Use the Apache Parquet Column framework in the Java Library for data analysis and query
Use the Apache Parquet Column framework in the Java Library for data analysis and query
Apache Parquet is an open source storage format for efficient storage and processing large -scale storage data.It is a optimized binary file format, which aims to improve data processing performance and storage efficiency.Apache Parquet provides a convenient way to process and query large -scale data sets, especially suitable for big data analysis.This article will introduce how to use the Apache Parquet Column framework in the Java library for data analysis and query.
First, we need to add the dependency item of Apache Parquet to our Java project.In the Maven project, you can add the following dependencies to the pom.xml file:
<dependency>
<groupId>org.apache.parquet</groupId>
<artifactId>parquet-column</artifactId>
<version>1.12.0</version>
</dependency>
Once we add dependencies, we can start using Apache Parquet Column framework for data analysis and query.Below is a simple example, showing how to read the ParQuet file and perform some basic query operations.
import org.apache.parquet.column.ColumnDescriptor;
import org.apache.parquet.column.ColumnReader;
import org.apache.parquet.column.ParquetProperties;
import org.apache.parquet.column.impl.ColumnReadStoreImpl;
import org.apache.parquet.column.page.PageReadStore;
import org.apache.parquet.column.page.PageReader;
import org.apache.parquet.example.data.Group;
import org.apache.parquet.example.data.simple.SimpleGroupFactory;
import org.apache.parquet.hadoop.ParquetFileReader;
import org.apache.parquet.hadoop.metadata.BlockMetaData;
import org.apache.parquet.hadoop.metadata.ColumnChunkMetaData;
import org.apache.parquet.hadoop.metadata.FileMetaData;
import org.apache.parquet.io.ColumnIOFactory;
import org.apache.parquet.io.MessageColumnIO;
import org.apache.parquet.io.ParquetDecodingException;
import org.apache.parquet.io.RecordReader;
import org.apache.parquet.schema.MessageType;
import org.apache.parquet.schema.PrimitiveType;
import org.apache.parquet.schema.Type;
import java.io.File;
import java.io.IOException;
public class ParquetColumnExample {
public static void main(String[] args) throws IOException {
// Read the Parquet file
String filePath = "path/to/parquet/file.parquet";
File parquetFile = new File(filePath);
ParquetFileReader fileReader = ParquetFileReader.open(ParquetIO.file(parquetFile));
FileMetaData fileMetaData = fileReader.getFileMetaData();
MessageType schema = fileMetaData.getSchema();
// Select columns to query
String columnName = "column_name";
ColumnDescriptor columnDescriptor = schema.getColumnDescription(new String[] {columnName});
// Traversing each data block
for (BlockMetaData blockMetaData : fileMetaData.getBlocks()) {
PageReadStore pageReadStore = fileReader.readNextRowGroup();
ColumnChunkMetaData columnChunkMetaData = blockMetaData.getColumns().get(columnDescriptor.getPath());
ColumnReader columnReader = new ColumnReadStoreImpl(pageReadStore, columnChunkMetaData, ParquetProperties.DEFAULT_COLUMN_PROPERTIES)
.getColumnReader(columnDescriptor);
// Traversing each data page
for (int pageIndex = 0; pageIndex < columnChunkMetaData.getPageCount(); pageIndex++) {
PageReader pageReader = pageReadStore.getPageReader(columnDescriptor);
pageReader.readPage();
RecordReader<Group> recordReader = columnReader.getRecordReader(pageReader);
Group record = recordReader.read();
// Execute some operations
// ...
}
}
// Close the file reader
fileReader.close();
}
}
In the above example, we first open the Parquet file in the read mode.Then, we obtain the file metadata and mode and choose the column to be queried.Next, we traverse each data block and traverse each data page in each data block.For each data page, we use ColumnReader to obtain a recording reader and read the data.In this example, we simply read the records, and you can perform more complicated operations according to your needs.
By using the Apache Parquet Column framework, we can efficiently process and query large -scale storage data.This allows us to better analyze and handle big data sets, thereby improving the efficiency and performance of data processing.
