spark-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From bo yang <>
Subject Re: Enabling fully disaggregated shuffle on Spark
Date Tue, 19 Nov 2019 22:43:32 GMT
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.


On Fri, Nov 15, 2019 at 4:10 PM Ben Sidhom <>

> 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
> <>, 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
> <>
> 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
> <>
> 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

View raw message