prometheus-community/postgres_exporter

PostgreSQL Server Exporter

Prometheus exporter for PostgreSQL server metrics.

CI Tested PostgreSQL versions: 9.4, 9.5, 9.6, 10, 11, 12, 13, 14

Quick Start

This package is available for Docker:

# Start an example database
docker run --net=host -it --rm -e POSTGRES_PASSWORD=password postgres
# Connect to it
docker run \
  --net=host \
  -e DATA_SOURCE_NAME="postgresql://postgres:password@localhost:5432/postgres?sslmode=disable" \
  quay.io/prometheuscommunity/postgres-exporter

Building and running

git clone https://github.com/prometheus-community/postgres_exporter.git
cd postgres_exporter
make build
./postgres_exporter <flags>

To build the Docker image:

make promu
promu crossbuild -p linux/amd64 -p linux/armv7 -p linux/amd64 -p linux/ppc64le
make docker

This will build the docker image as prometheuscommunity/postgres_exporter:${branch}.

Flags

• help
  Show context-sensitive help (also try --help-long and --help-man).
• collector.database
  Enable the pg_database collector. Default is enabled
• collector.bgwriter
  Enable the pg_stat_bgwriter collector. Default is enabled
• web.listen-address
  Address to listen on for web interface and telemetry. Default is :9187.
• web.telemetry-path
  Path under which to expose metrics. Default is /metrics.
• disable-default-metrics
  Use only metrics supplied from queries.yaml via --extend.query-path. Default is false.
• disable-settings-metrics
  Use the flag if you don't want to scrape pg_settings. Default is false.
• auto-discover-databases
  Whether to discover the databases on a server dynamically. Default is false.
• extend.query-path
  Path to a YAML file containing custom queries to run. Check out queries.yaml
  for examples of the format.
• dumpmaps
  Do not run - print the internal representation of the metric maps. Useful when debugging a custom
  queries file.
• constantLabels
  Labels to set in all metrics. A list of label=value pairs, separated by commas.
• version
  Show application version.
• exclude-databases
  A list of databases to remove when autoDiscoverDatabases is enabled.
• include-databases
  A list of databases to only include when autoDiscoverDatabases is enabled.
• log.level
  Set logging level: one of debug, info, warn, error.
• log.format
  Set the log format: one of logfmt, json.
• web.config.file
  Configuration file to use TLS and/or basic authentication. The format of the
  file is described in the exporter-toolkit repository.

Environment Variables

The following environment variables configure the exporter:

• DATA_SOURCE_NAME
  the default legacy format. Accepts URI form and key=value form arguments. The
  URI may contain the username and password to connect with.
• DATA_SOURCE_URI
  an alternative to DATA_SOURCE_NAME which exclusively accepts the hostname
  without a username and password component. For example, my_pg_hostname or
  my_pg_hostname?sslmode=disable.
• DATA_SOURCE_URI_FILE
  The same as above but reads the URI from a file.
• DATA_SOURCE_USER
  When using DATA_SOURCE_URI, this environment variable is used to specify
  the username.
• DATA_SOURCE_USER_FILE
  The same, but reads the username from a file.
• DATA_SOURCE_PASS
  When using DATA_SOURCE_URI, this environment variable is used to specify
  the password to connect with.
• DATA_SOURCE_PASS_FILE
  The same as above but reads the password from a file.
• PG_EXPORTER_WEB_LISTEN_ADDRESS
  Address to listen on for web interface and telemetry. Default is :9187.
• PG_EXPORTER_WEB_TELEMETRY_PATH
  Path under which to expose metrics. Default is /metrics.
• PG_EXPORTER_DISABLE_DEFAULT_METRICS
  Use only metrics supplied from queries.yaml. Value can be true or false. Default is false.
• PG_EXPORTER_DISABLE_SETTINGS_METRICS
  Use the flag if you don't want to scrape pg_settings. Value can be true or false. Default is false.
• PG_EXPORTER_AUTO_DISCOVER_DATABASES
  Whether to discover the databases on a server dynamically. Value can be true or false. Default is false.
• PG_EXPORTER_EXTEND_QUERY_PATH
  Path to a YAML file containing custom queries to run. Check out queries.yaml
  for examples of the format.
• PG_EXPORTER_CONSTANT_LABELS
  Labels to set in all metrics. A list of label=value pairs, separated by commas.
• PG_EXPORTER_EXCLUDE_DATABASES
  A comma-separated list of databases to remove when autoDiscoverDatabases is enabled. Default is empty string.
• PG_EXPORTER_INCLUDE_DATABASES
  A comma-separated list of databases to only include when autoDiscoverDatabases is enabled. Default is empty string,
  means allow all.
• PG_EXPORTER_METRIC_PREFIX
  A prefix to use for each of the default metrics exported by postgres-exporter. Default is pg

Settings set by environment variables starting with PG_ will be overwritten by the corresponding CLI flag if given.

Setting the Postgres server's data source name

The PostgreSQL server's data source name
must be set via the DATA_SOURCE_NAME environment variable.

For running it locally on a default Debian/Ubuntu install, this will work (transpose to init script as appropriate):

sudo -u postgres DATA_SOURCE_NAME="user=postgres host=/var/run/postgresql/ sslmode=disable" postgres_exporter

Also, you can set a list of sources to scrape different instances from the one exporter setup. Just define a comma separated string.

sudo -u postgres DATA_SOURCE_NAME="port=5432,port=6432" postgres_exporter

See the github.com/lib/pq module for other ways to format the connection string.

Adding new metrics

The exporter will attempt to dynamically export additional metrics if they are added in the
future, but they will be marked as "untyped". Additional metric maps can be easily created
from Postgres documentation by copying the tables and using the following Python snippet:

x = """tab separated raw text of a documentation table"""
for l in StringIO(x):
    column, ctype, description = l.split('\t')
    print """"{0}" : {{ prometheus.CounterValue, prometheus.NewDesc("pg_stat_database_{0}", "{2}", nil, nil) }}, """.format(column.strip(), ctype, description.strip())

Adjust the value of the resultant prometheus value type appropriately. This helps build
rich self-documenting metrics for the exporter.

Adding new metrics via a config file

The -extend.query-path command-line argument specifies a YAML file containing additional queries to run.
Some examples are provided in queries.yaml.

Disabling default metrics

To work with non-officially-supported postgres versions (e.g. 8.2.15),
or variants of postgres (e.g. Greenplum), you can disable the default metrics with the --disable-default-metrics
flag. This removes all built-in metrics, and uses only metrics defined by queries in the queries.yaml file you supply
(so you must supply one, otherwise the exporter will return nothing but internal statuses and not your database).

Automatically discover databases

To scrape metrics from all databases on a database server, the database DSN's can be dynamically discovered via the
--auto-discover-databases flag. When true, SELECT datname FROM pg_database WHERE datallowconn = true AND datistemplate = false and datname != current_database() is run for all configured DSN's. From the
result a new set of DSN's is created for which the metrics are scraped.

In addition, the option --exclude-databases adds the possibily to filter the result from the auto discovery to discard databases you do not need.

If you want to include only subset of databases, you can use option --include-databases. Exporter still makes request to
pg_database table, but do scrape from only if database is in include list.

Running as non-superuser

To be able to collect metrics from pg_stat* views as non-superuser in PostgreSQL
server versions >= 10 you can grant the pg_monitor or pg_read_all_stats built-in roles to the user. If
you need to monitor older PostgreSQL servers, you will have to create functions
and views as a superuser, and assign permissions separately to those.

-- To use IF statements, hence to be able to check if the user exists before
-- attempting creation, we need to switch to procedural SQL (PL/pgSQL)
-- instead of standard SQL.
-- More: https://www.postgresql.org/docs/9.3/plpgsql-overview.html
-- To preserve compatibility with <9.0, DO blocks are not used; instead,
-- a function is created and dropped.
CREATE OR REPLACE FUNCTION __tmp_create_user() returns void as $$
BEGIN
  IF NOT EXISTS (
          SELECT                       -- SELECT list can stay empty for this
          FROM   pg_catalog.pg_user
          WHERE  usename = 'postgres_exporter') THEN
    CREATE USER postgres_exporter;
  END IF;
END;
$$ language plpgsql;

SELECT __tmp_create_user();
DROP FUNCTION __tmp_create_user();

ALTER USER postgres_exporter WITH PASSWORD 'password';
ALTER USER postgres_exporter SET SEARCH_PATH TO postgres_exporter,pg_catalog;

-- If deploying as non-superuser (for example in AWS RDS), uncomment the GRANT
-- line below and replace <MASTER_USER> with your root user.
-- GRANT postgres_exporter TO <MASTER_USER>;

GRANT CONNECT ON DATABASE postgres TO postgres_exporter;

Run following command if you use PostgreSQL versions >= 10

GRANT pg_monitor to postgres_exporter;

Run following SQL commands only if you use PostgreSQL versions older than 10.
In PostgreSQL, views run with the permissions of the user that created them so
they can act as security barriers. Functions need to be created to share this
data with the non-superuser. Only creating the views will leave out the most
important bits of data.

CREATE SCHEMA IF NOT EXISTS postgres_exporter;
GRANT USAGE ON SCHEMA postgres_exporter TO postgres_exporter;

CREATE OR REPLACE FUNCTION get_pg_stat_activity() RETURNS SETOF pg_stat_activity AS
$$ SELECT * FROM pg_catalog.pg_stat_activity; $$
LANGUAGE sql
VOLATILE
SECURITY DEFINER;

CREATE OR REPLACE VIEW postgres_exporter.pg_stat_activity
AS
  SELECT * from get_pg_stat_activity();

GRANT SELECT ON postgres_exporter.pg_stat_activity TO postgres_exporter;

CREATE OR REPLACE FUNCTION get_pg_stat_replication() RETURNS SETOF pg_stat_replication AS
$$ SELECT * FROM pg_catalog.pg_stat_replication; $$
LANGUAGE sql
VOLATILE
SECURITY DEFINER;

CREATE OR REPLACE VIEW postgres_exporter.pg_stat_replication
AS
  SELECT * FROM get_pg_stat_replication();

GRANT SELECT ON postgres_exporter.pg_stat_replication TO postgres_exporter;

CREATE EXTENSION IF NOT EXISTS pg_stat_statements;
CREATE OR REPLACE FUNCTION get_pg_stat_statements() RETURNS SETOF pg_stat_statements AS
$$ SELECT * FROM public.pg_stat_statements; $$
LANGUAGE sql
VOLATILE
SECURITY DEFINER;

CREATE OR REPLACE VIEW postgres_exporter.pg_stat_statements
AS
  SELECT * FROM get_pg_stat_statements();

GRANT SELECT ON postgres_exporter.pg_stat_statements TO postgres_exporter;

NOTE

Remember to use postgres database name in the connection string:

DATA_SOURCE_NAME=postgresql://postgres_exporter:password@localhost:5432/postgres?sslmode=disable

Running the tests

# Run the unit tests
make test
# Start the test database with docker
docker run -p 5432:5432 -e POSTGRES_DB=circle_test -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=test -d postgres
# Run the integration tests
DATA_SOURCE_NAME='postgresql://postgres:test@localhost:5432/circle_test?sslmode=disable' GOOPTS='-v -tags integration' make test

PostgreSQL Exporter Helm Chart

The exporter, alert rule, and dashboard can be deployed in Kubernetes using the Helm chart. The Helm chart used for deployment is taken from the Prometheus community, which can be found here.

Installing PostgreSQL Server

If your Postgres server is not up and ready you can start it using Helm:

$ helm repo add bitnami https://charts.bitnami.com/bitnami
$ helm install my-release bitnami/postgresql

Note that bitnami charts allow you to deploy a Postgres exporter as part of the Helm chart. You can enable it by adding “--set metrics.enabled=true”

Installing PostgreSQL Exporter

helm repo add Prometheus-community https://prometheus-community.github.io/helm-charts

helm repo update
helm install my-release prometheus-community/prometheus-postgres-exporter

Some of the common parameters that must be changed in the values file include: 

config:
  datasource:
    # Specify one of both datasource or datasourceSecret
    host:
    user: postgres
    # Only one of password, passwordSecret and pgpassfile can be specified
    password:
    # Specify passwordSecret if DB password is stored in secret.
    passwordSecret: {}

All these parameters can be tuned via the values.yaml file here.

Scrape the metrics

There are multiple ways to scrape the metrics as discussed above. In addition to the native way of setting up Prometheus monitoring, a service monitor can be deployed (if a Prometheus operator is being used) to scrap the data from the Postgres exporter. With this approach, multiple Postgres servers can be scrapped without altering the Prometheus configuration. Every Postgres exporter comes with its own service monitor.
In the above-mentioned chart, a service monitor can be deployed by turning it on from the values.yaml file here.

serviceMonitor:
  # When set true then use a ServiceMonitor to configure scraping
  enabled: false
  # Set the namespace the ServiceMonitor should be deployed
  # namespace: monitoring
  # Set how frequently Prometheus should scrape
  # interval: 30s
  # Set path to cloudwatch-exporter telemtery-path
  # telemetryPath: /metrics
  # Set labels for the ServiceMonitor, use this to define your scrape label for Prometheus Operator
  # labels:
  # Set timeout for scrape
  # timeout: 10s
  # Set of labels to transfer from the Kubernetes Service onto the target
  # targetLabels: []
  # MetricRelabelConfigs to apply to samples before ingestion
  # metricRelabelings: []
  # Set relabel_configs as per https://prometheus.io/docs/prometheus/latest/configuration/configuration/#relabel_config
  # relabelings: []

Update the annotation section here if you are not using the Prometheus Operator.

service: 
  annotations:
    prometheus.io/path: /metrics
    prometheus.io/scrape: "true"

This concludes our discussion of the PostgreSQL exporter! If you have any questions, you can reach our team via support@nexclipper.io. Stay tuned for further exporter reviews and tips coming soon.

PostgreSQL Exporter Alerts

After digging into all the valuable metrics, this section explains in detail how we can get critical alerts.

PromQL is a query language for the Prometheus monitoring system. It is designed for building powerful yet simple queries for graphs, alerts, or derived time series (aka recording rules). PromQL is designed from scratch and has zero common grounds with other query languages used in time series databases, such as SQL in TimescaleDB, InfluxQL, or Flux. More details can be found here.

Prometheus comes with a built-in Alert Manager that is responsible for sending alerts (could be email, Slack, or any other supported channel) when any of the trigger conditions is met. Alerting rules allow users to define alerts based on Prometheus query expressions. They are defined based on the available metrics scraped by the exporter. Click here for a good source for community-defined alerts.

A general alert looks as follows:

- alert:(Alert Name)
expr: (Metric exported from exporter) >/</==/<=/=> (Value)
for: (wait for a certain duration between first encountering a new expression output vector element and counting an alert as firing for this element)
labels: (allows specifying a set of additional labels to be attached to the alert)
annotation: (specifies a set of informational labels that can be used to store longer additional information)

Some of the recommended PostgreSQL alerts are:

1. Alert - PostgreSQL is down

- alert: PostgresqlDown
    expr: pg_up == 0
    for: 0m
    labels:
      severity: critical
    annotations:
      summary: Postgresql down (instance {{ $labels.instance }})
      description: "Postgresql instance is down\n  VALUE = {{ $value }}\n  LABELS = {{ $labels }}"

2. Alert - Replication lag

  - alert: PostgresqlReplicationLag
    expr: pg_replication_lag > 30 and ON(instance) pg_replication_is_replica == 1
    for: 0m
    labels:
      severity: critical
    annotations:
      summary: Postgresql replication lag (instance {{ $labels.instance }})
      description: "PostgreSQL replication lag is going up (> 30s)\n  VALUE = {{ $value }}\n  LABELS = {{ $labels }}"

3.  Alert - Too many connections

 - alert: PostgresqlTooManyConnections
    expr: sum by (datname) (pg_stat_activity_count{datname!~"template.*|postgres"}) > pg_settings_max_connections * 0.8
    for: 2m
    labels:
      severity: warning
    annotations:
      summary: Postgresql too many connections (instance {{ $labels.instance }})
      description: "PostgreSQL instance has too many connections (> 80%).\n  VALUE = {{ $value }}\n  LABELS = {{ $labels }}"

4.   Alert -  Database size

- alert: PostgresqlHighDbSize
    expr: pg_database_size_bytes / (1024 * 1024 * 1024)
 > 100  # this value depends on available disk size 
    for: 0m
    labels:
      severity: critical
    annotations:
      summary: Postgresql DB size is more than 100 GB (instance {{ $labels.instance }})
      description: "Postgresql DB size is more than 100 GB\n  VALUE = {{ $value }}\n  LABELS = {{ $labels }}"

5.  Alert - Max transaction duration

- alert: PostgresqlTXDuration
    expr: pg_stat_activity_max_tx_duration{state="active"} > 2
    for: 0m
    labels:
      severity: critical
    annotations:
      summary: Postgresql active transaction takes more than 2 seconds to complete (instance {{ $labels.instance }})
      description: "PostgreSQL Postgresql active transaction takes more than 2 seconds\n  VALUE = {{ $value }}\n  LABELS = {{ $labels }}"

PostgreSQL Exporter Grafana

Graphs are easier to understand and more user-friendly than a row of numbers. For this purpose, users can plot their time series data in visualized format using Grafana.

Grafana is an open-source dashboarding tool used for visualizing metrics with the help of customizable and illustrative charts and graphs. It connects very well with Prometheus and makes monitoring easy and informative. Dashboards in Grafana are made up of panels, with each panel running a PromQL query to fetch metrics from Prometheus.
Grafana supports community-driven graphs for most of the widely used software, which can be directly imported to the Grafana Community.

NexClipper uses the PostgreSQL database by the Lucas Estienne dashboard, which is widely accepted and has a lot of useful panels.

What is a Panel?

Panels are the most basic component of a dashboard and can display information in various ways, such as gauge, text, bar chart, graph, and so on. They provide information in a very interactive way. Users can view every panel separately and check the value of metrics within a specific time range. 
The values on the panel are queried using PromQL, which is Prometheus Query Language. PromQL is a simple query language used to query metrics within Prometheus. It enables users to query data, aggregate and apply arithmetic functions to the metrics, and then further visualize them on panels.

Here are some examples of panels:

1. Database metrics and settings

2. Database statistics