How to use the Apache Parquet Column framework in the Java library for data compression
How to use the Apache Parquet Column framework in the Java library for data compression
Apache Parquet is an open source data storage format for column storage and streaming processing, which is very effective for large -scale data processing and analysis.It can significantly reduce the storage space of data through compression and coding technology, and improve the speed of reading and writing.In this article, we will introduce how to use the Apache Parquet Column framework in the Java class library for data compression.
First, we need to add Apache Parquet to the project.You can add the following dependencies to the pom.xml file in the Maven project:
<dependency>
<groupId>org.apache.parquet</groupId>
<artifactId>parquet-column</artifactId>
<version>${parquet.version}</version>
</dependency>
Next, we will demonstrate how to use the Parquet Column framework in Java for data compression through a simple example.
import org.apache.parquet.column.ColumnDescriptor;
import org.apache.parquet.column.ParquetProperties;
import org.apache.parquet.column.ParquetProperties.WriterVersion;
import org.apache.parquet.column.ParquetProperties.WriterVersion.PARQUET_2_0;
import org.apache.parquet.column.ParquetProperties.WriterVersion.PARQUET_1_0;
import org.apache.parquet.column.ParquetProperties.WriterVersion.PARQUET_1_6;
import org.apache.parquet.column.ParquetProperties.WriterVersion.PARQUET_1_0;
import org.apache.parquet.column.ParquetProperties.WriterVersion.PARQUET_2_0;
import org.apache.parquet.column.ParquetProperties.WriterVersion.PARQUET_1_6;
import org.apache.parquet.column.values.plain.PlainValuesWriter;
import org.apache.parquet.column.values.bitpacking.DevNullValuesWriter;
import org.apache.parquet.format.CompressionCodec;
import org.apache.parquet.ColumnWriteStore;
import org.apache.parquet.column.ColumnDescriptor;
import org.apache.parquet.column.ParquetProperties;
import org.apache.parquet.column.ParquetProperties.WriterVersion;
import org.apache.parquet.column.ParquetProperties.WriterVersion.PARQUET_2_0;
import org.apache.parquet.column.ParquetProperties.WriterVersion.PARQUET_1_0;
import org.apache.parquet.column.ParquetProperties.WriterVersion.PARQUET_1_6;
import org.apache.parquet.column.ParquetProperties.WriterVersion.PARQUET_1_0;
import org.apache.parquet.column.ParquetProperties.WriterVersion.PARQUET_2_0;
import org.apache.parquet.column.ParquetProperties.WriterVersion.PARQUET_1_6;
import org.apache.parquet.column.values.plain.PlainValuesWriter;
import org.apache.parquet.column.values.bitpacking.DevNullValuesWriter;
import org.apache.parquet.format.CompressionCodec;
import org.apache.parquet.hadoop.metadata.CompressionCodecName;
import org.apache.parquet.hadoop.metadata.CompressionCodecName.SNAPPY
import org.apache.parquet.column.values.bitpacking.IntPacker;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.parquet.hadoop.ParquetWriter;
import org.apache.parquet.hadoop.api.WriteSupport;
import org.apache.parquet.hadoop.example.GroupWriteSupport;
import org.apache.parquet.schema.MessageType;
import org.apache.parquet.schema.MessageTypeParser;
import java.io.IOException;
public class ParquetCompressionExample {
public static void main(String[] args) throws IOException {
// Prepare SCHEMA and data
String schemaString = "message example {
"
+ " optional int32 id;
"
+ " optional binary name (UTF8);
"
+ " optional int32 age;
"
+ "}";
MessageType schema = MessageTypeParser.parseMessageType(schemaString);
// Create the Writer of the written data
Path file = new Path("example.parquet");
ParquetWriter<Group> writer = new ParquetWriter<>(file, new GroupWriteSupport(), CompressionCodecName.SNAPPY, ParquetWriter.DEFAULT_BLOCK_SIZE, ParquetWriter.DEFAULT_PAGE_SIZE, ParquetWriter.DEFAULT_PAGE_SIZE, ParquetWriter.DEFAULT_IS_DICTIONARY_ENABLED, ParquetWriter.DEFAULT_IS_VALIDATING_ENABLED, ParquetWriter.DEFAULT_WRITER_VERSION, new Configuration());
// Write the data to the Parquet file
Group group1 = new SimpleGroup(schema);
group1.append("id", 1);
group1.append("name", "Alice");
group1.append("age", 25);
writer.write(group1);
Group group2 = new SimpleGroup(schema);
group2.append("id", 2);
group2.append("name", "Bob");
group2.append("age", 30);
writer.write(group2);
writer.close();
}
}
In the above example, we first define the schema of a Parquet file and create a ParQuetWriter instance.We then created two group objects, written the data into it, and wrote it into the ParQuet file.When creating a ParQuetWriter instance, we can specify the compression algorithm (Snappy here) and some other parameters, such as data block size and page size.
When using the Parquet Column framework for data compression, you need to pay attention to the following points:
- -Parquet Column framework to reduce the storage space of data by using appropriate compression algorithms and encoding methods.The compression algorithm can be configured by specifying the compression algorithm by creating a ParquetWriter instance.
-In when using the Parquet Column framework, a schema needs to be defined to ensure that the data can be written and read correctly.
-In the writing data, add the data to the corresponding column by using the group object, and write the group into the ParQuet file through the ParQuetWriter.
Summarize:
This article introduces how to use Apache Parquet Column framework in the Java library for data compression.By using the Parquet Column framework, we can reduce the storage space of the data by selecting the appropriate compression algorithm and encoding method and improve the read and write performance.
