Troubleshooting Amazon Kinesis Data Analytics for SQL Applications - Amazon Kinesis Data Analytics for SQL Applications Developer Guide

For new projects, we recommend that you use the new Kinesis Data Analytics Studio over Kinesis Data Analytics for SQL Applications. Kinesis Data Analytics Studio combines ease of use with advanced analytical capabilities, enabling you to build sophisticated stream processing applications in minutes.

Troubleshooting Amazon Kinesis Data Analytics for SQL Applications

The following can help you troubleshoot problems that you might encounter with Amazon Kinesis Data Analytics for SQL Applications.

Unable to Run SQL Code

If you need to figure out how to get a particular SQL statement to work correctly, you have several different resources when using Kinesis Data Analytics:

Unable to Detect or Discover My Schema

In some cases, Kinesis Data Analytics can't detect or discover a schema. In many of these cases, you can still use Kinesis Data Analytics.

Suppose that you have UTF-8 encoded data that doesn't use a delimiter, or data that uses a format other than comma-separated values (CSV), or the discovery API did not discover your schema. In these cases, you can define a schema manually or use string manipulation functions to structure your data.

To discover the schema for your stream, Kinesis Data Analytics randomly samples the latest data in your stream. If you aren't consistently sending data to your stream, Kinesis Data Analytics might not be able to retrieve a sample and detect a schema. For more information, see Using the Schema Discovery Feature on Streaming Data.

Reference Data is Out of Date

Reference data is loaded from the Amazon Simple Storage Service (Amazon S3) object into the application when the application is started or updated, or during application interruptions that are caused by service issues.

Reference data is not loaded into the application when updates are made to the underlying Amazon S3 object.

If the reference data in the application is not up to date, you can reload the data by following these steps:

  1. On the Kinesis Data Analytics console, choose the application name in the list, and then choose Application details.

  2. Choose Go to SQL editor to open the Real-time analytics page for the application.

  3. In the Source Data view, choose your reference data table name.

  4. Choose Actions, Synchronize reference data table.

Application Not Writing to Destination

If data is not being written to the destination, check the following:

If the role and destination configuration look correct, try restarting the application, specifying LAST_STOPPED_POINT for the InputStartingPositionConfiguration.

Important Application Health Parameters to Monitor

To make sure that your application is running correctly, we recommend that you monitor certain important parameters.

The most important parameter to monitor is the Amazon CloudWatch metric MillisBehindLatest. This metric represents how far behind the current time you are reading from the stream. This metric helps you determine whether you are processing records from the source stream fast enough.

As a general rule, you should set up a CloudWatch alarm to trigger if you fall behind more than one hour. However, the amount of time depends on your use case. You can adjust it as needed.

For more information, see Best Practices.

Invalid Code Errors When Running an Application

When you can't save and run the SQL code for your Amazon Kinesis Data Analytics application, the following are common causes:

  • The stream was redefined in your SQL code – After you create a stream and the pump associated with the stream, you can't redefine the same stream in your code. For more information about creating a stream, see CREATE STREAM in the Amazon Kinesis Data Analytics SQL Reference. For more information about creating a pump, see CREATE PUMP.

  • A GROUP BY clause uses multiple ROWTIME columns – You can specify only one ROWTIME column in the GROUP BY clause. For more information, see GROUP BY and ROWTIME in the Amazon Kinesis Data Analytics SQL Reference.

  • One or more data types have an invalid casting – In this case, your code has an invalid implicit cast. For example, you might be casting a timestamp to a bigint in your code.

  • A stream has the same name as a service reserved stream name – A stream can't have the same name as the service-reserved stream error_stream.

Application is Writing Errors to the Error Stream

If your application is writing errors to the in-application error stream, you can decode the value in the DATA_ROW field using standard libraries. For more information about the error stream, see Error Handling.

Insufficient Throughput or High MillisBehindLatest

If your application's MillisBehindLatest metric is steadily increasing or consistently is above 1000 (one second), it can be due to the following reasons:

  • Check your application's InputBytes CloudWatch metric. If you are ingesting more than 4 MB/sec, this can cause an increase in MillisBehindLatest. To improve your application's throughput, increase the value of the InputParallelism parameter. For more information, see Parallelizing Input Streams for Increased Throughput.

  • Check your application's output delivery Success metric for failures in delivering to your destination. Verify that you have correctly configured the output, and that your output stream has sufficient capacity.

  • If your application uses an AWS Lambda function for pre-processing or as an output, check the application’s InputProcessing.Duration or LambdaDelivery.Duration CloudWatch metric. If the Lambda function invocation duration is longer than 5 seconds, consider doing the following:

    • Increase the Lambda function’s Memory allocation. You can do this on the AWS Lambda console, on the Configuration page, under Basic settings. For more information, see Configuring Lambda Functions in the AWS Lambda Developer Guide.

    • Increase the number of shards in your input stream of the application. This increases the number of parallel functions that the application will invoke, which might increase throughput.

    • Verify that the function is not making blocking calls that are affecting performance, such as synchronous requests for external resources.

    • Examine your AWS Lambda function to see whether there are other areas where you can improve performance. Check the CloudWatch Logs of the application Lambda function. For more information, see Accessing Amazon CloudWatch Metrics for in the AWS Lambda Developer Guide.

  • Verify that your application is not reaching the default limit for Kinesis Processing Units (KPU). If your application is reaching this limit, you can request a limit increase. For more information, see Automatically Scaling Applications to Increase Throughput.