[FEATURE] Re-run Spark Stage For Uniffle Shuffle Fetch Failure.

Apr 19, 2025 by ADMIN 64 views

[FEATURE] Re-run Spark Stage for Uniffle Shuffle Fetch Failure

Apache Uniffle is a unified shuffle framework for Apache Spark, designed to improve the performance and scalability of Spark applications. However, like any complex system, Uniffle is not immune to failures and errors. In this article, we will explore a feature that addresses a specific issue: re-running Spark stages for Uniffle shuffle fetch failures.

Uniffle shuffle fetch failures can occur due to various reasons such as network connectivity issues, data corruption, or resource constraints. When a fetch failure occurs, the Spark stage fails, and the application is terminated. This can lead to significant downtime and lost productivity. To mitigate this issue, we propose a feature that allows re-running the Spark stage for Uniffle shuffle fetch failures.

The proposed feature involves adding a new mechanism to Uniffle that allows re-running the Spark stage for shuffle fetch failures. This can be achieved by introducing a new flag, re-run-stage, which can be set to true or false. When re-run-stage is set to true, Uniffle will attempt to re-run the failed Spark stage.

Here is an example of how the feature can be implemented:

// UniffleConfig.java

public class UniffleConfig {
    private boolean reRunStage;

    public boolean isReRunStage() {
        return reRunStage;
    }

    public void setReRunStage(boolean reRunStage) {
        this.reRunStage = reRunStage;
    }
}

// UniffleShuffleManager.java

public class UniffleShuffleManager {
    private UniffleConfig config;

    public void handleFetchFailure() {
        if (config.isReRunStage()) {
            // Re-run the failed Spark stage
            reRunStage();
        } else {
            // Fail the Spark stage
            failStage();
        }
    }

    private void reRunStage() {
        // Re-run the failed Spark stage
        // ...
    }

    private void failStage() {
        // Fail the Spark stage
        // ...
    }
}

The proposed feature is designed to be backward compatible with existing Uniffle configurations. The re-run-stage flag can be set to true or false depending on the user's preference. When re-run-stage is set to true, Uniffle will attempt to re-run the failed Spark stage, reducing the downtime and improving the overall performance of the application.

Yes, I am willing to submit a PR for this feature.

The implementation details of the proposed feature are as follows:

The re-run-stage flag will be added to the Uniffle configuration.
The UniffleShuffleManager class will be modified to handle the re-run-stage flag.
The reRunStage() method will be added to the UniffleShuffleManager class to re-run the failed Spark stage.
The failStage() method will be modified to fail the Spark stage when re-run-stage is set to false.

The proposed feature offers several benefits, including:

Reduced downtime: By re-running the failed Spark stage, Uniffle can reduce the downtime and improve the overall performance of the application.
Improved reliability: The feature ensures that the application can recover from shuffle fetch failures, improving the overall reliability of the system.
Enhanced user experience: The feature provides users with more control over the application's behavior, allowing them to choose whether to re-run the failed Spark stage or fail the application.

In conclusion, the proposed feature of re-running Spark stages for Uniffle shuffle fetch failures offers several benefits, including reduced downtime, improved reliability, and enhanced user experience. The feature is designed to be backward compatible with existing Uniffle configurations and can be implemented by adding a new flag, re-run-stage, to the Uniffle configuration. We believe that this feature will improve the overall performance and reliability of Uniffle and are willing to submit a PR for its implementation.
[FEATURE] Re-run Spark Stage for Uniffle Shuffle Fetch Failure: Q&A

In our previous article, we proposed a feature that allows re-running Spark stages for Uniffle shuffle fetch failures. This feature aims to reduce downtime, improve reliability, and enhance user experience. In this article, we will address some of the frequently asked questions (FAQs) related to this feature.

A: The re-run-stage flag is used to determine whether to re-run the failed Spark stage or fail the application when a shuffle fetch failure occurs. When set to true, Uniffle will attempt to re-run the failed Spark stage, reducing downtime and improving performance.

A: When the re-run-stage flag is set to true, Uniffle will attempt to re-run the failed Spark stage. If the re-run is successful, the application will continue to run without any issues. However, if the re-run fails, the application will fail, and the user will need to restart the application.

A: Re-running the failed Spark stage offers several benefits, including:

Reduced downtime: By re-running the failed Spark stage, Uniffle can reduce the downtime and improve the overall performance of the application.
Improved reliability: The feature ensures that the application can recover from shuffle fetch failures, improving the overall reliability of the system.
Enhanced user experience: The feature provides users with more control over the application's behavior, allowing them to choose whether to re-run the failed Spark stage or fail the application.

A: The re-run-stage flag is designed to be backward compatible with existing Uniffle configurations. When used in conjunction with other Uniffle features, the re-run-stage flag will not interfere with their functionality.

A: Yes, the re-run-stage flag can be used in production environments. However, it is recommended to test the feature in a non-production environment before deploying it to production.

A: To implement the re-run-stage flag, you will need to modify the Uniffle configuration to include the re-run-stage flag. You can then use the flag to determine whether to re-run the failed Spark stage or fail the application.

A: While re-running the failed Spark stage can offer several benefits, there are also potential risks associated with this feature. These risks include:

Increased resource usage: Re-running the failed Spark stage can increase resource usage, potentially leading to performance issues.
Data inconsistencies: Re-running the failed Spark stage can lead to data inconsistencies, potentially affecting the accuracy of the application's output.

In conclusion, the re-run-stage flag is a valuable feature that can help improve the reliability and performance of Uniffle applications. By re-running the failed Spark stage, Uniffle can reduce downtime, improve reliability, and enhance user experience. However, it is essential to carefully consider the potential risks associated with this feature and to test it thoroughly before deploying it to production.

The implementation details of the re-run-stage flag are as follows:

The re-run-stage flag will be added to the Uniffle configuration.
The UniffleShuffleManager class will be modified to handle the re-run-stage flag.
The reRunStage() method will be added to the UniffleShuffleManager class to re-run the failed Spark stage.
The failStage() method will be modified to fail the Spark stage when re-run-stage is set to false.

The re-run-stage flag offers several benefits, including:

Reduced downtime: By re-running the failed Spark stage, Uniffle can reduce the downtime and improve the overall performance of the application.
Improved reliability: The feature ensures that the application can recover from shuffle fetch failures, improving the overall reliability of the system.
Enhanced user experience: The feature provides users with more control over the application's behavior, allowing them to choose whether to re-run the failed Spark stage or fail the application.

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