Hi Ayan, , thanks for the explanation, I am aware of compression codecs.
How does locality level set? Is it done by Spark or yarn? Please let me know, Thanks, Yesh On Nov 22, 2016 5:13 PM, "ayan guha" <guha.a...@gmail.com> wrote: Hi RACK_LOCAL = Task running on the same rack but not on the same node where data is NODE_LOCAL = task and data is co-located. Probably you were looking for this one? GZIP - Read is through GZIP codec, but because it is non-splittable, so you can have atmost 1 task reading a gzip file. Now, the content of gzip may be across multiple node. Ex: GZIP file of say 1GB and block size is 256 MB (ie 4 blocks). Assume not all 4 blocks are on same data node. When you start reading the gzip file, 1 task will be assigned. It will read local blocks if available, and it will read remote blocks (streaming read). While reading the stream, gzip codec will uncompress the data. This is really is not a spark thing, but a hadoop input format discussion.... HTH? On Wed, Nov 23, 2016 at 10:00 AM, yeshwanth kumar <yeshwant...@gmail.com> wrote: > 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 >>>> >>>> >>> > -- Best Regards, Ayan Guha
