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From Stefan Richter <s.rich...@data-artisans.com>
Subject Re: Tuning RocksDB
Date Wed, 03 May 2017 16:05:11 GMT
Sorry, just saw that your question was actually mainly about checkpointing, but it can still
be related to my previous answer. I assume the checkpointing time is the time that is reported
in the web interface? This would be the end-to-end runtime of the checkpoint which does not
really tell you how much time is spend on writing the state itself, but you can find this
exact detail in the logging; you can grep for lines that start with "Asynchronous RocksDB
snapshot“. The background is that end-to-end also includes the time the checkpoint barrier
needs to travel to the operator. If there is a lot of backpressure and a lot of network buffers,
this can take a while. Still, the reason for the backpressure could still be in the way you
access RocksDB, as it seems you are de/serializing every time you update an ever-growing value
under a single key. I can see that accesses under this conditions could become very slow eventually,
but could remain fast on the FSBackend for the reason from my first answer.

> Am 03.05.2017 um 17:54 schrieb Stefan Richter <s.richter@data-artisans.com>:
> Hi,
> typically, I would expect that the bottleneck with the RocksDB backend is not RocksDB
itself, but your TypeSerializers. I suggest to first run a profiler/sampling attached to the
process and check if the problematic methods are in serialization or the actual accesses to
RocksDB. The RocksDB backend has to go through de/serialize roundtrips on every single state
access, while the FSBackend works on heap objects immediately. For checkpoints, the RocksDB
backend can write bytes directly whereas the FSBackend has to use the serializers to get from
objects to bytes, so their actions w.r.t. how serializers are used are kind of inverted between
operation and checkpointing. For Flink 1.3 we also will introduce incremental checkpoints
on RocksDB that piggyback on the SST files. Flink 1.2 is writing checkpoints and savepoints
fully and in a custom format.
> Best,
> Stefan
>> Am 03.05.2017 um 16:46 schrieb Jason Brelloch <jb.bc.flk@gmail.com <mailto:jb.bc.flk@gmail.com>>:
>> Hey all,
>> I am looking for some advice on tuning rocksDB for better performance in Flink 1.2.
 I created a pretty simple job with a single kafka source and one flatmap function that just
stores 50000 events in a single key of managed keyed state and then drops everything else,
to test checkpoint performance.  Using a basic FsStateBackend configured as:
>> val backend = new FsStateBackend("file:///home/jason/flink/checkpoint <file:///home/jason/flink/checkpoint>")
>> env.setStateBackend(backend)
>> With about 30MB of state we see the checkpoints completing in 151ms.  Using a RocksDBStateBackend
configured as:
>> val backend = new RocksDBStateBackend("file:///home/jason/flink/checkpoint <file:///home/jason/flink/checkpoint>")
>> backend.setDbStoragePath("file:///home/jason/flink/rocksdb <file:///home/jason/flink/rocksdb>")
>> backend.setPredefinedOptions(PredefinedOptions.FLASH_SSD_OPTIMIZED)
>> env.setStateBackend(backend)
>> Running the same test the checkpoint takes 3 minutes 42 seconds.
>> I expect it to be slower, but that seems excessive.  I am also a little confused
as to when rocksDB and flink decide to write to disk, because watching the database the .sst
file wasn't created until significantly after the checkpoint was completed, and the state
had not changed.  Is there anything I can do to increase the speed of the checkpoints, or
anywhere I can look to debug the issue?  (Nothing seems out of the ordinary in the flink logs
or rocksDB logs)
>> Thanks!
>> -- 
>> Jason Brelloch | Product Developer
>> 3405 Piedmont Rd. NE, Suite 325, Atlanta, GA 30305 
>>  <http://www.bettercloud.com/>
>> Subscribe to the BetterCloud Monitor <https://www.bettercloud.com/monitor?utm_source=bettercloud_email&utm_medium=email_signature&utm_campaign=monitor_launch>
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