如何使用自定义Parquet压缩算法？Spark能否配置自定义算法读写Parquet文件？

Alright, let's tackle your two questions one by one—they're both great ones for anyone looking to extend Parquet's compression capabilities!

To implement and use a custom compression algorithm with Parquet, you'll need to work with Parquet's core codec interface and register your implementation. Here's the step-by-step breakdown:

• Implement the Parquet CompressionCodec interface:
  Parquet relies on this interface to handle compression/decompression logic. You'll need to implement methods for creating compressed output streams and decompressed input streams, plus return a unique codec name that you'll use to reference it later.
  Example Java implementation snippet:

  import org.apache.parquet.hadoop.codec.CompressionCodec;
  import java.io.IOException;
  import java.io.InputStream;
  import java.io.OutputStream;
  
  public class MyCustomCompressionCodec implements CompressionCodec {
      @Override
      public InputStream createInputStream(InputStream in) throws IOException {
          // Add your custom decompression logic here
          return new MyCustomDecompressionStream(in);
      }
  
      @Override
      public OutputStream createOutputStream(OutputStream out) throws IOException {
          // Add your custom compression logic here
          return new MyCustomCompressionStream(out);
      }
  
      @Override
      public String getCodecName() {
          return "myalgo"; // Match the name you want to use in configs
      }
  }
  

• Register your codec with Parquet's CompressionCodecFactory:
  Parquet uses this factory to resolve codec names to implementations. You can extend the default factory to include your custom codec:

  import org.apache.parquet.hadoop.codec.CompressionCodecFactory;
  import org.apache.parquet.hadoop.codec.CodecConfig;
  
  public class CustomCodecFactory extends CompressionCodecFactory {
      public CustomCodecFactory(CodecConfig config) {
          super(config);
          // Register your custom codec using its unique name
          this.codecs.put("myalgo", new MyCustomCompressionCodec());
      }
  }
  

• Use the custom codec in Parquet readers/writers:
  When initializing ParquetWriter or ParquetReader, configure them to use your custom factory or explicitly specify the codec name. For example, with ParquetWriter:

  CodecConfig codecConfig = CodecConfig.from(hadoopConf);
  CompressionCodecFactory factory = new CustomCodecFactory(codecConfig);
  CompressionCodec codec = factory.getCodecByName("myalgo");
  
  ParquetWriter<MyRecord> writer = ParquetWriter.builder(new MyRecordWriteSupport())
      .withPath(new Path("output.parquet"))
      .withCompressionCodec(codec)
      .build();
  

Short answer: Not out of the box with the exact spark.sql.parquet.compression.codec config you mentioned, but it's possible with some extra setup. Here's how:

Key Limitation of Default Spark #

Spark's built-in Parquet data source only recognizes a fixed set of compression codecs (snappy, gzip, lz4, zstd, uncompressed). If you try to set spark.sql.parquet.compression.codec to "myalgo" directly, Spark will throw an error because it doesn't know how to map that name to an implementation.

How to Make It Work #

Step 1: Package and Deploy Your Custom Codec #

First, package your MyCustomCompressionCodec and CustomCodecFactory into a JAR file. Make sure this JAR is available to Spark:

• Add it to Spark's lib directory (for a cluster-wide setup)
• Or pass it via the --jars flag when submitting your Spark job:

  spark-submit --jars my-custom-codec.jar your-spark-app.jar
  

Step 2: Extend Spark's Parquet Handling (for Global Config Style) #

If you want to use the global spark.sql.parquet.compression.codec config like you mentioned, you'll need to extend Spark's Parquet configuration logic:

1. Modify Spark's ParquetOptions class (part of Spark's source code) to accept your custom codec name by updating the validateCompressionCodec method.
2. Recompile Spark with this change, or use a custom build that includes your modification.

Step 3: Alternative (No Spark Source Modification) #

If modifying Spark's source isn't feasible, you can explicitly set the codec when writing DataFrames, by ensuring Parquet uses your custom factory:
Example Scala code:

import org.apache.parquet.hadoop.codec.{CodecConfig, CompressionCodecFactory}
import com.yourpackage.CustomCodecFactory

// Get the Hadoop config from Spark
val hadoopConf = spark.sparkContext.hadoopConfiguration
val codecConfig = CodecConfig.from(hadoopConf)

// Initialize your custom codec factory
val customFactory = new CustomCodecFactory(codecConfig)
val customCodec = customFactory.getCodecByName("myalgo")

// Write the DataFrame using your custom codec
df.write
  .option("parquet.compression.codec", "myalgo")
  .parquet("path/to/your/output")

Reading Custom Compressed Parquet Files #

For reading, as long as your custom codec JAR is in Spark's classpath, Parquet will automatically resolve the codec name stored in the Parquet file metadata and use your implementation—no extra config needed, as long as the codec is registered correctly.

内容的提问来源于stack exchange，提问作者Jerome tan