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Documentation Grafana documentation Data sources PostgreSQL

PostgreSQL data source

Grafana ships with a built-in PostgreSQL data source plugin that allows you to query and visualize data from a PostgreSQL compatible database. This topic explains options, variables, querying, and other options specific to this data source. For instructions about how to add a data source to Grafana, refer to Add a data source. Only users with the organization admin role can add data sources.

PostgreSQL settings

To access PostgreSQL settings, hover your mouse over the Configuration (gear) icon, then click Data Sources, and then click the PostgreSQL data source.

Min time interval

A lower limit for the $__interval and $__interval_ms variables. Recommended to be set to write frequency, for example 1m if your data is written every minute. This option can also be overridden/configured in a dashboard panel under data source options. It’s important to note that this value needs to be formatted as a number followed by a valid time identifier, e.g. 1m (1 minute) or 30s (30 seconds). The following time identifiers are supported:

Database user permissions (Important!)

The database user you specify when you add the data source should only be granted SELECT permissions on the specified database and tables you want to query. Grafana does not validate that the query is safe. The query could include any SQL statement. For example, statements like DELETE FROM user; and DROP TABLE user; would be executed. To protect against this we highly recommend you create a specific PostgreSQL user with restricted permissions.

Example:

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 CREATE USER grafanareader WITH PASSWORD 'password';
 GRANT USAGE ON SCHEMA schema TO grafanareader;
 GRANT SELECT ON schema.table TO grafanareader;

Make sure the user does not get any unwanted privileges from the public role.

Query builder

The PostgreSQL query builder is available when editing a panel using a PostgreSQL data source. The built query can be run by pressing the Run query button in the top right corner of the editor.

Format

The response from PostgreSQL can be formatted as either a table or as a time series. To use the time series format one of the columns must be named time.

Dataset and Table selection

In the dataset dropdown, choose the PostgreSQL database to query. The dropdown is be populated with the databases that the user has access to. When the dataset is selected, the table dropdown is populated with the tables that are available.

Columns and Aggregation functions (SELECT)

Using the dropdown, select a column to include in the data. You can also specify an optional aggregation function.

Add further value columns by clicking the plus button and another column dropdown appears.

Filter data (WHERE)

To add a filter, flip the switch at the top of the editor. Using the first dropdown, select if all the filters need to match (AND) or if only one of the filters needs to match (OR).

To add more columns to filter on use the plus button.

Group By

To group the results by column, flip the group switch at the top of the editor. You can then choose which column to group the results by. The group by clause can be removed by pressing the X button.

Preview

By flipping the preview switch at the top of the editor, you can get a preview of the SQL query generated by the query builder.

Code editor

To make advanced queries, switch to the code editor by clicking code in the top right corner of the editor. The code editor support autocompletion of tables, columns, SQL keywords, standard sql functions, Grafana template variables and Grafana macros. Columns cannot be completed before a table has been specified.

You can expand the code editor by pressing the chevron pointing downwards in the lower right corner of the code editor.

CTRL/CMD + Return works as a keyboard shortcut to run the query.

Macros

Macros can be used within a query to simplify syntax and allow for dynamic parts.

We plan to add many more macros. If you have suggestions for what macros you would like to see, please open an issue in our GitHub repo.

Table queries

If the Format as query option is set to Table then you can basically do any type of SQL query. The table panel will automatically show the results of whatever columns and rows your query returns.

Query editor with example query:

The query:

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SELECT
  title as "Title",
  "user".login as "Created By",
  dashboard.created as "Created On"
FROM dashboard
INNER JOIN "user" on "user".id = dashboard.created_by
WHERE $__timeFilter(dashboard.created)

You can control the name of the Table panel columns by using regular as SQL column selection syntax.

The resulting table panel:

Time series queries

If you set Format as to Time series, then the query must have a column named time that returns either a SQL datetime or any numeric datatype representing Unix epoch in seconds. In addition, result sets of time series queries must be sorted by time for panels to properly visualize the result.

A time series query result is returned in a wide data frame format. Any column except time or of type string transforms into value fields in the data frame query result. Any string column transforms into field labels in the data frame query result.

For backward compatibility, there’s an exception to the above rule for queries that return three columns including a string column named metric. Instead of transforming the metric column into field labels, it becomes the field name, and then the series name is formatted as the value of the metric column. See the example with the metric column below.

To optionally customize the default series name formatting, refer to Standard options definitions.

Example with metric column:

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SELECT
  $__timeGroup("time_date_time",'5m'),
  min("value_double"),
  'min' as metric
FROM test_data
WHERE $__timeFilter("time_date_time")
GROUP BY time
ORDER BY time

Data frame result:

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+---------------------+-----------------+
| Name: time          | Name: min       |
| Labels:             | Labels:         |
| Type: []time.Time   | Type: []float64 |
+---------------------+-----------------+
| 2020-01-02 03:05:00 | 3               |
| 2020-01-02 03:10:00 | 6               |
+---------------------+-----------------+

Example using the fill parameter in the $__timeGroup macro to convert null values to be zero instead:

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SELECT
  $__timeGroup("createdAt",'5m',0),
  sum(value) as value,
  hostname
FROM test_data
WHERE
  $__timeFilter("createdAt")
GROUP BY time, hostname
ORDER BY time

Given the data frame result in the following example and using the graph panel, you will get two series named value 10.0.1.1 and value 10.0.1.2. To render the series with a name of 10.0.1.1 and 10.0.1.2 , use a Standard options definitions display value of ${__field.labels.hostname}.

Data frame result:

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+---------------------+---------------------------+---------------------------+
| Name: time          | Name: value               | Name: value               |
| Labels:             | Labels: hostname=10.0.1.1 | Labels: hostname=10.0.1.2 |
| Type: []time.Time   | Type: []float64           | Type: []float64           |
+---------------------+---------------------------+---------------------------+
| 2020-01-02 03:05:00 | 3                         | 4                         |
| 2020-01-02 03:10:00 | 6                         | 7                         |
+---------------------+---------------------------+---------------------------+

Example with multiple columns:

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SELECT
  $__timeGroup("time_date_time",'5m'),
  min("value_double") as "min_value",
  max("value_double") as "max_value"
FROM test_data
WHERE $__timeFilter("time_date_time")
GROUP BY time
ORDER BY time

Data frame result:

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+---------------------+-----------------+-----------------+
| Name: time          | Name: min_value | Name: max_value |
| Labels:             | Labels:         | Labels:         |
| Type: []time.Time   | Type: []float64 | Type: []float64 |
+---------------------+-----------------+-----------------+
| 2020-01-02 03:04:00 | 3               | 4               |
| 2020-01-02 03:05:00 | 6               | 7               |
+---------------------+-----------------+-----------------+

Templating

Instead of hard-coding things like server, application and sensor name in your metric queries you can use variables in their place. Variables are shown as dropdown select boxes at the top of the dashboard. These dropdowns make it easy to change the data being displayed in your dashboard.

Refer to Templates and variables for an introduction to the templating feature and the different types of template variables.

Query variable

If you add a template variable of the type Query, you can write a PostgreSQL query that can return things like measurement names, key names or key values that are shown as a dropdown select box.

For example, you can have a variable that contains all values for the hostname column in a table if you specify a query like this in the templating variable Query setting.

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SELECT hostname FROM host

A query can return multiple columns and Grafana will automatically create a list from them. For example, the query below will return a list with values from hostname and hostname2.

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SELECT host.hostname, other_host.hostname2 FROM host JOIN other_host ON host.city = other_host.city

To use time range dependent macros like $__timeFilter(column) in your query the refresh mode of the template variable needs to be set to On Time Range Change.

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SELECT event_name FROM event_log WHERE $__timeFilter(time_column)

Another option is a query that can create a key/value variable. The query should return two columns that are named __text and __value. The __text column value should be unique (if it is not unique then the first value is used). The options in the dropdown will have a text and value that allows you to have a friendly name as text and an id as the value. An example query with hostname as the text and id as the value:

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SELECT hostname AS __text, id AS __value FROM host

You can also create nested variables. Using a variable named region, you could have the hosts variable only show hosts from the current selected region with a query like this (if region is a multi-value variable then use the IN comparison operator rather than = to match against multiple values):

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SELECT hostname FROM host  WHERE region IN($region)

Using __searchFilter to filter results in Query Variable

Available from Grafana 6.5 and above

Using __searchFilter in the query field will filter the query result based on what the user types in the dropdown select box. When nothing has been entered by the user the default value for __searchFilter is %.

Important that you surround the __searchFilter expression with quotes as Grafana does not do this for you.

The example below shows how to use __searchFilter as part of the query field to enable searching for hostname while the user types in the dropdown select box.

Query

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SELECT hostname FROM my_host  WHERE hostname LIKE '$__searchFilter'

Using Variables in Queries

From Grafana 4.3.0 to 4.6.0, template variables are always quoted automatically. If your template variables are strings, do not wrap them in quotes in where clauses.

From Grafana 4.7.0, template variable values are only quoted when the template variable is a multi-value.

If the variable is a multi-value variable then use the IN comparison operator rather than = to match against multiple values.

There are two syntaxes:

$<varname> Example with a template variable named hostname:

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SELECT
  atimestamp as time,
  aint as value
FROM table
WHERE $__timeFilter(atimestamp) and hostname in($hostname)
ORDER BY atimestamp ASC

[[varname]] Example with a template variable named hostname:

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SELECT
  atimestamp as time,
  aint as value
FROM table
WHERE $__timeFilter(atimestamp) and hostname in([[hostname]])
ORDER BY atimestamp ASC

Disabling quoting for multi-value variables

Grafana automatically creates a quoted, comma-separated string for multi-value variables. For example: if server01 and server02 are selected then it will be formatted as: 'server01', 'server02'. To disable quoting, use the csv formatting option for variables:

${servers:csv}

Read more about variable formatting options in the Variables documentation.

Annotations

Annotations allow you to overlay rich event information on top of graphs. You add annotation queries via the Dashboard menu / Annotations view.

Example query using time column with epoch values:

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SELECT
  epoch_time as time,
  metric1 as text,
  concat_ws(', ', metric1::text, metric2::text) as tags
FROM
  public.test_data
WHERE
  $__unixEpochFilter(epoch_time)

Example region query using time and timeend columns with epoch values:

Only available in Grafana v6.6+.

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SELECT
  epoch_time as time,
  epoch_time_end as timeend,
  metric1 as text,
  concat_ws(', ', metric1::text, metric2::text) as tags
FROM
  public.test_data
WHERE
  $__unixEpochFilter(epoch_time)

Example query using time column of native SQL date/time data type:

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SELECT
  native_date_time as time,
  metric1 as text,
  concat_ws(', ', metric1::text, metric2::text) as tags
FROM
  public.test_data
WHERE
  $__timeFilter(native_date_time)

Alerting

Time series queries should work in alerting conditions. Table formatted queries are not yet supported in alert rule conditions.

Configure the data source with provisioning

It’s now possible to configure data sources using config files with Grafana’s provisioning system. You can read more about how it works and all the settings you can set for data sources on the provisioning docs page

Here are some provisioning examples for this data source.

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apiVersion: 1

datasources:
  - name: Postgres
    type: postgres
    url: localhost:5432
    database: grafana
    user: grafana
    secureJsonData:
      password: 'Password!'
    jsonData:
      sslmode: 'disable' # disable/require/verify-ca/verify-full
      maxOpenConns: 0 # Grafana v5.4+
      maxIdleConns: 2 # Grafana v5.4+
      connMaxLifetime: 14400 # Grafana v5.4+
      postgresVersion: 903 # 903=9.3, 904=9.4, 905=9.5, 906=9.6, 1000=10
      timescaledb: false

Note: In the above code, the postgresVersion value of 10 refers to version PostgreSQL 10 and above.

If you encounter metric request errors or other issues:

• Make sure your data source YAML file parameters exactly match the example. This includes parameter names and use of quotation marks.
• Make sure the database name is not included in the url.