Introduction to Apache Iceberg framework in the Java class library

Apache Iceberg is a data table format and real -time query engine of an open source code.It is designed to process and manage massive data in big data lakes.This article will introduce the basic concepts and characteristics of Apache Iceberg, and provide some Java code examples. Apache Iceberg is aimed at large -scale and diversified data, which provides a reliable, scalable structured data management solution.It supports the hierarchical structure of the concepts such as the concept of data tissue, sorting, and snapshot to support efficient query and data operations.Here are the key concepts of some Apache Iceberg: 1. Table: Iceberg uses tables to organize data.The form is a logical container of data, which contains all data rows and metadata.The Iceberg table can be regarded as a high -level representation of a distributed dataset. 2. Partition: Iceberg uses partitions to organize and manage data.Partition is a process of dividing the data in accordance with a specific column or expression.Partitions can make the query more efficient and facilitate data management. 3. Sorting: ICEBERG supports sorting data in the table.Sorting can improve the query performance, especially in some columns in scope query. 4. Snapshot: Iceberg uses snapshots to manage the form of the form.Each snapshot is the consistency view of the form, which can contain multiple partitions and data files.Snapshot can be used for query and rollback data. 5. Data File: Iceberg uses the actual data of the data file storage table.Each data file contains some data rows and corresponding metadata. Now let's take a look at a few Apache Iceberg Java code example: 1. Create an Iceberg form: import org.apache.iceberg.Table; import org.apache.iceberg.catalog.TableIdentifier; import org.apache.iceberg.spark.SparkCatalog; // Use SparkCatalog to create forms SparkCatalog catalog = new SparkCatalog(sparkSession); TableIdentifier tableId = TableIdentifier.of("database", "table"); Table table = catalog.createTable(tableId, schema); 2. Insert data into the Iceberg table: import org.apache.iceberg.AppendFiles; import org.apache.iceberg.Schema; import org.apache.iceberg.data.Record; import org.apache.iceberg.hadoop.HadoopTables; // Create Hadooptables object HadoopTables tables = new HadoopTables(hadoopConf); // Open the form Table table = tables.load(tablePath); // Construct a data line and insert form Schema schema = table.schema(); Record record = GenericRecord.create(schema); // Set the value of the data line record.setField("column1", "value1"); record.setField("column2", "value2"); // Insert data row AppendFiles append = table.newAppend(); append.appendFile(dataFile); append.commit(); 3. Query data in the Iceberg table: import org.apache.iceberg.data.GenericRecord; // Open the form Table table = tables.load(tablePath); // Construction query Iterable<Record> records = table .newScan() .filter(Expressions.equal("column1", "value1")) .<GenericRecord>select("column2") .limit(100) .planFiles(); // Traversing query results for (Record record : records) { System.out.println(record.get("column2")); } The above is the basic concept of some Apache Iceberg and examples of Java code.Apache Iceberg provides a reliable and easy -to -use method to process data in big data lakes, making data management and query more efficient.