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Supported Technologies

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Supported Technologies

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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

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The following Python versions are supported:

  • 2.7
  • 3.5
  • 3.6
  • 3.7
  • 3.8

Python 2.7 reached End of Life on January 1, 2020. The Elastic APM agent will stop supporting Python 2.7 starting in 6.0.0.

Django

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We support these Django versions:

  • 1.11
  • 2.0
  • 2.1
  • 2.2
  • 3.0

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

we currently don’t support Django running in ASGI mode.

Flask

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We support these Flask versions:

  • 0.10 (Deprecated)
  • 0.11 (Deprecated)
  • 0.12 (Deprecated)
  • 1.0
  • 1.1

Aiohttp Server

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We support these aiohttp versions:

  • 3.0+

Tornado

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We support these tornado versions:

  • 6.0+

Starlette/FastAPI

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We support these Starlette versions:

  • 0.13.0+

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

Automatic Instrumentation

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

Scheduling

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Celery
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We support these Celery versions:

  • 3.x
  • 4.x

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

Databases

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Elasticsearch

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Instrumented methods:

  • elasticsearch.connection.http_urllib3.Urllib3HttpConnection.perform_request
  • elasticsearch.connection.http_requests.RequestsHttpConnection.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)

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

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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

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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

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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

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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

PostgreSQL

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Library: psycopg2, psycopg2-binary (>=2.7)

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

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Library: aiopg (>=1.0)

Instrumented methods:

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

Collected trace data:

  • parametrized SQL query

asyncpg

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Library: asyncpg (>=0.20)

Instrumented methods:

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

Collected trace data:

  • parametrized SQL query

PyODBC

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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

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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

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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

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Library: redis (>=2.8,<3.2.0)

Instrumented methods:

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

Collected trace data:

  • Redis command name

Cassandra

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

Instrumented methods:

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

Collected trace data:

  • CQL query

Python Memcache

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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

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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)

External HTTP requests

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Standard library

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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

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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

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Instrumented methods:

  • requests.sessions.Session.send

Collected trace data:

  • HTTP method
  • requested URL

AIOHTTP Client

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Instrumented methods:

  • aiohttp.client.ClientSession._request

Collected trace data:

  • HTTP method
  • requested URL

httpx

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Instrumented methods:

  • `httpx.Client.send

Collected trace data:

  • HTTP method
  • requested URL

Services

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AWS Boto3 / Botocore

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Library: boto3 (>=1.0)

Instrumented methods:

  • botocore.client.BaseClient._make_api_call

Collected trace data:

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

Template Engines

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Django Template Language

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Library: Django (see Django for supported versions)

Instrumented methods:

  • django.template.Template.render

Collected trace data:

  • template name

Jinja2

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Library: jinja2

Instrumented methods:

  • jinja2.Template.render

Collected trace data:

  • template name
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