Database agnostic SQL exporter for Prometheus.
SQL Exporter is a configuration driven exporter that exposes metrics gathered from DBMSs, for use by the Prometheus monitoring system. Out of the box, it provides support for MySQL, PostgreSQL, Microsoft SQL Server and Clickhouse, but any DBMS for which a Go driver is available may be monitored after rebuilding the binary with the DBMS driver included.
The collected metrics and the queries that produce them are entirely configuration defined. SQL queries are grouped into collectors -- logical groups of queries, e.g. query stats or I/O stats, mapped to the metrics they populate. Collectors may be DBMS-specific (e.g. MySQL InnoDB stats) or custom, deployment specific (e.g. pricing data freshness). This means you can quickly and easily set up custom collectors to measure data quality, whatever that might mean in your specific case.
Per the Prometheus philosophy, scrapes are synchronous (metrics are collected on every
/metrics poll) but, in order to
keep load at reasonable levels, minimum collection intervals may optionally be set per collector, producing cached
metrics when queried more frequently than the configured interval.
$ go install github.com/free/sql_exporter/cmd/sql_exporter
then run it from the command line:
-help flag to get help information.
$ ./sql_exporter -help Usage of ./sql_exporter: -config.file string SQL Exporter configuration file name. (default "sql_exporter.yml") -web.listen-address string Address to listen on for web interface and telemetry. (default ":9399") -web.metrics-path string Path under which to expose metrics. (default "/metrics") [...]
SQL Exporter is deployed alongside the DB server it collects metrics from. If both the exporter and the DB
server are on the same host, they will share the same failure domain: they will usually be either both up and running
or both down. When the database is unreachable,
/metrics responds with HTTP code 500 Internal Server Error, causing
Prometheus to record
up=0 for that scrape. Only metrics defined by collectors are exported on the
SQL Exporter process metrics are exported at
The configuration examples listed here only cover the core elements. For a comprehensive and comprehensively documented
configuration file check out
You will find ready to use "standard" DBMS-specific collector definitions in the
examples directory. You may contribute your own collector
definitions and metric additions if you think they could be more widely useful, even if they are merely different takes
on already covered DBMSs.
# Global settings and defaults. global: # Subtracted from Prometheus' scrape_timeout to give us some headroom and prevent Prometheus from # timing out first. scrape_timeout_offset: 500ms # Minimum interval between collector runs: by default (0s) collectors are executed on every scrape. min_interval: 0s # Maximum number of open connections to any one target. Metric queries will run concurrently on # multiple connections. max_connections: 3 # Maximum number of idle connections to any one target. max_idle_connections: 3 # The target to monitor and the list of collectors to execute on it. target: # Data source name always has a URI schema that matches the driver name. In some cases (e.g. MySQL) # the schema gets dropped or replaced to match the driver expected DSN format. data_source_name: 'sqlserver://prom_user:firstname.lastname@example.org:1433' # Collectors (referenced by name) to execute on the target. collectors: [pricing_data_freshness] # Collector definition files. collector_files: - "*.collector.yml"
Collectors may be defined inline, in the exporter configuration file, under
collectors, or they may be defined in
separate files and referenced in the exporter configuration by name, making them easy to share and reuse.
The collector definition below generates gauge metrics of the form
# This collector will be referenced in the exporter configuration as `pricing_data_freshness`. collector_name: pricing_data_freshness # A Prometheus metric with (optional) additional labels, value and labels populated from one query. metrics: - metric_name: pricing_update_time type: gauge help: 'Time when prices for a market were last updated.' key_labels: # Populated from the `market` column of each row. - Market values: [LastUpdateTime] query: | SELECT Market, max(UpdateTime) AS LastUpdateTime FROM MarketPrices GROUP BY Market
Data Source Names
To keep things simple and yet allow fully configurable database connections to be set up, SQL Exporter uses DSNs (like
sqlserver://prom_user:email@example.com:1433) to refer to database instances. However, because the
sql library does not allow for automatic driver selection based on the DSN (i.e. an explicit driver name must be
specified) SQL Exporter uses the schema part of the DSN (the part before the
://) to determine which driver to use.
Unfortunately, while this works out of the box with the MS SQL Server and
PostgreSQL drivers, the MySQL driver DSNs format does not include
a schema and the Clickhouse one uses
tcp://. So SQL Exporter does a bit of massaging
of DSNs for the latter two drivers in order for this to work:
|DB||SQL Exporter expected DSN||Driver sees|
Why It Exists
SQL Exporter started off as an exporter for Microsoft SQL Server, for which no reliable exporters exist. But what is the point of a configuration driven SQL exporter, if you're going to use it along with 2 more exporters with wholly different world views and configurations, because you also have MySQL and PostgreSQL instances to monitor?
A couple of alternative database agnostic exporters are available -- https://github.com/justwatchcom/sql_exporter and https://github.com/chop-dbhi/prometheus-sql -- but they both do the collection at fixed intervals, independent of Prometheus scrapes. This is partly a philosophical issue, but practical issues are not all that difficult to imagine: jitter; duplicate data points; or collected but not scraped data points. The control they provide over which labels get applied is limited, and the base label set spammy. And finally, configurations are not easily reused without copy-pasting and editing across jobs and instances.