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.
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
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 settingdsn
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 tofalse
might be useful if queries on a database are run with very long interval, to avoid holding idle connections.
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.
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
to10.0
andmetric2
to20.0
.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).
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
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.
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