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From Robert Metzger <rmetz...@apache.org>
Subject Re: Best way to write data to HDFS by Flink
Date Wed, 22 Jul 2015 08:54:39 GMT
Hey,
can you measure how fast jmeter is able to push data into Kafka? Maybe that
is already the bottleneck.

Flink should be able to read from Kafka with 100k+ elements/second on a
single node.

On Mon, Jun 29, 2015 at 11:10 AM, Stephan Ewen <sewen@apache.org> wrote:

> Hi Hawin!
>
> The performance tuning of Kafka is much trickier than that of Flink. Your
> performance bottleneck may be Kafka at this point, not Flink.
> To make Kafka fast, make sure you have the right setup for the data
> directories, and you set up zookeeper properly (for good throughput).
>
> To test the Kafka throughput in isolation, push data into Kafka and just
> consume it with a command line client that pipes to /dev/null
>
> Greetings,
> Stephan
>
>
>
>
> On Mon, Jun 29, 2015 at 9:56 AM, Hawin Jiang <hawin.jiang@gmail.com>
> wrote:
>
>> Dear  Marton
>>
>>
>>
>> Thanks for your asking.  Yes. it is working now.
>>
>> But, the TPS is not very good.   I have met four issues as below
>>
>>
>>
>> 1.       My TPS around 2000 events per second.   But I saw some
>> companies achieved 132K per second on single node at 2015 Los Angeles big
>> data day yesterday.   For two nodes, the TPS is 282K per sec.  them used
>> kafka+Spark.
>>
>> As you knew  that I used kafka+Flink. Maybe we have to do more
>> investigations from my side.
>>
>>
>>
>> 2.       Regarding my performance testing, I used JMeter to producer
>> data to Kafka.  The total messages in JMeter side is not matched HDFS
>> side.   In the meantime, I used flink to write data to HDFS.
>>
>>
>>
>> 3.       I found that Flink randomly created 1, 2, 3 and 4 folders. Only
>> 1 and 4 folders have files.  The 2 and 3 folders don’t have any files.
>>
>>
>>
>> 4.       I am going to develop some codes to write data to
>> /data/flink/year/month/day/hour folder.  I think that folder structure is
>> good for flink table API in the future.
>>
>>
>>
>> Please let me know if you have some comments or suggests for me.
>>
>> Thanks.
>>
>>
>>
>>
>>
>>
>>
>> Best regards
>>
>> Hawin
>>
>>
>>
>> *From:* Márton Balassi [mailto:balassi.marton@gmail.com]
>> *Sent:* Sunday, June 28, 2015 9:09 PM
>> *To:* user@flink.apache.org
>> *Subject:* Re: Best way to write data to HDFS by Flink
>>
>>
>>
>> Dear Hawin,
>>
>>
>>
>> As for your issues with running the Flink Kafka examples: are those
>> resolved with Aljoscha's comment in the other thread? :)
>>
>>
>>
>> Best,
>>
>>
>>
>> Marton
>>
>>
>>
>> On Fri, Jun 26, 2015 at 8:40 AM, Hawin Jiang <hawin.jiang@gmail.com>
>> wrote:
>>
>> Hi Stephan
>>
>>
>>
>> Yes, that is a great idea.  if it is possible,  I will try my best to
>> contribute some codes to Flink.
>>
>> But I have to run some flink examples first to understand Apache Flink.
>>
>> I just run some kafka with flink examples.  No examples working for me.
>> I am so sad right now.
>>
>> I didn't get any troubles to run kafka examples from *kafka*.apache.org
>> so far.
>>
>> Please suggest me.
>>
>> Thanks.
>>
>>
>>
>>
>>
>>
>>
>> Best regards
>>
>> Hawin
>>
>>
>>
>>
>>
>> On Wed, Jun 24, 2015 at 1:02 AM, Stephan Ewen <sewen@apache.org> wrote:
>>
>> Hi Hawin!
>>
>>
>>
>> If you are creating code for such an output into different
>> files/partitions, it would be amazing if you could contribute this code to
>> Flink.
>>
>>
>>
>> It seems like a very common use case, so this functionality will be
>> useful to other user as well!
>>
>>
>>
>> Greetings,
>> Stephan
>>
>>
>>
>>
>>
>> On Tue, Jun 23, 2015 at 3:36 PM, Márton Balassi <balassi.marton@gmail.com>
>> wrote:
>>
>> Dear Hawin,
>>
>>
>>
>> We do not have out of the box support for that, it is something you would
>> need to implement yourself in a custom SinkFunction.
>>
>>
>>
>> Best,
>>
>>
>>
>> Marton
>>
>>
>>
>> On Mon, Jun 22, 2015 at 11:51 PM, Hawin Jiang <hawin.jiang@gmail.com>
>> wrote:
>>
>> Hi  Marton
>>
>>
>>
>> if we received a huge data from kafka and wrote to HDFS immediately.  We
>> should use buffer timeout based on your URL
>>
>> I am not sure you have flume experience.  Flume can be configured buffer
>> size and partition as well.
>>
>>
>>
>> What is the partition.
>>
>> For example:
>>
>> I want to write 1 minute buffer file to HDFS which is
>> /data/flink/year=2015/month=06/day=22/hour=21.
>>
>> if the partition(/data/flink/year=2015/month=06/day=22/hour=21) is there,
>> no need to create it. Otherwise, flume will create it automatically.
>>
>> Flume knows the coming data will come to right partition.
>>
>>
>>
>> I am not sure Flink also provided a similar partition API or
>> configuration for this.
>>
>> Thanks.
>>
>>
>>
>>
>>
>>
>>
>> Best regards
>>
>> Hawin
>>
>>
>>
>> On Wed, Jun 10, 2015 at 10:31 AM, Hawin Jiang <hawin.jiang@gmail.com>
>> wrote:
>>
>> Thanks Marton
>>
>> I will use this code to implement my testing.
>>
>>
>>
>>
>>
>>
>>
>> Best regards
>>
>> Hawin
>>
>>
>>
>> On Wed, Jun 10, 2015 at 1:30 AM, Márton Balassi <balassi.marton@gmail.com>
>> wrote:
>>
>> Dear Hawin,
>>
>>
>>
>> You can pass a hdfs path to DataStream's and DataSet's writeAsText and
>> writeAsCsv methods.
>>
>> I assume that you are running a Streaming topology, because your source
>> is Kafka, so it would look like the following:
>>
>>
>>
>> StreamExecutionEnvironment env =
>> StreamExecutionEnvironment.getExecutionEnvironment();
>>
>>
>>
>> env.addSource(PerisitentKafkaSource(..))
>>
>>       .map(/* do you operations*/)
>>
>>
>> .wirteAsText("hdfs://<namenode_name>:<namenode_port>/path/to/your/file");
>>
>>
>> Check out the relevant section of the streaming docs for more info. [1]
>>
>>
>>
>> [1]
>> http://ci.apache.org/projects/flink/flink-docs-master/apis/streaming_guide.html#connecting-to-the-outside-world
>>
>>
>>
>> Best,
>>
>>
>>
>> Marton
>>
>>
>>
>> On Wed, Jun 10, 2015 at 10:22 AM, Hawin Jiang <hawin.jiang@gmail.com>
>> wrote:
>>
>> Hi All
>>
>>
>>
>> Can someone tell me what is the best way to write data to HDFS when Flink
>> received data from Kafka?
>>
>> Big thanks for your example.
>>
>>
>>
>>
>>
>>
>>
>>
>>
>> Best regards
>>
>> Hawin
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>>
>
>

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