[
https://issues.apache.org/jira/browse/HIVE-23880?focusedWorklogId=469596&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-469596
]
ASF GitHub Bot logged work on HIVE-23880:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 12/Aug/20 08:44
Start Date: 12/Aug/20 08:44
Worklog Time Spent: 10m
Work Description: abstractdog commented on a change in pull request #1280:
URL: https://github.com/apache/hive/pull/1280#discussion_r469102366
##########
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() {
Review comment:
yeah, only for logging, it was for validating my executor shutdown
correctness...that can be misleading, I'm removing it
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Issue Time Tracking
-------------------
Worklog Id: (was: 469596)
Time Spent: 4h (was: 3h 50m)
> 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: 4h
> 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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