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Supported technologies [supported-technologies]

$$$framework-support$$$ The Elastic APM Python Agent comes with support for the following frameworks:

For other frameworks and custom Python code, the agent exposes a set of APIs for integration.

Python [supported-python]

The following Python versions are supported:

  • 3.6
  • 3.7
  • 3.8
  • 3.9
  • 3.10
  • 3.11
  • 3.12
  • 3.13

Django [supported-django]

We support these Django versions:

  • 1.11
  • 2.0
  • 2.1
  • 2.2
  • 3.0
  • 3.1
  • 3.2
  • 4.0
  • 4.2
  • 5.0

For upcoming Django versions, we generally aim to ensure compatibility starting with the first Release Candidate.

::::{note} we currently don’t support Django running in ASGI mode. ::::

Flask [supported-flask]

We support these Flask versions:

  • 0.10 (Deprecated)
  • 0.11 (Deprecated)
  • 0.12 (Deprecated)
  • 1.0
  • 1.1
  • 2.0
  • 2.1
  • 2.2
  • 2.3
  • 3.0

Aiohttp Server [supported-aiohttp]

We support these aiohttp versions:

  • 3.0+

Tornado [supported-tornado]

We support these tornado versions:

  • 6.0+

Sanic [supported-sanic]

We support these sanic versions:

  • 20.12.2+

Starlette/FastAPI [supported-starlette]

We support these Starlette versions:

  • 0.13.0+

Any FastAPI version which uses a supported Starlette version should also be supported.

GRPC [supported-grpc]

We support these grpcio versions:

  • 1.24.0+

Automatic Instrumentation [automatic-instrumentation]

The Python APM agent comes with automatic instrumentation of various 3rd party modules and standard library modules.

Scheduling [automatic-instrumentation-scheduling]

Celery [automatic-instrumentation-scheduling-celery]

We support these Celery versions:

  • 4.x (deprecated)
  • 5.x

Celery tasks will be recorded automatically with Django and Flask only.

Databases [automatic-instrumentation-db]

Elasticsearch [automatic-instrumentation-db-elasticsearch]

Instrumented methods:

  • elasticsearch.transport.Transport.perform_request
  • elasticsearch.connection.http_urllib3.Urllib3HttpConnection.perform_request
  • elasticsearch.connection.http_requests.RequestsHttpConnection.perform_request
  • elasticsearch._async.transport.AsyncTransport.perform_request
  • elasticsearch_async.connection.AIOHttpConnection.perform_request

Additionally, the instrumentation wraps the following methods of the Elasticsearch client class:

  • elasticsearch.client.Elasticsearch.delete_by_query
  • elasticsearch.client.Elasticsearch.search
  • elasticsearch.client.Elasticsearch.count
  • elasticsearch.client.Elasticsearch.update

Collected trace data:

  • the query string (if available)
  • the query element from the request body (if available)
  • the response status code
  • the count of affected rows (if available)

We recommend using keyword arguments only with elasticsearch-py, as recommended by the elasticsearch-py docs. If you are using positional arguments, we will be unable to gather the query element from the request body.

SQLite [automatic-instrumentation-db-sqlite]

Instrumented methods:

  • sqlite3.connect
  • sqlite3.dbapi2.connect
  • pysqlite2.dbapi2.connect

The instrumented connect method returns a wrapped connection/cursor which instruments the actual Cursor.execute calls.

Collected trace data:

  • parametrized SQL query

MySQLdb [automatic-instrumentation-db-mysql]

Library: MySQLdb

Instrumented methods:

  • MySQLdb.connect

The instrumented connect method returns a wrapped connection/cursor which instruments the actual Cursor.execute calls.

Collected trace data:

  • parametrized SQL query

mysql-connector [automatic-instrumentation-db-mysql-connector]

Library: mysql-connector-python

Instrumented methods:

  • mysql.connector.connect

The instrumented connect method returns a wrapped connection/cursor which instruments the actual Cursor.execute calls.

Collected trace data:

  • parametrized SQL query

pymysql [automatic-instrumentation-db-pymysql]

Library: pymysql

Instrumented methods:

  • pymysql.connect

The instrumented connect method returns a wrapped connection/cursor which instruments the actual Cursor.execute calls.

Collected trace data:

  • parametrized SQL query

aiomysql [automatic-instrumentation-db-aiomysql]

Library: aiomysql

Instrumented methods:

  • aiomysql.cursors.Cursor.execute

Collected trace data:

  • parametrized SQL query

PostgreSQL [automatic-instrumentation-db-postgres]

Library: psycopg2, psycopg2-binary (>=2.9)

Instrumented methods:

  • psycopg2.connect

The instrumented connect method returns a wrapped connection/cursor which instruments the actual Cursor.execute calls.

Collected trace data:

  • parametrized SQL query

aiopg [automatic-instrumentation-db-aiopg]

Library: aiopg (>=1.0)

Instrumented methods:

  • aiopg.cursor.Cursor.execute
  • aiopg.cursor.Cursor.callproc

Collected trace data:

  • parametrized SQL query

asyncpg [automatic-instrumentation-db-asyncg]

Library: asyncpg (>=0.20)

Instrumented methods:

  • asyncpg.connection.Connection.execute
  • asyncpg.connection.Connection.executemany

Collected trace data:

  • parametrized SQL query

PyODBC [automatic-instrumentation-db-pyodbc]

Library: pyodbc, (>=4.0)

Instrumented methods:

  • pyodbc.connect

The instrumented connect method returns a wrapped connection/cursor which instruments the actual Cursor.execute calls.

Collected trace data:

  • parametrized SQL query

MS-SQL [automatic-instrumentation-db-mssql]

Library: pymssql, (>=2.1.0)

Instrumented methods:

  • pymssql.connect

The instrumented connect method returns a wrapped connection/cursor which instruments the actual Cursor.execute calls.

Collected trace data:

  • parametrized SQL query

MongoDB [automatic-instrumentation-db-mongodb]

Library: pymongo, >=2.9,<3.8

Instrumented methods:

  • pymongo.collection.Collection.aggregate
  • pymongo.collection.Collection.bulk_write
  • pymongo.collection.Collection.count
  • pymongo.collection.Collection.create_index
  • pymongo.collection.Collection.create_indexes
  • pymongo.collection.Collection.delete_many
  • pymongo.collection.Collection.delete_one
  • pymongo.collection.Collection.distinct
  • pymongo.collection.Collection.drop
  • pymongo.collection.Collection.drop_index
  • pymongo.collection.Collection.drop_indexes
  • pymongo.collection.Collection.ensure_index
  • pymongo.collection.Collection.find_and_modify
  • pymongo.collection.Collection.find_one
  • pymongo.collection.Collection.find_one_and_delete
  • pymongo.collection.Collection.find_one_and_replace
  • pymongo.collection.Collection.find_one_and_update
  • pymongo.collection.Collection.group
  • pymongo.collection.Collection.inline_map_reduce
  • pymongo.collection.Collection.insert
  • pymongo.collection.Collection.insert_many
  • pymongo.collection.Collection.insert_one
  • pymongo.collection.Collection.map_reduce
  • pymongo.collection.Collection.reindex
  • pymongo.collection.Collection.remove
  • pymongo.collection.Collection.rename
  • pymongo.collection.Collection.replace_one
  • pymongo.collection.Collection.save
  • pymongo.collection.Collection.update
  • pymongo.collection.Collection.update_many
  • pymongo.collection.Collection.update_one

Collected trace data:

  • database name
  • method name

Redis [automatic-instrumentation-db-redis]

Library: redis (>=2.8)

Instrumented methods:

  • redis.client.Redis.execute_command
  • redis.client.Pipeline.execute

Collected trace data:

  • Redis command name

aioredis [automatic-instrumentation-db-aioredis]

Library: aioredis (<2.0)

Instrumented methods:

  • aioredis.pool.ConnectionsPool.execute
  • aioredis.commands.transaction.Pipeline.execute
  • aioredis.connection.RedisConnection.execute

Collected trace data:

  • Redis command name

Cassandra [automatic-instrumentation-db-cassandra]

Library: cassandra-driver (>=3.4,<4.0)

Instrumented methods:

  • cassandra.cluster.Session.execute
  • cassandra.cluster.Cluster.connect

Collected trace data:

  • CQL query

Python Memcache [automatic-instrumentation-db-python-memcache]

Library: python-memcached (>=1.51)

Instrumented methods:

  • memcache.Client.add
  • memcache.Client.append
  • memcache.Client.cas
  • memcache.Client.decr
  • memcache.Client.delete
  • memcache.Client.delete_multi
  • memcache.Client.disconnect_all
  • memcache.Client.flush_all
  • memcache.Client.get
  • memcache.Client.get_multi
  • memcache.Client.get_slabs
  • memcache.Client.get_stats
  • memcache.Client.gets
  • memcache.Client.incr
  • memcache.Client.prepend
  • memcache.Client.replace
  • memcache.Client.set
  • memcache.Client.set_multi
  • memcache.Client.touch

Collected trace data:

  • Destination (address and port)

pymemcache [automatic-instrumentation-db-pymemcache]

Library: pymemcache (>=3.0)

Instrumented methods:

  • pymemcache.client.base.Client.add
  • pymemcache.client.base.Client.append
  • pymemcache.client.base.Client.cas
  • pymemcache.client.base.Client.decr
  • pymemcache.client.base.Client.delete
  • pymemcache.client.base.Client.delete_many
  • pymemcache.client.base.Client.delete_multi
  • pymemcache.client.base.Client.flush_all
  • pymemcache.client.base.Client.get
  • pymemcache.client.base.Client.get_many
  • pymemcache.client.base.Client.get_multi
  • pymemcache.client.base.Client.gets
  • pymemcache.client.base.Client.gets_many
  • pymemcache.client.base.Client.incr
  • pymemcache.client.base.Client.prepend
  • pymemcache.client.base.Client.quit
  • pymemcache.client.base.Client.replace
  • pymemcache.client.base.Client.set
  • pymemcache.client.base.Client.set_many
  • pymemcache.client.base.Client.set_multi
  • pymemcache.client.base.Client.stats
  • pymemcache.client.base.Client.touch

Collected trace data:

  • Destination (address and port)

kafka-python [automatic-instrumentation-db-kafka-python]

Library: kafka-python (>=2.0)

Instrumented methods:

  • kafka.KafkaProducer.send,
  • kafka.KafkaConsumer.poll,
  • kafka.KafkaConsumer.\__next__

Collected trace data:

  • Destination (address and port)
  • topic (if applicable)

External HTTP requests [automatic-instrumentation-http]

Standard library [automatic-instrumentation-stdlib-urllib]

Library: urllib2 (Python 2) / urllib.request (Python 3)

Instrumented methods:

  • urllib2.AbstractHTTPHandler.do_open / urllib.request.AbstractHTTPHandler.do_open

Collected trace data:

  • HTTP method
  • requested URL

urllib3 [automatic-instrumentation-urllib3]

Library: urllib3

Instrumented methods:

  • urllib3.connectionpool.HTTPConnectionPool.urlopen

Additionally, we instrumented vendored instances of urllib3 in the following libraries:

  • requests
  • botocore

Both libraries have "unvendored" urllib3 in more recent versions, we recommend to use the newest versions.

Collected trace data:

  • HTTP method
  • requested URL

requests [automatic-instrumentation-requests]

Instrumented methods:

  • requests.sessions.Session.send

Collected trace data:

  • HTTP method
  • requested URL

AIOHTTP Client [automatic-instrumentation-aiohttp-client]

Instrumented methods:

  • aiohttp.client.ClientSession._request

Collected trace data:

  • HTTP method
  • requested URL

httpx [automatic-instrumentation-httpx]

Instrumented methods:

  • `httpx.Client.send

Collected trace data:

  • HTTP method
  • requested URL

Services [automatic-instrumentation-services]

AWS Boto3 / Botocore [automatic-instrumentation-boto3]

Library: boto3 (>=1.0)

Instrumented methods:

  • botocore.client.BaseClient._make_api_call

Collected trace data for all services:

  • AWS region (e.g. eu-central-1)
  • AWS service name (e.g. s3)
  • operation name (e.g. ListBuckets)

Additionally, some services collect more specific data

AWS Aiobotocore [automatic-instrumentation-aiobotocore]

Library: aiobotocore (>=2.2.0)

Instrumented methods:

  • aiobotocore.client.BaseClient._make_api_call

Collected trace data for all services:

  • AWS region (e.g. eu-central-1)
  • AWS service name (e.g. s3)
  • operation name (e.g. ListBuckets)

Additionally, some services collect more specific data

S3 [automatic-instrumentation-s3]
  • Bucket name
DynamoDB [automatic-instrumentation-dynamodb]
  • Table name
SNS [automatic-instrumentation-sns]
  • Topic name
SQS [automatic-instrumentation-sqs]
  • Queue name

Template Engines [automatic-instrumentation-template-engines]

Django Template Language [automatic-instrumentation-dtl]

Library: Django (see Django for supported versions)

Instrumented methods:

  • django.template.Template.render

Collected trace data:

  • template name

Jinja2 [automatic-instrumentation-jinja2]

Library: jinja2

Instrumented methods:

  • jinja2.Template.render

Collected trace data:

  • template name