Kusto Query Language (KQL): A Beginner’s Guide | Azure Data Explorer (2024)

Large datasets in Azure Data Explorer (ADX) and Azure Monitor can be accessed using the potent query language KQL. Data from logs, metrics, and traces are just a few of the sources that can be retrieved and analyzed using KQL. You may quickly and effectively filter, group, and aggregate data with KQL to learn more about your applications and systems.

Microsoft created KQL as a component of the Azure Data Explorer service. It was created to offer a strong and adaptable query language that can be used to instantly analyze huge datasets. Since then, KQL has been incorporated into further Microsoft services, such as Azure Monitor.

KQL is utilized in a wide range of fields and contexts, such as e-commerce, banking, security, and IT operations. KQL makes it simple and quick to analyze massive amounts of data in order to find anomalies, address problems, and improve performance. To manage and monitor your systems and apps, you may also utilize KQL to generate personalized dashboards and alerts.

In this section, we will cover some of the basic KQL queries that you can use to retrieve and analyze data.

The Project operator is used to select specific columns from a table. For example, the following query selects the Timestamp and Level columns from the MyTable table:

MyTable 
| project Timestamp, Level

The where operator is used to filter data based on specific conditions. For example, the following query filters the MyTable table to only include rows where the Level column is equal to Error:

MyTable 
| where Level == "Error"

The order by operator is used to sort data based on specific columns. For example, the following query sorts the MyTable table by the Timestamp column in ascending order:

MyTable 
| order by Timestamp asc

The top operator is used to limit the number of rows returned by a query. For example, the following query returns the top 10 rows from the MyTable table:

MyTable 
| top 10

KQL supports various data types, including string, integer, real, datetime, and timespan. KQL also supports literals, which are values that are explicitly specified in a query. For example, the following query specifies a string literal:

MyTable 
| where Level == "Error"

In this section, we will cover some of the advanced KQL queries that you can use to retrieve and analyze data.

The join operator is used to combine data from two or more tables. For example, the following query joins the MyTable and OtherTable tables based on the Id column:

MyTable 
| join OtherTable on Id</code>

The summarize operator is used to group data based on specific columns and calculate aggregate functions, such as count, avg, max, min, and sum. For example, the following query groups the MyTable table by the Level column and calculates the count of each level:

MyTable 
| summarize count() by Level

The extend operator is used to add new columns to a table based on calculations performed on existing columns. For example, the following query adds a new column called ResponseTimeInSeconds to the MyTable table by converting the ResponseTime column from milliseconds to seconds:MyTable
| extend ResponseTimeInSeconds = ResponseTime / 1000

MyTable
| extend ResponseTimeInSeconds = ResponseTime / 1000

The timechart operator is used to visualize data over time. For example, the following query groups the MyTable table by the Level column and displays the count of each level over time:

MyTable 
| summarize count() by Level, bin(Timestamp, 1h) 
| timechart

Here are some best practices to keep in mind when writing KQL queries:

• Use comments to document your queries and make them more readable
• Use variables to simplify complex queries and make them more reusable
• Use the top operator to limit the amount of data returned by a query
• Use the project operator to select only the columns you need
• Use the where operator to filter data as early as possible in the query
• Use the summarize operator to group and aggregate data
• Use the extend operator to add calculated columns to a table
• Use the order by operator to sort data as needed

Large datasets in Azure Data Explorer (ADX) and Azure Monitor can be accessed using the potent query language KQL. You may quickly and effectively filter, group, and aggregate data with KQL to learn more about your applications and systems. You can create KQL queries that are effective and efficient and offer insightful information about your data by adhering to the best practises described in this tutorial.

1. What is Azure Data Explorer (ADX)?
   - For real-time analysis of enormous volumes of data coming from applications, websites, IoT devices, and more, Azure Data Explorer (ADX) provides a quick, fully managed data analytics solution.
2. What is Azure Monitor?
   - The Azure Monitor service offers thorough application and system monitoring, including metrics, logs, and alerts.
3. Can KQL be used with other data sources besides Azure Data Explorer and Azure Monitor?
   - Yes, KQL may be used with different types of data sources, such as SQL databases, CSV files, and log files.
4. Is KQL difficult to learn for beginners?
   - KQL is straightforward to learn for novices with some programming knowledge because of its comparatively basic syntax. To assist you in getting started, Microsoft offers detailed guidance and examples.
5. Can KQL be used for real-time data analysis?
   - Yes, KQL is made for real-time data analysis and is capable of handling enormous amounts of streaming data.