def main(event, request):
context = dict({})
context[c.KEY_LAMBDA_FUNCTION] = request.function_name if hasattr(
request, 'function_name') else None
context[c.KEY_REQUEST_ID] = request.aws_request_id if hasattr(
request, 'aws_request_id') else None
stackid = os.environ[c.ENV_DEPLOYMENT_STACK_ARN]
context[c.KEY_DB] = DynamoDb(context)
context[c.KEY_ATHENA_QUERY] = Query(stackid)
context[c.KEY_GLUE_CRAWLER] = Glue()
thread_pool = ThreadPool(size=3)
crawler_name = context[c.KEY_GLUE_CRAWLER].get_crawler_name(stackid)
crawler = Crawler(context, os.environ[c.ENV_S3_STORAGE])
glue = Glue()
events = glue.get_events()
start = datetime.datetime.utcnow() - datetime.timedelta(hours=2)
now = datetime.datetime.utcnow()
found = False
for type in events:
dt = start
while dt <= now:
prefix = metric_schema.s3_key_format().format(
context[c.KEY_SEPERATOR_PARTITION], dt.year, dt.month, dt.day,
dt.hour, type, dt.strftime(util.partition_date_format()))
found = crawler.exists(prefix)
if found:
print "FOUND new events=>", prefix
break
dt += timedelta(hours=1)
if found:
break
if found:
thread_pool.add(crawl, context, crawler_name,
context[c.KEY_ATHENA_QUERY].execute_with_format)
thread_pool.wait()
return custom_resource_response.success_response({}, "*")
def launch(event, lambdacontext):
print "Start"
hours_delta = 36
context = dict({})
context[c.KEY_LAMBDA_FUNCTION] = lambdacontext.function_name if hasattr(
lambdacontext, 'function_name') else None
context[c.KEY_REQUEST_ID] = lambdacontext.aws_request_id if hasattr(
lambdacontext, 'aws_request_id') else None
global threadpool
global is_lambda
threadpool = ThreadPool(context, 8)
is_lambda = context[c.KEY_REQUEST_ID] is not None
available_amoeba_lambdas = []
available_amoeba_lambdas.append(c.ENV_AMOEBA_1)
available_amoeba_lambdas.append(c.ENV_AMOEBA_2)
available_amoeba_lambdas.append(c.ENV_AMOEBA_3)
available_amoeba_lambdas.append(c.ENV_AMOEBA_4)
available_amoeba_lambdas.append(c.ENV_AMOEBA_5)
db = DynamoDb(context)
crawler = Crawler(context, os.environ[c.ENV_S3_STORAGE])
glue = Glue()
events = glue.get_events()
#TODO: adjust the amoeba tree depth so that we have fully utilized all available amoebas; len(available_amoeba_lambdas) * 1000
#since the number of leaf nodes for the metric partitions can quickly get very large we use a 5 lambda pool to ensure we don't hit the 1000 invocation limit.
start = datetime.datetime.utcnow() - datetime.timedelta(hours=hours_delta)
now = datetime.datetime.utcnow()
for type in events:
dt = start
while dt <= now:
prefix = metric_schema.s3_key_format().format(
context[c.KEY_SEPERATOR_PARTITION], dt.year, dt.month, dt.day,
dt.hour, type, dt.strftime(util.partition_date_format()))
threadpool.add(crawler.crawl, prefix, available_amoeba_lambdas,
invoke_lambda)
dt += timedelta(hours=1)
threadpool.wait()
return custom_resource_response.success_response({"StatusCode": 200}, "*")