Hi Ayan, we have default rack topology.
-Yeshwanth Can you Imagine what I would do if I could do all I can - Art of War On Tue, Nov 22, 2016 at 6:37 AM, ayan guha <guha.a...@gmail.com> wrote: > Because snappy is not splittable, so single task makes sense. > > Are sure about rack topology? Ie 225 is in a different rack than 227 or > 228? What does your topology file says? > On 22 Nov 2016 10:14, "yeshwanth kumar" <yeshwant...@gmail.com> wrote: > >> Thanks for your reply, >> >> i can definitely change the underlying compression format. >> but i am trying to understand the Locality Level, >> why executor ran on a different node, where the blocks are not present, >> when Locality Level is RACK_LOCAL >> >> can you shed some light on this. >> >> >> Thanks, >> Yesh >> >> >> -Yeshwanth >> Can you Imagine what I would do if I could do all I can - Art of War >> >> On Mon, Nov 21, 2016 at 4:59 PM, Jörn Franke <jornfra...@gmail.com> >> wrote: >> >>> Use as a format orc, parquet or avro because they support any >>> compression type with parallel processing. Alternatively split your file in >>> several smaller ones. Another alternative would be bzip2 (but slower in >>> general) or Lzo (usually it is not included by default in many >>> distributions). >>> >>> On 21 Nov 2016, at 23:17, yeshwanth kumar <yeshwant...@gmail.com> wrote: >>> >>> Hi, >>> >>> we are running Hive on Spark, we have an external table over snappy >>> compressed csv file of size 917.4 M >>> HDFS block size is set to 256 MB >>> >>> as per my Understanding, if i run a query over that external table , it >>> should launch 4 tasks. one for each block. >>> but i am seeing one executor and one task processing all the file. >>> >>> trying to understand the reason behind, >>> >>> i went one step further to understand the block locality >>> when i get the block locations for that file, i found >>> >>> [DatanodeInfoWithStorage[10.11.0.226:50010,DS-bf39d33d-48e1- >>> 4a8f-be48-b0953fdaad37,DISK], >>> DatanodeInfoWithStorage[10.11.0.227:50010,DS-a760c1c8-ce0c- >>> 4eb8-8183-8d8ff5f24115,DISK], >>> DatanodeInfoWithStorage[10.11.0.228:50010,DS-0e5427e2-b030- >>> 43f8-91c9-d8517e68414a,DISK]] >>> >>> DatanodeInfoWithStorage[10.11.0.226:50010,DS-f50ddf2f-b827-4 >>> 845-b043-8b91ae4017c0,DISK], >>> DatanodeInfoWithStorage[10.11.0.228:50010,DS-e8c9785f-c352-4 >>> 89b-8209-4307f3296211,DISK], >>> DatanodeInfoWithStorage[10.11.0.225:50010,DS-6f6a3ffd-334b-4 >>> 5fd-ae0f-cc6eb268b0d2,DISK]] >>> >>> DatanodeInfoWithStorage[10.11.0.226:50010,DS-f8bea6a8-a433-4 >>> 601-8070-f6c5da840e09,DISK], >>> DatanodeInfoWithStorage[10.11.0.227:50010,DS-8aa3f249-790e-4 >>> 94d-87ee-bcfff2182a96,DISK], >>> DatanodeInfoWithStorage[10.11.0.228:50010,DS-d06714f4-2fbb-4 >>> 8d3-b858-a023b5c44e9c,DISK] >>> >>> DatanodeInfoWithStorage[10.11.0.226:50010,DS-b3a00781-c6bd-4 >>> 98c-a487-5ce6aaa66f48,DISK], >>> DatanodeInfoWithStorage[10.11.0.228:50010,DS-fa5aa339-e266-4 >>> e20-a360-e7cdad5dacc3,DISK], >>> DatanodeInfoWithStorage[10.11.0.225:50010,DS-9d597d3f-cd4f-4 >>> c8f-8a13-7be37ce769c9,DISK]] >>> >>> and in the spark UI i see the Locality Level is RACK_LOCAL. for that >>> task >>> >>> if it is RACK_LOCAL then it should run either in node 10.11.0.226 or >>> 10.11.0.228, because these 2 nodes has all the four blocks needed for >>> computation >>> but the executor is running in 10.11.0.225 >>> >>> my theory is not applying anywhere. >>> >>> please help me in understanding how spark/yarn calculates number of >>> executors/tasks. >>> >>> Thanks, >>> -Yeshwanth >>> >>> >>
