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Collect Microsoft SQL Server Logs and Metrics for Kubernetes environments

This page provides instructions to Collect Microsoft SQL Server Logs and Metrics for Kubernetes environments.

In a Kubernetes environment, we use the Telegraf Operator, which is packaged with our Kubernetes collection. You can learn more about it here.The diagram below illustrates how data is collected from SQL Server in Kubernetes environments. In the architecture shown below, there are four services that make up the metric collection pipeline: Telegraf, Prometheus, Fluentd and FluentBit.

The first service in the pipeline is Telegraf. Telegraf collects metrics from SQL Server. Note that we’re running Telegraf in each pod we want to collect metrics from as a sidecar deployment: i.e. Telegraf runs in the same pod as the containers it monitors. Telegraf uses the SQL Server input plugin to obtain metrics. (For simplicity, the diagram doesn’t show the input plugins.) The injection of the Telegraf sidecar container is done by the Telegraf Operator. We also have Fluentbit that collects logs written to standard out and forwards them to FluentD, which in turn sends all the logs and metrics data to a Sumo Logic HTTP Source.

Follow the below instructions to set up the metric collection:

  1. Configure Metrics Collection

    1. Setup Kubernetes Collection with the Telegraf operator

    2. Add annotations on your SQL Server pods

  2. Configure Logs Collection

    1. Configure logging in SQL Server.

    2. Add labels on your SQL Server pods to capture logs from standard output.

    3. Collecting SQL Server Logs from a Log file.

Prerequisites

It’s assumed that you are using the latest helm chart version if not upgrade using the instructions here.

Step 1 Configure Metrics Collection

This section explains the steps to collect SQL Server metrics from a Kubernetes environment.

In a Kubernetes environment, we use the Telegraf Operator, which is packaged with our Kubernetes collection. You can learn more on this here. Follow the steps listed below to collect metrics from a Kubernetes environment:

  1. Setup Kubernetes Collection with the Telegraf Operator.

  2. Add annotations on your SQL Server pods

Before you add annotations, you need to create a login on every SQL Server pod  you want to monitor, with following script:

USE master;
GO
CREATE LOGIN [Username_CHANGE_ME] WITH PASSWORD=N'Password_CHANGE_ME';
GO
GRANT VIEW SERVER STATE TO [Username_CHANGE_ME];
GO
GRANT VIEW ANY DEFINITION TO [Username_CHANGE_ME];
GO

On your SQL Server Pods, add the following annotations:

  annotations:
    telegraf.influxdata.com/class: sumologic-prometheus
    prometheus.io/scrape: "true"
    prometheus.io/port: "9273"
    telegraf.influxdata.com/inputs: |+
[[inputs.sql server]]
    servers = [“Server=<IP_CHANGE_ME>;Port=<Port_CHANGE_ME| default 1433>;User Id=<Username_CHANGE_ME>;Password=<Password_CHANGE_ME>;app name=telegraf;log=1;",]
   database_type = "SQLServer"
   exclude_query =  [ 'SQLServerSchedulers' , 'SQLServerRequests']
     [inputs.sqlserver.tags]
    environment="prod"
    component="database"
    db_cluster="sqlserver_on_k8s"
    db_system = "sqlserver"

Please enter in values for the following parameters (marked in bold above):

  • telegraf.influxdata.com/inputs - This contains the required configuration for the Telegraf SQL Server Input plugin. Please refer to this doc for more information on configuring the SQL Server input plugin for Telegraf. Note: As telegraf will be run as a sidecar the host should always be localhost.

    • In the input plugins section i.e. : 

      • servers - The URL to the SQLserver server. This can be a comma-separated list to connect to multiple SQLserver servers. Please see this doc for more information on additional parameters for configuring the SQLserver input plugin for Telegraf.

    • In the tags section i.e.  [inputs.sqlserver.tags]

      • environment - This is the deployment environment where the SQLserver cluster identified by the value of servers resides. For example: dev, prod or qa. While this value is optional we highly recommend setting it. 

      • db_cluster - Enter a name to identify this SQLserver cluster. This cluster name will be shown in the Sumo Logic dashboards. 

Here’s an explanation for additional values set by this configuration that we request you please do not modify as they will cause the Sumo Logic apps to not function correctly.

  • telegraf.influxdata.com/class: sumologic-prometheus - This instructs the Telegraf operator what output to use. This should not be changed.

  • prometheus.io/scrape: "true" - This ensures our Prometheus will scrape the metrics.

  • prometheus.io/port: "9273" - This tells prometheus what ports to scrape on. This should not be changed.

  • telegraf.influxdata.com/inputs

    • In the tags section i.e.  [inputs.sqlserver.tags]

      • component: “database” - This value is used by Sumo Logic apps to identify application components. 

      • db_system: “sqlserver” - This value identifies the database system.

For all other parameters please see this doc for more properties that can be configured in the Telegraf agent globally.

  1. Sumo Logic Kubernetes collection will automatically start collecting metrics from the pods having the labels and annotations defined in the previous step. 

  2. Verify metrics in Sumo Logic.

Step 2 Configure Logs Collection

This section explains the steps to collect SQL Server logs from a Kubernetes environment.

  1. (Recommended Method) Add labels on your SQL server pods to capture logs from standard output.

Make sure that the logs from SQL Server are sent to stdout. Follow the instructions below to capture SQL Server logs from stdout on Kubernetes.

  1. Apply following labels to the SQL server pods:

 labels:

    environment: "prod_CHANGE_ME"

    component: "database"

    db_system: "SQLserver"

    db_cluster: "SQLserver_prod_CHANGE_ME

Please enter in values for the following parameters (marked in bold and CHANGE_ME above):

  • environment - This is the deployment environment where the SQL server cluster identified by the value of servers resides. For example: dev, prod or qa. While this value is optional we highly recommend setting it.

  • db_cluster - Enter a name to identify this SQL server cluster. This cluster name will be shown in the Sumo Logic dashboards.

Here’s an explanation for additional values set by this configuration that we request you please do not modify as they will cause the Sumo Logic apps to not function correctly.

  • component: “database” - This value is used by Sumo Logic apps to identify application components. 

  • db_system: “SQLserver” - This value identifies the database system.

For all other parameters please see this doc for more properties that can be configured in the Telegraf agent globally.

  1. The Sumologic-Kubernetes-Collection will automatically capture the logs from stdout and will send the logs to Sumologic. For more information on deploying Sumologic-Kubernetes-Collection, visit here.

  2. Verify logs in Sumo Logic.

  1. (Optional) Collecting SQL server Logs from a Log File
    Follow the  steps below to capture SQL server logs from a log file on Kubernetes.

  1. Determine the location of the SQL server log file on Kubernetes. This can be determined from the SQLserver.conf for your SQL server cluster along with the mounts on the SQL server pods.

  2. Install the Sumo Logic tailing sidecar operator.

  3. Add the following annotation in addition to the existing annotations.

  1. Make sure that the SQL server pods are running and annotations are applied by using the command: kubectl describe pod <SQLserver_pod_name>

  2. Sumo Logic Kubernetes collection will automatically start collecting logs from the pods having the annotations defined above. 

  3. Verify logs in Sumo Logic.

  1. Add an FER to normalize the fields in Kubernetes environments
    Labels created in Kubernetes environments automatically are prefixed with pod_labels. To normalize these for our app to work, we need to create a Field Extraction Rule if not already created for Proxy Application Components. To do so:

  1. Go to Manage Data > Logs > Field Extraction Rules.

  2. Click the + Add button on the top right of the table.

  3. The following form appears:

  1. Enter the following options:

  • Rule Name. Enter the name as App Observability - Proxy.

  • Applied At. Choose Ingest Time

  • Scope. Select Specific Data

    • Scope: Enter the following keyword search expression:
      pod_labels_environment=* pod_labels_component=database
      pod_labels_db_system=*
      pod_labels_db_cluster=*

  • Parse Expression.Enter the following parse expression:

if (!isEmpty(pod_labels_environment), pod_labels_environment, "") as environment

| pod_labels_component as component
| pod_labels_db_system as db_system
| pod_labels_db_cluster as db_cluster

  1. Click Save to create the rule.