ClickHouse datasource plugin provides a support for ClickHouse as a backend database.

Install from grafana.net

OR

Copy files to your Grafana plugin directory. Restart Grafana, check datasources list at https://your.grafana.instance/datasources/new, choose ClickHouse option.

• Access to CH via HTTP
• Query setup
• Raw SQL editor
• Query formatting
• Macros support
• Additional functions
• Templates
• Table view
• SingleStat view
• Ad-hoc filters
• Annotations

Page configuration is standard

There is a small feature - ClickHouse treats HTTP Basic Authentication credentials as a database user and will try to run queries using its name.

Using of CHProxy will bring additional features:

• Easily setup HTTPS access to ClickHouse as shown here to provide secure access.
• Limit concurrency and execution time for requests from Grafana as shown here to prevent ClickHouse overloading from Grafana.
• Protection against request bursts for dashboards with numerous graphs. CHProxy allows to queue requests and execute them sequentially. To learn more - read about params max_queue_size and max_queue_time at CHProxy page.
• Response caching for the most frequent queries as shown here. Caching will protect ClickHouse from excessive refreshes and will be optimal option for popular dashboards.

Hint - if you need to cache requests like last 24h where timestamp changes constantly then try to use Round option at Raw Editor

Query setup interface:

First row FROM contains two options: database and table. Table values depends on selected database. Second row contains selectors for time filtering:

• Column:Date (EventDate)
• Column:DateTime (DateTime) or Column:TimeStamp (UInt32).

Plugin will try to detect date columns automatically

Column:DateTime or Column:TimeStamp are required for time-based macros and functions, because all analytics is based on these values

Button Go to Query is just a toggler to Raw SQL Editor

Raw Editor allows custom SQL queries to be written:

Raw Editor allows to type queries, get info about functions and macroses, format queries as Clickhouse do. Under the Editor you can find a raw query (all macros and functions have already been replaced) which will be sent directly to ClickHouse.

Plugin supports the following marcos:

• $table - replaced with selected table name from Query Builder
• $dateCol - replaced with Date:Col value from Query Builder
• $dateTimeCol - replaced with Column:DateTime or Column:TimeStamp value from Query Builder
• $from - replaced with timestamp/1000 value of selected "Time Range:From"
• $to - replaced with timestamp/1000 value of selected "Time Range:To"
• $interval - replaced with selected "Group by time interval" value (as a number of seconds)
• $timeFilter - replaced with currently selected "Time Range". Requires Column:Date and Column:DateTime or Column:TimeStamp to be selected
• $timeFilterByColumn($column) - replaced with currently selected "Time Range" for column passed as $column argument. Use it in queries or query variables as ...WHERE $timeFilterColumn($column)... or ...WHERE $timeFilterColumn(created_at)....
• $timeSeries - replaced with special ClickHouse construction to convert results as time-series data. Use it as "SELECT $timeSeries...".
• $unescape - unescapes variable value by removing single quotes. Used for multiple-value string variables: "SELECT $unescape($column) FROM requests WHERE $unescape($column) = 5"
• $adhoc - replaced with a rendered ad-hoc filter expression, or "1" if no ad-hoc filters exist. Since ad-hoc applies automatically only to outer queries the macros can be used for filtering in inner queries.

A description of macros is available by typing their names in Raw Editor

Functions are just templates of SQL queries and you can check the final query at Raw SQL Editor mode. If some additional complexity is needed - just copy raw sql into Raw Editor and make according changes. Remember that macros are still available to use.

There are some limits in function use because of poor query analysis:

• Column:Date and Column:DateTime or Column:TimeStamp must be set in Query Builder
• Query must begins from function name
• Only one function can be used per query

Plugin supports the following functions:

Example usage:

$rate(countIf(Type = 200) AS good, countIf(Type != 200) AS bad) FROM requests

Query will be transformed into:

SELECT 
    t, 
    good / runningDifference(t / 1000) AS goodRate, 
    bad / runningDifference(t / 1000) AS badRate
FROM 
(
    SELECT 
        (intDiv(toUInt32(EventTime), 60)) * 1000 AS t, 
        countIf(Type = 200) AS good, 
        countIf(Type != 200) AS bad
    FROM requests 
    WHERE ((EventDate >= toDate(1482796747)) AND (EventDate <= toDate(1482853383))) AND ((EventTime >= toDateTime(1482796747)) AND (EventTime <= toDateTime(1482853383)))
    GROUP BY t
    ORDER BY t ASC
) 

Example usage:

$columns(OSName, count(*) c) FROM requests

Query will be transformed into:

SELECT 
    t, 
    groupArray((OSName, c)) AS groupArr
FROM 
(
    SELECT 
        (intDiv(toUInt32(EventTime), 60) * 60) * 1000 AS t, 
        OSName, 
        count(*) AS c
    FROM requests 
    ANY INNER JOIN oses USING (OS)
    WHERE ((EventDate >= toDate(1482796627)) AND (EventDate <= toDate(1482853383))) AND ((EventTime >= toDateTime(1482796627)) AND (EventTime <= toDateTime(1482853383)))
    GROUP BY 
        t, 
        OSName
    ORDER BY 
        t ASC, 
        OSName ASC
) 
GROUP BY t
ORDER BY t ASC

This will help to build the next graph:

Example usage:

$rateColumns(OS, count(*) c) FROM requests

Query will be transformed into:

SELECT 
    t, 
    arrayMap(lambda(tuple(a), (a.1, a.2 / runningDifference(t / 1000))), groupArr)
FROM 
(
    SELECT 
        t, 
        groupArray((OS, c)) AS groupArr
    FROM 
    (
        SELECT 
            (intDiv(toUInt32(EventTime), 60) * 60) * 1000 AS t, 
            OS, 
            count(*) AS c
        FROM requests 
        WHERE ((EventDate >= toDate(1482796867)) AND (EventDate <= toDate(1482853383))) AND ((EventTime >= toDateTime(1482796867)) AND (EventTime <= toDateTime(1482853383)))
        GROUP BY 
            t, 
            OS
        ORDER BY 
            t ASC, 
            OS ASC
    ) 
    GROUP BY t
    ORDER BY t ASC
) 

Example usage:

$perSecond(total_requests) FROM requests

Query will be transformed into:

SELECT
    t,
    if(runningDifference(max_0) < 0, nan, runningDifference(max_0) / runningDifference(t / 1000)) AS max_0_Rate
FROM
(
    SELECT
        (intDiv(toUInt32(Time), 60) * 60) * 1000 AS t,
        max(total_requests) AS max_0
    FROM requests
    WHERE ((Date >= toDate(1535711819)) AND (Date <= toDate(1535714715)))
    AND ((Time >= toDateTime(1535711819)) AND (Time <= toDateTime(1535714715)))
    GROUP BY t
    ORDER BY t ASC
)

// see issue 78 for the background

Example usage:

$perSecondColumns(type, total) FROM requests WHERE Type in ('udp','tcp')

Query will be transformed into:

SELECT
    t,
    groupArray((type, max_0_Rate)) AS groupArr
FROM
(
    SELECT
        t,
        type,
        if(runningDifference(max_0) < 0, nan, runningDifference(max_0) / runningDifference(t / 1000)) AS max_0_Rate
    FROM
    (
        SELECT
            (intDiv(toUInt32(Time), 60) * 60) * 1000 AS t,
            type,
            max(total) AS max_0
        FROM requests
        WHERE ((Date >= toDate(1535711819)) AND (Date <= toDate(1535714715)))
        AND ((Time >= toDateTime(1535711819)) AND (Time <= toDateTime(1535714715)))
        AND (Type IN ('udp', 'tcp'))
        GROUP BY
            t,
            type
        ORDER BY
            type ASC,
            t ASC
    )
)
GROUP BY t
ORDER BY t ASC

// see issue 80 for the background

If you add a template variable of the type Query, you can write a ClickHouse 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.

SELECT hostname FROM host

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

SELECT event_name FROM event_log WHERE $timeFilterByColumn(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:

SELECT hostname AS __text, id AS __value FROM host

You can also create nested variables. For example if you had another variable named region. Then 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):

SELECT hostname FROM host WHERE region IN ($region)

If you are using templating to feed your predicate , you will face performance degradation when everything is selected as the predicate is not necessary. It's also true for textbox when nothing is enter , you have to write specific sql code to handle that.

To workaround this issue a new macro $conditionalTest(SQL Predicate,$variable) can be used to remove some part of the query. If the variable is type query with all selected or if the variable is a textbox with nothing enter , then the SQL Predicate is not included in the generated query.

To give an example: with 2 variables $var query with include All option $text textbox

The following query

 SELECT
   $timeSeries as t,
   count()
   FROM $table
   WHERE $timeFilter
    $conditionalTest(AND toLowerCase(column) in ($var),$var)
    $conditionalTest(AND toLowerCase(column2) like '%$text%',$text)
   GROUP BY t
   ORDER BY t

if the $var is all selected and the $text is empty , the query will be converted into

  SELECT
    $timeSeries as t,
    count()
     FROM $table
     WHERE $timeFilter
   GROUP BY t
   ORDER BY t

If $var have some element selected and the $text has at least one char , the query will be converted into

SELECT
    $timeSeries as t,
    count()
     FROM $table
     WHERE $timeFilter
   AND toLowerCase(column) in ($var)
   AND toLowerCase(column2) like '%$text%'
   GROUP BY t
   ORDER BY t

Remember that piechart plugin is not welcome for using in grafana - see https://grafana.com/blog/2015/12/04/friends-dont-let-friends-abuse-pie-charts

To create "Top 5" diagram we will need two queries: one for 'Top 5' rows and one for 'Other' row.

Top5:

SELECT
    1, /* fake timestamp value */
    UserName,
    sum(Reqs) AS Reqs
FROM requests
GROUP BY UserName
ORDER BY Reqs desc
LIMIT 5

Other:

SELECT
    1, /* fake timestamp value */
    UserName,
    sum(Reqs) AS Reqs
FROM requests
GROUP BY UserName
ORDER BY Reqs
LIMIT 5,10000000000000 /* select some ridiculous number after first 5 */

There are no any tricks in displaying time-series data. To print summary data, omit time column, and format the result as "Table".

SELECT
    UserName,
    sum(Reqs) as Reqs
FROM requests
GROUP BY
    UserName
ORDER BY 
    Reqs

To make vertical histogram from graph panel we will need to edit some settings:

• Display -> Draw Modes -> Bars
• Axes -> X-Axis -> Mode -> Series

And use next query:

$columns(
    Size,
    sum(Items) Items)
FROM some_table

// It is also possible to use query without macros

If you have a table with country/city codes:

SELECT
    1,
    CountryCode AS c,
    sum(requests) AS Reqs
FROM requests
GLOBAL ANY INNER JOIN
(
    SELECT Country country, CountryCode
    FROM countries
) USING (country)
WHERE $timeFilter
GROUP BY
    c
ORDER BY Reqs DESC

If you are using geohash set following options:

And make following query with Table formatting:

If there is an Ad-hoc variable, plugin will fetch all columns of all tables of all databases (except system database) as tags. So in dropdown menu will be options like database.table.column. If the default database is specified, it will only fetch tables and columns from that database, and the dropdown menu will have option like table.column. If there are ENUM columns, plugin will fetch their options and use them as tag values.

Plugin will apply Ad-hoc filters to all queries on the dashboard if their settings $database and $table are the same as Ad-hoc's database.table. If the ad-hoc filter doesn't specify table, it will apply to all queries regardless of the table. This is useful if the dashboard contains queries to multiple different tables.

There are no option to apply OR operator for multiple Ad-hoc filters - see grafana/grafana#10918

There are no option to use IN operator for Ad-hoc filters due to Grafana limitations

There may be cases when CH contains too many tables and columns so their fetching could take notably amount of time. And if you need to have multiple dashboards with different databases using of default database won't help. The best way to solve this will be to have parametrized ad-hoc variable in dashboard settings. Currently it's not supported by Grafana interface (see issue). As a temporary workaround, plugin will try to look for variable with name adhoc_query_filter and if it exists will use it's value as query to fetch columns. To do so we recommend to create some constant variable with name adhoc_query_filter and set value similar to following:

SELECT database, table, name, type FROM system.columns WHERE table='myTable' ORDER BY database, table

That should help to control data fetching by ad-hoc queries.

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

SELECT ClientID FROM events WHERE EventTime > $from AND EventTime < $to

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

Here are some provisioning example:

apiVersion: 1

datasources:
 - name: Clickhouse
   type: vertamedia-clickhouse-datasource
   access: proxy
   url: https://localhost:8123

   # <bool> enable/disable basic auth
   basicAuth:
   # <string> basic auth username
   basicAuthUser:
   # <string> basic auth password
   basicAuthPassword:
   # <bool> enable/disable with credentials headers
   withCredentials:
   # <bool> mark as default datasource. Max one per org
   isDefault:
   # <map> fields that will be converted to json and stored in json_data
   jsonData:
      # <bool> enable/disable sending 'add_http_cors_header=1' parameter
      addCorsHeader:
      # <bool> enable/disable using POST method for sending queries
      usePOST:
      # <string> default database name
      defaultDatabase:

Some settings and security params are the same for all datasources. You can find them here

Time series last point is not the real last point

Plugin extrapolates last datapoint if timerange is last N to avoid displaying of constantly decreasing graphs when timestamp in table is rounded to minute or bigger. If it so then in 99% cases last datapoint will be much less than previous one, because last minute is not finished yet. That's why plugin checks prev datapoints and tries to predict last datapoint value just as it was already written into db. This behavior could be turned off via "Extrapolation" checkbox in query editor.

Why no alerts support?

Alerts feature requires changes in Grafana's backend, which can be extended only for Grafana 6.5+. Grafana's maintainers are working on this feature. Current alerts support for clickhouse-grafana datasource in alpha.

There are following scripts defined in package.json:

• build:prod – production-ready build
• build:dev - development build (no uglify etc.)
• build:watch - automatically rebuilds code on change (handy while developing)
• test - runs test suite using Jest
• test:watch - runs test suite using Jest in watch mode. Automatically reruns tests on source change.

Each script can be run using NPM or Yarn package managers:

npm run <script>

or

yarn run <script>

(for example npm run build)

For test examples please see spec folder. We strongly encourage contributors to add tests to check new changes or functionality.

This is a simple demo which mounts the current dist directory inside the grafana container. The grafana container is connected to the docker clickhouse database container.

To run the development environment:

docker-compose up -d

after that open https://localhost:3000/ to open grafana instance with one clickhouse datasource

The frontend builder is the docker container used to transpile the typescript source code into the javascript found in the dist dir. This will affect the grafana query and configuration functionality.

To develop using docker, the process looks like:

1. change source files
2. docker-compose up frontend_builder
3. docker-compose restart grafana
4. open https://localhost:3000/

To develop without docker, the process looks like:

1. change source files
2. npm run test
3. npm run build:dev
4. docker-compose restart grafana
5. open https://localhost:3000/

The backend builder is the docker container used to compile the golang source code into the vertamedia-clickhouse-plugin_linux_amd64 binary in the dist dir. This will affect the grafana service used for running queries for alerting. The entrypoint for the go code is at plugin.go.

To develop using docker, the process looks like:

1. change source files
2. docker-compose up backend_builder
3. docker-compose restart grafana
4. open https://localhost:3000/

To format your go code, use the command:

docker-compose run --rm backend_builder go fmt .

The resulting alerts should look like this

If you have any idea for an improvement or found a bug do not hesitate to open an issue or submit a pull request. We will appreciate any help from the community which will make working with such amazing products as ClickHouse and Grafana more convenient.

Plugin creation was inspired by great grafana-sqldb-datasource

MIT License, please see LICENSE for details.