Use Apache Iceberg framework to implement data writing and reading in the Java library
Use Apache Iceberg framework to implement data writing and reading in the Java library
Abstract: Apache Iceberg is an open source framework for managing large -scale data sets.It provides a reliable and efficient way to read and read data, and has the characteristics of transactional and version control.This article will introduce how to use Apache Iceberg in the Java library to implement data writing and reading.
introduction:
As the amount of data continues to increase, the management of large -scale data sets has become increasingly important.Apache Iceberg is an open source framework for managing large -scale data sets. It provides a reliable and efficient way to read and read data.Using Iceberg, we can add transaction management and version control functions to the data to ensure the consistency and traceability of the data.
Features of Iceberg framework:
1. Affairs management: Iceberg framework supports atomic writing operations to ensure the consistency of data when writing data.If the writing operation fails, the framework will automatically roll back.
2. Version control: Iceberg framework uses snapshot to track different versions of the data.Each snapshot is an unsusable data set, and you can access the specific version of the data through a timestamp or version number.
3. Metal data management: The Iceberg framework provides a mechanism for managing data sets.Metal data includes table mode (SCHEMA), partition information, file index, etc.Through these metadata, we can easily conduct data query and operation.
Implement data writing:
1. Add dependencies: First, add Apache Iceberg dependencies to the project's Maven or Gradle configuration file.
Maven configuration:
<dependency>
<groupId>org.apache.iceberg</groupId>
<artifactId>iceberg-core</artifactId>
<version>0.11.0</version>
</dependency>
Gradle configuration:
groovy
implementation 'org.apache.iceberg:iceberg-core:0.11.0'
2. Create table: Use Iceberg to create a new data table and specify the required table mode.
import org.apache.iceberg.*;
import org.apache.iceberg.spark.*;
Schema schema = new Schema(
Types.NestedField.required(1, "id", Types.IntegerType.get()),
Types.NestedField.required(2, "name", Types.StringType.get())
);
Table table = new HadoopTables().create(
CreateTable.builder()
.identifier("hdfs://localhost:9000/data/my_table")
.schema(schema)
.build()
);
3. Write data: Use Iceberg API to write the data into the table.
Table table = new HadoopTables().load("hdfs://localhost:9000/data/my_table");
try (Transaction transaction = table.newTransaction()) {
DataFileWriter<Row> writer = Parquet.writeTable(table)
.createWriterFunc(GenericInMemoryWriter::build)
.open();
writer.write(row(1, "John"));
writer.write(row(2, "Jane"));
writer.close();
transaction.commit();
}
Realize data reading:
1. Read data: Read the data from the table with Iceberg API.
Table table = new HadoopTables().load("hdfs://localhost:9000/data/my_table");
try (CloseableIterable<Row> rows = table.newScan().planTasks())
rows.forEach(row -> {
int id = row.getField("id");
String name = row.getField("name");
System.out.println("ID: " + id + ", Name: " + name);
});
Summarize:
Apache Iceberg is a very powerful open source framework that helps us manage large -scale data sets.This article introduces how to use Iceberg to implement data in the Java library to write and read data.By achieving the characteristics of Iceberg, we can process a large amount of data and ensure the consistency and traceability of the data.
