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From Aljoscha Krettek <aljos...@apache.org>
Subject Re: Flink streaming throughput
Date Tue, 08 Mar 2016 12:36:10 GMT
Hi,
Another interesting test would be a combination of 3) and 2). I.e. no JSON parsing and no
sink. This would show what the raw throughput can be before being slowed down by writing to
Elasticsearch.

Also .print() is also not feasible for production since it just prints every element to the
stdout log on the TaskManagers, which itself can cause quite a slowdown. You could try:

datastream.addSink(new DiscardingSink())

which is a dummy sink that does nothing.

Cheers,
Aljoscha
> On 08 Mar 2016, at 13:31, おぎばやしひろのり <ogibayashi@gmail.com> wrote:
> 
> Stephan,
> 
> Sorry for the delay in my response.
> I tried 3 cases you suggested.
> 
> This time, I set parallelism to 1 for simpicity.
> 
> 0) base performance (same as the first e-mail): 1,480msg/sec
> 1) Disable checkpointing : almost same as 0)
> 2) No ES sink. just print() : 1,510msg/sec
> 3) JSON to TSV : 8,000msg/sec
> 
> So, as you can see, the bottleneck was JSON parsing. I also want to
> try eliminating Kafka to see
> if there is a room to improve performance.(Currently, I am using
> FlinkKafkaConsumer082 with Kafka 0.9
> I think I should try Flink 1.0 and FlinkKafkaConsumer09).
> Anyway, I think 8,000msg/sec with 1 CPU is not so bad thinking of
> Flink's scalability and fault tolerance.
> Thank you for your advice.
> 
> Regards,
> Hironori Ogibayashi
> 
> 2016-02-26 21:46 GMT+09:00 おぎばやしひろのり <ogibayashi@gmail.com>:
>> Stephan,
>> 
>> Thank you for your quick response.
>> I will try and post the result later.
>> 
>> Regards,
>> Hironori
>> 
>> 2016-02-26 19:45 GMT+09:00 Stephan Ewen <sewen@apache.org>:
>>> Hi!
>>> 
>>> I would try and dig bit by bit into what the bottleneck is:
>>> 
>>> 1) Disable the checkpointing, see what difference that makes
>>> 2) Use a dummy sink (discarding) rather than elastic search, to see if that
>>> is limiting
>>> 3) Check the JSON parsing. Many JSON libraries are very CPU intensive and
>>> easily dominate the entire pipeline.
>>> 
>>> Greetings,
>>> Stephan
>>> 
>>> 
>>> On Fri, Feb 26, 2016 at 11:23 AM, おぎばやしひろのり <ogibayashi@gmail.com>
wrote:
>>>> 
>>>> Hello,
>>>> 
>>>> I started evaluating Flink and tried simple performance test.
>>>> The result was just about 4000 messages/sec with 300% CPU usage. I
>>>> think this is quite low and wondering if it is a reasonable result.
>>>> If someone could check it, it would be great.
>>>> 
>>>> Here is the detail:
>>>> 
>>>> [servers]
>>>> - 3 Kafka broker with 3 partitions
>>>> - 3 Flink TaskManager + 1 JobManager
>>>> - 1 Elasticsearch
>>>> All of them are separate VM with 8vCPU, 8GB memory
>>>> 
>>>> [test case]
>>>> The application counts access log by URI with in 1 minute window and
>>>> send the result to Elasticsearch. The actual code is below.
>>>> I used '-p 3' option to flink run command, so the task was distributed
>>>> to 3 TaskManagers.
>>>> In the test, I sent about 5000 logs/sec to Kafka.
>>>> 
>>>> [result]
>>>> - From Elasticsearch records, the total access count for all URI was
>>>> about 260,000/min = 4300/sec. This is the entire throughput.
>>>> - Kafka consumer lag was keep growing.
>>>> - The CPU usage of each TaskManager machine was about 13-14%. From top
>>>> command output, Flink java process was using 100%(1 CPU full)
>>>> 
>>>> So I thought the bottleneck here was CPU used by Flink Tasks.
>>>> 
>>>> Here is the application code.
>>>> ---
>>>>    val env = StreamExecutionEnvironment.getExecutionEnvironment
>>>>    env.enableCheckpointing(1000)
>>>> ...
>>>>    val stream = env
>>>>      .addSource(new FlinkKafkaConsumer082[String]("kafka.dummy", new
>>>> SimpleStringSchema(), properties))
>>>>      .map{ json => JSON.parseFull(json).get.asInstanceOf[Map[String,
>>>> AnyRef]] }
>>>>      .map{ x => x.get("uri") match {
>>>>        case Some(y) => (y.asInstanceOf[String],1)
>>>>        case None => ("", 1)
>>>>      }}
>>>>      .keyBy(0)
>>>>      .timeWindow(Time.of(1, TimeUnit.MINUTES))
>>>>      .sum(1)
>>>>      .map{ x => (System.currentTimeMillis(), x)}
>>>>      .addSink(new ElasticsearchSink(config, transports, new
>>>> IndexRequestBuilder[Tuple2[Long, Tuple2[String, Int]]]  {
>>>>        override def createIndexRequest(element: Tuple2[Long,
>>>> Tuple2[String, Int]], ctx: RuntimeContext): IndexRequest = {
>>>>          val json = new HashMap[String, AnyRef]
>>>>          json.put("@timestamp", new Timestamp(element._1))
>>>>          json.put("uri", element._2._1)
>>>>          json.put("count", element._2._2: java.lang.Integer)
>>>>          println("SENDING: " + element)
>>>> 
>>>> Requests.indexRequest.index("dummy2").`type`("my-type").source(json)
>>>>        }
>>>>      }))
>>>> ---
>>>> 
>>>> Regards,
>>>> Hironori Ogibayashi
>>> 
>>> 


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