ClickHouse datasource for Grafana 4.6+
ClickHouse datasource plugin provides a support for ClickHouse as a backend database.
Install from grafana.net
- Access to CH via HTTP
- Query setup
- Raw SQL editor
- Query formatting
- Macros support
- Additional functions
- Table view
- SingleStat view
- Ad-hoc filters
Access to CH via HTTP
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
HTTPSaccess to ClickHouse as shown here to provide secure access.
- Limit concurrency and execution time for requests from
Grafanaas shown here to prevent
- Protection against request bursts for dashboards with numerous graphs.
CHProxyallows to queue requests and execute them sequentially. To learn more - read about params
max_queue_timeat CHProxy page.
- Response caching for the most frequent queries as shown here.
ClickHousefrom excessive refreshes and will be optimal option for popular dashboards.
Hint - if you need to cache requests like
last 24hwhere timestamp changes constantly then try to use
Query setup interface:
FROM contains two options: database and table. Table values depends on selected database.
Second row contains selectors for time filtering:
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
Go to Query is just a toggler to Raw SQL Editor
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". Require Column:Date and Column:DateTime or Column:TimeStamp to be selected
- $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:
$rate(cols...) - converts query results as "change rate per interval"
$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 )
$columns(key, value) - query values as array of [key, value], where key will be used as label
$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:
$rateColumns(key, value) - is a combination of $columns and $rate
$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 )
$perSecond(cols...) - converts query results as "change rate per interval" for Counter-like(growing only) metrics
$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
$perSecondColumns(key, value) - is a combination of $columns and $perSecond for Counter-like metrics
$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
Working with panels
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.
SELECT 1, /* fake timestamp value */ UserName, sum(Reqs) AS Reqs FROM requests GROUP BY UserName ORDER BY Reqs desc LIMIT 5
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
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
$table are the same
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
$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
Configure the Datasource with Provisioning
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: http://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.
Why no alerts support?
Alerts feature requires changes in
Grafana's backend, which can't be extended for now.
Grafana's maintainers are working on this feature.
The build works with either NPM or Yarn:
yarn run build
Tests can be run with Karma:
yarn run test
Since we developed this plugin only for internal needs we don't have some of Grafana's features:
- Alerts (this feature requires additional changes at backend and can't be solved by js-plugin)
We know that code quality needs a tons of improvements and unit-tests. We will continue working on 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.