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SQL Exporter for Prometheus

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Overview

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 the following databases and compatible interfaces:

In fact, 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.

Usage

Get Prometheus SQL Exporter, either as a packaged release, as a Docker image.

Use the -help flag to get help information.

$ ./sql_exporter -help
Usage of ./sql_exporter:
  -config.file string
      SQL Exporter configuration file path. (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")
  [...]

Build

Prerequisites:

By default we produce a binary with all the supported drivers with the following command:

make build

It's also possible to reduce the size of the binary by only including specific set of drivers like Postgres, MySQL and MSSQL. In this case we need to update drivers.go. To avoid manual manipulation there is a helper code generator available, so we can run the following commands:

make drivers-minimal
make build

The first command will regenerate drivers.go file with a minimal set of imported drivers using drivers_gen.go.

Running make drivers-all will regenerate driver set back to the current defaults.

Feel free to revisit and add more drivers as required. There's also the custom list that allows managing a separate list of drivers for special needs.

Configuration

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 /metrics endpoint. SQL Exporter process metrics are exported at /sql_exporter_metrics.

The configuration examples listed here only cover the core elements. For a comprehensive and comprehensively documented configuration file check out documentation/sql_exporter.yml. 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.

./sql_exporter.yml

# 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
  # Maximum amount of time a connection may be reused to any one target. Infinite by default.
  max_connection_lifetime: 10m

# The target to monitor and the list of collectors to execute on it.
target:
  # Target name (optional). Setting this field enables extra metrics e.g. `up` and `scrape_duration` with
  # the `target` label that are always returned on a scrape.
  name: "prices_db"
  # 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:prom_password@dbserver1.example.com:1433'

  # Collectors (referenced by name) to execute on the target.
  # Glob patterns are supported (see <https://pkg.go.dev/path/filepath#Match> for syntax).
  collectors: [pricing_data_freshness, pricing_*]

  # In case you need to connect to a backend that only responds to a limited set of commands (e.g. pgbouncer) or
  # a data warehouse you don't want to keep online all the time (due to the extra cost), you might want to disable `ping`
  # enable_ping: true

# Collector definition files.
# Glob patterns are supported (see <https://pkg.go.dev/path/filepath#Match> for syntax).
collector_files:
  - "*.collector.yml"

[!NOTE] The collectors and collector_files configurations support Glob pattern matching. To match names with literal pattern terms in them, e.g. collector_*1*, these must be escaped: collector_\*1\*.

Collectors

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 pricing_update_time{market="US"}.

./pricing_data_freshness.collector.yml

# 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
    static_labels:
      # Arbitrary key/value pair
      portfolio: income
    values: [LastUpdateTime]
    # Static metric value (optional). Useful in case we are interested in string data (key_labels) only. It's mutually
    # exclusive with `values` field.
    # static_value: 1
    # Timestamp value (optional). Should point at the existing column containing valid timestamps to return a metric
    # with an explicit timestamp.
    # timestamp_value: CreatedAt
    query: |
      SELECT Market, max(UpdateTime) AS LastUpdateTime
      FROM MarketPrices
      GROUP BY Market

Data Source Names (DSN)

To keep things simple and yet allow fully configurable database connections, SQL Exporter uses DSNs (like sqlserver://prom_user:prom_password@dbserver1.example.com:1433) to refer to database instances.

This exporter relies on xo/dburl package for parsing Data Source Names (DSN). The goal is to have a unified way to specify DSNs across all supported databases. This can potentially affect your connection to certain databases like MySQL, so you might want to adjust your connection string accordingly:

mysql://user:pass@localhost/dbname - for TCP connection
mysql:/var/run/mysqld/mysqld.sock - for Unix socket connection

[!IMPORTANT] If your DSN contains special characters in any part of your connection string (including passwords), you might need to apply URL encoding (percent-encoding) to them. For example, p@$$w0rd#abc then becomes p%40%24%24w0rd%23abc.

For additional details please refer to xo/dburl documentation.

Miscellaneous

<details> <summary>Multiple database connections</summary>

It is possible to run a single exporter instance against multiple database connections. In this case we need to configure jobs list instead of the target section as in the following example:

jobs:
  - job_name: db_targets
    collectors: [pricing_data_freshness, pricing_*]
    enable_ping: true # Optional, true by default. Set to `false` in case you connect to pgbouncer or a data warehouse
    static_configs:
      - targets:
          pg1: 'pg://db1@127.0.0.1:25432/postgres?sslmode=disable'
          pg2: 'postgresql://username:password@pg-host.example.com:5432/dbname?sslmode=disable'
        labels:  # Optional, arbitrary key/value pair for all targets
          cluster: cluster1

, where DSN strings are assigned to the arbitrary instance names (i.e. pg1 and pg2).

We can also define multiple jobs to run different collectors against different target sets.

Since v0.14, sql_exporter can be passed an optional list of job names to filter out metrics. The jobs[] query parameter may be used multiple times. In Prometheus configuration we can use this syntax under the scrape config:

  params:
    jobs[]:
      - db_targets1
      - db_targets2

This might be useful for scraping targets with different intervals or any other advanced use cases, when calling all jobs at once is undesired.

</details> <details> <summary>Scraping PgBouncer, ProxySQL, Clickhouse or Snowflake</summary>

Given that PgBouncer is a connection pooler, it doesn't support all the commands that a regular SQL database does, so we need to make some adjustments to the configuration:

For libpq (postgres) driver we only need to set no_prepared_statement: true parameter. For pgx driver, we also need to add default_query_exec_mode=simple_protocol parameter to the DSN (for v5).

Below is an example of a metric configuration for PgBouncer:

    metrics:
      - metric_name: max_connections
        no_prepared_statement: true
        type: gauge
        values: [max_connections]
        key_labels:
          - name
          - database
          - force_user
          - pool_mode
          - disabled
          - paused
          - current_connections
          - reserve_pool
          - min_pool_size
          - pool_size
          - port
        query: |
          SHOW DATABASES;

Same goes for ProxySQL and Clickhouse, where we need to add no_prepared_statement: true to the metric/job configuration, as these databases doesn't support prepared statements.

In case, you connect to a data warehouse (e.g. Snowflake) you don't want to keep online all the time (due to the extra cost), you might want to disable ping by setting enable_ping: false.

</details> <details> <summary>Scraping timestamp value from the result set</summary>

Some database drivers by default return DATE or DATETIME values as String type, whereas sql_exporter expects it to be Time.

This may result in the following error:

unsupported Scan, storing driver.Value type []uint8 into type *time.Time

To resolve the issue, make sure to include parseTime=true as a parameter on the DSN, so values with TIMESTAMP, DATETIME, TIME, DATE types will end up as time.Time type, which is a requirement on the sql_exporter side to process the value correctly.

</details> <details> <summary>Using AWS Secrets Manager</summary>

If the database runs on AWS EC2 instance, this is a secure option to store the DSN without having it in the configuration file. To use this option:

{
  "data_source_name": "sqlserver://prom_user:prom_password@dbserver1.example.com:1433"
}

...
target:
  aws_secret_name: '<AWS_SECRET_NAME>'
...

{
  "Version" : "2012-10-17",
  "Statement" : [
    {
      "Effect": "Allow",
      "Principal": {"AWS": "arn:aws:iam::123456789012:role/EC2RoleToAccessSecrets"},
      "Action": "secretsmanager:GetSecretValue",
      "Resource": "*",
    }
  ]
}

Currently, AWS Secret Manager integration is only available for a single target configuration.

</details> <details> <summary>Run as a Windows service</summary>

If you run SQL Exporter from Windows, it might come in handy to register it as a service to avoid interactive sessions. It is important to define --config.file parameter to load the configuration file. The other settings can be added as well. The registration itself is performed with Powershell or CMD (make sure you run it as Administrator):

Powershell:

New-Service -name "SqlExporterSvc" `
-BinaryPathName "%SQL_EXPORTER_PATH%\sql_exporter.exe --config.file %SQL_EXPORTER_PATH%\sql_exporter.yml" `
-StartupType Automatic `
-DisplayName "Prometheus SQL Exporter"

CMD:

sc.exe create SqlExporterSvc binPath= "%SQL_EXPORTER_PATH%\sql_exporter.exe --config.file %SQL_EXPORTER_PATH%\sql_exporter.yml" start= auto

%SQL_EXPORTER_PATH% is a path to the SQL Exporter binary executable. This document assumes that configuration files are in the same location.

In case you need a more sophisticated setup (e.g. with logging, environment variables, etc), you might want to use NSSM or WinSW. Please consult their documentation for more details.

</details> <details> <summary>Using WinSSPI/NTLM as the authentication mechanism for MSSQL</summary>

If sql_exporter is running in the same Windows domain as the MSSQL, then you can use the parameter authenticator=winsspi within the connection string to authenticate without any additional credentials:

sqlserver://@<HOST>:<PORT>?authenticator=winsspi

If you want to use Windows credentials to authenticate instead of MSSQL credentials, you can use the parameter authenticator=ntlm within the connection string. The USERNAME and PASSWORD then corresponds to a Windows username and password. The Windows domain may need to be prefixed to the username with a trailing \:

sqlserver://<DOMAIN\USERNAME>:<PASSWORD>@<HOST>:<PORT>?authenticator=ntlm
</details> <details> <summary>TLS and Basic Authentication</summary>

SQL Exporter supports TLS and Basic Authentication. This enables better control of the various HTTP endpoints.

To use TLS and/or Basic Authentication, you need to pass a configuration file using the --web.config.file parameter. The format of the file is described in the exporter-toolkit repository.

</details>

If you have an issue using sql_exporter, please check Discussions or closed Issues first. Chances are someone else has already encountered the same problem and there is a solution. If not, feel free to create a new discussion.

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:

However, 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:

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.

Credits

This is a permanent fork of Database agnostic SQL exporter for Prometheus created by @free.