query-exporter 1.9.3

Export Prometheus metrics generated from SQL queries

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 simply run as:

query-exporter config.yaml

where the passed configuration file contains the definitions of the databases to connect and queries to perform to update metrics.

Configuration file format

A sample configuration file for the application looks like this:

databases:
  db1:
    dsn: sqlite://
  db2:
    dsn: sqlite://
    keep-connected: false

metrics:
  metric1:
    type: gauge
    description: A sample gauge
  metric2:
    type: summary
    description: A sample summary
    labels: [l1, l2]
  metric3:
    type: histogram
    description: A sample histogram
    buckets: [10, 20, 50, 100, 1000]
  metric4:
    type: enum
    description: A sample enum
    states: [foo, bar, baz]

queries:
  query1:
    interval: 5
    databases: [db1]
    metrics: [metric1]
    sql: SELECT random() / 1000000000000000
  query2:
    interval: 20
    databases: [db1, db2]
    metrics: [metric2, metric3]
    sql: |
      SELECT abs(random() / 1000000000000000) AS metric2,
             abs(random() / 10000000000000000) AS metric3,
             "value1" AS l1,
             "value2" AS l2
  query3:
    interval: 10
    databases: [db2]
    metrics: [metric4]
    sql: |
      SELECT value FROM (
        SELECT "foo" AS value UNION
        SELECT "bar" UNION
        SELECT "baz")
      ORDER BY random()
      LIMIT 1

databases section

This section contains defintions for databases to connect to. Key names are arbitrary and only used to reference databases in the queries section.

Each database defintions can have the following keys:

dsn:

the connection string for the database, in the following format:

dialect[+driver]://[username:password][@host:port]/database[?option=value&...]

See SQLAlchemy documentation for details on available engines.

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

env:CONNECTION_STRING

keep-connected:

whether to keep the connection open for the database between queries, or disconnect after each one. If not specified, defaults to true. Setting this option to false might be useful if queries on a database are run with very long interval, to avoid holding idle connections.

metrics section

This section contains Prometheus metrics definitions. Keys are used as metric names, and must therefore be valid metric identifiers.

Each metric definition can have the following keys:

type:

the type of the metric, must be specified. The following metric types are supported:

• counter

• enum

• gauge

• histogram

• summary

description:

an optional description of the metric.

labels:

an optional list of label names to apply to the metric.

If specified, queries updating the metric must return rows that include values for each label in addition to the metric value. Column names must match metric and labels names.

buckets:

for histogram metrics, a list of buckets for the metrics.

If not specified, default buckets are applied.

states:

for enum metrics, a list of string values for possible states.

Queries for updating the enum must return valid states.

queries section

This section contains definitions for queries to perform. Key names are arbitrary and only used to identify queries in logs.

Each query definition can have the following keys:la-

interval:

the time interval at which queries are run.

The value is interpreted as seconds if no suffix is specified; valid suffixes are s, m, h, d. Only integer values are accepted.

If no value is specified (or specified as null), the query is only executed upon HTTP requests.

databases:

the list of databases to run the query on.

Names must match those defined in the databases section.

Metrics are automatically tagged with the database label so that indipendent series are generated for each database that a query is run on.

metrics:

the list of metrics that the query updates.

Names must match those defined in the metrics section.

sql:

the SQL text of the query.

The query must return a number of rows that match the number of metrics specified for the query plus labels for those metrics (if any).

The names of returned columns should match those of declared metrics and their labels. As an exception, if no metric for the query has labels and column names don’t match those of metrics, the order of metrics declaration is used. For example:

query:
  databases: [db]
  metrics: [metric1, metric2]
  sql: SELECT 10.0, 20.0

will update metric1 to 10.0 and metric2 to 20.0.

parameters:

an optional list of parameters sets to run the query with.

If a query is specified with parameters in its sql, it will be run once for every set of parameters specified in this list, for every interval.

Parameters sets can be specified as a list of positional parameters (using the ? syntax in the query SQL) or as a key/value dictionary when named parameters are used (e.g. :param).

As an example:

query1:
  databases: [db]
  metrics: [metric1]
  sql: |
    SELECT COUNT(*) FROM table
    WHERE id > :param1 AND id < :param2
  parameters:
    - param1: 10
      param2: 20
    - param1: 30
      param2: 40
query2:
  databases: [db]
  metrics: [metric2]
  sql: |
    SELECT COUNT(*) FROM othertable
    WHERE id > ? AND id < ?
  parameters:
    - [10, 20]
    - [30, 40]

All elements of the parameters list must be of the same kind (all lists or all dictionaries).

Metrics endpoint

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

For the configuration above, the endpoint would return something like this:

# HELP database_errors_total Number of database errors
# TYPE database_errors_total counter
# HELP queries_total Number of database queries
# TYPE queries_total counter
queries_total{database="db2",status="success"} 2.0
queries_total{database="db1",status="success"} 3.0
# TYPE queries_created gauge
queries_created{database="db2",status="success"} 1.558334663380845e+09
queries_created{database="db1",status="success"} 1.558334663381175e+09
# HELP metric1 A sample gauge
# TYPE metric1 gauge
metric1{database="db1"} 2580.0
# HELP metric2 A sample summary
# TYPE metric2 summary
metric2_count{database="db2",l1="value1",l2="value2"} 1.0
metric2_sum{database="db2",l1="value1",l2="value2"} 6476.0
metric2_count{database="db1",l1="value1",l2="value2"} 1.0
metric2_sum{database="db1",l1="value1",l2="value2"} 2340.0
# TYPE metric2_created gauge
metric2_created{database="db2",l1="value1",l2="value2"} 1.5583346633805697e+09
metric2_created{database="db1",l1="value1",l2="value2"} 1.5583346633816812e+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"} 135.0
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"} 164.0
# TYPE metric3_created gauge
metric3_created{database="db2"} 1.5583346633807e+09
metric3_created{database="db1"} 1.558334663381795e+09
# HELP metric4 A sample enum
# TYPE metric4 gauge
metric4{database="db2",metric4="foo"} 0.0
metric4{database="db2",metric4="bar"} 0.0
metric4{database="db2",metric4="baz"} 1.0

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. These can be installed as follows:

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

based on which database engines are needed.

See supported databases for details.

Install from Snap

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 configuration

• run sudo snap restart query-exporter

The snap has builtin support for the following databases:

• MySQL

• PostgreSQL

• SQLite

Run in Docker

query-exporter can be run inside Docker containers, and is availble from the Docker Hub:

docker run -p 9560:9560/tcp -v "$PWD/config.yaml:/config.yaml" --rm -it adonato/query-exporter:latest -- /config.yaml