class MsgpackProtocol(asyncio.Protocol): def __init__(self, routes): self.__routes = routes self.packer = Unpacker() def connection_made(self, transport): peername = transport.get_extra_info('peername') print('Connection from {}'.format(peername)) self.transport = transport self.transport.write(packb([2, 'peername', peername])) def data_received(self, data): self.packer.feed(data) for msg in self.packer: assert_request(msg) self.routing(msg) def routing(self, cmd): assert cmd[2] in self.__routes t = asyncio.ensure_future(response(cmd[1], self.transport, self.__routes[cmd[2]], cmd[3])) def eof_received(self): return True
def test_foobar(): unpacker = Unpacker(read_size=3) unpacker.feed("foobar") assert unpacker.unpack() == ord(b"f") assert unpacker.unpack() == ord(b"o") assert unpacker.unpack() == ord(b"o") assert unpacker.unpack() == ord(b"b") assert unpacker.unpack() == ord(b"a") assert unpacker.unpack() == ord(b"r") try: o = unpacker.unpack() print "Oops!", o assert 0 except StopIteration: assert 1 else: assert 0 unpacker.feed(b"foo") unpacker.feed(b"bar") k = 0 for o, e in zip(unpacker, b"foobarbaz"): assert o == ord(e) k += 1 assert k == len(b"foobar")
class SReader(): """ Define an asyncio msgpack stream decoder. """ def __init__(self, reader, writer): """ Pass ina stream reader to unmarshall msgpack objects from. """ self.reader = reader self.writer = writer self.decoder = make_decoder() self.unpacker = Unpacker(ext_hook=self.decoder, encoding="utf8") self.obj_buf = [] @asyncio.coroutine def get(self): """ The co-routine providing objects. """ while len(self.obj_buf) == 0: buf = yield from self.reader.read(1000) self.unpacker.feed(buf) for o in self.unpacker: self.obj_buf.append(o) return self.obj_buf.pop(0) def put(self, obj): """ Write an object to the channel. """ self.writer.write(encode(obj))
def test_foobar(): unpacker = Unpacker(read_size=3) unpacker.feed('foobar') assert_equal(unpacker.unpack(), ord('f')) assert_equal(unpacker.unpack(), ord('o')) assert_equal(unpacker.unpack(), ord('o')) assert_equal(unpacker.unpack(), ord('b')) assert_equal(unpacker.unpack(), ord('a')) assert_equal(unpacker.unpack(), ord('r')) try: o = unpacker.unpack() print "Oops!", o assert 0 except StopIteration: assert 1 else: assert 0 unpacker.feed('foo') unpacker.feed('bar') k = 0 for o, e in zip(unpacker, 'foobarbaz'): assert o == ord(e) k += 1 assert k == len('foobar')
def test3(): start = 0 end = 10 metric = "marion.channel-0" raw_series = REDIS_CONN.get(settings.FULL_NAMESPACE + metric) if not raw_series: resp = json.dumps({'results': 'Error: No metric by that name'}) return resp, 404 else: unpacker = Unpacker(use_list = False) unpacker.feed(raw_series) timeseries = [] point = {'x':datapoint[0],'y':datapoint[1]} if (start is None) and (end is not None): for datapoint in unpacker: if datapoint[0] < int(end): timeseries.append(point) elif (start is not None) and (end is None): for datapoint in unpacker: if datapoint[0] > int(start): timeseries.append(point) elif (start is not None) and (end is not None): for datapoint in unpacker: if (datapoint[0] > int(start)) and (datapoint[0] < int(end)): timeseries.append(point) elif (start is None) and (end is None): timeseries = [{'x':datapoint[0],'y':datapoint[1]} for datapoint in unpacker] resp = json.dumps({'results': timeseries}) return resp, 200
def anomalies(): resp = 'handle_data([])' try: analyzer_key_node = REDIS_BACKENDS.get_node(settings.ANALYZER_ANOMALY_KEY) anomaly_keys = RING.run('smembers', settings.ANALYZER_ANOMALY_KEY) anomalies = {} if not anomaly_keys: logger.info("No anomaly key found!") return resp, 200 for key in list(anomaly_keys): raw_anomalies = RING.run('get',key) if not raw_anomalies: logger.info("Can't get anomalies for key %s, removing it from set" % key) RING.run('srem', settings.ANALYZER_ANOMALY_KEY, key) continue unpacker = Unpacker(use_list = False) unpacker.feed(raw_anomalies) for item in unpacker: anomalies.update(item) anomaly_list = [] for anom, value in anomalies.iteritems(): anomaly_list.append([value, anom]) if len(anomaly_list) > 0: anomaly_list.sort(key=operator.itemgetter(1)) resp = 'handle_data(%s)' % anomaly_list except Exception as e: logger.error("Error getting anomalies: %s" % str(e)) return resp, 200
def test_foobar(): unpacker = Unpacker(read_size=3) unpacker.feed(b'foobar') assert unpacker.unpack() == ord(b'f') assert unpacker.unpack() == ord(b'o') assert unpacker.unpack() == ord(b'o') assert unpacker.unpack() == ord(b'b') assert unpacker.unpack() == ord(b'a') assert unpacker.unpack() == ord(b'r') try: o = unpacker.unpack() print(("Oops!", o)) assert 0 except StopIteration: assert 1 else: assert 0 unpacker.feed(b'foo') unpacker.feed(b'bar') k = 0 for o, e in zip(unpacker, b'foobarbaz'): assert o == e k += 1 assert k == len(b'foobar')
class ClientProtocol(asyncio.Protocol): def __init__(self): self._cpt = -1 self.packer = Unpacker() self._responses = dict() def connection_made(self, transport): print("connected") self.transport = transport def request(self, name, args, f): print("send request") self._cpt += 1 self._responses[self._cpt] = f self.transport.write(packb([0, self._cpt, name, args])) def data_received(self, data): self.packer.feed(data) for msg in self.packer: if msg[0] == 1: self._responses[msg[1]].set_result(msg) def connection_lost(self, exc): pass
def data(): metric = request.args.get('metric', None) start = request.args.get('start', None) end = request.args.get('end', None) if metric is None: metrics = ['channel-0', 'channel-1', 'channel-2', 'channel-3', 'channel-4', 'channel-5', 'channel-6', 'channel-7'] else: metrics = [metric] try: all_channels_data = [] for metric in metrics: single_channel_data = {} raw_series = REDIS_CONN.get(settings.FULL_NAMESPACE + metric) if not raw_series: resp = json.dumps({'results': 'Error: No metric by that name'}) return resp, 404 else: unpacker = Unpacker(use_list = False) unpacker.feed(raw_series) timeseries = [] if (start is None) and (end is not None): for datapoint in unpacker: if datapoint[0] < int(end): point = {'x' : datapoint[0], 'y':datapoint[1]} timeseries.append(point) elif (start is not None) and (end is None): for datapoint in unpacker: if datapoint[0] > int(start): point = {'x' : datapoint[0], 'y':datapoint[1]} timeseries.append(point) elif (start is not None) and (end is not None): for datapoint in unpacker: if (datapoint[0] > int(start)) and (datapoint[0] < int(end)): point = {'x' : datapoint[0], 'y':datapoint[1]} timeseries.append(point) elif (start is None) and (end is None): timeseries = [{'x' : datapoint[0], 'y':datapoint[1]} for datapoint in unpacker] single_channel_data['key'] = metric single_channel_data['values'] = timeseries all_channels_data.append(single_channel_data) resp = json.dumps({'results': all_channels_data}) return resp, 200 except Exception as e: error = "Error: " + e resp = json.dumps({'results': error}) return resp, 500 except Exception as e: error = "Error: " + e resp = json.dumps({'results': error}) return resp, 500
def mpdecode(iterable): unpacker = Unpacker(encoding='utf8') for chunk in iterable: unpacker.feed(chunk) # Each chunk can have none or many objects, # so here we dispatch any object ready for obj in unpacker: yield obj
def test_incorrect_type_nested_map(): unpacker = Unpacker() unpacker.feed(packb([{"a": "b"}])) try: unpacker.read_map_header() assert 0, "should raise exception" except UnexpectedTypeException: assert 1, "okay"
def test_correct_type_nested_array(): unpacker = Unpacker() unpacker.feed(packb({"a": ["b", "c", "d"]})) try: unpacker.read_array_header() assert 0, "should raise exception" except UnexpectedTypeException: assert 1, "okay"
def test_correct_type_nested_array(): unpacker = Unpacker() unpacker.feed(packb({'a': ['b', 'c', 'd']})) try: unpacker.read_array_header() assert 0, 'should raise exception' except UnexpectedTypeException: assert 1, 'okay'
def test_incorrect_type_nested_map(): unpacker = Unpacker() unpacker.feed(packb([{'a': 'b'}])) try: unpacker.read_map_header() assert 0, 'should raise exception' except UnexpectedTypeException: assert 1, 'okay'
def test_incorrect_type_array(): unpacker = Unpacker() unpacker.feed(packb(1)) try: unpacker.read_array_header() assert 0, 'should raise exception' except UnexpectedTypeException: assert 1, 'okay'
def setUp(self): address = 0xfa1afe1 device = "LivewareProblem" raw_packet = encode_erase_flash_page(address, device) unpacker = Unpacker() unpacker.feed(raw_packet) self.command = list(unpacker)[1:]
def unpack_gen(file, size): u = Unpacker() while True: data = file.read(size) if not data: break u.feed(data) for o in u: yield o
def test_auto_max_array_len(): packed = b'\xde\x00\x06zz' with pytest.raises(UnpackValueError): unpackb(packed, raw=False) unpacker = Unpacker(max_buffer_size=5, raw=False) unpacker.feed(packed) with pytest.raises(UnpackValueError): unpacker.unpack()
def test_auto_max_map_len(): # len(packed) == 6 -> max_map_len == 3 packed = b'\xde\x00\x04zzz' with pytest.raises(UnpackValueError): unpackb(packed, raw=False) unpacker = Unpacker(max_buffer_size=6, raw=False) unpacker.feed(packed) with pytest.raises(UnpackValueError): unpacker.unpack()
def mpack_handler(self, data, sock): unpacker = Unpacker() unpacker.feed(data) while 1: for msg in unpacker: self.on_message(msg) next = sock.recv(1000000) if not next: break unpacker.feed(next)
def test_foobar_skip(): unpacker = Unpacker(read_size=3, use_list=1) unpacker.feed(b'foobar') assert unpacker.unpack() == ord(b'f') unpacker.skip() assert unpacker.unpack() == ord(b'o') unpacker.skip() assert unpacker.unpack() == ord(b'a') unpacker.skip() with raises(OutOfData): unpacker.unpack()
def test_read_map_header(): unpacker = Unpacker() unpacker.feed(packb({'a': 'A'})) assert unpacker.read_map_header() == 1 assert unpacker.unpack() == B'a' assert unpacker.unpack() == B'A' try: unpacker.unpack() assert 0, 'should raise exception' except OutOfData: assert 1, 'okay'
class MsgpackStream(object): """Two-way msgpack stream that wraps a event loop byte stream. This wraps the event loop interface for reading/writing bytes and exposes an interface for reading/writing msgpack documents. """ def __init__(self, event_loop): """Wrap `event_loop` on a msgpack-aware interface.""" self.loop = event_loop self._packer = Packer(encoding='utf-8', unicode_errors=unicode_errors_default) self._unpacker = Unpacker() self._message_cb = None def threadsafe_call(self, fn): """Wrapper around `BaseEventLoop.threadsafe_call`.""" self.loop.threadsafe_call(fn) def send(self, msg): """Queue `msg` for sending to Nvim.""" debug('sent %s', msg) self.loop.send(self._packer.pack(msg)) def run(self, message_cb): """Run the event loop to receive messages from Nvim. While the event loop is running, `message_cb` will be called whenever a message has been successfully parsed from the input stream. """ self._message_cb = message_cb self.loop.run(self._on_data) self._message_cb = None def stop(self): """Stop the event loop.""" self.loop.stop() def close(self): """Close the event loop.""" self.loop.close() def _on_data(self, data): self._unpacker.feed(data) while True: try: debug('waiting for message...') msg = next(self._unpacker) debug('received message: %s', msg) self._message_cb(msg) except StopIteration: debug('unpacker needs more data...') break
def setUp(self): address = 0xdeadbeef data = bytes(range(4)) device = "dummy" raw_packet = encode_write_flash(data, address, device) unpacker = Unpacker() unpacker.feed(raw_packet) # Discards command set version self.command = list(unpacker)[1:]
def test_has_correct_protocol_version(self): """ Checks that the command encoding function works corectly. """ raw_packet = encode_command(command_code=10) unpacker = Unpacker() unpacker.feed(raw_packet) version, *_ = list(unpacker) self.assertEqual(2, version)
def test_foobar_skip(): unpacker = Unpacker(read_size=3, use_list=1) unpacker.feed(b"foobar") assert unpacker.unpack() == ord(b"f") unpacker.skip() assert unpacker.unpack() == ord(b"o") unpacker.skip() assert unpacker.unpack() == ord(b"a") unpacker.skip() with raises(OutOfData): unpacker.unpack()
def test_read_map_header(): unpacker = Unpacker() unpacker.feed(packb({"a": "A"})) assert unpacker.read_map_header() == 1 assert unpacker.unpack() == b"a" assert unpacker.unpack() == b"A" try: unpacker.unpack() assert 0, "should raise exception" except StopIteration: assert 1, "okay"
def mpack_handler(self, data, sock): unpacker = Unpacker() unpacker.feed(data) # default chunk size of memory buffer is 32MB RECV_SIZE = 32*1024*1024 while 1: for msg in unpacker: self.on_message(msg) next_data = sock.recv(RECV_SIZE) if not next_data: break unpacker.feed(next_data)
def test_max_ext_len(): d = ExtType(42, b"abc") packed = packb(d) unpacker = Unpacker(max_ext_len=3) unpacker.feed(packed) assert unpacker.unpack() == d unpacker = Unpacker(max_ext_len=2) with pytest.raises(ValueError): unpacker.feed(packed) unpacker.unpack()
def test_read_array_header(): unpacker = Unpacker() unpacker.feed(packb(['a', 'b', 'c'])) assert unpacker.read_array_header() == 3 assert unpacker.unpack() == b'a' assert unpacker.unpack() == b'b' assert unpacker.unpack() == b'c' try: unpacker.unpack() assert 0, 'should raise exception' except OutOfData: assert 1, 'okay'
def get_redis_metrics_timeseries(current_skyline_app, metrics, log=False): """ Return a dict of metrics timeseries as lists e.g. { 'base_name.1': [[ts, value], [ts, value], ..., [ts, value]], 'base_name.2': [[ts, value], [ts, value], ..., [ts, value]] } :param current_skyline_app: the app calling the function :param metrics: a list of base_names or full Redis metric names :param log: whether to log or not, optional, defaults to False :type current_skyline_app: str :type metrics: list :type log: boolean :return: metrics_timeseries :rtype: dict """ function_str = 'functions.redis.get_metrics_timeseries' if log: current_skyline_app_logger = current_skyline_app + 'Log' current_logger = logging.getLogger(current_skyline_app_logger) else: current_logger = None metrics_timeseries = {} try: redis_conn = get_redis_conn(current_skyline_app) except Exception as err: if not log: current_skyline_app_logger = current_skyline_app + 'Log' current_logger = logging.getLogger(current_skyline_app_logger) current_logger.error( 'error :: %s :: %s :: get_redis_conn failed - %s' % (current_skyline_app, function_str, str(err))) try: redis_conn_decoded = get_redis_conn_decoded(current_skyline_app) except Exception as err: if not log: current_skyline_app_logger = current_skyline_app + 'Log' current_logger = logging.getLogger(current_skyline_app_logger) current_logger.error( 'error :: %s :: %s :: get_redis_conn_decoded failed - %s' % (current_skyline_app, function_str, str(err))) assigned_metrics = [] base_names = [] for metric in metrics: if metric.startswith(FULL_NAMESPACE): metric_name = str(metric) base_name = metric.replace(FULL_NAMESPACE, '') else: metric_name = '%s%s' % (FULL_NAMESPACE, str(metric)) base_name = str(metric) assigned_metrics.append(metric_name) base_names.append(base_name) metrics_timeseries[base_name] = {} derivative_metrics = [] try: # @modified 20211012 - Feature #4280: aet.metrics_manager.derivative_metrics Redis hash # derivative_metrics = list(redis_conn_decoded.smembers('derivative_metrics')) derivative_metrics = list( redis_conn_decoded.smembers( 'aet.metrics_manager.derivative_metrics')) except Exception as err: if not log: current_skyline_app_logger = current_skyline_app + 'Log' current_logger = logging.getLogger(current_skyline_app_logger) current_logger.error(traceback.format_exc()) current_logger.error( 'error :: %s :: %s :: failed to get derivative_metrics from Redis - %s' % (current_skyline_app, function_str, str(err))) raw_assigned = {} try: raw_assigned = redis_conn.mget(assigned_metrics) except Exception as err: if not log: current_skyline_app_logger = current_skyline_app + 'Log' current_logger = logging.getLogger(current_skyline_app_logger) current_logger.error(traceback.format_exc()) current_logger.error( 'error :: %s :: %s :: failed to get raw_assigned from Redis - %s' % (current_skyline_app, function_str, str(err))) if raw_assigned: for index, metric_name in enumerate(assigned_metrics): timeseries = [] try: raw_series = raw_assigned[index] if raw_series: unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) except Exception as err: if not log: current_skyline_app_logger = current_skyline_app + 'Log' current_logger = logging.getLogger( current_skyline_app_logger) current_logger.error( 'error :: %s :: %s :: failed to unpack %s timeseries - %s' % (current_skyline_app, function_str, metric_name, str(err))) timeseries = [] if timeseries: # Convert Redis ts floats to ints timeseries = [[int(ts), value] for ts, value in timeseries] if timeseries: # To ensure that there are no unordered timestamps in the time # series which are artefacts of the collector or carbon-relay, sort # all time series by timestamp before analysis. original_timeseries = timeseries if original_timeseries: timeseries = sort_timeseries(original_timeseries) del original_timeseries if metric_name in derivative_metrics: if len(timeseries) > 3: try: derivative_timeseries = nonNegativeDerivative( timeseries) timeseries = derivative_timeseries except Exception as err: if not log: current_skyline_app_logger = current_skyline_app + 'Log' current_logger = logging.getLogger( current_skyline_app_logger) current_logger.error(traceback.format_exc()) current_logger.error( 'error :: %s :: %s :: nonNegativeDerivative failed on timeseries for %s - %s' % (current_skyline_app, function_str, metric_name, str(err))) if timeseries: base_name = base_names[index] metrics_timeseries[base_name] = timeseries return metrics_timeseries
class IControlTask(DeviceOperationMixIn): st_id = -3 # Device status ID main_e_axis = 0 # E axis control cmd_index = 0 # Command counter cmd_queue = None # Command store queue udp_sock = None # UDP socket to send status handler = None # Client TCP connection object known_position = None # Is toolhead position is known or not mainboard = None # Mainborad Controller toolhead = None # Headboard Controller head_resp_stack = None # Toolhead raw rasponse stack def __init__(self, stack, handler): super(IControlTask, self).__init__(stack, handler) self.handler = proxy(handler) self.handler.binary_mode = True self.cmd_queue = deque() self.meta = Metadata.instance() self._ready = 0 def on_mainboard_ready(ctrl): self._ready |= 1 self.mainboard.send_cmd("X8F") self.mainboard.send_cmd("T0") self.mainboard.send_cmd("G90") self.mainboard.send_cmd("G92E0") handler.send_text("ok") self.mainboard = MainController( self._sock_mb.fileno(), bufsize=14, empty_callback=self.on_mainboard_empty, sendable_callback=self.on_mainboard_sendable, ctrl_callback=self.on_mainboard_result) self.toolhead = HeadController( self._sock_th.fileno(), msg_callback=self.toolhead_message_callback) self.mainboard.bootstrap(on_mainboard_ready) self.unpacker = Unpacker() def on_toolhead_ready(self, ctrl): self._ready |= 2 @property def buflen(self): return len(self.cmd_queue) + self.mainboard.buffered_cmd_size def on_mainboard_empty(self, caller): self.fire() def on_mainboard_sendable(self, caller): self.fire() def toolhead_message_callback(self, sender, data): if data and self.head_resp_stack is not None and \ len(self.head_resp_stack) <= 32: self.head_resp_stack.append(data) self.send_udp1(sender) def on_binary(self, buf, handler): self.unpacker.feed(buf) for payload in self.unpacker: self.process_cmd(handler, *payload) def process_cmd(self, handler, index, cmd, *params): if index != self.cmd_index: logger.debug("Ignore %s 0x%02x %s", index, cmd, params) return fn = CMD_MATRIX.get(cmd) try: if cmd < 0xf0: fn(self, handler, *params) else: fn(self, handler, *params) self.cmd_index += 1 except InternalError as e: self.handler.send(packb((0xff, self.cmd_index, e[1]))) except Exception: logger.exception("Unknown error during processing command") self.handler.send(packb((0xff, self.cmd_index, MSG_UNKNOWN_ERROR))) self.on_require_kill(handler) def fire(self): if self.cmd_queue: target, cmd = self.cmd_queue[0] if target == TARGET_MAINBOARD: if self.mainboard.queue_full: return else: self.cmd_queue.popleft() self.mainboard.send_cmd(cmd) elif target == TARGET_TOOLHEAD: if self.mainboard.buffered_cmd_size == 0: if self.toolhead.sendable(): self.cmd_queue.popleft() # TODO self.toolhead.send_cmd(cmd, self) else: return def on_mainboard_message(self, watcher, revent): try: self.mainboard.handle_recv() except IOError: logger.error("Mainboard connection broken") self.stack.exit_task(self) self.send_udp0() except Exception: logger.exception("Unhandle Error") def on_headboard_message(self, watcher, revent): try: self.toolhead.handle_recv() check_toolhead_errno(self.toolhead, self.th_error_flag) self.fire() except IOError: logger.error("Headboard connection broken") self.stack.exit_task(self) except (HeadResetError, HeadOfflineError, HeadTypeError): self._ready &= ~2 except HeadError as e: logger.info("Head Error: %s", e) except Exception: logger.exception("Unhandle Error") def on_mainboard_result(self, controller, message): # Note: message will be... # "DATA HOME 12.3 -23.2 122.3" if message.startswith("DATA HOME"): position = [float(val) for val in message[10:].split(" ")] if float("nan") in position: self.handler.send(packb((CMD_G028, 1, None))) else: self.handler.send(packb((CMD_G028, 0, position))) self.known_position = [0, 0, 240] # "DATA READ X:0.124 Y:0.234 Z:0.534 F0:1 F1:0 MB:0" if message.startswith("DATA READ "): output = {} for key, val in ((p.split(":") for p in message[10:].split(" "))): if key in ("X", "Y", "Z"): output[key] = float(val) elif key in ("F0", "F1"): output[key] = (val == "1") elif key == "MB": output[key] = (val == "1") self.handler.send(packb((CMD_VALU, output))) # "DATA ZPROBE -0.5" if message.startswith("DATA ZPROBE "): self.handler.send(packb((CMD_G030, float(message[12:])))) def send_udp0(self): if self.udp_sock: try: buf = packb((0, "", self.cmd_index, self.buflen)) self.udp_sock.send(buf) except socket.error: pass def send_udp1(self, toolhead): if self.udp_sock: try: if self.head_resp_stack is not None: buf = packb((2, "", 0, len(self.head_resp_stack))) self.udp_sock.send(buf) if toolhead.ready: buf = packb( (1, "", 0, toolhead.error_code, toolhead.status)) self.udp_sock.send(buf) # elif toolhead.ready_flag > 0: # buf = packb((1, "", 0, -1, {})) # self.udp_sock.send(buf) else: buf = packb((1, "", 0, -2, {})) self.udp_sock.send(buf) except socket.error: pass def send_udps(self, signal): if self.udp_sock: try: self.udp_sock.send(packb((signal, ))) except socket.error: pass def on_timer(self, watcher, revent): self.meta.update_device_status(self.st_id, 0, "N/A", self.handler.address) self.send_udp0() if not self._ready & 2: self.send_udp1(self.toolhead) try: self.mainboard.patrol() except RuntimeError as e: logger.info("%s", e) except Exception: logger.exception("Mainboard dead") self.handler.send_text(packb((0xff, -1, 0xff, SUBSYSTEM_ERROR))) self.on_require_kill(self.handler) return try: self.toolhead.patrol() except (HeadOfflineError, HeadResetError) as e: logger.debug("Head Offline/Reset: %s", e) except RuntimeError as e: logger.info("%s", e) except socket.error: logger.warn("Socket IO Error") self.handler.close() except Exception: logger.exception("Toolhead dead") self.handler.send_text(packb((0xff, -1, 0xff, SUBSYSTEM_ERROR))) self.on_require_kill(self.handler) return def clean(self): self.mainboard.send_cmd("@HOME_BUTTON_TRIGGER\n") if self.toolhead: if self.toolhead.ready: self.toolhead.shutdown() self.toolhead = None if self.mainboard: self.mainboard.close() self.mainboard = None self.handler.binary_mode = False def append_cmd(self, target, cmd): self.cmd_queue.append((target, cmd)) self.fire() def create_movement_command(self, F=None, X=None, Y=None, Z=None, E0=None, E1=None, E2=None): # noqa target = self.known_position yield "G1" if F: yield "F%i" % F if X is not None or Y is not None or Z is not None: if self.known_position: if X is not None: target[0] = X yield "X%.5f" % X if Y is not None: target[1] = Y yield "Y%.5f" % Y if Z is not None: target[2] = Z yield "Z%.5f" % Z if (target[0]**2 + target[1]**2) > 28900: raise InternalError(CMD_G001, MSG_OPERATION_ERROR) elif target[2] > 240 or target[2] < 0: raise InternalError(CMD_G001, MSG_OPERATION_ERROR) else: raise InternalError(CMD_G001, MSG_OPERATION_ERROR) eflag = False for i, e in ((0, E0), (1, E1), (2, E2)): if e is not None: if eflag: raise InternalError(CMD_G001, MSG_OPERATION_ERROR) else: eflag = True if self.main_e_axis != i: yield "T%i" % i self.main_e_axis = i yield "E%.5f" % e self.known_position = target def on_move(self, handler, kw): try: cmd = "".join(self.create_movement_command(**kw)) self.append_cmd(TARGET_MAINBOARD, cmd) except TypeError: raise InternalError(CMD_G001, MSG_BAD_PARAMS) def on_sleep(self, handler, secondes): try: cmd = "G4S%.4f" % secondes self.append_cmd(TARGET_MAINBOARD, cmd) except TypeError: raise InternalError(CMD_G004, MSG_BAD_PARAMS) def on_scan_lasr(self, handler, flags): try: cmd = "X1E%i" % flags self.append_cmd(TARGET_MAINBOARD, cmd) except TypeError: raise InternalError(CMD_SLSR, MSG_BAD_PARAMS) def on_home(self, handler): self.append_cmd(TARGET_MAINBOARD, "X6") self.known_position = None def on_lock_motors(self, handler): self.append_cmd(TARGET_MAINBOARD, "M17") def on_release_motors(self, handler): self.append_cmd(TARGET_MAINBOARD, "M84") self.known_position = None def on_z_probe(self, handler, x, y): try: if self.known_position and x**2 + y**2 <= 7225: cmd = "G30X%.5fY%.5f" % (x, y) self.append_cmd(TARGET_MAINBOARD, cmd) else: raise InternalError(CMD_G030, MSG_OPERATION_ERROR) except TypeError: raise InternalError(CMD_G030, MSG_BAD_PARAMS) # def on_adjust(self, handler, kw): # pass def on_set_toolhead_temperature(self, handler, index, temperature): if index == 0 and temperature >= 0 and temperature <= 220: cmd = "H%i%.1f" % (index, temperature) self.append_cmd(TARGET_TOOLHEAD, cmd) else: raise InternalError(CMD_M104, MSG_OPERATION_ERROR) def on_set_toolhead_fan_speed(self, handler, index, speed): if index == 0 and speed >= 0 and speed <= 1: cmd = "F%i%.3f" % (index, speed) self.append_cmd(TARGET_TOOLHEAD, cmd) else: raise InternalError(CMD_M106, MSG_OPERATION_ERROR) def on_set_toolhead_pwm(self, handler, pwm): if pwm >= 0 and pwm <= 1: cmd = "X2O" % (pwm * 255) self.append_cmd(TARGET_MAINBOARD, cmd) else: raise InternalError(CMD_HLSR, MSG_OPERATION_ERROR) def on_query_value(self, handler, flags): self.append_cmd(TARGET_MAINBOARD, "X87F%i" % flags) def on_toolhead_profile(self, handler): buf = packb((CMD_THPF, self.toolhead.info())) self.handler.send(buf) def on_toolhead_raw_command(self, handler, cmd): self.append_cmd(TARGET_TOOLHEAD, cmd) def on_toolhead_raw_response(self, handler): buf = packb((CMD_THRR, self.head_resp_stack)) self.head_resp_stack = [] self.handler.send(buf) def on_require_sync(self, handler, ipaddr, port, salt): endpoint = (ipaddr, port) logger.debug("Create sync udp endpoint at %s", repr(endpoint)) try: s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) s.connect(endpoint) s.send(packb((0xff, ))) except (TypeError, OSError): raise InternalError(CMD_SYNC, MSG_OPERATION_ERROR) try: if self.udp_sock: self.udp_sock.close() finally: self.udp_sock = s def on_require_head(self, handler, head_type): self.toolhead = HeadController( self._sock_th.fileno(), required_module=head_type, msg_callback=self.toolhead_message_callback) self.head_resp_stack = [] if head_type == "USER" else None def on_bootstrap_toolhead(self, handler): self.toolhead.bootstrap(self.on_toolhead_ready) def on_clean_toolhead_error(self, handler): self.toolhead.errcode = 0 def on_require_quit(self, handler): if self.buflen: raise InternalError(CMD_QUIT, MSG_OPERATION_ERROR) self.stack.exit_task(self) self.handler.send(packb((CMD_QUIT, 0))) def on_require_kill(self, handler): try: self.send_udps(0xfe) self.stack.exit_task(self) finally: from fluxmonitor.hal.tools import reset_mb reset_mb() self.handler.send(packb((CMD_QUIT, 0)))
def get_correlations(base_name, anomaly_timestamp, anomalous_ts, assigned_metrics, raw_assigned, remote_assigned, anomalies): logger = logging.getLogger(skyline_app_logger) # Distill timeseries strings into lists start = timer() count = 0 metrics_checked_for_correlation = 0 # @added 20201203 - Feature #3860: luminosity - handle low frequency data # Determine data resolution resolution = determine_resolution(anomalous_ts) # Sample the time series # @modified 20180720 - Feature #2464: luminosity_remote_data # Added note here - if you modify the value of 600 here, it must be # modified in the luminosity_remote_data function in # skyline/webapp/backend.py as well # @modified 20201203 - Feature #3860: luminosity - handle low frequency data # from_timestamp = anomaly_timestamp - 600 from_timestamp = anomaly_timestamp - (resolution * 10) correlated_metrics = [] correlations = [] no_data = False if not anomalous_ts: no_data = True if not assigned_metrics: no_data = True if not raw_assigned: no_data = True if not anomalies: no_data = True if no_data: logger.error('error :: get_correlations :: no data') return (correlated_metrics, correlations) # @added 20200428 - Feature #3510: Enable Luminosity to handle correlating namespaces only # Feature #3500: webapp - crucible_process_metrics # Feature #1448: Crucible web UI # Discard the check if the anomaly_timestamp is not in FULL_DURATION as it # will have been added via the Crucible or webapp/crucible route start_timestamp_of_full_duration_data = int(time() - settings.FULL_DURATION) if anomaly_timestamp < (start_timestamp_of_full_duration_data + 2000): logger.info( 'get_correlations :: the anomaly_timestamp is too old not correlating' ) return (correlated_metrics, correlations) start_local_correlations = timer() local_redis_metrics_checked_count = 0 local_redis_metrics_correlations_count = 0 logger.info('get_correlations :: the local Redis metric count is %s' % str(len(assigned_metrics))) # @added 20200428 - Feature #3510: Enable Luminosity to handle correlating namespaces only # Removed here and handled in get_assigned_metrics for i, metric_name in enumerate(assigned_metrics): count += 1 # print(metric_name) # @modified 20180719 - Branch #2270: luminosity # Removed test limiting that was errorneously left in # if count > 1000: # break correlated = None # @modified 20200728 - Bug #3652: Handle multiple metrics in base_name conversion # metric_base_name = metric_name.replace(settings.FULL_NAMESPACE, '', 1) if metric_name.startswith(settings.FULL_NAMESPACE): metric_base_name = metric_name.replace(settings.FULL_NAMESPACE, '', 1) else: metric_base_name = metric_name if str(metric_base_name) == str(base_name): continue try: raw_series = raw_assigned[i] unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) except: timeseries = [] if not timeseries: # print('no time series data for %s' % base_name) continue # @added 20200507 - Feature #3532: Sort all time series # To ensure that there are no unordered timestamps in the time # series which are artefacts of the collector or carbon-relay, sort # all time series by timestamp before analysis. original_timeseries = timeseries if original_timeseries: timeseries = sort_timeseries(original_timeseries) del original_timeseries # Convert the time series if this is a known_derivative_metric known_derivative_metric = is_derivative_metric(skyline_app, metric_base_name) if known_derivative_metric: try: derivative_timeseries = nonNegativeDerivative(timeseries) timeseries = derivative_timeseries except: logger.error(traceback.format_exc()) logger.error('error :: nonNegativeDerivative') correlate_ts = [] # @added 20201203 - Feature #3860: luminosity - handle low frequency data # Determine data resolution resolution = determine_resolution(timeseries) for ts, value in timeseries: if int(ts) < from_timestamp: continue if int(ts) <= anomaly_timestamp: correlate_ts.append((int(ts), value)) # @modified 20180720 - Feature #2464: luminosity_remote_data # Added note here - if you modify the value of 61 here, it must be # modified in the luminosity_remote_data function in # skyline/webapp/backend.py as well # @modified 20201203 - Feature #3860: luminosity - handle low frequency data # Handle varying metric resolutions # if int(ts) > (anomaly_timestamp + 61): if int(ts) > (anomaly_timestamp + (resolution + 1)): break if not correlate_ts: continue local_redis_metrics_checked_count += 1 anomaly_ts_dict = dict(anomalous_ts) correlate_ts_dict = dict(correlate_ts) for a in anomalies: try: # @modified 20180720 - Feature #2464: luminosity_remote_data # Added note here - if you modify the value of 120 here, it must be # modified in the luminosity_remote_data function in # skyline/webapp/backend.py as well # @modified 20201203 - Feature #3860: luminosity - handle low frequency data # Handle varying metric resolutions # if int(a.exact_timestamp) < int(anomaly_timestamp - 120): # continue # if int(a.exact_timestamp) > int(anomaly_timestamp + 120): # continue if int(a.exact_timestamp) < int(anomaly_timestamp - (resolution * 2)): continue if int(a.exact_timestamp) > int(anomaly_timestamp + (resolution * 2)): continue except: continue try: # @modified 20201203 - Feature #3860: luminosity - handle low frequency data # Handle varying metric resolutions # time_period = (int(anomaly_timestamp - 120), int(anomaly_timestamp + 120)) time_period = (int(anomaly_timestamp - (resolution * 2)), int(anomaly_timestamp + (resolution * 2))) my_correlator = Correlator(anomaly_ts_dict, correlate_ts_dict, time_period) # For better correlation use 0.9 instead of 0.8 for the threshold # @modified 20180524 - Feature #2360: CORRELATE_ALERTS_ONLY # Branch #2270: luminosity # Feature #2378: Add redis auth to Skyline and rebrow # Added this to setting.py # if my_correlator.is_correlated(threshold=0.9): try: cross_correlation_threshold = settings.LUMINOL_CROSS_CORRELATION_THRESHOLD metrics_checked_for_correlation += 1 except: cross_correlation_threshold = 0.9 if my_correlator.is_correlated( threshold=cross_correlation_threshold): correlation = my_correlator.get_correlation_result() correlated = True correlations.append([ metric_base_name, correlation.coefficient, correlation.shift, correlation.shifted_coefficient ]) local_redis_metrics_correlations_count += 1 except: pass if correlated: correlated_metrics.append(metric_base_name) # @added 20180720 - Feature #2464: luminosity_remote_data # Added the correlation of preprocessed remote data end_local_correlations = timer() logger.info( 'get_correlations :: checked - local_redis_metrics_checked_count is %s' % str(local_redis_metrics_checked_count)) logger.info( 'get_correlations :: correlated - local_redis_metrics_correlations_count is %s' % str(local_redis_metrics_correlations_count)) logger.info( 'get_correlations :: processed %s correlations on local_redis_metrics_checked_count %s local metrics in %.6f seconds' % (str(local_redis_metrics_correlations_count), str(local_redis_metrics_checked_count), (end_local_correlations - start_local_correlations))) # @added 20201207 - Feature #3858: skyline_functions - correlate_or_relate_with do_not_correlate_with = [] remote_metrics_count = 0 remote_correlations_check_count = 0 remote_correlations_count = 0 logger.info('get_correlations :: remote_assigned count %s' % str(len(remote_assigned))) start_remote_correlations = timer() for ts_data in remote_assigned: remote_metrics_count += 1 correlated = None metric_name = str(ts_data[0]) # @modified 20200728 - Bug #3652: Handle multiple metrics in base_name conversion # metric_base_name = metric_name.replace(settings.FULL_NAMESPACE, '', 1) if metric_name.startswith(settings.FULL_NAMESPACE): metric_base_name = metric_name.replace(settings.FULL_NAMESPACE, '', 1) else: metric_base_name = metric_name if str(metric_base_name) == str(base_name): continue # @added 20201207 - Feature #3858: skyline_functions - correlate_or_relate_with try: correlate_or_relate = correlate_or_relate_with( skyline_app, base_name, metric_base_name) if not correlate_or_relate: do_not_correlate_with.append(metric_base_name) continue except: logger.error(traceback.format_exc()) logger.error( 'error :: get_remote_assigned :: failed to evaluate correlate_or_relate_with' ) timeseries = [] try: timeseries = ts_data[1] except: timeseries = [] if not timeseries: continue # @added 20201203 - Feature #3860: luminosity - handle low frequency data # Determine data resolution resolution = determine_resolution(timeseries) correlate_ts = [] for ts, value in timeseries: if int(ts) < from_timestamp: continue if int(ts) <= anomaly_timestamp: correlate_ts.append((int(ts), value)) # @modified 20180720 - Feature #2464: luminosity_remote_data # Added note here - if you modify the value of 61 here, it must be # modified in the luminosity_remote_data function in # skyline/webapp/backend.py as well # @modified 20201203 - Feature #3860: luminosity - handle low frequency data # Handle varying metric resolutions # if int(ts) > (anomaly_timestamp + 61): if int(ts) > (anomaly_timestamp + (resolution + 1)): break if not correlate_ts: continue anomaly_ts_dict = dict(anomalous_ts) correlate_ts_dict = dict(correlate_ts) for a in anomalies: try: # @modified 20180720 - Feature #2464: luminosity_remote_data # Added note here - if you modify the value of 120 here, it must be # modified in the luminosity_remote_data function in # skyline/webapp/backend.py as well # @modified 20201203 - Feature #3860: luminosity - handle low frequency data # Handle varying metric resolutions # if int(a.exact_timestamp) < int(anomaly_timestamp - 120): # continue # if int(a.exact_timestamp) > int(anomaly_timestamp + 120): # continue if int(a.exact_timestamp) < int(anomaly_timestamp - (resolution * 2)): continue if int(a.exact_timestamp) > int(anomaly_timestamp + (resolution * 2)): continue except: continue try: # @modified 20201203 - Feature #3860: luminosity - handle low frequency data # Handle varying metric resolutions # time_period = (int(anomaly_timestamp - 120), int(anomaly_timestamp + 120)) time_period = (int(anomaly_timestamp - (resolution * 2)), int(anomaly_timestamp + (resolution * 2))) my_correlator = Correlator(anomaly_ts_dict, correlate_ts_dict, time_period) metrics_checked_for_correlation += 1 remote_correlations_check_count += 1 try: cross_correlation_threshold = settings.LUMINOL_CROSS_CORRELATION_THRESHOLD except: cross_correlation_threshold = 0.9 if my_correlator.is_correlated( threshold=cross_correlation_threshold): correlation = my_correlator.get_correlation_result() correlated = True correlations.append([ metric_base_name, correlation.coefficient, correlation.shift, correlation.shifted_coefficient ]) remote_correlations_count += 1 except: pass if correlated: correlated_metrics.append(metric_base_name) end_remote_correlations = timer() # @added 20201207 - Feature #3858: skyline_functions - correlate_or_relate_with if len(do_not_correlate_with) > 0: logger.info( 'get_correlations :: discarded %s remote assigned metrics as not in a correlation group with %s' % (str(len(do_not_correlate_with)), base_name)) logger.info( 'get_correlations :: checked - remote_correlations_check_count is %s' % str(remote_correlations_check_count)) logger.info( 'get_correlations :: correlated - remote_correlations_count is %s' % str(remote_correlations_count)) logger.info( 'get_correlations :: processed remote correlations on remote_metrics_count %s local metric in %.6f seconds' % (str(remote_metrics_count), (end_remote_correlations - start_remote_correlations))) end = timer() logger.info( 'get_correlations :: checked a total of %s metrics and correlated %s metrics to %s anomaly, processed in %.6f seconds' % (str(metrics_checked_for_correlation), str( len(correlated_metrics)), base_name, (end - start))) # @added 20170720 - Task #2462: Implement useful metrics for Luminosity # Added runtime to calculate avg_runtime Graphite metric runtime = '%.6f' % (end - start) return (correlated_metrics, correlations, metrics_checked_for_correlation, runtime)
def spin_process(self, i, boundary_metrics): """ Assign a bunch of metrics for a process to analyze. """ # Determine assigned metrics bp = settings.BOUNDARY_PROCESSES bm_range = len(boundary_metrics) keys_per_processor = int(ceil(float(bm_range) / float(bp))) if i == settings.BOUNDARY_PROCESSES: assigned_max = len(boundary_metrics) else: # This is a skyine bug, the original skyline code uses 1 as the # beginning position of the index, python indices begin with 0 # assigned_max = len(boundary_metrics) # This closes the etsy/skyline pull request opened by @languitar on 17 Jun 2014 # https://github.com/etsy/skyline/pull/94 Fix analyzer worker metric assignment assigned_max = min(len(boundary_metrics), i * keys_per_processor) assigned_min = (i - 1) * keys_per_processor assigned_keys = range(assigned_min, assigned_max) # Compile assigned metrics assigned_metrics_and_algos = [ boundary_metrics[index] for index in assigned_keys ] if ENABLE_BOUNDARY_DEBUG: logger.info('debug - printing assigned_metrics_and_algos') for assigned_metric_and_algo in assigned_metrics_and_algos: logger.info('debug - assigned_metric_and_algo - %s' % str(assigned_metric_and_algo)) # Compile assigned metrics assigned_metrics = [] for i in assigned_metrics_and_algos: assigned_metrics.append(i[0]) # unique unhashed things def unique_noHash(seq): seen = set() return [ x for x in seq if str(x) not in seen and not seen.add(str(x)) ] unique_assigned_metrics = unique_noHash(assigned_metrics) if ENABLE_BOUNDARY_DEBUG: logger.info('debug - unique_assigned_metrics - %s' % str(unique_assigned_metrics)) logger.info('debug - printing unique_assigned_metrics:') for unique_assigned_metric in unique_assigned_metrics: logger.info('debug - unique_assigned_metric - %s' % str(unique_assigned_metric)) # Check if this process is unnecessary if len(unique_assigned_metrics) == 0: return # Multi get series try: raw_assigned = self.redis_conn.mget(unique_assigned_metrics) except: logger.error("failed to mget assigned_metrics from redis") return # Make process-specific dicts exceptions = defaultdict(int) anomaly_breakdown = defaultdict(int) # Reset boundary_algortims all_boundary_algorithms = [] for metric in BOUNDARY_METRICS: all_boundary_algorithms.append(metric[1]) # The unique algorithms that are being used boundary_algorithms = unique_noHash(all_boundary_algorithms) if ENABLE_BOUNDARY_DEBUG: logger.info('debug - boundary_algorithms - %s' % str(boundary_algorithms)) discover_run_metrics = [] # Distill metrics into a run list for i, metric_name, in enumerate(unique_assigned_metrics): self.check_if_parent_is_alive() try: if ENABLE_BOUNDARY_DEBUG: logger.info('debug - unpacking timeseries for %s - %s' % (metric_name, str(i))) raw_series = raw_assigned[i] unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) except Exception as e: exceptions['Other'] += 1 logger.error("redis data error: " + traceback.format_exc()) logger.error("error: %e" % e) base_name = metric_name.replace(FULL_NAMESPACE, '', 1) # Determine the metrics BOUNDARY_METRICS metric tuple settings for metrick in BOUNDARY_METRICS: CHECK_MATCH_PATTERN = metrick[0] check_match_pattern = re.compile(CHECK_MATCH_PATTERN) pattern_match = check_match_pattern.match(base_name) metric_pattern_matched = False if pattern_match: metric_pattern_matched = True algo_pattern_matched = False for algo in boundary_algorithms: for metric in BOUNDARY_METRICS: CHECK_MATCH_PATTERN = metric[0] check_match_pattern = re.compile( CHECK_MATCH_PATTERN) pattern_match = check_match_pattern.match( base_name) if pattern_match: if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - metric and algo pattern MATCHED - " + metric[0] + " | " + base_name + " | " + str(metric[1])) metric_expiration_time = False metric_min_average = False metric_min_average_seconds = False metric_trigger = False algorithm = False algo_pattern_matched = True algorithm = metric[1] try: if metric[2]: metric_expiration_time = metric[2] except: metric_expiration_time = False try: if metric[3]: metric_min_average = metric[3] except: metric_min_average = False try: if metric[4]: metric_min_average_seconds = metric[4] except: metric_min_average_seconds = 1200 try: if metric[5]: metric_trigger = metric[5] except: metric_trigger = False try: if metric[6]: alert_threshold = metric[6] except: alert_threshold = False try: if metric[7]: metric_alerters = metric[7] except: metric_alerters = False if metric_pattern_matched and algo_pattern_matched: if ENABLE_BOUNDARY_DEBUG: logger.info( 'debug - added metric - %s, %s, %s, %s, %s, %s, %s, %s, %s' % (str(i), metric_name, str(metric_expiration_time), str(metric_min_average), str(metric_min_average_seconds), str(metric_trigger), str(alert_threshold), metric_alerters, algorithm)) discover_run_metrics.append([ i, metric_name, metric_expiration_time, metric_min_average, metric_min_average_seconds, metric_trigger, alert_threshold, metric_alerters, algorithm ]) if ENABLE_BOUNDARY_DEBUG: logger.info('debug - printing discover_run_metrics') for discover_run_metric in discover_run_metrics: logger.info('debug - discover_run_metrics - %s' % str(discover_run_metric)) logger.info('debug - build unique boundary metrics to analyze') # Determine the unique set of metrics to run run_metrics = unique_noHash(discover_run_metrics) if ENABLE_BOUNDARY_DEBUG: logger.info('debug - printing run_metrics') for run_metric in run_metrics: logger.info('debug - run_metrics - %s' % str(run_metric)) # Distill timeseries strings and submit to run_selected_algorithm for metric_and_algo in run_metrics: self.check_if_parent_is_alive() try: raw_assigned_id = metric_and_algo[0] metric_name = metric_and_algo[1] base_name = metric_name.replace(FULL_NAMESPACE, '', 1) metric_expiration_time = metric_and_algo[2] metric_min_average = metric_and_algo[3] metric_min_average_seconds = metric_and_algo[4] metric_trigger = metric_and_algo[5] alert_threshold = metric_and_algo[6] metric_alerters = metric_and_algo[7] algorithm = metric_and_algo[8] if ENABLE_BOUNDARY_DEBUG: logger.info('debug - unpacking timeseries for %s - %s' % (metric_name, str(raw_assigned_id))) raw_series = raw_assigned[metric_and_algo[0]] unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) if ENABLE_BOUNDARY_DEBUG: logger.info('debug - unpacked OK - %s - %s' % (metric_name, str(raw_assigned_id))) autoaggregate = False autoaggregate_value = 0 # Determine if the namespace is to be aggregated if BOUNDARY_AUTOAGGRERATION: for autoaggregate_metric in BOUNDARY_AUTOAGGRERATION_METRICS: autoaggregate = False autoaggregate_value = 0 CHECK_MATCH_PATTERN = autoaggregate_metric[0] base_name = metric_name.replace(FULL_NAMESPACE, '', 1) check_match_pattern = re.compile(CHECK_MATCH_PATTERN) pattern_match = check_match_pattern.match(base_name) if pattern_match: autoaggregate = True autoaggregate_value = autoaggregate_metric[1] if ENABLE_BOUNDARY_DEBUG: logger.info( 'debug - BOUNDARY_AUTOAGGRERATION passed - %s - %s' % (metric_name, str(autoaggregate))) if ENABLE_BOUNDARY_DEBUG: logger.info( 'debug - analysing - %s, %s, %s, %s, %s, %s, %s, %s, %s, %s' % (metric_name, str(metric_expiration_time), str(metric_min_average), str(metric_min_average_seconds), str(metric_trigger), str(alert_threshold), metric_alerters, autoaggregate, autoaggregate_value, algorithm)) # Dump the the timeseries data to a file timeseries_dump_dir = "/tmp/skyline/boundary/" + algorithm self.mkdir_p(timeseries_dump_dir) timeseries_dump_file = timeseries_dump_dir + "/" + metric_name + ".json" with open(timeseries_dump_file, 'w+') as f: f.write(str(timeseries)) f.close() # Check if a metric has its own unique BOUNDARY_METRICS alert # tuple, this allows us to paint an entire metric namespace with # the same brush AND paint a unique metric or namespace with a # different brush or scapel has_unique_tuple = False run_tupple = False boundary_metric_tuple = (base_name, algorithm, metric_expiration_time, metric_min_average, metric_min_average_seconds, metric_trigger, alert_threshold, metric_alerters) wildcard_namespace = True for metric_tuple in BOUNDARY_METRICS: if not has_unique_tuple: CHECK_MATCH_PATTERN = metric_tuple[0] check_match_pattern = re.compile(CHECK_MATCH_PATTERN) pattern_match = check_match_pattern.match(base_name) if pattern_match: if metric_tuple[0] == base_name: wildcard_namespace = False if not has_unique_tuple: if boundary_metric_tuple == metric_tuple: has_unique_tuple = True run_tupple = True if ENABLE_BOUNDARY_DEBUG: logger.info('unique_tuple:') logger.info( 'boundary_metric_tuple: %s' % str(boundary_metric_tuple)) logger.info('metric_tuple: %s' % str(metric_tuple)) if not has_unique_tuple: if wildcard_namespace: if ENABLE_BOUNDARY_DEBUG: logger.info('wildcard_namespace:') logger.info('boundary_metric_tuple: %s' % str(boundary_metric_tuple)) run_tupple = True else: if ENABLE_BOUNDARY_DEBUG: logger.info( 'wildcard_namespace: BUT WOULD NOT RUN') logger.info('boundary_metric_tuple: %s' % str(boundary_metric_tuple)) if ENABLE_BOUNDARY_DEBUG: logger.info('WOULD RUN run_selected_algorithm = %s' % run_tupple) if run_tupple: # Submit the timeseries and settings to run_selected_algorithm anomalous, ensemble, datapoint, metric_name, metric_expiration_time, metric_min_average, metric_min_average_seconds, metric_trigger, alert_threshold, metric_alerters, algorithm = run_selected_algorithm( timeseries, metric_name, metric_expiration_time, metric_min_average, metric_min_average_seconds, metric_trigger, alert_threshold, metric_alerters, autoaggregate, autoaggregate_value, algorithm) if ENABLE_BOUNDARY_DEBUG: logger.info('debug - analysed - %s' % (metric_name)) else: anomalous = False if ENABLE_BOUNDARY_DEBUG: logger.info( 'debug - more unique metric tuple not analysed - %s' % (metric_name)) # If it's anomalous, add it to list if anomalous: anomalous_metric = [ datapoint, metric_name, metric_expiration_time, metric_min_average, metric_min_average_seconds, metric_trigger, alert_threshold, metric_alerters, algorithm ] self.anomalous_metrics.append(anomalous_metric) # Get the anomaly breakdown - who returned True? for index, value in enumerate(ensemble): if value: anomaly_breakdown[algorithm] += 1 # It could have been deleted by the Roomba except TypeError: exceptions['DeletedByRoomba'] += 1 except TooShort: exceptions['TooShort'] += 1 except Stale: exceptions['Stale'] += 1 except Boring: exceptions['Boring'] += 1 except: exceptions['Other'] += 1 logger.info("exceptions['Other'] traceback follows:") logger.info(traceback.format_exc()) # Add values to the queue so the parent process can collate for key, value in anomaly_breakdown.items(): self.anomaly_breakdown_q.put((key, value)) for key, value in exceptions.items(): self.exceptions_q.put((key, value))
def get_anomalous_ts(base_name, anomaly_timestamp): logger = logging.getLogger(skyline_app_logger) # @added 20180423 - Feature #2360: CORRELATE_ALERTS_ONLY # Branch #2270: luminosity # Only correlate metrics with an alert setting if correlate_alerts_only: try: # @modified 20191030 - Bug #3266: py3 Redis binary objects not strings # Branch #3262: py3 # smtp_alerter_metrics = list(redis_conn.smembers('analyzer.smtp_alerter_metrics')) # @modified 20200421 - Feature #3306: Record anomaly_end_timestamp # Branch #2270: luminosity # Branch #3262: py3 # Changed to use the aet Redis set, used to determine and record the # anomaly_end_timestamp, some transient sets need to copied so that # the data always exists, even if it is sourced from a transient set. # smtp_alerter_metrics = list(redis_conn_decoded.smembers('analyzer.smtp_alerter_metrics')) smtp_alerter_metrics = list( redis_conn_decoded.smembers( 'aet.analyzer.smtp_alerter_metrics')) except: smtp_alerter_metrics = [] if base_name not in smtp_alerter_metrics: logger.error('%s has no alerter setting, not correlating' % base_name) return [] if not base_name or not anomaly_timestamp: return [] # from skyline_functions import nonNegativeDerivative anomalous_metric = '%s%s' % (settings.FULL_NAMESPACE, base_name) unique_metrics = [] try: # @modified 20191030 - Bug #3266: py3 Redis binary objects not strings # Branch #3262: py3 # unique_metrics = list(redis_conn.smembers(settings.FULL_NAMESPACE + 'unique_metrics')) unique_metrics = list( redis_conn_decoded.smembers(settings.FULL_NAMESPACE + 'unique_metrics')) except: logger.error(traceback.format_exc()) logger.error('error :: get_assigned_metrics :: no unique_metrics') return [] # @added 20180720 - Feature #2464: luminosity_remote_data # Ensure that Luminosity only processes it's own Redis metrics so that if # multiple Skyline instances are running, Luminosity does not process an # anomaly_id for a metric that is not local to itself. This will stop the # call to the remote Redis with other_redis_conn below. With the # introduction of the preprocessing luminosity_remote_data API endpoint for # remote Skyline instances, there is no further requirement for Skyline # instances to have direct access to Redis on another Skyline instance. # A much better solution and means all data is preprocessed and encrypted, # there is no need for iptables other than 443 (or custom https port). # if anomalous_metric in unique_metrics: logger.info( '%s is a metric in Redis, processing on this Skyline instance' % base_name) else: logger.info( '%s is not a metric in Redis, not processing on this Skyline instance' % base_name) return [] assigned_metrics = [anomalous_metric] # @modified 20180419 - raw_assigned = [] try: raw_assigned = redis_conn.mget(assigned_metrics) except: raw_assigned = [] if raw_assigned == [None]: logger.info('%s data not retrieved from local Redis' % (str(base_name))) raw_assigned = [] # @modified 20180721 - Feature #2464: luminosity_remote_data # TO BE DEPRECATED settings.OTHER_SKYLINE_REDIS_INSTANCES # with the addition of the luminosity_remote_data API call and the above if not raw_assigned and settings.OTHER_SKYLINE_REDIS_INSTANCES: # @modified 20180519 - Feature #2378: Add redis auth to Skyline and rebrow # for redis_ip, redis_port in settings.OTHER_SKYLINE_REDIS_INSTANCES: for redis_ip, redis_port, redis_password in settings.OTHER_SKYLINE_REDIS_INSTANCES: if not raw_assigned: try: if redis_password: other_redis_conn = StrictRedis( host=str(redis_ip), port=int(redis_port), password=str(redis_password)) else: other_redis_conn = StrictRedis(host=str(redis_ip), port=int(redis_port)) raw_assigned = other_redis_conn.mget(assigned_metrics) if raw_assigned == [None]: logger.info( '%s data not retrieved from Redis at %s on port %s' % (str(base_name), str(redis_ip), str(redis_port))) raw_assigned = [] if raw_assigned: logger.info( '%s data retrieved from Redis at %s on port %s' % (str(base_name), str(redis_ip), str(redis_port))) except: logger.error(traceback.format_exc()) logger.error( 'error :: failed to connect to Redis at %s on port %s' % (str(redis_ip), str(redis_port))) raw_assigned = [] if not raw_assigned or raw_assigned == [None]: logger.info('%s data not retrieved' % (str(base_name))) return [] for i, metric_name in enumerate(assigned_metrics): try: raw_series = raw_assigned[i] unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) except: timeseries = [] # @added 20200507 - Feature #3532: Sort all time series # To ensure that there are no unordered timestamps in the time # series which are artefacts of the collector or carbon-relay, sort # all time series by timestamp before analysis. original_timeseries = timeseries if original_timeseries: timeseries = sort_timeseries(original_timeseries) del original_timeseries # Convert the time series if this is a known_derivative_metric known_derivative_metric = is_derivative_metric(skyline_app, base_name) if known_derivative_metric: derivative_timeseries = nonNegativeDerivative(timeseries) timeseries = derivative_timeseries # @added 20201203 - Feature #3860: luminosity - handle low frequency data # Determine data resolution resolution = determine_resolution(timeseries) # Sample the time series # @modified 20180720 - Feature #2464: luminosity_remote_data # Added note here - if you modify the value of 600 here, it must be # modified in the luminosity_remote_data function in # skyline/webapp/backend.py as well # @modified 20201203 - Feature #3860: luminosity - handle low frequency data # from_timestamp = anomaly_timestamp - 600 from_timestamp = anomaly_timestamp - (resolution * 10) anomaly_ts = [] for ts, value in timeseries: if int(ts) < from_timestamp: continue if int(ts) <= anomaly_timestamp: anomaly_ts.append((int(ts), value)) if int(ts) > anomaly_timestamp: break # @added 20190515 - Bug #3008: luminosity - do not analyse short time series # Only return a time series sample if the sample has sufficient data points # otherwise get_anomalies() will throw and error len_anomaly_ts = len(anomaly_ts) if len_anomaly_ts <= 9: logger.info( '%s insufficient data not retrieved, only %s data points surfaced, not correlating' % (str(base_name), str(len_anomaly_ts))) return [] return anomaly_ts
def spin_process(self, i, unique_metrics): """ Assign a bunch of metrics for a process to analyze. Multiple get the assigned_metrics to the process from Redis. For each metric:\n * unpack the `raw_timeseries` for the metric.\n * Analyse each timeseries against `ALGORITHMS` to determine if it is\n anomalous.\n * If anomalous add it to the :obj:`self.anomalous_metrics` list\n * Add what algorithms triggered to the :obj:`self.anomaly_breakdown_q` queue\n Add keys and values to the queue so the parent process can collate for:\n * :py:obj:`self.anomaly_breakdown_q` * :py:obj:`self.exceptions_q` """ spin_start = time() logger.info('spin_process started') # Discover assigned metrics keys_per_processor = int( ceil( float(len(unique_metrics)) / float(settings.ANALYZER_PROCESSES))) if i == settings.ANALYZER_PROCESSES: assigned_max = len(unique_metrics) else: assigned_max = min(len(unique_metrics), i * keys_per_processor) # Fix analyzer worker metric assignment #94 # https://github.com/etsy/skyline/pull/94 @languitar:worker-fix assigned_min = (i - 1) * keys_per_processor assigned_keys = range(assigned_min, assigned_max) # assigned_keys = range(300, 310) # Compile assigned metrics assigned_metrics = [unique_metrics[index] for index in assigned_keys] # Check if this process is unnecessary if len(assigned_metrics) == 0: return # Multi get series raw_assigned = self.redis_conn.mget(assigned_metrics) # Make process-specific dicts exceptions = defaultdict(int) anomaly_breakdown = defaultdict(int) # Distill timeseries strings into lists for i, metric_name in enumerate(assigned_metrics): self.check_if_parent_is_alive() # logger.info('analysing %s' % metric_name) try: raw_series = raw_assigned[i] unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) anomalous, ensemble, datapoint = run_selected_algorithm( timeseries, metric_name) # If it's anomalous, add it to list if anomalous: base_name = metric_name.replace(settings.FULL_NAMESPACE, '', 1) metric = [datapoint, base_name] self.anomalous_metrics.append(metric) # Get the anomaly breakdown - who returned True? triggered_algorithms = [] for index, value in enumerate(ensemble): if value: algorithm = settings.ALGORITHMS[index] anomaly_breakdown[algorithm] += 1 triggered_algorithms.append(algorithm) # It could have been deleted by the Roomba except TypeError: # logger.error('TypeError analysing %s' % metric_name) exceptions['DeletedByRoomba'] += 1 except TooShort: # logger.error('TooShort analysing %s' % metric_name) exceptions['TooShort'] += 1 except Stale: # logger.error('Stale analysing %s' % metric_name) exceptions['Stale'] += 1 except Boring: # logger.error('Boring analysing %s' % metric_name) exceptions['Boring'] += 1 except: # logger.error('Other analysing %s' % metric_name) exceptions['Other'] += 1 logger.info(traceback.format_exc()) # Add values to the queue so the parent process can collate for key, value in anomaly_breakdown.items(): self.anomaly_breakdown_q.put((key, value)) for key, value in exceptions.items(): self.exceptions_q.put((key, value)) spin_end = time() - spin_start logger.info('spin_process took %.2f seconds' % spin_end)
def vacuum(self, i, namespace, duration): """ Trim metrics that are older than settings.FULL_DURATION and purge old metrics. """ begin = time() logger.info('%s :: started vacuum' % (skyline_app)) # Discover assigned metrics namespace_unique_metrics = '%sunique_metrics' % str(namespace) unique_metrics = list(self.redis_conn.smembers(namespace_unique_metrics)) keys_per_processor = int(ceil(float(len(unique_metrics)) / float(settings.ROOMBA_PROCESSES))) if i == settings.ROOMBA_PROCESSES: assigned_max = len(unique_metrics) else: assigned_max = min(len(unique_metrics), i * keys_per_processor) assigned_min = (i - 1) * keys_per_processor assigned_keys = range(assigned_min, assigned_max) # Compile assigned metrics assigned_metrics = [unique_metrics[index] for index in assigned_keys] euthanized = 0 blocked = 0 trimmed_keys = 0 active_keys = 0 for i in xrange(len(assigned_metrics)): self.check_if_parent_is_alive() pipe = self.redis_conn.pipeline() now = time() key = assigned_metrics[i] try: # WATCH the key pipe.watch(key) # Everything below NEEDS to happen before another datapoint # comes in. If your data has a very small resolution (<.1s), # this technique may not suit you. raw_series = pipe.get(key) unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = sorted([unpacked for unpacked in unpacker]) # Put pipe back in multi mode pipe.multi() # There's one value. Purge if it's too old try: if python_version == 2: if not isinstance(timeseries[0], TupleType): if timeseries[0] < now - duration: pipe.delete(key) pipe.srem(namespace_unique_metrics, key) pipe.execute() euthanized += 1 continue if python_version == 3: if not isinstance(timeseries[0], tuple): if timeseries[0] < now - duration: pipe.delete(key) pipe.srem(namespace_unique_metrics, key) pipe.execute() euthanized += 1 continue except IndexError: continue # Check if the last value is too old and purge if timeseries[-1][0] < now - duration: pipe.delete(key) pipe.srem(namespace_unique_metrics, key) pipe.execute() euthanized += 1 continue # Remove old datapoints and duplicates from timeseries temp = set() temp_add = temp.add delta = now - duration trimmed = [ tuple for tuple in timeseries if tuple[0] > delta and tuple[0] not in temp and not temp_add(tuple[0]) ] # Purge if everything was deleted, set key otherwise if len(trimmed) > 0: # Serialize and turn key back into not-an-array btrimmed = packb(trimmed) if len(trimmed) <= 15: value = btrimmed[1:] elif len(trimmed) <= 65535: value = btrimmed[3:] trimmed_keys += 1 else: value = btrimmed[5:] trimmed_keys += 1 pipe.set(key, value) active_keys += 1 else: pipe.delete(key) pipe.srem(namespace_unique_metrics, key) euthanized += 1 pipe.execute() except WatchError: blocked += 1 assigned_metrics.append(key) except Exception as e: # If something bad happens, zap the key and hope it goes away pipe.delete(key) pipe.srem(namespace_unique_metrics, key) pipe.execute() euthanized += 1 logger.info(e) logger.info('%s :: vacuum Euthanizing %s' % (skyline_app, key)) finally: pipe.reset() logger.info( '%s :: vacuum operated on %s %d keys in %f seconds' % (skyline_app, namespace, len(assigned_metrics), time() - begin)) logger.info('%s :: vaccum %s keyspace is now %d keys' % (skyline_app, namespace, (len(assigned_metrics) - euthanized))) logger.info('%s :: vaccum blocked %d times' % (skyline_app, blocked)) logger.info('%s :: vacuum euthanized %d geriatric keys' % (skyline_app, euthanized)) logger.info('%s :: vacuum processed %d active keys' % (skyline_app, active_keys)) logger.info('%s :: vacuum potentially trimmed %d keys' % (skyline_app, trimmed_keys))
def get_correlations(base_name, anomaly_timestamp, anomalous_ts, assigned_metrics, raw_assigned, remote_assigned, anomalies): logger = logging.getLogger(skyline_app_logger) # Distill timeseries strings into lists start = timer() count = 0 metrics_checked_for_correlation = 0 # Sample the time series # @modified 20180720 - Feature #2464: luminosity_remote_data # Added note here - if you modify the value of 600 here, it must be # modified in the luminosity_remote_data function in # skyline/webapp/backend.py as well from_timestamp = anomaly_timestamp - 600 correlated_metrics = [] correlations = [] no_data = False if not anomalous_ts: no_data = True if not assigned_metrics: no_data = True if not raw_assigned: no_data = True if not anomalies: no_data = True if no_data: logger.error('error :: get_correlations :: no data') return (correlated_metrics, correlations) start_local_correlations = timer() local_redis_metrics_checked_count = 0 local_redis_metrics_correlations_count = 0 logger.info('get_correlations :: the local Redis metric count is %s' % str(len(assigned_metrics))) for i, metric_name in enumerate(assigned_metrics): count += 1 # print(metric_name) # @modified 20180719 - Branch #2270: luminosity # Removed test limiting that was errorneously left in # if count > 1000: # break correlated = None metric_base_name = metric_name.replace(settings.FULL_NAMESPACE, '', 1) if str(metric_base_name) == str(base_name): continue try: raw_series = raw_assigned[i] unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) except: timeseries = [] if not timeseries: # print('no time series data for %s' % base_name) continue # Convert the time series if this is a known_derivative_metric known_derivative_metric = is_derivative_metric(skyline_app, metric_base_name) if known_derivative_metric: try: derivative_timeseries = nonNegativeDerivative(timeseries) timeseries = derivative_timeseries except: logger.error(traceback.format_exc()) logger.error('error :: nonNegativeDerivative') correlate_ts = [] for ts, value in timeseries: if int(ts) < from_timestamp: continue if int(ts) <= anomaly_timestamp: correlate_ts.append((int(ts), value)) # @modified 20180720 - Feature #2464: luminosity_remote_data # Added note here - if you modify the value of 61 here, it must be # modified in the luminosity_remote_data function in # skyline/webapp/backend.py as well if int(ts) > (anomaly_timestamp + 61): break if not correlate_ts: continue local_redis_metrics_checked_count += 1 anomaly_ts_dict = dict(anomalous_ts) correlate_ts_dict = dict(correlate_ts) for a in anomalies: try: # @modified 20180720 - Feature #2464: luminosity_remote_data # Added note here - if you modify the value of 120 here, it must be # modified in the luminosity_remote_data function in # skyline/webapp/backend.py as well if int(a.exact_timestamp) < int(anomaly_timestamp - 120): continue if int(a.exact_timestamp) > int(anomaly_timestamp + 120): continue except: continue try: time_period = (int(anomaly_timestamp - 120), int(anomaly_timestamp + 120)) my_correlator = Correlator(anomaly_ts_dict, correlate_ts_dict, time_period) # For better correlation use 0.9 instead of 0.8 for the threshold # @modified 20180524 - Feature #2360: CORRELATE_ALERTS_ONLY # Branch #2270: luminosity # Feature #2378: Add redis auth to Skyline and rebrow # Added this to setting.py # if my_correlator.is_correlated(threshold=0.9): try: cross_correlation_threshold = settings.LUMINOL_CROSS_CORRELATION_THRESHOLD metrics_checked_for_correlation += 1 except: cross_correlation_threshold = 0.9 if my_correlator.is_correlated( threshold=cross_correlation_threshold): correlation = my_correlator.get_correlation_result() correlated = True correlations.append([ metric_base_name, correlation.coefficient, correlation.shift, correlation.shifted_coefficient ]) local_redis_metrics_correlations_count += 1 except: pass if correlated: correlated_metrics.append(metric_base_name) # @added 20180720 - Feature #2464: luminosity_remote_data # Added the correlation of preprocessed remote data end_local_correlations = timer() logger.info( 'get_correlations :: checked - local_redis_metrics_checked_count is %s' % str(local_redis_metrics_checked_count)) logger.info( 'get_correlations :: correlated - local_redis_metrics_correlations_count is %s' % str(local_redis_metrics_correlations_count)) logger.info( 'get_correlations :: processed %s correlations on local_redis_metrics_checked_count %s local metrics in %.6f seconds' % (str(local_redis_metrics_correlations_count), str(local_redis_metrics_checked_count), (end_local_correlations - start_local_correlations))) remote_metrics_count = 0 remote_correlations_check_count = 0 remote_correlations_count = 0 logger.info('get_correlations :: remote_assigned count %s' % str(len(remote_assigned))) start_remote_correlations = timer() for ts_data in remote_assigned: remote_metrics_count += 1 correlated = None metric_name = str(ts_data[0]) metric_base_name = metric_name.replace(settings.FULL_NAMESPACE, '', 1) if str(metric_base_name) == str(base_name): continue timeseries = [] try: timeseries = ts_data[1] except: timeseries = [] if not timeseries: continue correlate_ts = [] for ts, value in timeseries: if int(ts) < from_timestamp: continue if int(ts) <= anomaly_timestamp: correlate_ts.append((int(ts), value)) # @modified 20180720 - Feature #2464: luminosity_remote_data # Added note here - if you modify the value of 61 here, it must be # modified in the luminosity_remote_data function in # skyline/webapp/backend.py as well if int(ts) > (anomaly_timestamp + 61): break if not correlate_ts: continue anomaly_ts_dict = dict(anomalous_ts) correlate_ts_dict = dict(correlate_ts) for a in anomalies: try: # @modified 20180720 - Feature #2464: luminosity_remote_data # Added note here - if you modify the value of 120 here, it must be # modified in the luminosity_remote_data function in # skyline/webapp/backend.py as well if int(a.exact_timestamp) < int(anomaly_timestamp - 120): continue if int(a.exact_timestamp) > int(anomaly_timestamp + 120): continue except: continue try: time_period = (int(anomaly_timestamp - 120), int(anomaly_timestamp + 120)) my_correlator = Correlator(anomaly_ts_dict, correlate_ts_dict, time_period) metrics_checked_for_correlation += 1 remote_correlations_check_count += 1 try: cross_correlation_threshold = settings.LUMINOL_CROSS_CORRELATION_THRESHOLD except: cross_correlation_threshold = 0.9 if my_correlator.is_correlated( threshold=cross_correlation_threshold): correlation = my_correlator.get_correlation_result() correlated = True correlations.append([ metric_base_name, correlation.coefficient, correlation.shift, correlation.shifted_coefficient ]) remote_correlations_count += 1 except: pass if correlated: correlated_metrics.append(metric_base_name) end_remote_correlations = timer() logger.info( 'get_correlations :: checked - remote_correlations_check_count is %s' % str(remote_correlations_check_count)) logger.info( 'get_correlations :: correlated - remote_correlations_count is %s' % str(remote_correlations_count)) logger.info( 'get_correlations :: processed remote correlations on remote_metrics_count %s local metric in %.6f seconds' % (str(remote_metrics_count), (end_remote_correlations - start_remote_correlations))) end = timer() logger.info( 'get_correlations :: checked a total of %s metrics and correlated %s metrics to %s anomaly, processed in %.6f seconds' % (str(metrics_checked_for_correlation), str( len(correlated_metrics)), base_name, (end - start))) # @added 20170720 - Task #2462: Implement useful metrics for Luminosity # Added runtime to calculate avg_runtime Graphite metric runtime = '%.6f' % (end - start) return (correlated_metrics, correlations, metrics_checked_for_correlation, runtime)
def alert_smtp(alert, metric): """ Called by :func:`~trigger_alert` and sends an alert via smtp to the recipients that are configured for the metric. """ LOCAL_DEBUG = False logger = logging.getLogger(skyline_app_logger) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - sending smtp alert') logger.info('debug :: alert_smtp - Memory usage at start: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) # FULL_DURATION to hours so that analyzer surfaces the relevant timeseries data # in the graph full_duration_in_hours = int(settings.FULL_DURATION) / 3600 # For backwards compatibility if '@' in alert[1]: sender = settings.ALERT_SENDER recipient = alert[1] else: sender = settings.SMTP_OPTS['sender'] # @modified 20160806 - Added default_recipient try: recipients = settings.SMTP_OPTS['recipients'][alert[0]] use_default_recipient = False except: use_default_recipient = True if use_default_recipient: try: recipients = settings.SMTP_OPTS['default_recipient'] logger.info( 'alert_smtp - using default_recipient as no recipients are configured for %s' % str(alert[0])) except: logger.error( 'error :: alert_smtp - no known recipient for %s' % str(alert[0])) return False # Backwards compatibility if type(recipients) is str: recipients = [recipients] unencoded_graph_title = 'Skyline Analyzer - ALERT at %s hours %s - %s' % ( full_duration_in_hours, metric[1], metric[0]) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - unencoded_graph_title: %s' % unencoded_graph_title) graph_title_string = quote(unencoded_graph_title, safe='') graph_title = '&title=%s' % graph_title_string if settings.GRAPHITE_PORT != '': link = '%s://%s:%s/render/?from=-%shours&target=cactiStyle(%s)%s%s&colorList=orange' % ( settings.GRAPHITE_PROTOCOL, settings.GRAPHITE_HOST, settings.GRAPHITE_PORT, full_duration_in_hours, metric[1], settings.GRAPHITE_GRAPH_SETTINGS, graph_title) else: link = '%s://%s/render/?from=-%shours&target=cactiStyle(%s)%s%s&colorList=orange' % ( settings.GRAPHITE_PROTOCOL, settings.GRAPHITE_HOST, full_duration_in_hours, metric[1], settings.GRAPHITE_GRAPH_SETTINGS, graph_title) content_id = metric[1] image_data = None if settings.SMTP_OPTS.get('embed-images'): try: image_data = urllib2.urlopen(link).read() if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - image data OK') except urllib2.URLError: image_data = None if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - image data None') if LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - Memory usage after image_data: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) # If we failed to get the image or if it was explicitly disabled, # use the image URL instead of the content. if image_data is None: img_tag = '<img src="%s"/>' % link else: img_tag = '<img src="cid:%s"/>' % content_id if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - img_tag: %s' % img_tag) redis_image_data = None try: plot_redis_data = settings.PLOT_REDIS_DATA except: plot_redis_data = False if settings.SMTP_OPTS.get('embed-images') and plot_redis_data: # Create graph from Redis data try: REDIS_ALERTER_CONN = redis.StrictRedis( unix_socket_path=settings.REDIS_SOCKET_PATH) except: logger.error('error :: alert_smtp - redis connection failed') redis_metric_key = '%s%s' % (settings.FULL_NAMESPACE, metric[1]) try: raw_series = REDIS_ALERTER_CONN.get(redis_metric_key) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - raw_series: %s' % 'OK') except: if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - raw_series: %s' % 'FAIL') try: if LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - Memory usage before get Redis timeseries data: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) unpacker = Unpacker(use_list=True) unpacker.feed(raw_series) timeseries_x = [float(item[0]) for item in unpacker] unpacker = Unpacker(use_list=True) unpacker.feed(raw_series) timeseries_y = [item[1] for item in unpacker] unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) if LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - Memory usage after get Redis timeseries data: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) except: logger.error('error :: alert_smtp - unpack timeseries failed') timeseries = None pd_series_values = None if timeseries: try: if LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - Memory usage before pd.Series: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) values = pd.Series([x[1] for x in timeseries]) # Because the truth value of a Series is ambiguous pd_series_values = True if LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - Memory usage after pd.Series: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) except: logger.error( 'error :: alert_smtp - pandas value series on timeseries failed' ) if pd_series_values: try: array_median = np.median(values) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - values median: %s' % str(array_median)) array_amax = np.amax(values) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - array_amax: %s' % str(array_amax)) array_amin = np.amin(values) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - array_amin: %s' % str(array_amin)) mean = values.mean() if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - mean: %s' % str(mean)) stdDev = values.std() if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - stdDev: %s' % str(stdDev)) sigma3 = 3 * stdDev if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - sigma3: %s' % str(sigma3)) # sigma3_series = [sigma3] * len(values) sigma3_upper_bound = mean + sigma3 try: sigma3_lower_bound = mean - sigma3 except: sigma3_lower_bound = 0 sigma3_upper_series = [sigma3_upper_bound] * len(values) sigma3_lower_series = [sigma3_lower_bound] * len(values) amax_series = [array_amax] * len(values) amin_series = [array_amin] * len(values) mean_series = [mean] * len(values) except: logger.error( 'error :: alert_smtp - numpy ops on series failed') mean_series = None if mean_series: graph_title = 'Skyline Analyzer - ALERT - at %s hours - Redis data\n%s - anomalous value: %s' % ( full_duration_in_hours, metric[1], metric[0]) # @modified 20160814 - Bug #1558: Memory leak in Analyzer # I think the buf is causing a memory leak, trying a file # if python_version == 3: # buf = io.StringIO() # else: # buf = io.BytesIO() buf = '%s/%s.%s.%s.png' % (settings.SKYLINE_TMP_DIR, skyline_app, str(metric[0]), metric[0]) if LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - Memory usage before plot Redis data: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) # Too big # rcParams['figure.figsize'] = 12, 6 rcParams['figure.figsize'] = 8, 4 try: # fig = plt.figure() fig = plt.figure(frameon=False) ax = fig.add_subplot(111) ax.set_title(graph_title, fontsize='small') ax.set_axis_bgcolor('black') try: datetimes = [ dt.datetime.utcfromtimestamp(ts) for ts in timeseries_x ] if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - datetimes: %s' % 'OK') except: logger.error('error :: alert_smtp - datetimes: %s' % 'FAIL') plt.xticks(rotation=0, horizontalalignment='center') xfmt = DateFormatter('%a %H:%M') plt.gca().xaxis.set_major_formatter(xfmt) ax.xaxis.set_major_formatter(xfmt) ax.plot(datetimes, timeseries_y, color='orange', lw=0.6, zorder=3) ax.tick_params(axis='both', labelsize='xx-small') max_value_label = 'max - %s' % str(array_amax) ax.plot(datetimes, amax_series, lw=1, label=max_value_label, color='m', ls='--', zorder=4) min_value_label = 'min - %s' % str(array_amin) ax.plot(datetimes, amin_series, lw=1, label=min_value_label, color='b', ls='--', zorder=4) mean_value_label = 'mean - %s' % str(mean) ax.plot(datetimes, mean_series, lw=1.5, label=mean_value_label, color='g', ls='--', zorder=4) sigma3_text = (r'3$\sigma$') # sigma3_label = '%s - %s' % (str(sigma3_text), str(sigma3)) sigma3_upper_label = '%s upper - %s' % ( str(sigma3_text), str(sigma3_upper_bound)) ax.plot(datetimes, sigma3_upper_series, lw=1, label=sigma3_upper_label, color='r', ls='solid', zorder=4) if sigma3_lower_bound > 0: sigma3_lower_label = '%s lower - %s' % ( str(sigma3_text), str(sigma3_lower_bound)) ax.plot(datetimes, sigma3_lower_series, lw=1, label=sigma3_lower_label, color='r', ls='solid', zorder=4) ax.get_yaxis().get_major_formatter().set_useOffset(False) ax.get_yaxis().get_major_formatter().set_scientific(False) # Shrink current axis's height by 10% on the bottom box = ax.get_position() ax.set_position([ box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9 ]) # Put a legend below current axis ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=4, fontsize='x-small') plt.rc('lines', lw=2, color='w') plt.grid(True) ax.grid(b=True, which='both', axis='both', color='lightgray', linestyle='solid', alpha=0.5, linewidth=0.6) ax.set_axis_bgcolor('black') rcParams['xtick.direction'] = 'out' rcParams['ytick.direction'] = 'out' ax.margins(y=.02, x=.03) # tight_layout removes the legend box # fig.tight_layout() try: if LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - Memory usage before plt.savefig: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) plt.savefig(buf, format='png') # @added 20160814 - Bug #1558: Memory leak in Analyzer # As per http://www.mail-archive.com/[email protected]/msg13222.html # savefig in the parent process was causing the memory leak # the below fig.clf() and plt.close() did not resolve this # however spawing a multiprocessing process for alert_smtp # does solve this as issue as all memory is freed when the # process terminates. fig.clf() plt.close(fig) redis_graph_content_id = 'redis.%s' % metric[1] redis_image_data = True if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - savefig: %s' % 'OK') logger.info( 'debug :: alert_smtp - Memory usage after plt.savefig: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) except: logger.error('error :: alert_smtp - plt.savefig: %s' % 'FAIL') except: logger.error('error :: alert_smtp - could not build plot') logger.info(traceback.format_exc()) if LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - Memory usage before email: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) if redis_image_data: redis_img_tag = '<img src="cid:%s"/>' % redis_graph_content_id if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - redis_img_tag: %s' % str(redis_img_tag)) else: redis_img_tag = '<img src="none"/>' try: body = '<h3><font color="#dd3023">Sky</font><font color="#6698FF">line</font><font color="black"> Analyzer alert</font></h3><br>' body += '<font color="black">metric: <b>%s</b></font><br>' % metric[1] body += '<font color="black">Anomalous value: %s</font><br>' % str( metric[0]) body += '<font color="black">At hours: %s</font><br>' % str( full_duration_in_hours) body += '<font color="black">Next alert in: %s seconds</font><br>' % str( alert[2]) if redis_image_data: body += '<font color="black">min: %s | max: %s | mean: %s <br>' % ( str(array_amin), str(array_amax), str(mean)) body += '3-sigma: %s <br>' % str(sigma3) body += '3-sigma upper bound: %s | 3-sigma lower bound: %s <br></font>' % ( str(sigma3_upper_bound), str(sigma3_lower_bound)) body += '<h3><font color="black">Redis data at FULL_DURATION</font></h3><br>' body += '<div dir="ltr">:%s<br></div>' % redis_img_tag if image_data: body += '<h3><font color="black">Graphite data at FULL_DURATION (may be aggregated)</font></h3>' body += '<div dir="ltr"><a href="%s">%s</a><br></div><br>' % ( link, img_tag) body += '<font color="black">Clicking on the above graph will open to the Graphite graph with current data</font><br>' if redis_image_data: body += '<font color="black">To disable the Redis data graph view, set PLOT_REDIS_DATA to False in your settings.py, if the Graphite graph is sufficient for you,<br>' body += 'however do note that will remove the 3-sigma and mean value too.</font>' body += '<br>' body += '<div dir="ltr" align="right"><font color="#dd3023">Sky</font><font color="#6698FF">line</font><font color="black"> version :: %s</font></div><br>' % str( skyline_version) except: logger.error('error :: alert_smtp - could not build body') logger.info(traceback.format_exc()) for recipient in recipients: try: msg = MIMEMultipart('alternative') msg['Subject'] = '[Skyline alert] - Analyzer ALERT - ' + metric[1] msg['From'] = sender msg['To'] = recipient msg.attach(MIMEText(body, 'html')) if redis_image_data: try: # @modified 20160814 - Bug #1558: Memory leak in Analyzer # I think the buf is causing a memory leak, trying a file # buf.seek(0) # msg_plot_attachment = MIMEImage(buf.read()) # msg_plot_attachment = MIMEImage(buf.read()) try: with open(buf, 'r') as f: plot_image_data = f.read() try: os.remove(buf) except OSError: logger.error( 'error :: alert_smtp - failed to remove file - %s' % buf) logger.info(traceback.format_exc()) pass except: logger.error('error :: failed to read plot file - %s' % buf) plot_image_data = None msg_plot_attachment = MIMEImage(plot_image_data) msg_plot_attachment.add_header( 'Content-ID', '<%s>' % redis_graph_content_id) msg.attach(msg_plot_attachment) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - msg_plot_attachment - redis data done' ) except: logger.error('error :: alert_smtp - msg_plot_attachment') logger.info(traceback.format_exc()) if image_data is not None: try: msg_attachment = MIMEImage(image_data) msg_attachment.add_header('Content-ID', '<%s>' % content_id) msg.attach(msg_attachment) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - msg_attachment - Graphite img source done' ) except: logger.error('error :: alert_smtp - msg_attachment') logger.info(traceback.format_exc()) except: logger.error('error :: alert_smtp - could not attach') logger.info(traceback.format_exc()) s = SMTP('127.0.0.1') try: s.sendmail(sender, recipient, msg.as_string()) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - message sent to %s OK' % str(recipient)) except: logger.error('error :: alert_smtp - could not send email to %s' % str(recipient)) logger.info(traceback.format_exc()) s.quit() if LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - Memory usage after email: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) if redis_image_data: # buf.seek(0) # buf.write('none') if LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - Memory usage before del redis_image_data objects: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) del raw_series del unpacker del timeseries[:] del timeseries_x[:] del timeseries_y[:] del values del datetimes[:] del msg_plot_attachment del redis_image_data # We del all variables that are floats as they become unique objects and # can result in what appears to be a memory leak, but is not, it is # just the way Python handles floats del mean del array_amin del array_amax del stdDev del sigma3 if LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - Memory usage after del redis_image_data objects: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) if LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - Memory usage before del fig object: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) # @added 20160814 - Bug #1558: Memory leak in Analyzer # Issue #21 Memory leak in Analyzer - https://github.com/earthgecko/skyline/issues/21 # As per http://www.mail-archive.com/[email protected]/msg13222.html fig.clf() plt.close(fig) del fig if LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - Memory usage after del fig object: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) if LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - Memory usage before del other objects: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) del recipients[:] del body del msg del image_data del msg_attachment if LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - Memory usage after del other objects: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) return
def spin_process(self, i, unique_metrics): """ Assign a bunch of metrics for a process to analyze. """ # Discover assigned metrics keys_per_processor = int( ceil( float(len(unique_metrics)) / float(settings.ANALYZER_PROCESSES))) if i == settings.ANALYZER_PROCESSES: assigned_max = len(unique_metrics) else: assigned_max = i * keys_per_processor assigned_min = assigned_max - keys_per_processor assigned_keys = range(assigned_min, assigned_max) # Compile assigned metrics assigned_metrics = [unique_metrics[index] for index in assigned_keys] # Check if this process is unnecessary if len(assigned_metrics) == 0: return # Multi get series raw_assigned = self.redis_conn.mget(assigned_metrics) # Make process-specific dicts exceptions = defaultdict(int) anomaly_breakdown = defaultdict(int) # Distill timeseries strings into lists for i, metric_name in enumerate(assigned_metrics): self.check_if_parent_is_alive() try: raw_series = raw_assigned[i] unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) anomalous, ensemble, datapoint = run_selected_algorithm( timeseries, metric_name) # If it's anomalous, add it to list if anomalous: base_name = metric_name.replace(settings.FULL_NAMESPACE, '', 1) metric = [datapoint, base_name] self.anomalous_metrics.append(metric) # Get the anomaly breakdown - who returned True? for index, value in enumerate(ensemble): if value: algorithm = settings.ALGORITHMS[index] anomaly_breakdown[algorithm] += 1 # It could have been deleted by the Roomba except TypeError: exceptions['DeletedByRoomba'] += 1 except TooShort: exceptions['TooShort'] += 1 except Stale: exceptions['Stale'] += 1 except Boring: exceptions['Boring'] += 1 except: exceptions['Other'] += 1 logger.info(traceback.format_exc()) # Add values to the queue so the parent process can collate for key, value in anomaly_breakdown.items(): self.anomaly_breakdown_q.put((key, value)) for key, value in exceptions.items(): self.exceptions_q.put((key, value))
def test(self): """ Called when the process intializes. """ while 1: now = time() # Make sure Redis is up try: self.redis_conn.ping() except: logger.error( 'cloudbrain can\'t connect to redis at socket path %s' % settings.REDIS_SOCKET_PATH) sleep(10) self.redis_conn = StrictRedis( unix_socket_path=settings.REDIS_SOCKET_PATH) continue # Discover unique metrics unique_metrics = list( self.redis_conn.smembers(settings.FULL_NAMESPACE + 'unique_metrics')) if len(unique_metrics) == 0: logger.info( 'no metrics in redis. try adding some - see README') sleep(10) continue # Spawn processes pids = [] for i in range(1, settings.ANALYZER_PROCESSES + 1): if i > len(unique_metrics): logger.info( 'WARNING: cloudbrain is set for more cores than needed.' ) break p = Process(target=self.spin_process, args=(i, unique_metrics)) pids.append(p) p.start() # Send wait signal to zombie processes for p in pids: p.join() # Grab data from the queue and populate dictionaries exceptions = dict() anomaly_breakdown = dict() while 1: try: key, value = self.anomaly_breakdown_q.get_nowait() if key not in anomaly_breakdown.keys(): anomaly_breakdown[key] = value else: anomaly_breakdown[key] += value except Empty: break while 1: try: key, value = self.exceptions_q.get_nowait() if key not in exceptions.keys(): exceptions[key] = value else: exceptions[key] += value except Empty: break # Send alerts if settings.ENABLE_ALERTS: for alert in settings.ALERTS: for metric in self.anomalous_metrics: if alert[0] in metric[1]: cache_key = 'last_alert.%s.%s' % (alert[1], metric[1]) try: last_alert = self.redis_conn.get(cache_key) if not last_alert: self.redis_conn.setex( cache_key, alert[2], packb(metric[0])) trigger_alert(alert, metric) except Exception as e: logger.error("couldn't send alert: %s" % e) # Write anomalous_metrics to static webapp directory filename = path.abspath( path.join(path.dirname(__file__), '..', settings.ANOMALY_DUMP)) with open(filename, 'w') as fh: # Make it JSONP with a handle_data() function anomalous_metrics = list(self.anomalous_metrics) anomalous_metrics.sort(key=operator.itemgetter(1)) fh.write('handle_data(%s)' % anomalous_metrics) # Log progress logger.info('seconds to run :: %.2f' % (time() - now)) logger.info('total metrics :: %d' % len(unique_metrics)) logger.info('total analyzed :: %d' % (len(unique_metrics) - sum(exceptions.values()))) logger.info('total anomalies :: %d' % len(self.anomalous_metrics)) logger.info('exception stats :: %s' % exceptions) logger.info('anomaly breakdown :: %s' % anomaly_breakdown) # Log to Graphite self.send_graphite_metric('cloudbrain.analyzer.run_time', '%.2f' % (time() - now)) self.send_graphite_metric( 'cloudbrain.analyzer.total_analyzed', '%.2f' % (len(unique_metrics) - sum(exceptions.values()))) # Check canary metric raw_series = self.redis_conn.get(settings.FULL_NAMESPACE + settings.CANARY_METRIC) if raw_series is not None: unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) time_human = (timeseries[-1][0] - timeseries[0][0]) / 3600 projected = 24 * (time() - now) / time_human logger.info('canary duration :: %.2f' % time_human) self.send_graphite_metric('cloudbrain.analyzer.duration', '%.2f' % time_human) self.send_graphite_metric('cloudbrain.analyzer.projected', '%.2f' % projected) # Reset counters self.anomalous_metrics[:] = [] # Sleep if it went too fast if time() - now < 5: logger.info('sleeping due to low run time...') sleep(10)
def get_correlations(base_name, anomaly_timestamp, anomalous_ts, assigned_metrics, raw_assigned, anomalies): logger = logging.getLogger(skyline_app_logger) # Distill timeseries strings into lists start = timer() count = 0 # Sample the time series from_timestamp = anomaly_timestamp - 600 correlated_metrics = [] correlations = [] no_data = False if not anomalous_ts: no_data = True if not assigned_metrics: no_data = True if not raw_assigned: no_data = True if not anomalies: no_data = True if no_data: logger.error('error :: get_correlations :: no data') return (correlated_metrics, correlations) for i, metric_name in enumerate(assigned_metrics): count += 1 # print(metric_name) if count > 1000: break correlated = None metric_base_name = metric_name.replace(settings.FULL_NAMESPACE, '', 1) if str(metric_base_name) == str(base_name): continue try: raw_series = raw_assigned[i] unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) except: timeseries = [] if not timeseries: # print('no time series data for %s' % base_name) continue # Convert the time series if this is a known_derivative_metric known_derivative_metric = is_derivative_metric(skyline_app, metric_base_name) if known_derivative_metric: try: derivative_timeseries = nonNegativeDerivative(timeseries) timeseries = derivative_timeseries except: logger.error(traceback.format_exc()) logger.error('error :: nonNegativeDerivative') correlate_ts = [] for ts, value in timeseries: if int(ts) < from_timestamp: continue if int(ts) <= anomaly_timestamp: correlate_ts.append((int(ts), value)) if int(ts) > (anomaly_timestamp + 61): break if not correlate_ts: continue anomaly_ts_dict = dict(anomalous_ts) correlate_ts_dict = dict(correlate_ts) for a in anomalies: try: if int(a.exact_timestamp) < int(anomaly_timestamp - 120): continue if int(a.exact_timestamp) > int(anomaly_timestamp + 120): continue except: continue try: time_period = (int(anomaly_timestamp - 120), int(anomaly_timestamp + 120)) my_correlator = Correlator(anomaly_ts_dict, correlate_ts_dict, time_period) # For better correlation use 0.9 instead of 0.8 for the threshold # @modified 20180524 - Feature #2360: CORRELATE_ALERTS_ONLY # Branch #2270: luminosity # Feature #2378: Add redis auth to Skyline and rebrow # Added this to setting.py # if my_correlator.is_correlated(threshold=0.9): try: cross_correlation_threshold = settings.LUMINOL_CROSS_CORRELATION_THRESHOLD except: cross_correlation_threshold = 0.9 if my_correlator.is_correlated( threshold=cross_correlation_threshold): correlation = my_correlator.get_correlation_result() correlated = True correlations.append([ metric_base_name, correlation.coefficient, correlation.shift, correlation.shifted_coefficient ]) except: pass if correlated: correlated_metrics.append(metric_base_name) end = timer() logger.info( 'correlated %s metrics to %s anomaly, processed in %.6f seconds' % (str(len(correlated_metrics)), base_name, (end - start))) return (correlated_metrics, correlations)
def setUp(self): raw_packet = encode_ping() unpacker = Unpacker() unpacker.feed(raw_packet) self.command = list(unpacker)[1:]
def toJsonBody(self, mpackBody): unpacker = Unpacker(object_pairs_hook=OrderedDict) unpacker.feed(mpackBody) bodyMap = unpacker.unpack() newBody = json.dumps(bodyMap, ensure_ascii=False) return newBody
def setUp(self): raw_packet = encode_jump_to_main() unpacker = Unpacker() unpacker.feed(raw_packet) self.command = list(unpacker)[1:]
def run(self): """ Called when the process intializes. """ while 1: now = time() # Make sure Redis is up try: self.redis_conn.ping() except: logger.error( 'skyline can\'t connect to redis at socket path %s' % settings.REDIS_SOCKET_PATH) sleep(10) self.redis_conn = StrictRedis( unix_socket_path=settings.REDIS_SOCKET_PATH) continue # Discover unique metrics unique_metrics = list( self.redis_conn.smembers(settings.FULL_NAMESPACE + 'unique_metrics')) if len(unique_metrics) == 0: logger.info( 'no metrics in redis. try adding some - see README') sleep(10) continue # Reset boundary_metrics boundary_metrics = [] # Build boundary metrics for metric_name in unique_metrics: for metric in BOUNDARY_METRICS: CHECK_MATCH_PATTERN = metric[0] check_match_pattern = re.compile(CHECK_MATCH_PATTERN) base_name = metric_name.replace(FULL_NAMESPACE, '', 1) pattern_match = check_match_pattern.match(base_name) if pattern_match: if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - boundary metric - pattern MATCHED - " + metric[0] + " | " + base_name) boundary_metrics.append([metric_name, metric[1]]) if ENABLE_BOUNDARY_DEBUG: logger.info("debug - boundary metrics - " + str(boundary_metrics)) if len(boundary_metrics) == 0: logger.info( 'no metrics in redis. try adding some - see README') sleep(10) continue # Spawn processes pids = [] for i in range(1, settings.BOUNDARY_PROCESSES + 1): if i > len(boundary_metrics): logger.info( 'WARNING: skyline boundary is set for more cores than needed.' ) break p = Process(target=self.spin_process, args=(i, boundary_metrics)) pids.append(p) p.start() # Send wait signal to zombie processes for p in pids: p.join() # Grab data from the queue and populate dictionaries exceptions = dict() anomaly_breakdown = dict() while 1: try: key, value = self.anomaly_breakdown_q.get_nowait() if key not in anomaly_breakdown.keys(): anomaly_breakdown[key] = value else: anomaly_breakdown[key] += value except Empty: break while 1: try: key, value = self.exceptions_q.get_nowait() if key not in exceptions.keys(): exceptions[key] = value else: exceptions[key] += value except Empty: break # Send alerts if settings.BOUNDARY_ENABLE_ALERTS: for anomalous_metric in self.anomalous_metrics: datapoint = str(anomalous_metric[0]) metric_name = anomalous_metric[1] base_name = metric_name.replace(FULL_NAMESPACE, '', 1) expiration_time = str(anomalous_metric[2]) metric_trigger = str(anomalous_metric[5]) alert_threshold = int(anomalous_metric[6]) metric_alerters = anomalous_metric[7] algorithm = anomalous_metric[8] if ENABLE_BOUNDARY_DEBUG: logger.info("debug - anomalous_metric - " + str(anomalous_metric)) # Determine how many times has the anomaly been seen if the # ALERT_THRESHOLD is set to > 1 and create a cache key in # redis to keep count so that alert_threshold can be honored if alert_threshold == 0: times_seen = 1 if ENABLE_BOUNDARY_DEBUG: logger.info("debug - alert_threshold - " + str(alert_threshold)) if alert_threshold == 1: times_seen = 1 if ENABLE_BOUNDARY_DEBUG: logger.info("debug - alert_threshold - " + str(alert_threshold)) if alert_threshold > 1: if ENABLE_BOUNDARY_DEBUG: logger.info("debug - alert_threshold - " + str(alert_threshold)) anomaly_cache_key_count_set = False anomaly_cache_key_expiration_time = ( int(alert_threshold) + 1) * 60 anomaly_cache_key = 'anomaly_seen.%s.%s' % (algorithm, base_name) try: anomaly_cache_key_count = self.redis_conn.get( anomaly_cache_key) if not anomaly_cache_key_count: try: if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - redis no anomaly_cache_key - " + str(anomaly_cache_key)) times_seen = 1 if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - redis setex anomaly_cache_key - " + str(anomaly_cache_key)) self.redis_conn.setex( anomaly_cache_key, anomaly_cache_key_expiration_time, packb(int(times_seen))) logger.info( 'set anomaly seen key :: %s seen %s' % (anomaly_cache_key, str(times_seen))) except Exception as e: logger.error('redis setex failed :: %s' % str(anomaly_cache_key)) logger.error("couldn't set key: %s" % e) else: if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - redis anomaly_cache_key retrieved OK - " + str(anomaly_cache_key)) anomaly_cache_key_count_set = True except: if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - redis failed - anomaly_cache_key retrieval failed - " + str(anomaly_cache_key)) anomaly_cache_key_count_set = False if anomaly_cache_key_count_set: unpacker = Unpacker(use_list=False) unpacker.feed(anomaly_cache_key_count) raw_times_seen = list(unpacker) times_seen = int(raw_times_seen[0]) + 1 try: self.redis_conn.setex( anomaly_cache_key, anomaly_cache_key_expiration_time, packb(int(times_seen))) logger.info( 'set anomaly seen key :: %s seen %s' % (anomaly_cache_key, str(times_seen))) except: times_seen = 1 logger.error( 'set anomaly seen key failed :: %s seen %s' % (anomaly_cache_key, str(times_seen))) # Alert the alerters if times_seen > alert_threshold if times_seen >= alert_threshold: if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - times_seen %s is greater than or equal to alert_threshold %s" % (str(times_seen), str(alert_threshold))) for alerter in metric_alerters.split("|"): # Determine alerter limits send_alert = False alerts_sent = 0 if ENABLE_BOUNDARY_DEBUG: logger.info("debug - checking alerter - %s" % alerter) try: if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - determining alerter_expiration_time for settings" ) alerter_expiration_time_setting = settings.BOUNDARY_ALERTER_OPTS[ 'alerter_expiration_time'][alerter] alerter_expiration_time = int( alerter_expiration_time_setting) if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - determined alerter_expiration_time from settings - %s" % str(alerter_expiration_time)) except: # Set an arbitrary expiry time if not set alerter_expiration_time = 160 if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - could not determine alerter_expiration_time from settings" ) try: if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - determining alerter_limit from settings" ) alerter_limit_setting = settings.BOUNDARY_ALERTER_OPTS[ 'alerter_limit'][alerter] alerter_limit = int(alerter_limit_setting) alerter_limit_set = True if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - determined alerter_limit from settings - %s" % str(alerter_limit)) except: alerter_limit_set = False send_alert = True if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - could not determine alerter_limit from settings" ) # If the alerter_limit is set determine how many # alerts the alerter has sent if alerter_limit_set: alerter_sent_count_key = 'alerts_sent.%s' % ( alerter) try: alerter_sent_count_key_data = self.redis_conn.get( alerter_sent_count_key) if not alerter_sent_count_key_data: if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - redis no alerter key, no alerts sent for - " + str(alerter_sent_count_key)) alerts_sent = 0 send_alert = True if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - alerts_sent set to %s" % str(alerts_sent)) logger.info( "debug - send_alert set to %s" % str(sent_alert)) else: if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - redis alerter key retrieved, unpacking" + str(alerter_sent_count_key)) unpacker = Unpacker(use_list=False) unpacker.feed( alerter_sent_count_key_data) raw_alerts_sent = list(unpacker) alerts_sent = int(raw_alerts_sent[0]) if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - alerter %s alerts sent %s " % (str(alerter), str(alerts_sent))) except: logger.info("No key set - %s" % alerter_sent_count_key) alerts_sent = 0 send_alert = True if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - alerts_sent set to %s" % str(alerts_sent)) logger.info( "debug - send_alert set to %s" % str(send_alert)) if alerts_sent < alerter_limit: send_alert = True if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - alerts_sent %s is less than alerter_limit %s" % (str(alerts_sent), str(alerter_limit))) logger.info( "debug - send_alert set to %s" % str(send_alert)) # Send alert alerter_alert_sent = False if send_alert: cache_key = 'last_alert.boundary.%s.%s.%s' % ( alerter, base_name, algorithm) if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - checking cache_key - %s" % cache_key) try: last_alert = self.redis_conn.get(cache_key) if not last_alert: try: self.redis_conn.setex( cache_key, int(anomalous_metric[2]), packb(int( anomalous_metric[0]))) if ENABLE_BOUNDARY_DEBUG: logger.info( 'debug - key setex OK - %s' % (cache_key)) trigger_alert( alerter, datapoint, base_name, expiration_time, metric_trigger, algorithm) logger.info( 'alert sent :: %s - %s - via %s - %s' % (base_name, datapoint, alerter, algorithm)) trigger_alert( "syslog", datapoint, base_name, expiration_time, metric_trigger, algorithm) logger.info( 'alert sent :: %s - %s - via syslog - %s' % (base_name, datapoint, algorithm)) alerter_alert_sent = True except Exception as e: logger.error( 'alert failed :: %s - %s - via %s - %s' % (base_name, datapoint, alerter, algorithm)) logger.error( "couldn't send alert: %s" % str(e)) trigger_alert( "syslog", datapoint, base_name, expiration_time, metric_trigger, algorithm) else: if ENABLE_BOUNDARY_DEBUG: logger.info( "debug - cache_key exists not alerting via %s for %s is less than alerter_limit %s" % (alerter, cache_key)) trigger_alert("syslog", datapoint, base_name, expiration_time, metric_trigger, algorithm) logger.info( 'alert sent :: %s - %s - via syslog - %s' % (base_name, datapoint, algorithm)) except: trigger_alert("syslog", datapoint, base_name, expiration_time, metric_trigger, algorithm) logger.info( 'alert sent :: %s - %s - via syslog - %s' % (base_name, datapoint, algorithm)) else: trigger_alert("syslog", datapoint, base_name, expiration_time, metric_trigger, algorithm) logger.info( 'alert sent :: %s - %s - via syslog - %s' % (base_name, datapoint, algorithm)) # Update the alerts sent for the alerter cache key, # to allow for alert limiting if alerter_alert_sent and alerter_limit_set: try: alerter_sent_count_key = 'alerts_sent.%s' % ( alerter) new_alerts_sent = int(alerts_sent) + 1 self.redis_conn.setex( alerter_sent_count_key, alerter_expiration_time, packb(int(new_alerts_sent))) logger.info('set %s - %s' % (alerter_sent_count_key, str(new_alerts_sent))) except: logger.error('failed to set %s - %s' % (alerter_sent_count_key, str(new_alerts_sent))) else: # Always alert to syslog, even if alert_threshold is not # breached or if send_alert is not True trigger_alert("syslog", datapoint, base_name, expiration_time, metric_trigger, algorithm) logger.info('alert sent :: %s - %s - via syslog - %s' % (base_name, datapoint, algorithm)) # Write anomalous_metrics to static webapp directory if len(self.anomalous_metrics) > 0: filename = path.abspath( path.join(path.dirname(__file__), '..', settings.ANOMALY_DUMP)) with open(filename, 'w') as fh: # Make it JSONP with a handle_data() function anomalous_metrics = list(self.anomalous_metrics) anomalous_metrics.sort(key=operator.itemgetter(1)) fh.write('handle_data(%s)' % anomalous_metrics) # Log progress logger.info('seconds to run :: %.2f' % (time() - now)) logger.info('total metrics :: %d' % len(boundary_metrics)) logger.info('total analyzed :: %d' % (len(boundary_metrics) - sum(exceptions.values()))) logger.info('total anomalies :: %d' % len(self.anomalous_metrics)) logger.info('exception stats :: %s' % exceptions) logger.info('anomaly breakdown :: %s' % anomaly_breakdown) # Log to Graphite self.send_graphite_metric( 'skyline.boundary.' + SERVER_METRIC_PATH + 'run_time', '%.2f' % (time() - now)) self.send_graphite_metric( 'skyline.boundary.' + SERVER_METRIC_PATH + 'total_analyzed', '%.2f' % (len(boundary_metrics) - sum(exceptions.values()))) self.send_graphite_metric( 'skyline.boundary.' + SERVER_METRIC_PATH + 'total_anomalies', '%d' % len(self.anomalous_metrics)) self.send_graphite_metric( 'skyline.boundary.' + SERVER_METRIC_PATH + 'total_metrics', '%d' % len(boundary_metrics)) for key, value in exceptions.items(): send_metric = 'skyline.boundary.' + SERVER_METRIC_PATH + 'exceptions.%s' % key self.send_graphite_metric(send_metric, '%d' % value) for key, value in anomaly_breakdown.items(): send_metric = 'skyline.boundary.' + SERVER_METRIC_PATH + 'anomaly_breakdown.%s' % key self.send_graphite_metric(send_metric, '%d' % value) # Check canary metric raw_series = self.redis_conn.get(settings.FULL_NAMESPACE + settings.CANARY_METRIC) if raw_series is not None: unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) time_human = (timeseries[-1][0] - timeseries[0][0]) / 3600 projected = 24 * (time() - now) / time_human logger.info('canary duration :: %.2f' % time_human) self.send_graphite_metric( 'skyline.boundary.' + SERVER_METRIC_PATH + 'duration', '%.2f' % time_human) self.send_graphite_metric( 'skyline.boundary.' + SERVER_METRIC_PATH + 'projected', '%.2f' % projected) # Reset counters self.anomalous_metrics[:] = [] # Only run once per minute seconds_to_run = int((time() - now)) if seconds_to_run < 60: sleep_for_seconds = 60 - seconds_to_run else: sleep_for_seconds = 0 if sleep_for_seconds > 0: logger.info('sleeping for %s seconds' % sleep_for_seconds) sleep(sleep_for_seconds)
def get_anomalous_ts(base_name, anomaly_timestamp): logger = logging.getLogger(skyline_app_logger) # @added 20180423 - Feature #2360: CORRELATE_ALERTS_ONLY # Branch #2270: luminosity # Only correlate metrics with an alert setting if correlate_alerts_only: try: smtp_alerter_metrics = list( redis_conn.smembers('analyzer.smtp_alerter_metrics')) except: smtp_alerter_metrics = [] if base_name not in smtp_alerter_metrics: logger.error('%s has no alerter setting, not correlating' % base_name) return [] if not base_name or not anomaly_timestamp: return [] # from skyline_functions import nonNegativeDerivative anomalous_metric = '%s%s' % (settings.FULL_NAMESPACE, base_name) unique_metrics = [] try: unique_metrics = list( redis_conn.smembers(settings.FULL_NAMESPACE + 'unique_metrics')) except: logger.error(traceback.format_exc()) logger.error('error :: get_assigned_metrics :: no unique_metrics') return [] # @added 20180720 - Feature #2464: luminosity_remote_data # Ensure that Luminosity only processes it's own Redis metrics so that if # multiple Skyline instances are running, Luminosity does not process an # anomaly_id for a metric that is not local to itself. This will stop the # call to the remote Redis with other_redis_conn below. With the # introduction of the preprocessing luminosity_remote_data API endpoint for # remote Skyline instances, there is no further requirement for Skyline # instances to have direct access to Redis on another Skyline instance. # A much better solution and means all data is preprocessed and encrypted, # there is no need for iptables other than 443 (or custom https port). # if anomalous_metric in unique_metrics: logger.info( '%s is a metric in Redis, processing on this Skyline instance' % base_name) else: logger.info( '%s is not a metric in Redis, not processing on this Skyline instance' % base_name) return [] assigned_metrics = [anomalous_metric] # @modified 20180419 - raw_assigned = [] try: raw_assigned = redis_conn.mget(assigned_metrics) except: raw_assigned = [] if raw_assigned == [None]: logger.info('%s data not retrieved from local Redis' % (str(base_name))) raw_assigned = [] # @modified 20180721 - Feature #2464: luminosity_remote_data # TO BE DEPRECATED settings.OTHER_SKYLINE_REDIS_INSTANCES # with the addition of the luminosity_remote_data API call and the above if not raw_assigned and settings.OTHER_SKYLINE_REDIS_INSTANCES: # @modified 20180519 - Feature #2378: Add redis auth to Skyline and rebrow # for redis_ip, redis_port in settings.OTHER_SKYLINE_REDIS_INSTANCES: for redis_ip, redis_port, redis_password in settings.OTHER_SKYLINE_REDIS_INSTANCES: if not raw_assigned: try: if redis_password: other_redis_conn = StrictRedis( host=str(redis_ip), port=int(redis_port), password=str(redis_password)) else: other_redis_conn = StrictRedis(host=str(redis_ip), port=int(redis_port)) raw_assigned = other_redis_conn.mget(assigned_metrics) if raw_assigned == [None]: logger.info( '%s data not retrieved from Redis at %s on port %s' % (str(base_name), str(redis_ip), str(redis_port))) raw_assigned = [] if raw_assigned: logger.info( '%s data retrieved from Redis at %s on port %s' % (str(base_name), str(redis_ip), str(redis_port))) except: logger.error(traceback.format_exc()) logger.error( 'error :: failed to connect to Redis at %s on port %s' % (str(redis_ip), str(redis_port))) raw_assigned = [] if not raw_assigned or raw_assigned == [None]: logger.info('%s data not retrieved' % (str(base_name))) return [] for i, metric_name in enumerate(assigned_metrics): try: raw_series = raw_assigned[i] unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) except: timeseries = [] # Convert the time series if this is a known_derivative_metric known_derivative_metric = is_derivative_metric(skyline_app, base_name) if known_derivative_metric: derivative_timeseries = nonNegativeDerivative(timeseries) timeseries = derivative_timeseries # Sample the time series # @modified 20180720 - Feature #2464: luminosity_remote_data # Added note here - if you modify the value of 600 here, it must be # modified in the luminosity_remote_data function in # skyline/webapp/backend.py as well from_timestamp = anomaly_timestamp - 600 anomaly_ts = [] for ts, value in timeseries: if int(ts) < from_timestamp: continue if int(ts) <= anomaly_timestamp: anomaly_ts.append((int(ts), value)) if int(ts) > anomaly_timestamp: break return anomaly_ts
def luminosity_remote_data(anomaly_timestamp): """ Gets all the unique_metrics from Redis and then mgets Redis data for all metrics. The data is then preprocessed for the remote Skyline luminosity instance and only the relevant fragments of the time series are returned. This return is then gzipped by the Flask Webapp response to ensure the minimum about of bandwidth is used. :param anomaly_timestamp: the anomaly timestamp :type anomaly_timestamp: int :return: list :rtype: list """ message = 'luminosity_remote_data returned' success = False luminosity_data = [] logger.info('luminosity_remote_data :: determining unique_metrics') unique_metrics = [] # If you modify the values of 61 or 600 here, it must be modified in the # luminosity_remote_data function in # skyline/luminosity/process_correlations.py as well from_timestamp = int(anomaly_timestamp) - 600 until_timestamp = int(anomaly_timestamp) + 61 try: unique_metrics = list( REDIS_CONN.smembers(settings.FULL_NAMESPACE + 'unique_metrics')) except Exception as e: logger.error('error :: %s' % str(e)) logger.error( 'error :: luminosity_remote_data :: could not determine unique_metrics from Redis set' ) if not unique_metrics: message = 'error :: luminosity_remote_data :: could not determine unique_metrics from Redis set' return luminosity_data, success, message logger.info('luminosity_remote_data :: %s unique_metrics' % str(len(unique_metrics))) # assigned metrics assigned_min = 0 assigned_max = len(unique_metrics) assigned_keys = range(assigned_min, assigned_max) # Compile assigned metrics assigned_metrics = [unique_metrics[index] for index in assigned_keys] # Check if this process is unnecessary if len(assigned_metrics) == 0: message = 'error :: luminosity_remote_data :: assigned_metrics length is 0' logger.error(message) return luminosity_data, success, message # Multi get series raw_assigned_failed = True try: raw_assigned = REDIS_CONN.mget(assigned_metrics) raw_assigned_failed = False except: logger.info(traceback.format_exc()) message = 'error :: luminosity_remote_data :: failed to mget raw_assigned' logger.error(message) return luminosity_data, success, message if raw_assigned_failed: message = 'error :: luminosity_remote_data :: failed to mget raw_assigned' logger.error(message) return luminosity_data, success, message # Distill timeseries strings into lists for i, metric_name in enumerate(assigned_metrics): timeseries = [] try: raw_series = raw_assigned[i] unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) except: timeseries = [] if not timeseries: continue # Convert the time series if this is a known_derivative_metric base_name = metric_name.replace(settings.FULL_NAMESPACE, '', 1) known_derivative_metric = is_derivative_metric('webapp', base_name) if known_derivative_metric: try: derivative_timeseries = nonNegativeDerivative(timeseries) timeseries = derivative_timeseries except: logger.error('error :: nonNegativeDerivative failed') correlate_ts = [] for ts, value in timeseries: if int(ts) < from_timestamp: continue if int(ts) <= anomaly_timestamp: correlate_ts.append((int(ts), value)) if int(ts) > (anomaly_timestamp + until_timestamp): break if not correlate_ts: continue metric_data = [str(metric_name), correlate_ts] luminosity_data.append(metric_data) logger.info( 'luminosity_remote_data :: %s valid metric time series data preprocessed for the remote request' % str(len(luminosity_data))) return luminosity_data, success, message
def determine_id(table, key, value): """ Get the id of something from Redis or the database and insert a new record if one does not exist for the value. :param table: table name :param key: key name :param value: value name :type table: str :type key: str :type value: str :return: int or boolean """ query_cache_key = '%s.mysql_ids.%s.%s.%s' % (skyline_app, table, key, value) determined_id = None redis_determined_id = None if settings.ENABLE_PANORAMA_DEBUG: logger.info('debug :: query_cache_key - %s' % (query_cache_key)) try: redis_known_id = self.redis_conn.get(query_cache_key) except: redis_known_id = None if redis_known_id: unpacker = Unpacker(use_list=False) unpacker.feed(redis_known_id) redis_determined_id = list(unpacker) if redis_determined_id: determined_id = int(redis_determined_id[0]) if determined_id: if determined_id > 0: return determined_id # Query MySQL query = 'select id FROM %s WHERE %s=\'%s\'' % (table, key, value) results = self.mysql_select(query) determined_id = 0 if results: determined_id = int(results[0][0]) if determined_id > 0: # Set the key for a week if not redis_determined_id: try: self.redis_conn.setex(query_cache_key, 604800, packb(determined_id)) logger.info('set redis query_cache_key - %s - id: %s' % ( query_cache_key, str(determined_id))) except Exception as e: logger.error(traceback.format_exc()) logger.error('error :: failed to set query_cache_key - %s - id: %s' % ( query_cache_key, str(determined_id))) return int(determined_id) # INSERT because no known id insert_query = 'insert into %s (%s) VALUES (\'%s\')' % (table, key, value) logger.info('inserting %s into %s table' % (value, table)) try: results = self.mysql_insert(insert_query) except: logger.error(traceback.format_exc()) logger.error('error :: failed to determine the id of %s from the insert' % (value)) raise determined_id = 0 if results: determined_id = int(results) else: logger.error('error :: results not set') raise if determined_id > 0: # Set the key for a week if not redis_determined_id: try: self.redis_conn.setex(query_cache_key, 604800, packb(determined_id)) logger.info('set redis query_cache_key - %s - id: %s' % ( query_cache_key, str(determined_id))) except Exception as e: logger.error(traceback.format_exc()) logger.error('%s' % str(e)) logger.error('error :: failed to set query_cache_key - %s - id: %s' % ( query_cache_key, str(determined_id))) return determined_id logger.error('error :: failed to determine the inserted id for %s' % value) return False
def vacuum(self, i, namespace, duration): """ Trim metrics that are older than settings.FULL_DURATION and purge old metrics. """ begin = time() logger.info('%s :: started vacuum' % (skyline_app)) # Discover assigned metrics namespace_unique_metrics = '%sunique_metrics' % str(namespace) # @modified 20191030 - Bug #3266: py3 Redis binary objects not strings # Branch #3262: py3 # unique_metrics = list(self.redis_conn.smembers(namespace_unique_metrics)) unique_metrics = list( self.redis_conn_decoded.smembers(namespace_unique_metrics)) # @added 20200727 - Feature #3650: ROOMBA_DO_NOT_PROCESS_BATCH_METRICS # Feature #3480: batch_processing # Feature #3486: analyzer_batch if ROOMBA_DO_NOT_PROCESS_BATCH_METRICS and BATCH_PROCESSING and BATCH_PROCESSING_NAMESPACES: try: batch_metrics = list( self.redis_conn_decoded.smembers( 'aet.analyzer.batch_processing_metrics')) except: logger.error( 'error - failed to get Redis set aet.analyzer.batch_processing_metrics' ) batch_metrics = [] if batch_metrics: full_namespace_batch_metrics = [] for base_name in batch_metrics: metric = ''.join((settings.FULL_NAMESPACE, base_name)) full_namespace_batch_metrics.append(metric) del batch_metrics non_batch_unique_metrics = [] for metric in unique_metrics: if metric not in full_namespace_batch_metrics: non_batch_unique_metrics.append(metric) # @modified 20200815 - Feature #3650: ROOMBA_DO_NOT_PROCESS_BATCH_METRICS # del after log # UnboundLocalError: local variable 'full_namespace_batch_metrics' referenced before assignment # del full_namespace_batch_metrics if non_batch_unique_metrics: logger.info( 'roomba :: batch_processing :: removing %s batch metrics from unique_metrics' % str(len(full_namespace_batch_metrics))) unique_metrics = non_batch_unique_metrics del non_batch_unique_metrics # @added 20200815 - Feature #3650: ROOMBA_DO_NOT_PROCESS_BATCH_METRICS del full_namespace_batch_metrics keys_per_processor = int( ceil( float(len(unique_metrics)) / float(settings.ROOMBA_PROCESSES))) if i == settings.ROOMBA_PROCESSES: assigned_max = len(unique_metrics) else: assigned_max = min(len(unique_metrics), i * keys_per_processor) assigned_min = (i - 1) * keys_per_processor assigned_keys = range(assigned_min, assigned_max) # Compile assigned metrics assigned_metrics = [unique_metrics[index] for index in assigned_keys] euthanized = 0 blocked = 0 trimmed_keys = 0 active_keys = 0 # @modified 20191016 - Task #3280: Handle py2 xange and py3 range # Branch #3262: py3 # for i in xrange(len(assigned_metrics)): range_list = [] if python_version == 2: for i in xrange(len(assigned_metrics)): range_list.append(i) if python_version == 3: for i in range(len(assigned_metrics)): range_list.append(i) for i in range_list: self.check_if_parent_is_alive() pipe = self.redis_conn.pipeline() now = time() key = assigned_metrics[i] try: # WATCH the key pipe.watch(key) # Everything below NEEDS to happen before another datapoint # comes in. If your data has a very small resolution (<.1s), # this technique may not suit you. raw_series = pipe.get(key) unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = sorted([unpacked for unpacked in unpacker]) # Put pipe back in multi mode pipe.multi() # There's one value. Purge if it's too old try: if python_version == 2: if not isinstance(timeseries[0], TupleType): if timeseries[0] < now - duration: pipe.delete(key) pipe.srem(namespace_unique_metrics, key) pipe.execute() euthanized += 1 continue if python_version == 3: if not isinstance(timeseries[0], tuple): if timeseries[0] < now - duration: pipe.delete(key) pipe.srem(namespace_unique_metrics, key) pipe.execute() euthanized += 1 continue except IndexError: continue # Check if the last value is too old and purge if timeseries[-1][0] < now - duration: pipe.delete(key) pipe.srem(namespace_unique_metrics, key) pipe.execute() euthanized += 1 continue # Remove old datapoints and duplicates from timeseries temp = set() temp_add = temp.add delta = now - duration trimmed = [ tuple for tuple in timeseries if tuple[0] > delta and tuple[0] not in temp and not temp_add(tuple[0]) ] # Purge if everything was deleted, set key otherwise if len(trimmed) > 0: # Serialize and turn key back into not-an-array btrimmed = packb(trimmed) if len(trimmed) <= 15: value = btrimmed[1:] elif len(trimmed) <= 65535: value = btrimmed[3:] trimmed_keys += 1 else: value = btrimmed[5:] trimmed_keys += 1 pipe.set(key, value) active_keys += 1 else: pipe.delete(key) pipe.srem(namespace_unique_metrics, key) euthanized += 1 pipe.execute() except WatchError: blocked += 1 assigned_metrics.append(key) except Exception as e: # If something bad happens, zap the key and hope it goes away pipe.delete(key) pipe.srem(namespace_unique_metrics, key) pipe.execute() euthanized += 1 logger.info(e) logger.info('%s :: vacuum Euthanizing %s' % (skyline_app, key)) finally: pipe.reset() logger.info( '%s :: vacuum operated on %s %d keys in %f seconds' % (skyline_app, namespace, len(assigned_metrics), time() - begin)) logger.info('%s :: vaccum %s keyspace is now %d keys' % (skyline_app, namespace, (len(assigned_metrics) - euthanized))) logger.info('%s :: vaccum blocked %d times' % (skyline_app, blocked)) logger.info('%s :: vacuum euthanized %d geriatric keys' % (skyline_app, euthanized)) logger.info('%s :: vacuum processed %d active keys' % (skyline_app, active_keys)) logger.info('%s :: vacuum potentially trimmed %d keys' % (skyline_app, trimmed_keys))
def run(self): """ Called when the process intializes. """ while 1: now = time() # Make sure Redis is up try: self.redis_conn.ping() except: logger.error('skyline can\'t connect to redis at socket path %s' % settings.REDIS_SOCKET_PATH) sleep(10) self.redis_conn = StrictRedis(unix_socket_path=settings.REDIS_SOCKET_PATH) continue # Discover unique metrics unique_metrics = list(self.redis_conn.smembers(settings.FULL_NAMESPACE + 'unique_metrics')) if len(unique_metrics) == 0: logger.info('no metrics in redis. try adding some - see README') sleep(10) continue # Spawn processes pids = [] for i in range(1, settings.ANALYZER_PROCESSES + 1): if i > len(unique_metrics): logger.info('WARNING: skyline is set for more cores than needed.') break p = Process(target=self.spin_process, args=(i, unique_metrics)) pids.append(p) p.start() # Send wait signal to zombie processes for p in pids: p.join() # Grab data from the queue and populate dictionaries exceptions = dict() anomaly_breakdown = dict() while 1: try: key, value = self.anomaly_breakdown_q.get_nowait() if key not in anomaly_breakdown.keys(): anomaly_breakdown[key] = value else: anomaly_breakdown[key] += value except Empty: break while 1: try: key, value = self.exceptions_q.get_nowait() if key not in exceptions.keys(): exceptions[key] = value else: exceptions[key] += value except Empty: break # Send alerts if settings.ENABLE_ALERTS: for alert in settings.ALERTS: for metric in self.anomalous_metrics: ALERT_MATCH_PATTERN = alert[0] METRIC_PATTERN = metric[1] alert_match_pattern = re.compile(ALERT_MATCH_PATTERN) pattern_match = alert_match_pattern.match(METRIC_PATTERN) if pattern_match: cache_key = 'last_alert.%s.%s' % (alert[1], metric[1]) try: last_alert = self.redis_conn.get(cache_key) if not last_alert: try: SECOND_ORDER_RESOLUTION_FULL_DURATION = alert[3] logger.info('mirage check :: %s' % (metric[1])) # Write anomalous metric to test at second # order resolution by crucible to the check # file metric_timestamp = int(time()) anomaly_check_file = '%s/%s.%s.txt' % (settings.MIRAGE_CHECK_PATH, metric_timestamp, metric[1]) with open(anomaly_check_file, 'w') as fh: # metric_name, anomalous datapoint, hours to resolve, timestamp fh.write('metric = "%s"\nvalue = "%s"\nhours_to_resolve = "%s"\nmetric_timestamp = "%s"\n' % (metric[1], metric[0], alert[3], metric_timestamp)) logger.info('added mirage check :: %s,%s,%s' % (metric[1], metric[0], alert[3])) if settings.ENABLE_FULL_DURATION_ALERTS: self.redis_conn.setex(cache_key, alert[2], packb(metric[0])) trigger_alert(alert, metric) except: self.redis_conn.setex(cache_key, alert[2], packb(metric[0])) trigger_alert(alert, metric) except Exception as e: logger.error("couldn't send alert: %s" % e) # Write anomalous_metrics to static webapp directory if len(self.anomalous_metrics) > 0: filename = path.abspath(path.join(path.dirname(__file__), '..', settings.ANOMALY_DUMP)) with open(filename, 'w') as fh: # Make it JSONP with a handle_data() function anomalous_metrics = list(self.anomalous_metrics) anomalous_metrics.sort(key=operator.itemgetter(1)) fh.write('handle_data(%s)' % anomalous_metrics) # Log progress logger.info('seconds to run :: %.2f' % (time() - now)) logger.info('total metrics :: %d' % len(unique_metrics)) logger.info('total analyzed :: %d' % (len(unique_metrics) - sum(exceptions.values()))) logger.info('total anomalies :: %d' % len(self.anomalous_metrics)) logger.info('exception stats :: %s' % exceptions) logger.info('anomaly breakdown :: %s' % anomaly_breakdown) # Log to Graphite self.send_graphite_metric('skyline.analyzer.' + SERVER_METRIC_PATH + 'run_time', '%.2f' % (time() - now)) self.send_graphite_metric('skyline.analyzer.' + SERVER_METRIC_PATH + 'total_analyzed', '%.2f' % (len(unique_metrics) - sum(exceptions.values()))) self.send_graphite_metric('skyline.analyzer.' + SERVER_METRIC_PATH + 'total_anomalies', '%d' % len(self.anomalous_metrics)) self.send_graphite_metric('skyline.analyzer.' + SERVER_METRIC_PATH + 'total_metrics', '%d' % len(unique_metrics)) for key, value in exceptions.items(): send_metric = 'skyline.analyzer.' + SERVER_METRIC_PATH + 'exceptions.%s' % key self.send_graphite_metric(send_metric, '%d' % value) for key, value in anomaly_breakdown.items(): send_metric = 'skyline.analyzer.' + SERVER_METRIC_PATH + 'anomaly_breakdown.%s' % key self.send_graphite_metric(send_metric, '%d' % value) # Check canary metric raw_series = self.redis_conn.get(settings.FULL_NAMESPACE + settings.CANARY_METRIC) if raw_series is not None: unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) time_human = (timeseries[-1][0] - timeseries[0][0]) / 3600 projected = 24 * (time() - now) / time_human logger.info('canary duration :: %.2f' % time_human) self.send_graphite_metric('skyline.analyzer.' + SERVER_METRIC_PATH + 'duration', '%.2f' % time_human) self.send_graphite_metric('skyline.analyzer.' + SERVER_METRIC_PATH + 'projected', '%.2f' % projected) # Reset counters self.anomalous_metrics[:] = [] # Sleep if it went too fast if time() - now < 5: logger.info('sleeping due to low run time...') sleep(10)
def spin_process(self, i, unique_metrics): """ Assign a bunch of metrics for a process to analyze. Multiple get the assigned_metrics to the process from Redis. For each metric: - unpack the `raw_timeseries` for the metric. - Analyse each timeseries against `ALGORITHMS` to determine if it is anomalous. - If anomalous add it to the :obj:`self.anomalous_metrics` list - Add what algorithms triggered to the :obj:`self.anomaly_breakdown_q` queue - If :mod:`settings.ENABLE_CRUCIBLE` is ``True``: - Add a crucible data file with the details about the timeseries and anomaly. - Write the timeseries to a json file for crucible. Add keys and values to the queue so the parent process can collate for:\n * :py:obj:`self.anomaly_breakdown_q` * :py:obj:`self.exceptions_q` """ spin_start = time() logger.info('spin_process started') if LOCAL_DEBUG: logger.info('debug :: Memory usage spin_process start: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) # TESTING removal of p.join() from p.terminate() # sleep(4) # @modified 20160801 - Adding additional exception handling to Analyzer # Check the unique_metrics list is valid try: len(unique_metrics) except: logger.error('error :: the unique_metrics list is not valid') logger.info(traceback.format_exc()) logger.info('nothing to do, no unique_metrics') return # Discover assigned metrics keys_per_processor = int(ceil(float(len(unique_metrics)) / float(settings.ANALYZER_PROCESSES))) if i == settings.ANALYZER_PROCESSES: assigned_max = len(unique_metrics) else: assigned_max = min(len(unique_metrics), i * keys_per_processor) # Fix analyzer worker metric assignment #94 # https://github.com/etsy/skyline/pull/94 @languitar:worker-fix assigned_min = (i - 1) * keys_per_processor assigned_keys = range(assigned_min, assigned_max) # assigned_keys = range(300, 310) # Compile assigned metrics assigned_metrics = [unique_metrics[index] for index in assigned_keys] if LOCAL_DEBUG: logger.info('debug :: Memory usage spin_process after assigned_metrics: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) # @added 20190410 - Feature #2916: ANALYZER_ENABLED setting if not ANALYZER_ENABLED: len_assigned_metrics = len(assigned_metrics) logger.info('ANALYZER_ENABLED is set to %s removing the %s assigned_metrics' % ( str(ANALYZER_ENABLED), str(len_assigned_metrics))) assigned_metrics = [] del unique_metrics # Check if this process is unnecessary if len(assigned_metrics) == 0: return # Multi get series # @modified 20160801 - Adding additional exception handling to Analyzer raw_assigned_failed = True try: raw_assigned = self.redis_conn.mget(assigned_metrics) raw_assigned_failed = False if LOCAL_DEBUG: logger.info('debug :: Memory usage spin_process after raw_assigned: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) except: logger.info(traceback.format_exc()) logger.error('error :: failed to get assigned_metrics from Redis') # Make process-specific dicts exceptions = defaultdict(int) anomaly_breakdown = defaultdict(int) # @added 20160803 - Adding additional exception handling to Analyzer if raw_assigned_failed: return # @added 20161119 - Branch #922: ionosphere # Task #1718: review.tsfresh # Determine the unique Mirage and Ionosphere metrics once, which are # used later to determine how Analyzer should handle/route anomalies try: mirage_unique_metrics = list(self.redis_conn.smembers('mirage.unique_metrics')) except: mirage_unique_metrics = [] # @added 20190408 - Feature #2882: Mirage - periodic_check # Add Mirage periodic checks so that Mirage is analysing each metric at # least once per hour. mirage_periodic_check_metric_list = [] try: mirage_periodic_check_enabled = settings.MIRAGE_PERIODIC_CHECK except: mirage_periodic_check_enabled = False try: mirage_periodic_check_interval = settings.MIRAGE_PERIODIC_CHECK_INTERVAL except: mirage_periodic_check_interval = 3600 mirage_periodic_check_interval_minutes = int(int(mirage_periodic_check_interval) / 60) if mirage_unique_metrics and mirage_periodic_check_enabled: mirage_unique_metrics_count = len(mirage_unique_metrics) # Mirage periodic checks are only done on declared namespaces as to # process all Mirage metrics periodically would probably create a # substantial load on Graphite and is probably not required only key # metrics should be analysed by Mirage periodically. periodic_check_mirage_metrics = [] try: mirage_periodic_check_namespaces = settings.MIRAGE_PERIODIC_CHECK_NAMESPACES except: mirage_periodic_check_namespaces = [] for namespace in mirage_periodic_check_namespaces: for metric_name in mirage_unique_metrics: metric_namespace_elements = metric_name.split('.') mirage_periodic_metric = False for periodic_namespace in mirage_periodic_check_namespaces: if not namespace in mirage_periodic_check_namespaces: continue periodic_namespace_namespace_elements = periodic_namespace.split('.') elements_matched = set(metric_namespace_elements) & set(periodic_namespace_namespace_elements) if len(elements_matched) == len(periodic_namespace_namespace_elements): mirage_periodic_metric = True break if mirage_periodic_metric: if not metric_name in periodic_check_mirage_metrics: periodic_check_mirage_metrics.append(metric_name) periodic_check_mirage_metrics_count = len(periodic_check_mirage_metrics) logger.info( 'there are %s known Mirage periodic metrics' % ( str(periodic_check_mirage_metrics_count))) for metric_name in periodic_check_mirage_metrics: try: self.redis_conn.sadd('new.mirage.periodic_check.metrics.all', metric_name) except Exception as e: logger.error('error :: could not add %s to Redis set new.mirage.periodic_check.metrics.all: %s' % ( metric_name, e)) try: self.redis_conn.rename('mirage.periodic_check.metrics.all', 'mirage.periodic_check.metrics.all.old') except: pass try: self.redis_conn.rename('new.mirage.periodic_check.metrics.all', 'mirage.periodic_check.metrics.all') except: pass try: self.redis_conn.delete('mirage.periodic_check.metrics.all.old') except: pass if periodic_check_mirage_metrics_count > mirage_periodic_check_interval_minutes: mirage_periodic_checks_per_minute = periodic_check_mirage_metrics_count / mirage_periodic_check_interval_minutes else: mirage_periodic_checks_per_minute = 1 logger.info( '%s Mirage periodic checks can be added' % ( str(int(mirage_periodic_checks_per_minute)))) for metric_name in periodic_check_mirage_metrics: if len(mirage_periodic_check_metric_list) == int(mirage_periodic_checks_per_minute): break base_name = metric_name.replace(settings.FULL_NAMESPACE, '', 1) mirage_periodic_check_cache_key = 'mirage.periodic_check.%s' % base_name mirage_periodic_check_key = False try: mirage_periodic_check_key = self.redis_conn.get(mirage_periodic_check_cache_key) except Exception as e: logger.error('error :: could not query Redis for cache_key: %s' % e) if not mirage_periodic_check_key: try: key_created_at = int(time()) self.redis_conn.setex( mirage_periodic_check_cache_key, mirage_periodic_check_interval, key_created_at) logger.info( 'created Mirage periodic_check Redis key - %s' % (mirage_periodic_check_cache_key)) mirage_periodic_check_metric_list.append(metric_name) try: self.redis_conn.sadd('new.mirage.periodic_check.metrics', metric_name) except Exception as e: logger.error('error :: could not add %s to Redis set new.mirage.periodic_check.metrics: %s' % ( metric_name, e)) except: logger.error(traceback.format_exc()) logger.error( 'error :: failed to create Mirage periodic_check Redis key - %s' % (mirage_periodic_check_cache_key)) try: self.redis_conn.rename('mirage.periodic_check.metrics', 'mirage.periodic_check.metrics.old') except: pass try: self.redis_conn.rename('new.mirage.periodic_check.metrics', 'mirage.periodic_check.metrics') except: pass try: self.redis_conn.delete('mirage.periodic_check.metrics.old') except: pass mirage_periodic_check_metric_list_count = len(mirage_periodic_check_metric_list) logger.info( '%s Mirage periodic checks were added' % ( str(mirage_periodic_check_metric_list_count))) try: ionosphere_unique_metrics = list(self.redis_conn.smembers('ionosphere.unique_metrics')) except: ionosphere_unique_metrics = [] # @added 20170602 - Feature #2034: analyse_derivatives # In order to convert monotonic, incrementing metrics to a deriative # metric try: derivative_metrics = list(self.redis_conn.smembers('derivative_metrics')) except: derivative_metrics = [] try: non_derivative_metrics = list(self.redis_conn.smembers('non_derivative_metrics')) except: non_derivative_metrics = [] # This is here to refresh the sets try: manage_derivative_metrics = self.redis_conn.get('analyzer.derivative_metrics_expiry') except Exception as e: if LOCAL_DEBUG: logger.error('error :: could not query Redis for analyzer.derivative_metrics_expiry key: %s' % str(e)) manage_derivative_metrics = False # @added 20170901 - Bug #2154: Infrequent missing new_ Redis keys # If the analyzer.derivative_metrics_expiry is going to expire in the # next 60 seconds, just manage the derivative_metrics in the run as # there is an overlap some times where the key existed at the start of # the run but has expired by the end of the run. derivative_metrics_expiry_ttl = False if manage_derivative_metrics: try: derivative_metrics_expiry_ttl = self.redis_conn.ttl('analyzer.derivative_metrics_expiry') logger.info('the analyzer.derivative_metrics_expiry key ttl is %s' % str(derivative_metrics_expiry_ttl)) except: logger.error('error :: could not query Redis for analyzer.derivative_metrics_expiry key: %s' % str(e)) if derivative_metrics_expiry_ttl: if int(derivative_metrics_expiry_ttl) < 60: logger.info('managing derivative_metrics as the analyzer.derivative_metrics_expiry key ttl is less than 60 with %s' % str(derivative_metrics_expiry_ttl)) manage_derivative_metrics = False try: self.redis_conn.delete('analyzer.derivative_metrics_expiry') logger.info('deleted the Redis key analyzer.derivative_metrics_expiry') except: logger.error('error :: failed to delete Redis key :: analyzer.derivative_metrics_expiry') try: non_derivative_monotonic_metrics = settings.NON_DERIVATIVE_MONOTONIC_METRICS except: non_derivative_monotonic_metrics = [] # @added 20180519 - Feature #2378: Add redis auth to Skyline and rebrow # Added Redis sets for Boring, TooShort and Stale redis_set_errors = 0 # Distill timeseries strings into lists for i, metric_name in enumerate(assigned_metrics): self.check_if_parent_is_alive() # logger.info('analysing %s' % metric_name) try: raw_series = raw_assigned[i] unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) except: timeseries = [] # @added 20170602 - Feature #2034: analyse_derivatives # In order to convert monotonic, incrementing metrics to a deriative # metric known_derivative_metric = False unknown_deriv_status = True if metric_name in non_derivative_metrics: unknown_deriv_status = False if unknown_deriv_status: if metric_name in derivative_metrics: known_derivative_metric = True unknown_deriv_status = False # This is here to refresh the sets if not manage_derivative_metrics: unknown_deriv_status = True base_name = metric_name.replace(settings.FULL_NAMESPACE, '', 1) # @added 20170617 - Bug #2050: analyse_derivatives - change in monotonicity # First check if it has its own Redis z.derivative_metric key # that has not expired derivative_metric_key = 'z.derivative_metric.%s' % str(base_name) if unknown_deriv_status: # @added 20170617 - Bug #2050: analyse_derivatives - change in monotonicity last_derivative_metric_key = False try: last_derivative_metric_key = self.redis_conn.get(derivative_metric_key) except Exception as e: logger.error('error :: could not query Redis for last_derivative_metric_key: %s' % e) # Determine if it is a strictly increasing monotonically metric # or has been in last FULL_DURATION via its z.derivative_metric # key if not last_derivative_metric_key: is_strictly_increasing_monotonically = strictly_increasing_monotonicity(timeseries) if is_strictly_increasing_monotonically: try: last_expire_set = int(time()) self.redis_conn.setex( derivative_metric_key, settings.FULL_DURATION, last_expire_set) except Exception as e: logger.error('error :: could not set Redis derivative_metric key: %s' % e) else: # Until the z.derivative_metric key expires, it is classed # as such is_strictly_increasing_monotonically = True skip_derivative = in_list(base_name, non_derivative_monotonic_metrics) if skip_derivative: is_strictly_increasing_monotonically = False if is_strictly_increasing_monotonically: known_derivative_metric = True try: self.redis_conn.sadd('derivative_metrics', metric_name) except: logger.info(traceback.format_exc()) logger.error('error :: failed to add metric to Redis derivative_metrics set') try: self.redis_conn.sadd('new_derivative_metrics', metric_name) except: logger.info(traceback.format_exc()) logger.error('error :: failed to add metric to Redis new_derivative_metrics set') else: try: self.redis_conn.sadd('non_derivative_metrics', metric_name) except: logger.info(traceback.format_exc()) logger.error('error :: failed to add metric to Redis non_derivative_metrics set') try: self.redis_conn.sadd('new_non_derivative_metrics', metric_name) except: logger.info(traceback.format_exc()) logger.error('error :: failed to add metric to Redis new_non_derivative_metrics set') if known_derivative_metric: try: derivative_timeseries = nonNegativeDerivative(timeseries) timeseries = derivative_timeseries except: logger.error('error :: nonNegativeDerivative failed') # @added 20180903 - Feature #2580: illuminance # Feature #1986: flux try: illuminance_datapoint = timeseries[-1][1] if '.illuminance' not in metric_name: self.illuminance_datapoints.append(illuminance_datapoint) except: pass try: anomalous, ensemble, datapoint = run_selected_algorithm(timeseries, metric_name) # @added 20190408 - Feature #2882: Mirage - periodic_check # Add for Mirage periodic - is really anomalous add to # real_anomalous_metrics and if in mirage_periodic_check_metric_list # add as anomalous if anomalous: # @modified 20190412 - Bug #2932: self.real_anomalous_metrics not being populated correctly # Feature #2882: Mirage - periodic_check # self.real_anomalous_metrics.append(base_name) base_name = metric_name.replace(settings.FULL_NAMESPACE, '', 1) metric_timestamp = timeseries[-1][0] metric = [datapoint, base_name, metric_timestamp] self.real_anomalous_metrics.append(metric) if metric_name in mirage_periodic_check_metric_list: self.mirage_periodic_check_metrics.append(base_name) anomalous = True # If it's anomalous, add it to list if anomalous: base_name = metric_name.replace(settings.FULL_NAMESPACE, '', 1) metric_timestamp = timeseries[-1][0] metric = [datapoint, base_name, metric_timestamp] self.anomalous_metrics.append(metric) # Get the anomaly breakdown - who returned True? triggered_algorithms = [] for index, value in enumerate(ensemble): if value: algorithm = settings.ALGORITHMS[index] anomaly_breakdown[algorithm] += 1 triggered_algorithms.append(algorithm) # It could have been deleted by the Roomba except TypeError: # logger.error('TypeError analysing %s' % metric_name) exceptions['DeletedByRoomba'] += 1 except TooShort: # logger.error('TooShort analysing %s' % metric_name) exceptions['TooShort'] += 1 except Stale: # logger.error('Stale analysing %s' % metric_name) exceptions['Stale'] += 1 except Boring: # logger.error('Boring analysing %s' % metric_name) exceptions['Boring'] += 1 except: # logger.error('Other analysing %s' % metric_name) exceptions['Other'] += 1 logger.info(traceback.format_exc()) # Add values to the queue so the parent process can collate for key, value in anomaly_breakdown.items(): self.anomaly_breakdown_q.put((key, value)) for key, value in exceptions.items(): self.exceptions_q.put((key, value)) spin_end = time() - spin_start logger.info('spin_process took %.2f seconds' % spin_end)
def test_partialdata(): unpacker = Unpacker() unpacker.feed(b"\xa5") with raises(StopIteration): next(iter(unpacker)) unpacker.feed(b"h") with raises(StopIteration): next(iter(unpacker)) unpacker.feed(b"a") with raises(StopIteration): next(iter(unpacker)) unpacker.feed(b"l") with raises(StopIteration): next(iter(unpacker)) unpacker.feed(b"l") with raises(StopIteration): next(iter(unpacker)) unpacker.feed(b"o") assert next(iter(unpacker)) == "hallo"
def run(self): """ Called when the process intializes. Determine if Redis is up and discover the number of `unique metrics`. Divide the `unique_metrics` between the number of `ANALYZER_PROCESSES` and assign each process a set of metrics to analyse for anomalies. Wait for the processes to finish. Process the Determine whether if any anomalous metrics require:\n * alerting on (and set `EXPIRATION_TIME` key in Redis for alert).\n * feeding to another module e.g. mirage. Populated the webapp json the anomalous_metrics details. Log the details about the run to the skyline log. Send skyline.analyzer metrics to `GRAPHITE_HOST`, """ # Log management to prevent overwriting # Allow the bin/<skyline_app>.d to manage the log if os.path.isfile(skyline_app_logwait): try: os.remove(skyline_app_logwait) except OSError: logger.error('error - failed to remove %s, continuing' % skyline_app_logwait) pass now = time() log_wait_for = now + 5 while now < log_wait_for: if os.path.isfile(skyline_app_loglock): sleep(.1) now = time() else: now = log_wait_for + 1 logger.info('starting %s run' % skyline_app) if os.path.isfile(skyline_app_loglock): logger.error('error - bin/%s.d log management seems to have failed, continuing' % skyline_app) try: os.remove(skyline_app_loglock) logger.info('log lock file removed') except OSError: logger.error('error - failed to remove %s, continuing' % skyline_app_loglock) pass else: logger.info('bin/%s.d log management done' % skyline_app) if not os.path.exists(settings.SKYLINE_TMP_DIR): if python_version == 2: os.makedirs(settings.SKYLINE_TMP_DIR, 0750) if python_version == 3: os.makedirs(settings.SKYLINE_TMP_DIR, mode=0o750) # Initiate the algorithm timings if Analyzer is configured to send the # algorithm_breakdown metrics with ENABLE_ALGORITHM_RUN_METRICS algorithm_tmp_file_prefix = settings.SKYLINE_TMP_DIR + '/' + skyline_app + '.' algorithms_to_time = [] if send_algorithm_run_metrics: algorithms_to_time = settings.ALGORITHMS while 1: now = time() # Make sure Redis is up try: self.redis_conn.ping() except: logger.error('skyline can\'t connect to redis at socket path %s' % settings.REDIS_SOCKET_PATH) sleep(10) # @modified 20180519 - Feature #2378: Add redis auth to Skyline and rebrow if settings.REDIS_PASSWORD: self.redis_conn = StrictRedis(password=settings.REDIS_PASSWORD, unix_socket_path=settings.REDIS_SOCKET_PATH) else: self.redis_conn = StrictRedis(unix_socket_path=settings.REDIS_SOCKET_PATH) continue # Report app up self.redis_conn.setex(skyline_app, 120, now) # Discover unique metrics unique_metrics = list(self.redis_conn.smembers(settings.FULL_NAMESPACE + 'unique_metrics')) if len(unique_metrics) == 0: logger.info('no metrics in redis. try adding some - see README') sleep(10) continue # Using count files rather that multiprocessing.Value to enable metrics for # metrics for algorithm run times, etc for algorithm in algorithms_to_time: algorithm_count_file = algorithm_tmp_file_prefix + algorithm + '.count' algorithm_timings_file = algorithm_tmp_file_prefix + algorithm + '.timings' # with open(algorithm_count_file, 'a') as f: with open(algorithm_count_file, 'w') as f: pass with open(algorithm_timings_file, 'w') as f: pass # Spawn processes pids = [] pid_count = 0 for i in range(1, settings.ANALYZER_PROCESSES + 1): if i > len(unique_metrics): logger.info('WARNING: skyline is set for more cores than needed.') break p = Process(target=self.spin_process, args=(i, unique_metrics)) pids.append(p) pid_count += 1 logger.info('starting %s of %s spin_process/es' % (str(pid_count), str(settings.ANALYZER_PROCESSES))) p.start() # Send wait signal to zombie processes # for p in pids: # p.join() # Self monitor processes and terminate if any spin_process has run # for longer than 180 seconds p_starts = time() while time() - p_starts <= 180: if any(p.is_alive() for p in pids): # Just to avoid hogging the CPU sleep(.1) else: # All the processes are done, break now. time_to_run = time() - p_starts logger.info('%s :: %s spin_process/es completed in %.2f seconds' % (skyline_app, str(settings.ANALYZER_PROCESSES), time_to_run)) break else: # We only enter this if we didn't 'break' above. logger.info('%s :: timed out, killing all spin_process processes' % (skyline_app)) for p in pids: p.terminate() # p.join() # Grab data from the queue and populate dictionaries exceptions = dict() anomaly_breakdown = dict() while 1: try: key, value = self.anomaly_breakdown_q.get_nowait() if key not in anomaly_breakdown.keys(): anomaly_breakdown[key] = value else: anomaly_breakdown[key] += value except Empty: break while 1: try: key, value = self.exceptions_q.get_nowait() if key not in exceptions.keys(): exceptions[key] = value else: exceptions[key] += value except Empty: break # Push to panorama # if len(self.panorama_anomalous_metrics) > 0: # logger.info('to do - push to panorama') # Push to crucible # if len(self.crucible_anomalous_metrics) > 0: # logger.info('to do - push to crucible') # Write anomalous_metrics to static webapp directory # Using count files rather that multiprocessing.Value to enable metrics for # metrics for algorithm run times, etc for algorithm in algorithms_to_time: algorithm_count_file = algorithm_tmp_file_prefix + algorithm + '.count' algorithm_timings_file = algorithm_tmp_file_prefix + algorithm + '.timings' try: algorithm_count_array = [] with open(algorithm_count_file, 'r') as f: for line in f: value_string = line.replace('\n', '') unquoted_value_string = value_string.replace("'", '') float_value = float(unquoted_value_string) algorithm_count_array.append(float_value) except: algorithm_count_array = False if not algorithm_count_array: continue number_of_times_algorithm_run = len(algorithm_count_array) logger.info( 'algorithm run count - %s run %s times' % ( algorithm, str(number_of_times_algorithm_run))) if number_of_times_algorithm_run == 0: continue try: algorithm_timings_array = [] with open(algorithm_timings_file, 'r') as f: for line in f: value_string = line.replace('\n', '') unquoted_value_string = value_string.replace("'", '') float_value = float(unquoted_value_string) algorithm_timings_array.append(float_value) except: algorithm_timings_array = False if not algorithm_timings_array: continue number_of_algorithm_timings = len(algorithm_timings_array) logger.info( 'algorithm timings count - %s has %s timings' % ( algorithm, str(number_of_algorithm_timings))) if number_of_algorithm_timings == 0: continue try: _sum_of_algorithm_timings = sum(algorithm_timings_array) except: logger.error("sum error: " + traceback.format_exc()) _sum_of_algorithm_timings = round(0.0, 6) logger.error('error - sum_of_algorithm_timings - %s' % (algorithm)) continue sum_of_algorithm_timings = round(_sum_of_algorithm_timings, 6) # logger.info('sum_of_algorithm_timings - %s - %.16f seconds' % (algorithm, sum_of_algorithm_timings)) try: _median_algorithm_timing = determine_median(algorithm_timings_array) except: _median_algorithm_timing = round(0.0, 6) logger.error('error - _median_algorithm_timing - %s' % (algorithm)) continue median_algorithm_timing = round(_median_algorithm_timing, 6) # logger.info('median_algorithm_timing - %s - %.16f seconds' % (algorithm, median_algorithm_timing)) logger.info( 'algorithm timing - %s - total: %.6f - median: %.6f' % ( algorithm, sum_of_algorithm_timings, median_algorithm_timing)) send_mertic_name = 'algorithm_breakdown.' + algorithm + '.timing.times_run' self.send_graphite_metric(send_mertic_name, '%d' % number_of_algorithm_timings) send_mertic_name = 'algorithm_breakdown.' + algorithm + '.timing.total_time' self.send_graphite_metric(send_mertic_name, '%.6f' % sum_of_algorithm_timings) send_mertic_name = 'algorithm_breakdown.' + algorithm + '.timing.median_time' self.send_graphite_metric(send_mertic_name, '%.6f' % median_algorithm_timing) # Log progress logger.info('seconds to run :: %.2f' % (time() - now)) logger.info('total metrics :: %d' % len(unique_metrics)) logger.info('total analyzed :: %d' % (len(unique_metrics) - sum(exceptions.values()))) logger.info('total anomalies :: %d' % len(self.anomalous_metrics)) logger.info('exception stats :: %s' % exceptions) logger.info('anomaly breakdown :: %s' % anomaly_breakdown) # Log to Graphite self.send_graphite_metric('run_time', '%.2f' % (time() - now)) self.send_graphite_metric('total_analyzed', '%.2f' % (len(unique_metrics) - sum(exceptions.values()))) self.send_graphite_metric('total_anomalies', '%d' % len(self.anomalous_metrics)) self.send_graphite_metric('total_metrics', '%d' % len(unique_metrics)) for key, value in exceptions.items(): send_metric = 'exceptions.%s' % key self.send_graphite_metric(send_metric, '%d' % value) for key, value in anomaly_breakdown.items(): send_metric = 'anomaly_breakdown.%s' % key self.send_graphite_metric(send_metric, '%d' % value) # Check canary metric raw_series = self.redis_conn.get(settings.FULL_NAMESPACE + settings.CANARY_METRIC) if raw_series is not None: unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) time_human = (timeseries[-1][0] - timeseries[0][0]) / 3600 projected = 24 * (time() - now) / time_human logger.info('canary duration :: %.2f' % time_human) self.send_graphite_metric('duration', '%.2f' % time_human) self.send_graphite_metric('projected', '%.2f' % projected) # Reset counters self.anomalous_metrics[:] = [] # Sleep if it went too fast # if time() - now < 5: # logger.info('sleeping due to low run time...') # sleep(10) # @modified 20160504 - @earthgecko - development internal ref #1338, #1340) # Etsy's original if this was a value of 5 seconds which does # not make skyline Analyzer very efficient in terms of installations # where 100s of 1000s of metrics are being analyzed. This lead to # Analyzer running over several metrics multiple time in a minute # and always working. Therefore this was changed from if you took # less than 5 seconds to run only then sleep. This behaviour # resulted in Analyzer analysing a few 1000 metrics in 9 seconds and # then doing it again and again in a single minute. Therefore the # ANALYZER_OPTIMUM_RUN_DURATION setting was added to allow this to # self optimise in cases where skyline is NOT deployed to analyze # 100s of 1000s of metrics. This relates to optimising performance # for any deployments in the few 1000s and 60 second resolution # area, e.g. smaller and local deployments. process_runtime = time() - now analyzer_optimum_run_duration = settings.ANALYZER_OPTIMUM_RUN_DURATION if process_runtime < analyzer_optimum_run_duration: sleep_for = (analyzer_optimum_run_duration - process_runtime) # sleep_for = 60 logger.info('sleeping for %.2f seconds due to low run time...' % sleep_for) sleep(sleep_for)
def test_partialdata(): unpacker = Unpacker() unpacker.feed(b'\xa5') with raises(StopIteration): next(iter(unpacker)) unpacker.feed(b'h') with raises(StopIteration): next(iter(unpacker)) unpacker.feed(b'a') with raises(StopIteration): next(iter(unpacker)) unpacker.feed(b'l') with raises(StopIteration): next(iter(unpacker)) unpacker.feed(b'l') with raises(StopIteration): next(iter(unpacker)) unpacker.feed(b'o') assert next(iter(unpacker)) == b'hallo'
def run(self): """ Called when the process intializes. """ while 1: now = time() # Make sure Redis is up try: self.redis_conn.ping() except: logger.error( 'skyline can\'t connect to redis at socket path %s' % settings.REDIS_SOCKET_PATH) sleep(10) self.redis_conn = StrictRedis( unix_socket_path=settings.REDIS_SOCKET_PATH) continue # Discover unique metrics unique_metrics = list( self.redis_conn.smembers(settings.FULL_NAMESPACE + 'unique_metrics')) if len(unique_metrics) == 0: logger.info( 'no metrics in redis. try adding some - see README') sleep(10) continue # Spawn processes pids = [] for i in range(1, settings.ANALYZER_PROCESSES + 1): if i > len(unique_metrics): logger.info( 'WARNING: skyline is set for more cores than needed.') break p = Process(target=self.spin_process, args=(i, unique_metrics)) pids.append(p) p.start() # Send wait signal to zombie processes for p in pids: p.join() # Send alerts #if settings.ENABLE_ALERTS: # for alert in settings.ALERTS: # for metric in self.anomalous_metrics: # if alert[0] in metric[1]: # try: # last_alert = self.redis_conn.get('last_alert.' + metric[1]) # if not last_alert: # self.redis_conn.setex('last_alert.' + metric[1], alert[2], packb(metric[0])) # self.send_mail(alert, metric) # except Exception as e: # logger.error("couldn't send alert: %s" % e) # Write anomalous_metrics to static webapp directory filename = path.abspath( path.join(path.dirname(__file__), '..', settings.ANOMALY_DUMP)) with open(filename, 'w') as fh: # Make it JSONP with a handle_data() function anomalous_metrics = list(self.anomalous_metrics) anomalous_metrics.sort(key=operator.itemgetter(1)) fh.write('handle_data(%s)' % anomalous_metrics) # process anomalous metrics for metric in self.anomalous_metrics: try: last_save_key = 'last_save.%s.%s' % (metric[1], metric[2]) last_save = self.redis_conn.get(last_save_key) if not last_save: self.redis_conn.setex(last_save_key, settings.SKIP_FREQUENCY, packb(metric[0])) self.storage.save(metric) if settings.ENABLE_ALERTS: last_alert_key = 'last_alert.' + metric[1] last_alert = self.redis_conn.get(last_alert_key) if not last_alert: self.redis_conn.setex(last_alert_key, settings.SKIP_FREQUENCY, packb(metric[0])) self.alerter.add(metric) except Exception as e: logger.error( "Failed processing anomaly, pid: %s, metric: %s, error: %s", getpid(), metric[1], e) # send ready alerts if settings.ENABLE_ALERTS: try: self.alerter.send_alerts() except Exception as e: logger.error("Failed sending alerts, error: %s", e) # Log progress logger.info('seconds to run :: %.2f' % (time() - now)) logger.info('total metrics :: %d' % len(unique_metrics)) logger.info('total analyzed :: %d' % (len(unique_metrics) - sum(self.exceptions.values()))) logger.info('total anomalies :: %d' % len(self.anomalous_metrics)) logger.info('exception stats :: %s' % self.exceptions) logger.info('anomaly breakdown :: %s' % self.anomaly_breakdown) # Log to Graphite if settings.GRAPHITE_HOST != '': host = settings.GRAPHITE_HOST.replace('http://', '') system( 'echo skyline.analyzer.run_time %.2f %s | nc -w 3 %s 2003' % ((time() - now), now, host)) system( 'echo skyline.analyzer.total_analyzed %d %s | nc -w 3 %s 2003' % ((len(unique_metrics) - sum(self.exceptions.values())), now, host)) # Check canary metric raw_series = self.redis_conn.get(settings.FULL_NAMESPACE + settings.CANARY_METRIC) if raw_series is not None: unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) time_human = (timeseries[-1][0] - timeseries[0][0]) / 3600 projected = 24 * (time() - now) / time_human logger.info('canary duration :: %.2f' % time_human) if settings.GRAPHITE_HOST != '': host = settings.GRAPHITE_HOST.replace('http://', '') system( 'echo skyline.analyzer.duration %.2f %s | nc -w 3 %s 2003' % (time_human, now, host)) system( 'echo skyline.analyzer.projected %.2f %s | nc -w 3 %s 2003' % (projected, now, host)) # Reset counters self.anomalous_metrics[:] = [] self.exceptions = Manager().dict() self.anomaly_breakdown = Manager().dict() # Sleep if it went too fast if time() - now < 5: logger.info('sleeping due to low run time...') sleep(10)
from msgpack import Unpacker import json unpacked = [] unpacker = Unpacker() fname = r"test.bin" with open(fname, 'rb') as f: unpacker.feed(f.read()) for o in unpacker: print(json.dumps(o, indent=2))
def get_anomalous_ts(base_name, anomaly_timestamp): logger = logging.getLogger(skyline_app_logger) # @added 20180423 - Feature #2360: CORRELATE_ALERTS_ONLY # Branch #2270: luminosity # Only correlate metrics with an alert setting if correlate_alerts_only: try: smtp_alerter_metrics = list( redis_conn.smembers('analyzer.smtp_alerter_metrics')) except: smtp_alerter_metrics = [] if base_name not in smtp_alerter_metrics: logger.error('%s has no alerter setting, not correlating' % base_name) return False if not base_name or not anomaly_timestamp: return False # from skyline_functions import nonNegativeDerivative anomalous_metric = '%s%s' % (settings.FULL_NAMESPACE, base_name) assigned_metrics = [anomalous_metric] # @modified 20180419 - raw_assigned = [] try: raw_assigned = redis_conn.mget(assigned_metrics) except: raw_assigned = [] if raw_assigned == [None]: logger.info('%s data not retrieved from local Redis' % (str(base_name))) raw_assigned = [] if not raw_assigned and settings.OTHER_SKYLINE_REDIS_INSTANCES: # @modified 20180519 - Feature #2378: Add redis auth to Skyline and rebrow # for redis_ip, redis_port in settings.OTHER_SKYLINE_REDIS_INSTANCES: for redis_ip, redis_port, redis_password in settings.OTHER_SKYLINE_REDIS_INSTANCES: if not raw_assigned: try: if redis_password: other_redis_conn = StrictRedis( host=str(redis_ip), port=int(redis_port), password=str(redis_password)) else: other_redis_conn = StrictRedis(host=str(redis_ip), port=int(redis_port)) raw_assigned = other_redis_conn.mget(assigned_metrics) if raw_assigned == [None]: logger.info( '%s data not retrieved from Redis at %s on port %s' % (str(base_name), str(redis_ip), str(redis_port))) raw_assigned = [] if raw_assigned: logger.info( '%s data retrieved from Redis at %s on port %s' % (str(base_name), str(redis_ip), str(redis_port))) except: logger.error(traceback.format_exc()) logger.error( 'error :: failed to connect to Redis at %s on port %s' % (str(redis_ip), str(redis_port))) raw_assigned = [] if not raw_assigned or raw_assigned == [None]: logger.info('%s data not retrieved' % (str(base_name))) return False for i, metric_name in enumerate(assigned_metrics): try: raw_series = raw_assigned[i] unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) except: timeseries = [] # Convert the time series if this is a known_derivative_metric known_derivative_metric = is_derivative_metric(skyline_app, base_name) if known_derivative_metric: derivative_timeseries = nonNegativeDerivative(timeseries) timeseries = derivative_timeseries # Sample the time series from_timestamp = anomaly_timestamp - 600 anomaly_ts = [] for ts, value in timeseries: if int(ts) < from_timestamp: continue if int(ts) <= anomaly_timestamp: anomaly_ts.append((int(ts), value)) if int(ts) > anomaly_timestamp: break return anomaly_ts
def alert_smtp(alert, metric, second_order_resolution_seconds, context): """ Called by :func:`~trigger_alert` and sends an alert via smtp to the recipients that are configured for the metric. """ LOCAL_DEBUG = False logger = logging.getLogger(skyline_app_logger) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - sending smtp alert') logger.info('debug :: alert_smtp - Memory usage at start: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) # SECOND_ORDER_RESOLUTION_SECONDS to hours so that Mirage surfaces the # relevant timeseries data in the graph second_order_resolution_in_hours = int( second_order_resolution_seconds) / 3600 # @added 20161229 - Feature #1830: Ionosphere alerts # Added Ionosphere variables base_name = str(metric[1]).replace(settings.FULL_NAMESPACE, '', 1) if settings.IONOSPHERE_ENABLED: timeseries_dir = base_name.replace('.', '/') training_data_dir = '%s/%s/%s' % (settings.IONOSPHERE_DATA_FOLDER, str(int(metric[2])), timeseries_dir) graphite_image_file = '%s/%s.%s.graphite.%sh.png' % ( training_data_dir, base_name, skyline_app, str(int(second_order_resolution_in_hours))) json_file = '%s/%s.%s.redis.%sh.json' % ( training_data_dir, base_name, skyline_app, str(int(full_duration_in_hours))) training_data_redis_image = '%s/%s.%s.redis.plot.%sh.png' % ( training_data_dir, base_name, skyline_app, str(int(full_duration_in_hours))) # For backwards compatibility if '@' in alert[1]: sender = settings.ALERT_SENDER recipient = alert[1] logger.info('alert_smtp - recipient for %s are %s' % (str(alert[0]), str(recipient))) else: sender = settings.SMTP_OPTS['sender'] # @modified 20160806 - Added default_recipient try: recipients = settings.SMTP_OPTS['recipients'][alert[0]] use_default_recipient = False logger.info('alert_smtp - recipients for %s are %s' % (str(alert[0]), str(recipients))) logger.info('alert_smtp - recipients are:') try: for recip in recipients: logger.info('alert_smtp :: recipient - %s' % str(recip)) except: logger.error(traceback.format_exc()) logger.error( 'alert_smtp :: error :: could not iterate recipients list') except: use_default_recipient = True if use_default_recipient: try: recipients = settings.SMTP_OPTS['default_recipient'] logger.info( 'alert_smtp - using default_recipient as no recipients are configured for %s' % str(alert[0])) except: logger.error( 'error :: alert_smtp - no known recipient for %s' % str(alert[0])) return False # Backwards compatibility if type(recipients) is str: logger.info('alert_smtp :: recipients is a string') recipients = [recipients] else: logger.info('alert_smtp :: recipients is not a string, OK') # @added 20180524 - Task #2384: Change alerters to cc other recipients # The alerters did send an individual email to each recipient. This would be # more useful if one email was sent with the first smtp recipient being the # to recipient and the subsequent recipients were add in cc. if recipients: primary_recipient = False cc_recipients = False for i_recipient in recipients: if not primary_recipient: primary_recipient = str(i_recipient) if primary_recipient != i_recipient: if not cc_recipients: cc_recipients = str(i_recipient) else: new_cc_recipients = '%s,%s' % (str(cc_recipients), str(i_recipient)) cc_recipients = str(new_cc_recipients) logger.info( 'alert_smtp - will send to primary_recipient :: %s, cc_recipients :: %s' % (str(primary_recipient), str(cc_recipients))) # @modified 20161228 - Feature #1830: Ionosphere alerts # Ionosphere alerts unencoded_graph_title = 'Skyline %s - ALERT at %s hours - %s' % ( context, str(int(second_order_resolution_in_hours)), str(metric[0])) # @modified 20170603 - Feature #2034: analyse_derivatives # Added deriative functions to convert the values of metrics strictly # increasing monotonically to their deriative products in alert graphs and # specify it in the graph_title known_derivative_metric = False try: # @modified 20180519 - Feature #2378: Add redis auth to Skyline and rebrow if settings.REDIS_PASSWORD: REDIS_ALERTER_CONN = redis.StrictRedis( password=settings.REDIS_PASSWORD, unix_socket_path=settings.REDIS_SOCKET_PATH) else: REDIS_ALERTER_CONN = redis.StrictRedis( unix_socket_path=settings.REDIS_SOCKET_PATH) except: logger.error('error :: alert_smtp - redis connection failed') try: derivative_metrics = list( REDIS_ALERTER_CONN.smembers('derivative_metrics')) except: derivative_metrics = [] redis_metric_name = '%s%s' % (settings.FULL_NAMESPACE, str(base_name)) if redis_metric_name in derivative_metrics: known_derivative_metric = True if known_derivative_metric: try: non_derivative_monotonic_metrics = settings.NON_DERIVATIVE_MONOTONIC_METRICS except: non_derivative_monotonic_metrics = [] skip_derivative = in_list(redis_metric_name, non_derivative_monotonic_metrics) if skip_derivative: known_derivative_metric = False if known_derivative_metric: unencoded_graph_title = 'Skyline %s - ALERT at %s hours - derivative graph - %s' % ( context, str(int(second_order_resolution_in_hours)), str( metric[0])) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - unencoded_graph_title: %s' % unencoded_graph_title) graph_title_string = quote(unencoded_graph_title, safe='') graph_title = '&title=%s' % graph_title_string graphite_port = '80' if settings.GRAPHITE_PORT != '': graphite_port = str(settings.GRAPHITE_PORT) link = '%s://%s:%s/render/?from=-%shours&target=cactiStyle(%s)%s%s&colorList=orange' % ( settings.GRAPHITE_PROTOCOL, settings.GRAPHITE_HOST, graphite_port, str(int(second_order_resolution_in_hours)), metric[1], settings.GRAPHITE_GRAPH_SETTINGS, graph_title) # @added 20170603 - Feature #2034: analyse_derivatives if known_derivative_metric: link = '%s://%s:%s/render/?from=-%shours&target=cactiStyle(nonNegativeDerivative(%s))%s%s&colorList=orange' % ( settings.GRAPHITE_PROTOCOL, settings.GRAPHITE_HOST, graphite_port, str(int(second_order_resolution_in_hours)), metric[1], settings.GRAPHITE_GRAPH_SETTINGS, graph_title) content_id = metric[1] image_data = None if settings.SMTP_OPTS.get('embed-images'): # @added 20161229 - Feature #1830: Ionosphere alerts # Use existing data if files exist if os.path.isfile(graphite_image_file): try: with open(graphite_image_file, 'r') as f: image_data = f.read() logger.info('alert_smtp - using existing png - %s' % graphite_image_file) except: logger.error(traceback.format_exc()) logger.error( 'error :: alert_smtp - failed to read image data from existing png - %s' % graphite_image_file) logger.error('error :: alert_smtp - %s' % str(link)) image_data = None if image_data is None: try: # @modified 20170913 - Task #2160: Test skyline with bandit # Added nosec to exclude from bandit tests image_data = urllib2.urlopen(link).read() # nosec if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - image data OK') except urllib2.URLError: logger.error(traceback.format_exc()) logger.error('error :: alert_smtp - failed to get image graph') logger.error('error :: alert_smtp - %s' % str(link)) image_data = None if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - image data None') # If we failed to get the image or if it was explicitly disabled, # use the image URL instead of the content. if image_data is None: img_tag = '<img src="%s"/>' % link else: img_tag = '<img src="cid:%s"/>' % content_id if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - img_tag: %s' % img_tag) if settings.IONOSPHERE_ENABLED: # Create Ionosphere Graphite image # @modified 20161229 - Feature #1830: Ionosphere alerts # Only write the data to the file if it does not exist if not os.path.isfile(graphite_image_file): try: write_data_to_file(skyline_app, graphite_image_file, 'w', image_data) logger.info('added %s Ionosphere Graphite image :: %s' % (skyline_app, graphite_image_file)) except: logger.info(traceback.format_exc()) logger.error( 'error :: failed to add %s Ionosphere Graphite image' % (skyline_app, graphite_image_file)) else: logger.info( '%s Ionosphere Graphite image already exists :: %s' % (skyline_app, graphite_image_file)) redis_image_data = None try: plot_redis_data = settings.PLOT_REDIS_DATA except: plot_redis_data = False if settings.SMTP_OPTS.get('embed-images') and plot_redis_data: # Create graph from Redis data redis_metric_key = '%s%s' % (settings.FULL_NAMESPACE, metric[1]) try: raw_series = REDIS_ALERTER_CONN.get(redis_metric_key) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - raw_series: %s' % 'OK') except: if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - raw_series: %s' % 'FAIL') try: unpacker = Unpacker(use_list=True) unpacker.feed(raw_series) timeseries_x = [float(item[0]) for item in unpacker] unpacker = Unpacker(use_list=True) unpacker.feed(raw_series) timeseries_y = [item[1] for item in unpacker] unpacker = Unpacker(use_list=False) unpacker.feed(raw_series) timeseries = list(unpacker) except: logger.error('error :: alert_smtp - unpack timeseries failed') timeseries = None # @added 20170603 - Feature #2034: analyse_derivatives if known_derivative_metric: try: derivative_timeseries = nonNegativeDerivative(timeseries) timeseries = derivative_timeseries except: logger.error( 'error :: alert_smtp - nonNegativeDerivative failed') if settings.IONOSPHERE_ENABLED and timeseries: ''' .. todo: this is possibly to be used to allow the user to submit the FULL_DURATION duration data set for the features profile to be created against IF it is a Mirage metric. This would allow for additional granularity in Mirage metrics, thereby maintaining their seasonality, but allow user and Skyline to analyze the anomaly at a FULL_DURATION resolution as well. Not sure how to code that in Ionosphere context yet but could just be additonal flag in the Ionosphere record. In the Ionosphere frontend, the user would be given an option to either create the features profile on the Mirage timeseries or the redis FULL_DURATION timeseries. It is a little complicated, but doable. # @modified 20161229 - Feature #1828: ionosphere - mirage Redis data features However that ^^ is UNDESIRABLE in the Mirage/Ionosphere context at the moment. Ionosphere must only profile SECOND_ORDER_RESOLUTION_HOURS currently so as to not pollute the seasonality aspect of Mirage ''' # Create Ionosphere redis timeseries json if is does not exist # @modified 20161229 - Feature #1830: Ionosphere alerts # Only write the data to the file if it does not exist and replace # the timeseries object if a json file exists # @added 20170920 - Bug #2168: Strange Redis derivative graph using_original_redis_json = False if not os.path.isfile(json_file): timeseries_json = str(timeseries).replace('[', '(').replace( ']', ')') try: write_data_to_file(skyline_app, json_file, 'w', timeseries_json) logger.info( 'added %s Ionosphere Redis data timeseries json file :: %s' % (skyline_app, json_file)) except: logger.info(traceback.format_exc()) logger.error( 'error :: failed to add %s Ionosphere Redis data timeseries json file' % (skyline_app, json_file)) else: # Replace the timeseries object logger.info( '%s Ionosphere Redis data timeseries json file already exists, using :: %s' % (skyline_app, json_file)) anomaly_json = json_file try: # Read the timeseries json file with open(anomaly_json, 'r') as f: raw_timeseries = f.read() timeseries_array_str = str(raw_timeseries).replace( '(', '[').replace(')', ']') timeseries = literal_eval(timeseries_array_str) logger.info( '%s Redis timeseries replaced with timeseries from :: %s' % (skyline_app, anomaly_json)) timeseries_x = [float(item[0]) for item in timeseries] timeseries_y = [item[1] for item in timeseries] # @added 20170920 - Bug #2168: Strange Redis derivative graph # This already has nonNegativeDerivative applied to it using_original_redis_json = True except: logger.error(traceback.format_exc()) logger.error( 'error :: %s failed to read timeseries data from %s' % (skyline_app, anomaly_json)) timeseries = None # @added 20170823 - Feature #2034: analyse_derivatives # Originally patterned and added to analyzer/alerters.py on 20170603 if known_derivative_metric: # @added 20170920 - Bug #2168: Strange Redis derivative graph # If this is the Mirage Redis json it already has # nonNegativeDerivative applied to it if not using_original_redis_json: logger.info('alert_smtp - nonNegativeDerivative being applied') try: derivative_timeseries = nonNegativeDerivative(timeseries) timeseries = derivative_timeseries # @added 20170920 - Bug #2168: Strange Redis derivative graph logger.info('alert_smtp - nonNegativeDerivative applied') except: logger.error( 'error :: alert_smtp - nonNegativeDerivative failed') else: logger.info( 'alert_smtp - nonNegativeDerivative not being applied, as it will have been applied in the original json' ) # @added 21070823 - Bug #2068: Analyzer smtp alert error on Redis plot with derivative metrics # Originally patterned and added to analyzer/alerters.py on 20170726 # If the nonNegativeDerivative has been calculated we need to reset the # x and y as nonNegativeDerivative has to discard the first value as it # has no delta for it so the timeseries is 1 item less. timeseries_x = [float(item[0]) for item in timeseries] timeseries_y = [item[1] for item in timeseries] pd_series_values = None original_anomalous_datapoint = metric[0] if timeseries: try: values = pd.Series([x[1] for x in timeseries]) # Because the truth value of a Series is ambiguous pd_series_values = True except: logger.error( 'error :: alert_smtp - pandas value series on timeseries failed' ) # @added 20170307 - Feature #1960: ionosphere_layers # To display the original anomalous datapoint value in the Redis plot try: original_anomalous_datapoint = float(timeseries[-1][1]) except: logger.error( 'error :: alert_smtp - falied to determine the original_anomalous_datapoint from the timeseries' ) if pd_series_values: try: array_median = np.median(values) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - values median: %s' % str(array_median)) array_amax = np.amax(values) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - array_amax: %s' % str(array_amax)) array_amin = np.amin(values) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - array_amin: %s' % str(array_amin)) mean = values.mean() if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - mean: %s' % str(mean)) stdDev = values.std() if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - stdDev: %s' % str(stdDev)) sigma3 = 3 * stdDev if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - sigma3: %s' % str(sigma3)) # sigma3_series = [sigma3] * len(values) sigma3_upper_bound = mean + sigma3 try: sigma3_lower_bound = mean - sigma3 except: sigma3_lower_bound = 0 sigma3_upper_series = [sigma3_upper_bound] * len(values) sigma3_lower_series = [sigma3_lower_bound] * len(values) amax_series = [array_amax] * len(values) amin_series = [array_amin] * len(values) mean_series = [mean] * len(values) except: logger.error( 'error :: alert_smtp - numpy ops on series failed') mean_series = None if mean_series: # @modified 20170307 - Feature #1960: ionosphere_layers # To display the original anomalous datapoint value in the Redis plot # graph_title = 'Skyline %s - ALERT - at %s hours - Redis data\n%s - anomalous value: %s' % (context, str(int(full_duration_in_hours)), metric[1], str(metric[0])) graph_title = 'Skyline %s - ALERT - at %s hours - Redis data\n%s - anomalous value: %s' % ( context, str(int(full_duration_in_hours)), metric[1], str(original_anomalous_datapoint)) # @added 20170603 - Feature #2034: analyse_derivatives if known_derivative_metric: graph_title = 'Skyline %s - ALERT - at %s hours - Redis data (derivative graph)\n%s - anomalous value: %s' % ( context, str(int(full_duration_in_hours)), metric[1], str(original_anomalous_datapoint)) if python_version == 3: buf = io.StringIO() else: buf = io.BytesIO() # Too big # rcParams['figure.figsize'] = 12, 6 rcParams['figure.figsize'] = 8, 4 try: # fig = plt.figure() fig = plt.figure(frameon=False) ax = fig.add_subplot(111) ax.set_title(graph_title, fontsize='small') # @modified 20180417 - Bug #2358: set_axis_bgcolor method removed from Matplotlib - Luminosity # IssueID #49 'AxesSubplot' object has no attribute 'set_axis_bgcolor' # ax.set_axis_bgcolor('black') if hasattr(ax, 'set_facecolor'): ax.set_facecolor('black') else: ax.set_axis_bgcolor('black') try: datetimes = [ dt.datetime.utcfromtimestamp(ts) for ts in timeseries_x ] if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - datetimes: %s' % 'OK') except: logger.error('error :: alert_smtp - datetimes: %s' % 'FAIL') plt.xticks(rotation=0, horizontalalignment='center') xfmt = DateFormatter('%a %H:%M') plt.gca().xaxis.set_major_formatter(xfmt) ax.xaxis.set_major_formatter(xfmt) ax.plot(datetimes, timeseries_y, color='orange', lw=0.6, zorder=3) ax.tick_params(axis='both', labelsize='xx-small') max_value_label = 'max - %s' % str(array_amax) ax.plot(datetimes, amax_series, lw=1, label=max_value_label, color='m', ls='--', zorder=4) min_value_label = 'min - %s' % str(array_amin) ax.plot(datetimes, amin_series, lw=1, label=min_value_label, color='b', ls='--', zorder=4) mean_value_label = 'mean - %s' % str(mean) ax.plot(datetimes, mean_series, lw=1.5, label=mean_value_label, color='g', ls='--', zorder=4) sigma3_text = (r'3$\sigma$') # sigma3_label = '%s - %s' % (str(sigma3_text), str(sigma3)) sigma3_upper_label = '%s upper - %s' % ( str(sigma3_text), str(sigma3_upper_bound)) ax.plot(datetimes, sigma3_upper_series, lw=1, label=sigma3_upper_label, color='r', ls='solid', zorder=4) if sigma3_lower_bound > 0: sigma3_lower_label = '%s lower - %s' % ( str(sigma3_text), str(sigma3_lower_bound)) ax.plot(datetimes, sigma3_lower_series, lw=1, label=sigma3_lower_label, color='r', ls='solid', zorder=4) ax.get_yaxis().get_major_formatter().set_useOffset(False) ax.get_yaxis().get_major_formatter().set_scientific(False) # Shrink current axis's height by 10% on the bottom box = ax.get_position() ax.set_position([ box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9 ]) # Put a legend below current axis ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=4, fontsize='x-small') plt.rc('lines', lw=2, color='w') plt.grid(True) ax.grid(b=True, which='both', axis='both', color='lightgray', linestyle='solid', alpha=0.5, linewidth=0.6) # @modified 20180417 - Bug #2358: set_axis_bgcolor method removed from Matplotlib - Luminosity # IssueID #49 'AxesSubplot' object has no attribute 'set_axis_bgcolor' # ax.set_axis_bgcolor('black') if hasattr(ax, 'set_facecolor'): ax.set_facecolor('black') else: ax.set_axis_bgcolor('black') rcParams['xtick.direction'] = 'out' rcParams['ytick.direction'] = 'out' ax.margins(y=.02, x=.03) # tight_layout removes the legend box # fig.tight_layout() if settings.IONOSPHERE_ENABLED: if not os.path.exists(training_data_dir): mkdir_p(training_data_dir) logger.info('created dir - %s' % training_data_dir) if not os.path.isfile(training_data_redis_image): try: plt.savefig(training_data_redis_image, format='png') logger.info( 'alert_smtp - save Redis training data image - %s' % (training_data_redis_image)) except: logger.info(traceback.format_exc()) logger.error( 'error :: alert_smtp - could not save - %s' % (training_data_redis_image)) else: logger.info( 'alert_smtp - Redis training data image already exists - %s' % (training_data_redis_image)) try: plt.savefig(buf, format='png') # @added 20160814 - Bug #1558: Memory leak in Analyzer # As per http://www.mail-archive.com/[email protected]/msg13222.html # savefig in the parent process was causing the memory leak # the below fig.clf() and plt.close() did not resolve this # however spawing a multiprocessing process for alert_smtp # does solve this as issue as all memory is freed when the # process terminates. fig.clf() plt.close(fig) redis_graph_content_id = 'redis.%s' % metric[1] redis_image_data = True if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - savefig: %s' % 'OK') except: logger.info(traceback.format_exc()) logger.error('error :: alert_smtp - plt.savefig: %s' % 'FAIL') except: logger.error(traceback.format_exc()) logger.error('error :: alert_smtp - could not build plot') if redis_image_data: redis_img_tag = '<img src="cid:%s"/>' % redis_graph_content_id if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info('debug :: alert_smtp - redis_img_tag: %s' % str(redis_img_tag)) else: # @modified 20161229 - Feature #1830: Ionosphere alerts # @modified 20170108 - Feature #1852: Ionosphere - features_profile matched graphite graphs # Restored the previous redis_img_tag method as some smtp alerts were # coming without a Redis graph, not all but some and for some reason, # I am pretty certain retrospectively that it was done that way from # testing I just wanted to try and be cleaner. # The redis_img_tag was changed at # https://github.com/earthgecko/skyline/commit/31bcacf3f90f0953ebed0d57260cb937e01f887c#diff-520bf2a218f65074ffead4d8184c138dR489 redis_img_tag = '<img src="%s"/>' % 'none' # redis_img_tag = '<img src="none"/>' # @added 20170806 - Feature #1830: Ionosphere alerts # Show a human date in alerts alerted_at = str(dt.datetime.utcfromtimestamp(int(metric[2]))) try: body = '<h3><font color="#dd3023">Sky</font><font color="#6698FF">line</font><font color="black"> %s alert</font></h3><br>' % context body += '<font color="black">metric: <b>%s</b></font><br>' % metric[1] body += '<font color="black">Anomalous value: %s (Mirage)</font><br>' % str( metric[0]) body += '<font color="black">Original anomalous value: %s (Analyzer)</font><br>' % str( original_anomalous_datapoint) body += '<font color="black">Anomaly timestamp: %s</font><br>' % str( int(metric[2])) # @added 20170806 - Feature #1830: Ionosphere alerts # Show a human date in alerts body += '<font color="black">Anomalous at: %s</font><br>' % alerted_at body += '<font color="black">At hours: %s</font><br>' % str( int(second_order_resolution_in_hours)) body += '<font color="black">Next alert in: %s seconds</font><br>' % str( alert[2]) # @added 20170603 - Feature #2034: analyse_derivatives if known_derivative_metric: body += '<font color="black">Derivative graph: True</font><br>' more_body = '' if settings.IONOSPHERE_ENABLED: # @modified 20170823 - Bug #2142: 7bit SMTP encoding breaking long urls # Broke body into body and more_body to workaround the 990 character # limit per line for SMTP more_body += '<h3><font color="#dd3023">Ionosphere :: </font><font color="#6698FF">training data</font><font color="black"></font></h3>' ionosphere_link = '%s/ionosphere?timestamp=%s&metric=%s' % ( settings.SKYLINE_URL, str(int(metric[2])), str(metric[1])) more_body += '<font color="black">To use this timeseries to train Skyline that this is not anomalous manage this training data at:<br>' more_body += '<a href="%s">%s</a></font>' % (ionosphere_link, ionosphere_link) if image_data: more_body += '<h3><font color="black">Graphite data at SECOND_ORDER_RESOLUTION_HOURS (aggregated)</font></h3>' more_body += '<div dir="ltr"><a href="%s">%s</a><br></div><br>' % ( link, img_tag) more_body += '<font color="black">Clicking on the above graph will open to the Graphite graph with current data</font><br>' if redis_image_data: more_body += '<font color="black">min: %s | max: %s | mean: %s <br>' % ( str(array_amin), str(array_amax), str(mean)) more_body += '3-sigma: %s <br>' % str(sigma3) more_body += '3-sigma upper bound: %s | 3-sigma lower bound: %s <br></font>' % ( str(sigma3_upper_bound), str(sigma3_lower_bound)) more_body += '<h3><font color="black">Redis data at FULL_DURATION</font></h3><br>' more_body += '<div dir="ltr">:%s<br></div>' % redis_img_tag more_body += '<font color="black">To disable the Redis data graph view, set PLOT_REDIS_DATA to False in your settings.py, if the Graphite graph is sufficient for you,<br>' more_body += 'however do note that will remove the 3-sigma and mean value too.</font>' more_body += '<br>' more_body += '<div dir="ltr" align="right"><font color="#dd3023">Sky</font><font color="#6698FF">line</font><font color="black"> version :: %s</font></div><br>' % str( skyline_version) except: logger.error('error :: alert_smtp - could not build body') logger.info(traceback.format_exc()) # @modified 20180524 - Task #2384: Change alerters to cc other recipients # Do not send to each recipient, send to primary_recipient and cc the other # recipients, thereby sending only one email # for recipient in recipients: # logger.info('alert_smtp - sending alert to %s' % (str(recipient))) if primary_recipient: logger.info( 'alert_smtp - will send to primary_recipient :: %s, cc_recipients :: %s' % (str(primary_recipient), str(cc_recipients))) try: # @modified 20170823 - Bug #2142: 7bit SMTP encoding breaking long urls # Broke body into body and more_body to workaround the 990 character # limit per line for SMTP, using mixed as alternative indicates that # the client should select one of the parts for display and ignore # the rest (tripleee - https://stackoverflow.com/a/35115938) # msg = MIMEMultipart('alternative') msg = MIMEMultipart('mixed') # @added 20170812 - Bug #2142: 7bit SMTP encoding breaking long urls # set email charset and email encodings cs_ = charset.Charset('utf-8') cs_.header_encoding = charset.QP cs_.body_encoding = charset.QP msg.set_charset(cs_) msg['Subject'] = '[Skyline alert] - %s ALERT - %s' % (context, metric[1]) msg['From'] = sender # @modified 20180524 - Task #2384: Change alerters to cc other recipients # msg['To'] = recipient msg['To'] = primary_recipient # @added 20180524 - Task #2384: Change alerters to cc other recipients # Added Cc if cc_recipients: msg['Cc'] = cc_recipients msg.attach(MIMEText(body, 'html')) # @added 20170823 - Bug #2142: 7bit SMTP encoding breaking long urls # Broke body into body and more_body to workaround the 990 character # limit per line for SMTP msg.attach(MIMEText(more_body, 'html')) msg.replace_header('content-transfer-encoding', 'quoted-printable') if image_data is not None: try: msg_attachment = MIMEImage(image_data) msg_attachment.add_header('Content-ID', '<%s>' % content_id) msg.attach(msg_attachment) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - msg_attachment - Graphite img source done' ) except: logger.error('error :: alert_smtp - msg_attachment') logger.info(traceback.format_exc()) if redis_image_data: try: buf.seek(0) msg_plot_attachment = MIMEImage(buf.read()) msg_plot_attachment.add_header( 'Content-ID', '<%s>' % redis_graph_content_id) msg.attach(msg_plot_attachment) if settings.ENABLE_DEBUG or LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - msg_plot_attachment - redis data done' ) except: logger.error('error :: alert_smtp - msg_plot_attachment') logger.info(traceback.format_exc()) except: logger.error('error :: alert_smtp - could not attach') logger.info(traceback.format_exc()) s = SMTP('127.0.0.1') try: # @modified 20180524 - Task #2384: Change alerters to cc other recipients # Send to primary_recipient and cc_recipients # s.sendmail(sender, recipient, msg.as_string()) if cc_recipients: s.sendmail(sender, [primary_recipient, cc_recipients], msg.as_string()) else: s.sendmail(sender, primary_recipient, msg.as_string()) if settings.ENABLE_DEBUG or LOCAL_DEBUG: # logger.info('debug :: alert_smtp - message sent to %s OK' % str(recipient)) logger.info( 'debug :: alert_smtp - message sent OK to primary_recipient :: %s, cc_recipients :: %s' % (str(primary_recipient), str(cc_recipients))) except: logger.info(traceback.format_exc()) # logger.error('error :: alert_smtp - could not send email to %s' % str(recipient)) logger.error( 'error :: alert_smtp - could not send email to primary_recipient :: %s, cc_recipients :: %s' % (str(primary_recipient), str(cc_recipients))) s.quit() if LOCAL_DEBUG: logger.info( 'debug :: alert_smtp - Memory usage after email: %s (kb)' % resource.getrusage(resource.RUSAGE_SELF).ru_maxrss) return
# open the database redis_conn = StrictRedis(unix_socket_path='/tmp/redis.sock') full_list = list(redis_conn.smembers('system.unique_metrics')) if len(full_list) == 0: print "No metrics" exit() count = 0 start = time.time() for metric in full_list: count += 1 if not db.open("/opt/skyline/src/cabinet/" + metric + ".kct", DB.OWRITER | DB.OCREATE): print >> sys.stderr, metric + "open error: " + str(db.error()) raw_metric = redis_conn.mget(metric) for i, metric_name in enumerate(raw_metric): unpacker = Unpacker(use_list=False) unpacker.feed(metric_name) timeseries = list(unpacker) for value in timeseries: if db.check(value[0]) < 0: db.set(value[0], value[1]) #db.set(value[0], value[1]) db.close() if (count % 100) == 0: print "%s keys. Rate: %s" % (count, (100 / (time.time() - start))) start = time.time()