Examples with PairFlatMapFunction org.apache.spark.api.java.function.PairFlatMapFunction used on opensource projects

Example 1 with PairFlatMapFunction

use of org.apache.spark.api.java.function.PairFlatMapFunction in project cdap by caskdata.

the class SparkPageRankProgram method run.

@Override
public void run(JavaSparkExecutionContext sec) throws Exception {
    JavaSparkContext jsc = new JavaSparkContext();
    LOG.info("Processing backlinkURLs data");
    JavaPairRDD<Long, String> backlinkURLs = sec.fromStream("backlinkURLStream", String.class);
    int iterationCount = getIterationCount(sec);
    LOG.info("Grouping data by key");
    // Grouping backlinks by unique URL in key
    JavaPairRDD<String, Iterable<String>> links = backlinkURLs.values().mapToPair(new PairFunction<String, String, String>() {

        @Override
        public Tuple2<String, String> call(String s) {
            String[] parts = SPACES.split(s);
            return new Tuple2<>(parts[0], parts[1]);
        }
    }).distinct().groupByKey().cache();
    // Initialize default rank for each key URL
    JavaPairRDD<String, Double> ranks = links.mapValues(new Function<Iterable<String>, Double>() {

        @Override
        public Double call(Iterable<String> rs) {
            return 1.0;
        }
    });
    // Calculates and updates URL ranks continuously using PageRank algorithm.
    for (int current = 0; current < iterationCount; current++) {
        LOG.debug("Processing data with PageRank algorithm. Iteration {}/{}", current + 1, (iterationCount));
        // Calculates URL contributions to the rank of other URLs.
        JavaPairRDD<String, Double> contribs = links.join(ranks).values().flatMapToPair(new PairFlatMapFunction<Tuple2<Iterable<String>, Double>, String, Double>() {

            @Override
            public Iterable<Tuple2<String, Double>> call(Tuple2<Iterable<String>, Double> s) {
                LOG.debug("Processing {} with rank {}", s._1(), s._2());
                int urlCount = Iterables.size(s._1());
                List<Tuple2<String, Double>> results = new ArrayList<>();
                for (String n : s._1()) {
                    results.add(new Tuple2<>(n, s._2() / urlCount));
                }
                return results;
            }
        });
        // Re-calculates URL ranks based on backlink contributions.
        ranks = contribs.reduceByKey(new Sum()).mapValues(new Function<Double, Double>() {

            @Override
            public Double call(Double sum) {
                return 0.15 + sum * 0.85;
            }
        });
    }
    LOG.info("Writing ranks data");
    final ServiceDiscoverer discoveryServiceContext = sec.getServiceDiscoverer();
    final Metrics sparkMetrics = sec.getMetrics();
    JavaPairRDD<byte[], Integer> ranksRaw = ranks.mapToPair(new PairFunction<Tuple2<String, Double>, byte[], Integer>() {

        @Override
        public Tuple2<byte[], Integer> call(Tuple2<String, Double> tuple) throws Exception {
            LOG.debug("URL {} has rank {}", Arrays.toString(tuple._1().getBytes(Charsets.UTF_8)), tuple._2());
            URL serviceURL = discoveryServiceContext.getServiceURL(SparkPageRankApp.SERVICE_HANDLERS);
            if (serviceURL == null) {
                throw new RuntimeException("Failed to discover service: " + SparkPageRankApp.SERVICE_HANDLERS);
            }
            try {
                URLConnection connection = new URL(serviceURL, String.format("%s/%s", SparkPageRankApp.SparkPageRankServiceHandler.TRANSFORM_PATH, tuple._2().toString())).openConnection();
                try (BufferedReader reader = new BufferedReader(new InputStreamReader(connection.getInputStream(), Charsets.UTF_8))) {
                    String pr = reader.readLine();
                    if ((Integer.parseInt(pr)) == POPULAR_PAGE_THRESHOLD) {
                        sparkMetrics.count(POPULAR_PAGES, 1);
                    } else if (Integer.parseInt(pr) <= UNPOPULAR_PAGE_THRESHOLD) {
                        sparkMetrics.count(UNPOPULAR_PAGES, 1);
                    } else {
                        sparkMetrics.count(REGULAR_PAGES, 1);
                    }
                    return new Tuple2<>(tuple._1().getBytes(Charsets.UTF_8), Integer.parseInt(pr));
                }
            } catch (Exception e) {
                LOG.warn("Failed to read the Stream for service {}", SparkPageRankApp.SERVICE_HANDLERS, e);
                throw Throwables.propagate(e);
            }
        }
    });
    // Store calculated results in output Dataset.
    // All calculated results are stored in one row.
    // Each result, the calculated URL rank based on backlink contributions, is an entry of the row.
    // The value of the entry is the URL rank.
    sec.saveAsDataset(ranksRaw, "ranks");
    LOG.info("PageRanks successfuly computed and written to \"ranks\" dataset");
}

Example 2 with PairFlatMapFunction

use of org.apache.spark.api.java.function.PairFlatMapFunction in project cdap by caskdata.

the class SparkPageRankProgram method run.

@Override
public void run(JavaSparkExecutionContext sec) throws Exception {
    JavaSparkContext jsc = new JavaSparkContext();
    LOG.info("Processing backlinkURLs data");
    JavaPairRDD<Long, String> backlinkURLs = sec.fromStream("backlinkURLStream", String.class);
    int iterationCount = getIterationCount(sec);
    LOG.info("Grouping data by key");
    // Grouping backlinks by unique URL in key
    JavaPairRDD<String, Iterable<String>> links = backlinkURLs.values().mapToPair(new PairFunction<String, String, String>() {

        @Override
        public Tuple2<String, String> call(String s) {
            String[] parts = SPACES.split(s);
            return new Tuple2<>(parts[0], parts[1]);
        }
    }).distinct().groupByKey().cache();
    // Initialize default rank for each key URL
    JavaPairRDD<String, Double> ranks = links.mapValues(new Function<Iterable<String>, Double>() {

        @Override
        public Double call(Iterable<String> rs) {
            return 1.0;
        }
    });
    // Calculates and updates URL ranks continuously using PageRank algorithm.
    for (int current = 0; current < iterationCount; current++) {
        LOG.debug("Processing data with PageRank algorithm. Iteration {}/{}", current + 1, (iterationCount));
        // Calculates URL contributions to the rank of other URLs.
        JavaPairRDD<String, Double> contribs = links.join(ranks).values().flatMapToPair(new PairFlatMapFunction<Tuple2<Iterable<String>, Double>, String, Double>() {

            @Override
            public Iterable<Tuple2<String, Double>> call(Tuple2<Iterable<String>, Double> s) {
                LOG.debug("Processing {} with rank {}", s._1(), s._2());
                int urlCount = Iterables.size(s._1());
                List<Tuple2<String, Double>> results = new ArrayList<>();
                for (String n : s._1()) {
                    results.add(new Tuple2<>(n, s._2() / urlCount));
                }
                return results;
            }
        });
        // Re-calculates URL ranks based on backlink contributions.
        ranks = contribs.reduceByKey(new Sum()).mapValues(new Function<Double, Double>() {

            @Override
            public Double call(Double sum) {
                return 0.15 + sum * 0.85;
            }
        });
    }
    LOG.info("Writing ranks data");
    final ServiceDiscoverer discoveryServiceContext = sec.getServiceDiscoverer();
    final Metrics sparkMetrics = sec.getMetrics();
    JavaPairRDD<byte[], Integer> ranksRaw = ranks.mapToPair(new PairFunction<Tuple2<String, Double>, byte[], Integer>() {

        @Override
        public Tuple2<byte[], Integer> call(Tuple2<String, Double> tuple) throws Exception {
            LOG.debug("URL {} has rank {}", Arrays.toString(tuple._1().getBytes(Charsets.UTF_8)), tuple._2());
            URL serviceURL = discoveryServiceContext.getServiceURL(SparkPageRankApp.SERVICE_HANDLERS);
            if (serviceURL == null) {
                throw new RuntimeException("Failed to discover service: " + SparkPageRankApp.SERVICE_HANDLERS);
            }
            try {
                URLConnection connection = new URL(serviceURL, String.format("%s/%s", SparkPageRankApp.SparkPageRankServiceHandler.TRANSFORM_PATH, tuple._2().toString())).openConnection();
                try (BufferedReader reader = new BufferedReader(new InputStreamReader(connection.getInputStream(), Charsets.UTF_8))) {
                    String pr = reader.readLine();
                    if ((Integer.parseInt(pr)) == POPULAR_PAGE_THRESHOLD) {
                        sparkMetrics.count(POPULAR_PAGES, 1);
                    } else if (Integer.parseInt(pr) <= UNPOPULAR_PAGE_THRESHOLD) {
                        sparkMetrics.count(UNPOPULAR_PAGES, 1);
                    } else {
                        sparkMetrics.count(REGULAR_PAGES, 1);
                    }
                    return new Tuple2<>(tuple._1().getBytes(Charsets.UTF_8), Integer.parseInt(pr));
                }
            } catch (Exception e) {
                LOG.warn("Failed to read the Stream for service {}", SparkPageRankApp.SERVICE_HANDLERS, e);
                throw Throwables.propagate(e);
            }
        }
    });
    // Store calculated results in output Dataset.
    // All calculated results are stored in one row.
    // Each result, the calculated URL rank based on backlink contributions, is an entry of the row.
    // The value of the entry is the URL rank.
    sec.saveAsDataset(ranksRaw, "ranks");
    LOG.info("PageRanks successfuly computed and written to \"ranks\" dataset");
}

Example 3 with PairFlatMapFunction

use of org.apache.spark.api.java.function.PairFlatMapFunction in project gatk by broadinstitute.

the class AddContextDataToReadSpark method addUsingOverlapsPartitioning.

/**
     * Add context data ({@link ReadContextData}) to reads, using overlaps partitioning to avoid a shuffle.
     * @param ctx the Spark context
     * @param mappedReads the coordinate-sorted reads
     * @param referenceSource the reference source
     * @param variants the coordinate-sorted variants
     * @param sequenceDictionary the sequence dictionary for the reads
     * @param shardSize the maximum size of each shard, in bases
     * @param shardPadding amount of extra context around each shard, in bases
     * @return a RDD of read-context pairs, in coordinate-sorted order
     */
private static JavaPairRDD<GATKRead, ReadContextData> addUsingOverlapsPartitioning(final JavaSparkContext ctx, final JavaRDD<GATKRead> mappedReads, final ReferenceMultiSource referenceSource, final JavaRDD<GATKVariant> variants, final SAMSequenceDictionary sequenceDictionary, final int shardSize, final int shardPadding) {
    final List<SimpleInterval> intervals = IntervalUtils.getAllIntervalsForReference(sequenceDictionary);
    // use unpadded shards (padding is only needed for reference bases)
    final List<ShardBoundary> intervalShards = intervals.stream().flatMap(interval -> Shard.divideIntervalIntoShards(interval, shardSize, 0, sequenceDictionary).stream()).collect(Collectors.toList());
    final Broadcast<ReferenceMultiSource> bReferenceSource = ctx.broadcast(referenceSource);
    final IntervalsSkipList<GATKVariant> variantSkipList = new IntervalsSkipList<>(variants.collect());
    final Broadcast<IntervalsSkipList<GATKVariant>> variantsBroadcast = ctx.broadcast(variantSkipList);
    int maxLocatableSize = Math.min(shardSize, shardPadding);
    JavaRDD<Shard<GATKRead>> shardedReads = SparkSharder.shard(ctx, mappedReads, GATKRead.class, sequenceDictionary, intervalShards, maxLocatableSize);
    return shardedReads.flatMapToPair(new PairFlatMapFunction<Shard<GATKRead>, GATKRead, ReadContextData>() {

        private static final long serialVersionUID = 1L;

        @Override
        public Iterator<Tuple2<GATKRead, ReadContextData>> call(Shard<GATKRead> shard) throws Exception {
            // get reference bases for this shard (padded)
            SimpleInterval paddedInterval = shard.getInterval().expandWithinContig(shardPadding, sequenceDictionary);
            ReferenceBases referenceBases = bReferenceSource.getValue().getReferenceBases(null, paddedInterval);
            final IntervalsSkipList<GATKVariant> intervalsSkipList = variantsBroadcast.getValue();
            Iterator<Tuple2<GATKRead, ReadContextData>> transform = Iterators.transform(shard.iterator(), new Function<GATKRead, Tuple2<GATKRead, ReadContextData>>() {

                @Nullable
                @Override
                public Tuple2<GATKRead, ReadContextData> apply(@Nullable GATKRead r) {
                    List<GATKVariant> overlappingVariants;
                    if (SimpleInterval.isValid(r.getContig(), r.getStart(), r.getEnd())) {
                        overlappingVariants = intervalsSkipList.getOverlapping(new SimpleInterval(r));
                    } else {
                        //Sometimes we have reads that do not form valid intervals (reads that do not consume any ref bases, eg CIGAR 61S90I
                        //In those cases, we'll just say that nothing overlaps the read
                        overlappingVariants = Collections.emptyList();
                    }
                    return new Tuple2<>(r, new ReadContextData(referenceBases, overlappingVariants));
                }
            });
            // only include reads that start in the shard
            return Iterators.filter(transform, r -> r._1().getStart() >= shard.getStart() && r._1().getStart() <= shard.getEnd());
        }
    });
}