storage: kv-level memory accounting/bounding #19721

nvanbenschoten · 2017-11-01T20:15:20Z

#8691 introduces a mechanism to track and limit memory use at the SQL layer, but no effort has been put into tracking and limiting memory usage at any layers beneath this. Without this protection, scans over large tables can still result in trivial out-of-memory crashes, as we've recently seen. I think we'll want to add some memory accounting down in the kv-layer so that we can track memory across all memory-intensive operations in a node (MVCC scans, BatchResponse serialization/deserialization, etc.).

I also think we should add some kind of memory limit to BatchRequests so that responses will not exceed available memory for a client node. Even if one node has enough memory to field a KV request, another may not have enough to receive its response. This could also be accomplished by adding some kind of interceptor for BatchResponses that stops reading the response when it gets too large.

There have been some attempts to limit the size of single kv scans to reasonable numbers of rows. However, these attempts use a constant maximum row count. If rows are unexpectedly large than the current max (10,000 rows) may still be big enough to cause an OOM. We'll probably want to dynamically adjust this maximum limit based on the size of each row. Unfortunately, this problem is exacerbated by the fact that SQL scans can't push column filters down through ScanRequests. This means that large keys may be returned in a ScanResponse even if they are not necessary for the query, leading to unexpected OOMs. This is, for the most part, an orthogonal issue, but demonstrates why we might want memory accounting beneath SQL.

cc. @andreimatei @tschottdorf @knz

Jira issue: CRDB-5958

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jordanlewis · 2019-09-17T20:08:34Z

Bumping this - it's (more or less) the cause of the symptoms in #33660. Imagine a full table scan over a table with 512kb rows. The SQL fetchers ask for up to 10k rows at a time - this constant is pretty "magic", but it's been with us for quite some time and reducing it to hedge against large row sizes seems like a bad idea.

We'd love to do better, but it's quite difficult without having a bytes limit on requests instead of a keys limit.

Is there already an issue for this? Can we repurpose this issue to be the feature request:

Add a MaxSpanRequestBytes field to roachpb.Header so that clients can control the maximum number of bytes they'd like to process in memory at once, rather than the maximum number of returned KVs?

TODO: due to the RocksDB/pebble modes of operation, there are currently two ways to do anything. Thanks to the two possible response types for batches (KVs vs bytes) there is another factor of two. In short, I expect to have to make three more similar changes to fully be able to implement byte hints for scans. TODO: testing and potentially write the code in a saner way. ---- A fledgling step towards cockroachdb#19721 is allowing incoming KV requests to bound the size of the response in terms of bytes rather than rows. This commit provides the necessary functionality to pebbleMVCCScanner: The new maxBytes field stops the scan once the size of the result meets or exceeds the threshold (at least one key will be added, regardless of its size). The classic example of the problem this addresses is a table in which each row is, say, ~1mb in size. A full table scan will currently fetch data from KV in batches of [10k], causing at least 10GB of data held in memory at any given moment. This sort of thing does happen in practice; we have a long-failing roachtest cockroachdb#33660 because of just that, and anecdotally OOMs in production clusters are with regularity caused by individual queries consuming excessive amounts of memory at the KV level. Plumbing this limit into a header field on BatchRequest and down to the engine level will allow the batching in [10k] to become byte-sized in nature, thus avoiding one obvious source OOMs. This doesn't solve cockroachdb#19721 in general (many such requests could work together to consume a lot of memory after all), but it's a sane baby step that might just avoid a large portion of OOMs already. [10k]: https://github.com/cockroachdb/cockroach/blob/0a658c19cd164e7c021eaff7f73db173f0650e8c/pkg/sql/row/kv_batch_fetcher.go#L25-L29 Release note: None

A fledgling step towards cockroachdb#19721 is allowing incoming KV requests to bound the size of the response in terms of bytes rather than rows. This commit adds a TargetBytes field to MVCCScanOptions to address this need: scans stop once the size of the result meets or exceeds the threshold (at least one key will be added, regardless of its size), and returns a ResumeSpan as appropriate. The classic example of the problem this addresses is a table in which each row is, say, ~1mb in size. A full table scan will currently fetch data from KV in batches of [10k], causing at least 10GB of data held in memory at any given moment. This sort of thing does happen in practice; we have a long-failing roachtest cockroachdb#33660 because of just that, and anecdotally OOMs in production clusters are with regularity caused by individual queries consuming excessive amounts of memory at the KV level. Plumbing this limit into a header field on BatchRequest and down to the engine level will allow the batching in [10k] to become byte-sized in nature, thus avoiding one obvious source OOMs. This doesn't solve cockroachdb#19721 in general (many such requests could work together to consume a lot of memory after all), but it's a sane baby step that might just avoid a large portion of OOMs already. [10k]: https://github.com/cockroachdb/cockroach/blob/0a658c19cd164e7c021eaff7f73db173f0650e8c/pkg/sql/row/kv_batch_fetcher.go#L25-L29 Release note: None

44339: engine: add byte limit to MVCCScan r=petermattis,itsbilal a=tbg A fledgling step towards #19721 is allowing incoming KV requests to bound the size of the response in terms of bytes rather than rows. This commit adds a TargetBytes field to MVCCScanOptions to address this need: scans stop once the size of the result meets or exceeds the threshold (at least one key will be added, regardless of its size), and returns a ResumeSpan as appropriate. The classic example of the problem this addresses is a table in which each row is, say, ~1mb in size. A full table scan will currently fetch data from KV in batches of [10k], causing at least 10GB of data held in memory at any given moment. This sort of thing does happen in practice; we have a long-failing roachtest #33660 because of just that, and anecdotally OOMs in production clusters are with regularity caused by individual queries consuming excessive amounts of memory at the KV level. Plumbing this limit into a header field on BatchRequest and down to the engine level will allow the batching in [10k] to become byte-sized in nature, thus avoiding one obvious source OOMs. This doesn't solve #19721 in general (many such requests could work together to consume a lot of memory after all), but it's a sane baby step that might just avoid a large portion of OOMs already. [10k]: https://github.com/cockroachdb/cockroach/blob/0a658c19cd164e7c021eaff7f73db173f0650e8c/pkg/sql/row/kv_batch_fetcher.go#L25-L29 Release note: None Co-authored-by: Tobias Schottdorf <tobias.schottdorf@gmail.com>

lunevalex · 2020-07-27T15:09:51Z

Related discussion in #51551

tbg · 2020-08-21T10:22:10Z

Summarizing the discussion from #51551: now that we're using pebble, we should be able to accurately allocate from a memory budget in mvccScanner (we can budget in other places too - but this is the one that matters). It looks like there was consensus that that budget should be that given by the --sql-memory flag (i.e. we should not attempt to force users to try to partition memory between the SQL frontend and KV backend). There was some discussion on whether to queue for budget or to fail. I would say on the first stab we will definitely fail as what we're trying to achieve is the simplest means possible to avoid OOMs. Adding any kind of queuing is complex and comes with new debugging challenges.

It's worth noting that even with all of this in place, we still need to retain some form of the speculative accounting on the SQL side (table fetchers) for the common case in which the request does not hit the local fast-path. If it does hit the local fast path, we could conceivably "transfer" the allocated budget from KV back up to SQL, but there may be practical challenges to doing so. Naively, KV would release the allocation "after" the response has been sent to the client (though that "after" is likely difficult to implement, depending on what hooks gRPC offers here; it may need to happen in a custom marshaler or something like that, may not be worth it).

The goal is to track memory allocations for: - non-local SQL=>KV requests: this can happen with joins, multi-tenant clusters, and if ranges move between DistSQL planning and execution. - local SQL=>KV requests for the first request by a fetcher: in this case the fetcher reserves a modest 1KB which can be significantly exceeded by KV allocations. Only allocations in pebbleMVCCScanner for kv pairs and intents are tracked. The memory is released when returning from executeReadOnlyBatchWithServersideRefreshes since the chain of returns will end up in gRPC response processing and we can't hook into where that memory is released. This should still help for some cases of OOMs, and give some signal of memory overload that we can use elsewhere (e.g. admission control). The BytesMonitor is used to construct a BoundAccount that is wrapped in a narrower ScannerMemoryMonitor that is passed via the EvalContext interface. The other alternative would be for the engine to have a BytesMonitor at initialization time that it can use to construct a BoundAccount for each MVCC scan, and pass it back via MVCCScanResult. This would mean multiple BoundAccounts for a batch (since we don't want to release memory until all the requests in the batch are processed), and would be harder to extend to track additional request types compared to embedding in EvalContext. The rootSQLMonitor is reused for this memory allocation tracking. This tracking is always done for non-local requests, and for the first request by a fetcher for a local request. This is to avoid double-counting, the first request issued by a SQL fetcher only reserves 1KB, but subsequent ones have already reserved what was returned in the first response. So there is room to tighten this if we knew what had been reserved on the local client (there are complications because the batch may have been split to send to different nodes, only one of which was local). The AdmissionHeader.SourceLocation field is used to mark local requests and is set in rpc.internalClientAdapter. The first request is marked using the AdmissionHeader.NoMemoryReservedAtSource bit. Informs cockroachdb#19721 Release note (ops change): The memory pool used for SQL is now also used to cover KV memory used for scans.

The goal is to track memory allocations for: - non-local SQL=>KV requests: this can happen with joins, multi-tenant clusters, and if ranges move between DistSQL planning and execution. - local SQL=>KV requests for the first request by a fetcher: in this case the fetcher reserves a modest 1KB which can be significantly exceeded by KV allocations. Only allocations in pebbleMVCCScanner for kv pairs and intents are tracked. The memory is released when returning from executeReadOnlyBatchWithServersideRefreshes since the chain of returns will end up in gRPC response processing and we can't hook into where that memory is released. This should still help for some cases of OOMs, and give some signal of memory overload that we can use elsewhere (e.g. admission control). The BytesMonitor is used to construct a BoundAccount that is that is passed via the EvalContext interface. The other alternative would be for the engine to have a BytesMonitor at initialization time that it can use to construct a BoundAccount for each MVCC scan, and pass it back via MVCCScanResult. This would mean multiple BoundAccounts for a batch (since we don't want to release memory until all the requests in the batch are processed), and would be harder to extend to track additional request types compared to embedding in EvalContext. The rootSQLMonitor is reused for this memory allocation tracking. This tracking is always done for non-local requests, and for the first request by a fetcher for a local request. This is to avoid double-counting, the first request issued by a SQL fetcher only reserves 1KB, but subsequent ones have already reserved what was returned in the first response. So there is room to tighten this if we knew what had been reserved on the local client (there are complications because the batch may have been split to send to different nodes, only one of which was local). The AdmissionHeader.SourceLocation field is used to mark local requests and is set in rpc.internalClientAdapter. The first request is marked using the AdmissionHeader.NoMemoryReservedAtSource bit. Informs cockroachdb#19721 Release note (ops change): The memory pool used for SQL is now also used to cover KV memory used for scans.

66362: kv,storage: use a BytesMonitor to track memory allocations for scans r=sumeerbhola a=sumeerbhola The goal is to track memory allocations for: - non-local SQL=>KV requests: this can happen with joins, multi-tenant clusters, and if ranges move between DistSQL planning and execution. - local SQL=>KV requests for the first request by a fetcher: in this case the fetcher reserves a modest 1KB which can be significantly exceeded by KV allocations. Only allocations in pebbleMVCCScanner for kv pairs and intents are tracked. The memory is released when returning from executeReadOnlyBatchWithServersideRefreshes since the chain of returns will end up in gRPC response processing and we can't hook into where that memory is released. This should still help for some cases of OOMs, and give some signal of memory overload that we can use elsewhere (e.g. admission control). The BytesMonitor is used to construct a BoundAccount that is wrapped in a narrower ScannerMemoryMonitor that is passed via the EvalContext interface. The other alternative would be for the engine to have a BytesMonitor at initialization time that it can use to construct a BoundAccount for each MVCC scan, and pass it back via MVCCScanResult. This would mean multiple BoundAccounts for a batch (since we don't want to release memory until all the requests in the batch are processed), and would be harder to extend to track additional request types compared to embedding in EvalContext. The rootSQLMonitor is reused for this memory allocation tracking. This tracking is always done for non-local requests, and for the first request by a fetcher for a local request. This is to avoid double-counting, the first request issued by a SQL fetcher only reserves 1KB, but subsequent ones have already reserved what was returned in the first response. So there is room to tighten this if we knew what had been reserved on the local client (there are complications because the batch may have been split to send to different nodes, only one of which was local). The AdmissionHeader.SourceLocation field is used to mark local requests and is set in rpc.internalClientAdapter. The first request is marked using the AdmissionHeader.NoMemoryReservedAtSource bit. Informs #19721 Release note (ops change): The memory pool used for SQL is now also used to cover KV memory used for scans. 67451: colexec: optimize Bytes sorting with abbreviated values r=mgartner a=mgartner #### colexec: add benchmark for sorting UUIDs Release note: None #### colexec: optimize Bytes sorting with abbreviated values This commit introduces an optimization for sorting `Bytes` vectors. While sorting, a signification potion of time is spent performing comparisons between two datums. These comparisons are optimized by creating an abbreviated `uint64` value for each `[]byte` that represents up to the first eight bytes in the slice. The abbreviated value is used for a comparison fast path that avoid comparing all bytes in a slice in some cases. This technique is used by Postgres[1][2] and Pebble[3]. Abbreviating values to a `uint64` helps optimize comparisons for datums that are larger than the size of the system's native pointer (64 bits). Given datums `a` and `b`, the following properties must hold true for the respective abbreviated values `abbrA` and `abbrB`: - `abbrA > abbrB` iff `a > b` - `abbrA < abbrB` iff `a < b` - If `abbrA == abbrB`, it is unknown if `a` is greater than, less than, or equal to `b`. A full comparison of `a` and `b` is required. Comparing the abbreviated values first improves comparison performance for two reasons. First, comparing two `uint64`s is fast. It is a single CPU instruction. Any datum larger than 64 bits requires multiple instructions for comparison. For example, comparing two `[]byte`s requires iterating over each byte until non-equal bytes are found. Second, CPU caches are more efficiently used because the abbreviated values of a vector are packed into a `[]uint64` of contiguous memory. This increases the chance that two datums can be compared by only fetching information from CPU caches rather than main memory. In the benchmarks below, `rows` is the number of rows being sorted, `cols` is the number of sort columns, and `constAbbrPct` is the percentage of rows for which the abbreviated values are the same. The benchmarks show that in the best case, when the abbreviated values are all unique, sort throughput has increased by up to ~45%. In the worst case, where all values share the same abbreviated value, throughput has decreased by as much as ~8.5%. This is due to the extra overhead of creating abbreviated values and comparing them, only to have to fall back to a full comparison every time. name old speed new speed delta SortUUID/rows=2048/cols=1/constAbbrPct=0-16 38.0MB/s ± 2% 55.3MB/s ± 3% +45.30% SortUUID/rows=2048/cols=1/constAbbrPct=50-16 36.2MB/s ± 3% 43.0MB/s ± 4% +18.82% SortUUID/rows=2048/cols=1/constAbbrPct=75-16 36.0MB/s ± 4% 37.1MB/s ± 4% ~ SortUUID/rows=2048/cols=1/constAbbrPct=90-16 36.8MB/s ± 1% 36.2MB/s ± 3% ~ SortUUID/rows=2048/cols=1/constAbbrPct=100-16 37.0MB/s ± 1% 33.8MB/s ± 5% -8.66% SortUUID/rows=2048/cols=2/constAbbrPct=0-16 60.3MB/s ± 1% 74.0MB/s ± 3% +22.85% SortUUID/rows=2048/cols=2/constAbbrPct=50-16 58.7MB/s ± 1% 63.7MB/s ± 1% +8.54% SortUUID/rows=2048/cols=2/constAbbrPct=75-16 58.3MB/s ± 1% 56.6MB/s ± 6% ~ SortUUID/rows=2048/cols=2/constAbbrPct=90-16 58.7MB/s ± 1% 54.2MB/s ± 3% -7.69% SortUUID/rows=2048/cols=2/constAbbrPct=100-16 59.3MB/s ± 1% 56.1MB/s ± 4% -5.30% SortUUID/rows=16384/cols=1/constAbbrPct=0-16 30.7MB/s ± 2% 41.9MB/s ± 3% +36.24% SortUUID/rows=16384/cols=1/constAbbrPct=50-16 30.6MB/s ± 1% 32.0MB/s ± 8% ~ SortUUID/rows=16384/cols=1/constAbbrPct=75-16 30.2MB/s ± 2% 30.9MB/s ± 6% ~ SortUUID/rows=16384/cols=1/constAbbrPct=90-16 30.4MB/s ± 2% 27.6MB/s ± 3% -9.29% SortUUID/rows=16384/cols=1/constAbbrPct=100-16 30.6MB/s ± 2% 28.5MB/s ± 5% -6.98% SortUUID/rows=16384/cols=2/constAbbrPct=0-16 49.5MB/s ± 2% 59.0MB/s ± 2% +19.13% SortUUID/rows=16384/cols=2/constAbbrPct=50-16 48.6MB/s ± 1% 49.5MB/s ± 3% +1.84% SortUUID/rows=16384/cols=2/constAbbrPct=75-16 47.3MB/s ± 2% 47.3MB/s ± 2% ~ SortUUID/rows=16384/cols=2/constAbbrPct=90-16 48.5MB/s ± 2% 45.2MB/s ± 1% -6.65% SortUUID/rows=16384/cols=2/constAbbrPct=100-16 48.9MB/s ± 1% 44.4MB/s ± 2% -9.13% SortUUID/rows=262144/cols=1/constAbbrPct=0-16 25.4MB/s ± 2% 35.7MB/s ± 3% +40.53% SortUUID/rows=262144/cols=1/constAbbrPct=50-16 24.9MB/s ± 3% 28.8MB/s ± 3% +15.44% SortUUID/rows=262144/cols=1/constAbbrPct=75-16 25.4MB/s ± 2% 25.7MB/s ± 5% ~ SortUUID/rows=262144/cols=1/constAbbrPct=90-16 25.4MB/s ± 3% 24.7MB/s ± 2% -3.00% SortUUID/rows=262144/cols=1/constAbbrPct=100-16 25.1MB/s ± 3% 23.7MB/s ± 2% -5.73% SortUUID/rows=262144/cols=2/constAbbrPct=0-16 37.5MB/s ± 2% 43.7MB/s ± 5% +16.65% SortUUID/rows=262144/cols=2/constAbbrPct=50-16 37.5MB/s ± 1% 37.8MB/s ± 7% ~ SortUUID/rows=262144/cols=2/constAbbrPct=75-16 37.1MB/s ± 4% 36.4MB/s ± 1% ~ SortUUID/rows=262144/cols=2/constAbbrPct=90-16 36.9MB/s ± 5% 34.6MB/s ± 7% -6.26% SortUUID/rows=262144/cols=2/constAbbrPct=100-16 37.2MB/s ± 3% 34.0MB/s ± 2% -8.53% I experimented with several mitigations to the regressions, but did not have much luck. Postgres uses HyperLogLog while building abbreviated values to track their cardinality and abort the optimization if the cardinality is too low. I attempted this, but the overhead of HyperLogLog negated the benefits of the optimization entirely. Using a `map[uint64]struct{}` to track the cardinality of abbreviated values was the most promising. It was able to reduce the worst regressions at the expense of reducing the speedup of the best case benchmarks. These mechanisms for aborting the optimization when abbreviated value cardinality is low are essentially a trade-off between maximizing sort performance when their cardinality is high and minimizing the overhead of generating and comparing abbreviated values when their cardinality is low. It's worth future investigation to try to find a low-overhead method of aborting, and to be more thoughtful about the trade-offs. [1] https://brandur.org/sortsupport [2] http://pgeoghegan.blogspot.com/2015/01/abbreviated-keys-exploiting-locality-to.html [3] https://github.com/cockroachdb/pebble/blob/ea60b4722cca21fa0d3c2749c788e1ac76981f7d/internal/base/comparer.go#L28-L36 Release note (performance improvement): Sort performance has been improved when sorting columns of type STRING, BYTES, or UUID. 67606: sql: add sql shell telemetry counter r=rafiss,knz,arulajmani a=e-mbrown Fixes #62208 This change allows us to keep track of connections made using our internal SQl shell. Release note: None 68427: jobs: skip TestRegistryLifecycle r=adityamaru a=jbowens Refs: #68315 Reason: flaky test Generated by bin/skip-test. Release justification: non-production code changes Release note: None Co-authored-by: sumeerbhola <sumeer@cockroachlabs.com> Co-authored-by: Marcus Gartner <marcus@cockroachlabs.com> Co-authored-by: e-mbrown <ebsonari@gmail.com> Co-authored-by: Jackson Owens <jackson@cockroachlabs.com>

github-actions · 2023-09-26T11:09:56Z

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knz · 2023-10-09T12:16:59Z

still elevant

petermattis added C-enhancement Solution expected to add code/behavior + preserve backward-compat (pg compat issues are exception) A-kv-client Relating to the KV client and the KV interface. labels Jul 21, 2018

tbg modified the milestones: 2.1, 2.2 Jul 22, 2018

petermattis removed this from the 2.2 milestone Oct 5, 2018

RaduBerinde mentioned this issue Nov 22, 2019

opt: adjust costing of lookup-join #42707

Closed

jordanlewis mentioned this issue Nov 22, 2019

sql: create parallel lookup join hint to let people use the unsafe parallel lookup join when required #42708

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tbg mentioned this issue Jan 24, 2020

engine: add byte limit to MVCCScan #44339

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lunevalex mentioned this issue Jul 29, 2020

storage: usage accounting and enforcement #2185

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tbg added A-kv-observability A-kv-server Relating to the KV-level RPC server labels Sep 3, 2020

jlinder added the T-kv KV Team label Jun 16, 2021

tbg mentioned this issue Oct 25, 2021

roachtest: tpccbench/nodes=9/cpu=4/multi-region failed [sst raft oom] #71802

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nvanbenschoten mentioned this issue Mar 1, 2022

kv: limit aggregate memory across async IntentResolver tasks #77217

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github-actions bot closed this as not planned Won't fix, can't repro, duplicate, stale Oct 9, 2023

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storage: kv-level memory accounting/bounding #19721

storage: kv-level memory accounting/bounding #19721

nvanbenschoten commented Nov 1, 2017 •

edited by cockroach-jira-scripts

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jordanlewis commented Sep 17, 2019

lunevalex commented Jul 27, 2020

tbg commented Aug 21, 2020

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knz commented Oct 9, 2023

storage: kv-level memory accounting/bounding #19721

storage: kv-level memory accounting/bounding #19721

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