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.

1. The agent watches for any log files that match a specified name pattern on the local disk.
2. 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.
3. 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. You can use the following PowerShell script to create a custom table with your 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

Azure portal

Create a data collection rule, as described in Collect data with Azure Monitor Agent. In the Collect and deliver step, select Custom JSON Logs from the Data source type dropdown. Your DCR must include a column that exactly matches each JSON name-value pair for your data to properly upload. The following table describes the default columns that are available.

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.

ARM Template

Use the following ARM template to create a DCR for collecting JSON 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.

After you create the DCR using an ARM template, you must associate the DCR with the machine that will use it. You can use the Azure portal and select the machines 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')]"
                    }
                ]
            }
        }
    ]
}

Transformation

A transformation can modifies the incoming stream to filter records or to modify the schema to match the target table. The default transformation of source makes does not modify the table.

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:

• Azure Monitor Agent.
• Data collection rules.
• Best practices for cost management in Azure Monitor.