Application of the Apache Parquet Colmn framework in the Java class library
Apache Parquet is a column -type storage format that is used to store big data sets on the disk.It provides efficient reading and writing performance and compression technology, suitable for processing a large amount of structured data.In Java development, the Apache Parquet column framework is widely used in data warehouses, data analysis, and data lakes.This article will introduce the application of the Apache Parquet column framework in the Java library and give some Java code examples.
### 1. Import dependence
First of all, we need to import the relevant dependence of Apache Parqueet in the Java project.You can manage dependencies through Maven or Gradle.
Maven dependence:
<dependency>
<groupId>org.apache.parquet</groupId>
<artifactId>parquet-column</artifactId>
<version>1.12.0</version>
</dependency>
Gradle dependencies:
groovy
implementation 'org.apache.parquet:parquet-column:1.12.0'
### 2. Create a Parquet reader
Reading and writing data with Apache Parquet needs to create the corresponding reader.The following is a Java example of creating a writer:
import org.apache.parquet.column.ParquetProperties;
import org.apache.parquet.column.ParquetProperties.WriterVersion;
import org.apache.parquet.hadoop.ParquetWriter;
import org.apache.parquet.hadoop.api.WriteSupport;
import org.apache.parquet.hadoop.api.WriteSupport.WriteContext;
import org.apache.parquet.schema.MessageType;
import org.apache.parquet.schema.MessageTypeParser;
import org.apache.parquet.schema.Types;
import java.io.IOException;
public class ParquetWriterExample {
public static void main(String[] args) throws IOException {
// Define the Parquet mode
String schemaAsString = "message example {
" +
" required int32 id;
" +
" required binary name (UTF8);
" +
"}";
MessageType schema = MessageTypeParser.parseMessageType(schemaAsString);
// Define the configuration
ParquetWriter.Builder<Object> builder = ParquetWriter.builder((WriteSupport<Object>) new CustomWriteSupport(), "output.parquet")
.withWriteMode(ParquetFileWriter.Mode.OVERWRITE)
.withCompressionCodec(CompressionCodecName.SNAPPY)
.withPageSize(512 * 1024)
.withRowGroupSize(4 * 1024 * 1024)
.withDictionaryEncoding(true)
.withWriterVersion(WriterVersion.PARQUET_2_0);
// Create a write -offer
ParquetWriter<Object> writer = builder.build();
// data input
writer.write(new ExampleRecord(1, "John Doe"));
writer.write(new ExampleRecord(2, "Jane Smith"));
// Turn off the writer
writer.close();
}
}
class ExampleRecord {
int id;
String name;
ExampleRecord(int id, String name) {
this.id = id;
this.name = name;
}
}
class CustomWriteSupport extends WriteSupport<Object> {
@Override
public WriteContext init(Configuration configuration) {
MessageType schema = Types.buildMessage()
.requiredInt32("id")
.requiredBinary("name")
.named("example")
.named("root")
.build();
return new WriteContext(schema, new HashMap<>());
}
@Override
public void prepareForWrite(ParquetFileWriter parquetFileWriter) throws IOException {
}
}
In the above example, we first define a Parquet mode and specify the structure of the data set.Then, a Parquet writer was created and configured with writing parameters, such as writing mode, compression algorithm and dictionary coding.Next, we can write the data into the Parquet file.
### 3. Create a Parquet reader
To read the data in the ParQuet file, we need to create a Parquet reader.The following is a Java example:
import org.apache.parquet.hadoop.ParquetReader;
import org.apache.parquet.schema.MessageType;
import org.apache.parquet.schema.MessageTypeParser;
import org.apache.parquet.schema.Types;
import java.io.IOException;
public class ParquetReaderExample {
public static void main(String[] args) throws IOException {
// Define the Parquet mode
String schemaAsString = "message example {
" +
" required int32 id;
" +
" required binary name (UTF8);
" +
"}";
MessageType schema = MessageTypeParser.parseMessageType(schemaAsString);
// Create a reader
ParquetReader<Object> reader = ParquetReader.builder((ReadSupport<Object>) new CustomReadSupport(), "output.parquet").build();
// Read the data and print it
Object record;
while ((record = reader.read()) != null) {
System.out.println(record);
}
// Turn off the reader
reader.close();
}
}
class CustomReadSupport extends ReadSupport<Object> {
@Override
public ReadContext init(Configuration configuration, Map<String, String> keyValueMetaData, MessageType fileSchema) {
MessageType schema = Types.buildMessage()
.requiredInt32("id")
.requiredBinary("name")
.named("example")
.named("root")
.build();
return new ReadContext(schema, new HashMap<>(), true);
}
@Override
public RecordMaterializer<Object> prepareForRead(Configuration configuration, Map<String, String> keyValueMetaData, MessageType fileSchema, ReadContext readContext) {
return new RecordMaterializer<Object>() {
@Override
public Object getCurrentRecord() {
// Customized analytical logic
return null;
}
@Override
public void skipCurrentRecord() {
}
@Override
public void close() throws IOException {
}
};
}
}
In the above example, we define a Parquet mode to match our data structure.Then, a Parquet reader was created and configured the reading parameters, such as the Parquet file path and custom reading logic.Finally, read the data with the reader and perform the corresponding processing operation.
Summarize:
The application of the Apache Parquet series framework in the Java library is widely used.It provides an efficient columnist storage format that is suitable for processing a large number of structured data.This article shows how to read and write data in Java with the Apache Parquet column framework in Java.Through these examples, readers can understand how to use Apache Parquet to process big data sets and speed up data analysis and query.
