Official ClickHouse data source for Grafana

The ClickHouse data source plugin allows you to query and visualize ClickHouse data in Grafana.

Version compatibility

Users on Grafana v9.x and higher of Grafana can use v4. Users on Grafana v8.x are encouraged to continue using v2.2.0 of the plugin.

* As of 2.0 this plugin will only support ad hoc filters when using ClickHouse 22.7+

Installation

For detailed instructions on how to install the plugin on Grafana Cloud or locally, please checkout the Plugin installation docs.

Configuration

ClickHouse user for the data source

Set up an ClickHouse user account with readonly permission and access to databases and tables you want to query. Please note that Grafana does not validate that queries are safe. Queries can contain any SQL statement. For example, statements like ALTER TABLE system.users DELETE WHERE name='sadUser' and DROP TABLE sadTable; would be executed.

To configure a readonly user, follow these steps:

1. Create a readonly user profile following the Creating Users and Roles in ClickHouse guide.
2. Ensure the readonly user has enough permission to modify the max_execution_time setting required by the underlying clickhouse-go client.
3. If you're using a public Clickhouse instance, it's not recommended to set readonly=2 in the readonly profile. Instead, leave readonly=1 and set the constraint type of max_execution_time to changeable_in_readonly to allow modification of this setting.

ClickHouse protocol support

The plugin supports both Native (default) and HTTP transport protocols. This can be enabled in the configuration via the protocol configuration parameter. Both protocols exchange data with ClickHouse using optimized native format.

Note that the default ports for HTTP/S and Native differ:

• HTTP - 8123
• HTTPS - 8443
• Native - 9000
• Native with TLS - 9440

Manual configuration via UI

Once the plugin is installed on your Grafana instance, follow these instructions to add a new ClickHouse data source, and enter configuration options.

With a configuration file

It is possible to configure data sources using configuration files with Grafana’s provisioning system. To read about how it works, refer to Provisioning Grafana data sources.

Here are some provisioning examples for this data source using basic authentication:

apiVersion: 1
datasources:
  - name: ClickHouse
    type: grafana-clickhouse-datasource
    jsonData:
      defaultDatabase: database
      port: 9000
      host: localhost
      username: username
      tlsSkipVerify: false
      # tlsAuth: <bool>
      # tlsAuthWithCACert: <bool>
      # secure: <bool>
      # dialTimeout: <seconds>
      # queryTimeout: <seconds>
      # protocol: <native|http>
      # defaultTable: <string>
      # httpHeaders:
      # - name: X-Example-Header
      #   secure: false
      #   value: <string>
      # - name: Authorization
      #   secure: true
      # logs:
      #   defaultDatabase: <string>
      #   defaultTable: <string>
      #   otelEnabled: <bool>
      #   otelVersion: <string>
      #   timeColumn: <string>
      #   ...Column: <string>
      # traces:
      #   defaultDatabase: <string>
      #   defaultTable: <string>
      #   otelEnabled: <bool>
      #   otelVersion: <string>
      #   durationUnit: <seconds|milliseconds|microseconds|nanoseconds>
      #   traceIdColumn: <string>
      #   ...Column: <string>
    secureJsonData:
      password: password
      # tlsCACert: <string>
      # tlsClientCert: <string>
      # tlsClientKey: <string>
      # secureHttpHeaders.Authorization: <string>

Building queries

Queries can be built using the raw SQL editor or the query builder. Queries can contain macros which simplify syntax and allow for dynamic SQL generation.

Time series

Time series visualization options are selectable after adding a datetime field type to your query. This field will be used as the timestamp. You can select time series visualizations using the visualization options. Grafana interprets timestamp rows without explicit time zone as UTC. Any column except time is treated as a value column.

Multi-line time series

To create multi-line time series, the query must return at least 3 fields in the following order:

• field 1: datetime field with an alias of time
• field 2: value to group by
• field 3+: the metric values

For example:

SELECT log_time AS time, machine_group, avg(disk_free) AS avg_disk_free
FROM mgbench.logs1
GROUP BY machine_group, log_time
ORDER BY log_time

Tables

Table visualizations will always be available for any valid ClickHouse query.

Visualizing logs with the Logs Panel

To use the Logs panel your query must return a timestamp and string values. To default to the logs visualization in Explore mode, set the timestamp alias to log_time.

For example:

SELECT log_time AS log_time, machine_group, toString(avg(disk_free)) AS avg_disk_free
FROM logs1
GROUP BY machine_group, log_time
ORDER BY log_time

To force rendering as logs, in absence of a log_time column, set the Format to Logs (available from 2.2.0).

Visualizing traces with the Traces Panel

Ensure your data meets the requirements of the traces panel. This applies if using the visualization or Explore view.

Set the Format to Trace when constructing the query (available from 2.2.0).

If using the Open Telemetry Collector and ClickHouse exporter, the following query produces the required column names (these are case sensitive):

SELECT
    TraceId AS traceID,
    SpanId AS spanID,
    SpanName AS operationName,
    ParentSpanId AS parentSpanID,
    ServiceName AS serviceName,
    Duration / 1000000 AS duration,
    Timestamp AS startTime,
    arrayMap(key -> map('key', key, 'value', SpanAttributes[key]), mapKeys(SpanAttributes)) AS tags,
    arrayMap(key -> map('key', key, 'value', ResourceAttributes[key]), mapKeys(ResourceAttributes)) AS serviceTags
FROM otel.otel_traces
WHERE TraceId = '61d489320c01243966700e172ab37081'
ORDER BY startTime ASC

Macros

To simplify syntax and to allow for dynamic parts, like date range filters, the query can contain macros.

Here is an example of a query with a macro that will use Grafana's time filter:

SELECT date_time, data_stuff
FROM test_data
WHERE $__timeFilter(date_time)

Macro	Description	Output example
$__dateFilter(columnName)	Replaced by a conditional that filters the data (using the provided column) based on the date range of the panel	date >= toDate('2022-10-21') AND date <= toDate('2022-10-23')
$__timeFilter(columnName)	Replaced by a conditional that filters the data (using the provided column) based on the time range of the panel in seconds	time >= toDateTime(1415792726) AND time <= toDateTime(1447328726)
$__timeFilter_ms(columnName)	Replaced by a conditional that filters the data (using the provided column) based on the time range of the panel in milliseconds	time >= fromUnixTimestamp64Milli(1415792726123) AND time <= fromUnixTimestamp64Milli(1447328726456)
$__fromTime	Replaced by the starting time of the range of the panel casted to DateTime	toDateTime(1415792726)
$__toTime	Replaced by the ending time of the range of the panel casted to DateTime	toDateTime(1447328726)
$__fromTime_ms	Replaced by the starting time of the range of the panel casted to DateTime64(3)	fromUnixTimestamp64Milli(1415792726123)
$__toTime_ms	Replaced by the ending time of the range of the panel casted to DateTime64(3)	fromUnixTimestamp64Milli(1447328726456)
$__interval_s	Replaced by the interval in seconds	20
$__timeInterval(columnName)	Replaced by a function calculating the interval based on window size in seconds, useful when grouping	toStartOfInterval(toDateTime(column), INTERVAL 20 second)
$__timeInterval_ms(columnName)	Replaced by a function calculating the interval based on window size in milliseconds, useful when grouping	toStartOfInterval(toDateTime64(column, 3), INTERVAL 20 millisecond)
$__conditionalAll(condition, $templateVar)	Replaced by the first parameter when the template variable in the second parameter does not select every value. Replaced by the 1=1 when the template variable selects every value.	condition or 1=1

The plugin also supports notation using braces {}. Use this notation when queries are needed inside parameters.

Templates and variables

To add a new ClickHouse query variable, refer to Add a query variable.

After creating a variable, you can use it in your ClickHouse queries by using Variable syntax. For more information about variables, refer to Templates and variables.

Importing dashboards for ClickHouse

Follow these instructions to import a dashboard.

You can also find available, pre-made dashboards by navigating to the data sources configuration page, selecting the ClickHouse data source and clicking on the Dashboards tab.

We distribute the following dashboards with the plugin. These are aimed at assisting with support analysis of a ClickHouse cluster and do not rely on external datasets. The querying user requires access to the system database.

1. Cluster Analysis - an overview of configured clusters, merges, mutations and data replication.
2. Data Analysis - an overview of current databases and tables, including their respective sizes, partitions and parts.
3. Query Analysis - an analysis of queries by type, performance and resource consumption.

Ad Hoc Filters

Ad hoc filters are only supported with version 22.7+ of ClickHouse.

Ad hoc filters allow you to add key/value filters that are automatically added to all metric queries that use the specified data source, without being explicitly used in queries.

By default, Ad Hoc filters will be populated with all Tables and Columns. If you have a default database defined in the Datasource settings, all Tables from that database will be used to populate the filters. As this could be slow/expensive, you can introduce a second variable to allow limiting the Ad Hoc filters. It should be a constant type named clickhouse_adhoc_query and can contain: a comma delimited list of databases, just one database, or a database.table combination to show only columns for a single table.

For more information on Ad Hoc filters, check the Grafana docs

Using a query for Ad Hoc filters

The second clickhouse_adhoc_query also allows any valid Clickhouse query. The query results will be used to populate your ad-hoc filter's selectable filters. You may choose to hide this variable from view as it serves no further purpose.

For example, if clickhouse_adhoc_query is set to SELECT DISTINCT machine_name FROM mgbench.logs1 you would be able to select which machine names are filtered for in the dashboard.

Learn more

• Add Annotations.
• Configure and use Templates and variables.
• Add Transformations.
• Set up alerting; refer to Alerts overview.