forked from D-Net/dnet-hadoop
WIP: metadata collection in INCREMENTAL mode and relative test
This commit is contained in:
parent
bead34d11a
commit
8eaa1fd4b4
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@ -26,13 +26,13 @@ public class MetadataRecord implements Serializable {
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private String body;
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/** the date when the record has been stored */
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private long dateOfCollection;
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private Long dateOfCollection;
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/** the date when the record has been stored */
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private long dateOfTransformation;
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private Long dateOfTransformation;
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public MetadataRecord() {
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this.dateOfCollection = System.currentTimeMillis();
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}
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public MetadataRecord(
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@ -40,7 +40,7 @@ public class MetadataRecord implements Serializable {
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String encoding,
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Provenance provenance,
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String body,
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long dateOfCollection) {
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Long dateOfCollection) {
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this.originalId = originalId;
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this.encoding = encoding;
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@ -90,19 +90,19 @@ public class MetadataRecord implements Serializable {
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this.body = body;
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}
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public long getDateOfCollection() {
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public Long getDateOfCollection() {
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return dateOfCollection;
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}
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public void setDateOfCollection(long dateOfCollection) {
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public void setDateOfCollection(Long dateOfCollection) {
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this.dateOfCollection = dateOfCollection;
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}
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public long getDateOfTransformation() {
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public Long getDateOfTransformation() {
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return dateOfTransformation;
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}
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public void setDateOfTransformation(long dateOfTransformation) {
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public void setDateOfTransformation(Long dateOfTransformation) {
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this.dateOfTransformation = dateOfTransformation;
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}
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@ -8,21 +8,38 @@ import java.nio.charset.StandardCharsets;
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import org.apache.hadoop.fs.FSDataOutputStream;
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import org.apache.hadoop.fs.FileSystem;
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import org.apache.hadoop.fs.Path;
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import org.apache.spark.sql.Dataset;
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import org.apache.spark.sql.SaveMode;
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import org.apache.spark.sql.SparkSession;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import eu.dnetlib.dhp.collection.GenerateNativeStoreSparkJob;
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import eu.dnetlib.dhp.model.mdstore.MetadataRecord;
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public class AggregationUtility {
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private static final Logger log = LoggerFactory.getLogger(AggregationUtility.class);
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public static void writeTotalSizeOnHDFS(final SparkSession spark, final Long total, final String path)
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throws IOException {
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FileSystem fs = FileSystem.get(spark.sparkContext().hadoopConfiguration());
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log.info("writing size ({}) info file {}", total, path);
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try (FileSystem fs = FileSystem.get(spark.sparkContext().hadoopConfiguration());
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BufferedOutputStream os = new BufferedOutputStream(fs.create(new Path(path)))) {
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os.write(total.toString().getBytes(StandardCharsets.UTF_8));
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os.flush();
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}
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FSDataOutputStream output = fs.create(new Path(path));
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final BufferedOutputStream os = new BufferedOutputStream(output);
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os.write(total.toString().getBytes(StandardCharsets.UTF_8));
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os.close();
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}
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public static <T> void saveDataset(final Dataset<T> mdstore, final String targetPath) {
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log.info("saving dataset in: {}", targetPath);
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mdstore
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.write()
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.mode(SaveMode.Overwrite)
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.format("parquet")
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.save(targetPath);
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}
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}
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@ -1,23 +1,20 @@
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package eu.dnetlib.dhp.collection;
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import static eu.dnetlib.dhp.aggregation.common.AggregationUtility.*;
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import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession;
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import java.io.*;
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import java.nio.charset.StandardCharsets;
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import java.util.Collections;
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import java.util.List;
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import java.util.Objects;
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import java.util.Optional;
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import org.apache.commons.io.IOUtils;
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import org.apache.commons.lang3.StringUtils;
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import org.apache.hadoop.fs.FSDataOutputStream;
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import org.apache.hadoop.fs.FileSystem;
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import org.apache.hadoop.fs.Path;
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import org.apache.hadoop.io.IntWritable;
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import org.apache.hadoop.io.Text;
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import org.apache.spark.SparkConf;
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import org.apache.spark.api.java.JavaPairRDD;
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import org.apache.spark.api.java.JavaRDD;
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import org.apache.spark.api.java.JavaSparkContext;
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import org.apache.spark.api.java.function.MapFunction;
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@ -30,31 +27,155 @@ import org.dom4j.io.SAXReader;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import com.fasterxml.jackson.core.JsonProcessingException;
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import com.fasterxml.jackson.databind.ObjectMapper;
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import eu.dnetlib.data.mdstore.manager.common.model.MDStoreVersion;
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import eu.dnetlib.dhp.aggregation.common.AggregationUtility;
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import eu.dnetlib.dhp.application.ArgumentApplicationParser;
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import eu.dnetlib.dhp.collection.worker.CollectorWorkerApplication;
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import eu.dnetlib.dhp.model.mdstore.MetadataRecord;
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import eu.dnetlib.dhp.model.mdstore.Provenance;
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import net.sf.saxon.expr.Component;
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import scala.Tuple2;
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public class GenerateNativeStoreSparkJob {
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private static final Logger log = LoggerFactory.getLogger(GenerateNativeStoreSparkJob.class);
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private static final ObjectMapper MAPPER = new ObjectMapper();
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private static final String DATASET_NAME = "/store";
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public static void main(String[] args) throws Exception {
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final ArgumentApplicationParser parser = new ArgumentApplicationParser(
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IOUtils
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.toString(
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GenerateNativeStoreSparkJob.class
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.getResourceAsStream(
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"/eu/dnetlib/dhp/collection/collection_input_parameters.json")));
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parser.parseArgument(args);
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final String provenanceArgument = parser.get("provenance");
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log.info("Provenance is {}", provenanceArgument);
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final Provenance provenance = MAPPER.readValue(provenanceArgument, Provenance.class);
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final String dateOfCollectionArgs = parser.get("dateOfCollection");
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log.info("dateOfCollection is {}", dateOfCollectionArgs);
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final Long dateOfCollection = new Long(dateOfCollectionArgs);
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String mdStoreVersion = parser.get("mdStoreVersion");
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log.info("mdStoreVersion is {}", mdStoreVersion);
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final MDStoreVersion currentVersion = MAPPER.readValue(mdStoreVersion, MDStoreVersion.class);
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String readMdStoreVersionParam = parser.get("readMdStoreVersion");
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log.info("readMdStoreVersion is {}", readMdStoreVersionParam);
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final MDStoreVersion readMdStoreVersion = StringUtils.isBlank(readMdStoreVersionParam) ? null
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: MAPPER.readValue(readMdStoreVersionParam, MDStoreVersion.class);
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final String xpath = parser.get("xpath");
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log.info("xpath is {}", xpath);
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final String encoding = parser.get("encoding");
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log.info("encoding is {}", encoding);
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Boolean isSparkSessionManaged = Optional
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.ofNullable(parser.get("isSparkSessionManaged"))
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.map(Boolean::valueOf)
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.orElse(Boolean.TRUE);
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log.info("isSparkSessionManaged: {}", isSparkSessionManaged);
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SparkConf conf = new SparkConf();
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/*
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* conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer"); conf .registerKryoClasses( new
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* Class[] { MetadataRecord.class, Provenance.class });
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*/
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runWithSparkSession(
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conf,
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isSparkSessionManaged,
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spark -> createNativeMDStore(
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spark, provenance, dateOfCollection, xpath, encoding, currentVersion, readMdStoreVersion));
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}
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private static void createNativeMDStore(SparkSession spark,
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Provenance provenance,
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Long dateOfCollection,
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String xpath,
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String encoding,
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MDStoreVersion currentVersion,
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MDStoreVersion readVersion) throws IOException {
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final JavaSparkContext sc = JavaSparkContext.fromSparkContext(spark.sparkContext());
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final LongAccumulator totalItems = sc.sc().longAccumulator("TotalItems");
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final LongAccumulator invalidRecords = sc.sc().longAccumulator("InvalidRecords");
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final String seqFilePath = currentVersion.getHdfsPath() + CollectorWorkerApplication.SEQUENCE_FILE_NAME;
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final JavaRDD<MetadataRecord> nativeStore = sc
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.sequenceFile(seqFilePath, IntWritable.class, Text.class)
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.map(
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item -> parseRecord(
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item._2().toString(),
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xpath,
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encoding,
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provenance,
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dateOfCollection,
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totalItems,
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invalidRecords))
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.filter(Objects::nonNull)
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.distinct();
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final Encoder<MetadataRecord> encoder = Encoders.bean(MetadataRecord.class);
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final Dataset<MetadataRecord> mdstore = spark.createDataset(nativeStore.rdd(), encoder);
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final String targetPath = currentVersion.getHdfsPath() + DATASET_NAME;
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if (readVersion != null) { // INCREMENTAL MODE
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log.info("updating {} incrementally with {}", targetPath, readVersion.getHdfsPath());
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Dataset<MetadataRecord> currentMdStoreVersion = spark
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.read()
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.load(readVersion.getHdfsPath() + DATASET_NAME)
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.as(encoder);
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TypedColumn<MetadataRecord, MetadataRecord> aggregator = new MDStoreAggregator().toColumn();
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final Dataset<MetadataRecord> map = currentMdStoreVersion
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.union(mdstore)
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.groupByKey(
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(MapFunction<MetadataRecord, String>) MetadataRecord::getId,
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Encoders.STRING())
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.agg(aggregator)
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.map((MapFunction<Tuple2<String, MetadataRecord>, MetadataRecord>) Tuple2::_2, encoder);
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map.select("id").takeAsList(100).forEach(s -> log.info(s.toString()));
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saveDataset(map, targetPath);
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} else {
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saveDataset(mdstore, targetPath);
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}
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final Long total = spark.read().load(targetPath).count();
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log.info("collected {} records for datasource '{}'", total, provenance.getDatasourceName());
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writeTotalSizeOnHDFS(spark, total, currentVersion.getHdfsPath() + "/size");
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}
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public static class MDStoreAggregator extends Aggregator<MetadataRecord, MetadataRecord, MetadataRecord> {
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@Override
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public MetadataRecord zero() {
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return new MetadataRecord();
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return null;
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}
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@Override
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public MetadataRecord reduce(MetadataRecord b, MetadataRecord a) {
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return getLatestRecord(b, a);
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}
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@Override
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public MetadataRecord merge(MetadataRecord b, MetadataRecord a) {
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return getLatestRecord(b, a);
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}
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@ -68,136 +189,22 @@ public class GenerateNativeStoreSparkJob {
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}
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@Override
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public MetadataRecord merge(MetadataRecord b, MetadataRecord a) {
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return getLatestRecord(b, a);
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}
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@Override
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public MetadataRecord finish(MetadataRecord j) {
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return j;
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public MetadataRecord finish(MetadataRecord r) {
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return r;
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}
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@Override
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public Encoder<MetadataRecord> bufferEncoder() {
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return Encoders.kryo(MetadataRecord.class);
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return Encoders.bean(MetadataRecord.class);
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}
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@Override
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public Encoder<MetadataRecord> outputEncoder() {
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return Encoders.kryo(MetadataRecord.class);
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return Encoders.bean(MetadataRecord.class);
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}
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}
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public static void main(String[] args) throws Exception {
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final ArgumentApplicationParser parser = new ArgumentApplicationParser(
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IOUtils
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.toString(
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GenerateNativeStoreSparkJob.class
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.getResourceAsStream(
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"/eu/dnetlib/dhp/collection/collection_input_parameters.json")));
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parser.parseArgument(args);
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final ObjectMapper jsonMapper = new ObjectMapper();
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final String provenanceArgument = parser.get("provenance");
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log.info("Provenance is {}", provenanceArgument);
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final Provenance provenance = jsonMapper.readValue(provenanceArgument, Provenance.class);
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final String dateOfCollectionArgs = parser.get("dateOfCollection");
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log.info("dateOfCollection is {}", dateOfCollectionArgs);
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final long dateOfCollection = new Long(dateOfCollectionArgs);
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String mdStoreVersion = parser.get("mdStoreVersion");
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log.info("mdStoreVersion is {}", mdStoreVersion);
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final MDStoreVersion currentVersion = jsonMapper.readValue(mdStoreVersion, MDStoreVersion.class);
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String readMdStoreVersionParam = parser.get("readMdStoreVersion");
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log.info("readMdStoreVersion is {}", readMdStoreVersionParam);
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final MDStoreVersion readMdStoreVersion = StringUtils.isBlank(readMdStoreVersionParam) ? null
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: jsonMapper.readValue(readMdStoreVersionParam, MDStoreVersion.class);
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Boolean isSparkSessionManaged = Optional
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.ofNullable(parser.get("isSparkSessionManaged"))
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.map(Boolean::valueOf)
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.orElse(Boolean.TRUE);
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log.info("isSparkSessionManaged: {}", isSparkSessionManaged);
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SparkConf conf = new SparkConf();
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conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");
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conf.registerKryoClasses(Collections.singleton(MetadataRecord.class).toArray(new Class[] {}));
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runWithSparkSession(
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conf,
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isSparkSessionManaged,
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spark -> {
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final JavaSparkContext sc = JavaSparkContext.fromSparkContext(spark.sparkContext());
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final JavaPairRDD<IntWritable, Text> inputRDD = sc
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.sequenceFile(
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currentVersion.getHdfsPath() + CollectorWorkerApplication.SEQUENTIAL_FILE_NAME,
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IntWritable.class, Text.class);
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final LongAccumulator totalItems = sc.sc().longAccumulator("TotalItems");
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final LongAccumulator invalidRecords = sc.sc().longAccumulator("InvalidRecords");
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final JavaRDD<MetadataRecord> nativeStore = inputRDD
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.map(
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item -> parseRecord(
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item._2().toString(),
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parser.get("xpath"),
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parser.get("encoding"),
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provenance,
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dateOfCollection,
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totalItems,
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invalidRecords))
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.filter(Objects::nonNull)
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.distinct();
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final Encoder<MetadataRecord> encoder = Encoders.bean(MetadataRecord.class);
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final Dataset<MetadataRecord> mdstore = spark.createDataset(nativeStore.rdd(), encoder);
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final String targetPath = currentVersion.getHdfsPath() + DATASET_NAME;
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if (readMdStoreVersion != null) {
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// INCREMENTAL MODE
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Dataset<MetadataRecord> currentMdStoreVersion = spark
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.read()
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.load(readMdStoreVersion.getHdfsPath() + DATASET_NAME)
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.as(encoder);
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TypedColumn<MetadataRecord, MetadataRecord> aggregator = new MDStoreAggregator().toColumn();
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saveDataset(
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currentMdStoreVersion
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.union(mdstore)
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.groupByKey(
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(MapFunction<MetadataRecord, String>) MetadataRecord::getId,
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Encoders.STRING())
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.agg(aggregator)
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.map((MapFunction<Tuple2<String, MetadataRecord>, MetadataRecord>) Tuple2::_2, encoder),
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targetPath);
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} else {
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saveDataset(mdstore, targetPath);
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}
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final Long total = spark.read().load(targetPath).count();
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AggregationUtility.writeTotalSizeOnHDFS(spark, total, currentVersion.getHdfsPath() + "/size");
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});
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}
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private static void saveDataset(final Dataset<MetadataRecord> currentMdStore, final String targetPath) {
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currentMdStore
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.write()
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.mode(SaveMode.Overwrite)
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.format("parquet")
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.save(targetPath);
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}
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public static MetadataRecord parseRecord(
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final String input,
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final String xpath,
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@ -219,7 +226,7 @@ public class GenerateNativeStoreSparkJob {
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invalidRecords.add(1);
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return null;
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}
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return new MetadataRecord(originalIdentifier, encoding, provenance, input, dateOfCollection);
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return new MetadataRecord(originalIdentifier, encoding, provenance, document.asXML(), dateOfCollection);
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} catch (Throwable e) {
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invalidRecords.add(1);
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return null;
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|
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@ -1,11 +1,6 @@
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package eu.dnetlib.dhp.collection.worker;
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import java.io.File;
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import java.io.FileOutputStream;
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import java.io.OutputStream;
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import java.util.Properties;
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import org.apache.commons.io.IOUtils;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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@ -16,7 +11,6 @@ import eu.dnetlib.data.mdstore.manager.common.model.MDStoreVersion;
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import eu.dnetlib.dhp.application.ArgumentApplicationParser;
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import eu.dnetlib.dhp.collection.worker.utils.CollectorPluginFactory;
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import eu.dnetlib.dhp.collector.worker.model.ApiDescriptor;
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import eu.dnetlib.dhp.common.rest.DNetRestClient;
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/**
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* DnetCollectortWorkerApplication is the main class responsible to start the Dnet Collection into HDFS. This module
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|
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@ -31,7 +25,7 @@ public class CollectorWorkerApplication {
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private static final CollectorPluginFactory collectorPluginFactory = new CollectorPluginFactory();
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public static String SEQUENTIAL_FILE_NAME = "/sequence_file";
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public static String SEQUENCE_FILE_NAME = "/sequence_file";
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/**
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* @param args
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|
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@ -61,7 +55,7 @@ public class CollectorWorkerApplication {
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final ApiDescriptor api = jsonMapper.readValue(apiDescriptor, ApiDescriptor.class);
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final CollectorWorker worker = new CollectorWorker(collectorPluginFactory, api, hdfsuri,
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||||
currentVersion.getHdfsPath() + SEQUENTIAL_FILE_NAME);
|
||||
currentVersion.getHdfsPath() + SEQUENCE_FILE_NAME);
|
||||
worker.collect();
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,169 @@
|
|||
|
||||
package eu.dnetlib.dhp.collection;
|
||||
|
||||
import java.io.File;
|
||||
import java.io.FileOutputStream;
|
||||
import java.io.FileReader;
|
||||
import java.io.IOException;
|
||||
import java.nio.file.Files;
|
||||
import java.nio.file.Path;
|
||||
|
||||
import org.apache.commons.io.FileUtils;
|
||||
import org.apache.commons.io.IOUtils;
|
||||
import org.apache.hadoop.io.IntWritable;
|
||||
import org.apache.hadoop.io.Text;
|
||||
import org.apache.spark.SparkConf;
|
||||
import org.apache.spark.api.java.JavaSparkContext;
|
||||
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.junit.jupiter.api.*;
|
||||
import org.slf4j.Logger;
|
||||
import org.slf4j.LoggerFactory;
|
||||
|
||||
import com.fasterxml.jackson.databind.ObjectMapper;
|
||||
|
||||
import eu.dnetlib.data.mdstore.manager.common.model.MDStoreVersion;
|
||||
import eu.dnetlib.dhp.model.mdstore.MetadataRecord;
|
||||
|
||||
@TestMethodOrder(MethodOrderer.OrderAnnotation.class)
|
||||
public class GenerateNativeStoreSparkJobTest {
|
||||
|
||||
private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper();
|
||||
|
||||
private static SparkSession spark;
|
||||
|
||||
private static Path workingDir;
|
||||
|
||||
private static Encoder<MetadataRecord> encoder;
|
||||
|
||||
private static final String encoding = "XML";
|
||||
private static final String dateOfCollection = System.currentTimeMillis() + "";
|
||||
private static final String xpath = "//*[local-name()='header']/*[local-name()='identifier']";
|
||||
private static String provenance;
|
||||
|
||||
private static final Logger log = LoggerFactory.getLogger(GenerateNativeStoreSparkJobTest.class);
|
||||
|
||||
@BeforeAll
|
||||
public static void beforeAll() throws IOException {
|
||||
provenance = IOUtils.toString(GenerateNativeStoreSparkJobTest.class.getResourceAsStream("provenance.json"));
|
||||
workingDir = Files.createTempDirectory(GenerateNativeStoreSparkJobTest.class.getSimpleName());
|
||||
log.info("using work dir {}", workingDir);
|
||||
|
||||
SparkConf conf = new SparkConf();
|
||||
|
||||
conf.setAppName(GenerateNativeStoreSparkJobTest.class.getSimpleName());
|
||||
|
||||
conf.setMaster("local[*]");
|
||||
conf.set("spark.driver.host", "localhost");
|
||||
conf.set("hive.metastore.local", "true");
|
||||
conf.set("spark.ui.enabled", "false");
|
||||
conf.set("spark.sql.warehouse.dir", workingDir.toString());
|
||||
conf.set("hive.metastore.warehouse.dir", workingDir.resolve("warehouse").toString());
|
||||
|
||||
encoder = Encoders.bean(MetadataRecord.class);
|
||||
spark = SparkSession
|
||||
.builder()
|
||||
.appName(GenerateNativeStoreSparkJobTest.class.getSimpleName())
|
||||
.config(conf)
|
||||
.getOrCreate();
|
||||
}
|
||||
|
||||
@AfterAll
|
||||
public static void afterAll() throws IOException {
|
||||
FileUtils.deleteDirectory(workingDir.toFile());
|
||||
spark.stop();
|
||||
}
|
||||
|
||||
@Test
|
||||
@Order(1)
|
||||
public void testGenerateNativeStoreSparkJobRefresh() throws Exception {
|
||||
|
||||
MDStoreVersion mdStoreV1 = prepareVersion("mdStoreVersion_1.json");
|
||||
FileUtils.forceMkdir(new File(mdStoreV1.getHdfsPath()));
|
||||
|
||||
IOUtils
|
||||
.copy(
|
||||
getClass().getResourceAsStream("sequence_file"),
|
||||
new FileOutputStream(mdStoreV1.getHdfsPath() + "/sequence_file"));
|
||||
|
||||
GenerateNativeStoreSparkJob
|
||||
.main(
|
||||
new String[] {
|
||||
"-isSparkSessionManaged", Boolean.FALSE.toString(),
|
||||
"-encoding", encoding,
|
||||
"-dateOfCollection", dateOfCollection,
|
||||
"-provenance", provenance,
|
||||
"-xpath", xpath,
|
||||
"-mdStoreVersion", OBJECT_MAPPER.writeValueAsString(mdStoreV1),
|
||||
"-readMdStoreVersion", "",
|
||||
"-workflowId", "abc"
|
||||
});
|
||||
|
||||
verify(mdStoreV1);
|
||||
}
|
||||
|
||||
@Test
|
||||
@Order(2)
|
||||
public void testGenerateNativeStoreSparkJobIncremental() throws Exception {
|
||||
|
||||
MDStoreVersion mdStoreV2 = prepareVersion("mdStoreVersion_2.json");
|
||||
FileUtils.forceMkdir(new File(mdStoreV2.getHdfsPath()));
|
||||
|
||||
IOUtils
|
||||
.copy(
|
||||
getClass().getResourceAsStream("sequence_file"),
|
||||
new FileOutputStream(mdStoreV2.getHdfsPath() + "/sequence_file"));
|
||||
|
||||
MDStoreVersion mdStoreV1 = prepareVersion("mdStoreVersion_1.json");
|
||||
|
||||
GenerateNativeStoreSparkJob
|
||||
.main(
|
||||
new String[] {
|
||||
"-isSparkSessionManaged", Boolean.FALSE.toString(),
|
||||
"-encoding", encoding,
|
||||
"-dateOfCollection", dateOfCollection,
|
||||
"-provenance", provenance,
|
||||
"-xpath", xpath,
|
||||
"-mdStoreVersion", OBJECT_MAPPER.writeValueAsString(mdStoreV2),
|
||||
"-readMdStoreVersion", OBJECT_MAPPER.writeValueAsString(mdStoreV1),
|
||||
"-workflowId", "abc"
|
||||
});
|
||||
|
||||
verify(mdStoreV2);
|
||||
}
|
||||
|
||||
protected void verify(MDStoreVersion mdStoreVersion) throws IOException {
|
||||
Assertions.assertTrue(new File(mdStoreVersion.getHdfsPath()).exists());
|
||||
|
||||
final JavaSparkContext sc = JavaSparkContext.fromSparkContext(spark.sparkContext());
|
||||
long seqFileSize = sc
|
||||
.sequenceFile(mdStoreVersion.getHdfsPath() + "/sequence_file", IntWritable.class, Text.class)
|
||||
.count();
|
||||
|
||||
final Dataset<MetadataRecord> mdstore = spark.read().load(mdStoreVersion.getHdfsPath() + "/store").as(encoder);
|
||||
long mdStoreSize = mdstore.count();
|
||||
|
||||
long declaredSize = Long.parseLong(IOUtils.toString(new FileReader(mdStoreVersion.getHdfsPath() + "/size")));
|
||||
|
||||
Assertions.assertEquals(seqFileSize, declaredSize, "the size must be equal");
|
||||
Assertions.assertEquals(seqFileSize, mdStoreSize, "the size must be equal");
|
||||
|
||||
long uniqueIds = mdstore
|
||||
.map((MapFunction<MetadataRecord, String>) MetadataRecord::getId, Encoders.STRING())
|
||||
.distinct()
|
||||
.count();
|
||||
|
||||
Assertions.assertEquals(seqFileSize, uniqueIds, "the size must be equal");
|
||||
}
|
||||
|
||||
private MDStoreVersion prepareVersion(String filename) throws IOException {
|
||||
MDStoreVersion mdstore = OBJECT_MAPPER
|
||||
.readValue(IOUtils.toString(getClass().getResource(filename)), MDStoreVersion.class);
|
||||
mdstore.setHdfsPath(String.format(mdstore.getHdfsPath(), workingDir.toString()));
|
||||
return mdstore;
|
||||
}
|
||||
|
||||
}
|
||||
|
|
@ -1,9 +0,0 @@
|
|||
{
|
||||
"id": "md-7557225f-77cc-407d-bdf4-d2fe03131464-1611935085410",
|
||||
"mdstore": "md-7557225f-77cc-407d-bdf4-d2fe03131464",
|
||||
"writing": true,
|
||||
"readCount": 0,
|
||||
"lastUpdate": null,
|
||||
"size": 0,
|
||||
"hdfsPath": "/data/dnet.dev/mdstore/md-7557225f-77cc-407d-bdf4-d2fe03131464/md-7557225f-77cc-407d-bdf4-d2fe03131464-1611935085410"
|
||||
}
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
{
|
||||
"id":"md-84e86d00-5771-4ed9-b17f-177ef4b46e42-1612187678801",
|
||||
"mdstore":"md-84e86d00-5771-4ed9-b17f-177ef4b46e42",
|
||||
"writing":true,
|
||||
"readCount":0,
|
||||
"lastUpdate":null,
|
||||
"size":0,
|
||||
"hdfsPath":"%s/mdstore/md-84e86d00-5771-4ed9-b17f-177ef4b46e42/v1"
|
||||
}
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
{
|
||||
"id":"md-84e86d00-5771-4ed9-b17f-177ef4b46e42-1612187459108",
|
||||
"mdstore":"md-84e86d00-5771-4ed9-b17f-177ef4b46e42",
|
||||
"writing":false,
|
||||
"readCount":1,
|
||||
"lastUpdate":1612187563099,
|
||||
"size":71,
|
||||
"hdfsPath":"%s/mdstore/md-84e86d00-5771-4ed9-b17f-177ef4b46e42/v2"
|
||||
}
|
||||
|
|
@ -0,0 +1,5 @@
|
|||
{
|
||||
"datasourceId":"74912366-d6df-49c1-a1fd-8a52fa98ce5f_UmVwb3NpdG9yeVNlcnZpY2VSZXNvdXJjZXMvUmVwb3NpdG9yeVNlcnZpY2VSZXNvdXJjZVR5cGU\u003d",
|
||||
"datasourceName":"PSNC Institutional Repository",
|
||||
"nsPrefix":"psnc______pl"
|
||||
}
|
||||
Binary file not shown.
Loading…
Reference in New Issue