Azure.Monitor.Ingestion 1.0.0-beta.3

Azure Monitor Ingestion client library for .NET

The Azure Monitor Ingestion client library is used to send custom logs to Azure Monitor.

This library allows you to send data from virtually any source to supported built-in tables or to custom tables that you create in Log Analytics workspace. You can even extend the schema of built-in tables with custom columns.

Resources:

• Source code
• Package (NuGet)
• • Service documentation
• Change log

Getting started

Prerequisites

• An Azure subscription
• A Data Collection Endpoint
• A Data Collection Rule
• A Log Analytics workspace

Install the package

Install the Azure Monitor Ingestion client library for .NET with NuGet:

dotnet add package Azure.Monitor.Ingestion --prerelease

Authenticate the client

An authenticated client is required to ingest data. To authenticate, create an instance of a TokenCredential class (see Azure.Identity for DefaultAzureCredential and other TokenCredential implementations). Pass it to the constructor of your client class.

To authenticate, the following example uses DefaultAzureCredential from the Azure.Identity package:

var dataCollectionEndpoint = new Uri("...");
var credential = new DefaultAzureCredential();
var client = new LogsIngestionClient(dataCollectionEndpoint, credential);

Key concepts

Data Collection Endpoint

Data Collection Endpoints (DCEs) allow you to uniquely configure ingestion settings for Azure Monitor. This article provides an overview of data collection endpoints including their contents and structure and how you can create and work with them.

Data Collection Rule

Data collection rules (DCR) define data collected by Azure Monitor and specify how and where that data should be sent or stored. The REST API call must specify a DCR to use. A single DCE can support multiple DCRs, so you can specify a different DCR for different sources and target tables.

The DCR must understand the structure of the input data and the structure of the target table. If the two don't match, it can use a transformation to convert the source data to match the target table. You may also use the transform to filter source data and perform any other calculations or conversions.

For more details, refer to Data collection rules in Azure Monitor.

Log Analytics workspace tables

Custom logs can send data to any custom table that you create and to certain built-in tables in your Log Analytics workspace. The target table must exist before you can send data to it. The following built-in tables are currently supported:

• CommonSecurityLog
• SecurityEvents
• Syslog
• WindowsEvents

Thread safety

We guarantee that all client instance methods are thread-safe and independent of each other (guideline). This ensures that the recommendation of reusing client instances is always safe, even across threads.

Additional concepts

Client options | Accessing the response | Long-running operations | Handling failures | Diagnostics | Mocking | Client lifetime

Examples

• Upload custom logs
• Verify logs

You can familiarize yourself with different APIs using Samples.

Upload custom logs

You can create a client and call the client's Upload method. Take note of the data ingestion limits.

var dataCollectionEndpoint = new Uri("...");
var dataCollectionRuleImmutableId = "...";
var streamName = "...";

TokenCredential credential = new DefaultAzureCredential();
LogsIngestionClient client = new(dataCollectionEndpoint, credential);

DateTimeOffset currentTime = DateTimeOffset.UtcNow;

// Use BinaryData to serialize instances of an anonymous type into JSON
BinaryData data = BinaryData.FromObjectAsJson(
    new[] {
        new
        {
            Time = currentTime,
            Computer = "Computer1",
            AdditionalContext = new
            {
                InstanceName = "user1",
                TimeZone = "Pacific Time",
                Level = 4,
                CounterName = "AppMetric1",
                CounterValue = 15.3
            }
        },
        new
        {
            Time = currentTime,
            Computer = "Computer2",
            AdditionalContext = new
            {
                InstanceName = "user2",
                TimeZone = "Central Time",
                Level = 3,
                CounterName = "AppMetric1",
                CounterValue = 23.5
            }
        },
    });

// Upload our logs
Response response = client.Upload(dataCollectionRuleImmutableId, streamName, RequestContent.Create(data));

Verify logs

You can verify that your data has been uploaded correctly by using the Azure.Monitor.Query library. Run the Upload custom logs sample first before verifying the logs.

var workspaceId = "...";
var tableName = "...";

TokenCredential credential = new DefaultAzureCredential();

LogsQueryClient logsQueryClient = new(credential);
LogsBatchQuery batch = new();
string query = tableName + " | Count;";
string countQueryId = batch.AddWorkspaceQuery(
    workspaceId,
    query,
    new QueryTimeRange(TimeSpan.FromDays(1)));

Response<LogsBatchQueryResultCollection> queryResponse = logsQueryClient.QueryBatch(batch);

Console.WriteLine("Table entry Count: " + queryResponse.Value.GetResult<int>(countQueryId).Single());

Troubleshooting

Enabling logging

Azure SDKs for .Net offer a consistent logging story to help aid in troubleshooting application errors and expedite their resolution. The logs produced will capture the flow of an application before reaching the terminal state to help locate the root issue. View the logging wiki for guidance about enabling logging.

Next steps

To learn more about Azure Monitor, see the Azure Monitor service documentation.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.