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Tutorial: Transform text logs during ingestion in Azure Monitor Logs

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Ingestion-time transformations let you filter or modify incoming data before it's stored in a Log Analytics workspace. This article explains how to write a KQL query that transforms text log data and add the transformation to a data collection rule.

The procedure described here assumes you've already ingested some data from a text file, as described in Collect text logs with Azure Monitor Agent. In this tutorial, you'll:

  1. Write a KQL query to transform ingested data.
  2. Modify the target table schema.
  3. Add the transformation to your data collection rule.
  4. Verify that the transformation works correctly.

Prerequisites

To complete this procedure, you need:

  • Log Analytics workspace where you have at least contributor rights.
  • Data collection rule, data collection endpoint, and custom table, as described in Collect text logs with Azure Monitor Agent.
  • A VM, Virtual Machine Scale Set, or Arc-enabled on-premises server that writes logs to a text file. Text file requirements:
    • Store on the local drive of the machine on which Azure Monitor Agent is running.
    • Delineate with an end of line.
    • Use ASCII or UTF-8 encoding. Other formats such as UTF-16 aren't supported.
    • Don't allow circular logging, log rotation where the file is overwritten with new entries, or renaming where a file is moved and a new file with the same name is opened.

Write a KQL query to transform ingested data

  1. View the data in the target custom table in Log Analytics:

    1. In the Azure portal, select Log Analytics workspaces > your Log Analytics workspace > Logs.
    2. Run a basic query the custom logs table to view table data.

  2. Use the query window to write and test a query that transforms the raw data in your table.

    For information about the KQL operators that transformations support, see Structure of transformation in Azure Monitor.

    Note

    The only columns that are available to apply transforms against are TimeGenerated and RawData. Other columns are added to the table automatically after the transformation and are not available at the time of transformation. The _ResourceId column can't be used in the transformation.

    Example

    The sample uses basic KQL operators to parse the data in the RawData column into three new columns, called Time Ingested, RecordNumber, and RandomContent:

    • The extend operator adds new columns.
    • The project operator formats the output to match the columns of the target table schema:
    MyTable_CL
| extend d=todynamic(RawData)  
| project TimeGenerated,TimeIngested=tostring(d.Time), 
RecordNumber=tostring(d.RecordNumber), 
RandomContent=tostring(d.RandomContent), 
RawData

    Note

    Querying table data in this way doesn't actually modify the data in the table. Azure Monitor applies the transformation in the data ingestion pipeline after you add your transformation query to the data collection rule.

  3. Format the query into a single line and replace the table name in the first line of the query with the word source.

    For example:

    source | extend d=todynamic(RawData) | project TimeGenerated,TimeIngested=tostring(d.Time),RecordNumber=tostring(d.RecordNumber), RandomContent=tostring(d.RandomContent), RawData

  4. Copy the formatted query so you can paste it into the data collection rule configuration.

Modify the custom table to include the new columns

Add or delete columns in your custom table, based on your transformation query.

The example transformation query above adds three new columns of type string:

  • TimeIngested
  • RecordNumber
  • RandomContent

To support this transformation, add these three new columns to your custom table.

Screenshot of the Schema editor pane with the TimeIngested, RecordNumber, and RandomContent columns being defined.

Apply the transformation to your data collection rule

  1. On the Monitor menu, select Data Collection Rules > your data collection rule.

  2. Select Data sources > your data source.

  3. Paste the formatted transformation query in the Transform field on the Data source tab of the Add data source screen.

  4. Select Save.

    Screenshot of the Add data sources pane with the Transform field highlighted.

Check that the transformation works

View the data in the target custom table and check that data is being ingested correctly into the modified table:

  1. In the Azure portal, select Log Analytics workspaces > your Log Analytics workspace > Logs.
  2. Run a basic query the custom logs table to view table data.

Next steps

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