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Export Prometheus metrics generated from SQL queries.

Project description

query-exporter - Export Prometheus metrics from SQL queries

|Latest Version| |Build Status| |Coverage Status| |Snap Status|

``query-exporter`` is a Prometheus_ exporter which allows collecting metrics
from database queries, at specified time intervals.

It uses SQLAlchemy_ to connect to different database engines, including
PostgreSQL, MySQL, Oracle and Microsoft SQL Server.

Each query can be run on multiple databases, and update multiple metrics.

The application is called with a configuration file that looks like this:

.. code:: yaml

dsn: sqlite://
dsn: sqlite://

type: gauge
description: A sample gauge
type: summary
description: A sample summary
type: histogram
description: A sample histogram
buckets: [10, 20, 50, 100, 1000]
type: enum
description: A sample enum
states: [foo, bar, baz]

interval: 5
databases: [db1]
metrics: [metric1]
sql: SELECT random() / 1000000000000000
interval: 20
databases: [db1, db2]
metrics: [metric2, metric3]
sql: |
SELECT abs(random() / 1000000000000000),
abs(random() / 10000000000000000)
interval: 10
databases: [db2]
metrics: [metric4]
sql: |
SELECT 'foo' AS value UNION
SELECT 'baz')
ORDER BY random()

- The ``dsn`` connection string has the following format::


(see `SQLAlchemy documentation`_ for details on the available options).

It's also possible to get the connection string from an environment variable
(e.g. ``$CONNECTION_STRING``) by setting ``dsn`` to::


- The ``metrics`` list in the query configuration must match values returned by
the query defined in ``sql``.

- The ``interval`` value is interpreted as seconds if no suffix is specified;
valid suffix are ``s``, ``m``, ``h``, ``d``. Only integer values can be
specified. If no value is specified (or specified as ``null``), the query is
executed at every HTTP request.

Queries will usually return a single row, but multiple rows are supported, and
each row will cause an update of the related metrics. This is relevant for any
kind of metric except gauges, which will be effectively updated to the value
from the last row.

For the configuration above, exported metrics look like this::

# HELP database_errors_total Number of database errors
# TYPE database_errors_total counter
database_errors_total{database="db1"} 1.0
# TYPE database_errors_created gauge
database_errors_created{database="db1"} 1.5537735966162562e+09
# HELP queries_total Number of database queries
# TYPE queries_total counter
queries_total{database="db1",status="success"} 5.0
queries_total{database="db1",status="error"} 1.0
queries_total{database="db2",status="success"} 2.0
# TYPE queries_created gauge
queries_created{database="db1",status="success"} 1.5537735966273613e+09
queries_created{database="db1",status="error"} 1.5537735966273613e+09
queries_created{database="db2",status="success"} 1.553773596627851e+09
# HELP metric1 A sample gauge
# TYPE metric1 gauge
metric1{database="db1"} 1549.0
# HELP metric2 A sample summary
# TYPE metric2 summary
metric2_count{database="db2"} 1.0
metric2_sum{database="db2"} 5229.0
metric2_count{database="db1"} 1.0
metric2_sum{database="db1"} 4513.0
# TYPE metric2_created gauge
metric2_created{database="db2"} 1.5456472955657206e+09
metric2_created{database="db1"} 1.5456472955663064e+09
# HELP metric3 A sample histogram
# TYPE metric3 histogram
metric3_bucket{database="db2",le="10.0"} 0.0
metric3_bucket{database="db2",le="20.0"} 0.0
metric3_bucket{database="db2",le="50.0"} 0.0
metric3_bucket{database="db2",le="100.0"} 0.0
metric3_bucket{database="db2",le="1000.0"} 1.0
metric3_bucket{database="db2",le="+Inf"} 1.0
metric3_count{database="db2"} 1.0
metric3_sum{database="db2"} 714.0
metric3_bucket{database="db1",le="10.0"} 0.0
metric3_bucket{database="db1",le="20.0"} 0.0
metric3_bucket{database="db1",le="50.0"} 0.0
metric3_bucket{database="db1",le="100.0"} 0.0
metric3_bucket{database="db1",le="1000.0"} 1.0
metric3_bucket{database="db1",le="+Inf"} 1.0
metric3_count{database="db1"} 1.0
metric3_sum{database="db1"} 602.0
# TYPE metric3_created gauge
metric3_created{database="db2"} 1.545647295565831e+09
metric3_created{database="db1"} 1.5456472955663848e+09
# HELP metric4 A sample enum
# TYPE metric4 gauge
metric4{database="db2",metric4="foo"} 0.0
metric4{database="db2",metric4="bar"} 1.0
metric4{database="db2",metric4="baz"} 0.0

Metrics are automatically tagged with the ``database`` label so that
indipendent series are generated for each database.

The exporter uses port ``9560`` for exposting metrics, under the standard
``/metrics`` endpoint.

Database engines

SQLAlchemy doesn't depend on specific Python database modules at
installation. This means additional modules might need to be installed for
engines in use, as follows::

pip install SQLAlchemy[postgresql] SQLAlchemy[mysql] ...

based on which databased is in use.

See `supported databases`_ for details.

Install from Snap

|Get it from the Snap Store|

``query-exporter`` can be installed from `Snap Store`_ on systems where Snaps
are supported, via::

sudo snap install query-exporter

The snap provides both the ``query-exporter`` command and a deamon instance of
the command, managed via a Systemd service.

To configure the daemon:

- create or edit ``/var/snap/query-exporter/current/config.yaml`` with the
- run ``sudo snap restart query-exporter``

The snap has builtin support for the following databases::

- PostgreSQL
- SQLite

.. _Prometheus:
.. _SQLAlchemy:
.. _`SQLAlchemy documentation`:
.. _`supported databases`:
.. _`Snap Store`:

.. |Latest Version| image::
.. |Build Status| image::
.. |Coverage Status| image::
.. |Snap Status| image::
.. |Get it from the Snap Store| image::

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