I'm interested in remote shuffle services as well. I'd love to hear about what you're using in production!
rb On Tue, Nov 19, 2019 at 2:43 PM bo yang <bobyan...@gmail.com> wrote: > Hi Ben, > > Thanks for the writing up! This is Bo from Uber. I am in Felix's team in > Seattle, and working on disaggregated shuffle (we called it remote shuffle > service, RSS, internally). We have put RSS into production for a while, and > learned a lot during the work (tried quite a few techniques to improve the > remote shuffle performance). We could share our learning with the > community, and also would like to hear feedback/suggestions on how to > further improve remote shuffle performance. We could chat more details if > you or other people are interested. > > Best, > Bo > > On Fri, Nov 15, 2019 at 4:10 PM Ben Sidhom <sid...@google.com.invalid> > wrote: > >> I would like to start a conversation about extending the Spark shuffle >> manager surface to support fully disaggregated shuffle implementations. >> This is closely related to the work in SPARK-25299 >> <https://issues.apache.org/jira/browse/SPARK-25299>, which is focused on >> refactoring the shuffle manager API (and in particular, SortShuffleManager) >> to use a pluggable storage backend. The motivation for that SPIP is further >> enabling Spark on Kubernetes. >> >> >> The motivation for this proposal is enabling full externalized >> (disaggregated) shuffle service implementations. (Facebook’s Cosco >> shuffle >> <https://databricks.com/session/cosco-an-efficient-facebook-scale-shuffle-service> >> is one example of such a disaggregated shuffle service.) These changes >> allow the bulk of the shuffle to run in a remote service such that minimal >> state resides in executors and local disk spill is minimized. The net >> effect is increased job stability and performance improvements in certain >> scenarios. These changes should work well with or are complementary to >> SPARK-25299. Some or all points may be merged into that issue as >> appropriate. >> >> >> Below is a description of each component of this proposal. These changes >> can ideally be introduced incrementally. I would like to gather feedback >> and gauge interest from others in the community to collaborate on this. >> There are likely more points that would be useful to disaggregated shuffle >> services. We can outline a more concrete plan after gathering enough input. >> A working session could help us kick off this joint effort; maybe something >> in the mid-January to mid-February timeframe (depending on interest and >> availability. I’m happy to host at our Sunnyvale, CA offices. >> >> >> ProposalScheduling and re-executing tasks >> >> Allow coordination between the service and the Spark DAG scheduler as to >> whether a given block/partition needs to be recomputed when a task fails or >> when shuffle block data cannot be read. Having such coordination is >> important, e.g., for suppressing recomputation after aborted executors or >> for forcing late recomputation if the service internally acts as a cache. >> One catchall solution is to have the shuffle manager provide an indication >> of whether shuffle data is external to executors (or nodes). Another >> option: allow the shuffle manager (likely on the driver) to be queried for >> the existence of shuffle data for a given executor ID (or perhaps map task, >> reduce task, etc). Note that this is at the level of data the scheduler is >> aware of (i.e., map/reduce partitions) rather than block IDs, which are >> internal details for some shuffle managers. >> ShuffleManager API >> >> Add a heartbeat (keep-alive) mechanism to RDD shuffle output so that the >> service knows that data is still active. This is one way to enable >> time-/job-scoped data because a disaggregated shuffle service cannot rely >> on robust communication with Spark and in general has a distinct lifecycle >> from the Spark deployment(s) it talks to. This would likely take the form >> of a callback on ShuffleManager itself, but there are other approaches. >> >> >> Add lifecycle hooks to shuffle readers and writers (e.g., to >> close/recycle connections/streams/file handles as well as provide commit >> semantics). SPARK-25299 adds commit semantics to the internal data storage >> layer, but this is applicable to all shuffle managers at a higher level and >> should apply equally to the ShuffleWriter. >> >> >> Do not require ShuffleManagers to expose ShuffleBlockResolvers where they >> are not needed. Ideally, this would be an implementation detail of the >> shuffle manager itself. If there is substantial overlap between the >> SortShuffleManager and other implementations, then the storage details can >> be abstracted at the appropriate level. (SPARK-25299 does not currently >> change this.) >> >> >> Do not require MapStatus to include blockmanager IDs where they are not >> relevant. This is captured by ShuffleBlockInfo >> <https://docs.google.com/document/d/1d6egnL6WHOwWZe8MWv3m8n4PToNacdx7n_0iMSWwhCQ/edit#heading=h.imi27prnziyj> >> including an optional BlockManagerId in SPARK-25299. However, this >> change should be lifted to the MapStatus level so that it applies to all >> ShuffleManagers. Alternatively, use a more general data-location >> abstraction than BlockManagerId. This gives the shuffle manager more >> flexibility and the scheduler more information with respect to data >> residence. >> Serialization >> >> Allow serializers to be used more flexibly and efficiently. For example, >> have serializers support writing an arbitrary number of objects into an >> existing OutputStream or ByteBuffer. This enables objects to be serialized >> to direct buffers where doing so makes sense. More importantly, it allows >> arbitrary metadata/framing data to be wrapped around individual objects >> cheaply. Right now, that’s only possible at the stream level. (There are >> hacks around this, but this would enable more idiomatic use in efficient >> shuffle implementations.) >> >> >> Have serializers indicate whether they are deterministic. This provides >> much of the value of a shuffle service because it means that reducers do >> not need to spill to disk when reading/merging/combining inputs--the data >> can be grouped by the service, even without the service understanding data >> types or byte representations. Alternative (less preferable since it would >> break Java serialization, for example): require all serializers to be >> deterministic. >> >> >> >> -- >> >> - Ben >> > -- Ryan Blue Software Engineer Netflix
