def value_compare(left, right, ordering=1):
"""
SORT VALUES, NULL IS THE LEAST VALUE
:param left: LHS
:param right: RHS
:param ordering: (-1, 0, 0) TO AFFECT SORT ORDER
:return: The return value is negative if x < y, zero if x == y and strictly positive if x > y.
"""
try:
if isinstance(left, list) or isinstance(right, list):
if left == None:
return ordering
elif right == None:
return - ordering
left = listwrap(left)
right = listwrap(right)
for a, b in zip(left, right):
c = value_compare(a, b) * ordering
if c != 0:
return c
if len(left) < len(right):
return - ordering
elif len(left) > len(right):
return ordering
else:
return 0
ltype = type(left)
rtype = type(right)
ltype_num = TYPE_ORDER.get(ltype, 10)
rtype_num = TYPE_ORDER.get(rtype, 10)
type_diff = ltype_num - rtype_num
if type_diff != 0:
return ordering if type_diff > 0 else -ordering
if ltype_num == 9:
return 0
elif ltype is builtin_tuple:
for a, b in zip(left, right):
c = value_compare(a, b)
if c != 0:
return c * ordering
return 0
elif ltype in (dict, Data):
for k in sorted(set(left.keys()) | set(right.keys())):
c = value_compare(left.get(k), right.get(k)) * ordering
if c != 0:
return c
return 0
elif left > right:
return ordering
elif left < right:
return -ordering
else:
return 0
except Exception as e:
Log.error("Can not compare values {{left}} to {{right}}", left=left, right=right, cause=e)
def argparse(defs):
parser = _argparse.ArgumentParser()
for d in listwrap(defs):
args = d.copy()
name = args.name
args.name = None
parser.add_argument(*unwrap(listwrap(name)), **args)
namespace = parser.parse_args()
output = {k: getattr(namespace, k) for k in vars(namespace)}
return wrap(output)
def argparse(defs):
parser = _ArgParser()
for d in listwrap(defs):
args = d.copy()
name = args.name
args.name = None
parser.add_argument(*unwrap(listwrap(name)), **args)
namespace, unknown = parser.parse_known_args()
if unknown:
Log.warning("Ignoring arguments: {{unknown|json}}", unknown=unknown)
output = {k: getattr(namespace, k) for k in vars(namespace)}
return wrap(output)
def error(
cls,
template, # human readable template
default_params={}, # parameters for template
cause=None, # pausible cause
stack_depth=0,
**more_params
):
"""
raise an exception with a trace for the cause too
:param template: *string* human readable string with placeholders for parameters
:param default_params: *dict* parameters to fill in template
:param cause: *Exception* for chaining
:param stack_depth: *int* how many calls you want popped off the stack to report the *true* caller
:param log_context: *dict* extra key:value pairs for your convenience
:param more_params: *any more parameters (which will overwrite default_params)
:return:
"""
if not isinstance(template, text_type):
sys.stderr.write(str("Log.error was expecting a unicode template"))
Log.error("Log.error was expecting a unicode template")
if default_params and isinstance(listwrap(default_params)[0], BaseException):
cause = default_params
default_params = {}
params = dict(unwrap(default_params), **more_params)
add_to_trace = False
if cause == None:
causes = None
elif isinstance(cause, list):
causes = []
for c in listwrap(cause): # CAN NOT USE LIST-COMPREHENSION IN PYTHON3 (EXTRA STACK DEPTH FROM THE IN-LINED GENERATOR)
causes.append(Except.wrap(c, stack_depth=1))
causes = FlatList(causes)
elif isinstance(cause, BaseException):
causes = Except.wrap(cause, stack_depth=1)
else:
causes = None
Log.error("can only accept Exception, or list of exceptions")
trace = exceptions.extract_stack(stack_depth + 1)
if add_to_trace:
cause[0].trace.extend(trace[1:])
e = Except(type=exceptions.ERROR, template=template, params=params, cause=causes, trace=trace)
raise_from_none(e)
def value_compare(l, r, ordering=1):
"""
SORT VALUES, NULL IS THE LEAST VALUE
:param l: LHS
:param r: RHS
:param ordering: (-1, 0, 0) TO AFFECT SORT ORDER
:return: The return value is negative if x < y, zero if x == y and strictly positive if x > y.
"""
if l == None:
if r == None:
return 0
else:
return ordering
elif r == None:
return - ordering
if isinstance(l, list) or isinstance(r, list):
for a, b in zip(listwrap(l), listwrap(r)):
c = value_compare(a, b) * ordering
if c != 0:
return c
if len(l) < len(r):
return - ordering
elif len(l) > len(r):
return ordering
else:
return 0
elif isinstance(l, builtin_tuple) and isinstance(r, builtin_tuple):
for a, b in zip(l, r):
c = value_compare(a, b) * ordering
if c != 0:
return c
return 0
elif isinstance(l, Mapping):
if isinstance(r, Mapping):
for k in sorted(set(l.keys()) | set(r.keys())):
c = value_compare(l.get(k), r.get(k)) * ordering
if c != 0:
return c
return 0
else:
return 1
elif isinstance(r, Mapping):
return -1
else:
return cmp(l, r) * ordering
def insert_new(self, table_name, candidate_key, new_record):
candidate_key = listwrap(candidate_key)
condition = SQL_AND.join([
quote_column(k) + "=" + quote_value(new_record[k])
if new_record[k] != None
else quote_column(k) + SQL_IS_NULL
for k in candidate_key
])
command = (
"INSERT INTO " + quote_column(table_name) + sql_iso(sql_list(
quote_column(k) for k in new_record.keys()
)) +
SQL_SELECT + "a.*" + SQL_FROM + sql_iso(
SQL_SELECT + sql_list([quote_value(v) + " " + quote_column(k) for k, v in new_record.items()]) +
SQL_FROM + "DUAL"
) + " a" +
SQL_LEFT_JOIN + sql_iso(
SQL_SELECT + "'dummy' exist " +
SQL_FROM + quote_column(table_name) +
SQL_WHERE + condition +
SQL_LIMIT + SQL_ONE
) + " b ON " + SQL_TRUE + SQL_WHERE + " exist " + SQL_IS_NULL
)
self.execute(command, {})
def select(self, select):
selects = listwrap(select)
if len(selects) == 1 and is_op(selects[0].value, Variable) and selects[0].value.var == ".":
new_schema = self.schema
if selects[0].name == ".":
return self
else:
new_schema = None
if is_list(select):
if all(
is_op(s.value, Variable) and s.name == s.value.var
for s in select
):
names = set(s.value.var for s in select)
new_schema = Schema(".", [c for c in self.schema.columns if c.name in names])
push_and_pull = [(s.name, jx_expression_to_function(s.value)) for s in selects]
def selector(d):
output = Data()
for n, p in push_and_pull:
output[n] = unwraplist(p(wrap(d)))
return unwrap(output)
new_data = map(selector, self.data)
else:
select_value = jx_expression_to_function(select.value)
new_data = map(select_value, self.data)
if is_op(select.value, Variable):
column = copy(first(c for c in self.schema.columns if c.name == select.value.var))
column.name = '.'
new_schema = Schema("from " + self.name, [column])
return ListContainer("from "+self.name, data=new_data, schema=new_schema)
def _send_email(self):
try:
if not self.accumulation:
return
with Emailer(self.settings) as emailer:
# WHO ARE WE SENDING TO
emails = Data()
for template, params in self.accumulation:
content = expand_template(template, params)
emails[literal_field(self.settings.to_address)] += [content]
for c in self.cc:
if any(d in params.params.error for d in c.contains):
emails[literal_field(c.to_address)] += [content]
# SEND TO EACH
for to_address, content in emails.items():
emailer.send_email(
from_address=self.settings.from_address,
to_address=listwrap(to_address),
subject=self.settings.subject,
text_data="\n\n".join(content)
)
self.accumulation = []
except Exception as e:
Log.warning("Could not send", e)
finally:
self.next_send = Date.now() + self.settings.average_interval * (2 * Random.float())
def _normalize_groupby(groupby, limit, schema=None):
if groupby == None:
return None
output = wrap([n for ie, e in enumerate(listwrap(groupby)) for n in _normalize_group(e, ie, limit, schema=schema) ])
if any(o==None for o in output):
Log.error("not expected")
return output
def wrap(cls, e, stack_depth=0):
"""
ENSURE THE STACKTRACE AND CAUSAL CHAIN IS CAPTURED, PLUS ADD FEATURES OF Except
:param e: AN EXCEPTION OF ANY TYPE
:param stack_depth: HOW MANY CALLS TO TAKE OFF THE TOP OF THE STACK TRACE
:return: A Except OBJECT OF THE SAME
"""
if e == None:
return Null
elif isinstance(e, (list, Except)):
return e
elif is_data(e):
e.cause = unwraplist([Except.wrap(c) for c in listwrap(e.cause)])
return Except(**e)
else:
tb = getattr(e, '__traceback__', None)
if tb is not None:
trace = _parse_traceback(tb)
else:
trace = _extract_traceback(0)
cause = Except.wrap(getattr(e, '__cause__', None))
if hasattr(e, "message") and e.message:
output = Except(context=ERROR, template=text_type(e.message), trace=trace, cause=cause)
else:
output = Except(context=ERROR, template=text_type(e), trace=trace, cause=cause)
trace = extract_stack(stack_depth + 2) # +2 = to remove the caller, and it's call to this' Except.wrap()
output.trace.extend(trace)
return output
def query(self, _query):
try:
query = QueryOp.wrap(_query, container=self, namespace=self.namespace)
for s in listwrap(query.select):
if s.aggregate != None and not aggregates.get(s.aggregate):
Log.error(
"ES can not aggregate {{name}} because {{aggregate|quote}} is not a recognized aggregate",
name=s.name,
aggregate=s.aggregate
)
frum = query["from"]
if isinstance(frum, QueryOp):
result = self.query(frum)
q2 = query.copy()
q2.frum = result
return jx.run(q2)
if is_deepop(self.es, query):
return es_deepop(self.es, query)
if is_aggsop(self.es, query):
return es_aggsop(self.es, frum, query)
if is_setop(self.es, query):
return es_setop(self.es, query)
Log.error("Can not handle")
except Exception as e:
e = Except.wrap(e)
if "Data too large, data for" in e:
http.post(self.es.cluster.url / "_cache/clear")
Log.error("Problem (Tried to clear Elasticsearch cache)", e)
Log.error("problem", e)
def __init__(self, **desc):
Domain.__init__(self, **desc)
self.type = "range"
self.NULL = Null
if self.partitions:
# IGNORE THE min, max, interval
if not self.key:
Log.error("Must have a key value")
parts = listwrap(self.partitions)
for i, p in enumerate(parts):
self.min = MIN([self.min, p.min])
self.max = MAX([self.max, p.max])
if p.dataIndex != None and p.dataIndex != i:
Log.error("Expecting `dataIndex` to agree with the order of the parts")
if p[self.key] == None:
Log.error("Expecting all parts to have {{key}} as a property", key=self.key)
p.dataIndex = i
# VERIFY PARTITIONS DO NOT OVERLAP, HOLES ARE FINE
for p, q in itertools.product(parts, parts):
if p.min <= q.min and q.min < p.max:
Log.error("partitions overlap!")
self.partitions = parts
return
elif any([self.min == None, self.max == None, self.interval == None]):
Log.error("Can not handle missing parameter")
self.key = "min"
self.partitions = wrap([{"min": v, "max": v + self.interval, "dataIndex": i} for i, v in enumerate(frange(self.min, self.max, self.interval))])
def read_settings(filename=None, defs=None):
# READ SETTINGS
if filename:
settings_file = File(filename)
if not settings_file.exists:
Log.error("Can not file settings file {{filename}}", {
"filename": settings_file.abspath
})
settings = mo_json_config.get("file:///" + settings_file.abspath)
if defs:
settings.args = argparse(defs)
return settings
else:
defs = listwrap(defs)
defs.append({
"name": ["--settings", "--settings-file", "--settings_file"],
"help": "path to JSON file with settings",
"type": str,
"dest": "filename",
"default": "./settings.json",
"required": False
})
args = argparse(defs)
settings = mo_json_config.get("file://" + args.filename.replace(os.sep, "/"))
settings.args = args
return settings
def read_settings(filename=None, defs=None):
"""
:param filename: Force load a file
:param defs: arguments you want to accept
:param default_filename: A config file from an environment variable (a fallback config file, if no other provided)
:return:
"""
# READ SETTINGS
defs = listwrap(defs)
defs.append({
"name": ["--config", "--settings", "--settings-file", "--settings_file"],
"help": "path to JSON file with settings",
"type": str,
"dest": "filename",
"default": None,
"required": False
})
args = argparse(defs)
args.filename = coalesce(filename, args.filename, "./config.json")
settings_file = File(args.filename)
if not settings_file.exists:
Log.error("Can not read configuration file {{filename}}", {
"filename": settings_file.abspath
})
settings = mo_json_config.get_file(settings_file)
settings.args = args
return settings
def __getitem__(self, item):
for s in listwrap(self.cube.select):
if s.name == item:
return self.cube.data[item]
for i, e in enumerate(self.cube.edges):
if e.name == item:
return e.domain.partition[self.coord[i]]
def _send_email(self):
try:
if not self.accumulation:
return
with Closer(connect_to_region(
self.settings.region,
aws_access_key_id=unwrap(self.settings.aws_access_key_id),
aws_secret_access_key=unwrap(self.settings.aws_secret_access_key)
)) as conn:
# WHO ARE WE SENDING TO
emails = Data()
for template, params in self.accumulation:
content = expand_template(template, params)
emails[literal_field(self.settings.to_address)] += [content]
for c in self.cc:
if any(c in params.params.error for c in c.contains):
emails[literal_field(c.to_address)] += [content]
# SEND TO EACH
for to_address, content in emails.items():
conn.send_email(
source=self.settings.from_address,
to_addresses=listwrap(to_address),
subject=self.settings.subject,
body="\n\n".join(content),
format="text"
)
self.next_send = Date.now() + self.settings.max_interval
self.accumulation = []
except Exception as e:
self.next_send = Date.now() + self.settings.max_interval
Log.warning("Could not send", e)
def is_terms(query):
select = listwrap(query.select)
isSimple = not query.select or AND(aggregates[s.aggregate] in ("none", "count") for s in select)
if isSimple:
return True
return False
def __init__(
self,
host,
index,
port=9200,
type="log",
queue_size=1000,
batch_size=100,
kwargs=None,
):
"""
settings ARE FOR THE ELASTICSEARCH INDEX
"""
kwargs.timeout = Duration(coalesce(kwargs.timeout, "30second")).seconds
kwargs.retry.times = coalesce(kwargs.retry.times, 3)
kwargs.retry.sleep = Duration(coalesce(kwargs.retry.sleep, MINUTE)).seconds
kwargs.host = Random.sample(listwrap(host), 1)[0]
schema = json2value(value2json(SCHEMA), leaves=True)
schema.mappings[type].properties["~N~"].type = "nested"
self.es = Cluster(kwargs).get_or_create_index(
schema=schema,
limit_replicas=True,
typed=True,
kwargs=kwargs,
)
self.batch_size = batch_size
self.es.add_alias(coalesce(kwargs.alias, kwargs.index))
self.queue = Queue("debug logs to es", max=queue_size, silent=True)
self.worker = Thread.run("add debug logs to es", self._insert_loop)
def vars(self, exclude_where=False, exclude_select=False):
"""
:return: variables in query
"""
def edges_get_all_vars(e):
output = set()
if isinstance(e.value, text_type):
output.add(e.value)
if isinstance(e.value, Expression):
output |= e.value.vars()
if e.domain.key:
output.add(e.domain.key)
if e.domain.where:
output |= e.domain.where.vars()
if e.range:
output |= e.range.min.vars()
output |= e.range.max.vars()
if e.domain.partitions:
for p in e.domain.partitions:
if p.where:
output |= p.where.vars()
return output
output = set()
try:
output |= self.frum.vars()
except Exception:
pass
if not exclude_select:
for s in listwrap(self.select):
output |= s.value.vars()
for s in listwrap(self.edges):
output |= edges_get_all_vars(s)
for s in listwrap(self.groupby):
output |= edges_get_all_vars(s)
if not exclude_where:
output |= self.where.vars()
for s in listwrap(self.sort):
output |= s.value.vars()
try:
output |= UNION(e.vars() for e in self.window)
except Exception:
pass
return output
def new_instance(desc):
return Except(
context=desc.context,
template=desc.template,
params=desc.params,
cause=[Except.new_instance(c) for c in listwrap(desc.cause)],
trace=desc.trace
)
def _get_schema_from_list(frum, table_name, prefix_path, nested_path, columns):
"""
:param frum: The list
:param table_name: Name of the table this list holds records for
:param prefix_path: parent path
:param nested_path: each nested array, in reverse order
:param columns: map from full name to column definition
:return:
"""
for d in frum:
row_type = _type_to_name[d.__class__]
if row_type != "object":
full_name = join_field(prefix_path)
column = columns[full_name]
if not column:
column = Column(
names={table_name: full_name},
es_column=full_name,
es_index=".",
type="undefined",
nested_path=nested_path
)
columns.add(column)
column.type = _merge_type[column.type][row_type]
else:
for name, value in d.items():
full_name = join_field(prefix_path + [name])
column = columns[full_name]
if not column:
column = Column(
names={table_name: full_name},
es_column=full_name,
es_index=".",
type="undefined",
nested_path=nested_path
)
columns.add(column)
if isinstance(value, list):
if len(value) == 0:
this_type = "undefined"
elif len(value) == 1:
this_type = _type_to_name[value[0].__class__]
else:
this_type = _type_to_name[value[0].__class__]
if this_type == "object":
this_type = "nested"
else:
this_type = _type_to_name[value.__class__]
new_type = _merge_type[column.type][this_type]
column.type = new_type
if this_type == "object":
_get_schema_from_list([value], table_name, prefix_path + [name], nested_path, columns)
elif this_type == "nested":
np = listwrap(nested_path)
newpath = unwraplist([join_field(split_field(np[0])+[name])]+np)
_get_schema_from_list(value, table_name, prefix_path + [name], newpath, columns)
def fatal(
cls,
template, # human readable template
default_params={}, # parameters for template
cause=None, # pausible cause
stack_depth=0,
log_context=None,
**more_params
):
"""
SEND TO STDERR
:param template: *string* human readable string with placeholders for parameters
:param default_params: *dict* parameters to fill in template
:param cause: *Exception* for chaining
:param stack_depth: *int* how many calls you want popped off the stack to report the *true* caller
:param log_context: *dict* extra key:value pairs for your convenience
:param more_params: *any more parameters (which will overwrite default_params)
:return:
"""
if default_params and isinstance(listwrap(default_params)[0], BaseException):
cause = default_params
default_params = {}
params = dict(unwrap(default_params), **more_params)
cause = unwraplist([Except.wrap(c) for c in listwrap(cause)])
trace = exceptions.extract_stack(stack_depth + 1)
e = Except(exceptions.ERROR, template, params, cause, trace)
str_e = unicode(e)
error_mode = cls.error_mode
with suppress_exception:
if not error_mode:
cls.error_mode = True
Log.note(
"{{error|unicode}}",
error=e,
log_context=set_default({"context": exceptions.FATAL}, log_context),
stack_depth=stack_depth + 1
)
cls.error_mode = error_mode
sys.stderr.write(str_e.encode('utf8'))
def start(cls, settings=None):
"""
RUN ME FIRST TO SETUP THE THREADED LOGGING
http://victorlin.me/2012/08/good-logging-practice-in-python/
log - LIST OF PARAMETERS FOR LOGGER(S)
trace - SHOW MORE DETAILS IN EVERY LOG LINE (default False)
cprofile - True==ENABLE THE C-PROFILER THAT COMES WITH PYTHON (default False)
USE THE LONG FORM TO SET THE FILENAME {"enabled": True, "filename": "cprofile.tab"}
profile - True==ENABLE pyLibrary SIMPLE PROFILING (default False) (eg with Profiler("some description"):)
USE THE LONG FORM TO SET FILENAME {"enabled": True, "filename": "profile.tab"}
constants - UPDATE MODULE CONSTANTS AT STARTUP (PRIMARILY INTENDED TO CHANGE DEBUG STATE)
"""
global _Thread
if not settings:
return
settings = wrap(settings)
Log.stop()
cls.settings = settings
cls.trace = coalesce(settings.trace, False)
if cls.trace:
from mo_threads import Thread as _Thread
_ = _Thread
# ENABLE CPROFILE
if settings.cprofile is False:
settings.cprofile = {"enabled": False}
elif settings.cprofile is True:
if isinstance(settings.cprofile, bool):
settings.cprofile = {"enabled": True, "filename": "cprofile.tab"}
if settings.cprofile.enabled:
from mo_threads import profiles
profiles.enable_profilers(settings.cprofile.filename)
if settings.profile is True or (isinstance(settings.profile, Mapping) and settings.profile.enabled):
Log.error("REMOVED 2018-09-02, Activedata revision 3f30ff46f5971776f8ba18")
# from mo_logs import profiles
#
# if isinstance(settings.profile, bool):
# profiles.ON = True
# settings.profile = {"enabled": True, "filename": "profile.tab"}
#
# if settings.profile.enabled:
# profiles.ON = True
if settings.constants:
constants.set(settings.constants)
if settings.log:
cls.logging_multi = StructuredLogger_usingMulti()
for log in listwrap(settings.log):
Log.add_log(Log.new_instance(log))
from mo_logs.log_usingThread import StructuredLogger_usingThread
cls.main_log = StructuredLogger_usingThread(cls.logging_multi)
def _select(self, select):
selects = listwrap(select)
is_aggregate = OR(s.aggregate != None and s.aggregate != "none" for s in selects)
if is_aggregate:
values = {s.name: Matrix(value=self.data[s.value].aggregate(s.aggregate)) for s in selects}
return Cube(select, [], values)
else:
values = {s.name: self.data[s.value] for s in selects}
return Cube(select, self.edges, values)
def errors(e, _buffer): # HANDLE ERRORS FROM extend()
if e.cause.cause:
not_possible = [f for f in listwrap(e.cause.cause) if any(h in f for h in HOPELESS)]
still_have_hope = [f for f in listwrap(e.cause.cause) if all(h not in f for h in HOPELESS)]
else:
not_possible = [e]
still_have_hope = []
if still_have_hope:
if "429 EsRejectedExecutionException[rejected execution (queue capacity" in e:
Log.note("waiting for ES to be free ({{num}} pending)", num=len(_buffer))
elif "503 UnavailableShardsException" in e:
Log.note("waiting for ES to initialize shards ({{num}} pending)", num=len(_buffer))
else:
Log.warning("Problem with sending to ES ({{num}} pending)", num=len(_buffer), cause=still_have_hope)
elif not_possible:
# THERE IS NOTHING WE CAN DO
Log.warning("Not inserted, will not try again", cause=not_possible[0:10:])
del _buffer[:]
def _convert_window(self, window):
return Data(
name=coalesce(window.name, window.value),
value=window.value,
edges=[self._convert_edge(e) for e in listwrap(window.edges)],
sort=self._convert_sort(window.sort),
aggregate=window.aggregate,
range=self._convert_range(window.range),
where=self._convert_where(window.where)
)
def groupby(data, keys=None, size=None, min_size=None, max_size=None, contiguous=False):
"""
:param data:
:param keys:
:param size:
:param min_size:
:param max_size:
:param contiguous: MAINTAIN THE ORDER OF THE DATA, STARTING THE NEW GROUP WHEN THE SELECTOR CHANGES
:return: return list of (keys, values) PAIRS, WHERE
keys IS IN LEAF FORM (FOR USE WITH {"eq": terms} OPERATOR
values IS GENERATOR OF ALL VALUE THAT MATCH keys
contiguous -
"""
if isinstance(data, Container):
return data.groupby(keys)
if size != None or min_size != None or max_size != None:
if size != None:
max_size = size
return groupby_min_max_size(data, min_size=min_size, max_size=max_size)
try:
keys = listwrap(keys)
if not contiguous:
from jx_python import jx
data = jx.sort(data, keys)
if not data:
return Null
if any(isinstance(k, Expression) for k in keys):
Log.error("can not handle expressions")
else:
accessor = jx_expression_to_function(jx_expression({"tuple": keys})) # CAN RETURN Null, WHICH DOES NOT PLAY WELL WITH __cmp__
def _output():
start = 0
prev = accessor(data[0])
for i, d in enumerate(data):
curr = accessor(d)
if curr != prev:
group = {}
for k, gg in zip(keys, prev):
group[k] = gg
yield Data(group), data[start:i:]
start = i
prev = curr
group = {}
for k, gg in zip(keys, prev):
group[k] = gg
yield Data(group), data[start::]
return _output()
except Exception as e:
Log.error("Problem grouping", cause=e)
def _request_spot_instances(self, price, availability_zone_group, instance_type, kwargs):
kwargs.kwargs = None
# m3 INSTANCES ARE NOT ALLOWED PLACEMENT GROUP
if instance_type.startswith("m3."):
kwargs.placement_group = None
kwargs.network_interfaces = NetworkInterfaceCollection(*(
NetworkInterfaceSpecification(**i)
for i in listwrap(kwargs.network_interfaces)
if self.vpc_conn.get_all_subnets(subnet_ids=i.subnet_id, filters={"availabilityZone": availability_zone_group})
))
if len(kwargs.network_interfaces) == 0:
Log.error("No network interface specifications found for {{availability_zone}}!", availability_zone=kwargs.availability_zone_group)
block_device_map = BlockDeviceMapping()
# GENERIC BLOCK DEVICE MAPPING
for dev, dev_settings in kwargs.block_device_map.items():
block_device_map[dev] = BlockDeviceType(
delete_on_termination=True,
**dev_settings
)
kwargs.block_device_map = block_device_map
# INCLUDE EPHEMERAL STORAGE IN BlockDeviceMapping
num_ephemeral_volumes = ephemeral_storage[instance_type]["num"]
for i in range(num_ephemeral_volumes):
letter = convert.ascii2char(98 + i) # START AT "b"
kwargs.block_device_map["/dev/sd" + letter] = BlockDeviceType(
ephemeral_name='ephemeral' + unicode(i),
delete_on_termination=True
)
if kwargs.expiration:
kwargs.valid_until = (Date.now() + Duration(kwargs.expiration)).format(ISO8601)
kwargs.expiration = None
# ATTACH NEW EBS VOLUMES
for i, drive in enumerate(self.settings.utility[instance_type].drives):
letter = convert.ascii2char(98 + i + num_ephemeral_volumes)
device = drive.device = coalesce(drive.device, "/dev/sd" + letter)
d = drive.copy()
d.path = None # path AND device PROPERTY IS NOT ALLOWED IN THE BlockDeviceType
d.device = None
if d.size:
kwargs.block_device_map[device] = BlockDeviceType(
delete_on_termination=True,
**d
)
output = list(self.ec2_conn.request_spot_instances(**kwargs))
return output
def __contains__(self, value):
if isinstance(value, text_type):
if self.template.find(value) >= 0 or self.message.find(value) >= 0:
return True
if self.type == value:
return True
for c in listwrap(self.cause):
if value in c:
return True
return False
def __contains__(self, value):
if is_text(value):
if self.template.find(value) >= 0 or self.message.find(value) >= 0:
return True
if self.context == value:
return True
for c in listwrap(self.cause):
if value in c:
return True
return False
def _get_schema_from_list(
frum, # The list
table_name, # Name of the table this list holds records for
parent, # parent path
nested_path, # each nested array, in reverse order
columns, # map from full name to column definition
native_type_to_json_type, # dict from storage type name to json type name
):
for d in frum:
row_type = python_type_to_json_type[d.__class__]
if row_type != "object":
# EXPECTING PRIMITIVE VALUE
full_name = parent
column = columns[full_name]
if not column:
es_type = d.__class__
jx_type =native_type_to_json_type[es_type]
column = Column(
name=concat_field(table_name, json_type_to_inserter_type[jx_type]),
es_column=full_name,
es_index=".",
es_type=es_type,
jx_type=jx_type,
last_updated=Date.now(),
nested_path=nested_path,
multi=1,
)
columns.add(column)
else:
column.es_type = _merge_python_type(column.es_type, d.__class__)
column.jx_type = native_type_to_json_type[column.es_type]
else:
for name, value in d.items():
full_name = concat_field(parent, name)
column = columns[full_name]
if is_container(value): # GET TYPE OF MULTIVALUE
v = list(value)
if len(v) == 0:
es_type = none_type.__name__
elif len(v) == 1:
es_type = v[0].__class__.__name__
else:
es_type = reduce(
_merge_python_type, (vi.__class__.__name__ for vi in value)
)
else:
es_type = value.__class__.__name__
if not column:
jx_type = native_type_to_json_type[es_type]
column = Column(
name=concat_field(table_name, full_name),
es_column=full_name,
es_index=".",
es_type=es_type,
jx_type=jx_type,
last_updated=Date.now(),
nested_path=nested_path,
cardinality=1 if jx_type == OBJECT else None,
multi=1
)
columns.add(column)
else:
column.es_type = _merge_python_type(column.es_type, es_type)
try:
column.jx_type = native_type_to_json_type[column.es_type]
except Exception as e:
raise e
if es_type in {"object", "dict", "Mapping", "Data"}:
_get_schema_from_list(
[value],
table_name,
full_name,
nested_path,
columns,
native_type_to_json_type,
)
elif es_type in {"list", "FlatList"}:
np = listwrap(nested_path)
newpath = unwraplist([join_field(split_field(np[0]) + [name])] + np)
_get_schema_from_list(
value, table_name, full_name, newpath, columns
)
def update(self, command):
"""
EXPECTING command == {"set":term, "where":where}
THE set CLAUSE IS A DICT MAPPING NAMES TO VALUES
THE where CLAUSE IS AN ES FILTER
"""
command = wrap(command)
schema = self._es.get_properties()
# GET IDS OF DOCUMENTS
results = self._es.search({
"fields": listwrap(schema._routing.path),
"query": {
"filtered": {
"filter": jx_expression(command.where).to_esfilter(Null)
}
},
"size": 10000
})
# SCRIPT IS SAME FOR ALL (CAN ONLY HANDLE ASSIGNMENT TO CONSTANT)
scripts = FlatList()
for k, v in command.set.items():
if not is_variable_name(k):
Log.error("Only support simple paths for now")
if isinstance(v, Mapping) and v.doc:
scripts.append({"doc": v.doc})
else:
v = scrub(v)
scripts.append({
"script":
"ctx._source." + k + " = " +
jx_expression(v).to_ruby(schema).script(schema)
})
if results.hits.hits:
updates = []
for h in results.hits.hits:
for s in scripts:
updates.append({
"update": {
"_id":
h._id,
"_routing":
unwraplist(h.fields[literal_field(
schema._routing.path)])
}
})
updates.append(s)
content = ("\n".join(convert.value2json(c)
for c in updates) + "\n").encode('utf-8')
response = self._es.cluster.post(
self._es.path + "/_bulk",
data=content,
headers={"Content-Type": "application/json"},
timeout=self.settings.timeout,
params={
"wait_for_active_shards":
self.settings.wait_for_active_shards
})
if response.errors:
Log.error("could not update: {{error}}",
error=[
e.error for i in response["items"]
for e in i.values() if e.status not in (200, 201)
])
def to_sql(self, schema, not_null=False, boolean=False):
if self.var == GUID:
return wrap([{
"name": ".",
"sql": {
"s": quoted_GUID
},
"nested_path": ROOT_PATH
}])
vars = schema[self.var]
if not vars:
# DOES NOT EXIST
return wrap([{
"name": ".",
"sql": {
"0": SQL_NULL
},
"nested_path": ROOT_PATH
}])
var_name = list(set(listwrap(vars).names.get('\\.')))
if len(var_name) > 1:
Log.error("do not know how to handle")
var_name = var_name[0]
cols = schema.leaves(self.var)
acc = {}
if boolean:
for col in cols:
cname = relative_field(col.names['.'], var_name)
nested_path = col.nested_path[0]
if col.type == OBJECT:
value = SQL_TRUE
elif col.type == BOOLEAN:
value = quote_column(col.es_column)
else:
value = quote_column(col.es_column) + SQL_IS_NOT_NULL
tempa = acc.setdefault(nested_path, {})
tempb = tempa.setdefault(get_property_name(cname), {})
tempb['b'] = value
else:
for col in cols:
cname = relative_field(col.names['.'], var_name)
if col.type == OBJECT:
prefix = self.var + "."
for cn, cs in schema.items():
if cn.startswith(prefix):
for child_col in cs:
tempa = acc.setdefault(child_col.nested_path[0],
{})
tempb = tempa.setdefault(get_property_name(cname),
{})
tempb[json_type_to_sql_type[
col.type]] = quote_column(child_col.es_column)
else:
nested_path = col.nested_path[0]
tempa = acc.setdefault(nested_path, {})
tempb = tempa.setdefault(get_property_name(cname), {})
tempb[json_type_to_sql_type[col.type]] = quote_column(
col.es_column)
return wrap([{
"name": cname,
"sql": types,
"nested_path": nested_path
} for nested_path, pairs in acc.items() for cname, types in pairs.items()])
def columns(self):
return listwrap(self.select) + coalesce(self.edges, self.groupby)
def start(cls, settings=None):
"""
RUN ME FIRST TO SETUP THE THREADED LOGGING
http://victorlin.me/2012/08/good-logging-practice-in-python/
log - LIST OF PARAMETERS FOR LOGGER(S)
trace - SHOW MORE DETAILS IN EVERY LOG LINE (default False)
cprofile - True==ENABLE THE C-PROFILER THAT COMES WITH PYTHON (default False)
USE THE LONG FORM TO SET THE FILENAME {"enabled": True, "filename": "cprofile.tab"}
profile - True==ENABLE pyLibrary SIMPLE PROFILING (default False) (eg with Profiler("some description"):)
USE THE LONG FORM TO SET FILENAME {"enabled": True, "filename": "profile.tab"}
constants - UPDATE MODULE CONSTANTS AT STARTUP (PRIMARILY INTENDED TO CHANGE DEBUG STATE)
"""
global _Thread
if not settings:
return
settings = wrap(settings)
Log.stop()
cls.settings = settings
cls.trace = coalesce(settings.trace, False)
if cls.trace:
from mo_threads import Thread as _Thread
_ = _Thread
# ENABLE CPROFILE
if settings.cprofile is False:
settings.cprofile = {"enabled": False}
elif settings.cprofile is True:
if isinstance(settings.cprofile, bool):
settings.cprofile = {
"enabled": True,
"filename": "cprofile.tab"
}
if settings.cprofile.enabled:
from mo_threads import profiles
profiles.enable_profilers(settings.cprofile.filename)
if settings.profile is True or (is_data(settings.profile)
and settings.profile.enabled):
Log.error(
"REMOVED 2018-09-02, Activedata revision 3f30ff46f5971776f8ba18"
)
# from mo_logs import profiles
#
# if isinstance(settings.profile, bool):
# profiles.ON = True
# settings.profile = {"enabled": True, "filename": "profile.tab"}
#
# if settings.profile.enabled:
# profiles.ON = True
if settings.constants:
constants.set(settings.constants)
if settings.log:
cls.logging_multi = StructuredLogger_usingMulti()
for log in listwrap(settings.log):
Log.add_log(Log.new_instance(log))
from mo_logs.log_usingThread import StructuredLogger_usingThread
cls.main_log = StructuredLogger_usingThread(cls.logging_multi)
def __data__(self):
output = Data({k:getattr(self,k) for k in vars(self)})
output.cause=unwraplist([c.__data__() for c in listwrap(output.cause)])
return output
def command_loop(local):
STDOUT.write(b'{"out":"ok"}\n')
DEBUG and Log.note("python process running")
file = File
while not please_stop:
line = STDIN.readline()
try:
command = json2value(line.decode('utf8'))
DEBUG and Log.note("got {{command}}", command=command)
if "import" in command:
dummy={}
if is_text(command['import']):
exec ("from " + command['import'] + " import *", dummy, context)
else:
exec ("from " + command['import']['from'] + " import " + ",".join(listwrap(command['import']['vars'])), dummy, context)
STDOUT.write(DONE)
elif "set" in command:
for k, v in command.set.items():
context[k] = v
STDOUT.write(DONE)
elif "get" in command:
STDOUT.write(value2json({"out": coalesce(local.get(command['get']), context.get(command['get']))}).encode('utf8'))
STDOUT.write(b'\n')
elif "stop" in command:
STDOUT.write(DONE)
please_stop.go()
elif "exec" in command:
if not is_text(command['exec']):
Log.error("exec expects only text")
exec (command['exec'], context, local)
STDOUT.write(DONE)
else:
for k, v in command.items():
if is_list(v):
exec ("_return = " + k + "(" + ",".join(map(value2json, v)) + ")", context, local)
else:
exec ("_return = " + k + "(" + ",".join(kk + "=" + value2json(vv) for kk, vv in v.items()) + ")", context, local)
STDOUT.write(value2json({"out": local['_return']}).encode('utf8'))
STDOUT.write(b'\n')
except Exception as e:
e = Except.wrap(e)
STDOUT.write(value2json({"err": e}).encode('utf8'))
STDOUT.write(b'\n')
finally:
STDOUT.flush()
def values(self):
return listwrap(dict.values(self))
def value_compare(left, right, ordering=1):
"""
SORT VALUES, NULL IS THE LEAST VALUE
:param left: LHS
:param right: RHS
:param ordering: (-1, 0, 0) TO AFFECT SORT ORDER
:return: The return value is negative if x < y, zero if x == y and strictly positive if x > y.
"""
try:
if isinstance(left, list) or isinstance(right, list):
if left == None:
return ordering
elif right == None:
return -ordering
left = listwrap(left)
right = listwrap(right)
for a, b in zip(left, right):
c = value_compare(a, b) * ordering
if c != 0:
return c
if len(left) < len(right):
return -ordering
elif len(left) > len(right):
return ordering
else:
return 0
ltype = type(left)
rtype = type(right)
ltype_num = TYPE_ORDER.get(ltype, 10)
rtype_num = TYPE_ORDER.get(rtype, 10)
type_diff = ltype_num - rtype_num
if type_diff != 0:
return ordering if type_diff > 0 else -ordering
if ltype_num == 9:
return 0
elif ltype is builtin_tuple:
for a, b in zip(left, right):
c = value_compare(a, b)
if c != 0:
return c * ordering
return 0
elif ltype in (dict, Data):
for k in sorted(set(left.keys()) | set(right.keys())):
c = value_compare(left.get(k), right.get(k)) * ordering
if c != 0:
return c
return 0
elif left > right:
return ordering
elif left < right:
return -ordering
else:
return 0
except Exception as e:
Log.error("Can not compare values {{left}} to {{right}}",
left=left,
right=right,
cause=e)
def es_aggsop(es, frum, query):
query = query.copy() # WE WILL MARK UP THIS QUERY
schema = frum.schema
select = listwrap(query.select)
es_query = Data()
new_select = Data() # MAP FROM canonical_name (USED FOR NAMES IN QUERY) TO SELECT MAPPING
formula = []
for s in select:
if s.aggregate == "count" and isinstance(s.value, Variable) and s.value.var == ".":
if schema.query_path == ".":
s.pull = jx_expression_to_function("doc_count")
else:
s.pull = jx_expression_to_function({"coalesce": ["_nested.doc_count", "doc_count", 0]})
elif isinstance(s.value, Variable):
if s.aggregate == "count":
new_select["count_"+literal_field(s.value.var)] += [s]
else:
new_select[literal_field(s.value.var)] += [s]
elif s.aggregate:
formula.append(s)
for canonical_name, many in new_select.items():
for s in many:
columns = frum.schema.values(s.value.var)
if s.aggregate == "count":
canonical_names = []
for column in columns:
cn = literal_field(column.es_column + "_count")
if column.jx_type == EXISTS:
canonical_names.append(cn + ".doc_count")
es_query.aggs[cn].filter.range = {column.es_column: {"gt": 0}}
else:
canonical_names.append(cn+ ".value")
es_query.aggs[cn].value_count.field = column.es_column
if len(canonical_names) == 1:
s.pull = jx_expression_to_function(canonical_names[0])
else:
s.pull = jx_expression_to_function({"add": canonical_names})
elif s.aggregate == "median":
if len(columns) > 1:
Log.error("Do not know how to count columns with more than one type (script probably)")
# ES USES DIFFERENT METHOD FOR PERCENTILES
key = literal_field(canonical_name + " percentile")
es_query.aggs[key].percentiles.field = columns[0].es_column
es_query.aggs[key].percentiles.percents += [50]
s.pull = jx_expression_to_function(key + ".values.50\\.0")
elif s.aggregate == "percentile":
if len(columns) > 1:
Log.error("Do not know how to count columns with more than one type (script probably)")
# ES USES DIFFERENT METHOD FOR PERCENTILES
key = literal_field(canonical_name + " percentile")
if isinstance(s.percentile, text_type) or s.percetile < 0 or 1 < s.percentile:
Log.error("Expecting percentile to be a float from 0.0 to 1.0")
percent = Math.round(s.percentile * 100, decimal=6)
es_query.aggs[key].percentiles.field = columns[0].es_column
es_query.aggs[key].percentiles.percents += [percent]
es_query.aggs[key].percentiles.compression = 2
s.pull = jx_expression_to_function(key + ".values." + literal_field(text_type(percent)))
elif s.aggregate == "cardinality":
canonical_names = []
for column in columns:
cn = literal_field(column.es_column + "_cardinality")
canonical_names.append(cn)
es_query.aggs[cn].cardinality.field = column.es_column
if len(columns) == 1:
s.pull = jx_expression_to_function(canonical_names[0] + ".value")
else:
s.pull = jx_expression_to_function({"add": [cn + ".value" for cn in canonical_names], "default": 0})
elif s.aggregate == "stats":
if len(columns) > 1:
Log.error("Do not know how to count columns with more than one type (script probably)")
# REGULAR STATS
stats_name = literal_field(canonical_name)
es_query.aggs[stats_name].extended_stats.field = columns[0].es_column
# GET MEDIAN TOO!
median_name = literal_field(canonical_name + "_percentile")
es_query.aggs[median_name].percentiles.field = columns[0].es_column
es_query.aggs[median_name].percentiles.percents += [50]
s.pull = get_pull_stats(stats_name, median_name)
elif s.aggregate == "union":
pulls = []
for column in columns:
stats_name = encode_property(column.es_column)
if column.nested_path[0] == ".":
es_query.aggs[stats_name] = {"terms": {
"field": column.es_column,
"size": Math.min(s.limit, MAX_LIMIT)
}}
pulls.append(get_bucket_keys(stats_name))
else:
es_query.aggs[stats_name] = {
"nested": {"path": column.nested_path[0]},
"aggs": {"_nested": {"terms": {
"field": column.es_column,
"size": Math.min(s.limit, MAX_LIMIT)
}}}
}
pulls.append(get_bucket_keys(stats_name+"._nested"))
if len(pulls) == 0:
s.pull = NULL
elif len(pulls) == 1:
s.pull = pulls[0]
else:
s.pull = lambda row: UNION(
p(row)
for p in pulls
)
else:
if len(columns) > 1:
Log.error("Do not know how to count columns with more than one type (script probably)")
# PULL VALUE OUT OF THE stats AGGREGATE
es_query.aggs[literal_field(canonical_name)].extended_stats.field = columns[0].es_column
s.pull = jx_expression_to_function({"coalesce": [literal_field(canonical_name) + "." + aggregates[s.aggregate], s.default]})
for i, s in enumerate(formula):
canonical_name = literal_field(s.name)
if isinstance(s.value, TupleOp):
if s.aggregate == "count":
# TUPLES ALWAYS EXIST, SO COUNTING THEM IS EASY
s.pull = "doc_count"
else:
Log.error("{{agg}} is not a supported aggregate over a tuple", agg=s.aggregate)
elif s.aggregate == "count":
es_query.aggs[literal_field(canonical_name)].value_count.script = s.value.partial_eval().to_es_script(schema).script(schema)
s.pull = jx_expression_to_function(literal_field(canonical_name) + ".value")
elif s.aggregate == "median":
# ES USES DIFFERENT METHOD FOR PERCENTILES THAN FOR STATS AND COUNT
key = literal_field(canonical_name + " percentile")
es_query.aggs[key].percentiles.script = s.value.to_es_script(schema).script(schema)
es_query.aggs[key].percentiles.percents += [50]
s.pull = jx_expression_to_function(key + ".values.50\\.0")
elif s.aggregate == "percentile":
# ES USES DIFFERENT METHOD FOR PERCENTILES THAN FOR STATS AND COUNT
key = literal_field(canonical_name + " percentile")
percent = Math.round(s.percentile * 100, decimal=6)
es_query.aggs[key].percentiles.script = s.value.to_es_script(schema).script(schema)
es_query.aggs[key].percentiles.percents += [percent]
s.pull = jx_expression_to_function(key + ".values." + literal_field(text_type(percent)))
elif s.aggregate == "cardinality":
# ES USES DIFFERENT METHOD FOR CARDINALITY
key = canonical_name + " cardinality"
es_query.aggs[key].cardinality.script = s.value.to_es_script(schema).script(schema)
s.pull = jx_expression_to_function(key + ".value")
elif s.aggregate == "stats":
# REGULAR STATS
stats_name = literal_field(canonical_name)
es_query.aggs[stats_name].extended_stats.script = s.value.to_es_script(schema).script(schema)
# GET MEDIAN TOO!
median_name = literal_field(canonical_name + " percentile")
es_query.aggs[median_name].percentiles.script = s.value.to_es_script(schema).script(schema)
es_query.aggs[median_name].percentiles.percents += [50]
s.pull = get_pull_stats(stats_name, median_name)
elif s.aggregate=="union":
# USE TERMS AGGREGATE TO SIMULATE union
stats_name = literal_field(canonical_name)
es_query.aggs[stats_name].terms.script_field = s.value.to_es_script(schema).script(schema)
s.pull = jx_expression_to_function(stats_name + ".buckets.key")
else:
# PULL VALUE OUT OF THE stats AGGREGATE
s.pull = jx_expression_to_function(canonical_name + "." + aggregates[s.aggregate])
es_query.aggs[canonical_name].extended_stats.script = s.value.to_es_script(schema).script(schema)
decoders = get_decoders_by_depth(query)
start = 0
#<TERRIBLE SECTION> THIS IS WHERE WE WEAVE THE where CLAUSE WITH nested
split_where = split_expression_by_depth(query.where, schema=frum.schema)
if len(split_field(frum.name)) > 1:
if any(split_where[2::]):
Log.error("Where clause is too deep")
for d in decoders[1]:
es_query = d.append_query(es_query, start)
start += d.num_columns
if split_where[1]:
#TODO: INCLUDE FILTERS ON EDGES
filter_ = AndOp("and", split_where[1]).to_esfilter(schema)
es_query = Data(
aggs={"_filter": set_default({"filter": filter_}, es_query)}
)
es_query = wrap({
"aggs": {"_nested": set_default(
{"nested": {"path": schema.query_path[0]}},
es_query
)}
})
else:
if any(split_where[1::]):
Log.error("Where clause is too deep")
if decoders:
for d in jx.reverse(decoders[0]):
es_query = d.append_query(es_query, start)
start += d.num_columns
if split_where[0]:
#TODO: INCLUDE FILTERS ON EDGES
filter = AndOp("and", split_where[0]).to_esfilter(schema)
es_query = Data(
aggs={"_filter": set_default({"filter": filter}, es_query)}
)
# </TERRIBLE SECTION>
if not es_query:
es_query = wrap({"query": {"match_all": {}}})
es_query.size = 0
with Timer("ES query time") as es_duration:
result = es_post(es, es_query, query.limit)
try:
format_time = Timer("formatting")
with format_time:
decoders = [d for ds in decoders for d in ds]
result.aggregations.doc_count = coalesce(result.aggregations.doc_count, result.hits.total) # IT APPEARS THE OLD doc_count IS GONE
formatter, groupby_formatter, aggop_formatter, mime_type = format_dispatch[query.format]
if query.edges:
output = formatter(decoders, result.aggregations, start, query, select)
elif query.groupby:
output = groupby_formatter(decoders, result.aggregations, start, query, select)
else:
output = aggop_formatter(decoders, result.aggregations, start, query, select)
output.meta.timing.formatting = format_time.duration
output.meta.timing.es_search = es_duration.duration
output.meta.content_type = mime_type
output.meta.es_query = es_query
return output
except Exception as e:
if query.format not in format_dispatch:
Log.error("Format {{format|quote}} not supported yet", format=query.format, cause=e)
Log.error("Some problem", cause=e)
def es_setop(es, query):
schema = query.frum.schema
es_query, filters = es_query_template(schema.query_path[0])
nested_filter = None
set_default(filters[0], query.where.partial_eval().to_es14_filter(schema))
es_query.size = coalesce(query.limit, DEFAULT_LIMIT)
es_query.fields = FlatList()
selects = wrap([s.copy() for s in listwrap(query.select)])
new_select = FlatList()
schema = query.frum.schema
# columns = schema.columns
# nested_columns = set(c.name for c in columns if c.nested_path[0] != ".")
es_query.sort = jx_sort_to_es_sort(query.sort, schema)
put_index = 0
for select in selects:
# IF THERE IS A *, THEN INSERT THE EXTRA COLUMNS
if isinstance(select.value, LeavesOp) and isinstance(
select.value.term, Variable):
term = select.value.term
leaves = schema.leaves(term.var)
for c in leaves:
full_name = concat_field(
select.name, relative_field(untype_path(c.name), term.var))
if c.jx_type == NESTED:
es_query.fields = ["_source"]
new_select.append({
"name": full_name,
"value": Variable(c.es_column),
"put": {
"name": literal_field(full_name),
"index": put_index,
"child": "."
},
"pull": get_pull_source(c.es_column)
})
put_index += 1
elif c.nested_path[0] != ".":
pass # THE NESTED PARENT WILL CAPTURE THIS
else:
es_query.fields += [c.es_column]
new_select.append({
"name": full_name,
"value": Variable(c.es_column),
"put": {
"name": literal_field(full_name),
"index": put_index,
"child": "."
}
})
put_index += 1
elif isinstance(select.value, Variable):
s_column = select.value.var
# LEAVES OF OBJECT
leaves = schema.leaves(s_column)
nested_selects = {}
if leaves:
if s_column == "." or any(c.jx_type == NESTED for c in leaves):
# PULL WHOLE NESTED ARRAYS
es_query.fields = ["_source"]
for c in leaves:
if len(c.nested_path) == 1:
jx_name = untype_path(c.name)
new_select.append({
"name":
select.name,
"value":
Variable(c.es_column),
"put": {
"name": select.name,
"index": put_index,
"child": relative_field(jx_name, s_column)
},
"pull":
get_pull_source(c.es_column)
})
else:
# PULL ONLY WHAT'S NEEDED
for c in leaves:
if len(c.nested_path) == 1:
jx_name = untype_path(c.name)
if c.jx_type == NESTED:
es_query.fields = ["_source"]
new_select.append({
"name":
select.name,
"value":
Variable(c.es_column),
"put": {
"name": select.name,
"index": put_index,
"child":
relative_field(jx_name, s_column)
},
"pull":
get_pull_source(c.es_column)
})
else:
es_query.fields += [c.es_column]
new_select.append({
"name":
select.name,
"value":
Variable(c.es_column),
"put": {
"name": select.name,
"index": put_index,
"child":
relative_field(jx_name, s_column)
}
})
else:
if not nested_filter:
where = filters[0].copy()
nested_filter = [where]
for k in filters[0].keys():
filters[0][k] = None
set_default(
filters[0],
es_and([where, es_or(nested_filter)]))
nested_path = c.nested_path[0]
if nested_path not in nested_selects:
where = nested_selects[nested_path] = Data()
nested_filter += [where]
where.nested.path = nested_path
where.nested.query.match_all = {}
where.nested.inner_hits._source = False
where.nested.inner_hits.fields += [c.es_column]
child = relative_field(
untype_path(
relative_field(c.name,
schema.query_path[0])),
s_column)
pull = accumulate_nested_doc(
nested_path,
Variable(
relative_field(
s_column,
unnest_path(nested_path))))
new_select.append({
"name": select.name,
"value": select.value,
"put": {
"name": select.name,
"index": put_index,
"child": child
},
"pull": pull
})
else:
nested_selects[
nested_path].nested.inner_hits.fields += [
c.es_column
]
else:
new_select.append({
"name": select.name,
"value": Variable("$dummy"),
"put": {
"name": select.name,
"index": put_index,
"child": "."
}
})
put_index += 1
else:
painless = select.value.partial_eval().to_es14_script(schema)
es_query.script_fields[literal_field(select.name)] = es_script(
painless.script(schema))
new_select.append({
"name":
select.name,
"pull":
jx_expression_to_function("fields." +
literal_field(select.name)),
"put": {
"name": select.name,
"index": put_index,
"child": "."
}
})
put_index += 1
for n in new_select:
if n.pull:
continue
elif isinstance(n.value, Variable):
if es_query.fields[0] == "_source":
es_query.fields = ["_source"]
n.pull = get_pull_source(n.value.var)
if n.value.var == "_id":
n.pull = jx_expression_to_function("_id")
else:
n.pull = jx_expression_to_function(
concat_field("fields", literal_field(n.value.var)))
else:
Log.error("Do not know what to do")
with Timer("call to ES", silent=not DEBUG) as call_timer:
data = es_post(es, es_query, query.limit)
T = data.hits.hits
try:
formatter, groupby_formatter, mime_type = format_dispatch[query.format]
output = formatter(T, new_select, query)
output.meta.timing.es = call_timer.duration
output.meta.content_type = mime_type
output.meta.es_query = es_query
return output
except Exception as e:
Log.error("problem formatting", e)
def get_revision(self,
revision,
locale=None,
get_diff=False,
get_moves=True):
"""
EXPECTING INCOMPLETE revision OBJECT
RETURNS revision
"""
rev = revision.changeset.id
if not rev:
return Null
elif rev == "None":
return Null
elif revision.branch.name == None:
return Null
locale = coalesce(locale, revision.branch.locale, DEFAULT_LOCALE)
output = self._get_from_elasticsearch(revision,
locale=locale,
get_diff=get_diff)
if output:
if not get_diff: # DIFF IS BIG, DO NOT KEEP IT IF NOT NEEDED
output.changeset.diff = None
if not get_moves:
output.changeset.moves = None
DEBUG and Log.note(
"Got hg ({{branch}}, {{locale}}, {{revision}}) from ES",
branch=output.branch.name,
locale=locale,
revision=output.changeset.id)
if output.push.date >= Date.now() - MAX_TODO_AGE:
self.todo.add((output.branch, listwrap(output.parents)))
self.todo.add((output.branch, listwrap(output.children)))
if output.push.date:
return output
# RATE LIMIT CALLS TO HG (CACHE MISSES)
next_cache_miss = self.last_cache_miss + (
Random.float(WAIT_AFTER_CACHE_MISS.seconds * 2) * SECOND)
self.last_cache_miss = Date.now()
if next_cache_miss > self.last_cache_miss:
Log.note("delaying next hg call for {{seconds|round(decimal=1)}}",
seconds=next_cache_miss - self.last_cache_miss)
Till(till=next_cache_miss.unix).wait()
found_revision = copy(revision)
if isinstance(found_revision.branch, (text_type, binary_type)):
lower_name = found_revision.branch.lower()
else:
lower_name = found_revision.branch.name.lower()
if not lower_name:
Log.error("Defective revision? {{rev|json}}",
rev=found_revision.branch)
b = found_revision.branch = self.branches[(lower_name, locale)]
if not b:
b = found_revision.branch = self.branches[(lower_name,
DEFAULT_LOCALE)]
if not b:
Log.warning("can not find branch ({{branch}}, {{locale}})",
branch=lower_name,
locale=locale)
return Null
if Date.now() - Date(b.etl.timestamp) > _OLD_BRANCH:
self.branches = _hg_branches.get_branches(kwargs=self.settings)
push = self._get_push(found_revision.branch,
found_revision.changeset.id)
url1 = found_revision.branch.url.rstrip(
"/") + "/json-info?node=" + found_revision.changeset.id[0:12]
url2 = found_revision.branch.url.rstrip(
"/") + "/json-rev/" + found_revision.changeset.id[0:12]
with Explanation("get revision from {{url}}", url=url1, debug=DEBUG):
raw_rev2 = Null
try:
raw_rev1 = self._get_raw_json_info(url1, found_revision.branch)
raw_rev2 = self._get_raw_json_rev(url2, found_revision.branch)
except Exception as e:
if "Hg denies it exists" in e:
raw_rev1 = Data(node=revision.changeset.id)
else:
raise e
output = self._normalize_revision(set_default(raw_rev1, raw_rev2),
found_revision, push, get_diff,
get_moves)
if output.push.date >= Date.now() - MAX_TODO_AGE:
self.todo.add((output.branch, listwrap(output.parents)))
self.todo.add((output.branch, listwrap(output.children)))
if not get_diff: # DIFF IS BIG, DO NOT KEEP IT IF NOT NEEDED
output.changeset.diff = None
if not get_moves:
output.changeset.moves = None
return output
def create_table(
self,
table,
schema=None,
typed=True,
read_only=True, # TO PREVENT ACCIDENTAL WRITING
sharded=False,
partition=Null, # PARTITION RULES
cluster=None, # TUPLE OF FIELDS TO SORT DATA
top_level_fields=Null,
kwargs=None,
):
if kwargs.lookup != None or kwargs.flake != None:
Log.error("expecting schema, not lookup")
full_name = self.full_name + escape_name(table)
if not schema:
# WE MUST HAVE SOMETHING
if typed:
schema = copy(DEFAULT_TYPED_SCHEMA)
else:
schema = copy(DEFAULT_SCHEMA)
flake = Snowflake(text(full_name),
top_level_fields,
partition,
schema=schema)
if read_only:
Log.error("Can not create a table for read-only use")
if sharded:
shard_name = escape_name(table + "_" +
"".join(Random.sample(ALLOWED, 20)))
shard_api_name = self.full_name + shard_name
_shard = bigquery.Table(text(shard_api_name),
schema=flake.to_bq_schema())
_shard.time_partitioning = unwrap(
flake._partition.bq_time_partitioning)
_shard.clustering_fields = [
c.es_column for f in listwrap(cluster)
for c in [first(flake.leaves(f))] if c
] or None
self.shard = self.client.create_table(_shard)
self.create_view(full_name, shard_api_name)
else:
_table = bigquery.Table(text(full_name),
schema=flake.to_bq_schema())
_table.time_partitioning = unwrap(
flake._partition.bq_time_partitioning)
_table.clustering_fields = [
l.es_column for f in listwrap(cluster) for l in flake.leaves(f)
] or None
self.client.create_table(_table)
DEBUG and Log.note("created table {{table}}",
table=_table.table_id)
return Table(
table=table,
typed=typed,
read_only=read_only,
sharded=sharded,
partition=partition,
top_level_fields=top_level_fields,
kwargs=kwargs,
container=self,
)
def es_setop(es, mvel, query):
FromES = es09.util.build_es_query(query)
select = listwrap(query.select)
isDeep = len(split_field(
query.frum.name)) > 1 # LOOKING INTO NESTED WILL REQUIRE A SCRIPT
isComplex = OR([
s.value == None and s.aggregate not in ("count", "none")
for s in select
]) # CONVERTING esfilter DEFINED PARTS WILL REQUIRE SCRIPT
if not isDeep and not isComplex:
if len(select) == 1 and not select[0].value or select[0].value == "*":
FromES = wrap({
"query": {
"filtered": {
"query": {
"match_all": {}
},
"filter":
simplify_esfilter(
jx_expression(query.where).to_esfilter())
}
},
"sort": query.sort,
"size": 1
})
elif all(isinstance(v, Variable) for v in select.value):
FromES = wrap({
"query": {
"filtered": {
"query": {
"match_all": {}
},
"filter": simplify_esfilter(query.where.to_esfilter())
}
},
"fields": select.value,
"sort": query.sort,
"size": coalesce(query.limit, 200000)
})
elif not isDeep:
simple_query = query.copy()
simple_query.where = TRUE_FILTER # THE FACET FILTER IS FASTER
FromES.facets.mvel = {
"terms": {
"script_field": mvel.code(simple_query),
"size": coalesce(simple_query.limit, 200000)
},
"facet_filter":
simplify_esfilter(jx_expression(query.where).to_esfilter())
}
else:
FromES.facets.mvel = {
"terms": {
"script_field": mvel.code(query),
"size": coalesce(query.limit, 200000)
},
"facet_filter":
simplify_esfilter(jx_expression(query.where).to_esfilter())
}
data = es09.util.post(es, FromES, query.limit)
if len(select) == 1 and not select[0].value or select[0].value == "*":
# SPECIAL CASE FOR SINGLE COUNT
cube = wrap(data).hits.hits._source
elif isinstance(select[0].value, Variable):
# SPECIAL CASE FOR SINGLE TERM
cube = wrap(data).hits.hits.fields
else:
data_list = unpack_terms(data.facets.mvel, select)
if not data_list:
cube = Cube(select, [], {s.name: Matrix.wrap([]) for s in select})
else:
output = zip(*data_list)
cube = Cube(
select, [],
{s.name: Matrix(list=output[i])
for i, s in enumerate(select)})
return Data(meta={"esquery": FromES}, data=cube)
def new_instance(desc):
return Except(desc.type, desc.template, desc.params,
[Except.new_instance(c) for c in listwrap(desc.cause)],
desc.trace)
def _setop(self, query):
"""
NO AGGREGATION, SIMPLE LIST COMPREHENSION
"""
if isinstance(query.select, list):
# RETURN BORING RESULT SET
selects = FlatList()
for s in listwrap(query.select):
if isinstance(s.value, Mapping):
for k, v in s.value.items:
selects.append(v + " AS " + self.db.quote_column(s.name + "." + k))
if isinstance(s.value, list):
for i, ss in enumerate(s.value):
selects.append(s.value + " AS " + self.db.quote_column(s.name + "," + str(i)))
else:
selects.append(s.value + " AS " + self.db.quote_column(s.name))
sql = expand_template("""
SELECT
{{selects}}
FROM
{{table}}
{{where}}
{{sort}}
{{limit}}
""", {
"selects": SQL(",\n".join(selects)),
"table": self._subquery(query["from"])[0],
"where": self._where2sql(query.where),
"limit": self._limit2sql(query.limit),
"sort": self._sort2sql(query.sort)
})
def post_process(sql):
result = self.db.query(sql)
for s in listwrap(query.select):
if isinstance(s.value, Mapping):
for r in result:
r[s.name] = {}
for k, v in s.value:
r[s.name][k] = r[s.name + "." + k]
r[s.name + "." + k] = None
if isinstance(s.value, list):
# REWRITE AS TUPLE
for r in result:
r[s.name] = tuple(r[s.name + "," + str(i)] for i, ss in enumerate(s.value))
for i, ss in enumerate(s.value):
r[s.name + "," + str(i)] = None
expand_json(result)
return result
return sql, post_process # RETURN BORING RESULT SET
else:
# RETURN LIST OF VALUES
if query.select.value == ".":
select = "*"
else:
name = query.select.name
select = query.select.value + " AS " + self.db.quote_column(name)
sql = expand_template("""
SELECT
{{selects}}
FROM
{{table}}
{{where}}
{{sort}}
{{limit}}
""", {
"selects": SQL(select),
"table": self._subquery(query["from"])[0],
"where": self._where2sql(query.where),
"limit": self._limit2sql(query.limit),
"sort": self._sort2sql(query.sort)
})
if query.select.value == ".":
def post(sql):
result = self.db.query(sql)
expand_json(result)
return result
return sql, post
else:
return sql, lambda sql: [r[name] for r in self.db.query(sql)] # RETURNING LIST OF VALUES
def cube_aggs(frum, query):
select = listwrap(query.select)
#MATCH EDGES IN QUERY TO ONES IN frum
for e in query.edges:
for fs in frum.select:
if fs.name == e.value:
Log.error("Not implemented yet")
if isinstance(e.domain, DefaultDomain):
# DEFAULT DOMAINS CAN EASILY BE LOOKED UP FROM frum
for fe in frum.edges:
if fe.name == e.value:
e.domain = SimpleSetDomain(**fe.domain.__data__())
e.value = e.value + "." + fe.domain.key
break
else:
for fe in frum.edges:
if fe.name == e.value:
e.value = e.value + "." + fe.domain.key
break
result = {
s.name: Matrix(dims=[
len(e.domain.partitions) + (1 if e.allowNulls else 0)
for e in query.edges
],
zeros=s.default)
for s in select
}
where = jx_expression_to_function(query.where)
for d in filter(where, frum.values()):
coord = [
] # LIST OF MATCHING COORDINATE FAMILIES, USUALLY ONLY ONE PER FAMILY BUT JOINS WITH EDGES CAN CAUSE MORE
for e in query.edges:
matches = get_matches(e, d)
coord.append(matches)
if len(matches) == 1 and d[e.name] == None:
d[e.name] = e.domain.partitions[matches[0]]
for s in select:
mat = result[s.name]
agg = s.aggregate
var = s.value
expr = jx_expression_to_function(var)
val = expr(d)
if agg == "count":
if var == "." or var == None:
for c in itertools.product(*coord):
mat[c] += 1
continue
if val != None:
for c in itertools.product(*coord):
mat[c] += 1
else:
for c in itertools.product(*coord):
acc = mat[c]
if acc == None:
acc = windows.name2accumulator.get(agg)
if acc == None:
Log.error(
"select aggregate {{agg}} is not recognized",
agg=agg)
acc = acc(**s)
mat[c] = acc
acc.add(val)
for s in select:
if s.aggregate == "count":
continue
m = result[s.name]
for c, var in m.items():
if var != None:
m[c] = var.end()
from pyLibrary.queries.containers.cube import Cube
return Cube(select, query.edges, result)
def _normalize_edges(edges, limit, schema=None):
return list_to_data([
n for ie, e in enumerate(listwrap(edges))
for n in _normalize_edge(e, ie, limit=limit, schema=schema)
])
def is_aggsop(es, query):
if query.edges or query.groupby or any(
a != None and a != "none"
for a in listwrap(query.select).aggregate):
return True
return False
def value_compare(left, right, ordering=1):
"""
SORT VALUES, NULL IS THE LEAST VALUE
:param left: LHS
:param right: RHS
:param ordering: (-1, 0, 1) TO AFFECT SORT ORDER
:return: The return value is negative if x < y, zero if x == y and strictly positive if x > y.
"""
try:
ltype = left.__class__
rtype = right.__class__
if ltype in list_types or rtype in list_types:
if left == None:
return ordering
elif right == None:
return -ordering
left = listwrap(left)
right = listwrap(right)
for a, b in zip(left, right):
c = value_compare(a, b) * ordering
if c != 0:
return c
if len(left) < len(right):
return -ordering
elif len(left) > len(right):
return ordering
else:
return 0
if ltype is float and isnan(left):
left = None
ltype = none_type
if rtype is float and isnan(right):
right = None
rtype = none_type
null_order = ordering * 10
ltype_num = TYPE_ORDER.get(ltype, null_order)
rtype_num = TYPE_ORDER.get(rtype, null_order)
type_diff = ltype_num - rtype_num
if type_diff != 0:
return ordering if type_diff > 0 else -ordering
if ltype_num == null_order:
return 0
elif ltype is builtin_tuple:
for a, b in zip(left, right):
c = value_compare(a, b)
if c != 0:
return c * ordering
return 0
elif ltype in data_types:
for k in sorted(set(left.keys()) | set(right.keys())):
c = value_compare(left.get(k), right.get(k)) * ordering
if c != 0:
return c
return 0
elif left > right:
return ordering
elif left < right:
return -ordering
else:
return 0
except Exception as e:
Log.error("Can not compare values {{left}} to {{right}}",
left=left,
right=right,
cause=e)
def _set_op(self, query, frum):
# GET LIST OF COLUMNS
base_name, primary_nested_path = tail_field(frum)
vars_ = UNION([
v.var for select in listwrap(query.select)
for v in select.value.vars()
])
schema = self.sf.tables[primary_nested_path].schema
active_columns = {".": set()}
for v in vars_:
for c in schema.leaves(v):
nest = c.nested_path[0]
active_columns.setdefault(nest, set()).add(c)
# ANY VARS MENTIONED WITH NO COLUMNS?
for v in vars_:
if not any(startswith_field(cname, v) for cname in schema.keys()):
active_columns["."].add(
Column(name=v,
jx_type=IS_NULL,
es_column=".",
es_index=".",
nested_path=["."]))
# EVERY COLUMN, AND THE INDEX IT TAKES UP
index_to_column = {} # MAP FROM INDEX TO COLUMN (OR SELECT CLAUSE)
index_to_uid = {} # FROM NESTED PATH TO THE INDEX OF UID
sql_selects = [
] # EVERY SELECT CLAUSE (NOT TO BE USED ON ALL TABLES, OF COURSE)
nest_to_alias = {
nested_path: "__" + unichr(ord('a') + i) + "__"
for i, (nested_path,
sub_table) in enumerate(self.sf.tables.items())
}
sorts = []
if query.sort:
for select in query.sort:
col = select.value.to_sql(schema)[0]
for t, sql in col.sql.items():
json_type = sql_type_to_json_type[t]
if json_type in STRUCT:
continue
column_number = len(sql_selects)
# SQL HAS ABS TABLE REFERENCE
column_alias = _make_column_name(column_number)
sql_selects.append(sql_alias(sql, column_alias))
if select.sort == -1:
sorts.append(column_alias + SQL_IS_NOT_NULL)
sorts.append(column_alias + " DESC")
else:
sorts.append(column_alias + SQL_IS_NULL)
sorts.append(column_alias)
primary_doc_details = Data()
# EVERY SELECT STATEMENT THAT WILL BE REQUIRED, NO MATTER THE DEPTH
# WE WILL CREATE THEM ACCORDING TO THE DEPTH REQUIRED
nested_path = []
for step, sub_table in self.sf.tables.items():
nested_path.insert(0, step)
nested_doc_details = {
"sub_table": sub_table,
"children": [],
"index_to_column": {},
"nested_path": nested_path
}
# INSERT INTO TREE
if not primary_doc_details:
primary_doc_details = nested_doc_details
else:
def place(parent_doc_details):
if startswith_field(step,
parent_doc_details['nested_path'][0]):
for c in parent_doc_details['children']:
if place(c):
return True
parent_doc_details['children'].append(
nested_doc_details)
place(primary_doc_details)
alias = nested_doc_details['alias'] = nest_to_alias[step]
# WE ALWAYS ADD THE UID
column_number = index_to_uid[step] = nested_doc_details[
'id_coord'] = len(sql_selects)
sql_select = join_column(alias, quoted_UID)
sql_selects.append(
sql_alias(sql_select, _make_column_name(column_number)))
if step != ".":
# ID AND ORDER FOR CHILD TABLES
index_to_column[column_number] = ColumnMapping(
sql=sql_select,
type="number",
nested_path=nested_path,
column_alias=_make_column_name(column_number))
column_number = len(sql_selects)
sql_select = join_column(alias, quoted_ORDER)
sql_selects.append(
sql_alias(sql_select, _make_column_name(column_number)))
index_to_column[column_number] = ColumnMapping(
sql=sql_select,
type="number",
nested_path=nested_path,
column_alias=_make_column_name(column_number))
# WE DO NOT NEED DATA FROM TABLES WE REQUEST NOTHING FROM
if step not in active_columns:
continue
# ADD SQL SELECT COLUMNS FOR EACH jx SELECT CLAUSE
si = 0
for select in listwrap(query.select):
try:
column_number = len(sql_selects)
select.pull = get_column(column_number)
db_columns = select.value.partial_eval().to_sql(schema)
for column in db_columns:
if is_list(column.nested_path):
column.nested_path = column.nested_path[
0] # IN THE EVENT THIS "column" IS MULTIVALUED
for t, unsorted_sql in column.sql.items():
json_type = sql_type_to_json_type[t]
if json_type in STRUCT:
continue
column_number = len(sql_selects)
column_alias = _make_column_name(column_number)
sql_selects.append(
sql_alias(unsorted_sql, column_alias))
if startswith_field(primary_nested_path,
step) and is_op(
select.value, LeavesOp):
# ONLY FLATTEN primary_nested_path AND PARENTS, NOT CHILDREN
index_to_column[
column_number] = nested_doc_details[
'index_to_column'][
column_number] = ColumnMapping(
push_name=literal_field(
get_property_name(
concat_field(
select.name,
column.name))),
push_child=".",
push_column_name=
get_property_name(
concat_field(
select.name,
column.name)),
push_column=si,
pull=get_column(column_number),
sql=unsorted_sql,
type=json_type,
column_alias=column_alias,
nested_path=nested_path)
si += 1
else:
index_to_column[
column_number] = nested_doc_details[
'index_to_column'][
column_number] = ColumnMapping(
push_name=select.name,
push_child=column.name,
push_column_name=select.name,
push_column=si,
pull=get_column(column_number),
sql=unsorted_sql,
type=json_type,
column_alias=column_alias,
nested_path=nested_path)
finally:
si += 1
where_clause = BooleanOp(query.where).partial_eval().to_sql(
schema, boolean=True)[0].sql.b
unsorted_sql = self._make_sql_for_one_nest_in_set_op(
".", sql_selects, where_clause, active_columns, index_to_column)
for n, _ in self.sf.tables.items():
sorts.append(quote_column(COLUMN + text_type(index_to_uid[n])))
ordered_sql = (SQL_SELECT + "*" + SQL_FROM + sql_iso(unsorted_sql) +
SQL_ORDERBY + sql_list(sorts) + SQL_LIMIT +
quote_value(query.limit))
self.db.create_new_functions() # creating new functions: regexp
result = self.db.query(ordered_sql)
def _accumulate_nested(rows, row, nested_doc_details, parent_doc_id,
parent_id_coord):
"""
:param rows: REVERSED STACK OF ROWS (WITH push() AND pop())
:param row: CURRENT ROW BEING EXTRACTED
:param nested_doc_details: {
"nested_path": wrap_nested_path(nested_path),
"index_to_column": map from column number to column details
"children": all possible direct decedents' nested_doc_details
}
:param parent_doc_id: the id of the parent doc (for detecting when to step out of loop)
:param parent_id_coord: the column number for the parent id (so we ca extract from each row)
:return: the nested property (usually an array)
"""
previous_doc_id = None
doc = Data()
output = []
id_coord = nested_doc_details['id_coord']
while True:
doc_id = row[id_coord]
if doc_id == None or (parent_id_coord is not None and
row[parent_id_coord] != parent_doc_id):
rows.append(
row
) # UNDO PREVIOUS POP (RECORD IS NOT A NESTED RECORD OF parent_doc)
return output
if doc_id != previous_doc_id:
previous_doc_id = doc_id
doc = Data()
curr_nested_path = nested_doc_details['nested_path'][0]
index_to_column = nested_doc_details[
'index_to_column'].items()
if index_to_column:
for i, c in index_to_column:
value = row[i]
if is_list(query.select) or is_op(
query.select.value, LeavesOp):
# ASSIGN INNER PROPERTIES
relative_field = concat_field(
c.push_name, c.push_child)
else: # FACT IS EXPECTED TO BE A SINGLE VALUE, NOT AN OBJECT
relative_field = c.push_child
if relative_field == ".":
if value == '':
doc = Null
else:
doc = value
elif value != None and value != '':
doc[relative_field] = value
for child_details in nested_doc_details['children']:
# EACH NESTED TABLE MUST BE ASSEMBLED INTO A LIST OF OBJECTS
child_id = row[child_details['id_coord']]
if child_id is not None:
nested_value = _accumulate_nested(
rows, row, child_details, doc_id, id_coord)
if nested_value:
push_name = child_details['nested_path'][0]
if is_list(query.select) or is_op(
query.select.value, LeavesOp):
# ASSIGN INNER PROPERTIES
relative_field = relative_field(
push_name, curr_nested_path)
else: # FACT IS EXPECTED TO BE A SINGLE VALUE, NOT AN OBJECT
relative_field = "."
if relative_field == "." and doc is Null:
doc = nested_value
elif relative_field == ".":
doc = unwraplist(nested_value)
else:
doc[relative_field] = unwraplist(nested_value)
output.append(doc)
try:
row = rows.pop()
except IndexError:
return output
cols = tuple(
[i for i in index_to_column.values() if i.push_name != None])
rows = list(reversed(unwrap(result.data)))
if rows:
row = rows.pop()
data = _accumulate_nested(rows, row, primary_doc_details, None,
None)
else:
data = result.data
if query.format == "cube":
for f, _ in self.sf.tables.items():
if frum.endswith(f) or (test_dots(cols)
and is_list(query.select)):
num_rows = len(result.data)
num_cols = MAX([c.push_column
for c in cols]) + 1 if len(cols) else 0
map_index_to_name = {
c.push_column: c.push_column_name
for c in cols
}
temp_data = [[None] * num_rows for _ in range(num_cols)]
for rownum, d in enumerate(result.data):
for c in cols:
if c.push_child == ".":
temp_data[c.push_column][rownum] = c.pull(d)
else:
column = temp_data[c.push_column][rownum]
if column is None:
column = temp_data[
c.push_column][rownum] = {}
column[c.push_child] = c.pull(d)
output = Data(meta={"format": "cube"},
data={
n: temp_data[c]
for c, n in map_index_to_name.items()
},
edges=[{
"name": "rownum",
"domain": {
"type": "rownum",
"min": 0,
"max": num_rows,
"interval": 1
}
}])
return output
if is_list(query.select) or is_op(query.select.value, LeavesOp):
num_rows = len(data)
temp_data = {
c.push_column_name: [None] * num_rows
for c in cols
}
for rownum, d in enumerate(data):
for c in cols:
temp_data[c.push_column_name][rownum] = d[c.push_name]
return Data(meta={"format": "cube"},
data=temp_data,
edges=[{
"name": "rownum",
"domain": {
"type": "rownum",
"min": 0,
"max": num_rows,
"interval": 1
}
}])
else:
num_rows = len(data)
map_index_to_name = {
c.push_column: c.push_column_name
for c in cols
}
temp_data = [data]
return Data(meta={"format": "cube"},
data={
n: temp_data[c]
for c, n in map_index_to_name.items()
},
edges=[{
"name": "rownum",
"domain": {
"type": "rownum",
"min": 0,
"max": num_rows,
"interval": 1
}
}])
elif query.format == "table":
for f, _ in self.sf.tables.items():
if frum.endswith(f):
num_column = MAX([c.push_column for c in cols]) + 1
header = [None] * num_column
for c in cols:
header[c.push_column] = c.push_column_name
output_data = []
for d in result.data:
row = [None] * num_column
for c in cols:
set_column(row, c.push_column, c.push_child,
c.pull(d))
output_data.append(row)
return Data(meta={"format": "table"},
header=header,
data=output_data)
if is_list(query.select) or is_op(query.select.value, LeavesOp):
column_names = [None] * (max(c.push_column for c in cols) + 1)
for c in cols:
column_names[c.push_column] = c.push_column_name
temp_data = []
for rownum, d in enumerate(data):
row = [None] * len(column_names)
for c in cols:
row[c.push_column] = d[c.push_name]
temp_data.append(row)
return Data(meta={"format": "table"},
header=column_names,
data=temp_data)
else:
column_names = listwrap(query.select).name
return Data(meta={"format": "table"},
header=column_names,
data=[[d] for d in data])
else:
for f, _ in self.sf.tables.items():
if frum.endswith(f) or (test_dots(cols)
and is_list(query.select)):
data = []
for d in result.data:
row = Data()
for c in cols:
if c.push_child == ".":
row[c.push_name] = c.pull(d)
elif c.num_push_columns:
tuple_value = row[c.push_name]
if not tuple_value:
tuple_value = row[c.push_name] = [
None
] * c.num_push_columns
tuple_value[c.push_child] = c.pull(d)
else:
row[c.push_name][c.push_child] = c.pull(d)
data.append(row)
return Data(meta={"format": "list"}, data=data)
if is_list(query.select) or is_op(query.select.value, LeavesOp):
temp_data = []
for rownum, d in enumerate(data):
row = {}
for c in cols:
row[c.push_column_name] = d[c.push_name]
temp_data.append(row)
return Data(meta={"format": "list"}, data=temp_data)
else:
return Data(meta={"format": "list"}, data=data)
def es_aggsop(es, frum, query):
query = query.copy() # WE WILL MARK UP THIS QUERY
schema = frum.schema
query_path = schema.query_path[0]
select = listwrap(query.select)
new_select = Data(
) # MAP FROM canonical_name (USED FOR NAMES IN QUERY) TO SELECT MAPPING
formula = []
for s in select:
if is_op(s.value, Variable_):
s.query_path = query_path
if s.aggregate == "count":
new_select["count_" + literal_field(s.value.var)] += [s]
else:
new_select[literal_field(s.value.var)] += [s]
elif s.aggregate:
split_select = split_expression_by_path(s.value,
schema,
lang=Painless)
for si_key, si_value in split_select.items():
if si_value:
if s.query_path:
Log.error(
"can not handle more than one depth per select")
s.query_path = si_key
formula.append(s)
acc = Aggs()
for _, many in new_select.items():
for s in many:
canonical_name = s.name
if s.aggregate in ("value_count", "count"):
columns = frum.schema.values(s.value.var,
exclude_type=(OBJECT, NESTED))
else:
columns = frum.schema.values(s.value.var)
if s.aggregate == "count":
canonical_names = []
for column in columns:
es_name = column.es_column + "_count"
if column.jx_type == EXISTS:
if column.nested_path[0] == query_path:
canonical_names.append("doc_count")
acc.add(
NestedAggs(column.nested_path[0]).add(
CountAggs(s)))
else:
canonical_names.append("value")
acc.add(
NestedAggs(column.nested_path[0]).add(
ExprAggs(es_name, {
"value_count": {
"field": column.es_column
}
}, s)))
if len(canonical_names) == 1:
s.pull = jx_expression_to_function(canonical_names[0])
else:
s.pull = jx_expression_to_function(
{"add": canonical_names})
elif s.aggregate == "median":
columns = [
c for c in columns if c.jx_type in (NUMBER, INTEGER)
]
if len(columns) != 1:
Log.error(
"Do not know how to perform median on columns with more than one type (script probably)"
)
# ES USES DIFFERENT METHOD FOR PERCENTILES
key = canonical_name + " percentile"
acc.add(
ExprAggs(
key, {
"percentiles": {
"field": first(columns).es_column,
"percents": [50]
}
}, s))
s.pull = jx_expression_to_function("values.50\\.0")
elif s.aggregate == "percentile":
columns = [
c for c in columns if c.jx_type in (NUMBER, INTEGER)
]
if len(columns) != 1:
Log.error(
"Do not know how to perform percentile on columns with more than one type (script probably)"
)
# ES USES DIFFERENT METHOD FOR PERCENTILES
key = canonical_name + " percentile"
if is_text(
s.percentile) or s.percetile < 0 or 1 < s.percentile:
Log.error(
"Expecting percentile to be a float from 0.0 to 1.0")
percent = mo_math.round(s.percentile * 100, decimal=6)
acc.add(
ExprAggs(
key, {
"percentiles": {
"field": first(columns).es_column,
"percents": [percent],
"tdigest": {
"compression": 2
}
}
}, s))
s.pull = jx_expression_to_function(
join_field(["values", text(percent)]))
elif s.aggregate == "cardinality":
for column in columns:
path = column.es_column + "_cardinality"
acc.add(
ExprAggs(path,
{"cardinality": {
"field": column.es_column
}}, s))
s.pull = jx_expression_to_function("value")
elif s.aggregate == "stats":
columns = [
c for c in columns if c.jx_type in (NUMBER, INTEGER)
]
if len(columns) != 1:
Log.error(
"Do not know how to perform stats on columns with more than one type (script probably)"
)
# REGULAR STATS
acc.add(
ExprAggs(canonical_name, {
"extended_stats": {
"field": first(columns).es_column
}
}, s))
s.pull = get_pull_stats()
# GET MEDIAN TOO!
select_median = s.copy()
select_median.pull = jx_expression_to_function(
{"select": [{
"name": "median",
"value": "values.50\\.0"
}]})
acc.add(
ExprAggs(
canonical_name + "_percentile", {
"percentiles": {
"field": first(columns).es_column,
"percents": [50]
}
}, select_median))
elif s.aggregate == "union":
for column in columns:
script = {
"scripted_metric": {
'init_script':
'params._agg.terms = new HashSet()',
'map_script':
'for (v in doc[' + quote(column.es_column) +
'].values) params._agg.terms.add(v);',
'combine_script':
'return params._agg.terms.toArray()',
'reduce_script':
'HashSet output = new HashSet(); for (a in params._aggs) { if (a!=null) for (v in a) {output.add(v)} } return output.toArray()',
}
}
stats_name = column.es_column
acc.add(
NestedAggs(column.nested_path[0]).add(
ExprAggs(stats_name, script, s)))
s.pull = jx_expression_to_function("value")
elif s.aggregate == "count_values":
# RETURN MAP FROM VALUE TO THE NUMBER OF TIMES FOUND IN THE DOCUMENTS
# NOT A NESTED DOC, RATHER A MULTIVALUE FIELD
for column in columns:
script = {
"scripted_metric": {
'params': {
"_agg": {}
},
'init_script':
'params._agg.terms = new HashMap()',
'map_script':
'for (v in doc[' + quote(column.es_column) +
'].values) params._agg.terms.put(v, Optional.ofNullable(params._agg.terms.get(v)).orElse(0)+1);',
'combine_script':
'return params._agg.terms',
'reduce_script':
'''
HashMap output = new HashMap();
for (agg in params._aggs) {
if (agg!=null){
for (e in agg.entrySet()) {
String key = String.valueOf(e.getKey());
output.put(key, e.getValue() + Optional.ofNullable(output.get(key)).orElse(0));
}
}
}
return output;
'''
}
}
stats_name = encode_property(column.es_column)
acc.add(
NestedAggs(column.nested_path[0]).add(
ExprAggs(stats_name, script, s)))
s.pull = jx_expression_to_function("value")
else:
if not columns:
s.pull = jx_expression_to_function(NULL)
else:
for c in columns:
acc.add(
NestedAggs(c.nested_path[0]).add(
ExprAggs(
canonical_name,
{"extended_stats": {
"field": c.es_column
}}, s)))
s.pull = jx_expression_to_function(aggregates[s.aggregate])
for i, s in enumerate(formula):
s_path = [
k for k, v in split_expression_by_path(
s.value, schema=schema, lang=Painless).items() if v
]
if len(s_path) == 0:
# FOR CONSTANTS
nest = NestedAggs(query_path)
acc.add(nest)
elif len(s_path) == 1:
nest = NestedAggs(first(s_path))
acc.add(nest)
else:
Log.error("do not know how to handle")
canonical_name = s.name
if is_op(s.value, TupleOp):
if s.aggregate == "count":
# TUPLES ALWAYS EXIST, SO COUNTING THEM IS EASY
s.pull = jx_expression_to_function("doc_count")
elif s.aggregate in ('max', 'maximum', 'min', 'minimum'):
if s.aggregate in ('max', 'maximum'):
dir = 1
op = "max"
else:
dir = -1
op = 'min'
nully = Painless[TupleOp(
[NULL] *
len(s.value.terms))].partial_eval().to_es_script(schema)
selfy = text(
Painless[s.value].partial_eval().to_es_script(schema))
script = {
"scripted_metric": {
'init_script':
'params._agg.best = ' + nully + '.toArray();',
'map_script':
'params._agg.best = ' + expand_template(
MAX_OF_TUPLE, {
"expr1": "params._agg.best",
"expr2": selfy,
"dir": dir,
"op": op
}) + ";",
'combine_script':
'return params._agg.best',
'reduce_script':
'return params._aggs.stream().' + op + '(' +
expand_template(COMPARE_TUPLE, {
"dir": dir,
"op": op
}) + ').get()',
}
}
nest.add(
NestedAggs(query_path).add(
ExprAggs(canonical_name, script, s)))
s.pull = jx_expression_to_function("value")
else:
Log.error("{{agg}} is not a supported aggregate over a tuple",
agg=s.aggregate)
elif s.aggregate == "count":
nest.add(
ExprAggs(
canonical_name, {
"value_count": {
"script":
text(Painless[s.value].partial_eval().to_es_script(
schema))
}
}, s))
s.pull = jx_expression_to_function("value")
elif s.aggregate == "median":
# ES USES DIFFERENT METHOD FOR PERCENTILES THAN FOR STATS AND COUNT
key = literal_field(canonical_name + " percentile")
nest.add(
ExprAggs(
key, {
"percentiles": {
"script": text(
Painless[s.value].to_es_script(schema)),
"percents": [50]
}
}, s))
s.pull = jx_expression_to_function(join_field(["50.0"]))
elif s.aggregate == "percentile":
# ES USES DIFFERENT METHOD FOR PERCENTILES THAN FOR STATS AND COUNT
key = literal_field(canonical_name + " percentile")
percent = mo_math.round(s.percentile * 100, decimal=6)
nest.add(
ExprAggs(
key, {
"percentiles": {
"script": text(
Painless[s.value].to_es_script(schema)),
"percents": [percent]
}
}, s))
s.pull = jx_expression_to_function(
join_field(["values", text(percent)]))
elif s.aggregate == "cardinality":
# ES USES DIFFERENT METHOD FOR CARDINALITY
key = canonical_name + " cardinality"
nest.add(
ExprAggs(
key, {
"cardinality": {
"script": text(
Painless[s.value].to_es_script(schema))
}
}, s))
s.pull = jx_expression_to_function("value")
elif s.aggregate == "stats":
# REGULAR STATS
nest.add(
ExprAggs(
canonical_name, {
"extended_stats": {
"script": text(
Painless[s.value].to_es_script(schema))
}
}, s))
s.pull = get_pull_stats()
# GET MEDIAN TOO!
select_median = s.copy()
select_median.pull = jx_expression_to_function(
{"select": [{
"name": "median",
"value": "values.50\\.0"
}]})
nest.add(
ExprAggs(
canonical_name + "_percentile", {
"percentiles": {
"script": text(
Painless[s.value].to_es_script(schema)),
"percents": [50]
}
}, select_median))
s.pull = get_pull_stats()
elif s.aggregate == "union":
# USE TERMS AGGREGATE TO SIMULATE union
nest.add(
TermsAggs(canonical_name, {
"script_field":
text(Painless[s.value].to_es_script(schema))
}, s))
s.pull = jx_expression_to_function("key")
else:
# PULL VALUE OUT OF THE stats AGGREGATE
s.pull = jx_expression_to_function(aggregates[s.aggregate])
nest.add(
ExprAggs(
canonical_name, {
"extended_stats": {
"script":
text(
NumberOp(s.value).partial_eval().to_es_script(
schema))
}
}, s))
acc = NestedAggs(query_path).add(acc)
split_decoders = get_decoders_by_path(query)
split_wheres = split_expression_by_path(query.where,
schema=frum.schema,
lang=ES52)
start = 0
decoders = [None] * (len(query.edges) + len(query.groupby))
paths = list(reversed(sorted(split_wheres.keys() | split_decoders.keys())))
for path in paths:
literal_path = literal_field(path)
decoder = split_decoders[literal_path]
where = split_wheres[literal_path]
for d in decoder:
decoders[d.edge.dim] = d
acc = d.append_query(path, acc)
start += d.num_columns
if where:
acc = FilterAggs("_filter", AndOp(where), None).add(acc)
acc = NestedAggs(path).add(acc)
acc = NestedAggs('.').add(acc)
acc = simplify(acc)
es_query = wrap(acc.to_es(schema))
es_query.size = 0
with Timer("ES query time", silent=not DEBUG) as es_duration:
result = es_post(es, es_query, query.limit)
try:
format_time = Timer("formatting", silent=not DEBUG)
with format_time:
# result.aggregations.doc_count = coalesce(result.aggregations.doc_count, result.hits.total) # IT APPEARS THE OLD doc_count IS GONE
aggs = unwrap(result.aggregations)
formatter, groupby_formatter, aggop_formatter, mime_type = format_dispatch[
query.format]
if query.edges:
output = formatter(aggs, acc, query, decoders, select)
elif query.groupby:
output = groupby_formatter(aggs, acc, query, decoders, select)
else:
output = aggop_formatter(aggs, acc, query, decoders, select)
output.meta.timing.formatting = format_time.duration
output.meta.timing.es_search = es_duration.duration
output.meta.content_type = mime_type
output.meta.es_query = es_query
return output
except Exception as e:
if query.format not in format_dispatch:
Log.error("Format {{format|quote}} not supported yet",
format=query.format,
cause=e)
Log.error("Some problem", cause=e)
def _grouped(self, query, stacked=False):
select = listwrap(query.select)
# RETURN SINGLE OBJECT WITH AGGREGATES
for s in select:
if s.aggregate not in aggregates:
Log.error("Expecting all columns to have an aggregate: {{select}}", select=s)
selects = FlatList()
groups = FlatList()
edges = query.edges
for e in edges:
if e.domain.type != "default":
Log.error("domain of type {{type}} not supported, yet", type=e.domain.type)
groups.append(e.value)
selects.append(e.value + " AS " + self.db.quote_column(e.name))
for s in select:
selects.append(aggregates[s.aggregate].replace("{{code}}", s.value) + " AS " + self.db.quote_column(s.name))
sql = expand_template("""
SELECT
{{selects}}
FROM
{{table}}
{{where}}
GROUP BY
{{groups}}
""", {
"selects": SQL(",\n".join(selects)),
"groups": SQL(",\n".join(groups)),
"table": self._subquery(query["from"])[0],
"where": self._where2sql(query.where)
})
def post_stacked(sql):
# RETURN IN THE USUAL DATABASE RESULT SET FORMAT
return self.db.query(sql)
def post(sql):
# FIND OUT THE default DOMAIN SIZES
result = self.db.column_query(sql)
num_edges = len(edges)
for e, edge in enumerate(edges):
domain = edge.domain
if domain.type == "default":
domain.type = "set"
parts = set(result[e])
domain.partitions = [{"index": i, "value": p} for i, p in enumerate(parts)]
domain.map = {p: i for i, p in enumerate(parts)}
else:
Log.error("Do not know what to do here, yet")
# FILL THE DATA CUBE
maps = [(unwrap(e.domain.map), result[i]) for i, e in enumerate(edges)]
cubes = FlatList()
for c, s in enumerate(select):
data = Matrix(*[len(e.domain.partitions) + (1 if e.allow_nulls else 0) for e in edges])
for rownum, value in enumerate(result[c + num_edges]):
coord = [m[r[rownum]] for m, r in maps]
data[coord] = value
cubes.append(data)
if isinstance(query.select, list):
return cubes
else:
return cubes[0]
return sql, post if not stacked else post_stacked
def groupby(data,
keys=None,
size=None,
min_size=None,
max_size=None,
contiguous=False):
"""
:param data:
:param keys:
:param size:
:param min_size:
:param max_size:
:param contiguous: MAINTAIN THE ORDER OF THE DATA, STARTING THE NEW GROUP WHEN THE SELECTOR CHANGES
:return: return list of (keys, values) PAIRS, WHERE
keys IS IN LEAF FORM (FOR USE WITH {"eq": terms} OPERATOR
values IS GENERATOR OF ALL VALUE THAT MATCH keys
contiguous -
"""
if isinstance(data, Container):
return data.groupby(keys)
if size != None or min_size != None or max_size != None:
if size != None:
max_size = size
return groupby_min_max_size(data, min_size=min_size, max_size=max_size)
try:
keys = listwrap(keys)
if not contiguous:
from jx_python import jx
data = jx.sort(data, keys)
if not data:
return Null
if any(is_expression(k) for k in keys):
Log.error("can not handle expressions")
else:
accessor = jx_expression_to_function(jx_expression({
"tuple": keys
})) # CAN RETURN Null, WHICH DOES NOT PLAY WELL WITH __cmp__
def _output():
start = 0
prev = accessor(data[0])
for i, d in enumerate(data):
curr = accessor(d)
if curr != prev:
group = {}
for k, gg in zip(keys, prev):
group[k] = gg
yield Data(group), data[start:i:]
start = i
prev = curr
group = {}
for k, gg in zip(keys, prev):
group[k] = gg
yield Data(group), data[start::]
return _output()
except Exception as e:
Log.error("Problem grouping", cause=e)
def es_deepop(es, query):
schema = query.frum.schema
query_path = schema.query_path[0]
# TODO: FIX THE GREAT SADNESS CAUSED BY EXECUTING post_expressions
# THE EXPRESSIONS SHOULD BE PUSHED TO THE CONTAINER: ES ALLOWS
# {"inner_hit":{"script_fields":[{"script":""}...]}}, BUT THEN YOU
# LOOSE "_source" BUT GAIN "fields", FORCING ALL FIELDS TO BE EXPLICIT
post_expressions = {}
es_query, es_filters = es_query_template(query_path)
# SPLIT WHERE CLAUSE BY DEPTH
wheres = split_expression_by_depth(query.where, schema)
for f, w in zip_longest(es_filters, wheres):
script = ES52[AndOp(w)].partial_eval().to_esfilter(schema)
set_default(f, script)
if not wheres[1]:
# INCLUDE DOCS WITH NO NESTED DOCS
more_filter = {
"bool": {
"filter":
[AndOp(wheres[0]).partial_eval().to_esfilter(schema)],
"must_not": {
"nested": {
"path": query_path,
"query": MATCH_ALL
}
}
}
}
else:
more_filter = None
es_query.size = coalesce(query.limit, DEFAULT_LIMIT)
# es_query.sort = jx_sort_to_es_sort(query.sort)
map_to_es_columns = schema.map_to_es()
# {c.name: c.es_column for c in schema.leaves(".")}
query_for_es = query.map(map_to_es_columns)
es_query.sort = jx_sort_to_es_sort(query_for_es.sort, schema)
es_query.stored_fields = []
is_list = is_list_(query.select)
selects = wrap([unwrap(s.copy()) for s in listwrap(query.select)])
new_select = FlatList()
put_index = 0
for select in selects:
if is_op(select.value, LeavesOp) and is_op(select.value.term,
Variable):
# IF THERE IS A *, THEN INSERT THE EXTRA COLUMNS
leaves = schema.leaves(select.value.term.var)
col_names = set()
for c in leaves:
if c.nested_path[0] == ".":
if c.jx_type == NESTED:
continue
es_query.stored_fields += [c.es_column]
c_name = untype_path(relative_field(c.name, query_path))
col_names.add(c_name)
new_select.append({
"name": concat_field(select.name, c_name),
"nested_path": c.nested_path[0],
"put": {
"name": concat_field(select.name,
literal_field(c_name)),
"index": put_index,
"child": "."
},
"pull": get_pull_function(c)
})
put_index += 1
# REMOVE DOTS IN PREFIX IF NAME NOT AMBIGUOUS
for n in new_select:
if n.name.startswith("..") and n.name.lstrip(
".") not in col_names:
n.put.name = n.name = n.name.lstrip(".")
col_names.add(n.name)
elif is_op(select.value, Variable):
net_columns = schema.leaves(select.value.var)
if not net_columns:
new_select.append({
"name": select.name,
"nested_path": ".",
"put": {
"name": select.name,
"index": put_index,
"child": "."
},
"pull": NULL
})
else:
for n in net_columns:
pull = get_pull_function(n)
if n.nested_path[0] == ".":
if n.jx_type == NESTED:
continue
es_query.stored_fields += [n.es_column]
# WE MUST FIGURE OUT WHICH NAMESSPACE s.value.var IS USING SO WE CAN EXTRACT THE child
for np in n.nested_path:
c_name = untype_path(relative_field(n.name, np))
if startswith_field(c_name, select.value.var):
child = relative_field(c_name, select.value.var)
break
else:
continue
# REMOVED BECAUSE SELECTING INNER PROPERTIES IS NOT ALLOWED
# child = relative_field(untype_path(relative_field(n.name, n.nested_path[0])), s.value.var)
new_select.append({
"name": select.name,
"pull": pull,
"nested_path": n.nested_path[0],
"put": {
"name": select.name,
"index": put_index,
"child": child
}
})
put_index += 1
else:
expr = select.value
for v in expr.vars():
for c in schema[v.var]:
if c.nested_path[0] == ".":
es_query.stored_fields += [c.es_column]
# else:
# Log.error("deep field not expected")
pull_name = EXPRESSION_PREFIX + select.name
map_to_local = MapToLocal(schema)
pull = jx_expression_to_function(pull_name)
post_expressions[pull_name] = jx_expression_to_function(
expr.map(map_to_local))
new_select.append({
"name": select.name if is_list else ".",
"pull": pull,
"value": expr.__data__(),
"put": {
"name": select.name,
"index": put_index,
"child": "."
}
})
put_index += 1
# <COMPLICATED> ES needs two calls to get all documents
more = []
def get_more(please_stop):
more.append(
es_post(
es,
Data(query=more_filter, stored_fields=es_query.stored_fields),
query.limit))
if more_filter:
need_more = Thread.run("get more", target=get_more)
with Timer("call to ES") as call_timer:
data = es_post(es, es_query, query.limit)
# EACH A HIT IS RETURNED MULTIPLE TIMES FOR EACH INNER HIT, WITH INNER HIT INCLUDED
def inners():
for t in data.hits.hits:
for i in t.inner_hits[literal_field(query_path)].hits.hits:
t._inner = i._source
for k, e in post_expressions.items():
t[k] = e(t)
yield t
if more_filter:
Thread.join(need_more)
for t in more[0].hits.hits:
yield t
# </COMPLICATED>
try:
formatter, groupby_formatter, mime_type = format_dispatch[query.format]
output = formatter(inners(), new_select, query)
output.meta.timing.es = call_timer.duration
output.meta.content_type = mime_type
output.meta.es_query = es_query
return output
except Exception as e:
Log.error("problem formatting", e)
def query(self, query):
"""
:param query: JSON Query Expression, SET `format="container"` TO MAKE NEW TABLE OF RESULT
:return:
"""
if not query.get('from'):
query['from'] = self.name
elif not startswith_field(query['from'], self.name):
Log.error("Expecting table, or some nested table")
query = QueryOp.wrap(query, self.container, self.namespace)
new_table = "temp_" + unique_name()
if query.format == "container":
create_table = SQL_CREATE + quote_column(new_table) + SQL_AS
else:
create_table = ""
if query.groupby and query.format != "cube":
op, index_to_columns = self._groupby_op(query, self.schema)
command = create_table + op
elif query.groupby:
query.edges, query.groupby = query.groupby, query.edges
op, index_to_columns = self._edges_op(query, self.schema)
command = create_table + op
query.edges, query.groupby = query.groupby, query.edges
elif query.edges or any(a != "none"
for a in listwrap(query.select).aggregate):
op, index_to_columns = self._edges_op(query, query.frum.schema)
command = create_table + op
else:
op = self._set_op(query)
return op
result = self.db.query(command)
if query.format == "container":
output = QueryTable(new_table,
db=self.db,
uid=self.uid,
exists=True)
elif query.format == "cube" or (not query.format and query.edges):
column_names = [None
] * (max(c.push_column
for c in index_to_columns.values()) + 1)
for c in index_to_columns.values():
column_names[c.push_column] = c.push_column_name
if len(query.edges) == 0 and len(query.groupby) == 0:
data = {n: Data() for n in column_names}
for s in index_to_columns.values():
data[s.push_name][s.push_child] = unwrap(
s.pull(result.data[0]))
if is_list(query.select):
select = [{"name": s.name} for s in query.select]
else:
select = {"name": query.select.name}
return Data(data=unwrap(data),
select=select,
meta={"format": "cube"})
if not result.data:
edges = []
dims = []
for i, e in enumerate(query.edges + query.groupby):
allowNulls = coalesce(e.allowNulls, True)
if e.domain.type == "set" and e.domain.partitions:
domain = SimpleSetDomain(
partitions=e.domain.partitions.name)
elif e.domain.type == "range":
domain = e.domain
elif is_op(e.value, TupleOp):
pulls = jx.sort([
c for c in index_to_columns.values()
if c.push_name == e.name
], "push_child").pull
parts = [
tuple(p(d) for p in pulls) for d in result.data
]
domain = SimpleSetDomain(
partitions=jx.sort(set(parts)))
else:
domain = SimpleSetDomain(partitions=[])
dims.append(1 if allowNulls else 0)
edges.append(
Data(name=e.name, allowNulls=allowNulls,
domain=domain))
data = {}
for si, s in enumerate(listwrap(query.select)):
if s.aggregate == "count":
data[s.name] = Matrix(dims=dims, zeros=0)
else:
data[s.name] = Matrix(dims=dims)
if is_list(query.select):
select = [{"name": s.name} for s in query.select]
else:
select = {"name": query.select.name}
return Data(meta={"format": "cube"},
edges=edges,
select=select,
data={k: v.cube
for k, v in data.items()})
columns = None
edges = []
dims = []
for g in query.groupby:
g.is_groupby = True
for i, e in enumerate(query.edges + query.groupby):
allowNulls = coalesce(e.allowNulls, True)
if e.domain.type == "set" and e.domain.partitions:
domain = SimpleSetDomain(
partitions=e.domain.partitions.name)
elif e.domain.type == "range":
domain = e.domain
elif e.domain.type == "time":
domain = wrap(mo_json.scrub(e.domain))
elif e.domain.type == "duration":
domain = wrap(mo_json.scrub(e.domain))
elif is_op(e.value, TupleOp):
pulls = jx.sort([
c for c in index_to_columns.values()
if c.push_name == e.name
], "push_child").pull
parts = [tuple(p(d) for p in pulls) for d in result.data]
domain = SimpleSetDomain(partitions=jx.sort(set(parts)))
else:
if not columns:
columns = transpose(*result.data)
parts = set(columns[i])
if e.is_groupby and None in parts:
allowNulls = True
parts -= {None}
if query.sort[i].sort == -1:
domain = SimpleSetDomain(
partitions=wrap(sorted(parts, reverse=True)))
else:
domain = SimpleSetDomain(partitions=jx.sort(parts))
dims.append(len(domain.partitions) + (1 if allowNulls else 0))
edges.append(
Data(name=e.name, allowNulls=allowNulls, domain=domain))
data_cubes = {}
for si, s in enumerate(listwrap(query.select)):
if s.aggregate == "count":
data_cubes[s.name] = Matrix(dims=dims, zeros=0)
else:
data_cubes[s.name] = Matrix(dims=dims)
r2c = index_to_coordinate(
dims) # WORKS BECAUSE THE DATABASE SORTED THE EDGES TO CONFORM
for rownum, row in enumerate(result.data):
coord = r2c(rownum)
for i, s in enumerate(index_to_columns.values()):
if s.is_edge:
continue
if s.push_child == ".":
data_cubes[s.push_name][coord] = s.pull(row)
else:
data_cubes[s.push_name][coord][s.push_child] = s.pull(
row)
if query.select == None:
select = Null
elif is_list(query.select):
select = [{"name": s.name} for s in query.select]
else:
select = {"name": query.select.name}
return Data(meta={"format": "cube"},
edges=edges,
select=select,
data={k: v.cube
for k, v in data_cubes.items()})
elif query.format == "table" or (not query.format and query.groupby):
column_names = [None
] * (max(c.push_column
for c in index_to_columns.values()) + 1)
for c in index_to_columns.values():
column_names[c.push_column] = c.push_column_name
data = []
for d in result.data:
row = [None for _ in column_names]
for s in index_to_columns.values():
if s.push_child == ".":
row[s.push_column] = s.pull(d)
elif s.num_push_columns:
tuple_value = row[s.push_column]
if tuple_value == None:
tuple_value = row[
s.push_column] = [None] * s.num_push_columns
tuple_value[s.push_child] = s.pull(d)
elif row[s.push_column] == None:
row[s.push_column] = Data()
row[s.push_column][s.push_child] = s.pull(d)
else:
row[s.push_column][s.push_child] = s.pull(d)
data.append(tuple(unwrap(r) for r in row))
output = Data(meta={"format": "table"},
header=column_names,
data=data)
elif query.format == "list" or (not query.edges and not query.groupby):
if not query.edges and not query.groupby and any(
listwrap(query.select).aggregate):
if is_list(query.select):
data = Data()
for c in index_to_columns.values():
if c.push_child == ".":
if data[c.push_name] == None:
data[c.push_name] = c.pull(result.data[0])
elif is_list(data[c.push_name]):
data[c.push_name].append(c.pull(
result.data[0]))
else:
data[c.push_name] = [
data[c.push_name],
c.pull(result.data[0])
]
else:
data[c.push_name][c.push_child] = c.pull(
result.data[0])
output = Data(meta={"format": "value"}, data=data)
else:
data = Data()
for s in index_to_columns.values():
if not data[s.push_child]:
data[s.push_child] = s.pull(result.data[0])
else:
data[s.push_child] += [s.pull(result.data[0])]
output = Data(meta={"format": "value"}, data=unwrap(data))
else:
data = []
for rownum in result.data:
row = Data()
for c in index_to_columns.values():
if c.push_child == ".":
row[c.push_name] = c.pull(rownum)
elif c.num_push_columns:
tuple_value = row[c.push_name]
if not tuple_value:
tuple_value = row[
c.push_name] = [None] * c.num_push_columns
tuple_value[c.push_child] = c.pull(rownum)
else:
row[c.push_name][c.push_child] = c.pull(rownum)
data.append(row)
output = Data(meta={"format": "list"}, data=data)
else:
Log.error("unknown format {{format}}", format=query.format)
return output
def column_names(self):
return listwrap(self.select).name + self.edges.name + self.groupby.name
def get_selects(query):
schema = query.frum.schema
split_select = {".": ESSelect(".")}
def get_select(path):
es_select = split_select.get(path)
if not es_select:
es_select = split_select[path] = ESSelect(path)
return es_select
selects = wrap([unwrap(s.copy()) for s in listwrap(query.select)])
new_select = FlatList()
put_index = 0
for select in selects:
# IF THERE IS A *, THEN INSERT THE EXTRA COLUMNS
if is_op(select.value, LeavesOp) and is_op(select.value.term,
Variable):
term = select.value.term
leaves = schema.leaves(term.var)
for c in leaves:
full_name = concat_field(
select.name, relative_field(untype_path(c.name), term.var))
if c.jx_type == NESTED:
get_select(".").set_op = True
new_select.append({
"name": full_name,
"value": Variable(c.es_column),
"put": {
"name": literal_field(full_name),
"index": put_index,
"child": ".",
},
"pull": get_pull_source(c.es_column),
})
put_index += 1
else:
get_select(c.nested_path[0]).fields.append(c.es_column)
new_select.append({
"name": full_name,
"value": Variable(c.es_column),
"put": {
"name": literal_field(full_name),
"index": put_index,
"child": ".",
},
})
put_index += 1
elif is_op(select.value, Variable):
s_column = select.value.var
if s_column == ".":
# PULL ALL SOURCE
get_select(".").set_op = True
new_select.append({
"name": select.name,
"value": select.value,
"put": {
"name": select.name,
"index": put_index,
"child": "."
},
"pull": get_pull_source("."),
})
continue
leaves = schema.leaves(s_column) # LEAVES OF OBJECT
# nested_selects = {}
if leaves:
if any(c.jx_type == NESTED for c in leaves):
# PULL WHOLE NESTED ARRAYS
get_select(".").set_op = True
for c in leaves:
if (
len(c.nested_path) == 1
): # NESTED PROPERTIES ARE IGNORED, CAPTURED BY THESE FIRST LEVEL PROPERTIES
pre_child = join_field(
decode_property(n)
for n in split_field(c.name))
new_select.append({
"name":
select.name,
"value":
Variable(c.es_column),
"put": {
"name":
select.name,
"index":
put_index,
"child":
untype_path(
relative_field(pre_child, s_column)),
},
"pull":
get_pull_source(c.es_column),
})
else:
# PULL ONLY WHAT'S NEEDED
for c in leaves:
c_nested_path = c.nested_path[0]
if c_nested_path == ".":
if c.es_column == "_id":
new_select.append({
"name":
select.name,
"value":
Variable(c.es_column),
"put": {
"name": select.name,
"index": put_index,
"child": ".",
},
"pull":
lambda row: row._id,
})
elif c.jx_type == NESTED:
get_select(".").set_op = True
pre_child = join_field(
decode_property(n)
for n in split_field(c.name))
new_select.append({
"name":
select.name,
"value":
Variable(c.es_column),
"put": {
"name":
select.name,
"index":
put_index,
"child":
untype_path(
relative_field(
pre_child, s_column)),
},
"pull":
get_pull_source(c.es_column),
})
else:
get_select(c_nested_path).fields.append(
c.es_column)
pre_child = join_field(
decode_property(n)
for n in split_field(c.name))
new_select.append({
"name":
select.name,
"value":
Variable(c.es_column),
"put": {
"name":
select.name,
"index":
put_index,
"child":
untype_path(
relative_field(
pre_child, s_column)),
},
})
else:
es_select = get_select(c_nested_path)
es_select.fields.append(c.es_column)
child = relative_field(
untype_path(
relative_field(c.name,
schema.query_path[0])),
s_column,
)
pull = accumulate_nested_doc(
c_nested_path,
Variable(
relative_field(
s_column, unnest_path(c_nested_path))),
)
new_select.append({
"name": select.name,
"value": select.value,
"put": {
"name": select.name,
"index": put_index,
"child": child,
},
"pull": pull,
})
else:
new_select.append({
"name": select.name,
"value": Variable("$dummy"),
"put": {
"name": select.name,
"index": put_index,
"child": "."
},
})
put_index += 1
else:
split_scripts = split_expression_by_path(select.value,
schema,
lang=Painless)
for p, script in split_scripts.items():
es_select = get_select(p)
es_select.scripts[select.name] = {
"script":
text(Painless[first(script)].partial_eval().to_es_script(
schema))
}
new_select.append({
"name":
select.name,
"pull":
jx_expression_to_function("fields." +
literal_field(select.name)),
"put": {
"name": select.name,
"index": put_index,
"child": "."
},
})
put_index += 1
for n in new_select:
if n.pull:
continue
elif is_op(n.value, Variable):
if get_select(".").set_op:
n.pull = get_pull_source(n.value.var)
elif n.value == "_id":
n.pull = jx_expression_to_function("_id")
else:
n.pull = jx_expression_to_function(
concat_field("fields", literal_field(n.value.var)))
else:
Log.error("Do not know what to do")
return new_select, split_select
def values(self):
d = self._internal_dict
return listwrap(list(d.values()))
def update(self, command):
"""
:param command: EXPECTING dict WITH {"set": s, "clear": c, "where": w} FORMAT
"""
command = wrap(command)
# REJECT DEEP UPDATES
touched_columns = command.set.keys() | set(listwrap(command['clear']))
for c in self.schema.columns:
if c.name in touched_columns and len(c.nested_path) > 1:
Log.error("Deep update not supported")
# ADD NEW COLUMNS
where = jx_expression(command.where)
_vars = where.vars()
_map = {
v: c.es_column
for v in _vars for c in self.columns.get(v, Null)
if c.jx_type not in STRUCT
}
where_sql = where.map(_map).to_sql(self.schema)
new_columns = set(command.set.keys()) - set(self.columns.keys())
for new_column_name in new_columns:
nested_value = command.set[new_column_name]
ctype = get_jx_type(nested_value)
column = Column(name=new_column_name,
jx_type=ctype,
es_index=self.name,
es_type=json_type_to_sqlite_type(ctype),
es_column=typed_column(new_column_name, ctype),
last_updated=Date.now())
self.add_column(column)
# UPDATE THE NESTED VALUES
for nested_column_name, nested_value in command.set.items():
if get_jx_type(nested_value) == "nested":
nested_table_name = concat_field(self.name, nested_column_name)
nested_table = nested_tables[nested_column_name]
self_primary_key = sql_list(
quote_column(c.es_column) for u in self.uid
for c in self.columns[u])
extra_key_name = UID + text(len(self.uid))
extra_key = [e
for e in nested_table.columns[extra_key_name]][0]
sql_command = (
SQL_DELETE + SQL_FROM + quote_column(nested_table.name) +
SQL_WHERE + "EXISTS" +
sql_iso(SQL_SELECT + SQL_ONE + SQL_FROM +
sql_alias(quote_column(nested_table.name), "n") +
SQL_INNER_JOIN +
sql_iso(SQL_SELECT + self_primary_key + SQL_FROM +
quote_column(abs_schema.fact) + SQL_WHERE +
where_sql) + " t ON " +
SQL_AND.join(
quote_column("t", c.es_column) + SQL_EQ +
quote_column("n", c.es_column)
for u in self.uid for c in self.columns[u])))
self.db.execute(sql_command)
# INSERT NEW RECORDS
if not nested_value:
continue
doc_collection = {}
for d in listwrap(nested_value):
nested_table.flatten(d,
Data(),
doc_collection,
path=nested_column_name)
prefix = SQL_INSERT + quote_column(nested_table.name) + sql_iso(
sql_list([self_primary_key] + [quote_column(extra_key)] + [
quote_column(c.es_column)
for c in doc_collection.get(".", Null).active_columns
]))
# BUILD THE PARENT TABLES
parent = (SQL_SELECT + self_primary_key + SQL_FROM +
quote_column(abs_schema.fact) + SQL_WHERE +
jx_expression(command.where).to_sql(schema))
# BUILD THE RECORDS
children = SQL_UNION_ALL.join(
SQL_SELECT +
sql_alias(quote_value(i), extra_key.es_column) +
SQL_COMMA + sql_list(
sql_alias(quote_value(row[c.name]),
quote_column(c.es_column))
for c in doc_collection.get(".", Null).active_columns)
for i, row in enumerate(
doc_collection.get(".", Null).rows))
sql_command = (prefix + SQL_SELECT + sql_list([
quote_column("p", c.es_column) for u in self.uid
for c in self.columns[u]
] + [quote_column("c", extra_key)] + [
quote_column("c", c.es_column)
for c in doc_collection.get(".", Null).active_columns
]) + SQL_FROM + sql_iso(parent) + " p" + SQL_INNER_JOIN +
sql_iso(children) + " c" + SQL_ON + SQL_TRUE)
self.db.execute(sql_command)
# THE CHILD COLUMNS COULD HAVE EXPANDED
# ADD COLUMNS TO SELF
for n, cs in nested_table.columns.items():
for c in cs:
column = Column(name=c.name,
jx_type=c.jx_type,
es_type=c.es_type,
es_index=c.es_index,
es_column=c.es_column,
nested_path=[nested_column_name] +
c.nested_path,
last_updated=Date.now())
if c.name not in self.columns:
self.columns[column.name] = {column}
elif c.jx_type not in [
c.jx_type for c in self.columns[c.name]
]:
self.columns[column.name].add(column)
command = (
SQL_UPDATE + quote_column(abs_schema.fact) + SQL_SET + sql_list([
quote_column(c) + SQL_EQ +
quote_value(get_if_type(v, c.jx_type))
for k, v in command.set.items() if get_jx_type(v) != "nested"
for c in self.columns[k]
if c.jx_type != "nested" and len(c.nested_path) == 1
] + [
quote_column(c) + SQL_EQ + SQL_NULL
for k in listwrap(command['clear']) if k in self.columns
for c in self.columns[k]
if c.jx_type != "nested" and len(c.nested_path) == 1
]) + SQL_WHERE + where_sql)
self.db.execute(command)
