Examining Kusto Query Language (KQL) in Azure DevOps

Examining Kusto Query Language (KQL) in Azure DevOps

Kusto Query Language (KQL) is a powerful query language used to interact with data in Azure Log Analytics, which is a core part of Azure Monitor. It is designed for querying, analyzing, and visualizing data collected from various sources like application logs, infrastructure logs, metrics, and telemetry data. KQL is used to work with large datasets and allows for advanced analysis to uncover patterns, monitor performance, and troubleshoot issues.

When integrated with Azure DevOps, KQL can be used to query data about your CI/CD pipelines, builds, releases, test results, and other DevOps-related telemetry collected in Azure Monitor or Log Analytics.

Overview of Kusto Query Language (KQL)

KQL is a declarative query language that allows users to search, filter, and manipulate data using a simple yet powerful syntax. It is designed for high-performance, large-scale data analytics. KQL queries are optimized for speed and are ideal for large amounts of data collected from cloud applications, infrastructure, and services.

Key features of KQL:

1. Powerful query capabilities: Allows filtering, aggregation, joining, and complex data manipulations.

2. Data exploration: Provides a fast and interactive way to analyze data.

3. Rich syntax: Supports advanced operators, functions, and expressions for data transformation.

4. Real-time insights: Enables interactive analysis for monitoring and troubleshooting.

KQL in Azure DevOps

Azure DevOps provides several ways to use KQL within the context of DevOps pipelines, logs, and monitoring. Most commonly, KQL is used within Azure Log Analytics to analyze logs generated by your Azure DevOps environment, including pipeline runs, build statuses, test results, and other activities.

Key Use Cases of KQL in Azure DevOps:

1. Build and Release Pipeline Analytics: Track and analyze builds and releases, identifying patterns such as slow builds, test failures, and bottlenecks.

2. Test Results Analysis: Use KQL to analyze and filter test results, identify failing tests, and monitor the status of test execution across builds.

3. Performance Monitoring: Monitor application performance by querying telemetry data from Azure DevOps pipelines.

4. Security and Compliance: Query logs related to access, permissions, and security settings to ensure compliance.

Structure of a KQL Query

A typical KQL query follows a simple structure of a data source followed by a series of operators for data manipulation.

xxxxxxxxxx
4
1
<datatable>
2
| <operator1> <arguments>
3
| <operator2> <arguments>
4
| ...

Example:

Querying build logs in Log Analytics:

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4
1
AzureDevOpsBuilds
2
| where Status == "Failed"
3
| summarize FailedBuildsCount = count() by BuildID, ProjectName, RepoName
4
| order by FailedBuildsCount desc

This query does the following:

1. Searches the AzureDevOpsBuilds table for builds with a failed status.

2. Summarizes the count of failed builds by BuildID, ProjectName, and RepoName.

3. Orders the results by FailedBuildsCount in descending order.

Common KQL Operators and Functions

1. where: Filtering data

The where operator filters rows based on specified conditions.

Example: Filter logs for failed builds.

xxxxxxxxxx
2
1
AzureDevOpsBuilds
2
| where Status == "Failed"

2. project: Selecting specific columns

The project operator is used to select specific columns of data.

Example: Show only the BuildID, ProjectName, and Status.

xxxxxxxxxx
2
1
AzureDevOpsBuilds
2
| project BuildID, ProjectName, Status

3. summarize: Aggregating data

The summarize operator allows you to perform aggregation operations like count, sum, average, etc.

Example: Count the number of failed builds by project.

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3
1
AzureDevOpsBuilds
2
| where Status == "Failed"
3
| summarize Count = count() by ProjectName

4. extend: Creating calculated columns

The extend operator allows you to add new calculated columns based on existing columns.

Example: Calculate the duration of each build.

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1
AzureDevOpsBuilds
2
| extend BuildDuration = EndTime - StartTime
3
| project BuildID, BuildDuration

5. order by: Sorting data

The order by operator is used to sort data in ascending or descending order.

Example: Sort builds by their duration in descending order.

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3
1
AzureDevOpsBuilds
2
| extend BuildDuration = EndTime - StartTime
3
| order by BuildDuration desc

6. join: Combining multiple datasets

The join operator allows you to combine data from two tables based on common columns.

Example: Join build data with release data by BuildID.

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2
1
AzureDevOpsBuilds
2
| join kind=inner (AzureDevOpsReleases) on BuildID

7. top: Retrieving the top N results

The top operator is used to get the top N records based on a specific field (e.g., top 5 slowest builds).

Example: Get the top 5 longest-running builds.

xxxxxxxxxx
3
1
AzureDevOpsBuilds
2
| extend BuildDuration = EndTime - StartTime
3
| top 5 by BuildDuration desc

Examples of KQL Queries in Azure DevOps Monitoring

1. Analyzing Build Failures

You can use KQL to identify which builds have failed in your Azure DevOps pipelines, which can help in quickly pinpointing issues that need attention.

xxxxxxxxxx
4
1
AzureDevOpsBuilds
2
| where Status == "Failed"
3
| summarize FailedBuildsCount = count() by BuildID, ProjectName, RepoName
4
| order by FailedBuildsCount desc

This query gives you a summary of failed builds, grouped by BuildID, ProjectName, and RepoName.

2. Tracking Build Duration

If you want to analyze build durations, KQL allows you to compute the time between the StartTime and EndTime for each build and then order them by the longest duration.

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1
AzureDevOpsBuilds
2
| extend BuildDuration = EndTime - StartTime
3
| project BuildID, BuildDuration, ProjectName
4
| order by BuildDuration desc

3. Examining Test Failures

You can use KQL to analyze test results and identify failing tests across builds or releases.

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4
1
AzureDevOpsTestResults
2
| where TestResult == "Failed"
3
| summarize FailedTestsCount = count() by TestName, BuildID
4
| order by FailedTestsCount desc

This query finds all test failures, counts them per test and build, and orders them by the number of failures.

4. Performance Metrics of Releases

To analyze the performance of releases, you can query the data about release pipeline executions and measure metrics such as deployment times.

xxxxxxxxxx
4
1
AzureDevOpsReleases
2
| extend DeploymentDuration = EndTime - StartTime
3
| project ReleaseID, DeploymentDuration, ProjectName
4
| top 10 by DeploymentDuration desc

This query retrieves the top 10 longest-running deployments in your release pipeline, which is useful for identifying slow pipelines or inefficient stages.

Best Practices for Using KQL in Azure DevOps

1. Start Simple: Begin with simple queries and gradually add more complexity as you get comfortable with KQL.

2. Use Dashboards: Visualize your KQL queries in Azure Dashboards to provide real-time insights into your Azure DevOps pipelines.

3. Use Alerts: Set up alerts based on KQL query results (e.g., alert when builds fail or when specific performance thresholds are exceeded).

4. Leverage the KQL Query Explorer: In the Azure portal, the Query Explorer helps you craft queries by showing sample queries and suggesting operators.

Summary

Kusto Query Language (KQL) is an incredibly powerful tool for querying and analyzing data in Azure Monitor and Azure Log Analytics. By integrating KQL with Azure DevOps, teams can gain deep insights into their CI/CD pipelines, application performance, and infrastructure health. This allows for better decision-making, faster issue resolution, and more efficient DevOps processes.

Whether you are tracking build failures, analyzing test results, or optimizing pipeline performance, KQL provides the flexibility and scalability needed to analyze massive datasets and uncover critical insights in real-time.