Thanks , is there a metric or other way to know how much space each task/job is taking? Does execution plan has these details?
Thanks On Mon, Feb 19, 2018 at 10:54 AM, Fabian Hueske <fhue...@gmail.com> wrote: > Hi, > > that's a difficult question without knowing the details of your job. > A NoSpaceLeftOnDevice error occurs when a file system is full. > > This can happen if: > - A Flink algorithm writes to disk, e.g., an external sort or the hash > table of a hybrid hash join. This can happen for GroupBy, Join, Distinct, > or any other operation that requires to group or join data. Filters will > never spill to disk. > - An OutputFormat writes to disk. > > The data is written to a temp directory, that can be configured in the > ./conf/flink-conf.yaml file. > > Did you check how the tasks are distributed across the task managers? > The web UI can help to diagnose such problems. > > Best, Fabian > > 2018-02-19 11:22 GMT+01:00 Darshan Singh <darshan.m...@gmail.com>: > >> Thanks Fabian for such detailed explanation. >> >> I am using a datset in between so i guess csv is read once. Now to my >> real issue i have 6 task managers each having 4 cores and i have 2 slots >> per task manager. >> >> Now my csv file is jus 1 gb and i create table and transform to dataset >> and then run 15 different filters and extra processing which all run in >> almost parallel. >> >> However it fails with error no space left on device on one of the task >> manager. Space on each task manager is 32 gb in /tmp. So i am not sure why >> it is running out of space. I do use some joins with othrr tables but those >> are few megabytes. >> >> So i was assuming that somehow all parallel executions were storing data >> in /tmp and were filling it. >> >> So i would like to know wht could be filling space. >> >> Thanks >> >> On 19 Feb 2018 10:10 am, "Fabian Hueske" <fhue...@gmail.com> wrote: >> >> Hi, >> >> this works as follows. >> >> - Table API and SQL queries are translated into regular DataSet jobs >> (assuming you are running in a batch ExecutionEnvironment). >> - A query is translated into a sequence of DataSet operators when you 1) >> transform the Table into a DataSet or 2) write it to a TableSink. In both >> cases, the optimizer is invoked and recursively goes back from the >> converted/emitted Table back to its roots, i.e., a TableSource or a >> DataSet. >> >> This means, that if you create a Table from a TableSource and apply >> multiple filters on it and write each filter to a TableSink, the CSV file >> will be read 10 times, filtered 10 times and written 10 times. This is not >> efficient, because, you could also just read the file once and apply all >> filters in parallel. >> You can do this by converting the Table that you read with a TableSource >> into a DataSet and register the DataSet again as a Table. In that case, the >> translations of all TableSinks will stop at the DataSet and not include the >> TableSource which reads the file. >> >> The following figures illustrate the difference: >> >> 1) Without DataSet in the middle: >> >> TableSource -> Filter1 -> TableSink1 >> TableSource -> Filter2 -> TableSink2 >> TableSource -> Filter3 -> TableSink3 >> >> 2) With DataSet in the middle: >> >> /-> Filter1 -> TableSink1 >> TableSource -<-> Filter2 -> TableSink2 >> \-> Filter3 -> TableSink3 >> >> I'll likely add a feature to internally translate an intermediate Table >> to make this a bit easier. >> The underlying problem is that the SQL optimizer cannot translate queries >> with multiple sinks. >> Instead, each sink is individually translated and the optimizer does not >> know that common execution paths could be shared. >> >> Best, >> Fabian >> >> >> 2018-02-19 2:19 GMT+01:00 Darshan Singh <darshan.m...@gmail.com>: >> >>> Thanks for reply. >>> >>> I guess I am not looking for alternate. I am trying to understand what >>> flink does in this scenario and if 10 tasks ar egoing in parallel I am sure >>> they will be reading csv as there is no other way. >>> >>> Thanks >>> >>> On Mon, Feb 19, 2018 at 12:48 AM, Niclas Hedhman <nic...@hedhman.org> >>> wrote: >>> >>>> >>>> Do you really need the large single table created in step 2? >>>> >>>> If not, what you typically do is that the Csv source first do the >>>> common transformations. Then depending on whether the 10 outputs have >>>> different processing paths or not, you either do a split() to do individual >>>> processing depending on some criteria, or you just have the sink put each >>>> record in separate tables. >>>> You have full control, at each step along the transformation path >>>> whether it can be parallelized or not, and if there are no sequential >>>> constraints on your model, then you can easily fill all cores on all hosts >>>> quite easily. >>>> >>>> Even if you need the step 2 table, I would still just treat that as a >>>> split(), a branch ending in a Sink that does the storage there. No need to >>>> read records from file over and over again, nor to store them first in step >>>> 2 table and read them out again. >>>> >>>> Don't ask *me* about what happens in failure scenarios... I have myself >>>> not figured that out yet. >>>> >>>> HTH >>>> Niclas >>>> >>>> On Mon, Feb 19, 2018 at 3:11 AM, Darshan Singh <darshan.m...@gmail.com> >>>> wrote: >>>> >>>>> Hi I would like to understand the execution model. >>>>> >>>>> 1. I have a csv files which is say 10 GB. >>>>> 2. I created a table from this file. >>>>> >>>>> 3. Now I have created filtered tables on this say 10 of these. >>>>> 4. Now I created a writetosink for all these 10 filtered tables. >>>>> >>>>> Now my question is that are these 10 filetered tables be written in >>>>> parallel (suppose i have 40 cores and set up parallelism to say 40 as >>>>> well. >>>>> >>>>> Next question I have is that the table which I created form the csv >>>>> file which is common wont be persisted by flink internally rather for all >>>>> 10 filtered tables it will read csv files and then apply the filter and >>>>> write to sink. >>>>> >>>>> I think that for all 10 filtered tables it will read csv again and >>>>> again in this case it will be read 10 times. Is my understanding correct >>>>> or I am missing something. >>>>> >>>>> What if I step 2 I change table to dataset and back? >>>>> >>>>> Thanks >>>>> >>>> >>>> >>>> >>>> -- >>>> Niclas Hedhman, Software Developer >>>> http://polygene.apache.org - New Energy for Java >>>> >>> >>> >> >> >
