WIP: metadata collection in INCREMENTAL mode and relative test

This commit is contained in:
Claudio Atzori 2021-02-01 19:29:10 +01:00
parent bead34d11a
commit 8eaa1fd4b4
10 changed files with 360 additions and 159 deletions

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@ -26,13 +26,13 @@ public class MetadataRecord implements Serializable {
private String body;
/** the date when the record has been stored */
private long dateOfCollection;
private Long dateOfCollection;
/** the date when the record has been stored */
private long dateOfTransformation;
private Long dateOfTransformation;
public MetadataRecord() {
this.dateOfCollection = System.currentTimeMillis();
}
public MetadataRecord(
@ -40,7 +40,7 @@ public class MetadataRecord implements Serializable {
String encoding,
Provenance provenance,
String body,
long dateOfCollection) {
Long dateOfCollection) {
this.originalId = originalId;
this.encoding = encoding;
@ -90,19 +90,19 @@ public class MetadataRecord implements Serializable {
this.body = body;
}
public long getDateOfCollection() {
public Long getDateOfCollection() {
return dateOfCollection;
}
public void setDateOfCollection(long dateOfCollection) {
public void setDateOfCollection(Long dateOfCollection) {
this.dateOfCollection = dateOfCollection;
}
public long getDateOfTransformation() {
public Long getDateOfTransformation() {
return dateOfTransformation;
}
public void setDateOfTransformation(long dateOfTransformation) {
public void setDateOfTransformation(Long dateOfTransformation) {
this.dateOfTransformation = dateOfTransformation;
}

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@ -8,21 +8,38 @@ import java.nio.charset.StandardCharsets;
import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.SaveMode;
import org.apache.spark.sql.SparkSession;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import eu.dnetlib.dhp.collection.GenerateNativeStoreSparkJob;
import eu.dnetlib.dhp.model.mdstore.MetadataRecord;
public class AggregationUtility {
private static final Logger log = LoggerFactory.getLogger(AggregationUtility.class);
public static void writeTotalSizeOnHDFS(final SparkSession spark, final Long total, final String path)
throws IOException {
FileSystem fs = FileSystem.get(spark.sparkContext().hadoopConfiguration());
FSDataOutputStream output = fs.create(new Path(path));
final BufferedOutputStream os = new BufferedOutputStream(output);
log.info("writing size ({}) info file {}", total, path);
try (FileSystem fs = FileSystem.get(spark.sparkContext().hadoopConfiguration());
BufferedOutputStream os = new BufferedOutputStream(fs.create(new Path(path)))) {
os.write(total.toString().getBytes(StandardCharsets.UTF_8));
os.close();
os.flush();
}
}
public static <T> void saveDataset(final Dataset<T> mdstore, final String targetPath) {
log.info("saving dataset in: {}", targetPath);
mdstore
.write()
.mode(SaveMode.Overwrite)
.format("parquet")
.save(targetPath);
}
}

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@ -1,23 +1,20 @@
package eu.dnetlib.dhp.collection;
import static eu.dnetlib.dhp.aggregation.common.AggregationUtility.*;
import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession;
import java.io.*;
import java.nio.charset.StandardCharsets;
import java.util.Collections;
import java.util.List;
import java.util.Objects;
import java.util.Optional;
import org.apache.commons.io.IOUtils;
import org.apache.commons.lang3.StringUtils;
import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.MapFunction;
@ -30,31 +27,155 @@ import org.dom4j.io.SAXReader;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.ObjectMapper;
import eu.dnetlib.data.mdstore.manager.common.model.MDStoreVersion;
import eu.dnetlib.dhp.aggregation.common.AggregationUtility;
import eu.dnetlib.dhp.application.ArgumentApplicationParser;
import eu.dnetlib.dhp.collection.worker.CollectorWorkerApplication;
import eu.dnetlib.dhp.model.mdstore.MetadataRecord;
import eu.dnetlib.dhp.model.mdstore.Provenance;
import net.sf.saxon.expr.Component;
import scala.Tuple2;
public class GenerateNativeStoreSparkJob {
private static final Logger log = LoggerFactory.getLogger(GenerateNativeStoreSparkJob.class);
private static final ObjectMapper MAPPER = new ObjectMapper();
private static final String DATASET_NAME = "/store";
public static void main(String[] args) throws Exception {
final ArgumentApplicationParser parser = new ArgumentApplicationParser(
IOUtils
.toString(
GenerateNativeStoreSparkJob.class
.getResourceAsStream(
"/eu/dnetlib/dhp/collection/collection_input_parameters.json")));
parser.parseArgument(args);
final String provenanceArgument = parser.get("provenance");
log.info("Provenance is {}", provenanceArgument);
final Provenance provenance = MAPPER.readValue(provenanceArgument, Provenance.class);
final String dateOfCollectionArgs = parser.get("dateOfCollection");
log.info("dateOfCollection is {}", dateOfCollectionArgs);
final Long dateOfCollection = new Long(dateOfCollectionArgs);
String mdStoreVersion = parser.get("mdStoreVersion");
log.info("mdStoreVersion is {}", mdStoreVersion);
final MDStoreVersion currentVersion = MAPPER.readValue(mdStoreVersion, MDStoreVersion.class);
String readMdStoreVersionParam = parser.get("readMdStoreVersion");
log.info("readMdStoreVersion is {}", readMdStoreVersionParam);
final MDStoreVersion readMdStoreVersion = StringUtils.isBlank(readMdStoreVersionParam) ? null
: MAPPER.readValue(readMdStoreVersionParam, MDStoreVersion.class);
final String xpath = parser.get("xpath");
log.info("xpath is {}", xpath);
final String encoding = parser.get("encoding");
log.info("encoding is {}", encoding);
Boolean isSparkSessionManaged = Optional
.ofNullable(parser.get("isSparkSessionManaged"))
.map(Boolean::valueOf)
.orElse(Boolean.TRUE);
log.info("isSparkSessionManaged: {}", isSparkSessionManaged);
SparkConf conf = new SparkConf();
/*
* conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer"); conf .registerKryoClasses( new
* Class[] { MetadataRecord.class, Provenance.class });
*/
runWithSparkSession(
conf,
isSparkSessionManaged,
spark -> createNativeMDStore(
spark, provenance, dateOfCollection, xpath, encoding, currentVersion, readMdStoreVersion));
}
private static void createNativeMDStore(SparkSession spark,
Provenance provenance,
Long dateOfCollection,
String xpath,
String encoding,
MDStoreVersion currentVersion,
MDStoreVersion readVersion) throws IOException {
final JavaSparkContext sc = JavaSparkContext.fromSparkContext(spark.sparkContext());
final LongAccumulator totalItems = sc.sc().longAccumulator("TotalItems");
final LongAccumulator invalidRecords = sc.sc().longAccumulator("InvalidRecords");
final String seqFilePath = currentVersion.getHdfsPath() + CollectorWorkerApplication.SEQUENCE_FILE_NAME;
final JavaRDD<MetadataRecord> nativeStore = sc
.sequenceFile(seqFilePath, IntWritable.class, Text.class)
.map(
item -> parseRecord(
item._2().toString(),
xpath,
encoding,
provenance,
dateOfCollection,
totalItems,
invalidRecords))
.filter(Objects::nonNull)
.distinct();
final Encoder<MetadataRecord> encoder = Encoders.bean(MetadataRecord.class);
final Dataset<MetadataRecord> mdstore = spark.createDataset(nativeStore.rdd(), encoder);
final String targetPath = currentVersion.getHdfsPath() + DATASET_NAME;
if (readVersion != null) { // INCREMENTAL MODE
log.info("updating {} incrementally with {}", targetPath, readVersion.getHdfsPath());
Dataset<MetadataRecord> currentMdStoreVersion = spark
.read()
.load(readVersion.getHdfsPath() + DATASET_NAME)
.as(encoder);
TypedColumn<MetadataRecord, MetadataRecord> aggregator = new MDStoreAggregator().toColumn();
final Dataset<MetadataRecord> map = currentMdStoreVersion
.union(mdstore)
.groupByKey(
(MapFunction<MetadataRecord, String>) MetadataRecord::getId,
Encoders.STRING())
.agg(aggregator)
.map((MapFunction<Tuple2<String, MetadataRecord>, MetadataRecord>) Tuple2::_2, encoder);
map.select("id").takeAsList(100).forEach(s -> log.info(s.toString()));
saveDataset(map, targetPath);
} else {
saveDataset(mdstore, targetPath);
}
final Long total = spark.read().load(targetPath).count();
log.info("collected {} records for datasource '{}'", total, provenance.getDatasourceName());
writeTotalSizeOnHDFS(spark, total, currentVersion.getHdfsPath() + "/size");
}
public static class MDStoreAggregator extends Aggregator<MetadataRecord, MetadataRecord, MetadataRecord> {
@Override
public MetadataRecord zero() {
return new MetadataRecord();
return null;
}
@Override
public MetadataRecord reduce(MetadataRecord b, MetadataRecord a) {
return getLatestRecord(b, a);
}
@Override
public MetadataRecord merge(MetadataRecord b, MetadataRecord a) {
return getLatestRecord(b, a);
}
@ -68,136 +189,22 @@ public class GenerateNativeStoreSparkJob {
}
@Override
public MetadataRecord merge(MetadataRecord b, MetadataRecord a) {
return getLatestRecord(b, a);
}
@Override
public MetadataRecord finish(MetadataRecord j) {
return j;
public MetadataRecord finish(MetadataRecord r) {
return r;
}
@Override
public Encoder<MetadataRecord> bufferEncoder() {
return Encoders.kryo(MetadataRecord.class);
return Encoders.bean(MetadataRecord.class);
}
@Override
public Encoder<MetadataRecord> outputEncoder() {
return Encoders.kryo(MetadataRecord.class);
return Encoders.bean(MetadataRecord.class);
}
}
public static void main(String[] args) throws Exception {
final ArgumentApplicationParser parser = new ArgumentApplicationParser(
IOUtils
.toString(
GenerateNativeStoreSparkJob.class
.getResourceAsStream(
"/eu/dnetlib/dhp/collection/collection_input_parameters.json")));
parser.parseArgument(args);
final ObjectMapper jsonMapper = new ObjectMapper();
final String provenanceArgument = parser.get("provenance");
log.info("Provenance is {}", provenanceArgument);
final Provenance provenance = jsonMapper.readValue(provenanceArgument, Provenance.class);
final String dateOfCollectionArgs = parser.get("dateOfCollection");
log.info("dateOfCollection is {}", dateOfCollectionArgs);
final long dateOfCollection = new Long(dateOfCollectionArgs);
String mdStoreVersion = parser.get("mdStoreVersion");
log.info("mdStoreVersion is {}", mdStoreVersion);
final MDStoreVersion currentVersion = jsonMapper.readValue(mdStoreVersion, MDStoreVersion.class);
String readMdStoreVersionParam = parser.get("readMdStoreVersion");
log.info("readMdStoreVersion is {}", readMdStoreVersionParam);
final MDStoreVersion readMdStoreVersion = StringUtils.isBlank(readMdStoreVersionParam) ? null
: jsonMapper.readValue(readMdStoreVersionParam, MDStoreVersion.class);
Boolean isSparkSessionManaged = Optional
.ofNullable(parser.get("isSparkSessionManaged"))
.map(Boolean::valueOf)
.orElse(Boolean.TRUE);
log.info("isSparkSessionManaged: {}", isSparkSessionManaged);
SparkConf conf = new SparkConf();
conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");
conf.registerKryoClasses(Collections.singleton(MetadataRecord.class).toArray(new Class[] {}));
runWithSparkSession(
conf,
isSparkSessionManaged,
spark -> {
final JavaSparkContext sc = JavaSparkContext.fromSparkContext(spark.sparkContext());
final JavaPairRDD<IntWritable, Text> inputRDD = sc
.sequenceFile(
currentVersion.getHdfsPath() + CollectorWorkerApplication.SEQUENTIAL_FILE_NAME,
IntWritable.class, Text.class);
final LongAccumulator totalItems = sc.sc().longAccumulator("TotalItems");
final LongAccumulator invalidRecords = sc.sc().longAccumulator("InvalidRecords");
final JavaRDD<MetadataRecord> nativeStore = inputRDD
.map(
item -> parseRecord(
item._2().toString(),
parser.get("xpath"),
parser.get("encoding"),
provenance,
dateOfCollection,
totalItems,
invalidRecords))
.filter(Objects::nonNull)
.distinct();
final Encoder<MetadataRecord> encoder = Encoders.bean(MetadataRecord.class);
final Dataset<MetadataRecord> mdstore = spark.createDataset(nativeStore.rdd(), encoder);
final String targetPath = currentVersion.getHdfsPath() + DATASET_NAME;
if (readMdStoreVersion != null) {
// INCREMENTAL MODE
Dataset<MetadataRecord> currentMdStoreVersion = spark
.read()
.load(readMdStoreVersion.getHdfsPath() + DATASET_NAME)
.as(encoder);
TypedColumn<MetadataRecord, MetadataRecord> aggregator = new MDStoreAggregator().toColumn();
saveDataset(
currentMdStoreVersion
.union(mdstore)
.groupByKey(
(MapFunction<MetadataRecord, String>) MetadataRecord::getId,
Encoders.STRING())
.agg(aggregator)
.map((MapFunction<Tuple2<String, MetadataRecord>, MetadataRecord>) Tuple2::_2, encoder),
targetPath);
} else {
saveDataset(mdstore, targetPath);
}
final Long total = spark.read().load(targetPath).count();
AggregationUtility.writeTotalSizeOnHDFS(spark, total, currentVersion.getHdfsPath() + "/size");
});
}
private static void saveDataset(final Dataset<MetadataRecord> currentMdStore, final String targetPath) {
currentMdStore
.write()
.mode(SaveMode.Overwrite)
.format("parquet")
.save(targetPath);
}
public static MetadataRecord parseRecord(
final String input,
final String xpath,
@ -219,7 +226,7 @@ public class GenerateNativeStoreSparkJob {
invalidRecords.add(1);
return null;
}
return new MetadataRecord(originalIdentifier, encoding, provenance, input, dateOfCollection);
return new MetadataRecord(originalIdentifier, encoding, provenance, document.asXML(), dateOfCollection);
} catch (Throwable e) {
invalidRecords.add(1);
return null;

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@ -1,11 +1,6 @@
package eu.dnetlib.dhp.collection.worker;
import java.io.File;
import java.io.FileOutputStream;
import java.io.OutputStream;
import java.util.Properties;
import org.apache.commons.io.IOUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
@ -16,7 +11,6 @@ import eu.dnetlib.data.mdstore.manager.common.model.MDStoreVersion;
import eu.dnetlib.dhp.application.ArgumentApplicationParser;
import eu.dnetlib.dhp.collection.worker.utils.CollectorPluginFactory;
import eu.dnetlib.dhp.collector.worker.model.ApiDescriptor;
import eu.dnetlib.dhp.common.rest.DNetRestClient;
/**
* DnetCollectortWorkerApplication is the main class responsible to start the Dnet Collection into HDFS. This module
@ -31,7 +25,7 @@ public class CollectorWorkerApplication {
private static final CollectorPluginFactory collectorPluginFactory = new CollectorPluginFactory();
public static String SEQUENTIAL_FILE_NAME = "/sequence_file";
public static String SEQUENCE_FILE_NAME = "/sequence_file";
/**
* @param args
@ -61,7 +55,7 @@ public class CollectorWorkerApplication {
final ApiDescriptor api = jsonMapper.readValue(apiDescriptor, ApiDescriptor.class);
final CollectorWorker worker = new CollectorWorker(collectorPluginFactory, api, hdfsuri,
currentVersion.getHdfsPath() + SEQUENTIAL_FILE_NAME);
currentVersion.getHdfsPath() + SEQUENCE_FILE_NAME);
worker.collect();
}

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@ -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;
}
}

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@ -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"
}

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@ -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"
}

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@ -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"
}

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@ -0,0 +1,5 @@
{
"datasourceId":"74912366-d6df-49c1-a1fd-8a52fa98ce5f_UmVwb3NpdG9yeVNlcnZpY2VSZXNvdXJjZXMvUmVwb3NpdG9yeVNlcnZpY2VSZXNvdXJjZVR5cGU\u003d",
"datasourceName":"PSNC Institutional Repository",
"nsPrefix":"psnc______pl"
}