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From "ASF GitHub Bot (Jira)" <j...@apache.org>
Subject [jira] [Work logged] (HIVE-23880) Bloom filters can be merged in a parallel way in VectorUDAFBloomFilterMerge
Date Thu, 13 Aug 2020 07:56:00 GMT

     [ https://issues.apache.org/jira/browse/HIVE-23880?focusedWorklogId=470126&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-470126
]

ASF GitHub Bot logged work on HIVE-23880:
-----------------------------------------

                Author: ASF GitHub Bot
            Created on: 13/Aug/20 07:55
            Start Date: 13/Aug/20 07:55
    Worklog Time Spent: 10m 
      Work Description: abstractdog commented on a change in pull request #1280:
URL: https://github.com/apache/hive/pull/1280#discussion_r469765404



##########
File path: ql/src/java/org/apache/hadoop/hive/ql/exec/vector/expressions/aggregates/VectorUDAFBloomFilterMerge.java
##########
@@ -77,6 +75,211 @@ public void reset() {
       // Do not change the initial bytes which contain NumHashFunctions/NumBits!
       Arrays.fill(bfBytes, BloomKFilter.START_OF_SERIALIZED_LONGS, bfBytes.length, (byte)
0);
     }
+
+    public boolean mergeBloomFilterBytesFromInputColumn(BytesColumnVector inputColumn,
+        int batchSize, boolean selectedInUse, int[] selected, Configuration conf) {
+      // already set in previous iterations, no need to call initExecutor again
+      if (numThreads == 0) {
+        return false;
+      }
+      if (executor == null) {
+        initExecutor(conf, batchSize);
+        if (!isParallel) {
+          return false;
+        }
+      }
+
+      // split every bloom filter (represented by a part of a byte[]) across workers
+      for (int j = 0; j < batchSize; j++) {
+        if (!selectedInUse && inputColumn.noNulls) {
+          splitVectorAcrossWorkers(workers, inputColumn.vector[j], inputColumn.start[j],
+              inputColumn.length[j]);
+        } else if (!selectedInUse) {
+          if (!inputColumn.isNull[j]) {
+            splitVectorAcrossWorkers(workers, inputColumn.vector[j], inputColumn.start[j],
+                inputColumn.length[j]);
+          }
+        } else if (inputColumn.noNulls) {
+          int i = selected[j];
+          splitVectorAcrossWorkers(workers, inputColumn.vector[i], inputColumn.start[i],
+              inputColumn.length[i]);
+        } else {
+          int i = selected[j];
+          if (!inputColumn.isNull[i]) {
+            splitVectorAcrossWorkers(workers, inputColumn.vector[i], inputColumn.start[i],
+                inputColumn.length[i]);
+          }
+        }
+      }
+
+      return true;
+    }
+
+    private void initExecutor(Configuration conf, int batchSize) {
+      numThreads = conf.getInt(HiveConf.ConfVars.TEZ_BLOOM_FILTER_MERGE_THREADS.varname,
+          HiveConf.ConfVars.TEZ_BLOOM_FILTER_MERGE_THREADS.defaultIntVal);
+      LOG.info("Number of threads used for bloom filter merge: {}", numThreads);
+
+      if (numThreads < 0) {
+        throw new RuntimeException(
+            "invalid number of threads for bloom filter merge: " + numThreads);
+      }
+      if (numThreads == 0) { // disable parallel feature
+        return; // this will leave isParallel=false
+      }
+      isParallel = true;
+      executor = Executors.newFixedThreadPool(numThreads);
+
+      workers = new BloomFilterMergeWorker[numThreads];
+      for (int f = 0; f < numThreads; f++) {
+        workers[f] = new BloomFilterMergeWorker(bfBytes, 0, bfBytes.length);
+      }
+
+      for (int f = 0; f < numThreads; f++) {
+        executor.submit(workers[f]);
+      }
+    }
+
+    public int getNumberOfWaitingMergeTasks(){
+      int size = 0;
+      for (BloomFilterMergeWorker w : workers){
+        size += w.queue.size();
+      }
+      return size;
+    }
+
+    public int getNumberOfMergingWorkers() {
+      int working = 0;
+      for (BloomFilterMergeWorker w : workers) {
+        if (w.isMerging.get()) {
+          working += 1;
+        }
+      }
+      return working;
+    }
+
+    private static void splitVectorAcrossWorkers(BloomFilterMergeWorker[] workers, byte[]
bytes,
+        int start, int length) {
+      if (bytes == null || length == 0) {
+        return;
+      }
+      /*
+       * This will split a byte[] across workers as below:
+       * let's say there are 10 workers for 7813 bytes, in this case
+       * length: 7813, elementPerBatch: 781
+       * bytes assigned to workers: inclusive lower bound, exclusive upper bound
+       * 1. worker: 5 -> 786
+       * 2. worker: 786 -> 1567
+       * 3. worker: 1567 -> 2348
+       * 4. worker: 2348 -> 3129
+       * 5. worker: 3129 -> 3910
+       * 6. worker: 3910 -> 4691
+       * 7. worker: 4691 -> 5472
+       * 8. worker: 5472 -> 6253
+       * 9. worker: 6253 -> 7034
+       * 10. worker: 7034 -> 7813 (last element per batch is: 779)
+       *
+       * This way, a particular worker will be given with the same part
+       * of all bloom filters along with the shared base bloom filter,
+       * so the bitwise OR function will not be a subject of threading/sync issues.
+       */
+      int elementPerBatch =
+          (int) Math.ceil((double) (length - START_OF_SERIALIZED_LONGS) / workers.length);
+
+      for (int w = 0; w < workers.length; w++) {
+        int modifiedStart = START_OF_SERIALIZED_LONGS + w * elementPerBatch;
+        int modifiedLength = (w == workers.length - 1)
+          ? length - (START_OF_SERIALIZED_LONGS + w * elementPerBatch) : elementPerBatch;
+
+        ElementWrapper wrapper =
+            new ElementWrapper(bytes, start, length, modifiedStart, modifiedLength);
+        workers[w].add(wrapper);
+      }
+    }
+
+    public void shutdownAndWaitForMergeTasks() {
+      /**
+       * Executor.shutdownNow() is supposed to send Thread.interrupt to worker threads, and
they are
+       * supposed to finish their work.
+       */
+      executor.shutdownNow();
+      try {
+        executor.awaitTermination(180, TimeUnit.SECONDS);
+      } catch (InterruptedException e) {
+        LOG.warn("Bloom filter merge is interrupted while waiting to finish, this is unexpected",
+            e);
+      }
+    }
+  }
+
+  private static class BloomFilterMergeWorker implements Runnable {
+    private BlockingQueue<ElementWrapper> queue;
+    private byte[] bfAggregation;
+    private int bfAggregationStart;
+    private int bfAggregationLength;
+    AtomicBoolean isMerging = new AtomicBoolean(false);
+
+    public BloomFilterMergeWorker(byte[] bfAggregation, int bfAggregationStart,
+        int bfAggregationLength) {
+      this.bfAggregation = bfAggregation;
+      this.bfAggregationStart = bfAggregationStart;
+      this.bfAggregationLength = bfAggregationLength;
+      this.queue = new ArrayBlockingQueue<>(VectorizedRowBatch.DEFAULT_SIZE * 2);

Review comment:
       if there are 1000 upstream mapper tasks (creating bloom filters), there will be 1000
rowbatches (=1000 bloom filters), for example on TPCDS 30GB there were 1000<x<2000...anyway,
you're absolutely right, I don't want to take care of correct bounds, which is unpredictable,
I've just chosen a wrong implementation...I'm going to change this to LinkedBlockingDeque
and letting this size confusion go




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Issue Time Tracking
-------------------

    Worklog Id:     (was: 470126)
    Time Spent: 7h 10m  (was: 7h)

> Bloom filters can be merged in a parallel way in VectorUDAFBloomFilterMerge
> ---------------------------------------------------------------------------
>
>                 Key: HIVE-23880
>                 URL: https://issues.apache.org/jira/browse/HIVE-23880
>             Project: Hive
>          Issue Type: Improvement
>            Reporter: László Bodor
>            Assignee: László Bodor
>            Priority: Major
>              Labels: pull-request-available
>         Attachments: lipwig-output3605036885489193068.svg
>
>          Time Spent: 7h 10m
>  Remaining Estimate: 0h
>
> Merging bloom filters in semijoin reduction can become the main bottleneck in case of
large number of source mapper tasks (~1000, Map 1 in below example) and a large amount of
expected entries (50M) in bloom filters.
> For example in TPCDS Q93:
> {code}
> select /*+ semi(store_returns, sr_item_sk, store_sales, 70000000)*/ ss_customer_sk
>             ,sum(act_sales) sumsales
>       from (select ss_item_sk
>                   ,ss_ticket_number
>                   ,ss_customer_sk
>                   ,case when sr_return_quantity is not null then (ss_quantity-sr_return_quantity)*ss_sales_price
>                                                             else (ss_quantity*ss_sales_price)
end act_sales
>             from store_sales left outer join store_returns on (sr_item_sk = ss_item_sk
>                                                                and sr_ticket_number =
ss_ticket_number)
>                 ,reason
>             where sr_reason_sk = r_reason_sk
>               and r_reason_desc = 'reason 66') t
>       group by ss_customer_sk
>       order by sumsales, ss_customer_sk
> limit 100;
> {code}
> On 10TB-30TB scale there is a chance that from 3-4 mins of query runtime 1-2 mins are
spent with merging bloom filters (Reducer 2), as in:  [^lipwig-output3605036885489193068.svg]

> {code}
> ----------------------------------------------------------------------------------------------
>         VERTICES      MODE        STATUS  TOTAL  COMPLETED  RUNNING  PENDING  FAILED
 KILLED
> ----------------------------------------------------------------------------------------------
> Map 3 ..........      llap     SUCCEEDED      1          1        0        0       0
      0
> Map 1 ..........      llap     SUCCEEDED   1263       1263        0        0       0
      0
> Reducer 2             llap       RUNNING      1          0        1        0       0
      0
> Map 4                 llap       RUNNING   6154          0      207     5947       0
      0
> Reducer 5             llap        INITED     43          0        0       43       0
      0
> Reducer 6             llap        INITED      1          0        0        1       0
      0
> ----------------------------------------------------------------------------------------------
> VERTICES: 02/06  [====>>----------------------] 16%   ELAPSED TIME: 149.98 s
> ----------------------------------------------------------------------------------------------
> {code}
> For example, 70M entries in bloom filter leads to a 436 465 696 bits, so merging 1263
bloom filters means running ~ 1263 * 436 465 696 bitwise OR operation, which is very hot codepath,
but can be parallelized.



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