Comments (2)
I think I can envision how this would work in practice, namely, I can tradeoff the efficiency of processing larger batches to gain more streaming/pipelining, which I think is totally appropriate. How would operations like cogroup work though? Can they still be incremental, I would imagine not?
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I think I can envision how this would work in practice, namely, I can tradeoff the efficiency of processing larger batches to gain more streaming/pipelining, which I think is totally appropriate. How would operations like cogroup work though? Can they still be incremental, I would imagine not?
They can be: cogroup would checkpoint its state as, e.g., a mapio sstable; it can then emit rows for any newly modified keys.
There is also the opportunity, later, to introduce a notion of a "diff+patch" approach, where operators emit fine-grained changes that can be reconstructed downstream. But this requires a fairly different data model, though it could nevertheless be built on top of this proposal.
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Related Issues (20)
- Proposal: memory reclamation
- Support cluster shrinking
- Support heterogeneous clusters
- Support different instance types per computation
- Compress data between nodes
- Fail typechecking for functions passed to `bigslice.Func` that take `func` and channel arguments HOT 1
- Use static typechecking with generics over runtime type checking in Slice interface
- a question on sharding.. HOT 8
- Provide user-controlled sharding HOT 5
- Filter and counts, maybe consider mapreduce counters HOT 12
- exec: add some form of log.Flush.Sync mechanism HOT 15
- Metrics should follow data-flow
- Is there a way to distinguish the master from workers? HOT 3
- ReaderFunc for multiple files.. HOT 6
- Add example code for (*Result).Discard
- Proposal: row streaming
- bigslice run: command-line arguments parsing bug HOT 2
- Address pain points from latest onboarding
- Support shared backing store
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