ClickHouse data source for Grafana

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

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.

Manual configuration

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

Note: this plugin uses the native ClickHouse TCP interface to connect and run queries. Make sure you configure the server address and port accordingly.

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, including all the settings that you can set for this data source, 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
server: localhost
username: username
tlsSkipVerify: false
secureJsonData:
password: password

Building queries

The query editor allows you to query ClickHouse to return time series or tabular data. Queries can contain macros which simplify syntax and allow for dynamic parts.

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

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)

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 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.