[SUPPORT] How to configure spark and flink to write mor tables using bucket indexes?

[xiaofan2022]

I want to use flink and spark to write to the mor table, and use bucket CONSISTENT_HASHING for the index, but I find that spark is very fast to write the full amount and flink is very slow(flink write 100record/s) to write increments.
spark sql：

CREATE TABLE test.tableA ()
USING hudi
TBLPROPERTIES (
'connector' = 'hudi',
'index.type'='BUCKET',
'hoodie.index.type'='BUCKET',
'hoodie.index.bucket.engine'='CONSISTENT_HASHING',
'hoodie.datasource.write.recordkey.field' = '',
'path' = '',
'preCombineField' = 'create_time',
'precombine.field' = 'create_time',
'primaryKey' = '',
'table.type' = 'MERGE_ON_READ',
'write.rate.limit'='10000',--flink配置
'write.tasks'='2',--flink配置
'write.utc-timezone'='false',
 'type' = 'mor');

How to optimize？

[danny0405]

Did you have chance to check the thread dump of the operators?

[xiaofan2022]

"consistent_bucket_write: test.fin_ipr_inmaininfo_test (1/2)#0" Id=89 TIMED_WAITING on java.util.LinkedList@37d9fd7
at java.lang.Object.wait(Native Method)
- waiting on java.util.LinkedList@37d9fd7
at org.apache.hadoop.hdfs.DataStreamer.waitForAckedSeqno(DataStreamer.java:924)
at org.apache.hadoop.hdfs.DFSOutputStream.flushOrSync(DFSOutputStream.java:692)
at org.apache.hadoop.hdfs.DFSOutputStream.hsync(DFSOutputStream.java:587)
at org.apache.hadoop.fs.FSDataOutputStream.hsync(FSDataOutputStream.java:145)
at org.apache.hadoop.fs.FSDataOutputStream.hsync(FSDataOutputStream.java:145)
at org.apache.hudi.common.table.log.HoodieLogFormatWriter.flush(HoodieLogFormatWriter.java:261)
at org.apache.hudi.common.table.log.HoodieLogFormatWriter.appendBlocks(HoodieLogFormatWriter.java:194)
at org.apache.hudi.io.HoodieAppendHandle.appendDataAndDeleteBlocks(HoodieAppendHandle.java:479)
at org.apache.hudi.io.HoodieAppendHandle.doAppend(HoodieAppendHandle.java:450)
at org.apache.hudi.table.action.commit.delta.BaseFlinkDeltaCommitActionExecutor.handleUpdate(BaseFlinkDeltaCommitActionExecutor.java:54)
at org.apache.hudi.table.action.commit.BaseFlinkCommitActionExecutor.handleUpsertPartition(BaseFlinkCommitActionExecutor.java:191)
at org.apache.hudi.table.action.commit.BaseFlinkCommitActionExecutor.execute(BaseFlinkCommitActionExecutor.java:109)
at org.apache.hudi.table.action.commit.BaseFlinkCommitActionExecutor.execute(BaseFlinkCommitActionExecutor.java:71)
at org.apache.hudi.table.action.commit.FlinkWriteHelper.write(FlinkWriteHelper.java:77)
at org.apache.hudi.table.action.commit.delta.FlinkUpsertDeltaCommitActionExecutor.execute(FlinkUpsertDeltaCommitActionExecutor.java:51)
at org.apache.hudi.table.HoodieFlinkMergeOnReadTable.upsert(HoodieFlinkMergeOnReadTable.java:73)
at org.apache.hudi.client.HoodieFlinkWriteClient.upsert(HoodieFlinkWriteClient.java:148)
at org.apache.hudi.sink.StreamWriteFunction.lambda$initWriteFunction$1(StreamWriteFunction.java:192)
at org.apache.hudi.sink.StreamWriteFunction$$Lambda$1275/530894042.apply(Unknown Source)
at org.apache.hudi.sink.bucket.ConsistentBucketStreamWriteFunction.lambda$writeBucket$0(ConsistentBucketStreamWriteFunction.java:80)
at org.apache.hudi.sink.bucket.ConsistentBucketStreamWriteFunction$$Lambda$1599/372489614.apply(Unknown Source)
at java.util.stream.ReferencePipeline$7$1.accept(ReferencePipeline.java:267)
at java.util.Collections$2.tryAdvance(Collections.java:4717)
at java.util.Collections$2.forEachRemaining(Collections.java:4725)
at java.util.stream.AbstractPipeline.copyInto(AbstractPipeline.java:481)
at java.util.stream.AbstractPipeline.wrapAndCopyInto(AbstractPipeline.java:471)
at java.util.stream.ReduceOps$ReduceOp.evaluateSequential(ReduceOps.java:708)
at java.util.stream.AbstractPipeline.evaluate(AbstractPipeline.java:234)
at java.util.stream.ReferencePipeline.collect(ReferencePipeline.java:499)
at org.apache.hudi.sink.bucket.ConsistentBucketStreamWriteFunction.writeBucket(ConsistentBucketStreamWriteFunction.java:81)
at org.apache.hudi.sink.StreamWriteFunction.lambda$flushRemaining$7(StreamWriteFunction.java:467)
at org.apache.hudi.sink.StreamWriteFunction$$Lambda$1593/379154034.accept(Unknown Source)
at java.util.LinkedHashMap$LinkedValues.forEach(LinkedHashMap.java:608)
at org.apache.hudi.sink.StreamWriteFunction.flushRemaining(StreamWriteFunction.java:463)
at org.apache.hudi.sink.StreamWriteFunction.snapshotState(StreamWriteFunction.java:137)
at org.apache.hudi.sink.bucket.ConsistentBucketStreamWriteFunction.snapshotState(ConsistentBucketStreamWriteFunction.java:69)
at org.apache.hudi.sink.common.AbstractStreamWriteFunction.snapshotState(AbstractStreamWriteFunction.java:167)
at org.apache.flink.streaming.util.functions.StreamingFunctionUtils.trySnapshotFunctionState(StreamingFunctionUtils.java:118)
at org.apache.flink.streaming.util.functions.StreamingFunctionUtils.snapshotFunctionState(StreamingFunctionUtils.java:99)
at org.apache.flink.streaming.api.operators.AbstractUdfStreamOperator.snapshotState(AbstractUdfStreamOperator.java:88)
at org.apache.flink.streaming.api.operators.StreamOperatorStateHandler.snapshotState(StreamOperatorStateHandler.java:222)
at org.apache.flink.streaming.api.operators.StreamOperatorStateHandler.snapshotState(StreamOperatorStateHandler.java:173)
at org.apache.flink.streaming.api.operators.AbstractStreamOperator.snapshotState(AbstractStreamOperator.java:336)
at org.apache.flink.streaming.runtime.tasks.RegularOperatorChain.checkpointStreamOperator(RegularOperatorChain.java:228)
at org.apache.flink.streaming.runtime.tasks.RegularOperatorChain.buildOperatorSnapshotFutures(RegularOperatorChain.java:213)
at org.apache.flink.streaming.runtime.tasks.RegularOperatorChain.snapshotState(RegularOperatorChain.java:192)
at org.apache.flink.streaming.runtime.tasks.SubtaskCheckpointCoordinatorImpl.takeSnapshotSync(SubtaskCheckpointCoordinatorImpl.java:715)
at org.apache.flink.streaming.runtime.tasks.SubtaskCheckpointCoordinatorImpl.checkpointState(SubtaskCheckpointCoordinatorImpl.java:350)
at org.apache.flink.streaming.runtime.tasks.StreamTask.lambda$performCheckpoint$14(StreamTask.java:1299)
...

Is hdfs write performance problematic? If I use simple index for spark, flink uses bucket index very quickly 1k-2krecord/s

[danny0405]

The performance should be very near for consistent hashing and simple hashing, but from the stacktrace, it looks like the appending to files takes time.

[xiaofan2022]

So how can this be optimized? This speed is too slow

[danny0405]

@beyond1920 can you help with the performance issue here?

[danny0405]

@xiaofan2022 Did you schedule the clustering for expanding the consistent hashing ring already? Did you check the tablePath/.bucket_index/consistent_hashing_metadata for the number of consistent hashing nodes?

[xiaofan2022]

hdfs dfs -cat hdfs://nameservice1/apps/spark/warehouse/test.db/file_test/.hoodie/.bucket_index/consistent_hashing_metadata/00000000000000.hashing_meta | grep "value" | wc -l

result=>>>256

[danny0405]

So you have 256 initial buckets?

[xiaofan2022]

Yes, I set up 'hoodie. Bucket. Index. Max. Num. Buckets' =' 32 ',
'the hoodie. Bucket. Index. Min. Num. Buckets' =' 4 ', but found that there are still 256 buckets

[danny0405]

yeah, let's figure out the reason, too many buckets would not perform well for streaming write.

[ad1happy2go]

@xiaofan2022 Any updates on this ticket? Were you able to find out the reason why we see 256 buckets?

[xiaofan2022]

I first create tables through spark and import full data. Then flink updates incremental data in real time, but the default bucket in spark is 4