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 >>> >> >> > >
