Azure<\/a> generates huge amounts of log and telemetry data across services and resources. This data holds valuable insights on the health and performance of your Azure environment. However, making sense of such large volumes of data is challenging. <\/p>\n\n\n\nKQL provides a powerful and flexible query language that can be used to analyze and derive insights from the massive amounts of log and telemetry data in Azure. KQL is a read-only language, so you don\u2019t need to think about INSERT<\/strong>, DELETE<\/strong>, UPDATE<\/strong> and so on. <\/p>\n\n\n\nCheck out this example to show all Azure blobs<\/p>\n\n\n\n
<\/circle><\/circle><\/circle><\/g><\/svg><\/span><\/path><\/path><\/svg><\/span>\/\/<\/span> Show blob storage requests<\/span><\/span>\nStorageBlobLogs<\/span><\/span><\/code><\/pre><\/div>\n\n\n\nPretty amazing, huh! <\/p>\n\n\n\n
Here are some key reasons why KQL is crucial for Azure monitoring:<\/p>\n\n\n\n
\nFast and scalable<\/strong>: KQL is designed for massive data volumes. Queries execute quickly over terabytes of data. This enables analyzing data in near real-time.<\/li>\n\n\n\nPowerful analytics<\/strong>: KQL includes abilities like aggregation, joins, ranking, and more that make analysis easier. You can get started with simple queries and evolve to build advanced analytics.<\/li>\n\n\n\nCustomizable<\/strong>: KQL is customizable to augment built-in functionality with user-defined functions, custom aggregations, and more.<\/li>\n<\/ul>\n\n\n\n<\/span>1. Centralized Data Access<\/span><\/h3>\n\n\n\nAzure services generate an abundance of logs, metrics, and telemetry data. KQL provides a unified language to query this data, enabling you to access information from various sources in one place.<\/p>\n\n\n\n
KQL includes abilities like aggregation, joins, ranking, and more that make analysis easier. You can get started with simple queries and evolve to build advanced analytics.<\/p>\n\n\n\n
<\/span>2. Real-time Insights<\/span><\/h3>\n\n\n\nKQL allows you to perform real-time analysis on your Azure resources. You can quickly identify and respond to issues, making it an invaluable tool for maintaining system health.<\/p>\n\n\n\n
KQL is designed for massive data volumes. Queries execute quickly over terabytes of data. This enables analyzing data in near real-time.<\/p>\n\n\n\n
<\/span>3. Customized Analytics<\/span><\/h3>\n\n\n\nKQL is incredibly flexible. You can write custom queries to gain deep insights into your specific Azure resources and applications, tailoring your monitoring to your organization’s unique needs.<\/p>\n\n\n\n
KQL is customizable to augment built-in functionality with user-defined functions, custom aggregations, and more.<\/p>\n\n\n\n
<\/span>4. Unified query language<\/strong><\/span><\/h3>\n\n\n\nAzure Monitor uses KQL as its query language, making it a seamless choice for monitoring Azure resources. KQL provides a common syntax and semantics that works across various Azure data sources like Log Analytics, Application Insights, etc.<\/p>\n\n\n\n
It’s tightly integrated with Azure services like Log Analytics, Application Insights, and Security Center. This avoids having to learn different query languages.<\/p>\n\n\n\n
Now that we understand why KQL is essential, let’s explore some practical examples of how to utilize it effectively for monitoring Azure services and resources.<\/p>\n\n\n\n
<\/span>Real KQL Examples<\/span><\/h2>\n\n\n\nImportant thing to note in KQL is the | (pipe) element. KQL queries use pipe as symbol to delineate each operation. Additionally the operations are executed in the order they are written. <\/p>\n\n\n\n
Simply put, the data set is manipulated at each step, and then the resulting set is \u201cpiped\u201d to the next step. <\/p>\n\n\n\n
<\/span>Example 1: Querying Azure Activity Logs<\/span><\/h3>\n\n\n\nAzure Activity Logs record all operations performed on resources in your Azure subscription. You can use KQL to query these logs to track changes and audit activities. Here’s an example:<\/p>\n\n\n\n
<\/circle><\/circle><\/circle><\/g><\/svg><\/span><\/path><\/path><\/svg><\/span>AzureActivity<\/span><\/span>\n| <\/span>where<\/span> ResourceGroup <\/span>==<\/span> <\/span>"YourResourceGroup"<\/span> <\/span>and<\/span> OperationName <\/span>==<\/span> <\/span>"Microsoft.Compute\/virtualMachines\/delete"<\/span><\/span>\n| project <\/span>Resource<\/span>, <\/span>Caller<\/span>, ActivityStatus, ActivityDateTime<\/span><\/span>\n| <\/span>order by<\/span> ActivityDateTime <\/span>desc<\/span><\/span><\/code><\/pre><\/div>\n\n\n\nThis query retrieves information about deleted virtual machines in a specific resource group, including the user who initiated the operation and the timestamp.<\/p>\n\n\n\n
<\/span>Example 2: Analyzing Azure Metrics<\/span><\/h3>\n\n\n\nAzure Monitor Metrics provide data on the performance of your Azure resources. You can use KQL to analyze these metrics and create custom alerts. Here’s an example querying CPU usage for a virtual machine:<\/p>\n\n\n\n
<\/circle><\/circle><\/circle><\/g><\/svg><\/span><\/path><\/path><\/svg><\/span>Perf<\/span><\/span>\n| <\/span>where<\/span> ObjectName <\/span>==<\/span> <\/span>"Processor"<\/span> <\/span>and<\/span> CounterName <\/span>==<\/span> <\/span>"% Processor Time"<\/span> <\/span>and<\/span> InstanceName <\/span>==<\/span> <\/span>"_Total"<\/span><\/span>\n| summarize <\/span>avg<\/span>(CounterValue) <\/span>by<\/span> bin(TimeGenerated, 1h), Computer<\/span><\/span>\n| render timechart<\/span><\/span><\/code><\/pre><\/div>\n\n\n\nThis query plots a time chart of the average CPU usage for all virtual machines in your environment over the last hour.<\/p>\n\n\n\n
<\/span>Example 3: Identifying Security Threats<\/span><\/h3>\n\n\n\nAzure Security Center generates security alerts and recommendations. You can leverage KQL to investigate and respond to security threats. Here’s a simple query to find high-severity security alerts:<\/p>\n\n\n\n
<\/circle><\/circle><\/circle><\/g><\/svg><\/span><\/path><\/path><\/svg><\/span>SecurityAlert<\/span><\/span>\n| <\/span>where<\/span> Severity <\/span>==<\/span> <\/span>"High"<\/span><\/span>\n| project TimeGenerated, AlertName, ResourceName, ResourceGroup, <\/span>Status<\/span><\/span><\/code><\/pre><\/div>\n\n\n\nThis query lists high-severity security alerts, along with relevant information like the resource name and alert status.<\/p>\n\n\n\n
<\/span>Example 4: Query Application Insights data<\/strong><\/span><\/h3>\n\n\n\n<\/circle><\/circle><\/circle><\/g><\/svg><\/span><\/path><\/path><\/svg><\/span>app(<\/span>'myApplication'<\/span>).requests<\/span><\/span>\n| summarize requestCount<\/span>=<\/span>count<\/span>(), durationAvg<\/span>=<\/span>avg<\/span>(duration) <\/span>by<\/span> <\/span>name<\/span><\/span>\n| <\/span>order by<\/span> requestCount <\/span>desc<\/span><\/span><\/code><\/pre><\/div>\n\n\n\nThis query fetches HTTP request data from an Application Insights application, calculates the count and average duration for each request name and sorts the results by highest count.<\/p>\n\n\n\n
<\/span>Example 5: Analyze Azure activity logs<\/span><\/h3>\n\n\n\n<\/circle><\/circle><\/circle><\/g><\/svg><\/span><\/path>