To collect custom metrics with the Postgres integration, use the custom_queries option in the conf.d/postgres.d/conf.yaml file at the root of your Agent’s configuration directory. See the sample postgres.d/conf.yaml for more details.

Note: When generating custom metrics that require querying additional tables, you may need to grant the SELECT permission on those tables to the Postgres user. Example: grant SELECT on <TABLE_NAME> to <USER>;

Configuration

custom_queries has the following options:

metric_prefix

Required: Yes
Each metric starts with the chosen prefix.

query

Required: Yes
The SQL to execute. It can be a simple statement or a multi-line script. All of the rows of the results are evaluated. Use the pipe if you require a multi-line script.

columns

Required: Yes
A list representing each column ordered sequentially from left to right. There are 2 required pieces of data:
- name: The suffix to append to the metric_prefix to form the full metric name. If the type is specified as tag, the column is instead applied as a tag to every metric collected by this query.
- type: The submission method (for example, gauge, count, rate, etc.). This can also be set to tag to tag each metric in the row with the name and value (<name>:<row_value>) of the item in this column.

tags

Required: No
A list of static tags to apply to each metric.

Notes

• At least one of the items in defined columns should be a metric type (gauge, count, rate, etc.). For more information about metrics submission from an Agent Check, see Metrics Types.

• The number of items defined in columns must equal the number of columns returned in the query.

• The order in which the items in columns are defined must be same order returned in the query.

  custom_queries:
    - query: Select F3, F2, F1 from Table;
      columns:
        - {name: f3_metric_alias, type: gauge}
        - {name: f2_tagkey      , type: tag  }
        - {name: f1_metric_alias, type: count}
      [...]
  

Example

Database and table

Below is the company table from testdb database. The table contains 3 employee records:

testdb=# SELECT * FROM company;

id| name  | age| address    |salary | entry_date | last_raise_time
-------------------------------------------------------------------
1 | Paul  | 32 | California | 20000 | 1457570000 | 1457570300
2 | Allen | 25 | Texas      | 30000 | 1457570060 | 1457570300
3 | Teddy | 23 | Norway     | 45000 | 1457570120 | 1457570300

From a SQL query to the YAML configuration

The goal is to capture the age and salary of Paul as metric values with his name and address as tags.

SQL query:

SELECT age,salary,name,address FROM company WHERE name = 'Paul'

Corresponding custom_queries YAML configuration:

custom_queries:
  - metric_prefix: postgresql
    query: SELECT age,salary,name,address FROM company WHERE name = 'Paul'
    columns:
      - name: employee_age
        type: gauge
      - name: employee_salary
        type: gauge
      - name: name
        type: tag
      - name: localisation
        type: tag
    tags:
      - query:custom

After updating the Postgres YAML file, restart the Datadog Agent.

Validation

To verify the result, search for the metrics using the Metrics Explorer:

Debugging

Run the Agent’s status subcommand and look for postgres under the Checks section:

postgres
--------
  - instance #0 [ERROR]: 'Missing metric_prefix parameter in custom_queries'
  - Collected 0 metrics, 0 events & 0 service checks

Additionally, the Agent’s logs may provide useful information.

Further Reading

추가 유용한 문서, 링크 및 기사:

Postgres IntegrationDOCUMENTATION