Basic steps to manipulate Parquet files using Java

Detailed explanation of Parquet file

Apache ParquetIt is an open source columnar storage format designed for big data processing and is especially suitable for analytical data storage. It is widely used in big data frameworks (such as Hadoop, Spark, Hive, etc.) because it supports efficient compression and query optimization and is suitable for processing large-scale data sets.

Parquet is designed to have many advantages when processing big data, especially in storage, compression, query and processing speed.

1. Parquet's design concept

• Column storage: Unlike row-based storage (such as CSV, JSON), Parquet stores data in columns rather than rows. This means that the data of each column will be stored together, and a column can be read and processed efficiently.
• High efficiency compression: Because the same type of data is stored together, Parquet can perform highly optimized compression and reduce storage space.
• Support complex data structures:Parquet supports complex data types such as nested structures, arrays, dictionaries, etc., which enables it to effectively process structured and semi-structured data.
• Cross-platform support:Parquet is an open source format that supports multiple programming languages ​​and big data processing frameworks (such as Apache Spark, Hadoop, Hive, Presto, etc.).

2. Parquet file format

The Parquet file is made byFile header、Metadata、Data blocksComposition of other parts. Its structural design is very efficient, and the specific format includes the following important parts:

2.1 File Header

• The Parquet file header contains the format information of the file. Each Parquet file is in a fixed sequence of bytesPAR1(i.e. ASCII charactersPAR1) Start and end, this tag is used to identify that the file is a Parquet file.

2.2 Metadata

• File-level metadata: Includes information such as the schema of the file, the column name and type of the data.
• Column family metadata: Describe each column of data, including the name, type, data encoding method, compression method, etc.
• Page-level metadata: Each column of data in the Parquet file is divided into multiple data blocks (pages), and each data block will also contain its own metadata.

2.3 Data Pages

• The minimum unit of data storage is a data page, each data page contains a certain number of column data. Each data page has its own metadata (such as compression format, number of rows, etc.) and is stored in columns. Each column of data is also divided into multiple pages to store.

2.4 Checksum

• Each data block and data page has a checksum to ensure data integrity and ensure that there is no data corruption during reading.

2.5 File Footer

• The end of the Parquet file contains the file's index and metadata offset, so that relevant data blocks and column metadata can be quickly positioned during reading.

3. Advantages of Parquet

3.1 Efficient storage and compression

• Column storageThis enables the Parquet format to optimize storage and compression of data in the same column, greatly reducing disk space usage.
• Supports multiple compression algorithms, such asSnappy、GZIP、Brotlietc. You can choose different compression methods according to the data characteristics.
• In terms of compression, columnar storage formats can usually save more storage space than row storage formats.

3.2 Efficient query performance

• Since the data is stored in columns, it can only read data from specific columns, greatly improving query efficiency. For example, in a table with multiple columns, if you only care about a few columns of data, Parquet can only load those columns, thereby reducing I/O operations.
• Parquet format supportPredicate pushdown, that is, when querying, filtering conditions are applied directly to data on disk, thereby reducing the burden of transmission and calculation.

3.3 Support complex data structures

• Parquet can efficiently handle nested data types, such as arrays, dictionaries, structures, etc. It can represent complex data patterns, so that structured and semi-structured data in big data environments can be effectively stored and processed.

3.4 Cross-platform and cross-language support

• Parquet is an open source project that supports a wide range of different programming languages ​​and big data frameworks. For example, Apache Hive, Apache Impala, Apache Spark, Apache Drill, etc. all natively support the Parquet format.
• Its format is designed to be cross-platform, supporting a variety of storage engines and processing tools.

3.5 Scalability

• Since Parquet is a columnar storage format, it can maintain good performance and scalability when facing massive data. It supportsDistributed StorageandDistributed computing, very suitable forBig Data PlatformUse on.

4. Comparison between Parquet and other formats

5. How to use Parquet files

In actual development, the operation using the Parquet file usually involves the following steps:

Create a Parquet file：

• In Spark, Hive, or other big data processing frameworks, you can save a data frame (DataFrame) or table in Parquet format. For example, in Apache Spark:

().parquet("path/to/");

Read Parquet file：

• Reading a Parquet file is also very simple. For example, read a Parquet file in Apache Spark:

Dataset<Row> df = ().parquet("path/to/");
();

Optimize query：

• By leveraging the columnar storage advantages of Parquet, efficient queries can be performed. For example, use predicate pushdown in Spark to speed up query operations:

Dataset<Row> result = ().parquet("path/to/")
                              .filter("age > 30")
                              .select("name", "age");
();

Summarize

ParquetIt is a powerful columnar storage format suitable for big data scenarios and can efficiently compress, query and store data. It is particularly suitable for application scenarios that require high-performance queries, large-scale data processing, and support for complex data structures. Parquet is often the preferred file format when using Apache Spark, Hive, or other big data frameworks.

Operation of Parquet files using java

Using Apache Spark to read and write Parquet files in Java is a common task, especially when dealing with large-scale data. The Parquet format is widely used for its efficient columnar storage features. Here are the basic steps for how to use Spark in Java to read and write Parquet files.

1. Add dependencies

First, you need to add Apache Spark and Parquet dependencies to your project. If you are usingMaven, you need toAdd the following dependencies:

<dependencies>
    <!-- Spark Core -->
    <dependency>
        <groupId></groupId>
        <artifactId>spark-core_2.12</artifactId>
        <version>3.3.1</version>
    </dependency>

    <!-- Spark SQL (for Parquet support) -->
    <dependency>
        <groupId></groupId>
        <artifactId>spark-sql_2.12</artifactId>
        <version>3.3.1</version>
    </dependency>

    <!-- Spark Hadoop Dependencies (if using HDFS) -->
    <dependency>
        <groupId></groupId>
        <artifactId>hadoop-common</artifactId>
        <version>3.3.1</version>
    </dependency>

    <!-- Parquet Dependencies -->
    <dependency>
        <groupId></groupId>
        <artifactId>parquet-hadoop</artifactId>
        <version>1.12.0</version>
    </dependency>
</dependencies>

Note: The version number should be adjusted according to your Spark and Hadoop versions.

2. Create SparkSession

In Java, you need to create a SparkSession, which is the main entry to access Spark SQL features in the Spark version. You can configure the logic for reading and writing Parquet files in SparkSession.

import ;

public class ParquetExample {
    public static void main(String[] args) {
        // Create SparkSession        SparkSession spark = ()
                .appName("Parquet Example")
                .master("local[*]") // You can adjust it to cluster mode as needed                .getOrCreate();

        // The code to read and write Parquet files will be performed here    }
}

3. Read the Parquet file

Reading Parquet files is very simple, you only need to useSparkSessionofreadAPI and specify file path. Spark will automatically infer the file's schema.

import ;
import ;

public class ParquetExample {
    public static void main(String[] args) {
        // Create SparkSession        SparkSession spark = ()
                .appName("Parquet Example")
                .master("local[*]") // You can adjust it to cluster mode as needed                .getOrCreate();

        // Read Parquet file        Dataset<Row> parquetData = ().parquet("path/to/your/parquet/file");

        // Display data        ();
    }
}

Notice：

• You can replace"path/to/your/parquet/file"The path to the Parquet file on your local file system or HDFS.
• Dataset<Row>It is a data structure in Spark SQL, representing tabular data.

4. Write to Parquet file

Writing data to a Parquet file is very simple. You just need to usewrite()API and specify the target path.

import ;
import ;

public class ParquetExample {
    public static void main(String[] args) {
        // Create SparkSession        SparkSession spark = ()
                .appName("Parquet Example")
                .master("local[*]") // You can adjust it to cluster mode as needed                .getOrCreate();

        // Create a sample dataset        Dataset<Row> data = ().json("path/to/your/json/file");

        // Write to Parquet file        ().parquet("path/to/output/parquet");

        // You can also configure Parquet write options, such as overwriting files, partitions, etc.        // ().mode("overwrite").parquet("path/to/output/parquet");
    }
}

Write mode:

• By default, Spark will useappendMode write data. You can use.mode("overwrite")to overwrite existing Parquet files, or use.mode("ignore")to ignore write conflicts.

5. Advanced operations for reading and writing Parquet data

You can perform some more complex operations, such as:

• Read multiple Parquet files: By providing multiple file paths or directory paths, Spark automatically reads all matching Parquet files.

Dataset<Row> parquetData = ().parquet("path/to/files/*.parquet");

• Read/write Parquet files using partitions: On large data sets, partitioning can significantly improve read and write performance.

// Partition data when writing().partitionBy("columnName").parquet("path/to/output/parquet");

• Custom mode: Sometimes you may want to explicitly specify the schema of the Parquet file, especially if the file format is not standardized or contains nested data.

import .*;

StructType schema = new StructType()
    .add("name", )
    .add("age", );

Dataset<Row> parquetData = ().schema(schema).parquet("path/to/parquet/file");

6. Optimize Parquet read and write performance

• Compress using Snappy:Spark uses Snappy compression by default, which usually provides good compression and decompression speed.

().option("compression", "snappy").parquet("path/to/output/parquet");

• Inferred mode: If you have a very large Parquet file and don't want to load the entire file to infer the pattern, you can useinferSchemaor predefined patterns to avoid overhead.

Summarize

Reading and writing Parquet files with Apache Spark is very simple, and the Spark SQL API allows easy integration of data processing flow into the Parquet format, taking advantage of Parquet's advantages in big data storage and queries. Spark provides rich functions to optimize the reading and writing of Parquet files, including automatic inference mode, support for columnar storage compression and partitioning, making it a very efficient tool for processing large-scale data.

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