Flink HDFS

Share

                              Flink HDFS

Flink is a powerful and versatile stream processing and batch processing framework that can interact with Hadoop Distributed File System (HDFS) for reading and writing data. Flink provides connectors and APIs to seamlessly integrate with HDFS, allowing users to process, analyze, and store data from HDFS in real-time and batch processing applications. Here’s how Flink interacts with HDFS:

  1. Reading Data from HDFS:

    • Flink can read data from HDFS by utilizing the TextInputFormat or other custom formats. You can specify the HDFS file path or directory as a source in your Flink application.
    • Here’s an example of reading data from HDFS using Flink’s Java API:
      java
      ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); DataSet<String> data = env.readTextFile("hdfs://<namenode>:<port>/path/to/hdfs/file");
  2. Writing Data to HDFS:

    • Flink can write processed data back to HDFS using the TextOutputFormat or other custom output formats. You can specify the HDFS destination path in your Flink application.
    • Here’s an example of writing data to HDFS using Flink’s Java API:
      java
      DataSet<String> data = ... // Your processed data data.writeAsText("hdfs://<namenode>:<port>/path/to/output/directory", WriteMode.OVERWRITE);
  3. Parquet Integration:

    • Flink provides built-in support for reading and writing Parquet files in HDFS. Parquet is a columnar storage format optimized for analytical processing, and it is commonly used in big data environments.
    • Flink’s Parquet connectors enable efficient data exchange between Flink and HDFS while maintaining data schema and compression.
  4. Checkpointing and Data Durability:

    • Flink supports checkpointing, which allows you to create consistent snapshots of your application’s state. This ensures that data processed from HDFS is reliably processed and that the application can recover from failures.
    • When writing data to HDFS, Flink can provide strong data durability guarantees, ensuring that data is safely written and not lost due to failures.
  5. Streaming and Batch Processing:

    • Flink is known for its ability to process both streaming and batch data. It can continuously read data from HDFS as new data arrives (streaming) or process existing datasets in HDFS (batch).
  6. Integration with Hadoop Ecosystem:

    • Flink can be seamlessly integrated with other Hadoop ecosystem components, such as Hive and HBase, to perform complex data processing tasks that involve multiple data sources and sinks.

Hadoop Training Demo Day 1 Video:

 
You can find more information about Hadoop Training in this Hadoop Docs Link

 

Conclusion:

Unogeeks is the No.1 IT Training Institute for Hadoop Training. Anyone Disagree? Please drop in a comment

You can check out our other latest blogs on Hadoop Training here – Hadoop Blogs

Please check out our Best In Class Hadoop Training Details here – Hadoop Training

💬 Follow & Connect with us:

———————————-

For Training inquiries:

Call/Whatsapp: +91 73960 33555

Mail us at: info@unogeeks.com

Our Website ➜ https://unogeeks.com

Follow us:

Instagram: https://www.instagram.com/unogeeks

Facebook:https://www.facebook.com/UnogeeksSoftwareTrainingInstitute

Twitter: https://twitter.com/unogeeks


Share

Leave a Reply

Your email address will not be published. Required fields are marked *