def keepalive_fn(scheduler: sched.scheduler, params: inputs.Inputs,
context: LambdaContext, keepalive_state: KeepaliveState,
cache: Cache):
''' Each iteration of keepalive_thread runs this code. Add the next iteration of keepalive before exiting to
continue the keepalive thread. Otherwise keepalives will stop '''
try:
update_keepalive(params, keepalive_state, cache)
keepalive_fn.num_keepalives += 1
if keepalive_fn.num_keepalives % defaults.KEEPALIVE_PRINT_EVERY == 0:
print("keepalive_fn: keepalive #{}: state={}".format(
keepalive_fn.num_keepalives, keepalive_state))
if context.invoked_function_arn and context.get_remaining_time_in_millis(
) < defaults.RETRIGGER_BEFORE_EXPIRY_MS:
# if invoked as lambda (not CLI), then retrigger backing job if this instance of it will expire soon
cache_keys = keepalive_state.cache_keys
lastaccess_ms = int(cache.get(cache_keys.lastaccess))
lastaccess_age_ms = utils.millitime() - lastaccess_ms
if lastaccess_age_ms > (defaults.BACKING_JOB_LIFETIME_MS * 0.9):
# There were no recent calls to fetch the data produced by this backing job. No need to re-issue
print(
"Exiting backing job by ending keepalive thread. lastaccess_age_ms = ",
lastaccess_age_ms)
return False
if not params.is_streaming():
''' Fixed time-range jobs need not be reissued '''
print(
"keepalive_fn: backing job won't be restarted because it is not a streaming job",
params)
return False
# Restart this job again in another lambda invocation.
# Before doing that, don't keepalive for a while to make it stale. Otherwise the new invocation
# will assume there is another backing job already running and will auto-exit
print(
"keepalive_fn: backing job needs to be restarted. lastaccess_age_ms =",
lastaccess_age_ms)
time.sleep(defaults.KEEPALIVE_INTERVAL_SEC *
defaults.KEEPALIVE_EXPIRY_MULTIPLE)
start_backing_job_if_necessary(params, context, keepalive_state,
cache)
print(
"keepalive_fn: exiting current backing job after re-issuing a new one"
)
return False
except Exception as e:
print("keepalive_fn: exception", e, traceback.format_exc())
# schedule the next iteration of keepalive thread
scheduler.enter(defaults.KEEPALIVE_INTERVAL_SEC,
1,
keepalive_fn,
argument=(scheduler, params, context, keepalive_state,
cache))
def start_backing_job_if_necessary(params: inputs.Inputs,
context: LambdaContext, cache: Cache):
''' If no backing job is running for a given signalflow program and duration, start one
Returns keepalive_state from cache if active backing job is found (to prevent a duplicate cache read by callers '''
def start_backing_job_as_lambda(params: inputs.Inputs, tstart, tend,
context: LambdaContext):
# Start new backing job that runs as a lambda function
print("start_backing_job_as_lambda: started")
import boto3
lambda_client = boto3.client('lambda')
lambda_client.invoke(FunctionName=context.invoked_function_arn,
InvocationType='Event',
Payload=json.dumps({
"program": params.program,
"start_time_ms": tstart,
"end_time_ms": tend,
"resolution_hint_ms":
params.resolution_hint_ms,
"api_token": params.api_token,
"api_endpoint": params.api_endpoint,
"daemon": True
}))
def start_backing_job_as_process(params: inputs.Inputs, tstart, tend):
# Start new backing job that runs as a python process
print("start_backing_job_as_process: started")
cmd: str = "nohup python3 {script} --program=\"{program}\" --token={token} \
--start_time_ms={tstart} --end_time_ms={tend} --resolution_hint_ms={res} --endpoint={endpoint}".format(
script=__file__,
program=params.program,
tstart=tstart,
tend=tend,
res=params.resolution_hint_ms,
token=params.api_token,
endpoint=params.api_endpoint)
cmd += " --daemon > /tmp/{}.log 2>&1 &".format(
params.cache_key_prefix())
print("start_backing_job_as_process:", cmd)
os.system(cmd)
# begin code for start_backing_job_if_necessary()
try:
cache_keys = CacheKeys(params.cache_key_prefix())
print("start_backing_job_if_necessary: started", cache_keys)
now_ms = utils.millitime()
cached_state: KeepaliveState = pickle.loads(
cache.get(cache_keys.keepalive))
keepalive_age_ms = now_ms - cached_state.last_keepalive_ms
expiry_ms = defaults.KEEPALIVE_EXPIRY_MULTIPLE * defaults.KEEPALIVE_INTERVAL_SEC * 1000
if keepalive_age_ms < expiry_ms:
print(
"start_backing_job_if_necessary: found active backing job already running. keepalive_age_ms =",
keepalive_age_ms)
return cached_state
print(
"start_backing_job_if_necessary: found expired keepalive_age_ms =",
keepalive_age_ms)
cache.set(cache_keys.keepalive, None)
except Exception as e:
print("start_backing_job_if_necessary: no keeplive found in cache", e)
tstart = params.start_time_ms
tend = params.end_time_ms
if not params.is_streaming():
tstart = params.absolute_ms(tstart)
tend = params.absolute_ms(tend)
if context.invoked_function_arn:
# This backing job was invoked as a lambda. So invoke a new lambda
start_backing_job_as_lambda(params, tstart, tend, context)
else:
start_backing_job_as_process(params, tstart, tend)
return None
def get_cached_result(params: inputs.Inputs, context: LambdaContext,
cache: Cache):
''' Backing job is already running. So just query cached data from and return result '''
def wait_for_backing_job_to_exit_batch_phase(
keepalive_state: KeepaliveState, cache: Cache,
cache_keys: CacheKeys, wait_until_ms: int):
print("wait_for_backing_job_to_exit_batch_phase: started",
cache_keys.keepalive)
while not keepalive_state or not keepalive_state.in_streaming_phase:
# wait for backing job to be running and advance to streaming state
if utils.millitime() > wait_until_ms:
raise Exception(
"wait_for_backing_job_to_exit_batch_phase: timed out")
print(
"get_cached_result: waiting for batch phase to end. keepalive_state=",
keepalive_state)
time.sleep(1)
try:
keepalive_state: KeepaliveState = pickle.loads(
cache.get(cache_keys.keepalive))
except Exception as e:
print(
"wait_for_backing_job_to_exit_batch_phase: failed to read keepalive from cache",
cache_keys.keepalive, e)
print("wait_for_backing_job_to_exit_batch_phase: backing job is ready",
keepalive_state)
return keepalive_state
print("get_cached_result: started")
# Update 'lastaccess' timestamp in memcache to indicate the corresponding backing job's data was recently queried
cache_keys: CacheKeys = CacheKeys(params.cache_key_prefix())
now_ms = params.invoke_time_ms
try:
cache.set(cache_keys.lastaccess, now_ms)
except Exception as e:
print(
"get_cached_result: failed to set lastaccess cache key {}={}, {}".
format(cache_keys.lastaccess, now_ms, e))
# start the backing job if one is not running, or if the backing job's keepalive timestamp is stale
keepalive_state: KeepaliveState = start_backing_job_if_necessary(
params, context, cache)
# now that backing job is surely running, wait for it to become 'ready' - i.e. go from batch to streaming phase
keepalive_state = wait_for_backing_job_to_exit_batch_phase(
keepalive_state, cache, cache_keys, now_ms + defaults.API_TIMEOUT_MS)
# compute which cache keys need to be fetched
if not params.is_streaming():
tstart = params.absolute_ms(params.start_time_ms)
tend = params.absolute_ms(params.end_time_ms)
else:
tend = now_ms
tstart = tend - params.duration_ms()
timestamps = sorted([
ts for ts in keepalive_state.data_timestamps
if ts >= tstart and ts <= tend
])
data_keys = [cache_keys.data_prefix + str(ts) for ts in timestamps]
# retrieve metadata and data from cache. retry if necessary
metadata = cache.get(cache_keys.metadata)
if len(timestamps):
print(
"get_cached_result: fetching {} timestamps {} - {} @ {}ms".format(
len(timestamps), time.ctime(timestamps[0] / 1000),
time.ctime(timestamps[-1] / 1000),
keepalive_state.resolution_ms))
data = cache.multiget(data_keys)
missing_keys = set(data_keys) - set(data.keys())
if (len(missing_keys)):
print("get_cached_result: retrying fetch of {}/{} keys: {}".format(
len(missing_keys), len(data_keys), sorted(missing_keys)))
data.update(cache.multiget(list(missing_keys)))
# Fill in results in results struct
result = {
"start_time_ms": tstart,
"end_time_ms": tend,
"earliest_result_ms": 0,
"latest_result_ms": 0,
"resolution_ms": keepalive_state.resolution_ms,
"metadata": metadata,
"data": {},
"missing_timestamps_ms": []
}
# First fill in retrieved data
tsids = set()
missing_timestamps = []
for timestamp in timestamps:
k = cache_keys.data_prefix + str(timestamp)
if k in data.keys():
for tsid, value in data[k].items():
if not result["earliest_result_ms"]:
result["earliest_result_ms"] = timestamp
if timestamp > result["latest_result_ms"]:
result["latest_result_ms"] = timestamp
tsids.add(tsid)
result["data"].setdefault(tsid, [])
result["data"][tsid].append([timestamp, value])
else:
missing_timestamps.append(timestamp)
# Second, fill in metadata of only the relevant mts that have data
remove_metadata_ids = set(metadata.keys()).difference(tsids)
for tsid in remove_metadata_ids:
metadata.pop(tsid)
result["missing_timestamps_ms"] = missing_timestamps
return result
def data_consumer_thread_fn(params: inputs.Inputs, context: LambdaContext,
data_queue: Queue, keepalive_state: KeepaliveState,
cache: Cache):
''' Thread that consumes data messages from analytics job, and sticks each one individually into cache.
Also detects when job moves from batch to stream phase. Unfortunately that requires 'auto-detection' where
data does not arrive for close to a second :-( '''
print("data_consumer_thread_fn: started")
try:
cache_keys = keepalive_state.cache_keys
data_to_encache = {}
last_datamsg_walltime_ms = 0
while True:
now_ms = utils.millitime()
try:
if params.is_streaming():
# remove trailing data keys that are beyond the scope of the current 'window' of a streaming job
valid_timestamps = [
ts for ts in keepalive_state.data_timestamps
if ts >= (now_ms - params.job_duration_ms() -
keepalive_state.resolution_ms)
]
keepalive_state.data_timestamps = set(valid_timestamps)
msg = data_queue.get(False)
last_datamsg_walltime_ms = utils.millitime()
data_to_encache.setdefault(msg.logical_timestamp_ms, {})
data_to_encache[msg.logical_timestamp_ms].update(msg.data)
except Exception as e:
# No data found in queue. However there may be pending data from previous messages that need caching
timestamps_encached = set()
for timestamp, values in data_to_encache.items():
try:
cache.set(cache_keys.data_prefix + str(timestamp),
values)
timestamps_encached.add(timestamp)
except Exception as e:
# Failed to set data in cache
None
for timestamp_encached in timestamps_encached:
data_to_encache.pop(timestamp_encached)
keepalive_state.data_timestamps.add(timestamp_encached)
if data_to_encache:
print(
"data_consumer_thread_fn: will retry writing {} data keys to cache {}"
.format(len(data_to_encache), list(data_to_encache)))
elif not keepalive_state.in_streaming_phase:
# Now that all data is successfully published, 'Auto-detect' whether we have completed batch phase
# and entered stream phase. If so, update keepalive_state
if last_datamsg_walltime_ms > 0 and (
now_ms - last_datamsg_walltime_ms
) > defaults.STREAM_PHASE_DETECTION_INTERVAL_MS:
keepalive_state.in_streaming_phase = True
print(
"data_consumer_thread_fn: backing job entered stream phase after {} datapoints. now={}, last={}"
.format(len(keepalive_state.data_timestamps),
now_ms, last_datamsg_walltime_ms))
# start healthcheck thread now that data is flowing in
threading.Thread(target=healthcheck_thread_fn,
args=(params, context,
keepalive_state,
cache)).start()
time.sleep(defaults.STREAM_PHASE_DETECTION_INTERVAL_MS / 1000 /
5)
except Exception as e:
print("data_consumer_thread_fn exception", e, traceback.format_exc())
finally:
print("data_consumer_thread_fn: ended")
