https://github.com/sqlalchemy/sqlalchemy-collectd

Send statistics on SQLAlchemy connection and transaction metrics used by Python applications to the collectd service.
https://github.com/sqlalchemy/sqlalchemy-collectd

collectd sqlalchemy

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Send statistics on SQLAlchemy connection and transaction metrics used by Python applications to the collectd service.

• Host: GitHub
• URL: https://github.com/sqlalchemy/sqlalchemy-collectd
• Owner: sqlalchemy
• License: mit
• Created: 2018-02-10T17:36:54.000Z (almost 7 years ago)
• Default Branch: main
• Last Pushed: 2024-06-19T22:06:35.000Z (5 months ago)
• Last Synced: 2024-09-23T13:39:59.870Z (about 2 months ago)
• Topics: collectd, sqlalchemy
• Language: Python
• Size: 805 KB
• Stars: 30
• Watchers: 7
• Forks: 11
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • Changelog: changelog.rst
  • License: LICENSE

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README

        
===================

sqlalchemy-collectd

===================

Send statistics on `SQLAlchemy `_ connection and

transaction metrics used by Python applications to the

`collectd `_ service.

sqlalchemy-collectd works as a SQLAlchemy plugin invoked via the database URL,

so can be used in any SQLAlchemy application (1.1 or greater) that accepts

arbitrary connection URLs.   The plugin is loaded using setuptools

entrypoints and no code changes to the application are required.   There

are no dependencies on database backends or drivers.

sqlalchemy-collectd is oriented towards providing a unified view of

application-side database metrics in sprawling, many-host / many-process

environments that may make use of any number of topologically complicating

technologies such as database clusters, proxy servers, large numbers of

client applications, multi-process applications, and containers.

What's collectd?

================

collectd is a statistics collection daemon that is easy to run.   It serves as

a collector and re-broadcaster of runtime statistics for a wide variety of

performance and other metrics.   Once a set of stats are in collectd, they can

be broadcast out virtually anywhere, including to `RRD `_

databases and front-ends, to metrics

reporting applications like `Graphite `_ and `Grafana

`_, and to `other collectd servers

`_.

Architecture Nutshell

=====================

::

  +---------------------------------------------------------+

  |                                                         |

  |                      host A                             |

  |  +------------------------------+                       |

  |  |                              |                       |

  |  |     Python Process A         |                       |

  |  |                              |     +--------------+  |

  |  | +----------+ +-------------+ |     |              |  |

  |  | |SQLAlchemy+-+ SQLAlchemy- +-+-----+              +--+-(optional ---+

  |  | +------+---+ |  collectd   | |     |              |  |  direct host |

  |  |        |     +-------------+ |     | local        |  |  monitor)    |

  |  |        |                     |     | collectd     |  |              |

  |  +--------+---------------------+     | daemon       |  |              |

  |           |                           |              |  |     +--------+----+

  |           |        +-----------+      | +----------+ |  |     |             |

  |           |        | Python    +------+ | SQLA-    | |  |     | connmon     |

  |           | +------+ Process B |      | | collectd | |  |     | console TUI |

  |           | |      +-----------+      | | plugin   | |  |     | (runs on    |

  |           | |                         | +----------+ |  |     |  any host)  |

  |           | |      +-----------+      |              |  |     | -- -------- |

  |           | |      | Python    +------+              |  |     | -- -------- |

  |           | | +----+ Process C |      |              |  |     | -- -------- |

  |           | | |    +-----------+      +----+---------+  |     | -- -------- |

  |           | | |                            |            |     | - --  - --  |

  |           | | |                            |            |     +----+--------+

  +-----------+-+-+----------------------------+------------+          |

              | | |                            |                       |

              | | |                            |                       |

    +---------+-+-+-------+          +--------------------+        (optional

    | host B  | | |       |          | host C  |          |         cluster-wide

    |     +---+-+-+-+     |          |  +------+--------+ |         monitor)

    |    |           |    |          |  | collectd      | |            |

    |    | Database  |    |          |  | aggregator    +-+------------+

    |    |           |    |          |  | (optional)    | |

    |     +---------+     |          |  +--+----+----+--+ |

    |                     |          |     |    |    |    |

    +---------------------+          +-----+----+----+----+

                                           |    |    |

                                    +------+    |    +-------+

                                    |           |            |

                                +---+----+  +---+----+ +-----+----+

                                | host D |  | host E | | host ... |

                                +--------+  +--------+ +----------+

sqlalchemy-collectd gathers its statistics from **within** the Python

application itself, and delivers live metrics to a collectd service over UDP.

To achieve this, it's client portion is loaded within the process as a

SQLAlchemy **engine plugin** that attaches listeners to a

``sqlalchemy.engine.Engine`` object as well as the connection pool within it.

A background thread running within each process periodically sends a snapshot

of statistics out over UDP.

On the collectd side, a Python plugin listens on the same UDP port and

aggregates statistics received from any number of Python processes and hosts,

and then delivers them to the collectd engine itself as a series of

per-host and per-program statistics.

A key goal of this architecture is to allow a Python program that uses

multiple processes (e.g. via Python ``multiprocessing`` or just plain

``fork``) to report **unified** information on each engine/connection pool

within each subprocess, as well as to track multiple instances of the

same application running from many hosts (and of course it can track

any number of applications and hosts simultaneously).   Having a full

client/server model allows the collectd service itself to be located not only

on the same host as the application itself, but on any other host on the

network.

The network model itself makes use of collectd's own binary protocol; while

not strictly necessary, this is because originally the plan was to use the

collectd "network" plugin as the receiver, however after observing limitations

in collectd's "aggregation" plugin this was replaced with a full Python plugin

that does everything needed in a much more straightforward way.

How is this different from using database monitoring?

=====================================================

When you run a database like MySQL or Postgresql, there's lots of ways to see

activity in the database; you can list out statistics regarding connections,

transactions, threads / processes in use, and in most cases you can integrate

these server-side statistics with collectd itself to watch trends in real time.

However, while gathering stats from the server can provide insights into

activity, including being able to look at the originating host as well as the

specific database(s) being accessed by each client, in a large scale

environment it's difficult to get a unified, real-time picture for how each

process on each host is making use of its database connections, especially if

there are additional layers of indirection between application and

databases present, such as proxy servers like HAProxy, ProxySQL or PGBouncer,

as well as when databases and/or applications are containerized and potentially

hopping over additional network translation layers.   This kind of analysis

requires being able to relate database connections reported by the database

to the originating hosts and individual processes on each host.

SQLAlchemy-oriented applications usually make use of process-local connection pools as

well, and it is valuable to be able to see how well these pools are being

utilized, which includes being able to see how many connections are sitting

idle vs. how often does the application need to  create new connections in

order to respond to requests.   These are still things that can probably be

gleaned from the database itself from things like connection idle time, but

especially when layers of indirection are in place, it's simpler to get the

performance metrics you care about (e.g., how well are the **applications**

performing) from the applications themselves, as they can give you the exact

information about what they are doing without having to reverse-engineer it

from database servers and network status.

Of course, this assumes the applications are Python applications using

SQLAlchemy.   Which of course they should be! :)

Installation

============

To use SQLAlchemy-collectd, you need to have:

* SQLAlchemy-collectd installed as a Python library alongside SQLAlchemy

  itself, in all Python environments that run a SQLAlchemy-oriented

  application.

* The collectd service itself somewhere on the network.

* The collectd-python plugin, which may be delivered as a separate package

  depending on distribution

* SQLAlchemy-collectd installed as a Python library alongside the collectd

  server itself, either as part of the system Python which collectd-python

  accesses by default, **or** the SQLAlchemy-collectd application can be

  extracted into any arbitrary location that can be set up as an additional

  system path with collectd.

Without using a package manager, SQLAlchemy-collectd can be installed via

pip using::

	pip install sqlalchemy-collectd

Configuration

=============

Configuration involves both a client-side configuration as well as a server

side configuration.  Both are very simple.

Client

------

SQLAlchemy applications use a database connection URL, usually loaded

from a configuration system of some kind.  Wherever this URL is for your

target application, basically add ``?plugin=collectd`` to it (or ``&plugin=collectd``

if other query parameters already there).  Such as::

	mysql+pymysql://user:password@databasehost/dbname?charset=utf8&plugin=collectd

Using a URL as above, the sqlalchemy-collectd plugin will be pulled in where it

will attempt to send messages to a collectd server listening on localhost port

25827 (note this is one higher than the default collectd network plugin port of

25826).

Destination Host

^^^^^^^^^^^^^^^^

To send stats to collectd on a different host, add ``collectd_host``

(currently ipv4 only) and optionally ``collectd_port``::

	mysql+pymysql://user:password@databasehost/dbname?charset=utf8&plugin=collectd&collectd_host=172.18.5.2&collectd_port=25827

Program Name

^^^^^^^^^^^^

Another important configuration is the "program name" - this is the application

name that sqlalchemy-collectd will report within statistics.   Within a particular

"program name" on a particular host, statistics are **aggregated across all

processes**, regardless of parent process.

By default, the "program name" comes from ``sys.argv[0]``, but this is not

always what's desired; for example, if you're running from within mod_wsgi,

this will likely return ``httpd`` which is more vague that most would prefer.

Additionally, a single application might create connections to multiple

databases for different purposes, and one might want to separate the reporting

for these into different sections.  To set up this program name, add

``collectd_program_name``::

	mysql+pymysql://user:password@databasehost/dbname?charset=utf8&plugin=collectd&collectd_program_name=nova_api&collectd_host=172.18.5.2

With the above URL, all Python processes that use this URL on a single host

will aggregate their connection use statistics under the name ``nova_api``.

Startup

^^^^^^^

After the URL is configured, the vast majority of applications probably

need to be restarted for the change to take effect.

The plugin will transparently spawn a background thread for each individual process

that starts up which also connects to the database (don't worry, these work

if you are using gevent, eventlet, asyncio, gunicorn, etc.  threads are your

friend).

Server

------

sqlalchemy-collectd uses a Python plugin, so in your collectd.conf or in a

collectd.d/sqlalchemy.conf file, assuming a system-installed sqlalchemy-collectd::

	LoadPlugin python

	

	    LogTraces true

	    Import "sqlalchemy_collectd.server.plugin"

	    

	    	# ipv4 only for the moment

	        listen "0.0.0.0" 25827

	        # set to "debug" to show messages received

	        loglevel "info"

	    

	

Above, the plugin will listen for UDP on port 25827 of the default network

interface.  It can also be configured to listen on "localhost" or any

other IP number (currently ipv4 only) on the host.

Custom Module Path

^^^^^^^^^^^^^^^^^^

To reference sqlalchemy-collectd extracted into an arbitrary file location,

add ``ModulePath``::

	LoadPlugin python

	

		ModulePath "/path/to/sqlalchemy-collectd/"

	    LogTraces true

	    Import "sqlalchemy_collectd.server.plugin"

	    

	    	# ipv4 only for the moment

	        listen "0.0.0.0" 25827

	        # set to "debug" to show messages received

	        loglevel "info"

	    

	

For further information about the Python plugin system see

`collectd-python `_.

The collectd server is typically restarted for the configurational change

to take effect.

TODO

^^^^

* ipv6 support

* security layer (e.g. network packet signing / encryption)

Stats

=====

Now that sqlalchemy-collectd is running, what stats can we see?

Supposing we have the plugin turned on for the applications ``neutron``

and ``nova``, the namespace we would see in a tool like graphana would

look like::

	hostname

		sqlalchemy-host

			count-checkedin

			count-checkedout

			count-connections

			count-detached

			count-numpools

			count-numprocs

			derive-checkouts

			derive-connects

			derive-disconnects

			derive-invalidated

			derive-commits

			derive-rollbacks

			derive-transactions

		sqlalchemy-neutron

			count-checkedin

			count-checkedout

			count-connections

			count-detached

			... everything else

		sqlalchemy-nova

			count-checkedin

			count-checkedout

			count-connections

			count-detached

			... everything else

Above, we first see that all stats are grouped per-hostname.   Within that,

we have a fixed *plugin instance* called "host", which renders as ``sqlalchemy-host``.

This represents aggregated statistics for the entire host, that is, statistics

that take into account all database connections used by all applications (that

use sqlalchemy-collectd) on this particular host.

Following that, we can see there are groups for the individual ``program_name``

we set up, for ``nova`` and ``neutron`` we get stats aggregated for that

name specifically.

The statistics themselves are labeled ``count-`` or ``derive-``,

which correspond to pre-supplied collectd types ``count`` and ``derive`` (see

"collectd types" below for why the naming is done this way).  The stats labeled

``count`` are integers representing the current count of a resource or

activity:

* ``count-checkedin`` - current number of connections that are checked in to the

  connection pool

* ``count-checkedout`` - current number of connections that are checked out from

  the connection pool, e.g. are in use by the application to talk to the

  database.

* ``count-connections`` - total number of connections to the database at this moment,

  checked out, checked in, detached, or soft-invalidated.

* ``count-detached`` - total number of connections that are **detached**; meaning

  they have been disconnected from the engine/pool using the ``.detach()``

  method but are still being used as a database connection.

* ``count-numpools`` - the number of connection pools in use.  A SQLAlchemy

  ``Engine`` features exactly one connection pool.  If an application connects

  to two different database URLs in a process and creates two different

  ``Engine`` objects, then you'd have two pools.  If that same application

  spawns off into ten subprocesses, then you have 20 or 22 pools in use,

  depending on how the parent uses the database also.   Use ``count-numpools``

  to make sure this number is what you expect.  A poorly written application

  that is spawning a brand new ``Engine`` for each request will have a

  dramatically larger number here (as well as one that is changing constantly)

  and that is an immediate red flag that the application should be fixed.

* ``count-numprocs`` - the total number of Python processes, e.g. parent and

  subprocesses, that are contributing to the connection statistics in this

  group.   This number will match ``count-numpools`` if you have one

  ``Engine`` per process.

  Both the ``count-numpools`` and ``count-numprocs`` values provide context to

  when one looks at the total connections and  checkouts. If connection pools

  are configured to allow at most 20 connections max, and you have 10

  connection pools on the host, now you can have 200  connections max to your

  database.

The stats labeled ``derive`` are floating point values representing a

**rate** of activity.   sqlalchemy-collectd sends these numbers to the

collectd server as a total number of events occurred as of a specific

timestamp; collectd then compares this to the previous value to determine

the rate.  How the rate is reported (e.g. number per second, etc.) depends

on the reporting tools being used.

* ``derive-checkouts`` - rate of connections being checked out.

* ``derive-connects`` - rate of new connections made to the database

* ``derive-disconnects`` - rate of database connections being closed

* ``derive-invalidated`` - rate of connections that are explicitly **invalidated**,

  e.g. have encountered a connectivity error which made the program invalidate

  the connection.  The application may or may not have tried to connect

  again immediately depending on how it is using this feature.  See the

  section on "invalidated connections" below for details on this.

* ``derive-commits`` - (TODO: not implemented yet) rate of calls to ``transaction.commit()``.  This value

  can be used to estimate TPS, e.g. transactions per second, however note that

  this is limited to SQLAlchemy-explicit transactions where the Engine-level

  begin() / commit() methods are being invoked.   When using the SQLAlchemy

  ORM with the ``Session``, this rate should be tracking the rate of

  calls to ``Session.commit()``.

* ``derive-rollbacks`` - (TODO: not implemented yet) rate of calls to ``transaction.rollback()``.

* ``derive-transactions`` - (TODO: not implemented yet) rate of transactions overall.  This should add up

  to the commit and rollback rates combined, however may be higher than that

  if the application also discards transactions and/or ``Session`` objects

  without calling ``.commit()`` or ``.rollback()``.

Invalidated Connections

-----------------------

The ``derive-invalidated`` stat records the rate of invalidations.

By invalidated, we mean the ``.invalidated()`` method on the connection

is called, which marks this connection as no longer usable and marks it

for refresh on next use (soft invalidation) or more commonly closes it

immediately (hard invalidation).   Typically, when a connection is invalidated,

the application is either pre-pinging the database and will try to connect

again, or it was in the middle of an operation when the database got

cut off, in which case depending on how the application was designed it

may or may not try the operation again.

Invalidation usually corresponds to a

connection that reported a problem in being able to communicate with the

database, and for which an error was raised.  For this reason, the

"invalidated" rate should be considered to be roughly an "error" rate -

each count here usually corresponds to a connectivity error encountered by the

application to which it responded by invalidating the connection, which results

either in immediate or eventual reconnection.

For most invalidation scenarios, the entire pool of connections is

invalidated at once using a "freshness" timestamp; any connection older than

this timestamp is refreshed on next use.  This is to suit the case of assuming

that the database was probably restarted, so all connections need to be

reconnected.  These connections which have been **implicitly** invalidated

are **not** included in this count.

Collectd Types

--------------

These funny names ``count-`` and ``derive-`` are an artifact of how

collectd provides *types*.  collectd has a fixed list of "types" which it

lists in a file called ``types.db``. The server does not accept type names

that are not either in this file or in a separately configured custom types file,

as each type is accompanied by a template for what kinds of values it

carries.  Annoyingly, collectd does not let us add these names within the

regular .conf file, which would make it very easy for us to include

our own custom names; it instead requires they be listed in completely separate file that must be

explicitly referred to by absolute path within a conf file, and then to

make matters worse when this option is used, we have to uncomment the location

of the default types.db file in the central collectd.conf else it will

no longer be able to find it.  Given the choice between "very nice names"

and "no need to set up three separate config files", we chose the latter :)

connmon mode

============

As an added feature, the **connmon** UX has now been integrated into SQLAlchemy-collectd.

This is a console application that displays a "top"-like display of the current

status of connections.

Using the configuration above, we can add a a plugin configuration for the

connmon server plugin::

    LoadPlugin python

    

        LogTraces true

        Import "sqlalchemy_collectd.server.plugin"

        

            # ipv4 only for the moment

            listen "0.0.0.0" 25827

            # set to "debug" to show messages received

            loglevel "info"

        

        Import "sqlalchemy_collectd.connmon.plugin"

        

            monitor "localhost" 25828

            # set to "debug" to show messaging

            #loglevel "debug"

        

    

We can now run "connmon" on localhost port 25828::

    connmon --port 25828

The connmon plugin and command line tool as of version 0.6 works independently

of the "server" plugin, and may be configured by itself without the server

plugin being present.  It now consumes sqlalchemy-collectd events not only from

the local server plugin if present, but from also from any other

sqlalchemy-collectd messages that are forwarded to the server from elsewhere,

typically via the "network" plugin.

Screenshot of connmon:

|connmon_screenshot|

.. |connmon_screenshot| image:: connmon.png

   :width: 800

The SQLAlchemy Project

======================

SQLAlchemy-collectd is part of the `SQLAlchemy Project `_ and

adheres to the same standards and conventions as the core project.

Development / Bug reporting / Pull requests

--------------------------------------------

Please refer to the

`SQLAlchemy Community Guide `_ for

guidelines on coding and participating in this project.

Code of Conduct

---------------

Above all, SQLAlchemy places great emphasis on polite, thoughtful, and

constructive communication between users and developers.

Please see our current Code of Conduct at

`Code of Conduct `_.

License

=======

SQLAlchemy-collectd is distributed under the `MIT license

`_.