The advantages and characteristics of Apache Parquet Colmn framework in the Java class library
Apache Parquet is a columnist storage format that is widely used in big data processing.It has many advantages and characteristics to help users process and analyze data more efficiently.
1. High performance: Apache Parquet can greatly improve the query and analysis of performance by storing the data according to columns.It uses coding and compression technology to reduce storage space, and support optimization technologies such as predicate push and tailoring to make the query operation faster.
2. Scalability: Apache Parquet can effectively handle large -scale data sets.It supports the division and partition of data, and can read and write data in parallel.This allows users to easily process a large amount of data and expand and distribute the data horizontally as needed.
3. Cross -platform compatibility: Apache Parquet is an open source code item and can be used on various platforms.It provides binding of Java libraries and other languages (such as Python and C ++), allowing users to use the Parquet file format in many environments.
4. Data model flexibility: Apache Parquet supports complex data models, which can store nested data structures such as nested records and lists.This makes it very suitable for storing semi -structured and multi -level data, such as JSON files or nested AVRO records.
The following is a simple Java code example. It shows how to read and write the ParQuet file with Apache Parquet Column framework:
import org.apache.parquet.column.ColumnDescriptor;
import org.apache.parquet.column.ParquetProperties;
import org.apache.parquet.column.ParquetProperties.WriterVersion;
import org.apache.parquet.example.data.Group;
import org.apache.parquet.example.data.simple.SimpleGroupFactory;
import org.apache.parquet.hadoop.ParquetReader;
import org.apache.parquet.hadoop.ParquetWriter;
import org.apache.parquet.hadoop.example.GroupReadSupport;
import org.apache.parquet.hadoop.example.GroupWriteSupport;
import org.apache.parquet.schema.MessageType;
import org.apache.parquet.schema.MessageTypeParser;
import org.apache.parquet.schema.Types;
import java.io.IOException;
public class ParquetColumnExample {
public static void main(String[] args) throws IOException {
// Create the schema of the Parquet file
String schemaString = "message User {
" +
" required int32 id;
" +
" required binary name;
" +
" optional int32 age;
" +
"}";
MessageType schema = MessageTypeParser.parseMessageType(schemaString);
// Create a writer of Parquet file
Path filePath = new Path("example.parquet");
Configuration configuration = new Configuration();
GroupWriteSupport.setSchema(schema, configuration);
ParquetWriter<Group> writer = new ParquetWriter<>(filePath, new GroupWriteSupport(), ParquetWriter.DEFAULT_COMPRESSION_CODEC_NAME, 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, configuration);
// Write the data to the Parquet file
SimpleGroupFactory groupFactory = new SimpleGroupFactory(schema);
for (int i = 1; i <= 10; i++) {
Group group = groupFactory.newGroup()
.append("id", i)
.append("name", "User " + i)
.append("age", 20 + i);
writer.write(group);
}
writer.close();
// Create a reader of the Parquet file
ParquetReader<Group> reader = ParquetReader.builder(new GroupReadSupport(), filePath).build();
// Read data from the Parqueet file
Group rowGroup;
while ((rowGroup = reader.read()) != null) {
ColumnDescriptor idColumn = rowGroup.getType().getColumnDescription(new String[]{"id"});
ColumnDescriptor nameColumn = rowGroup.getType().getColumnDescription(new String[]{"name"});
ColumnDescriptor ageColumn = rowGroup.getType().getColumnDescription(new String[]{"age"});
int id = rowGroup.getInteger(idColumn.getMaxDefinitionLevel(), idColumn.getMaxRepetitionLevel(), idColumn.getId().getFirst());
String name = rowGroup.getString(nameColumn.getMaxDefinitionLevel(), nameColumn.getMaxRepetitionLevel(), nameColumn.getId().getFirst());
int age = rowGroup.getInteger(ageColumn.getMaxDefinitionLevel(), ageColumn.getMaxRepetitionLevel(), ageColumn.getId().getFirst());
System.out.println("ID: " + id + ", Name: " + name + ", Age: " + age);
}
reader.close();
}
}
This example demonstrates how to use the Column framework to create the schema of the Parquet file and how to use ParquetWriter to write the data into the file.Then, the example uses the ParQuetReader to read the data from the file, and uses the COLUMNDEScriptor to extract the data from the reading line.Finally, print the extracted data to the console.
These advantages and characteristics of Apache Parquet make it an ideal choice for big data processing and analysis.Whether in large -scale data storage, fast query, or data model flexibility, Parquet provides efficient and powerful solutions.
