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From Mayuresh Kunjir <mayuresh.kun...@gmail.com>
Subject Re: Bagel caching issues
Date Thu, 05 Dec 2013 14:54:19 GMT
Thanks Jay for your response. Stragglers are a big problem here. I am
seeing such tasks in many stages of the workflow on a consistent basis.
It's not due to any particular nodes being slow since the slow tasks are
observed on all the nodes at different points in time.
The distribution of task completion times is too skewed for my liking.
GC delays is a possible reason, but I am just speculating.

~Mayuresh




On Thu, Dec 5, 2013 at 5:31 AM, huangjay <jayin@live.cn> wrote:

> Hi,
>
> Maybe you need to check those nodes. It's very slow.
>
>
> 3487SUCCESSPROCESS_LOCALip-10-60-150-111.ec2.internal 2013/12/01 02:11:3817.7
> m16.3 m 23.3 MB3447SUCCESS PROCESS_LOCALip-10-12-54-63.ec2.internal2013/12/01
> 02:11:26 20.1 m13.9 m50.9 MB
>
> 在 2013年12月1日,上午10:59,"Mayuresh Kunjir" <mayuresh.kunjir@gmail.com>
写道:
>
> I tried passing DISK_ONLY storage level to Bagel's run method. It's
> running without any error (so far) but is too slow. I am attaching details
> for a stage corresponding to second iteration of my algorithm. (foreach
> at Bagel.scala:237<http://ec2-54-234-176-171.compute-1.amazonaws.com:4040/stages/stage?id=23>)
> It's been running for more than 35 minutes. I am noticing very high GC time
> for some tasks. Listing below the setup parameters.
>
> #nodes = 16
> SPARK_WORKER_MEMORY = 13G
> SPARK_MEM = 13G
> RDD storage fraction = 0.5
> degree of parallelism = 192 (16 nodes * 4 cores each * 3)
> Serializer = Kryo
> Vertex data size after serialization = ~12G (probably too high, but it's
> the bare minimum required for the algorithm.)
>
> I would be grateful if you could suggest some further optimizations or
> point out reasons why/if Bagel is not suitable for this data size. I need
> to further scale my cluster and not feeling confident at all looking at
> this.
>
> Thanks and regards,
> ~Mayuresh
>
>
> On Sat, Nov 30, 2013 at 3:07 PM, Mayuresh Kunjir <
> mayuresh.kunjir@gmail.com> wrote:
>
>> Hi Spark users,
>>
>> I am running a pagerank-style algorithm on Bagel and bumping into "out of
>> memory" issues with that.
>>
>> Referring to the following table, rdd_120 is the rdd of vertices,
>> serialized and compressed in memory. On each iteration, Bagel deserializes
>> the compressed rdd. e.g. rdd_126 shows the uncompressed version of rdd_120
>> persisted in memory and disk. As iterations keep piling on, the cached
>> partitions start getting evicted. The moment a rdd_120 partition gets
>> evicted, it necessitates a recomputations and the performance goes for a
>> toss. Although we don't need uncompressed rdds from previous iterations,
>> they are the last ones to get evicted thanks to LRU policy.
>>
>> Should I make Bagel use DISK_ONLY persistence? How much of a performance
>> hit would that be? Or maybe there is a better solution here.
>>
>> Storage
>>  RDD NameStorage Level Cached PartitionsFraction Cached Size in MemorySize
>> on Disk rdd_83<http://ec2-54-234-176-171.compute-1.amazonaws.com:4040/storage/rdd?id=83>Memory
Serialized1x Replicated2312%83.7 MB0.0 B
>> rdd_95<http://ec2-54-234-176-171.compute-1.amazonaws.com:4040/storage/rdd?id=95>Memory
Serialized1x Replicated23
>> 12% 2.5 MB 0.0 B rdd_120<http://ec2-54-234-176-171.compute-1.amazonaws.com:4040/storage/rdd?id=120>Memory
Serialized1x Replicated2513%761.1 MB0.0 B
>> rdd_126<http://ec2-54-234-176-171.compute-1.amazonaws.com:4040/storage/rdd?id=126>Disk
Memory Deserialized 1x Replicated192
>> 100% 77.9 GB 1016.5 MB rdd_134<http://ec2-54-234-176-171.compute-1.amazonaws.com:4040/storage/rdd?id=134>Disk
Memory Deserialized 1x Replicated18596%60.8 GB475.4 MB
>> Thanks and regards,
>> ~Mayuresh
>>
>
> <BigFrame - Details for Stage 23.htm>
>
>

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