bypass PageCache for L0 flush #7418
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part of #7124 # Problem (Re-stating the problem from #7124 for posterity) The `test_bulk_ingest` benchmark shows about 2x lower throughput with `tokio-epoll-uring` compared to `std-fs`. That's why we temporarily disabled it in #7238. The reason for this regression is that the benchmark runs on a system without memory pressure and thus std-fs writes don't block on disk IO but only copy the data into the kernel page cache. `tokio-epoll-uring` cannot beat that at this time, and possibly never. (However, under memory pressure, std-fs would stall the executor thread on kernel page cache writeback disk IO. That's why we want to use `tokio-epoll-uring`. And we likely want to use O_DIRECT in the future, at which point std-fs becomes an absolute show-stopper.) More elaborate analysis: https://neondatabase.notion.site/Why-test_bulk_ingest-is-slower-with-tokio-epoll-uring-918c5e619df045a7bd7b5f806cfbd53f?pvs=4 # Changes This PR increases the buffer size of `blob_io` and `EphemeralFile` from PAGE_SZ=8k to 64k. Longer-term, we probably want to do double-buffering / pipelined IO. # Resource Usage We currently do not flush the buffer when freezing the InMemoryLayer. That means a single Timeline can have multiple 64k buffers alive, esp if flushing is slow. This poses an OOM risk. We should either bound the number of frozen layers (#7317). Or we should change the freezing code to flush the buffer and drop the allocation. However, that's future work. # Performance (Measurements done on i3en.3xlarge.) The `test_bulk_insert.py` is too noisy, even with instance storage. It varies by 30-40%. I suspect that's due to compaction. Raising amount of data by 10x doesn't help with the noisiness.) So, I used the `bench_ingest` from @jcsp 's #7409 . Specifically, the `ingest-small-values/ingest 128MB/100b seq` and `ingest-small-values/ingest 128MB/100b seq, no delta` benchmarks. | | | seq | seq, no delta | |-----|-------------------|-----|---------------| | 8k | std-fs | 55 | 165 | | 8k | tokio-epoll-uring | 37 | 107 | | 64k | std-fs | 55 | 180 | | 64k | tokio-epoll-uring | 48 | 164 | The `8k` is from before this PR, the `64k` is with this PR. The values are the throughput reported by the benchmark (MiB/s). We see that this PR gets `tokio-epoll-uring` from 67% to 87% of `std-fs` performance in the `seq` benchmark. Notably, `seq` appears to hit some other bottleneck at `55 MiB/s`. CC'ing #7418 due to the apparent bottlenecks in writing delta layers. For `seq, no delta`, this PR gets `tokio-epoll-uring` from 64% to 91% of `std-fs` performance.
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…e is empty Found this while doing research for #7418
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…at_n`, fix UB for engine `std-fs` (#8186) part of #7418 I reviewed how the VirtualFile API's `read` methods look like and came to the conclusion that we've been using `IoBufMut` / `BoundedBufMut` / `Slice` wrong. This patch rectifies the situation. # Change 1: take `tokio_epoll_uring::Slice` in the read APIs Before, we took an `IoBufMut`, which is too low of a primitive and while it _seems_ convenient to be able to pass in a `Vec<u8>` without any fuzz, it's actually very unclear at the callsite that we're going to fill up that `Vec` up to its `capacity()`, because that's what `IoBuf::bytes_total()` returns and that's what `VirtualFile::read_exact_at` fills. By passing a `Slice` instead, a caller that "just wants to read into a `Vec`" is forced to be explicit about it, adding either `slice_full()` or `slice(x..y)`, and these methods panic if the read is outside of the bounds of the `Vec::capacity()`. Last, passing slices is more similar to what the `std::io` APIs look like. # Change 2: fix UB in `virtual_file_io_engine=std-fs` While reviewing call sites, I noticed that the `io_engine::IoEngine::read_at` method for `StdFs` mode has been constructing an `&mut[u8]` from raw parts that were uninitialized. We then used `std::fs::File::read_exact` to initialize that memory, but, IIUC we must not even be constructing an `&mut[u8]` where some of the memory isn't initialized. So, stop doing that and add a helper ext trait on `Slice` to do the zero-initialization. # Change 3: eliminate `read_exact_at_n` The `read_exact_at_n` doesn't make sense because the caller can just 1. `slice = buf.slice()` the exact memory it wants to fill 2. `slice = read_exact_at(slice)` 3. `buf = slice.into_inner()` Again, the `std::io` APIs specify the length of the read via the Rust slice length. We should do the same for the owned buffers IO APIs, i.e., via `Slice::bytes_total()`. # Change 4: simplify filling of `PageWriteGuard` The `PageWriteGuardBuf::init_up_to` was never necessary. Remove it. See changes to doc comment for more details. --- Reviewers should probably look at the added test case first, it illustrates my case a bit.
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part of #7418 # Motivation (reproducing #7418) When we do an `InMemoryLayer::write_to_disk`, there is a tremendous amount of random read I/O, as deltas from the ephemeral file (written in LSN order) are written out to the delta layer in key order. In benchmarks (#7409) we can see that this delta layer writing phase is substantially more expensive than the initial ingest of data, and that within the delta layer write a significant amount of the CPU time is spent traversing the page cache. # High-Level Changes Add a new mode for L0 flush that works as follows: * Read the full ephemeral file into memory -- layers are much smaller than total memory, so this is afforable * Do all the random reads directly from this in memory buffer instead of using blob IO/page cache/disk reads. * Add a semaphore to limit how many timelines may concurrently do this (limit peak memory). * Make the semaphore configurable via PS config. # Implementation Details The new `BlobReaderRef::Slice` is a temporary hack until we can ditch `blob_io` for `InMemoryLayer` => Plan for this is laid out in #8183 # Correctness The correctness of this change is quite obvious to me: we do what we did before (`blob_io`) but read from memory instead of going to disk. The highest bug potential is in doing owned-buffers IO. I refactored the API a bit in preliminary PR #8186 to make it less error-prone, but still, careful review is requested. # Performance I manually measured single-client ingest performance from `pgbench -i ...`. Full report: https://neondatabase.notion.site/2024-06-28-benchmarking-l0-flush-performance-e98cff3807f94cb38f2054d8c818fe84?pvs=4 tl;dr: * no speed improvements during ingest, but * significantly lower pressure on PS PageCache (eviction rate drops to 1/3) * (that's why I'm working on this) * noticable but modestly lower CPU time This is good enough for merging this PR because the changes require opt-in. We'll do more testing in staging & pre-prod. # Stability / Monitoring **memory consumption**: there's no _hard_ limit on max `InMemoryLayer` size (aka "checkpoint distance") , hence there's no hard limit on the memory allocation we do for flushing. In practice, we a) [log a warning](https://github.com/neondatabase/neon/blob/23827c6b0d400cbb9a972d4d05d49834816c40d1/pageserver/src/tenant/timeline.rs#L5741-L5743) when we flush oversized layers, so we'd know which tenant is to blame and b) if we were to put a hard limit in place, we would have to decide what to do if there is an InMemoryLayer that exceeds the limit. It seems like a better option to guarantee a max size for frozen layer, dependent on `checkpoint_distance`. Then limit concurrency based on that. **metrics**: we do have the [flush_time_histo](https://github.com/neondatabase/neon/blob/23827c6b0d400cbb9a972d4d05d49834816c40d1/pageserver/src/tenant/timeline.rs#L3725-L3726), but that includes the wait time for the semaphore. We could add a separate metric for the time spent after acquiring the semaphore, so one can infer the wait time. Seems unnecessary at this point, though.
VladLazar
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part of #7418 # Motivation (reproducing #7418) When we do an `InMemoryLayer::write_to_disk`, there is a tremendous amount of random read I/O, as deltas from the ephemeral file (written in LSN order) are written out to the delta layer in key order. In benchmarks (#7409) we can see that this delta layer writing phase is substantially more expensive than the initial ingest of data, and that within the delta layer write a significant amount of the CPU time is spent traversing the page cache. # High-Level Changes Add a new mode for L0 flush that works as follows: * Read the full ephemeral file into memory -- layers are much smaller than total memory, so this is afforable * Do all the random reads directly from this in memory buffer instead of using blob IO/page cache/disk reads. * Add a semaphore to limit how many timelines may concurrently do this (limit peak memory). * Make the semaphore configurable via PS config. # Implementation Details The new `BlobReaderRef::Slice` is a temporary hack until we can ditch `blob_io` for `InMemoryLayer` => Plan for this is laid out in #8183 # Correctness The correctness of this change is quite obvious to me: we do what we did before (`blob_io`) but read from memory instead of going to disk. The highest bug potential is in doing owned-buffers IO. I refactored the API a bit in preliminary PR #8186 to make it less error-prone, but still, careful review is requested. # Performance I manually measured single-client ingest performance from `pgbench -i ...`. Full report: https://neondatabase.notion.site/2024-06-28-benchmarking-l0-flush-performance-e98cff3807f94cb38f2054d8c818fe84?pvs=4 tl;dr: * no speed improvements during ingest, but * significantly lower pressure on PS PageCache (eviction rate drops to 1/3) * (that's why I'm working on this) * noticable but modestly lower CPU time This is good enough for merging this PR because the changes require opt-in. We'll do more testing in staging & pre-prod. # Stability / Monitoring **memory consumption**: there's no _hard_ limit on max `InMemoryLayer` size (aka "checkpoint distance") , hence there's no hard limit on the memory allocation we do for flushing. In practice, we a) [log a warning](https://github.com/neondatabase/neon/blob/23827c6b0d400cbb9a972d4d05d49834816c40d1/pageserver/src/tenant/timeline.rs#L5741-L5743) when we flush oversized layers, so we'd know which tenant is to blame and b) if we were to put a hard limit in place, we would have to decide what to do if there is an InMemoryLayer that exceeds the limit. It seems like a better option to guarantee a max size for frozen layer, dependent on `checkpoint_distance`. Then limit concurrency based on that. **metrics**: we do have the [flush_time_histo](https://github.com/neondatabase/neon/blob/23827c6b0d400cbb9a972d4d05d49834816c40d1/pageserver/src/tenant/timeline.rs#L3725-L3726), but that includes the wait time for the semaphore. We could add a separate metric for the time spent after acquiring the semaphore, so one can infer the wait time. Seems unnecessary at this point, though.
VladLazar
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part of #7418 # Motivation (reproducing #7418) When we do an `InMemoryLayer::write_to_disk`, there is a tremendous amount of random read I/O, as deltas from the ephemeral file (written in LSN order) are written out to the delta layer in key order. In benchmarks (#7409) we can see that this delta layer writing phase is substantially more expensive than the initial ingest of data, and that within the delta layer write a significant amount of the CPU time is spent traversing the page cache. # High-Level Changes Add a new mode for L0 flush that works as follows: * Read the full ephemeral file into memory -- layers are much smaller than total memory, so this is afforable * Do all the random reads directly from this in memory buffer instead of using blob IO/page cache/disk reads. * Add a semaphore to limit how many timelines may concurrently do this (limit peak memory). * Make the semaphore configurable via PS config. # Implementation Details The new `BlobReaderRef::Slice` is a temporary hack until we can ditch `blob_io` for `InMemoryLayer` => Plan for this is laid out in #8183 # Correctness The correctness of this change is quite obvious to me: we do what we did before (`blob_io`) but read from memory instead of going to disk. The highest bug potential is in doing owned-buffers IO. I refactored the API a bit in preliminary PR #8186 to make it less error-prone, but still, careful review is requested. # Performance I manually measured single-client ingest performance from `pgbench -i ...`. Full report: https://neondatabase.notion.site/2024-06-28-benchmarking-l0-flush-performance-e98cff3807f94cb38f2054d8c818fe84?pvs=4 tl;dr: * no speed improvements during ingest, but * significantly lower pressure on PS PageCache (eviction rate drops to 1/3) * (that's why I'm working on this) * noticable but modestly lower CPU time This is good enough for merging this PR because the changes require opt-in. We'll do more testing in staging & pre-prod. # Stability / Monitoring **memory consumption**: there's no _hard_ limit on max `InMemoryLayer` size (aka "checkpoint distance") , hence there's no hard limit on the memory allocation we do for flushing. In practice, we a) [log a warning](https://github.com/neondatabase/neon/blob/23827c6b0d400cbb9a972d4d05d49834816c40d1/pageserver/src/tenant/timeline.rs#L5741-L5743) when we flush oversized layers, so we'd know which tenant is to blame and b) if we were to put a hard limit in place, we would have to decide what to do if there is an InMemoryLayer that exceeds the limit. It seems like a better option to guarantee a max size for frozen layer, dependent on `checkpoint_distance`. Then limit concurrency based on that. **metrics**: we do have the [flush_time_histo](https://github.com/neondatabase/neon/blob/23827c6b0d400cbb9a972d4d05d49834816c40d1/pageserver/src/tenant/timeline.rs#L3725-L3726), but that includes the wait time for the semaphore. We could add a separate metric for the time spent after acquiring the semaphore, so one can infer the wait time. Seems unnecessary at this point, though.
VladLazar
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Jul 8, 2024
part of #7418 # Motivation (reproducing #7418) When we do an `InMemoryLayer::write_to_disk`, there is a tremendous amount of random read I/O, as deltas from the ephemeral file (written in LSN order) are written out to the delta layer in key order. In benchmarks (#7409) we can see that this delta layer writing phase is substantially more expensive than the initial ingest of data, and that within the delta layer write a significant amount of the CPU time is spent traversing the page cache. # High-Level Changes Add a new mode for L0 flush that works as follows: * Read the full ephemeral file into memory -- layers are much smaller than total memory, so this is afforable * Do all the random reads directly from this in memory buffer instead of using blob IO/page cache/disk reads. * Add a semaphore to limit how many timelines may concurrently do this (limit peak memory). * Make the semaphore configurable via PS config. # Implementation Details The new `BlobReaderRef::Slice` is a temporary hack until we can ditch `blob_io` for `InMemoryLayer` => Plan for this is laid out in #8183 # Correctness The correctness of this change is quite obvious to me: we do what we did before (`blob_io`) but read from memory instead of going to disk. The highest bug potential is in doing owned-buffers IO. I refactored the API a bit in preliminary PR #8186 to make it less error-prone, but still, careful review is requested. # Performance I manually measured single-client ingest performance from `pgbench -i ...`. Full report: https://neondatabase.notion.site/2024-06-28-benchmarking-l0-flush-performance-e98cff3807f94cb38f2054d8c818fe84?pvs=4 tl;dr: * no speed improvements during ingest, but * significantly lower pressure on PS PageCache (eviction rate drops to 1/3) * (that's why I'm working on this) * noticable but modestly lower CPU time This is good enough for merging this PR because the changes require opt-in. We'll do more testing in staging & pre-prod. # Stability / Monitoring **memory consumption**: there's no _hard_ limit on max `InMemoryLayer` size (aka "checkpoint distance") , hence there's no hard limit on the memory allocation we do for flushing. In practice, we a) [log a warning](https://github.com/neondatabase/neon/blob/23827c6b0d400cbb9a972d4d05d49834816c40d1/pageserver/src/tenant/timeline.rs#L5741-L5743) when we flush oversized layers, so we'd know which tenant is to blame and b) if we were to put a hard limit in place, we would have to decide what to do if there is an InMemoryLayer that exceeds the limit. It seems like a better option to guarantee a max size for frozen layer, dependent on `checkpoint_distance`. Then limit concurrency based on that. **metrics**: we do have the [flush_time_histo](https://github.com/neondatabase/neon/blob/23827c6b0d400cbb9a972d4d05d49834816c40d1/pageserver/src/tenant/timeline.rs#L3725-L3726), but that includes the wait time for the semaphore. We could add a separate metric for the time spent after acquiring the semaphore, so one can infer the wait time. Seems unnecessary at this point, though.
VladLazar
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Jul 8, 2024
part of #7418 # Motivation (reproducing #7418) When we do an `InMemoryLayer::write_to_disk`, there is a tremendous amount of random read I/O, as deltas from the ephemeral file (written in LSN order) are written out to the delta layer in key order. In benchmarks (#7409) we can see that this delta layer writing phase is substantially more expensive than the initial ingest of data, and that within the delta layer write a significant amount of the CPU time is spent traversing the page cache. # High-Level Changes Add a new mode for L0 flush that works as follows: * Read the full ephemeral file into memory -- layers are much smaller than total memory, so this is afforable * Do all the random reads directly from this in memory buffer instead of using blob IO/page cache/disk reads. * Add a semaphore to limit how many timelines may concurrently do this (limit peak memory). * Make the semaphore configurable via PS config. # Implementation Details The new `BlobReaderRef::Slice` is a temporary hack until we can ditch `blob_io` for `InMemoryLayer` => Plan for this is laid out in #8183 # Correctness The correctness of this change is quite obvious to me: we do what we did before (`blob_io`) but read from memory instead of going to disk. The highest bug potential is in doing owned-buffers IO. I refactored the API a bit in preliminary PR #8186 to make it less error-prone, but still, careful review is requested. # Performance I manually measured single-client ingest performance from `pgbench -i ...`. Full report: https://neondatabase.notion.site/2024-06-28-benchmarking-l0-flush-performance-e98cff3807f94cb38f2054d8c818fe84?pvs=4 tl;dr: * no speed improvements during ingest, but * significantly lower pressure on PS PageCache (eviction rate drops to 1/3) * (that's why I'm working on this) * noticable but modestly lower CPU time This is good enough for merging this PR because the changes require opt-in. We'll do more testing in staging & pre-prod. # Stability / Monitoring **memory consumption**: there's no _hard_ limit on max `InMemoryLayer` size (aka "checkpoint distance") , hence there's no hard limit on the memory allocation we do for flushing. In practice, we a) [log a warning](https://github.com/neondatabase/neon/blob/23827c6b0d400cbb9a972d4d05d49834816c40d1/pageserver/src/tenant/timeline.rs#L5741-L5743) when we flush oversized layers, so we'd know which tenant is to blame and b) if we were to put a hard limit in place, we would have to decide what to do if there is an InMemoryLayer that exceeds the limit. It seems like a better option to guarantee a max size for frozen layer, dependent on `checkpoint_distance`. Then limit concurrency based on that. **metrics**: we do have the [flush_time_histo](https://github.com/neondatabase/neon/blob/23827c6b0d400cbb9a972d4d05d49834816c40d1/pageserver/src/tenant/timeline.rs#L3725-L3726), but that includes the wait time for the semaphore. We could add a separate metric for the time spent after acquiring the semaphore, so one can infer the wait time. Seems unnecessary at this point, though.
VladLazar
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Jul 8, 2024
part of #7418 # Motivation (reproducing #7418) When we do an `InMemoryLayer::write_to_disk`, there is a tremendous amount of random read I/O, as deltas from the ephemeral file (written in LSN order) are written out to the delta layer in key order. In benchmarks (#7409) we can see that this delta layer writing phase is substantially more expensive than the initial ingest of data, and that within the delta layer write a significant amount of the CPU time is spent traversing the page cache. # High-Level Changes Add a new mode for L0 flush that works as follows: * Read the full ephemeral file into memory -- layers are much smaller than total memory, so this is afforable * Do all the random reads directly from this in memory buffer instead of using blob IO/page cache/disk reads. * Add a semaphore to limit how many timelines may concurrently do this (limit peak memory). * Make the semaphore configurable via PS config. # Implementation Details The new `BlobReaderRef::Slice` is a temporary hack until we can ditch `blob_io` for `InMemoryLayer` => Plan for this is laid out in #8183 # Correctness The correctness of this change is quite obvious to me: we do what we did before (`blob_io`) but read from memory instead of going to disk. The highest bug potential is in doing owned-buffers IO. I refactored the API a bit in preliminary PR #8186 to make it less error-prone, but still, careful review is requested. # Performance I manually measured single-client ingest performance from `pgbench -i ...`. Full report: https://neondatabase.notion.site/2024-06-28-benchmarking-l0-flush-performance-e98cff3807f94cb38f2054d8c818fe84?pvs=4 tl;dr: * no speed improvements during ingest, but * significantly lower pressure on PS PageCache (eviction rate drops to 1/3) * (that's why I'm working on this) * noticable but modestly lower CPU time This is good enough for merging this PR because the changes require opt-in. We'll do more testing in staging & pre-prod. # Stability / Monitoring **memory consumption**: there's no _hard_ limit on max `InMemoryLayer` size (aka "checkpoint distance") , hence there's no hard limit on the memory allocation we do for flushing. In practice, we a) [log a warning](https://github.com/neondatabase/neon/blob/23827c6b0d400cbb9a972d4d05d49834816c40d1/pageserver/src/tenant/timeline.rs#L5741-L5743) when we flush oversized layers, so we'd know which tenant is to blame and b) if we were to put a hard limit in place, we would have to decide what to do if there is an InMemoryLayer that exceeds the limit. It seems like a better option to guarantee a max size for frozen layer, dependent on `checkpoint_distance`. Then limit concurrency based on that. **metrics**: we do have the [flush_time_histo](https://github.com/neondatabase/neon/blob/23827c6b0d400cbb9a972d4d05d49834816c40d1/pageserver/src/tenant/timeline.rs#L3725-L3726), but that includes the wait time for the semaphore. We could add a separate metric for the time spent after acquiring the semaphore, so one can infer the wait time. Seems unnecessary at this point, though.
VladLazar
pushed a commit
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Jul 8, 2024
part of #7418 # Motivation (reproducing #7418) When we do an `InMemoryLayer::write_to_disk`, there is a tremendous amount of random read I/O, as deltas from the ephemeral file (written in LSN order) are written out to the delta layer in key order. In benchmarks (#7409) we can see that this delta layer writing phase is substantially more expensive than the initial ingest of data, and that within the delta layer write a significant amount of the CPU time is spent traversing the page cache. # High-Level Changes Add a new mode for L0 flush that works as follows: * Read the full ephemeral file into memory -- layers are much smaller than total memory, so this is afforable * Do all the random reads directly from this in memory buffer instead of using blob IO/page cache/disk reads. * Add a semaphore to limit how many timelines may concurrently do this (limit peak memory). * Make the semaphore configurable via PS config. # Implementation Details The new `BlobReaderRef::Slice` is a temporary hack until we can ditch `blob_io` for `InMemoryLayer` => Plan for this is laid out in #8183 # Correctness The correctness of this change is quite obvious to me: we do what we did before (`blob_io`) but read from memory instead of going to disk. The highest bug potential is in doing owned-buffers IO. I refactored the API a bit in preliminary PR #8186 to make it less error-prone, but still, careful review is requested. # Performance I manually measured single-client ingest performance from `pgbench -i ...`. Full report: https://neondatabase.notion.site/2024-06-28-benchmarking-l0-flush-performance-e98cff3807f94cb38f2054d8c818fe84?pvs=4 tl;dr: * no speed improvements during ingest, but * significantly lower pressure on PS PageCache (eviction rate drops to 1/3) * (that's why I'm working on this) * noticable but modestly lower CPU time This is good enough for merging this PR because the changes require opt-in. We'll do more testing in staging & pre-prod. # Stability / Monitoring **memory consumption**: there's no _hard_ limit on max `InMemoryLayer` size (aka "checkpoint distance") , hence there's no hard limit on the memory allocation we do for flushing. In practice, we a) [log a warning](https://github.com/neondatabase/neon/blob/23827c6b0d400cbb9a972d4d05d49834816c40d1/pageserver/src/tenant/timeline.rs#L5741-L5743) when we flush oversized layers, so we'd know which tenant is to blame and b) if we were to put a hard limit in place, we would have to decide what to do if there is an InMemoryLayer that exceeds the limit. It seems like a better option to guarantee a max size for frozen layer, dependent on `checkpoint_distance`. Then limit concurrency based on that. **metrics**: we do have the [flush_time_histo](https://github.com/neondatabase/neon/blob/23827c6b0d400cbb9a972d4d05d49834816c40d1/pageserver/src/tenant/timeline.rs#L3725-L3726), but that includes the wait time for the semaphore. We could add a separate metric for the time spent after acquiring the semaphore, so one can infer the wait time. Seems unnecessary at this point, though.
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Currently, when we do an
InMemoryLayer::write_to_disk, there is a tremendous amount of random read I/O, as deltas from the ephemeral file (written in LSN order) are written out to the delta layer in key order.In benchmarks (#7409) we can see that this delta layer writing phase is substantially more expensive than the initial ingest of data, and that within the delta layer write a significant amount of the CPU time is spent traversing the page cache.
It's really slow: like tens of megabytes per second on a fast desktop CPU.
Since this is a background task whose concurrency we can limit, we can simplify and accelerate this by doing the whole thing in memory:
Impl
Slicein the read APIs, eliminateread_exact_at_n, fix UB for enginestd-fs#8186The text was updated successfully, but these errors were encountered: