Examples with CdcJdbcIO com.google.cloud.teleport.v2.io.CdcJdbcIO used on opensource projects

Example 1 with CdcJdbcIO

use of com.google.cloud.teleport.v2.io.CdcJdbcIO in project DataflowTemplates by GoogleCloudPlatform.

the class DataStreamToSQL method run.

/**
 * Runs the pipeline with the supplied options.
 *
 * @param options The execution parameters to the pipeline.
 * @return The result of the pipeline execution.
 */
public static PipelineResult run(Options options) {
    /*
     * Stages:
     *   1) Ingest and Normalize Data to FailsafeElement with JSON Strings
     *   2) Write JSON Strings to SQL DML Objects
     *   3) Filter stale rows using stateful PK transform
     *   4) Write DML statements to SQL Database via jdbc
     */
    Pipeline pipeline = Pipeline.create(options);
    CdcJdbcIO.DataSourceConfiguration dataSourceConfiguration = getDataSourceConfiguration(options);
    validateOptions(options, dataSourceConfiguration);
    Map<String, String> schemaMap = parseSchemaMap(options.getSchemaMap());
    /*
     * Stage 1: Ingest and Normalize Data to FailsafeElement with JSON Strings
     *   a) Read DataStream data from GCS into JSON String FailsafeElements (datastreamJsonRecords)
     */
    PCollection<FailsafeElement<String, String>> datastreamJsonRecords = pipeline.apply(new DataStreamIO(options.getStreamName(), options.getInputFilePattern(), options.getInputFileFormat(), options.getGcsPubSubSubscription(), options.getRfcStartDateTime()).withLowercaseSourceColumns().withHashColumnValue("_metadata_row_id", "rowid"));
    /*
     * Stage 2: Write JSON Strings to SQL Insert Strings
     *   a) Convert JSON String FailsafeElements to TableRow's (tableRowRecords)
     * Stage 3) Filter stale rows using stateful PK transform
     */
    PCollection<DmlInfo> dmlStatements = datastreamJsonRecords.apply("Format to DML", CreateDml.of(dataSourceConfiguration).withSchemaMap(schemaMap)).apply("DML Stateful Processing", ProcessDml.statefulOrderByPK());
    /*
     * Stage 4: Write Inserts to CloudSQL
     */
    dmlStatements.apply("Write to SQL", CdcJdbcIO.<DmlInfo>write().withDataSourceConfiguration(dataSourceConfiguration).withStatementFormatter(new CdcJdbcIO.StatementFormatter<DmlInfo>() {

        public String formatStatement(DmlInfo element) {
            return element.getDmlSql();
        }
    }));
    // Execute the pipeline and return the result.
    return pipeline.run();
}

Example 2 with CdcJdbcIO

use of com.google.cloud.teleport.v2.io.CdcJdbcIO in project DataflowTemplates by GoogleCloudPlatform.

the class DataStreamToPostgres method run.

/**
 * Runs the pipeline with the supplied options.
 *
 * @param options The execution parameters to the pipeline.
 * @return The result of the pipeline execution.
 */
public static PipelineResult run(Options options) {
    /*
     * Stages:
     *   1) Ingest and Normalize Data to FailsafeElement with JSON Strings
     *   2) Write JSON Strings to Postgres DML Objects
     *   3) Filter stale rows using stateful PK transform
     *   4) Write DML statements to Postgres
     */
    Pipeline pipeline = Pipeline.create(options);
    String jdbcDriverConnectionString = String.format("jdbc:postgresql://%s:%s/%s", options.getDatabaseHost(), options.getDatabasePort(), options.getDatabaseName());
    CdcJdbcIO.DataSourceConfiguration dataSourceConfiguration = CdcJdbcIO.DataSourceConfiguration.create("org.postgresql.Driver", jdbcDriverConnectionString).withUsername(options.getDatabaseUser()).withPassword(options.getDatabasePassword()).withMaxIdleConnections(new Integer(0));
    validateOptions(options, dataSourceConfiguration);
    /*
     * Stage 1: Ingest and Normalize Data to FailsafeElement with JSON Strings
     *   a) Read DataStream data from GCS into JSON String FailsafeElements (datastreamJsonRecords)
     */
    PCollection<FailsafeElement<String, String>> datastreamJsonRecords = pipeline.apply(new DataStreamIO(options.getStreamName(), options.getInputFilePattern(), options.getInputFileFormat(), options.getGcsPubSubSubscription(), options.getRfcStartDateTime()).withLowercaseSourceColumns().withHashColumnValue("_metadata_row_id", "rowid"));
    /*
     * Stage 2: Write JSON Strings to Postgres Insert Strings
     *   a) Convert JSON String FailsafeElements to TableRow's (tableRowRecords)
     * Stage 3) Filter stale rows using stateful PK transform
     */
    PCollection<DmlInfo> dmlStatements = datastreamJsonRecords.apply("Format to Postgres DML", CreateDml.createDmlObjects(dataSourceConfiguration)).apply("DML Stateful Processing", ProcessDml.statefulOrderByPK());
    /*
     * Stage 4: Write Inserts to CloudSQL
     */
    dmlStatements.apply("Write to Postgres", CdcJdbcIO.<DmlInfo>write().withDataSourceConfiguration(dataSourceConfiguration).withStatementFormatter(new CdcJdbcIO.StatementFormatter<DmlInfo>() {

        public String formatStatement(DmlInfo element) {
            return element.getDmlSql();
        }
    }));
    // Execute the pipeline and return the result.
    return pipeline.run();
}