Examples with SQLRelationDefinition io.cdap.cdap.etl.api.engine.sql.request.SQLRelationDefinition used on opensource projects

Example 1 with SQLRelationDefinition

use of io.cdap.cdap.etl.api.engine.sql.request.SQLRelationDefinition in project cdap by caskdata.

the class BatchSQLEngineAdapter method tryRelationalTransform.

/**
 * This method is called when engine is present and is willing to try performing a relational transform.
 *
 * @param stageSpec stage specification
 * @param transform transform plugin
 * @param input     input collections
 * @return resulting collection or empty optional if tranform can't be done with this engine
 */
public Optional<SQLEngineJob<SQLDataset>> tryRelationalTransform(StageSpec stageSpec, RelationalTransform transform, Map<String, SparkCollection<Object>> input) {
    String stageName = stageSpec.getName();
    Map<String, Relation> inputRelations = input.entrySet().stream().collect(Collectors.toMap(Map.Entry::getKey, e -> sqlEngine.getRelation(new SQLRelationDefinition(e.getKey(), stageSpec.getInputSchemas().get(e.getKey())))));
    BasicRelationalTransformContext pluginContext = new BasicRelationalTransformContext(getSQLRelationalEngine(), inputRelations, stageSpec.getInputSchemas(), stageSpec.getOutputSchema());
    if (!transform.transform(pluginContext)) {
        // Plugin was not able to do relational tranform with this engine
        return Optional.empty();
    }
    if (pluginContext.getOutputRelation() == null) {
        // Plugin said that tranformation was success but failed to set output
        throw new IllegalStateException("Plugin " + transform + " did not produce a relational output");
    }
    if (!pluginContext.getOutputRelation().isValid()) {
        // An output is set to invalid relation, probably some of transforms are not supported by an engine
        return Optional.empty();
    }
    // Ensure input and output schemas for this stage are supported by the engine
    if (stageSpec.getInputSchemas().values().stream().anyMatch(s -> !sqlEngine.supportsInputSchema(s))) {
        return Optional.empty();
    }
    if (!sqlEngine.supportsOutputSchema(stageSpec.getOutputSchema())) {
        return Optional.empty();
    }
    // Validate transformation definition with engine
    SQLTransformDefinition transformDefinition = new SQLTransformDefinition(stageName, pluginContext.getOutputRelation(), stageSpec.getOutputSchema(), Collections.emptyMap(), Collections.emptyMap());
    if (!sqlEngine.canTransform(transformDefinition)) {
        return Optional.empty();
    }
    return Optional.of(runJob(stageSpec.getName(), SQLEngineJobType.EXECUTE, () -> {
        // Push all stages that need to be pushed to execute this aggregation
        input.forEach((name, collection) -> {
            if (!exists(name)) {
                push(name, stageSpec.getInputSchemas().get(name), collection);
            }
        });
        // Initialize metrics collector
        DefaultStageMetrics stageMetrics = new DefaultStageMetrics(metrics, stageName);
        StageStatisticsCollector statisticsCollector = statsCollectors.get(stageName);
        // Collect input datasets and execute transformation
        Map<String, SQLDataset> inputDatasets = input.keySet().stream().collect(Collectors.toMap(Function.identity(), this::getDatasetForStage));
        // Count input records
        for (SQLDataset inputDataset : inputDatasets.values()) {
            countRecordsIn(inputDataset, statisticsCollector, stageMetrics);
        }
        // Execute transform
        SQLTransformRequest sqlContext = new SQLTransformRequest(inputDatasets, stageSpec.getName(), pluginContext.getOutputRelation(), stageSpec.getOutputSchema());
        SQLDataset transformed = sqlEngine.transform(sqlContext);
        // Count output records
        countRecordsOut(transformed, statisticsCollector, stageMetrics);
        return transformed;
    }));
}