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From "Evan R. Sparks" <>
Subject Re: performance
Date Fri, 10 Jan 2014 03:43:26 GMT
If your left outer join is slow and one of the tables is relatively small,
you could consider broadcasting the smaller table and doing a join like in
slide 11 of this presentation:

If both tables are big, there has been some work on IndexedRDDs which might
help speed things up, but this feature hasn't made it into spark master

On Thu, Jan 9, 2014 at 2:11 PM, Yann Luppo <> wrote:

>  Thank you guys that was really helpful in identifying the slow step,
> which in our case is the leftouterjoin.
> I'm checking with our admins to see if we have some sort of distributed
> system monitoring in place, which I'm sure we do.
>  Now just out of curiosity, what would be the rule of thumb or general
> guideline for the number of partitions and the number of reducers?
> Should it be some kind of factor of the number of cores available? Of
> nodes available? Should the number of partitions match the number of
> reducers or at least be some multiple of it for better performance?
>  Thanks,
> Yann
>   From: Evan Sparks <>
> Reply-To: "" <
> Date: Wednesday, January 8, 2014 5:28 PM
> To: "" <>
> Cc: "" <>
> Subject: Re: performance
>   On this note - the ganglia web front end that runs on the master
> (assuming you're launching with the ec2 scripts) is great for this.
>  Also, a common technique for diagnosing "which step is slow" is to run a
> '.cache' and a '.count' on the RDD after each step. This forces the RDD to
> be materialized, which subverts the lazy evaluation that causes such
> diagnosis to be hard sometimes.
>  - Evan
> On Jan 8, 2014, at 2:57 PM, Andrew Ash <> wrote:
>   My first thought on hearing that you're calling collect is that taking
> all the data back to the driver is intensive on the network.  Try checking
> the basic systems stuff on the machines to get a sense of what's being
> heavily used:
>  disk IO
> network
>  Any kind of distributed system monitoring framework should be able to
> handle these sorts of things.
>  Cheers!
> Andrew
> On Wed, Jan 8, 2014 at 1:49 PM, Yann Luppo <>wrote:
>>  Hi,
>>  I have what I hope is a simple question. What's a typical approach to
>> diagnostic performance issues on a Spark cluster?
>> We've followed all the pertinent parts of the following document already:
>> But we seem to still have issues. More specifically we have a
>> leftouterjoin followed by a flatmap and then a collect running a bit long.
>>  How would I go about determining the bottleneck operation(s) ?
>> Is our leftouterjoin taking a long time?
>> Is the function we send to the flatmap not optimized?
>>  Thanks,
>> Yann

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