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 >
