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From Mich Talebzadeh <mich.talebza...@gmail.com>
Subject Re: Spark Streaming Advice
Date Mon, 10 Oct 2016 16:38:13 GMT
Hi Kevin,

What is the streaming interval (batch interval) above?

I do analytics on streaming trade data but after manipulation of individual
messages I store the selected on in Hbase. Very fast.

HTH

Dr Mich Talebzadeh



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On 10 October 2016 at 15:25, Kevin Mellott <kevin.r.mellott@gmail.com>
wrote:

> Whilst working on this application, I found a setting that drastically
> improved the performance of my particular Spark Streaming application. I'm
> sharing the details in hopes that it may help somebody in a similar
> situation.
>
> As my program ingested information into HDFS (as parquet files), I noticed
> that the time to process each batch was significantly greater than I
> anticipated. Whether I was writing a single parquet file (around 8KB) or
> around 10-15 files (8KB each), that step of the processing was taking
> around 30 seconds. Once I set the configuration below, this operation
> reduced from 30 seconds to around 1 second.
>
> // ssc = instance of SparkStreamingContext
> ssc.sparkContext.hadoopConfiguration.set("parquet.enable.summary-metadata",
> "false")
>
> I've also verified that the parquet files being generated are usable by
> both Hive and Impala.
>
> Hope that helps!
> Kevin
>
> On Thu, Oct 6, 2016 at 4:22 PM, Kevin Mellott <kevin.r.mellott@gmail.com>
> wrote:
>
>> I'm attempting to implement a Spark Streaming application that will
>> consume application log messages from a message broker and store the
>> information in HDFS. During the data ingestion, we apply a custom schema to
>> the logs, partition by application name and log date, and then save the
>> information as parquet files.
>>
>> All of this works great, except we end up having a large number of
>> parquet files created. It's my understanding that Spark Streaming is unable
>> to control the number of files that get generated in each partition; can
>> anybody confirm that is true?
>>
>> Also, has anybody else run into a similar situation regarding data
>> ingestion with Spark Streaming and do you have any tips to share? Our end
>> goal is to store the information in a way that makes it efficient to query,
>> using a tool like Hive or Impala.
>>
>> Thanks,
>> Kevin
>>
>
>

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