Ismael, that should already be true. If not using dynamic destinations there might be some edges in the graph that are never used (i.e. no records are ever published on them), but that should not affect performance. If this is not the case we should fix it.
Reuven On Wed, Aug 22, 2018 at 8:50 AM Ismaël Mejía <ieme...@gmail.com> wrote: > Spark runner uses the Spark broadcast mechanism to materialize the > side input PCollections in the workers, not sure exactly if this is > efficient assigned in an optimal way but seems logical at least. > > Just wondering if we shouldn't better first tackle the fact that if > the pipeline does not have dynamic destinations (this case) WriteFiles > should not be doing so much extra magic? > > On Wed, Aug 22, 2018 at 5:26 PM Reuven Lax <re...@google.com> wrote: > > > > Often only the metadata (i.e. temp file names) are shuffled, except in > the "spilling" case (which should only happen when using dynamic > destinations). > > > > WriteFiles depends heavily on side inputs. How are side inputs > implemented in the Spark runner? > > > > On Wed, Aug 22, 2018 at 8:21 AM Robert Bradshaw <rober...@google.com> > wrote: > >> > >> Yes, I stand corrected, dynamic writes is now much more than the > >> primitive window-based naming we used to have. > >> > >> It would be interesting to visualize how much of this codepath is > >> metatada vs. the actual data. > >> > >> In the case of file writing, it seems one could (maybe?) avoid > >> requiring a stable input, as shards are accepted as a whole (unlike, > >> say, sinks where a deterministic uid is needed for deduplication on > >> retry). > >> > >> On Wed, Aug 22, 2018 at 4:55 PM Reuven Lax <re...@google.com> wrote: > >> > > >> > Robert - much of the complexity isn't due to streaming, but rather > because WriteFiles supports "dynamic" output (where the user can choose a > destination file based on the input record). In practice if a pipeline is > not using dynamic destinations the full graph is still generated, but much > of that graph is never used (empty PCollections). > >> > > >> > On Wed, Aug 22, 2018 at 3:12 AM Robert Bradshaw <rober...@google.com> > wrote: > >> >> > >> >> I agree that this is concerning. Some of the complexity may have also > >> >> been introduced to accommodate writing files in Streaming mode, but > it > >> >> seems we should be able to execute this as a single Map operation. > >> >> > >> >> Have you profiled to see which stages and/or operations are taking > up the time? > >> >> On Wed, Aug 22, 2018 at 11:29 AM Tim Robertson > >> >> <timrobertson...@gmail.com> wrote: > >> >> > > >> >> > Hi folks, > >> >> > > >> >> > I've recently been involved in projects rewriting Avro files and > have discovered a concerning performance trait in Beam. > >> >> > > >> >> > I have observed Beam between 6-20x slower than native Spark or > MapReduce code for a simple pipeline of read Avro, modify, write Avro. > >> >> > > >> >> > - Rewriting 200TB of Avro files (big cluster): 14 hrs using > Beam/Spark, 40 minutes with a map-only MR job > >> >> > - Rewriting 1.5TB Avro file (small cluster): 2 hrs using > Beam/Spark, 18 minutes using vanilla Spark code. Test code available [1] > >> >> > > >> >> > These tests were running Beam 2.6.0 on Cloudera 5.12.x clusters > (Spark / YARN) on reference Dell / Cloudera hardware. > >> >> > > >> >> > I have only just started exploring but I believe the cause is > rooted in the WriteFiles which is used by all our file based IO. WriteFiles > is reasonably complex with reshuffles, spilling to temporary files > (presumably to accommodate varying bundle sizes/avoid small files), a > union, a GBK etc. > >> >> > > >> >> > Before I go too far with exploration I'd appreciate thoughts on > whether we believe this is a concern (I do), if we should explore > optimisations or any insight from previous work in this area. > >> >> > > >> >> > Thanks, > >> >> > Tim > >> >> > > >> >> > [1] https://github.com/gbif/beam-perf/tree/master/avro-to-avro >
