SQL 🔗

Caution

Smart Agent monitors are being deprecated. To collect SQL metrics use the native OpenTelemetry SQL Query receiver component.

The SQL monitor gathers database usage metrics from SQL queries on your databases. It’s available for Kubernetes, Windows, and Linux.

Configuration 🔗

To use this integration of a Smart Agent monitor with the Collector:

Include the Smart Agent receiver in your configuration file.
Add the monitor type to the Collector configuration, both in the receiver and pipelines sections.

See how to Use Smart Agent monitors with the Collector.
See how to set up the Smart Agent receiver.
For a list of common configuration options, refer to Common configuration settings for monitors.
Learn more about the Collector at Get started: Understand and use the Collector.

Example 🔗

To activate this integration, add the following to your Collector configuration:

receivers:
  smartagent/sql:
    type: sql
    ...  # Additional config

Next, add the monitor to the service.pipelines.metrics.receivers section of your configuration file:

service:
  pipelines:
    metrics:
      receivers: [smartagent/sql]

Configuration settings 🔗

The following tables show the configuration options for this monitor:

Option	Required	Type	Description
`queries`	Yes	`list of objects (see below)`	A list of queries that generate data points.
`host`	No	`string`	Host or address of the SQL instance.
`port`	No	`integer`	Port of the SQL instance. The default value is `0`.
`params`	No	`map of strings`	Replaceable parameters, in the form of key-value pairs. The system inserts the values into `connectionString` for a specified key, using Go template syntax. For example, `{{.key}}`.
`dbDriver`	No	`string`	The database driver to use. Valid values are `postgres`, `mysql`, `sqlserver`, and `snowflake`.
`connectionString`	No	`string`	Connection string and replaceable parameters used to connect to the database. To learn more, see the list of connection string parameters for the Go `pq` package.
`logQueries`	No	`bool`	(default: `false`) If true, log query results infolevel.

The nested queries configuration object has the following fields:

Option	Required	Type	Description
`query`	Yes	`string`	An SQL query text that selects one or more rows from a database.
`params`	No	`list of values`	Optional parameters that replace placeholders in the query string.
`metrics`	No	`list of objects (see below)`	Metrics generated from the query.
`datapointExpressions`	No	`list of strings`	A set of expressions that convert each row to a set of metrics. Each of these run for each row in the query result set, allowing you to generate multiple data points per row. Each expression must evaluate to a single data point or nil.

The nested metrics configuration object has the following fields:

Option	Required	Type	Description
`metricName`	Yes	`string`	The name of the metric as it appears in Splunk Observability Cloud.
`valueColumn`	Yes	`string`	The column name that holds the data point value.
`dimensionColumns`	No	`list of strings`	The names of the columns that make up the dimensions of the data point.
`isCumulative`	No	`bool`	Whether the value is a cumulative counters (true) or gauge (false). If you set this to the wrong value and send in your first data point for the metric name with the wrong type, you have to manually change the type, as it is set in the system based on the first type seen. The default value is `false`.
`dimensionPropertyColumns`	No	`map of lists`	Mapping between dimensions and the columns to be used to attach corresponding properties.

Supported drivers 🔗

You must specify the dbDriver option that contains the name of the database driver to use. These names are the same as the name of the Golang SQL driver used in the agent. The monitor formats the connectionString according to the driver you specify.

Note

Please be sure to use the correct connection string syntax based on the driver you’re using. For example, if you use the mysql driver, you must use the connection string syntax for the mysql driver.

This is the list of the drivers currently supported:

hana.
sqlserver.
mysql.
postgres.
pq.
snowflake.

Parameterized connection string 🔗

The integration treats the value of connectionString as a Golang template with a context consisting of the variables host and port and all the parameters from the params option. To add a variable to the template, use the Golang {{.varname}} template syntax.

See the following example:

smartagent/sql:
  type: sql
  host: localhost
  port: 1433
  dbDriver: sqlserver
  connectionString: 'Server=127.0.0.1;Database=WideWorldImporters;User Id=sa;Password=123456;'
  queries:
    - query: 'SELECT COUNT(*) as count FROM Sales.Orders'
      metrics:
        - metricName: "orders"
          valueColumn: "count"

Collect Snowflake performance and usage metrics 🔗

To configure the agents to collect Snowflake performance and usage metrics, do the following:

Copy the pkg/sql/snowflake-metrics.yaml file from the sql monitor repo into the same location as your agent.yaml file. For example, /etc/splunk. Find the latest version of snowflake-metrics.yaml in our GitHub repo.
Configure the SQL monitor as follows:

receivers:
  smartagent/sql:
    type: sql
    intervalSeconds: 3600
    dbDriver: snowflake
    params:
      account: "account.region"
      database: "SNOWFLAKE"
      schema: "ACCOUNT_USAGE"
      role: "ACCOUNTADMIN"
      user: "user"
      password: "password"
    connectionString: "{{.user}}:{{.password}}@{{.account}}/{{.database}}/{{.schema}}?role={{.role}}"
    queries:
      {"#from": "/etc/signalfx/snowflake-metrics.yaml"}

You can also copy the contents of snowflake-metrics.yaml into agent.yaml under queries. Edit snowflake-metrics.yaml to only include the metrics you want to monitor.

Using the monitor 🔗

Consider the following customers database table:

id	name	country	status
1	Bill	USA	active
2	Mary	USA	inactive
3	Joe	USA	active
4	Elizabeth	Germany	active

Use the following monitor configuration to generate metrics about active users and customer counts by country:

receivers:
  smartagent/sql:
    type: sql
    host: localhost
    port: 5432
    dbDriver: postgres
    params:
      user: "${env:SQL_USERNAME}"
      password: "${env:SQL_PASSWORD}"
    # The `host` and `port` values shown in this example (also provided through autodiscovery) are interpolated
    # to the connection string as appropriate for the database driver.
    # Also, the values from the `params` configuration option above can be
    # interpolated.
    connectionString: 'host={{.host}} port={{.port}} dbname=main user={{.user}} password={{.password}} sslmode=disable'
    queries:
      - query: 'SELECT COUNT(*) as count, country, status FROM customers GROUP BY country, status;'
        metrics:
          - metricName: "customers"
            valueColumn: "count"
            dimensionColumns: ["country", "status"]

When you use this configuration, you get series of MTS, all with the metric name customers. Each MTS has a county and status dimension. The dimension value is the number of customers that belong to that combination of country and status. You can also specify multiple metrics items to generate more than one metric from a single query.

Using metric expressions 🔗

If you need to do more complex logic than mapping columns to metric values and dimensions, use the datapointExpressions option that’s available for individual metric configurations. Create more sophisticated logic to derive data points from individual rows by using the expr expression language. These expressions must evaluate to data points created by the GAUGE or CUMULATIVE helper functions available in the expression’s context. You can also have the expression evaluate to nil if you don’t need to generate a data point for a particular row.

Both the GAUGE and CUMULATIVE functions have the following signature:

(metricName, dimensions, value)

metricName: Must be a string
dimensions: Must be a map of string keys and values, and
value: Must be a numeric value.

Each of the columns in the row maps to a variable in the context of the expression with the same name. For example, if you have a column called name in your SQL query result, you can use a variable called name in the expression. In your expression, surround string values with single quotes ('').

Metrics 🔗

This integration doesn’t produce any metrics.

Troubleshooting 🔗

If you are a Splunk Observability Cloud customer and are not able to see your data in Splunk Observability Cloud, you can get help in the following ways.

Available to Splunk Observability Cloud customers

Submit a case in the Splunk Support Portal .
Contact Splunk Support .

Available to prospective customers and free trial users

Ask a question and get answers through community support at Splunk Answers .
Join the Splunk #observability user group Slack channel to communicate with customers, partners, and Splunk employees worldwide. To join, see Chat groups in the Get Started with Splunk Community manual.

This page was last updated on Dec 09, 2024.

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