Kusto Query Language (KQL): A Beginner’s Guide | Azure Data Explorer
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.
Brief history of KQL and its development
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.
Use cases and applications of KQL
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.
Basic KQL Queries
In this section, we will cover some of the basic KQL queries that you can use to retrieve and analyze data.
Selecting data using the Project
operator
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
Filtering data using the where
operator
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"
Sorting data using the order by
operator
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
Limiting data using the top
operator
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
Understanding data types and literals in KQL
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"
Advanced KQL Queries
In this section, we will cover some of the advanced KQL queries that you can use to retrieve and analyze data.
Joining tables using the join
operator
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>
Grouping data using the summarize
operator
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
Aggregating data using the extend
operator
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
Best Practices for Writing KQL Queries
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
Conclusion
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.
FAQs
- 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. - What is Azure Monitor?
- The Azure Monitor service offers thorough application and system monitoring, including metrics, logs, and alerts. - 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. - 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. - 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.
Originally published at https://cybercrip.com on May 16, 2023.