Efficiently Configure Flink 1.11 StreamingFileSink for Early Flushing of Records скачать в хорошем качестве

Efficiently Configure Flink 1.11 StreamingFileSink for Early Flushing of Records

Discover how to manage `Flink 1.11 StreamingFileSink` to effectively flush records to in-progress files for near-real-time data processing. --- This video is based on the question https://stackoverflow.com/q/70717500/ asked by the user 'sunrize' ( https://stackoverflow.com/u/9728653/ ) and on the answer https://stackoverflow.com/a/70736015/ provided by the user 'ChangLi' ( https://stackoverflow.com/u/16867587/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Flink 1.11 StreamingFileSink flush record(s) early to in-progress file Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Efficiently Configure Flink 1.11 StreamingFileSink for Early Flushing of Records When dealing with stream processing in Apache Flink, especially with the StreamingFileSink, there can be challenges regarding how soon records are written to an in-progress file. For users running hourly bucketed streams to HDFS, infrequent records can be a significant issue. You may find yourself asking, How can I configure the sink to flush records as soon as they arrive, instead of allowing them to buffer? This need often arises when a successor data analysis process must access the data in near real-time. Let's explore how you can achieve this in a clear and structured manner. Understanding the Problem In your current configuration, records are held in buffer as they arrive at the StreamingFileSink. The use of InactivityInterval can reduce the waiting time for records to be written but can lead to the undesirable outcome of generating many small files. This is not optimal for performance or storage management. Key Challenges: Buffers within the FileOutputStream: These may prevent immediate writing to disk. Excessive small files: Lowering the inactivity interval might create too many files. The Solution: Directly Writing to HDFS To address these concerns, you can modify the implementation of the write function. Here’s how you can do that effectively: Leverage StreamingFileSink Implementations StreamingFileSink incorporates various implementations that allow for real-time data writes. By focusing on the inner workings of the InProgressFileWriter, you can modify its operations to achieve immediate flushing of records. Implementation Steps Accessing InProgressFileWriter: This interface enables you to write elements to part files. Pay attention to the following methods: write(final IN element, final long currentTime): Directly write the incoming record to the part file. persist(): Keep your current part file's state information. closeForCommit(): Finalize the pending file for data commitments. dispose(): Safely dispose of part files once they are no longer needed. Here is a snippet to illustrate: [[See Video to Reveal this Text or Code Snippet]] Advantages of This Approach Reduced Latency: Attaining near-real-time data availability for your downstream processing. Controlled File Output: You maintain control over how data is written, minimizing the chances of creating an excessive number of small files. Flexibility: This method allows you to customize behavior depending on your specific use case. Final thoughts By focusing on the underlying implementations of StreamingFileSink and properly adjusting the InProgressFileWriter, you can flush records to in-progress files promptly and efficiently while minimizing undesirable file generation. Exploring these details will offer you a way to optimize your data pipeline for real-time analytics in Flink. Implementing these changes can significantly enhance your data processing capabilities, allowing you to gain insights faster and more effectively. Make the Most Out of Flink Always stay updated with best practices in Apache Flink, experiment with your configurations, and ensure you are utilizing the sink capabilities to their fullest potential for the best performance.