Guide on how to add and propagate new metadata fields in Apache Beam's WindowedValue, extending protos, windmill persistence, and runner interfaces to avoid metadata loss.
日本語の概要は準備中です。原文の説明を表示しています。
Guides development and usage of I/O connectors in Apache Beam. Use when working with I/O connectors, creating new connectors, or debugging data source/sink issues.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
I/O connectors enable reading from and writing to external data sources. Beam provides 51+ Java I/O connectors and several Python connectors.
sdks/java/io/
| Category | Connectors |
|---|---|
| Cloud Storage | google-cloud-platform (BigQuery, Bigtable, Spanner, Pub/Sub, GCS), amazon-web-services2, azure, azure-cosmos |
| Databases | jdbc, mongodb, cassandra, hbase, redis, neo4j, clickhouse, influxdb, singlestore, elasticsearch |
| Messaging | kafka, pulsar, rabbitmq, amqp, jms, mqtt, solace |
| File Formats | parquet, csv, json, xml, thrift, iceberg |
| Other | snowflake, splunk, cdap, debezium, hadoop-format, kudu, solr, tika |
./gradlew :sdks:java:io:kafka:test
./gradlew :sdks:java:io:jdbc:test
./gradlew :sdks:java:io:google-cloud-platform:integrationTest
./gradlew :sdks:java:io:google-cloud-platform:integrationTest \
-PgcpProject=<project> \
-PgcpTempRoot=gs://<bucket>/path
./gradlew :sdks:java:io:jdbc:integrationTest \
-DbeamTestPipelineOptions='["--runner=TestDirectRunner"]'
Located at it/ directory:
it/common/ - Common test utilitiesit/google-cloud-platform/ - GCP-specific test infrastructureit/jdbc/ - JDBC test infrastructureit/kafka/ - Kafka test infrastructureit/testcontainers/ - Testcontainers support@RunWith(JUnit4.class)
public class MyIOIT {
@Rule public TestPipeline readPipeline = TestPipeline.create();
@Rule public TestPipeline writePipeline = TestPipeline.create();
@Test
public void testWriteAndRead() {
// Write data
writePipeline.apply(Create.of(testData))
.apply(MyIO.write().to(destination));
writePipeline.run().waitUntilFinish();
// Read and verify
PCollection<String> results = readPipeline.apply(MyIO.read().from(destination));
PAssert.that(results).containsInAnyOrder(expectedData);
readPipeline.run().waitUntilFinish();
}
}
@Rule public TestPipeline pipeline = TestPipeline.create();
TestPipeline:
beamTestPipelineOptions system property// Read
pipeline.apply(BigQueryIO.readTableRows().from("project:dataset.table"));
// Write
data.apply(BigQueryIO.writeTableRows()
.to("project:dataset.table")
.withSchema(schema)
.withWriteDisposition(WriteDisposition.WRITE_APPEND));
// Read
pipeline.apply(PubsubIO.readStrings().fromTopic("projects/project/topics/topic"));
// Write
data.apply(PubsubIO.writeStrings().to("projects/project/topics/topic"));
// Read
pipeline.apply(TextIO.read().from("gs://bucket/path/*.txt"));
// Write
data.apply(TextIO.write().to("gs://bucket/output").withSuffix(".txt"));
// Read
pipeline.apply(KafkaIO.<String, String>read()
.withBootstrapServers("localhost:9092")
.withTopic("topic")
.withKeyDeserializer(StringDeserializer.class)
.withValueDeserializer(StringDeserializer.class));
// Write
data.apply(KafkaIO.<String, String>write()
.withBootstrapServers("localhost:9092")
.withTopic("topic")
.withKeySerializer(StringSerializer.class)
.withValueSerializer(StringSerializer.class));
// Read
pipeline.apply(JdbcIO.<Row>read()
.withDataSourceConfiguration(JdbcIO.DataSourceConfiguration
.create("org.postgresql.Driver", "jdbc:postgresql://host/db"))
.withQuery("SELECT * FROM table"));
// Write
data.apply(JdbcIO.<Row>write()
.withDataSourceConfiguration(config)
.withStatement("INSERT INTO table VALUES (?, ?)"));
sdks/python/apache_beam/io/
textio - Text filesfileio - General file operationsavroio - Avro filesparquetio - Parquet filesgcp/ - GCP connectors (BigQuery, Pub/Sub, Datastore, etc.)Beam supports using I/O connectors from one SDK in another via the expansion service.
# Start Java expansion service
./gradlew :sdks:java:io:expansion-service:runExpansionService
Key components:
For more detailed information on developing new I/O connectors see the Developing new I/O connectors SKILL.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Guide on how to add and propagate new metadata fields in Apache Beam's WindowedValue, extending protos, windmill persistence, and runner interfaces to avoid metadata loss.
日本語の概要は準備中です。原文の説明を表示しています。
Explains core Apache Beam programming model concepts including PCollections, PTransforms, Pipelines, and Runners. Use when learning Beam fundamentals or explaining pipeline concepts.
日本語の概要は準備中です。原文の説明を表示しています。
Guides understanding and working with Apache Beam's CI/CD system using GitHub Actions. Use when debugging CI failures, understanding test workflows, or modifying CI configuration.
日本語の概要は準備中です。原文の説明を表示しています。
Guides the contribution workflow for Apache Beam, including creating PRs, issue management, code review process, release cycles, and rigorous evaluation rules for high-risk core component changes. Use when contributing code, creating PRs, or modifying core Beam components.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end guide on developing new Apache Beam I/O connectors correctly, including core IO transforms, SchemaTransforms, URN proto definitions, Managed API integration, cross-language expansion service, and testing.
日本語の概要は準備中です。原文の説明を表示しています。
Guides understanding and using the Gradle build system in Apache Beam. Use when building projects, understanding dependencies, or troubleshooting build issues.
日本語の概要は準備中です。原文の説明を表示しています。