page_service: add benchmark for batching #9820
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5607 tests run: 5371 passed, 0 failed, 236 skipped (full report)Code coverage* (full report)
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18f2964 at 2024-11-25T09:13:22.691Z :recycle: |
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test_runner/performance/pageserver/test_pageserver_getpage_merge.py
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# Problem The timeout-based batching adds latency to unbatchable workloads. We can choose a short batching timeout (e.g. 10us) but that requires high-resolution timers, which tokio doesn't have. I thoroughly explored options to use OS timers (see [this](#9822) abandoned PR). In short, it's not an attractive option because any timer implementation adds non-trivial overheads. # Solution The insight is that, in the steady state of a batchable workload, the time we spend in `get_vectored` will be hundreds of microseconds anyway. If we prepare the next batch concurrently to `get_vectored`, we will have a sizeable batch ready once `get_vectored` of the current batch is done and do not need an explicit timeout. This can be reasonably described as **pipelining of the protocol handler**. # Implementation We model the sub-protocol handler for pagestream requests (`handle_pagrequests`) as two futures that form a pipeline: 2. Batching: read requests from the connection and fill the current batch 3. Execution: `take` the current batch, execute it using `get_vectored`, and send the response. The Reading and Batching stage are connected through a new type of channel called `spsc_fold`. See the long comment in the `handle_pagerequests_pipelined` for details. # Changes - Refactor `handle_pagerequests` - separate functions for - reading one protocol message; produces a `BatchedFeMessage` with just one page request in it - batching; tried to merge an incoming `BatchedFeMessage` into an existing `BatchedFeMessage`; returns `None` on success and returns back the incoming message in case merging isn't possible - execution of a batched message - unify the timeline handle acquisition & request span construction; it now happen in the function that reads the protocol message - Implement serial and pipelined model - serial: what we had before any of the batching changes - read one protocol message - execute protocol messages - pipelined: the design described above - optionality for execution of the pipeline: either via concurrent futures vs tokio tasks - Pageserver config - remove batching timeout field - add ability to configure pipelining mode - add ability to limit max batch size for pipelined configurations (required for the rollout, cf neondatabase/cloud#20620 ) - ability to configure execution mode - Tests - remove `batch_timeout` parametrization - rename `test_getpage_merge_smoke` to `test_throughput` - add parametrization to test different max batch sizes and execution moes - rename `test_timer_precision` to `test_latency` - rename the test case file to `test_page_service_batching.py` - better descriptions of what the tests actually do ## On the holding The `TimelineHandle` in the pending batch While batching, we hold the `TimelineHandle` in the pending batch. Therefore, the timeline will not finish shutting down while we're batching. This is not a problem in practice because the concurrently ongoing `get_vectored` call will fail quickly with an error indicating that the timeline is shutting down. This results in the Execution stage returning a `QueryError::Shutdown`, which causes the pipeline / entire page service connection to shut down. This drops all references to the `Arc<Mutex<Option<Box<BatchedFeMessage>>>>` object, thereby dropping the contained `TimelineHandle`s. - => fixes #9850 # Performance Local run of the benchmarks, results in [this empty commit](1cf5b14) in the PR branch. Key take-aways: * `concurrent-futures` and `tasks` deliver identical `batching_factor` * tail latency impact unknown, cf #9837 * `concurrent-futures` has higher throughput than `tasks` in all workloads (=lower `time` metric) * In unbatchable workloads, `concurrent-futures` has 5% higher `CPU-per-throughput` than that of `tasks`, and 15% higher than that of `serial`. * In batchable-32 workload, `concurrent-futures` has 8% lower `CPU-per-throughput` than that of `tasks` (comparison to tput of `serial` is irrelevant) * in unbatchable workloads, mean and tail latencies of `concurrent-futures` is practically identical to `serial`, whereas `tasks` adds 20-30us of overhead Overall, `concurrent-futures` seems like a slightly more attractive choice. # Rollout This change is disabled-by-default. Rollout plan: - neondatabase/cloud#20620 # Refs - epic: #9376 - this sub-task: #9377 - the abandoned attempt to improve batching timeout resolution: #9820 - closes #9850 - fixes #9835
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# Problem The timeout-based batching adds latency to unbatchable workloads. We can choose a short batching timeout (e.g. 10us) but that requires high-resolution timers, which tokio doesn't have. I thoroughly explored options to use OS timers (see [this](#9822) abandoned PR). In short, it's not an attractive option because any timer implementation adds non-trivial overheads. # Solution The insight is that, in the steady state of a batchable workload, the time we spend in `get_vectored` will be hundreds of microseconds anyway. If we prepare the next batch concurrently to `get_vectored`, we will have a sizeable batch ready once `get_vectored` of the current batch is done and do not need an explicit timeout. This can be reasonably described as **pipelining of the protocol handler**. # Implementation We model the sub-protocol handler for pagestream requests (`handle_pagrequests`) as two futures that form a pipeline: 2. Batching: read requests from the connection and fill the current batch 3. Execution: `take` the current batch, execute it using `get_vectored`, and send the response. The Reading and Batching stage are connected through a new type of channel called `spsc_fold`. See the long comment in the `handle_pagerequests_pipelined` for details. # Changes - Refactor `handle_pagerequests` - separate functions for - reading one protocol message; produces a `BatchedFeMessage` with just one page request in it - batching; tried to merge an incoming `BatchedFeMessage` into an existing `BatchedFeMessage`; returns `None` on success and returns back the incoming message in case merging isn't possible - execution of a batched message - unify the timeline handle acquisition & request span construction; it now happen in the function that reads the protocol message - Implement serial and pipelined model - serial: what we had before any of the batching changes - read one protocol message - execute protocol messages - pipelined: the design described above - optionality for execution of the pipeline: either via concurrent futures vs tokio tasks - Pageserver config - remove batching timeout field - add ability to configure pipelining mode - add ability to limit max batch size for pipelined configurations (required for the rollout, cf neondatabase/cloud#20620 ) - ability to configure execution mode - Tests - remove `batch_timeout` parametrization - rename `test_getpage_merge_smoke` to `test_throughput` - add parametrization to test different max batch sizes and execution moes - rename `test_timer_precision` to `test_latency` - rename the test case file to `test_page_service_batching.py` - better descriptions of what the tests actually do ## On the holding The `TimelineHandle` in the pending batch While batching, we hold the `TimelineHandle` in the pending batch. Therefore, the timeline will not finish shutting down while we're batching. This is not a problem in practice because the concurrently ongoing `get_vectored` call will fail quickly with an error indicating that the timeline is shutting down. This results in the Execution stage returning a `QueryError::Shutdown`, which causes the pipeline / entire page service connection to shut down. This drops all references to the `Arc<Mutex<Option<Box<BatchedFeMessage>>>>` object, thereby dropping the contained `TimelineHandle`s. - => fixes #9850 # Performance Local run of the benchmarks, results in [this empty commit](1cf5b14) in the PR branch. Key take-aways: * `concurrent-futures` and `tasks` deliver identical `batching_factor` * tail latency impact unknown, cf #9837 * `concurrent-futures` has higher throughput than `tasks` in all workloads (=lower `time` metric) * In unbatchable workloads, `concurrent-futures` has 5% higher `CPU-per-throughput` than that of `tasks`, and 15% higher than that of `serial`. * In batchable-32 workload, `concurrent-futures` has 8% lower `CPU-per-throughput` than that of `tasks` (comparison to tput of `serial` is irrelevant) * in unbatchable workloads, mean and tail latencies of `concurrent-futures` is practically identical to `serial`, whereas `tasks` adds 20-30us of overhead Overall, `concurrent-futures` seems like a slightly more attractive choice. # Rollout This change is disabled-by-default. Rollout plan: - neondatabase/cloud#20620 # Refs - epic: #9376 - this sub-task: #9377 - the abandoned attempt to improve batching timeout resolution: #9820 - closes #9850 - fixes #9835
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This PR adds two benchmark to demonstrate the effect of server-side
getpage request batching added in #9321.
For the CPU usage, I found the the
prometheuscrate's built-in CPU usage accounts the seconds at integer granularity. That's not enough you reduce the target benchmark runtime for local iteration. So, add a newlibmetricsmetric and report that.The benchmarks are disabled because on our benchmark nodes, timer resolution isn't high enough.
They work (no statement about quality) on my bare-metal devbox.
They will be refined and enabled once we find a fix. Candidates at time of writing are:
Refs: