Introduction to Apache Parquet Column framework
Apache Parquet is a column -type storage format that is used to store and process large -scale data sets efficiently.It is widely used in big data processing tools and distributed computing frameworks, such as Apache Hadoop and Apache Spark.This article will introduce the basic concepts and characteristics of Apache Parquet, and provide some Java code examples to illustrate its usage.
1. What is Apache Parquet?
Apache Parquet is an open source storage format. Its design goal is to store and process large -scale structured data in an efficient and scalable way.Compared with the traditional line storage format (such as CSV and JSON), the column storage format has many advantages.
In the traditional line storage format, the data is stored according to row, and each line contains the values of all fields.When a single field query, the entire line needs to be read, causing reading redundant data and reducing query efficiency.In the column storage format, the data is stored by column, and each column contains all the values of the same field.This means that when a single field query, you only need to read the column of the field, which greatly improves the query efficiency.
In addition, the column storage format has better compression capabilities.Because the same type of data is continuously stored in a column, more effective compression algorithms can be used.This not only reduces storage demand, but also improves data reading speed.
2. The characteristics of Apache Parquet
Apache Parquet has the following characteristics:
1. High -efficiency inquiries: Because the data is stored according to columns, Parqueet can only read the required list to improve the query efficiency.
2. High compression ratio: Parquet uses multiple compression algorithms, including Snappy, GZIP, and LZO, which can minimize storage demand.
3. Column storage: The data is stored by columns, so that Parquet is suitable for efficiently handling large -scale data sets.
4. column coding: Parquet uses a variety of column encoding algorithms, such as Run Length Encoding (RLE) and Dictionary Encoding, which further improves the compression ratio and query efficiency.
5. Cross -platform: Parquet is an open storage format that can be used in various platforms and distributed computing frameworks.
Third, example of Apache Parquet
Below is a simple Java code example, demonstrating how to read and write data with Apache Parquet.
1. Import related ParQuet dependence library:
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.parquet.column.ColumnDescriptor;
import org.apache.parquet.column.ParquetProperties;
import org.apache.parquet.column.ParquetStore;
import org.apache.parquet.column.ParquetWriteSupport;
import org.apache.parquet.example.data.Group;
import org.apache.parquet.hadoop.ParquetFileReader;
import org.apache.parquet.hadoop.ParquetFileWriter;
import org.apache.parquet.hadoop.example.GroupReadSupport;
import org.apache.parquet.hadoop.example.GroupWriteSupport;
import org.apache.parquet.hadoop.metadata.CompressionCodecName;
import org.apache.parquet.hadoop.util.HadoopInputFile;
import org.apache.parquet.hadoop.util.HadoopOutputFile;
2. Read the Parquet file:
public List<Group> readParquetFile(String parquetFilePath) throws IOException {
Configuration configuration = new Configuration();
ParquetFileReader reader = ParquetFileReader.open(HadoopInputFile.fromPath(new Path(parquetFilePath), configuration));
GroupReadSupport readSupport = new GroupReadSupport();
ParquetProperties properties = ParquetProperties.builder().build();
ParquetStore<Group> store = new ParquetStore<>(reader, readSupport, properties);
List<Group> groups = new ArrayList<>();
while (store.hasNext()) {
groups.add(store.next());
}
return groups;
}
3. Write into the Parquet file:
public void writeParquetFile(List<Group> groups, String parquetFilePath) throws IOException {
Configuration configuration = new Configuration();
GroupWriteSupport writeSupport = new GroupWriteSupport();
ParquetOutputFile outputFile = HadoopOutputFile.fromPath(new Path(parquetFilePath), configuration);
ParquetFileWriter writer = new ParquetFileWriter(outputFile, CompressionCodecName.SNAPPY, ParquetFileWriter.Mode.CREATE, ParquetWriteSupport.PARQUET_VERSION, PARQUET_ROW_GROUP_SIZE_DEFAULT, PAGE_SIZE_DEFAULT, DICTIONARY_PAGE_SIZE_DEFAULT);
writer.open();
for (Group group : groups) {
writer.write(group);
}
writer.close();
}
Through the above example, we can see how to use the Apache Parquet library to read and write the Parquet file.In practical applications, appropriate configuration and optimization can be performed according to specific needs.
Summarize:
Apache Parquet is an efficient columnist storage format that plays an important role in big data processing.It provides efficient query performance and high compression ratio by storing data and using effective encoding and compression algorithms.With the help of Java code examples, we can better understand and use Apache Parquet, and use its advantages to process large -scale data sets in actual development.
