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/**
* DataCleaner (community edition)
* Copyright (C) 2014 Neopost - Customer Information Management
*
* This copyrighted material is made available to anyone wishing to use, modify,
* copy, or redistribute it subject to the terms and conditions of the GNU
* Lesser General Public License, as published by the Free Software Foundation.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
* or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License
* for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this distribution; if not, write to:
* Free Software Foundation, Inc.
* 51 Franklin Street, Fifth Floor
* Boston, MA 02110-1301 USA
*/
package org.datacleaner.spark.functions;
import java.io.File;
import java.net.URI;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Iterator;
import java.util.List;
import java.util.ListIterator;
import java.util.Set;
import org.apache.metamodel.csv.CsvConfiguration;
import org.apache.metamodel.util.FileResource;
import org.apache.metamodel.util.HdfsResource;
import org.apache.metamodel.util.Resource;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFlatMapFunction;
import org.datacleaner.api.AnalyzerResult;
import org.datacleaner.api.AnalyzerResultFuture;
import org.datacleaner.api.HasAnalyzerResult;
import org.datacleaner.api.InputRow;
import org.datacleaner.configuration.DataCleanerConfiguration;
import org.datacleaner.connection.CsvDatastore;
import org.datacleaner.connection.Datastore;
import org.datacleaner.connection.JsonDatastore;
import org.datacleaner.connection.ResourceDatastore;
import org.datacleaner.connection.UpdateableDatastore;
import org.datacleaner.descriptors.ConfiguredPropertyDescriptor;
import org.datacleaner.extension.output.CreateCsvFileAnalyzer;
import org.datacleaner.job.AnalysisJob;
import org.datacleaner.job.builder.AnalysisJobBuilder;
import org.datacleaner.job.builder.ComponentBuilder;
import org.datacleaner.job.runner.ActiveOutputDataStream;
import org.datacleaner.job.runner.ConsumeRowHandler;
import org.datacleaner.job.runner.RowProcessingConsumer;
import org.datacleaner.lifecycle.LifeCycleHelper;
import org.datacleaner.spark.NamedAnalyzerResult;
import org.datacleaner.spark.SparkAnalysisRunner;
import org.datacleaner.spark.SparkJobContext;
import org.datacleaner.spark.utils.HdfsHelper;
import org.datacleaner.util.HadoopResource;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import scala.Tuple2;
/**
* The main Spark function which applies the DataCleaner row processing
* framework onto RDDs of InputRows.
*
* The main vehicle used to do this is the {@link ConsumeRowHandler}.
*
* This class implements two interfaces because it has two (quite similar)
* styles of usages in the {@link SparkAnalysisRunner}.
*/
public final class RowProcessingFunction implements
Function2, Iterator>>,
PairFlatMapFunction, String, NamedAnalyzerResult> {
private static final Logger logger = LoggerFactory.getLogger(RowProcessingFunction.class);
private static final long serialVersionUID = 1L;
private final SparkJobContext _sparkJobContext;
public RowProcessingFunction(final SparkJobContext sparkJobContext) {
_sparkJobContext = sparkJobContext;
}
@Override
public Iterable> call(Iterator inputRowIterator) throws Exception {
logger.info("call(Iterator) invoked");
final AnalysisJob analysisJob = _sparkJobContext.getAnalysisJob();
final List> analyzerResults = executePartition(inputRowIterator,
analysisJob);
logger.info("call(Iterator) finished, returning {} results", analyzerResults.size());
return analyzerResults;
}
@Override
public Iterator> call(Integer partitionNumber,
Iterator inputRowIterator) throws Exception {
logger.info("call({}, Iterator) invoked", partitionNumber);
final AnalysisJobBuilder jobBuilder = _sparkJobContext.getAnalysisJobBuilder();
configureComponentsBeforeBuilding(jobBuilder, partitionNumber.intValue());
final AnalysisJob analysisJob = jobBuilder.toAnalysisJob();
final List> analyzerResults = executePartition(inputRowIterator,
analysisJob);
logger.info("call({}, Iterator) finished, returning {} results", partitionNumber, analyzerResults.size());
return analyzerResults.iterator();
}
/**
* Applies any partition-specific configuration to the job builder before
* building it.
*
* @param jobBuilder
* @param partitionNumber
*/
private void configureComponentsBeforeBuilding(AnalysisJobBuilder jobBuilder, int partitionNumber) {
// update datastores and resource properties to point to node-specific
// targets if possible. This way parallel writing to files on HDFS does
// not cause any inconsistencies because each node is writing to a
// separate file.
for (final ComponentBuilder cb : jobBuilder.getComponentBuilders()) {
// find any datastore properties that point to HDFS files
final Set targetDatastoreProperties = cb.getDescriptor()
.getConfiguredPropertiesByType(UpdateableDatastore.class, false);
for (final ConfiguredPropertyDescriptor targetDatastoreProperty : targetDatastoreProperties) {
final Object datastoreObject = cb.getConfiguredProperty(targetDatastoreProperty);
if (datastoreObject instanceof ResourceDatastore) {
final ResourceDatastore resourceDatastore = (ResourceDatastore) datastoreObject;
final Resource resource = resourceDatastore.getResource();
final Resource replacementResource = createReplacementResource(resource, partitionNumber);
if (replacementResource != null) {
final ResourceDatastore replacementDatastore = createReplacementDatastore(cb, resourceDatastore,
replacementResource);
if (replacementDatastore != null) {
cb.setConfiguredProperty(targetDatastoreProperty, replacementDatastore);
}
}
}
}
final Set resourceProperties = cb.getDescriptor()
.getConfiguredPropertiesByType(Resource.class, false);
for (final ConfiguredPropertyDescriptor resourceProperty : resourceProperties) {
final Resource resource = (Resource) cb.getConfiguredProperty(resourceProperty);
final Resource replacementResource = createReplacementResource(resource, partitionNumber);
if (replacementResource != null) {
cb.setConfiguredProperty(resourceProperty, replacementResource);
}
}
// special handlings of specific component types are handled here
if (cb.getComponentInstance() instanceof CreateCsvFileAnalyzer) {
if (partitionNumber > 0) {
// ensure header is only created once
cb.setConfiguredProperty(CreateCsvFileAnalyzer.PROPERTY_INCLUDE_HEADER, false);
}
}
}
// recursively apply this function also on output data stream jobs
final List children = jobBuilder.getConsumedOutputDataStreamsJobBuilders();
for (AnalysisJobBuilder childJobBuilder : children) {
configureComponentsBeforeBuilding(childJobBuilder, partitionNumber);
}
}
/**
* Creates a {@link Resource} replacement to use for configured properties.
*
* @param resource
* @param partitionNumber
* @return a replacement resource, or null if it shouldn't be replaced
*/
private Resource createReplacementResource(final Resource resource, int partitionNumber) {
final String formattedPartitionNumber = String.format("%05d", partitionNumber);
if (resource instanceof HdfsResource || resource instanceof HadoopResource) {
final String path = resource.getQualifiedPath() + "/part-" + formattedPartitionNumber;
final URI uri = URI.create(path);
final Resource replacementResource = HdfsHelper.createHelper().getResourceToUse(uri);
return replacementResource;
}
if (resource instanceof FileResource) {
final File file = ((FileResource) resource).getFile();
if (file.exists() && file.isFile()) {
// a file already exists - we cannot just create a directory
// then
return resource;
}
if (!file.exists()) {
file.mkdirs();
}
final FileResource fileResource = new FileResource(resource.getQualifiedPath() + "/part-"
+ formattedPartitionNumber);
return fileResource;
}
return null;
}
/**
* Creates a {@link Datastore} replacement to use for configured properties
*
* @param cb
* @param datastore
* @param replacementResource
* @return a replacement datastore, or null if it shouldn't be replaced
*/
private ResourceDatastore createReplacementDatastore(ComponentBuilder cb, ResourceDatastore datastore,
Resource replacementResource) {
final String name = datastore.getName();
if (datastore instanceof CsvDatastore) {
final CsvConfiguration csvConfiguration = ((CsvDatastore) datastore).getCsvConfiguration();
return new CsvDatastore(name, replacementResource, csvConfiguration);
}
if (datastore instanceof JsonDatastore) {
return new JsonDatastore(name, replacementResource, ((JsonDatastore) datastore).getSchemaBuilder());
}
logger.warn("Could not replace datastore '{}' because it is of an unsupported type: ", name, datastore
.getClass().getSimpleName());
return datastore;
}
private List> executePartition(Iterator inputRowIterator,
final AnalysisJob analysisJob) {
_sparkJobContext.triggerOnPartitionProcessingStart();
final DataCleanerConfiguration configuration = _sparkJobContext.getConfiguration();
// set up processing stream (this also initializes the components)
final ConsumeRowHandler consumeRowHandler;
{
final ConsumeRowHandler.Configuration handlerConfiguration = new ConsumeRowHandler.Configuration();
handlerConfiguration.includeAnalyzers = true;
handlerConfiguration.includeNonDistributedTasks = false;
consumeRowHandler = new ConsumeRowHandler(analysisJob, configuration, handlerConfiguration);
}
// fire row processing on each row
while (inputRowIterator.hasNext()) {
final InputRow inputRow = inputRowIterator.next();
consumeRowHandler.consumeRow(inputRow);
logger.debug("Consumed row no. {}", inputRow.getId());
}
logger.info("Row processing complete - continuing to fetching results");
// collect results
final List> analyzerResults = getAnalyzerResults(consumeRowHandler
.getConsumers());
// await any future results
for (ListIterator> it = analyzerResults.listIterator(); it.hasNext();) {
final Tuple2 tuple = it.next();
final NamedAnalyzerResult namedAnalyzerResult = tuple._2;
final AnalyzerResult analyzerResult = namedAnalyzerResult.getAnalyzerResult();
if (analyzerResult instanceof AnalyzerResultFuture) {
final AnalyzerResult awaitedResult = ((AnalyzerResultFuture) analyzerResult).get();
final NamedAnalyzerResult awaitedResultTuple = new NamedAnalyzerResult(namedAnalyzerResult.getName(),
awaitedResult);
it.set(new Tuple2<>(tuple._1, awaitedResultTuple));
}
}
// close components
final LifeCycleHelper lifeCycleHelper = new LifeCycleHelper(configuration, analysisJob, false);
for (RowProcessingConsumer consumer : consumeRowHandler.getConsumers()) {
lifeCycleHelper.close(consumer.getComponentJob().getDescriptor(), consumer.getComponent(), true);
}
_sparkJobContext.triggerOnPartitionProcessingEnd();
return analyzerResults;
}
private List> getAnalyzerResults(
Collection rowProcessingConsumers) {
final List> analyzerResults = new ArrayList<>();
for (RowProcessingConsumer consumer : rowProcessingConsumers) {
if (consumer.isResultProducer()) {
final HasAnalyzerResult resultProducer = (HasAnalyzerResult) consumer.getComponent();
final AnalyzerResult analyzerResult = resultProducer.getResult();
final String key = _sparkJobContext.getComponentKey(consumer.getComponentJob());
final NamedAnalyzerResult namedAnalyzerResult = new NamedAnalyzerResult(key, analyzerResult);
final Tuple2 tuple = new Tuple2<>(key, namedAnalyzerResult);
analyzerResults.add(tuple);
}
for (ActiveOutputDataStream activeOutputDataStream : consumer.getActiveOutputDataStreams()) {
List outputDataStreamConsumers = activeOutputDataStream.getPublisher()
.getConsumers();
List> outputDataStreamsAnalyzerResults = getAnalyzerResults(
outputDataStreamConsumers);
analyzerResults.addAll(outputDataStreamsAnalyzerResults);
}
}
return analyzerResults;
}
}
