Avro Parquet

The Avro Parquet connector provides an Apache Pekko Stream Source, Sink and Flow for push and pull data to and from Parquet files.

For more information about Apache Parquet please visit the official documentation.

Artifacts

sbt

val PekkoVersion = "1.1.3"
libraryDependencies ++= Seq(
  "org.apache.pekko" %% "pekko-connectors-avroparquet" % "1.1.0",
  "org.apache.pekko" %% "pekko-stream" % PekkoVersion
)

Maven

<properties>
  <pekko.version>1.1.3</pekko.version>
  <scala.binary.version>2.13</scala.binary.version>
</properties>
<dependencies>
  <dependency>
    <groupId>org.apache.pekko</groupId>
    <artifactId>pekko-connectors-avroparquet_${scala.binary.version}</artifactId>
    <version>1.1.0</version>
  </dependency>
  <dependency>
    <groupId>org.apache.pekko</groupId>
    <artifactId>pekko-stream_${scala.binary.version}</artifactId>
    <version>${pekko.version}</version>
  </dependency>
</dependencies>

Gradle

def versions = [
  PekkoVersion: "1.1.3",
  ScalaBinary: "2.13"
]
dependencies {
  implementation "org.apache.pekko:pekko-connectors-avroparquet_${versions.ScalaBinary}:1.1.0"
  implementation "org.apache.pekko:pekko-stream_${versions.ScalaBinary}:${versions.PekkoVersion}"
}

The table below shows direct dependencies of this module and the second tab shows all libraries it depends on transitively.

Source Initiation

Sometimes it might be useful to use a Parquet file as stream Source. For this we will need to create an AvroParquetReader instance which will produce records as subtypes of GenericRecord, the Avro record’s abstract representation.

Scala

copysourceimport org.apache.hadoop.conf.Configuration
import org.apache.parquet.avro.AvroReadSupport

val conf: Configuration = new Configuration()
conf.setBoolean(AvroReadSupport.AVRO_COMPATIBILITY, true)
val reader =
  AvroParquetReader.builder[GenericRecord](HadoopInputFile.fromPath(new Path(file), conf)).withConf(conf).build()

Java

copysourceimport org.apache.parquet.hadoop.ParquetReader;
import org.apache.avro.generic.GenericRecord;
import org.apache.hadoop.fs.Path;
import org.apache.avro.Schema;
import org.apache.pekko.stream.javadsl.Source;
import org.apache.parquet.avro.AvroParquetReader;

Configuration conf = new Configuration();

ParquetReader<GenericRecord> reader =
    AvroParquetReader.<GenericRecord>builder(
            HadoopInputFile.fromPath(new Path("./test.parquet"), conf))
        .disableCompatibility()
        .build();

After that, you can create the Parquet Source from the initialisation of AvroParquetReader. This object requires an instance of a org.apache.parquet.hadoop.ParquetReader typed by a subtype of GenericRecord.

Scala

copysourceval source: Source[GenericRecord, NotUsed] = AvroParquetSource(reader)
val source: Source[GenericRecord, NotUsed] = AvroParquetSource(reader)

Java

copysourceSource<GenericRecord, NotUsed> source = AvroParquetSource.create(reader);

Sink Initiation

On the other hand, you can use AvroParquetWriter as the Apache Pekko Streams Sink implementation for writing to Parquet. In that case, its initialisation would require an instance of org.apache.parquet.hadoop.ParquetWriter. It will also expect any subtype of GenericRecord to be passed.

Scala

copysourceimport com.sksamuel.avro4s.Record
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.Path
import org.apache.parquet.avro.AvroReadSupport

val file = "./sample/path/test.parquet"
val conf = new Configuration()
conf.setBoolean(AvroReadSupport.AVRO_COMPATIBILITY, true)
val writer =
  AvroParquetWriter.builder[Record](new Path(file)).withConf(conf).withSchema(schema).build()

Java

copysourceimport org.apache.parquet.hadoop.ParquetWriter;
import org.apache.avro.Schema;
import org.apache.avro.generic.GenericRecord;
import org.apache.avro.generic.GenericRecordBuilder;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.parquet.hadoop.util.HadoopInputFile;

Configuration conf = new Configuration();
conf.setBoolean(AvroReadSupport.AVRO_COMPATIBILITY, true);
ParquetWriter<GenericRecord> writer =
    AvroParquetWriter.<GenericRecord>builder(new Path(file))
        .withConf(conf)
        .withWriteMode(ParquetFileWriter.Mode.OVERWRITE)
        .withSchema(schema)
        .build();

After that, the AvroParquet Sink can already be used.

Scala

copysourceval records: List[Record] = documents.map(format.to(_))
val source: Source[Record, NotUsed] = Source(records)
val result: Future[Done] = source
  .runWith(AvroParquetSink(writer))

Java

copysourceSink<GenericRecord, CompletionStage<Done>> sink = AvroParquetSink.create(writer);

Flow Initiation

The representation of a ParquetWriter as a Flow is also available to use as a streams flow stage, in which as well as the other representations, it will expect subtypes of the Parquet GenericRecord type to be passed. As a result, it writes into a Parquet file and returns the same GenericRecords. Such a Flow stage can be easily created by using the AvroParquetFlow and providing an AvroParquetWriter instance as a parameter.

Scala

copysourceval records: List[GenericRecord]
val source: Source[GenericRecord, NotUsed] = Source(records)
val avroParquet: Flow[GenericRecord, GenericRecord, NotUsed] = AvroParquetFlow(writer)
val result =
  source
    .via(avroParquet)
    .runWith(Sink.seq)

This is all the preparation that we are going to need.

Java

copysourceParquetWriter<GenericRecord> writer =
    AvroParquetWriter.<GenericRecord>builder(new Path("./test.parquet"))
        .withConf(conf)
        .withSchema(schema)
        .build();

Flow<GenericRecord, GenericRecord, NotUsed> flow = AvroParquetFlow.create(writer);

source.via(flow).runWith(Sink.ignore(), system);

Running the example code

The code in this guide is part of the runnable tests of this project. You are welcome to edit the code and run it in sbt.

Scala

sbt
> avroparquet/test