Using Microsoft SQL Server in Grafana

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

    A lower limit for the $__interval and 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

    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 MS SQL user with restricted permissions.

    Example:

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

    If you’re using an older version of Microsoft SQL Server like 2008 and 2008R2 you may need to disable encryption to be able to connect. If possible, we recommend you to use the latest service pack available for optimal compatibility.

    Query Editor

    Microsoft SQL Server - 图2

    You will find the MSSQL query editor in the metrics tab in Graph, Singlestat or Table panel’s edit mode. You enter edit mode by clicking the panel title, then edit. The editor allows you to define a SQL query to select data to be visualized.

    1. Select Format as Time series (for use in Graph or Singlestat panel’s among others) or Table (for use in Table panel among others).
    2. This is the actual editor where you write your SQL queries.
    3. Show help section for MSSQL below the query editor.
    4. Show actual executed SQL query. Will be available first after a successful query has been executed.
    5. Add an additional query where an additional query editor will be displayed.

    Macros

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

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

    The query editor has a link named Generated SQL that shows up after a query has been executed, while in panel edit mode. Click on it and it will expand and show the raw interpolated SQL string that was executed.

    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.

    Example database table:

    1. CREATE TABLE [event] (
    2.   time_sec bigint,
    3.   description nvarchar(100),
    4.   tags nvarchar(100),
    5. )
    1. CREATE TABLE [mssql_types] (
    2.   c_bit bit, c_tinyint tinyint, c_smallint smallint, c_int int, c_bigint bigint, c_money money, c_smallmoney smallmoney, c_numeric numeric(10,5),
    3.   c_real real, c_decimal decimal(10,2), c_float float,
    4.   c_char char(10), c_varchar varchar(10), c_text text,
    5.   c_nchar nchar(12), c_nvarchar nvarchar(12), c_ntext ntext,
    6.   c_datetime datetime,  c_datetime2 datetime2, c_smalldatetime smalldatetime, c_date date, c_time time, c_datetimeoffset datetimeoffset
    7. )
    9. INSERT INTO [mssql_types]
    10. SELECT
    11.   1, 5, 20020, 980300, 1420070400, '$20000.15', '£2.15', 12345.12,
    12.   1.11, 2.22, 3.33,
    13.   'char10', 'varchar10', 'text',
    14.   N'☺nchar12☺', N'☺nvarchar12☺', N'☺text☺',
    15.   GETDATE(), CAST(GETDATE() AS DATETIME2), CAST(GETDATE() AS SMALLDATETIME), CAST(GETDATE() AS DATE), CAST(GETDATE() AS TIME), SWITCHOFFSET(CAST(GETDATE() AS DATETIMEOFFSET), '-07:00')

    Query editor with example query:

    Microsoft SQL Server - 图4

    The query:

    1. SELECT * FROM [mssql_types]

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

    1. SELECT
    2.   c_bit as [column1], c_tinyint as [column2]
    3. FROM
    4.   [mssql_types]

    The resulting table panel:

    Microsoft SQL Server - 图6

    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.

    Example with metric column:

    1. SELECT
    2.   $__timeGroup(time_date_time, '5m') as time,
    3.   min("value_double"),
    4.   'min' as metric
    5. FROM test_data
    6. WHERE $__timeFilter(time_date_time)
    7. GROUP BY $__timeGroup(time_date_time, '5m')
    8. ORDER BY 1

    Data frame result:

    1. +---------------------+-----------------+
    2. | Name: time          | Name: min       |
    3. | Labels:             | Labels:         |
    4. | Type: []time.Time   | Type: []float64 |
    5. +---------------------+-----------------+
    6. | 2020-01-02 03:05:00 | 3               |
    7. | 2020-01-02 03:10:00 | 6               |
    8. +---------------------+-----------------+

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

    1. SELECT
    2.   $__timeGroup(createdAt, '5m', 0) as time,
    3.   sum(value) as value,
    5. FROM test_data
    6. WHERE
    7.   $__timeFilter(createdAt)
    8. GROUP BY
    9.   $__timeGroup(createdAt, '5m', 0),
    10. ORDER BY 1

    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 value of ${__field.labels.hostname}.

    Data frame result:

    1. +---------------------+---------------------------+---------------------------+
    2. | Name: time          | Name: value               | Name: value               |
    3. | Labels:             | Labels: hostname=10.0.1.1 | Labels: hostname=10.0.1.2 |
    4. | Type: []time.Time   | Type: []float64           | Type: []float64           |
    5. +---------------------+---------------------------+---------------------------+
    6. | 2020-01-02 03:05:00 | 3                         | 4                         |
    7. | 2020-01-02 03:10:00 | 6                         | 7                         |
    8. +---------------------+---------------------------+---------------------------+

    Example with multiple columns:

    Data frame result:

    1. +---------------------+-----------------+-----------------+
    2. | Name: time          | Name: min_value | Name: max_value |
    3. | Labels:             | Labels:         | Labels:         |
    4. | Type: []time.Time   | Type: []float64 | Type: []float64 |
    5. +---------------------+-----------------+-----------------+
    6. | 2020-01-02 03:04:00 | 3               | 4               |
    7. | 2020-01-02 03:05:00 | 6               | 7               |
    8. +---------------------+-----------------+-----------------+

    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.

    Check out the documentation 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 MS SQL 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.

    1. 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.

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

    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 allow 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:

    1. SELECT hostname __text, id __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):

    1. SELECT hostname FROM host WHERE region IN ($region)

    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:

    1. SELECT
    2.   atimestamp time,
    3.   aint value
    4. FROM table
    5. WHERE $__timeFilter(atimestamp) and hostname in($hostname)
    6. ORDER BY atimestamp

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

    1. SELECT
    2.   atimestamp as time,
    3.   aint as value
    4. FROM table
    5. WHERE $__timeFilter(atimestamp) and hostname in([[hostname]])
    6. ORDER BY atimestamp

    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'. Do disable quoting, use the csv formatting option for variables:

    ${servers:csv}

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

    Columns:

    Example database tables:

    1. CREATE TABLE [events] (
    2.   time_sec bigint,
    3.   description nvarchar(100),
    4.   tags nvarchar(100),
    5. )

    We also use the database table defined in .

    Example query using time column with epoch values:

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

    1. SELECT
    2.   time_sec as time,
    3.   time_end_sec as timeend,
    4.   description as [text],
    5.   tags
    6. FROM
    7.   [events]
    8. WHERE
    9.   $__unixEpochFilter(time_sec)
    10. ORDER BY 1

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

    1. SELECT
    2.   time,
    3.   measurement as text,
    4.   convert(varchar, valueOne) + ',' + convert(varchar, valueTwo) as tags
    5. FROM
    6.   metric_values
    7. WHERE
    8.   $__timeFilter(time_column)
    9. ORDER BY 1

    Stored procedure support

    Stored procedures have been verified to work. However, please note that we haven’t done anything special to support this, so there might be edge cases where it won’t work as you would expect. Stored procedures should be supported in table, time series and annotation queries as long as you use the same naming of columns and return data in the same format as describe above under respective section.

    Please note that any macro function will not work inside a stored procedure.

    Examples

    Microsoft SQL Server - 图8

    For the following examples, the database table is defined in . Let’s say that we want to visualize four series in a graph panel, such as all combinations of columns valueOne, valueTwo and measurement. Graph panel to the right visualizes what we want to achieve. To solve this, we need to use two queries:

    First query:

    1. SELECT
    3.   measurement + ' - value one' as metric,
    4.   avg(valueOne) as valueOne
    5. FROM
    6.   metric_values
    7. WHERE
    8.   $__timeFilter(time)
    9. GROUP BY
    10.   $__timeGroup(time, '5m'),
    11.   measurement
    12. ORDER BY 1

    Second query:

    1. SELECT
    2.   $__timeGroup(time, '5m') as time,
    3.   measurement + ' - value two' as metric,
    4.   avg(valueTwo) as valueTwo
    5. FROM
    6. GROUP BY
    7.   $__timeGroup(time, '5m'),
    8.   measurement
    9. ORDER BY 1

    Stored procedure using time in epoch format

    We can define a stored procedure that will return all data we need to render 4 series in a graph panel like above. In this case the stored procedure accepts two parameters @from and @to of int data types which should be a timerange (from-to) in epoch format which will be used to filter the data to return from the stored procedure.

    We’re mimicking the $__timeGroup(time, '5m') in the select and group by expressions, and that’s why there are a lot of lengthy expressions needed - these could be extracted to MS SQL functions, if wanted.

    1. CREATE PROCEDURE sp_test_epoch(
    2.   @from int,
    3.   @to     int
    4. )    AS
    5. BEGIN
    6.   SELECT
    7.     cast(cast(DATEDIFF(second, {d '1970-01-01'}, DATEADD(second, DATEDIFF(second,GETDATE(),GETUTCDATE()), time))/600 as int)*600 as int) as time,
    8.     measurement + ' - value one' as metric,
    9.     avg(valueOne) as value
    10.   FROM
    11.     metric_values
    12.   WHERE
    13.     time >= DATEADD(s, @from, '1970-01-01') AND time <= DATEADD(s, @to, '1970-01-01')
    14.   GROUP BY
    15.     cast(cast(DATEDIFF(second, {d '1970-01-01'}, DATEADD(second, DATEDIFF(second,GETDATE(),GETUTCDATE()), time))/600 as int)*600 as int),
    16.     measurement
    17.   UNION ALL
    18.   SELECT
    19.     cast(cast(DATEDIFF(second, {d '1970-01-01'}, DATEADD(second, DATEDIFF(second,GETDATE(),GETUTCDATE()), time))/600 as int)*600 as int) as time,
    20.     measurement + ' - value two' as metric,
    21.     avg(valueTwo) as value
    22.   FROM
    23.     metric_values
    24.   WHERE
    25.     time >= DATEADD(s, @from, '1970-01-01') AND time <= DATEADD(s, @to, '1970-01-01')
    26.   GROUP BY
    27.     cast(cast(DATEDIFF(second, {d '1970-01-01'}, DATEADD(second, DATEDIFF(second,GETDATE(),GETUTCDATE()), time))/600 as int)*600 as int),
    28.     measurement
    29.   ORDER BY 1
    30. END

    Then we can use the following query for our graph panel.

    1. DECLARE
    2.   @from int = $__unixEpochFrom(),
    3.   @to int = $__unixEpochTo()
    5. EXEC dbo.sp_test_epoch @from, @to

    Stored procedure using time in datetime format

    We can define a stored procedure that will return all data we need to render 4 series in a graph panel like above. In this case the stored procedure accepts two parameters @from and @to of datetime data types which should be a timerange (from-to) which will be used to filter the data to return from the stored procedure.

    We’re mimicking the $__timeGroup(time, '5m') in the select and group by expressions and that’s why there’s a lot of lengthy expressions needed - these could be extracted to MS SQL functions, if wanted.

    1. CREATE PROCEDURE sp_test_datetime(
    2.   @from datetime,
    3.   @to     datetime
    4. )    AS
    5. BEGIN
    6.   SELECT
    7.     cast(cast(DATEDIFF(second, {d '1970-01-01'}, time)/600 as int)*600 as int) as time,
    8.     measurement + ' - value one' as metric,
    9.     avg(valueOne) as value
    10.   FROM
    11.     metric_values
    12.   WHERE
    13.     time >= @from AND time <= @to
    14.   GROUP BY
    15.     cast(cast(DATEDIFF(second, {d '1970-01-01'}, time)/600 as int)*600 as int),
    16.     measurement
    17.   UNION ALL
    18.   SELECT
    19.     cast(cast(DATEDIFF(second, {d '1970-01-01'}, time)/600 as int)*600 as int) as time,
    20.     measurement + ' - value two' as metric,
    21.     avg(valueTwo) as value
    22.   FROM
    23.     metric_values
    24.   WHERE
    25.     time >= @from AND time <= @to
    26.   GROUP BY
    27.     cast(cast(DATEDIFF(second, {d '1970-01-01'}, time)/600 as int)*600 as int),
    28.     measurement
    29.   ORDER BY 1
    30. END

    Then we can use the following query for our graph panel.

    1. DECLARE
    2.   @from datetime = $__timeFrom(),
    3.   @to datetime = $__timeTo()
    5. EXEC dbo.sp_test_datetime @from, @to

    Alerting

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

    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