Collect logs from a JSON file with Azure Monitor Agent
Custom JSON Logs is one of the data sources used in a data collection rule (DCR). Details for the creation of the DCR are provided in Collect data with Azure Monitor Agent. This article provides additional details for the text and JSON logs type.
Many applications and services will log information to a JSON files instead of standard logging services such as Windows Event log or Syslog. This data can be collected with Azure Monitor Agent and stored in a Log Analytics workspace with data collected from other sources.
Prerequisites
- Log Analytics workspace where you have at least contributor rights.
- A data collection endpoint (DCE) in the same region as the Log Analytics workspace. See How to set up data collection endpoints based on your deployment for details.
- Either a new or existing DCR described in Collect data with Azure Monitor Agent.
Basic operation
The following diagram shows the basic operation of collecting log data from a json file.
- The agent watches for any log files that match a specified name pattern on the local disk.
- Each entry in the log is collected and sent to Azure Monitor. The incoming stream defined by the user is used to parse the log data into columns.
- A default transformation is used if the schema of the incoming stream matches the schema of the target table.
JSON file requirements and best practices
The file that the Azure Monitor Agent is monitoring must meet the following requirements:
- The file must be stored on the local drive of the machine with the Azure Monitor Agent in the directory that is being monitored.
- Each record must be delineated with an end of line.
- The file must use ASCII or UTF-8 encoding. Other formats such as UTF-16 aren't supported.
- New records should be appended to the end of the file and not overwrite old records. Overwriting will cause data loss.
- JSON text must be contained in a single row. The JSON body format is not supported. See sample below.
Adhere to the following recommendations to ensure that you don't experience data loss or performance issues:
- Create a new log file every day so that you can easily clean up old files.
- Continuously clean up log files in the monitored directory. Tracking many log files can drive up agent CPU and Memory usage. Wait for at least 2 days to allow ample time for all logs to be processed.
- Don't rename a file that matches the file scan pattern to another name that also matches the file scan pattern. This will cause duplicate data to be ingested.
- Don't rename or copy large log files that match the file scan pattern into the monitored directory. If you must, do not exceed 50MB per minute.
Custom table
Before you can collect log data from a JSON file, you must create a custom table in your Log Analytics workspace to receive the data. The table schema must match the columns in the incoming stream, or you must add a transformation to ensure that the output schema matches the table.
Warning
You shouldn't use an existing custom table used by Log Analytics agent. The legacy agents won't be able to write to the table once the first Azure Monitor agent writes to it. Create a new table for Azure Monitor agent to use to prevent Log Analytics agent data loss.
For example, you can use the following PowerShell script to create a custom table with multiple columns.
$tableParams = @'
{
"properties": {
"schema": {
"name": "{TableName}_CL",
"columns": [
{
"name": "TimeGenerated",
"type": "DateTime"
},
{
"name": "MyStringColumn",
"type": "string"
},
{
"name": "MyIntegerColumn",
"type": "int"
},
{
"name": "MyRealColumn",
"type": "real"
},
{
"name": "MyBooleanColumn",
"type": "bool"
},
{
"name": "FilePath",
"type": "string"
},
{
"name": "Computer",
"type": "string"
}
]
}
}
}
'@
Invoke-AzRestMethod -Path "/subscriptions/{subscription}/resourcegroups/{resourcegroup}/providers/microsoft.operationalinsights/workspaces/{WorkspaceName}/tables/{TableName}_CL?api-version=2021-12-01-preview" -Method PUT -payload $tableParams
Create a data collection rule for a JSON file
Note
The agent based JSON custom file ingestion is currently in preview and does not have a complete UI experience in the portal yet. While you can create the DCR using the portal, you must modify it to define the columns in the incoming stream. This section includes details on creating the DCR using an ARM template.
Incoming stream schema
Note
Multiline support that uses an ISO 8601 time stamp to delimited events is expected mid-October 2024
JSON files include a property name with each value, and the incoming stream in the DCR needs to include a column matching the name of each property. You need to modify the columns section of the ARM template with the columns from your log.
The following table describes optional columns that you can include in addition to the columns defining the data in your log file.
| Column | Type | Description |
|---|---|---|
TimeGenerated |
datetime | The time the record was generated. This value will be automatically populated with the time the record is added to the Log Analytics workspace if it's not included in the incoming stream. |
FilePath |
string | If you add this column to the incoming stream in the DCR, it will be populated with the path to the log file. This column is not created automatically and can't be added using the portal. You must manually modify the DCR created by the portal or create the DCR using another method where you can explicitly define the incoming stream. |
Computer |
string | If you add this column to the incoming stream in the DCR, it will be populated with the name of the computer with the log file. This column is not created automatically and can't be added using the portal. You must manually modify the DCR created by the portal or create the DCR using another method where you can explicitly define the incoming stream. |
Transformation
The transformation potentially modifies the incoming stream to filter records or to modify the schema to match the target table. If the schema of the incoming stream is the same as the target table, then you can use the default transformation of source. If not, then modify the transformKql section of tee ARM template with a KQL query that returns the required schema.
ARM template
Use the following ARM template to create a DCR for collecting text log files, making the changes described in the previous sections. The following table describes the parameters that require values when you deploy the template.
| Setting | Description |
|---|---|
| Data collection rule name | Unique name for the DCR. |
| Data collection endpoint resource ID | Resource ID of the data collection endpoint (DCE). |
| Location | Region for the DCR. Must be the same location as the Log Analytics workspace. |
| File patterns | Identifies the location and name of log files on the local disk. Use a wildcard for filenames that vary, for example when a new file is created each day with a new name. You can enter multiple file patterns separated by commas (AMA version 1.26 or higher required for multiple file patterns on Linux). Examples: - C:\Logs\MyLog.json - C:\Logs\MyLog*.json - C:\App01\AppLog.json, C:\App02\AppLog.json - /var/mylog.json - /var/mylog*.json |
| Table name | Name of the destination table in your Log Analytics Workspace. |
| Workspace resource ID | Resource ID of the Log Analytics workspace with the target table. |
Important
When you create the DCR using an ARM template, you still must associate the DCR with the agents that will use it. You can edit the DCR in the Azure portal and select the agents as described in Add resources
{
"$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
"contentVersion": "1.0.0.0",
"parameters": {
"dataCollectionRuleName": {
"type": "string",
"metadata": {
"description": "Unique name for the DCR. "
}
},
"dataCollectionEndpointResourceId": {
"type": "string",
"metadata": {
"description": "Resource ID of the data collection endpoint (DCE)."
}
},
"location": {
"type": "string",
"metadata": {
"description": "Region for the DCR. Must be the same location as the Log Analytics workspace. "
}
},
"filePatterns": {
"type": "string",
"metadata": {
"description": "Path on the local disk for the log file to collect. May include wildcards.Enter multiple file patterns separated by commas (AMA version 1.26 or higher required for multiple file patterns on Linux)."
}
},
"tableName": {
"type": "string",
"metadata": {
"description": "Name of destination table in your Log Analytics workspace. "
}
},
"workspaceResourceId": {
"type": "string",
"metadata": {
"description": "Resource ID of the Log Analytics workspace with the target table."
}
}
},
"variables": {
"tableOutputStream": "[concat('Custom-', parameters('tableName'))]"
},
"resources": [
{
"type": "Microsoft.Insights/dataCollectionRules",
"apiVersion": "2022-06-01",
"name": "[parameters('dataCollectionRuleName')]",
"location": "[parameters('location')]",
"properties": {
"dataCollectionEndpointId": "[parameters('dataCollectionEndpointResourceId')]",
"streamDeclarations": {
"Custom-Json-stream": {
"columns": [
{
"name": "TimeGenerated",
"type": "datetime"
},
{
"name": "FilePath",
"type": "string"
},
{
"name": "MyStringColumn",
"type": "string"
},
{
"name": "MyIntegerColumn",
"type": "int"
},
{
"name": "MyRealColumn",
"type": "real"
},
{
"name": "MyBooleanColumn",
"type": "boolean"
}
]
}
},
"dataSources": {
"logFiles": [
{
"streams": [
"Custom-Json-stream"
],
"filePatterns": [
"[parameters('filePatterns')]"
],
"format": "json",
"name": "Custom-Json-stream"
}
]
},
"destinations": {
"logAnalytics": [
{
"workspaceResourceId": "[parameters('workspaceResourceId')]",
"name": "workspace"
}
]
},
"dataFlows": [
{
"streams": [
"Custom-Json-stream"
],
"destinations": [
"workspace"
],
"transformKql": "source",
"outputStream": "[variables('tableOutputStream')]"
}
]
}
}
]
}
Troubleshooting
Go through the following steps if you aren't collecting data from the JSON log that you're expecting.
- Verify that data is being written to the log file being collected.
- Verify that the name and location of the log file matches the file pattern you specified.
- Verify that the schema of the incoming stream in the DCR matches the schema in the log file.
- Verify that the schema of the target table matches the incoming stream or that you have a transformation that will convert the incoming stream to the correct schema.
- See Verify operation to verify whether the agent is operational and data is being received.
Next steps
Learn more about: