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https://issues.apache.org/jira/browse/DRILL-7607?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17048165#comment-17048165
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ASF GitHub Bot commented on DRILL-7607:
---------------------------------------
paul-rogers commented on pull request #2000: DRILL-7607: support dynamic credit
based flow control
URL: https://github.com/apache/drill/pull/2000#discussion_r385998974
##########
File path:
exec/java-exec/src/main/java/org/apache/drill/exec/work/batch/UnlimitedRawBatchBuffer.java
##########
@@ -90,14 +100,39 @@ public boolean isEmpty() {
@Override
public void add(RawFragmentBatch batch) {
+ int recordCount = batch.getHeader().getDef().getRecordCount();
+ long bathByteSize = batch.getByteCount();
+ if (recordCount != 0) {
+ //skip first header batch
+ totalBatchSize += bathByteSize;
+ sampleTimes++;
+ }
+ if (sampleTimes == maxSampleTimes) {
+ long averageBathSize = totalBatchSize / sampleTimes;
+ //make a decision
+ long limit = context.getAllocator().getLimit();
Review comment:
Another issue is the question of how many of these receivers exist per
Drillbit. I don't know the answer. If I have 5 minor fragments on this
Drillbit, will all 5 have their own flow control calcs? Will I have 5 fragments
each trying to use 50% of 10GB for a total of 20GB of buffering? Will this be a
problem?
Also, how can this algorithm go wrong? Suppose I have a set of files
organized by time. I do a time range query. The first few batches might have
very few rows because the filter is picking up just a few early arrivals. We
see three batches, say, where the filter had low selectivity, of a few dozen
rows, then decide we can hold many batches.
Later, the scan hits the bulk of my time ranges and the batches have far
fewer rows filtered out. Suddenly, we need far more memory for these
low-selectivity batches.
Do we need a safety valve that says that we will back off if we suddenly see
large batches?
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> Dynamic credit based flow control
> ---------------------------------
>
> Key: DRILL-7607
> URL: https://issues.apache.org/jira/browse/DRILL-7607
> Project: Apache Drill
> Issue Type: New Feature
> Components: Server, Execution - RPC
> Affects Versions: 1.17.0
> Reporter: Weijie Tong
> Assignee: Weijie Tong
> Priority: Major
> Fix For: 1.18.0
>
>
> Drill current has a static credit based flow control between the batch sender
> and receiver. That means ,all the sender send out their batch through the
> DataTunnel by a static 3 semaphore. To the receiver side , there's two cases,
> the UnlimitedRawBatchBuffer has a 6 * fragmentCount receiver semaphore, the
> SpoolingRawBatchBuffer acts as having unlimited receiving semaphore as it
> could flush data to disk.
> The static credit has the following weak points:
> 1. While the send batch data size is low(e.g. it has only one column bigint
> data) and the receiver has larger memory space, the sender still could not
> send out its data rapidly.
> 2. As the static credit assumption does not set the semaphore number
> according to the corresponding receiver memory space, it still have the risk
> to make the receiver OOM.
> 3. As the sender semaphore is small, it could not send its batch
> consecutively due to wait for an Ack to release one semaphore , and then ,
> the sender's corresponding execution pipeline would be halt, also the same to
> its leaf execution nodes.
> The dynamic credit based flow control could solve these problems. It starts
> from the static credit flow control. Then the receiver collects some batch
> datas to calculate the average batch size. According to the receiver side
> memory space, the receiver make a runtime sender credit and receiver side
> total credit. The receiver sends out the runtime sender credit number to the
> sender by the Ack response. The sender change to the runtime sender credit
> number when receives the Ack response with a runtime credit value.
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