BrBETA_dnet-hadoop/dhp-workflows/dhp-aggregation/src/main/java/eu/dnetlib/dhp/transformation/TransformSparkJobNode.java

162 lines
6.2 KiB
Java

package eu.dnetlib.dhp.transformation;
import static eu.dnetlib.dhp.common.Constants.*;
import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession;
import static eu.dnetlib.dhp.utils.DHPUtils.*;
import java.io.IOException;
import java.util.Map;
import java.util.Optional;
import org.apache.commons.io.IOUtils;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.MapFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoder;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.util.LongAccumulator;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import eu.dnetlib.dhp.aggregation.common.AggregationCounter;
import eu.dnetlib.dhp.application.ArgumentApplicationParser;
import eu.dnetlib.dhp.common.aggregation.AggregatorReport;
import eu.dnetlib.dhp.common.vocabulary.VocabularyGroup;
import eu.dnetlib.dhp.message.MessageSender;
import eu.dnetlib.dhp.schema.mdstore.MDStoreVersion;
import eu.dnetlib.dhp.schema.mdstore.MetadataRecord;
import eu.dnetlib.dhp.utils.ISLookupClientFactory;
import eu.dnetlib.enabling.is.lookup.rmi.ISLookUpService;
public class TransformSparkJobNode {
private static final Logger log = LoggerFactory.getLogger(TransformSparkJobNode.class);
private static final int RECORDS_PER_TASK = 200;
public static void main(String[] args) throws Exception {
final ArgumentApplicationParser parser = new ArgumentApplicationParser(
IOUtils
.toString(
TransformSparkJobNode.class
.getResourceAsStream(
"/eu/dnetlib/dhp/transformation/transformation_input_parameters.json")));
parser.parseArgument(args);
Boolean isSparkSessionManaged = Optional
.ofNullable(parser.get("isSparkSessionManaged"))
.map(Boolean::valueOf)
.orElse(Boolean.TRUE);
log.info("isSparkSessionManaged: {}", isSparkSessionManaged);
final String mdstoreInputVersion = parser.get("mdstoreInputVersion");
final String mdstoreOutputVersion = parser.get("mdstoreOutputVersion");
final MDStoreVersion nativeMdStoreVersion = MAPPER.readValue(mdstoreInputVersion, MDStoreVersion.class);
final String inputPath = nativeMdStoreVersion.getHdfsPath() + MDSTORE_DATA_PATH;
log.info("inputPath: {}", inputPath);
final MDStoreVersion cleanedMdStoreVersion = MAPPER.readValue(mdstoreOutputVersion, MDStoreVersion.class);
final String outputBasePath = cleanedMdStoreVersion.getHdfsPath();
log.info("outputBasePath: {}", outputBasePath);
final String isLookupUrl = parser.get("isLookupUrl");
log.info("isLookupUrl: {}", isLookupUrl);
final String dateOfTransformation = parser.get("dateOfTransformation");
log.info("dateOfTransformation: {}", dateOfTransformation);
final Integer rpt = Optional
.ofNullable(parser.get("recordsPerTask"))
.map(Integer::valueOf)
.orElse(RECORDS_PER_TASK);
final ISLookUpService isLookupService = ISLookupClientFactory.getLookUpService(isLookupUrl);
final VocabularyGroup vocabularies = VocabularyGroup.loadVocsFromIS(isLookupService);
log.info("Retrieved {} vocabularies", vocabularies.vocabularyNames().size());
SparkConf conf = new SparkConf();
runWithSparkSession(
conf,
isSparkSessionManaged,
spark -> {
transformRecords(
parser.getObjectMap(), isLookupService, spark, inputPath, outputBasePath, rpt);
});
}
public static void transformRecords(final Map<String, String> args, final ISLookUpService isLookUpService,
final SparkSession spark, final String inputPath, final String outputBasePath, final Integer rpt)
throws DnetTransformationException, IOException {
final LongAccumulator totalItems = spark.sparkContext().longAccumulator(CONTENT_TOTALITEMS);
final LongAccumulator errorItems = spark.sparkContext().longAccumulator(CONTENT_INVALIDRECORDS);
final LongAccumulator transformedItems = spark.sparkContext().longAccumulator(CONTENT_TRANSFORMEDRECORDS);
final AggregationCounter ct = new AggregationCounter(totalItems, errorItems, transformedItems);
final Encoder<MetadataRecord> encoder = Encoders.bean(MetadataRecord.class);
final String dnetMessageManagerURL = args.get(DNET_MESSAGE_MGR_URL);
log.info("dnetMessageManagerURL is {}", dnetMessageManagerURL);
final String workflowId = args.get("workflowId");
log.info("workflowId is {}", workflowId);
MapFunction<MetadataRecord, MetadataRecord> x = TransformationFactory
.getTransformationPlugin(args, ct, isLookUpService);
final Dataset<MetadataRecord> inputMDStore = spark
.read()
.format("parquet")
.load(inputPath)
.as(encoder);
final long totalInput = inputMDStore.count();
final MessageSender messageSender = new MessageSender(dnetMessageManagerURL, workflowId);
try (AggregatorReport report = new AggregatorReport(messageSender)) {
try {
JavaRDD<MetadataRecord> mdstore = inputMDStore
.javaRDD()
.repartition(getRepartitionNumber(totalInput, rpt))
.map((Function<MetadataRecord, MetadataRecord>) x::call);
saveDataset(spark.createDataset(mdstore.rdd(), encoder), outputBasePath + MDSTORE_DATA_PATH);
log.info("Transformed item {}", ct.getProcessedItems().count());
log.info("Total item {}", ct.getTotalItems().count());
log.info("Transformation Error item {}", ct.getErrorItems().count());
final long mdStoreSize = spark.read().load(outputBasePath + MDSTORE_DATA_PATH).count();
writeHdfsFile(
spark.sparkContext().hadoopConfiguration(),
"" + mdStoreSize, outputBasePath + MDSTORE_SIZE_PATH);
} catch (Throwable e) {
log.error("error during record transformation", e);
report.put(TransformSparkJobNode.class.getSimpleName(), e.getMessage());
report.put(CONTENT_TOTALITEMS, ct.getTotalItems().value().toString());
report.put(CONTENT_INVALIDRECORDS, ct.getErrorItems().value().toString());
report.put(CONTENT_TRANSFORMEDRECORDS, ct.getProcessedItems().value().toString());
throw e;
}
}
}
/**
* Calculates the number of partitions allocating at most @rpt records for a single transformation task.
* @param totalInput
* @param rpt
* @return
*/
private static int getRepartitionNumber(long totalInput, Integer rpt) {
return Math.max(1, (int) (totalInput / rpt));
}
}