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zyonU$G2+|1^^l>?RUOp7!218G8U)5t1Lw^o3qT8r{RYfjTp@sc`(Lu^zj>V$Imf6^ z%m@G5h(~=fCL(~Yk;}Lt8Cu%@|L#Y|XAxYx75vjAGygWp^%sOfdCYEsLNPG#dtN{i MqOu}oA9Z~H7g4_53jhEB literal 0 HcmV?d00001 diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md new file mode 100644 index 000000000..7f6da4c6a --- /dev/null +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -0,0 +1,161 @@ +--- +title: 'Speeding up V8 heap snapshots' +description: 'This post about V8 heap snapshots presents some performance problems found by Bloomberg engineers, and how we fixed them to make JavaScript memory analysis faster than ever.' +author: 'Jose Dapena Paz' +date: 2023-06-23 +tags: + - memory + - tools +--- +*This blog post has been authored by José Dapena Paz (Igalia), with contributions from Jason Williams (Bloomberg), Ashley Claymore (Bloomberg), and Rob Palmer (Bloomberg).* + +In this post about V8 heap snapshots, I will talk about some performance problems found by Bloomberg engineers, and how we fixed them to make JavaScript memory analysis faster than ever. + +## The Problem + +Ashley Claymore was working on diagnosing a memory leak in a JavaScript application. It was failing with *Out-Of-Memory* errors. Despite the process having access to plenty of system memory, V8 places a hard limit on the amount of memory dedicated to the garbage-collected heap from which all JavaScript objects are allocated. This V8 heap limit (~1400MB) was being hit. + +The standard way to debug a routine memory leak scenario like this is to capture a heap snapshot and then inspect the various summaries and object attributes using DevTools "Memory" tab to find out what is consuming the most memory. In DevTools, you click the round button marked "Take heap snapshot" to perform the capture. For Node.js applications, you can [trigger the snapshot](https://nodejs.org/en/docs/guides/diagnostics/memory/using-heap-snapshot) programatically using this API: + +```javascript +require('v8').writeHeapSnapshot(); +``` + +The desire was to capture several snapshots at different points in the application's life, so that DevTools Memory viewer could be used to show the difference between each. The problem was that capturing a single full-size (500MB) snapshot was taking **over 30 minutes** alone! + +It was this slowness in the memory analysis workflow that we needed to solve. + +## Narrowing down the Problem + +Jason Williams started investigating the issue using some V8 parameters. As described in the previous post, V8 has some nice command line parameters that can help with that. These options were used to curate the heap snapshots , simplify the reproduction, and improve observability: + +`--max-old-space-size=100` +: This limits the heap to 100 megabytes and helps to reproduce the issue much faster. + +`--heapsnapshot-near-heap-limit=10` +: This tells V8 to produce a maximum of 10 snapshots, each time it comes close to running out of memory. This prevents thrashing where the memory-starved program spends a long time producing more snapshots than needed. + +`--enable-etw-stack-walking` +: This allows tools such as ETW, WPA & xperf to see the JS stack which has been called in V8. (Node v20+) + +`--interpreted-frames-native-stack` +: This flag is used in combination with tools like ETW, WPA & xperf to see the native stack when profiling. (Node v20+). + +After each snapshot, V8 tries to force garbage collection to reduce memory usage and avoid hitting the limit. In the test case, the memory usage increases, but, after several iterations, garbage collection ultimately could not free up enough space and so the application terminated with an *Out-Of-Memory* error. + +Jason took recordings using Windows Performance Analyzer (see below) in order to narrow down the issue. This revealed that most CPU time was being spent within the V8 Heap Explorer. Specifically, around 30 minutes would elapse just walking through the heap to visit each node and collect the name. This didn’t seem to make much sense - why would recording the name of each property take so long? + +This is when I was asked to take a look. + +## Quantifying the Problem + +We knew the snapshots were dramatically increasing execution time, so the first step was adding support in V8 to better understand the time used by heap snapshotting. The capture process itself is split into two phases: generation, then serialization. + +We landed [this patch](https://chromium-review.googlesource.com/c/v8/v8/+/4428810) upstream to introduce a new command line flag `--profile_heap_snapshot` to V8, which enables recording of both the generation and serialization times. + +Using this flag, we learn some interesting things! + +First, we could observe the exact time the CPU was using for each snapshot. In our reduced test case, the first took 5 minutes, the second took 8 minutes, and each subsequent snapshot kept on taking longer and longer. Nearly all of this time was spent in the generation phase. + +We also identified other widely-used JavaScript applications that demonstrated the same slowdown, in particular, running ESLint on TypeScript. The problem was not app-specific. + +Furthermore, we found the problem happened on both Windows and Linux. The problem was also not platform-specific. + +## Windows Performance Analyzer to the rescue + +As the problem was initially reported on a Windows platform, I used [Windows Performance Toolkit](https://learn.microsoft.com/en-us/windows-hardware/test/wpt/), based on [ETW](https://learn.microsoft.com/en-us/windows-hardware/drivers/devtest/event-tracing-for-windows--etw-), for analysis. This is a powerful low-level expert tool to find out exactly what a program is doing on Windows. + +To record the session, I followed these steps: + +1. Open [Windows Performance Recorder](https://learn.microsoft.com/en-us/windows-hardware/test/wpt/windows-performance-recorder) and selected CPU profiling, verbose detail level, general scenario, and file logging mode. + + ![](/_img/speeding-up-v8-heap-snapshots/d62c099f-6218-4991-a298-c735afe3d6ce.png){.no-darkening} + + +2. After that, I started the recording session (pressing the Start button). +3. Then, I executed the failing script with `NODE_OPTIONS="--max-old-space-size=100 --heapsnapshot-near-heap-limit=10 --profile-heap-snapshot`. I had to modify Node.js to accept --profile-heap-snapshot, as its command line parameters filter does not allow passing it in `NODE_OPTIONS` right now. +4. I just let it run a couple of dumps (it would already take over 10 minutes!) and then I stopped the recording. + +## First Optimization: Improved StringsStorage hashing + +When I opened the recording with [Windows Performance Analyzer](https://learn.microsoft.com/en-us/windows-hardware/test/wpt/windows-performance-analyzer), what did I find? This: + +![](/_img/speeding-up-v8-heap-snapshots/345360c1-297e-43d2-901f-ea4fa81ede3c.png){.no-darkening} + + +One third of the samples was spent in `v8::internal::StringsStorage::GetEntry`: + +```javascript +181 base::HashMap::Entry* StringsStorage::GetEntry(const char* str, int len) { +182 uint32_t hash = ComputeStringHash(str, len); +183 return names_.LookupOrInsert(const_cast(str), hash); +184 } +``` + +Where exactly? I added the source line numbers to the break down and found this: + +![](/_img/speeding-up-v8-heap-snapshots/59293b17-6d52-4d9e-8737-5b23d038d50a.png){.no-darkening} + +So, that inlined `ComputeStringHash` call is causing over 30% of the delay! But why? + +Let’s first talk about `StringsStorage`. Its purpose is to store a unique copy of all the strings that will be used in the heap snapshot. For fast access and handling uniqueness, this class uses a flatmap: a hashmap backed by an array, where collisions are handled by storing elements in the next positions of the array. + +I started to suspect the problem could be too many collisions, which could lead to long searches in the array. But I needed to prove it. So I added exhaustive logs to see the generated hash keys and, on insertion, see how far an entry could end after the expected position for its hash key. + +Things were… not right: the offset of many items was over 20 entries. But some cases were really bad, in the order of thousands of positions off from their expected position! + +Part of the problem was caused by the scripts using strings for lots of numbers - and especially a big range of numbers from 1 to several hundreds without gaps. The hash key algorithm had two implementations, one for numbers and another for other strings. While the string hash function was quite classical, the problem came with the numbers implementation that would basically be the value of the number - with a prefix for the number of digits. + +Some examples of problems with this hash function: + +* Once we had strings with a hash key value in lower positions, then the storing of new numbers would offset all the positions. +* Or even worse: if we would introduce a string with a hash key value that would be a low number, and we already found several hundreds or thousands of consecutive numbers, then that value would be moved several hundreds or thousands of positions. + +What did I do to fix it? As the problem comes mostly from numbers represented as strings that would fall in consecutive positions, I modified the hash function so we would rotate the resulting hash value 2 positions to the left. So, for each number, we would introduce 3 free positions. Why 2? Empirical testing across several work-sets showed this number was the best choice to minimize collisions. + +[This hashing fix](https://chromium-review.googlesource.com/c/v8/v8/+/4428811) has landed in V8. + +## Second Optimization: Caching source positions + +After fixing the hashing, we re-profiled and found a further optimization opportunity that would take a significant part of the time. *An old friend*. + +For each allocation in the V8 heap, the heap snapshot tries to record the call stack responsible for the allocation. Therefore, for each stack frame, it needs to know the function name and its source location (filename, line number, column number). It was the fetching of this information that turned out to be super slow! + +What was happening? It was something similar to [what I fixed in the ETW stack walk](https://chromium-review.googlesource.com/c/v8/v8/+/3669698) and that I explained in [this post](https://blogs.igalia.com/dape/2022/12/21/native-call-stack-profiling-3-3-2022-work-in-v8/). + +To compute the source code lines of a function, V8 knows the linear position of the function in the file. But, to find the source line, it needs to traverse the whole file to identify where each newline occurs. This is expensive. + +When requesting line information, V8 already knows how to cache the source line positions per-script. But the heap snapshot implementation was not using a cache! + +The solution? Before generating the heap snapshot, we now iterate over all the scripts in the V8 context to compute and cache the source line positions. + +[This source position caching fix](https://chromium-review.googlesource.com/c/v8/v8/+/4538766) has also landed in V8. + +## Did we make it fast? + +After enabling both fixes, we re-profiled. Both of our fixes only affect snapshot generation time, so, as expected, snapshot serialization times were unaffected. + +When operating on a JS program containing... + +* Unoptimized development JS, generation time is **50% faster** 👍 +* Optimized production JS, generation time is **90% faster** 😮 + +Why such a massive difference between optimized and unoptimized code? It is mostly because of the source code line optimization. In code optimized using bundling and minification, there are fewer JS files and these files tend to be large, which means finding the newlines takes longer. And therefore, these larger files benefit most from the source position caching that only performs this work once. + +The optimizations were validated on both Windows and Linux target environments. + +For the particularly challenging problem originally faced by the Bloomberg engineers, the total end-to-end time to capture a 100MB snapshot was reduced from a painful 10 minutes down to a very pleasant 6 seconds. That is **a 100x win!** 🔥 + +The optimizations are generic wins that we expect to be widely applicable to anyone performing memory debugging on V8, Node, and Chromium. These wins shipped in V8 11.5.130, which means they are found in Chromium 115.0.5576.0. We look forward to Node gaining these optimizations once it adopts the newer V8 release. + +## What’s next? + +First, it would be useful for Node.js to accept the new `--profile-heap-snapshot` flag. Today, it filters V8 command line options, and only allows a known subset. + +Information accuracy in snapshots can be improved further. Today, each script source code line information is stored in a representation in the V8 heap itself. And that’s a problem because we want to measure the heap precisely without the observability infrastructure affecting the subject we are observing. Ideally, we would store the cache of line information outside the V8 heap in order to make heap snapshot information more accurate. + +Finally, now that we improved the generation phase, the biggest cost is now the serialization phase. Further analysis may reveal new optimization opportunities in serialization. + +## The end + +This work was possible thanks to the sponsorship of [Igalia](https://www.igalia.com) and [Bloomberg](https://techatbloomberg.com). From 485d04f764add8361c0a2d7c90a51675c28351fb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Dapena=20Paz?= Date: Wed, 21 Jun 2023 11:47:44 +0200 Subject: [PATCH 02/27] Apply suggestions from Rob Palmer Co-authored-by: Rob Palmer --- src/blog/speeding-up-v8-heap-snapshots.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index 7f6da4c6a..e4f6ad06c 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -15,7 +15,7 @@ In this post about V8 heap snapshots, I will talk about some performance problem Ashley Claymore was working on diagnosing a memory leak in a JavaScript application. It was failing with *Out-Of-Memory* errors. Despite the process having access to plenty of system memory, V8 places a hard limit on the amount of memory dedicated to the garbage-collected heap from which all JavaScript objects are allocated. This V8 heap limit (~1400MB) was being hit. -The standard way to debug a routine memory leak scenario like this is to capture a heap snapshot and then inspect the various summaries and object attributes using DevTools "Memory" tab to find out what is consuming the most memory. In DevTools, you click the round button marked "Take heap snapshot" to perform the capture. For Node.js applications, you can [trigger the snapshot](https://nodejs.org/en/docs/guides/diagnostics/memory/using-heap-snapshot) programatically using this API: +The standard way to debug a routine memory leak scenario like this is to capture a heap snapshot and then inspect the various summaries and object attributes using DevTools "Memory" tab to find out what is consuming the most memory. In DevTools, you click the round button marked _"Take heap snapshot"_ to perform the capture. For Node.js applications, you can [trigger the snapshot](https://nodejs.org/en/docs/guides/diagnostics/memory/using-heap-snapshot) programmatically using this API: ```javascript require('v8').writeHeapSnapshot(); @@ -53,7 +53,7 @@ We knew the snapshots were dramatically increasing execution time, so the first We landed [this patch](https://chromium-review.googlesource.com/c/v8/v8/+/4428810) upstream to introduce a new command line flag `--profile_heap_snapshot` to V8, which enables recording of both the generation and serialization times. -Using this flag, we learn some interesting things! +Using this flag, we learned some interesting things! First, we could observe the exact time the CPU was using for each snapshot. In our reduced test case, the first took 5 minutes, the second took 8 minutes, and each subsequent snapshot kept on taking longer and longer. Nearly all of this time was spent in the generation phase. @@ -73,7 +73,7 @@ To record the session, I followed these steps: 2. After that, I started the recording session (pressing the Start button). -3. Then, I executed the failing script with `NODE_OPTIONS="--max-old-space-size=100 --heapsnapshot-near-heap-limit=10 --profile-heap-snapshot`. I had to modify Node.js to accept --profile-heap-snapshot, as its command line parameters filter does not allow passing it in `NODE_OPTIONS` right now. +3. Then, I executed the failing script with `NODE_OPTIONS="--max-old-space-size=100 --heapsnapshot-near-heap-limit=10 --profile-heap-snapshot`. I had to modify Node.js to accept `--profile-heap-snapshot`, as its command line parameters filter has not yet been updated to accept the newer V8 flags. 4. I just let it run a couple of dumps (it would already take over 10 minutes!) and then I stopped the recording. ## First Optimization: Improved StringsStorage hashing @@ -98,7 +98,7 @@ Where exactly? I added the source line numbers to the break down and found this: So, that inlined `ComputeStringHash` call is causing over 30% of the delay! But why? -Let’s first talk about `StringsStorage`. Its purpose is to store a unique copy of all the strings that will be used in the heap snapshot. For fast access and handling uniqueness, this class uses a flatmap: a hashmap backed by an array, where collisions are handled by storing elements in the next positions of the array. +Let’s first talk about `StringsStorage`. Its purpose is to store a unique copy of all the strings that will be used in the heap snapshot. For fast access and handling uniqueness, this class uses a flatmap: a hashmap backed by an array, where collisions are handled by storing elements in the next position of the array. I started to suspect the problem could be too many collisions, which could lead to long searches in the array. But I needed to prove it. So I added exhaustive logs to see the generated hash keys and, on insertion, see how far an entry could end after the expected position for its hash key. @@ -108,7 +108,7 @@ Part of the problem was caused by the scripts using strings for lots of numbers Some examples of problems with this hash function: -* Once we had strings with a hash key value in lower positions, then the storing of new numbers would offset all the positions. +* Once we had strings with a hash key value in lower positions, then the storing of new numbers would offset all the positions. * Or even worse: if we would introduce a string with a hash key value that would be a low number, and we already found several hundreds or thousands of consecutive numbers, then that value would be moved several hundreds or thousands of positions. What did I do to fix it? As the problem comes mostly from numbers represented as strings that would fall in consecutive positions, I modified the hash function so we would rotate the resulting hash value 2 positions to the left. So, for each number, we would introduce 3 free positions. Why 2? Empirical testing across several work-sets showed this number was the best choice to minimize collisions. From 3dbf61b78da8b1ab80e05e12f7a63456899262e5 Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Thu, 22 Jun 2023 17:52:17 +0200 Subject: [PATCH 03/27] Fix lint issues --- src/blog/speeding-up-v8-heap-snapshots.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index e4f6ad06c..1ee420532 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -108,8 +108,8 @@ Part of the problem was caused by the scripts using strings for lots of numbers Some examples of problems with this hash function: -* Once we had strings with a hash key value in lower positions, then the storing of new numbers would offset all the positions. -* Or even worse: if we would introduce a string with a hash key value that would be a low number, and we already found several hundreds or thousands of consecutive numbers, then that value would be moved several hundreds or thousands of positions. +- Once we had strings with a hash key value in lower positions, then the storing of new numbers would offset all the positions. +- Or even worse: if we would introduce a string with a hash key value that would be a low number, and we already found several hundreds or thousands of consecutive numbers, then that value would be moved several hundreds or thousands of positions. What did I do to fix it? As the problem comes mostly from numbers represented as strings that would fall in consecutive positions, I modified the hash function so we would rotate the resulting hash value 2 positions to the left. So, for each number, we would introduce 3 free positions. Why 2? Empirical testing across several work-sets showed this number was the best choice to minimize collisions. @@ -137,8 +137,8 @@ After enabling both fixes, we re-profiled. Both of our fixes only affect snapsho When operating on a JS program containing... -* Unoptimized development JS, generation time is **50% faster** 👍 -* Optimized production JS, generation time is **90% faster** 😮 +- Unoptimized development JS, generation time is **50% faster** 👍 +- Optimized production JS, generation time is **90% faster** 😮 Why such a massive difference between optimized and unoptimized code? It is mostly because of the source code line optimization. In code optimized using bundling and minification, there are fewer JS files and these files tend to be large, which means finding the newlines takes longer. And therefore, these larger files benefit most from the source position caching that only performs this work once. From b6860f4f2802bfc886a201fc92ffb5d106c4fd25 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Dapena=20Paz?= Date: Tue, 27 Jun 2023 10:50:11 +0200 Subject: [PATCH 04/27] Apply suggestions from Joyee Cheung Several fixes in accuracy, specially related to NODE_OPTIONS processing, coming from the code review. Also some writing improvements. Co-authored-by: Joyee Cheung --- src/blog/speeding-up-v8-heap-snapshots.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index 1ee420532..76a1a9eef 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -43,7 +43,7 @@ Jason Williams started investigating the issue using some V8 parameters. As desc After each snapshot, V8 tries to force garbage collection to reduce memory usage and avoid hitting the limit. In the test case, the memory usage increases, but, after several iterations, garbage collection ultimately could not free up enough space and so the application terminated with an *Out-Of-Memory* error. -Jason took recordings using Windows Performance Analyzer (see below) in order to narrow down the issue. This revealed that most CPU time was being spent within the V8 Heap Explorer. Specifically, around 30 minutes would elapse just walking through the heap to visit each node and collect the name. This didn’t seem to make much sense - why would recording the name of each property take so long? +Jason took recordings using Windows Performance Analyzer (see below) in order to narrow down the issue. This revealed that most CPU time was being spent within the V8 Heap Explorer. Specifically, it took around 30 minutes just to walk through the heap to visit each node and collect the name. This didn’t seem to make much sense - why would recording the name of each property take so long? This is when I was asked to take a look. @@ -73,7 +73,7 @@ To record the session, I followed these steps: 2. After that, I started the recording session (pressing the Start button). -3. Then, I executed the failing script with `NODE_OPTIONS="--max-old-space-size=100 --heapsnapshot-near-heap-limit=10 --profile-heap-snapshot`. I had to modify Node.js to accept `--profile-heap-snapshot`, as its command line parameters filter has not yet been updated to accept the newer V8 flags. +3. Then, I executed the failing script with `NODE_OPTIONS="--max-old-space-size=100 --heapsnapshot-near-heap-limit=10 --profile-heap-snapshot`. I had to modify Node.js to accept `--profile-heap-snapshot` in `NODE_OPTIONS`, as it uses an allowlist to filter V8 flags that can be configured through the environment variable. 4. I just let it run a couple of dumps (it would already take over 10 minutes!) and then I stopped the recording. ## First Optimization: Improved StringsStorage hashing @@ -123,7 +123,7 @@ For each allocation in the V8 heap, the heap snapshot tries to record the call s What was happening? It was something similar to [what I fixed in the ETW stack walk](https://chromium-review.googlesource.com/c/v8/v8/+/3669698) and that I explained in [this post](https://blogs.igalia.com/dape/2022/12/21/native-call-stack-profiling-3-3-2022-work-in-v8/). -To compute the source code lines of a function, V8 knows the linear position of the function in the file. But, to find the source line, it needs to traverse the whole file to identify where each newline occurs. This is expensive. +To compute the source code lines of a function, V8 knows the linear position of the function in the script. But, to find the source line, it needs to traverse the whole script to identify where each newline occurs. This is expensive. When requesting line information, V8 already knows how to cache the source line positions per-script. But the heap snapshot implementation was not using a cache! @@ -150,7 +150,7 @@ The optimizations are generic wins that we expect to be widely applicable to any ## What’s next? -First, it would be useful for Node.js to accept the new `--profile-heap-snapshot` flag. Today, it filters V8 command line options, and only allows a known subset. +First, it would be useful for Node.js to accept the new `--profile-heap-snapshot` flag in `NODE_OPTIONS`. In some use cases, users cannot control the the command line options passed to Node.js directly and have to configure them through the environment variable `NODE_OPTIONS`. Today, Node.js filters V8 command line options set in the environment variable, and only allows a known subset, which could make it harder to test new V8 flags in Node.js, as what happened in our case. Information accuracy in snapshots can be improved further. Today, each script source code line information is stored in a representation in the V8 heap itself. And that’s a problem because we want to measure the heap precisely without the observability infrastructure affecting the subject we are observing. Ideally, we would store the cache of line information outside the V8 heap in order to make heap snapshot information more accurate. From 77c2744ae3e842e84634fb49ceac3aabe59d1408 Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Tue, 27 Jun 2023 11:03:56 +0200 Subject: [PATCH 05/27] More clarifications of Node.js specific parts When we tried to make the post less Node.js specific, we want a bit too far, as the parts related to --heapsnapshot-near-heap-limit are indeed Node.js specific. Provided more clarity on that. --- src/blog/speeding-up-v8-heap-snapshots.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index 76a1a9eef..38744cac4 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -21,7 +21,7 @@ The standard way to debug a routine memory leak scenario like this is to capture require('v8').writeHeapSnapshot(); ``` -The desire was to capture several snapshots at different points in the application's life, so that DevTools Memory viewer could be used to show the difference between each. The problem was that capturing a single full-size (500MB) snapshot was taking **over 30 minutes** alone! +The desire was to capture several snapshots at different points in the application's life, so that DevTools Memory viewer could be used to show the difference between the heaps at different times. The problem was that capturing a single full-size (500MB) snapshot was taking **over 30 minutes** alone! It was this slowness in the memory analysis workflow that we needed to solve. @@ -33,7 +33,7 @@ Jason Williams started investigating the issue using some V8 parameters. As desc : This limits the heap to 100 megabytes and helps to reproduce the issue much faster. `--heapsnapshot-near-heap-limit=10` -: This tells V8 to produce a maximum of 10 snapshots, each time it comes close to running out of memory. This prevents thrashing where the memory-starved program spends a long time producing more snapshots than needed. +: This is a Node.js specific command line parameter that tells V8 to produce a maximum of 10 snapshots, each time it comes close to running out of memory. This prevents thrashing where the memory-starved program spends a long time producing more snapshots than needed. `--enable-etw-stack-walking` : This allows tools such as ETW, WPA & xperf to see the JS stack which has been called in V8. (Node v20+) @@ -41,7 +41,7 @@ Jason Williams started investigating the issue using some V8 parameters. As desc `--interpreted-frames-native-stack` : This flag is used in combination with tools like ETW, WPA & xperf to see the native stack when profiling. (Node v20+). -After each snapshot, V8 tries to force garbage collection to reduce memory usage and avoid hitting the limit. In the test case, the memory usage increases, but, after several iterations, garbage collection ultimately could not free up enough space and so the application terminated with an *Out-Of-Memory* error. +When V8 is close to the heap limit, it forces a garbage collection to reduce the memory usage and avoid hitting the limit. With the provided Node.js `--heapsnapshot-near-heap-limit` flag it will also dump a new heap snapshot. In the test case, the memory usage increases, but, after several iterations, garbage collection ultimately can not free up enough space and so the application is terminated with an *Out-Of-Memory* error. Jason took recordings using Windows Performance Analyzer (see below) in order to narrow down the issue. This revealed that most CPU time was being spent within the V8 Heap Explorer. Specifically, it took around 30 minutes just to walk through the heap to visit each node and collect the name. This didn’t seem to make much sense - why would recording the name of each property take so long? From 9eac65c914db6bda6589df86a8db3b4947035b6a Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Tue, 27 Jun 2023 11:12:33 +0200 Subject: [PATCH 06/27] Added Joyee Cheung to the initial paragraph list of contributors --- src/blog/speeding-up-v8-heap-snapshots.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index 38744cac4..cc66f5aee 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -7,7 +7,7 @@ tags: - memory - tools --- -*This blog post has been authored by José Dapena Paz (Igalia), with contributions from Jason Williams (Bloomberg), Ashley Claymore (Bloomberg), and Rob Palmer (Bloomberg).* +*This blog post has been authored by José Dapena Paz (Igalia), with contributions from Jason Williams (Bloomberg), Ashley Claymore (Bloomberg), Rob Palmer (Bloomberg) and Joyee Cheung(Igalia).* In this post about V8 heap snapshots, I will talk about some performance problems found by Bloomberg engineers, and how we fixed them to make JavaScript memory analysis faster than ever. From 53d996a6f22ada6d0617cdb03ed9ee5bbad155cb Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Wed, 28 Jun 2023 12:30:17 +0200 Subject: [PATCH 07/27] More accurate description of --heapsnapshot-near-heap-limit implementation Co-authored-by: Joyee Cheung --- src/blog/speeding-up-v8-heap-snapshots.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index cc66f5aee..c94c90a24 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -41,7 +41,7 @@ Jason Williams started investigating the issue using some V8 parameters. As desc `--interpreted-frames-native-stack` : This flag is used in combination with tools like ETW, WPA & xperf to see the native stack when profiling. (Node v20+). -When V8 is close to the heap limit, it forces a garbage collection to reduce the memory usage and avoid hitting the limit. With the provided Node.js `--heapsnapshot-near-heap-limit` flag it will also dump a new heap snapshot. In the test case, the memory usage increases, but, after several iterations, garbage collection ultimately can not free up enough space and so the application is terminated with an *Out-Of-Memory* error. +When V8 is close to the heap limit, it forces a garbage collection to reduce the memory usage and avoid hitting the limit. It also notifies the embedder that the heap is about to reach the memory limit. The `--heapsnapshot-near-heap-limit` flag in Node.js dumps a new heap snapshot upon notification. In the test case, the memory usage decreases, but, after several iterations, garbage collection ultimately can not free up enough space and so the application is terminated with an *Out-Of-Memory* error. Jason took recordings using Windows Performance Analyzer (see below) in order to narrow down the issue. This revealed that most CPU time was being spent within the V8 Heap Explorer. Specifically, it took around 30 minutes just to walk through the heap to visit each node and collect the name. This didn’t seem to make much sense - why would recording the name of each property take so long? From a0518ffcce5567c9556f2a58c42a4bac3fe7cd57 Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Wed, 28 Jun 2023 12:58:58 +0200 Subject: [PATCH 08/27] More details of source position caching fix The level of details in teh source position caching fix explanation was quite poor, not even explaining why the source line position was not cached. Expand it after proposal from Joyee Cheung. --- src/blog/speeding-up-v8-heap-snapshots.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index c94c90a24..90f8c318c 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -125,9 +125,9 @@ What was happening? It was something similar to [what I fixed in the ETW stack w To compute the source code lines of a function, V8 knows the linear position of the function in the script. But, to find the source line, it needs to traverse the whole script to identify where each newline occurs. This is expensive. -When requesting line information, V8 already knows how to cache the source line positions per-script. But the heap snapshot implementation was not using a cache! +When requesting line information, V8 already implements caching the source line positions per-script in method `Script::InitLineEnds()`. That information is stored in each script using a new heap object. Unfortunately, that was a problem that prevented the heap snapshot from using the cache. This is because, when the snapshot implementation traverses the heap, it cannot modify it, so the newly calculated line information cannot be cached. -The solution? Before generating the heap snapshot, we now iterate over all the scripts in the V8 context to compute and cache the source line positions. +The solution? Before generating the heap snapshot, we now iterate over all the scripts in the V8 context to compute and cache the new source line positions information. As this is done out of the heap traversal done while capturing the snapshot, it is still possible to modify the heap to store the cached source code lines information. [This source position caching fix](https://chromium-review.googlesource.com/c/v8/v8/+/4538766) has also landed in V8. From f02741a7a85cfa8b0a931087555c796b276c3826 Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Wed, 28 Jun 2023 13:04:51 +0200 Subject: [PATCH 09/27] Reduce ambiguity of development and production JS code reference In previous version, we talked about "unoptimized development JS" and "optimized production JS", that could be misleading, as Joyee Cheung pointed out, because it could induce reader to think about JS runtime optimizations. So this change removes reference there to optimized/unoptimized JS to later detail how production source code is optimized using bundling and minification. --- src/blog/speeding-up-v8-heap-snapshots.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index 90f8c318c..ad5177fea 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -137,10 +137,10 @@ After enabling both fixes, we re-profiled. Both of our fixes only affect snapsho When operating on a JS program containing... -- Unoptimized development JS, generation time is **50% faster** 👍 -- Optimized production JS, generation time is **90% faster** 😮 +- Development JS, generation time is **50% faster** 👍 +- Production JS, generation time is **90% faster** 😮 -Why such a massive difference between optimized and unoptimized code? It is mostly because of the source code line optimization. In code optimized using bundling and minification, there are fewer JS files and these files tend to be large, which means finding the newlines takes longer. And therefore, these larger files benefit most from the source position caching that only performs this work once. +Why such a massive difference between production and development code? It is mostly because of the production source code optimizations. In production, code is optimized using bundling and minification, so there are fewer JS files and these files tend to be large, which means finding the newlines takes longer. And therefore, these larger files benefit most from the source position caching that only performs this work once. The optimizations were validated on both Windows and Linux target environments. From bc0bcee1df6bcb8789f014d6a2e8abf4314f13cd Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Wed, 28 Jun 2023 13:15:03 +0200 Subject: [PATCH 10/27] Rewrite description of V8 heap limit configuration. In the original description, we wrongly state that the heap limit is set by V8 to be 1400MB. Instead of this, it was an application specific limit. Also clarify that, in the test case, hitting Out-Of-Memory would hint there was a leak. Co-authored-by: Joyee Cheung --- src/blog/speeding-up-v8-heap-snapshots.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index ad5177fea..efe369ac9 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -13,7 +13,7 @@ In this post about V8 heap snapshots, I will talk about some performance problem ## The Problem -Ashley Claymore was working on diagnosing a memory leak in a JavaScript application. It was failing with *Out-Of-Memory* errors. Despite the process having access to plenty of system memory, V8 places a hard limit on the amount of memory dedicated to the garbage-collected heap from which all JavaScript objects are allocated. This V8 heap limit (~1400MB) was being hit. +Ashley Claymore was working on diagnosing a memory leak in a JavaScript application. It was failing with *Out-Of-Memory* errors. Despite the process having access to plenty of system memory, V8 places a hard limit on the amount of memory dedicated to the garbage-collected heap from which all JavaScript objects are allocated. For the tested application, this limit was configured to be around 1400MB. Normally V8's garbage collector should be able to keep the heap usage under that limit, so the failures indicated that there was a leak. The standard way to debug a routine memory leak scenario like this is to capture a heap snapshot and then inspect the various summaries and object attributes using DevTools "Memory" tab to find out what is consuming the most memory. In DevTools, you click the round button marked _"Take heap snapshot"_ to perform the capture. For Node.js applications, you can [trigger the snapshot](https://nodejs.org/en/docs/guides/diagnostics/memory/using-heap-snapshot) programmatically using this API: From 91525cd4b535e05710d61fa1090403ce1f294e78 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Dapena=20Paz?= Date: Tue, 11 Jul 2023 20:06:56 +0200 Subject: [PATCH 11/27] Apply suggestions from Joyee code review Many insightful improvements to the writing of heap snapshot optimizations blog post, that can be landed altogether. Co-authored-by: Joyee Cheung --- src/blog/speeding-up-v8-heap-snapshots.md | 50 +++++++++++------------ 1 file changed, 24 insertions(+), 26 deletions(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index efe369ac9..10bfeb6ff 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -13,33 +13,33 @@ In this post about V8 heap snapshots, I will talk about some performance problem ## The Problem -Ashley Claymore was working on diagnosing a memory leak in a JavaScript application. It was failing with *Out-Of-Memory* errors. Despite the process having access to plenty of system memory, V8 places a hard limit on the amount of memory dedicated to the garbage-collected heap from which all JavaScript objects are allocated. For the tested application, this limit was configured to be around 1400MB. Normally V8's garbage collector should be able to keep the heap usage under that limit, so the failures indicated that there was a leak. +Ashley Claymore was working on diagnosing a memory leak in a JavaScript application. It was failing with *Out-Of-Memory* errors. For the tested application, the V8 heap limit was configured to be around 1400MB. Normally V8's garbage collector should be able to keep the heap usage under that limit, so the failures indicated that there was a leak. -The standard way to debug a routine memory leak scenario like this is to capture a heap snapshot and then inspect the various summaries and object attributes using DevTools "Memory" tab to find out what is consuming the most memory. In DevTools, you click the round button marked _"Take heap snapshot"_ to perform the capture. For Node.js applications, you can [trigger the snapshot](https://nodejs.org/en/docs/guides/diagnostics/memory/using-heap-snapshot) programmatically using this API: +A common technique to debug a routine memory leak scenario like this is to capture a heap snapshot first, then load it in the DevTools "Memory" tab and find out what is consuming the most memory by inspecting the various summaries and object attributes. In the DevTools UI, the heap snapshot can be taken in the "Memory" tab. For Node.js applications, the heap snapshot [can be triggered programmatically](https://nodejs.org/en/docs/guides/diagnostics/memory/using-heap-snapshot) using this API: ```javascript require('v8').writeHeapSnapshot(); ``` -The desire was to capture several snapshots at different points in the application's life, so that DevTools Memory viewer could be used to show the difference between the heaps at different times. The problem was that capturing a single full-size (500MB) snapshot was taking **over 30 minutes** alone! +We wanted to capture several snapshots at different points in the application's life, so that DevTools Memory viewer could be used to show the difference between the heaps at different times. The problem was that capturing a single full-size (500MB) snapshot was taking **over 30 minutes** alone! It was this slowness in the memory analysis workflow that we needed to solve. ## Narrowing down the Problem -Jason Williams started investigating the issue using some V8 parameters. As described in the previous post, V8 has some nice command line parameters that can help with that. These options were used to curate the heap snapshots , simplify the reproduction, and improve observability: +Jason Williams started investigating the issue using some V8 parameters. As described in the previous post, V8 has some nice command line parameters that can help with that. These options were used to create the heap snapshots, simplify the reproduction, and improve observability: `--max-old-space-size=100` : This limits the heap to 100 megabytes and helps to reproduce the issue much faster. `--heapsnapshot-near-heap-limit=10` -: This is a Node.js specific command line parameter that tells V8 to produce a maximum of 10 snapshots, each time it comes close to running out of memory. This prevents thrashing where the memory-starved program spends a long time producing more snapshots than needed. +: This is a Node.js specific command line parameter that tells Node.js to generate a snapshot each time it comes close to running out of memory. It is configured to generate up to 10 snapshots in total. This prevents thrashing where the memory-starved program spends a long time producing more snapshots than needed. `--enable-etw-stack-walking` -: This allows tools such as ETW, WPA & xperf to see the JS stack which has been called in V8. (Node v20+) +: This allows tools such as ETW, WPA & xperf to see the JS stack which has been called in V8. (available in Node.js v20+) `--interpreted-frames-native-stack` -: This flag is used in combination with tools like ETW, WPA & xperf to see the native stack when profiling. (Node v20+). +: This flag is used in combination with tools like ETW, WPA & xperf to see the native stack when profiling. (available in Node.js v20+). When V8 is close to the heap limit, it forces a garbage collection to reduce the memory usage and avoid hitting the limit. It also notifies the embedder that the heap is about to reach the memory limit. The `--heapsnapshot-near-heap-limit` flag in Node.js dumps a new heap snapshot upon notification. In the test case, the memory usage decreases, but, after several iterations, garbage collection ultimately can not free up enough space and so the application is terminated with an *Out-Of-Memory* error. @@ -49,9 +49,7 @@ This is when I was asked to take a look. ## Quantifying the Problem -We knew the snapshots were dramatically increasing execution time, so the first step was adding support in V8 to better understand the time used by heap snapshotting. The capture process itself is split into two phases: generation, then serialization. - -We landed [this patch](https://chromium-review.googlesource.com/c/v8/v8/+/4428810) upstream to introduce a new command line flag `--profile_heap_snapshot` to V8, which enables recording of both the generation and serialization times. +The first step was adding support in V8 to better understand where time is spent during the capturing of heap snapshots. The capture process itself is split into two phases: generation, then serialization. We landed [this patch](https://chromium-review.googlesource.com/c/v8/v8/+/4428810) upstream to introduce a new command line flag `--profile_heap_snapshot` to V8, which enables logging of both the generation and serialization times. Using this flag, we learned some interesting things! @@ -67,18 +65,18 @@ As the problem was initially reported on a Windows platform, I used [Windows Per To record the session, I followed these steps: -1. Open [Windows Performance Recorder](https://learn.microsoft.com/en-us/windows-hardware/test/wpt/windows-performance-recorder) and selected CPU profiling, verbose detail level, general scenario, and file logging mode. +1. Opened [Windows Performance Recorder](https://learn.microsoft.com/en-us/windows-hardware/test/wpt/windows-performance-recorder) and selected CPU profiling, verbose detail level, general scenario, and file logging mode. ![](/_img/speeding-up-v8-heap-snapshots/d62c099f-6218-4991-a298-c735afe3d6ce.png){.no-darkening} 2. After that, I started the recording session (pressing the Start button). 3. Then, I executed the failing script with `NODE_OPTIONS="--max-old-space-size=100 --heapsnapshot-near-heap-limit=10 --profile-heap-snapshot`. I had to modify Node.js to accept `--profile-heap-snapshot` in `NODE_OPTIONS`, as it uses an allowlist to filter V8 flags that can be configured through the environment variable. -4. I just let it run a couple of dumps (it would already take over 10 minutes!) and then I stopped the recording. +4. I just let it run to generate a couple of heap snapshots (it would already take over 10 minutes!) and then I stopped the recording. ## First Optimization: Improved StringsStorage hashing -When I opened the recording with [Windows Performance Analyzer](https://learn.microsoft.com/en-us/windows-hardware/test/wpt/windows-performance-analyzer), what did I find? This: +When I opened the recording with [Windows Performance Analyzer](https://learn.microsoft.com/en-us/windows-hardware/test/wpt/windows-performance-analyzer), this was what I found: ![](/_img/speeding-up-v8-heap-snapshots/345360c1-297e-43d2-901f-ea4fa81ede3c.png){.no-darkening} @@ -92,24 +90,24 @@ One third of the samples was spent in `v8::internal::StringsStorage::GetEntry`: 184 } ``` -Where exactly? I added the source line numbers to the break down and found this: +Because this was run with a release build, the information of the inlined function calls were folded into `StringsStorage::GetEntry()`. To figure out exactly how much time the inlined function calls were taking, I added the "Source Line Number" column to the breakdown and found that most of the time was spent on line 182, which was a call to `ComputeStringHash()`: ![](/_img/speeding-up-v8-heap-snapshots/59293b17-6d52-4d9e-8737-5b23d038d50a.png){.no-darkening} -So, that inlined `ComputeStringHash` call is causing over 30% of the delay! But why? +So over 30% of the snapshot generation time was spent on `ComputeStringHash()`, but why? -Let’s first talk about `StringsStorage`. Its purpose is to store a unique copy of all the strings that will be used in the heap snapshot. For fast access and handling uniqueness, this class uses a flatmap: a hashmap backed by an array, where collisions are handled by storing elements in the next position of the array. +Let’s first talk about `StringsStorage`. Its purpose is to store a unique copy of all the strings that will be used in the heap snapshot. For fast access and avoiding duplicates, this class uses a flatmap: a hashmap backed by an array, where collisions are handled by storing elements in the next position of the array. -I started to suspect the problem could be too many collisions, which could lead to long searches in the array. But I needed to prove it. So I added exhaustive logs to see the generated hash keys and, on insertion, see how far an entry could end after the expected position for its hash key. +I started to suspect that the problem could be caused by collisions, which could lead to long searches in the array. But I needed to prove it. So I added exhaustive logs to see the generated hash keys and, on insertion, see how far it was between the expected position calculated from the hash key and the actual position the entry ended up in due to collisions. -Things were… not right: the offset of many items was over 20 entries. But some cases were really bad, in the order of thousands of positions off from their expected position! +In the logs, things were… not right: the offset of many items was over 20, and in the worst case, in the order of thousands! -Part of the problem was caused by the scripts using strings for lots of numbers - and especially a big range of numbers from 1 to several hundreds without gaps. The hash key algorithm had two implementations, one for numbers and another for other strings. While the string hash function was quite classical, the problem came with the numbers implementation that would basically be the value of the number - with a prefix for the number of digits. +Part of the problem was caused by the scripts using strings for lots of numbers - and especially a big range of numbers from 1 to several hundreds without gaps. The hash key algorithm had two implementations, one for numbers and another for other strings. While the string hash function was quite classical, the implementation for the numbers would basically return the value of the number prefixed by the number of digits, which was problematic. Some examples of problems with this hash function: -- Once we had strings with a hash key value in lower positions, then the storing of new numbers would offset all the positions. -- Or even worse: if we would introduce a string with a hash key value that would be a low number, and we already found several hundreds or thousands of consecutive numbers, then that value would be moved several hundreds or thousands of positions. +- Once we inserted a string whose hash key value was a small number, we would run into collisions when we tried to store another number in that location, and there would be similar collisions if we tried to store subsequent numbers consecutively. +- Or even worse: if there were already a lot of consecutive numbers stored in the map, and we wanted to insert a string whose hash key value was in that range, we had to move the entry along all the occupied locations to find a free location. What did I do to fix it? As the problem comes mostly from numbers represented as strings that would fall in consecutive positions, I modified the hash function so we would rotate the resulting hash value 2 positions to the left. So, for each number, we would introduce 3 free positions. Why 2? Empirical testing across several work-sets showed this number was the best choice to minimize collisions. @@ -125,9 +123,9 @@ What was happening? It was something similar to [what I fixed in the ETW stack w To compute the source code lines of a function, V8 knows the linear position of the function in the script. But, to find the source line, it needs to traverse the whole script to identify where each newline occurs. This is expensive. -When requesting line information, V8 already implements caching the source line positions per-script in method `Script::InitLineEnds()`. That information is stored in each script using a new heap object. Unfortunately, that was a problem that prevented the heap snapshot from using the cache. This is because, when the snapshot implementation traverses the heap, it cannot modify it, so the newly calculated line information cannot be cached. +When requesting line information, V8 already implements caching of the source line positions per-script in method `Script::InitLineEnds()`. That information is stored in each script using a new heap object. Unfortunately, the snapshot implementation cannot modify the heap when traversing it, so the newly calculated line information cannot be cached. -The solution? Before generating the heap snapshot, we now iterate over all the scripts in the V8 context to compute and cache the new source line positions information. As this is done out of the heap traversal done while capturing the snapshot, it is still possible to modify the heap to store the cached source code lines information. +The solution? Before generating the heap snapshot, we now iterate over all the scripts in the V8 context to compute and cache the source line positions. As this is not done when we traverse the heap for heap snapshot generation, it is still possible to modify the heap and store the source line positions as a cache. [This source position caching fix](https://chromium-review.googlesource.com/c/v8/v8/+/4538766) has also landed in V8. @@ -140,19 +138,19 @@ When operating on a JS program containing... - Development JS, generation time is **50% faster** 👍 - Production JS, generation time is **90% faster** 😮 -Why such a massive difference between production and development code? It is mostly because of the production source code optimizations. In production, code is optimized using bundling and minification, so there are fewer JS files and these files tend to be large, which means finding the newlines takes longer. And therefore, these larger files benefit most from the source position caching that only performs this work once. +Why was there a massive difference between production and development code? The production code is optimized using bundling and minification, so there are fewer JS files, and these files tend to be large. It takes longer to calculate source lines positions for these large files, so they benefit the most when we can cache the source position and avoid repeating calculations. The optimizations were validated on both Windows and Linux target environments. For the particularly challenging problem originally faced by the Bloomberg engineers, the total end-to-end time to capture a 100MB snapshot was reduced from a painful 10 minutes down to a very pleasant 6 seconds. That is **a 100x win!** 🔥 -The optimizations are generic wins that we expect to be widely applicable to anyone performing memory debugging on V8, Node, and Chromium. These wins shipped in V8 11.5.130, which means they are found in Chromium 115.0.5576.0. We look forward to Node gaining these optimizations once it adopts the newer V8 release. +The optimizations are generic wins that we expect to be widely applicable to anyone performing memory debugging on V8, Node.js, and Chromium. These wins were shipped in V8 11.5.130, which means they are found in Chromium 115.0.5576.0. We look forward to Node.js gaining these optimizations in the next semver-major release. ## What’s next? First, it would be useful for Node.js to accept the new `--profile-heap-snapshot` flag in `NODE_OPTIONS`. In some use cases, users cannot control the the command line options passed to Node.js directly and have to configure them through the environment variable `NODE_OPTIONS`. Today, Node.js filters V8 command line options set in the environment variable, and only allows a known subset, which could make it harder to test new V8 flags in Node.js, as what happened in our case. -Information accuracy in snapshots can be improved further. Today, each script source code line information is stored in a representation in the V8 heap itself. And that’s a problem because we want to measure the heap precisely without the observability infrastructure affecting the subject we are observing. Ideally, we would store the cache of line information outside the V8 heap in order to make heap snapshot information more accurate. +Information accuracy in snapshots can be improved further. Today, each script source code line information is stored in a representation in the V8 heap itself. And that’s a problem because we want to measure the heap precisely without the performance measurement overhead affecting the subject we are observing. Ideally, we would store the cache of line information outside the V8 heap in order to make heap snapshot information more accurate. Finally, now that we improved the generation phase, the biggest cost is now the serialization phase. Further analysis may reveal new optimization opportunities in serialization. From 69a958e30288421ad71329bbe35f7956542d90ce Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Dapena=20Paz?= Date: Tue, 11 Jul 2023 20:22:41 +0200 Subject: [PATCH 12/27] Remove unneeded reference from heap snapshot optimization post "And old friend" reference was for the ETW fix that I presented in a different blog post that is not even linked. And it is redundant as later I explain that this issue is similar to another one fixed in ETW. Co-authored-by: Joyee Cheung --- src/blog/speeding-up-v8-heap-snapshots.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index 10bfeb6ff..b14621184 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -115,7 +115,7 @@ What did I do to fix it? As the problem comes mostly from numbers represented as ## Second Optimization: Caching source positions -After fixing the hashing, we re-profiled and found a further optimization opportunity that would take a significant part of the time. *An old friend*. +After fixing the hashing, we re-profiled and found a further optimization opportunity that would reduce a significant part of the overhead. For each allocation in the V8 heap, the heap snapshot tries to record the call stack responsible for the allocation. Therefore, for each stack frame, it needs to know the function name and its source location (filename, line number, column number). It was the fetching of this information that turned out to be super slow! From 168995896e4ca95adad90bcd92f8389d19e4d696 Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Tue, 11 Jul 2023 19:46:41 +0200 Subject: [PATCH 13/27] Crop images to improve readability The images in the v8 heap snapshot optimizations post were too much big, with lots of empty space that was not relevant and made it harder to read the contents. 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zF?A-HMl6J6bjV<&D*{k$UedpT0woS&vH<2{d`tS=Sn1*G4#;29QiU+p(62H{EirR-poCP)R02AD~FuAbTStl57 z9sSr?1y$?XoLRLEfEpx^aqC9sRbx!WPtAIqJYs7|oyx(Lln$QCp7Ts1r|My6%CY6} Date: Wed, 12 Jul 2023 11:27:23 +0200 Subject: [PATCH 14/27] Apply suggestions from @syg code review Co-authored-by: Shu-yu Guo --- src/blog/speeding-up-v8-heap-snapshots.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index 68f5b1949..66bbf77b9 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -25,7 +25,7 @@ We wanted to capture several snapshots at different points in the application's It was this slowness in the memory analysis workflow that we needed to solve. -## Narrowing down the Problem +## Narrowing the Problem Jason Williams started investigating the issue using some V8 parameters. As described in the previous post, V8 has some nice command line parameters that can help with that. These options were used to create the heap snapshots, simplify the reproduction, and improve observability: @@ -154,6 +154,6 @@ Information accuracy in snapshots can be improved further. Today, each script so Finally, now that we improved the generation phase, the biggest cost is now the serialization phase. Further analysis may reveal new optimization opportunities in serialization. -## The end +## Credits This work was possible thanks to the sponsorship of [Igalia](https://www.igalia.com) and [Bloomberg](https://techatbloomberg.com). From 91dc5a8f0fccc5325267a12830fa906bd44e4bad Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Wed, 12 Jul 2023 11:06:53 +0200 Subject: [PATCH 15/27] Apply suggestions from @syg in pull request. Manually apply suggestions that could not be applied directly from the GH UI. --- src/blog/speeding-up-v8-heap-snapshots.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index 66bbf77b9..8c55688d8 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -13,7 +13,7 @@ In this post about V8 heap snapshots, I will talk about some performance problem ## The Problem -Ashley Claymore was working on diagnosing a memory leak in a JavaScript application. It was failing with *Out-Of-Memory* errors. For the tested application, the V8 heap limit was configured to be around 1400MB. Normally V8's garbage collector should be able to keep the heap usage under that limit, so the failures indicated that there was a leak. +Ashley Claymore was working on diagnosing a memory leak in a JavaScript application. It was failing with *Out-Of-Memory* errors. For the tested application, the V8 heap limit was configured to be around 1400MB. Normally V8's garbage collector should be able to keep the heap usage under that limit, so the failures indicated that there was likely a leak. A common technique to debug a routine memory leak scenario like this is to capture a heap snapshot first, then load it in the DevTools "Memory" tab and find out what is consuming the most memory by inspecting the various summaries and object attributes. In the DevTools UI, the heap snapshot can be taken in the "Memory" tab. For Node.js applications, the heap snapshot [can be triggered programmatically](https://nodejs.org/en/docs/guides/diagnostics/memory/using-heap-snapshot) using this API: From 3899164800b7ee8bd980fcf1fa82ec87c12fadb0 Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Wed, 12 Jul 2023 11:28:48 +0200 Subject: [PATCH 16/27] Added @syg to the list of contributors in first paragraph --- src/blog/speeding-up-v8-heap-snapshots.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index 8c55688d8..e65f7f398 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -7,7 +7,7 @@ tags: - memory - tools --- -*This blog post has been authored by José Dapena Paz (Igalia), with contributions from Jason Williams (Bloomberg), Ashley Claymore (Bloomberg), Rob Palmer (Bloomberg) and Joyee Cheung(Igalia).* +*This blog post has been authored by José Dapena Paz (Igalia), with contributions from Jason Williams (Bloomberg), Ashley Claymore (Bloomberg), Rob Palmer (Bloomberg), Joyee Cheung (Igalia) and Shu-yu Guo (Google).* In this post about V8 heap snapshots, I will talk about some performance problems found by Bloomberg engineers, and how we fixed them to make JavaScript memory analysis faster than ever. From de70d289365423d2b2297226859625411139cd7f Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Wed, 12 Jul 2023 11:29:40 +0200 Subject: [PATCH 17/27] Do not use specific names explaining the heap snapshot performance investigation Following @syg recommendation, avoid explicit engineers reference while explaining the procedure to investigate the performance problem. --- src/blog/speeding-up-v8-heap-snapshots.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index e65f7f398..e0907a4fb 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -13,7 +13,7 @@ In this post about V8 heap snapshots, I will talk about some performance problem ## The Problem -Ashley Claymore was working on diagnosing a memory leak in a JavaScript application. It was failing with *Out-Of-Memory* errors. For the tested application, the V8 heap limit was configured to be around 1400MB. Normally V8's garbage collector should be able to keep the heap usage under that limit, so the failures indicated that there was likely a leak. +Bloomberg engineers were working on diagnosing a memory leak in a JavaScript application. It was failing with *Out-Of-Memory* errors. For the tested application, the V8 heap limit was configured to be around 1400MB. Normally V8's garbage collector should be able to keep the heap usage under that limit, so the failures indicated that there was likely a leak. A common technique to debug a routine memory leak scenario like this is to capture a heap snapshot first, then load it in the DevTools "Memory" tab and find out what is consuming the most memory by inspecting the various summaries and object attributes. In the DevTools UI, the heap snapshot can be taken in the "Memory" tab. For Node.js applications, the heap snapshot [can be triggered programmatically](https://nodejs.org/en/docs/guides/diagnostics/memory/using-heap-snapshot) using this API: @@ -21,13 +21,13 @@ A common technique to debug a routine memory leak scenario like this is to captu require('v8').writeHeapSnapshot(); ``` -We wanted to capture several snapshots at different points in the application's life, so that DevTools Memory viewer could be used to show the difference between the heaps at different times. The problem was that capturing a single full-size (500MB) snapshot was taking **over 30 minutes** alone! +They wanted to capture several snapshots at different points in the application's life, so that DevTools Memory viewer could be used to show the difference between the heaps at different times. The problem was that capturing a single full-size (500MB) snapshot was taking **over 30 minutes** alone! It was this slowness in the memory analysis workflow that we needed to solve. ## Narrowing the Problem -Jason Williams started investigating the issue using some V8 parameters. As described in the previous post, V8 has some nice command line parameters that can help with that. These options were used to create the heap snapshots, simplify the reproduction, and improve observability: +Then, Bloomberg engineers started investigating the issue using some V8 parameters. As described in the previous post, V8 has some nice command line parameters that can help with that. These options were used to create the heap snapshots, simplify the reproduction, and improve observability: `--max-old-space-size=100` : This limits the heap to 100 megabytes and helps to reproduce the issue much faster. @@ -43,7 +43,7 @@ Jason Williams started investigating the issue using some V8 parameters. As desc When V8 is close to the heap limit, it forces a garbage collection to reduce the memory usage and avoid hitting the limit. It also notifies the embedder that the heap is about to reach the memory limit. The `--heapsnapshot-near-heap-limit` flag in Node.js dumps a new heap snapshot upon notification. In the test case, the memory usage decreases, but, after several iterations, garbage collection ultimately can not free up enough space and so the application is terminated with an *Out-Of-Memory* error. -Jason took recordings using Windows Performance Analyzer (see below) in order to narrow down the issue. This revealed that most CPU time was being spent within the V8 Heap Explorer. Specifically, it took around 30 minutes just to walk through the heap to visit each node and collect the name. This didn’t seem to make much sense - why would recording the name of each property take so long? +They took recordings using Windows Performance Analyzer (see below) in order to narrow down the issue. This revealed that most CPU time was being spent within the V8 Heap Explorer. Specifically, it took around 30 minutes just to walk through the heap to visit each node and collect the name. This didn’t seem to make much sense - why would recording the name of each property take so long? This is when I was asked to take a look. From 3811c746122c6294e65d242b039f505fa5a4af4c Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Wed, 12 Jul 2023 11:31:34 +0200 Subject: [PATCH 18/27] Remove sponsorship reference in heap optimizations post V8 team is not sponsored. Work is actually done by engineers of different companies. Rewrite the credits section accordingly. --- src/blog/speeding-up-v8-heap-snapshots.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index e0907a4fb..978e00790 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -156,4 +156,4 @@ Finally, now that we improved the generation phase, the biggest cost is now the ## Credits -This work was possible thanks to the sponsorship of [Igalia](https://www.igalia.com) and [Bloomberg](https://techatbloomberg.com). +This was possible thanks to the work of [Igalia](https://www.igalia.com) and [Bloomberg](https://techatbloomberg.com) engineers. From c89f34f491f2565246dc12a4f3ecd5ef2244444d Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Thu, 13 Jul 2023 13:56:43 +0200 Subject: [PATCH 19/27] Provided function snippet and value examples of new Hash function --- src/blog/speeding-up-v8-heap-snapshots.md | 37 +++++++++++++++++++++-- 1 file changed, 34 insertions(+), 3 deletions(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index 978e00790..9d548f7e1 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -102,14 +102,45 @@ I started to suspect that the problem could be caused by collisions, which could In the logs, things were… not right: the offset of many items was over 20, and in the worst case, in the order of thousands! -Part of the problem was caused by the scripts using strings for lots of numbers - and especially a big range of numbers from 1 to several hundreds without gaps. The hash key algorithm had two implementations, one for numbers and another for other strings. While the string hash function was quite classical, the implementation for the numbers would basically return the value of the number prefixed by the number of digits, which was problematic. +Part of the problem was caused by the scripts using strings for lots of numbers - and especially a big range of numbers from 1 to several hundreds without gaps. The hash key algorithm had two implementations, one for numbers and another for other strings. While the string hash function was quite classical, the implementation for the numbers would basically return the value of the number prefixed by the number of digits: -Some examples of problems with this hash function: +``` +ValueBits := 24 +Mask := (1 << ValueBits) - 1 /* 0xffffff */ +OriginalHash(x) := (digits(x) << ValueBits) | (x & Mask) +``` + +| x | OriginalHash(x) | +| -: | -: | +| 0 | `0x1000000` | +| 1 | `0x1000001` | +| 2 | `0x1000002` | +| 3 | `0x1000003` | +| 10 | `0x200000a` | +| 11 | `0x200000b` | +| 100 | `0x3000064` | + +This function was problematic. Some examples of problems with this hash function: - Once we inserted a string whose hash key value was a small number, we would run into collisions when we tried to store another number in that location, and there would be similar collisions if we tried to store subsequent numbers consecutively. - Or even worse: if there were already a lot of consecutive numbers stored in the map, and we wanted to insert a string whose hash key value was in that range, we had to move the entry along all the occupied locations to find a free location. -What did I do to fix it? As the problem comes mostly from numbers represented as strings that would fall in consecutive positions, I modified the hash function so we would rotate the resulting hash value 2 positions to the left. So, for each number, we would introduce 3 free positions. Why 2? Empirical testing across several work-sets showed this number was the best choice to minimize collisions. +What did I do to fix it? As the problem comes mostly from numbers represented as strings that would fall in consecutive positions, I modified the hash function so we would rotate the resulting hash value 2 positions to the left. + +``` +NewHash(x) := OriginalHash(x) << 2 +`````` + +| x | OriginalHash(x) | NewHash(x) | +| -: | -: | -: | +| 0 | `0x1000000` | `0x4000000` | +| 1 | `0x1000001` | `0x4000004` | +| 2 | `0x1000002` | `0x4000008` | +| 3 | `0x1000003` | `0x400000c` | +| 10 | `0x200000a` | `0x8000028` | +| 11 | `0x200000b` | `0x800002c` | + +So, for each number, we would introduce 3 free positions. Why rotating 2 bits? Empirical testing across several work-sets showed this number was the best choice to minimize collisions. [This hashing fix](https://chromium-review.googlesource.com/c/v8/v8/+/4428811) has landed in V8. From 239123a6376f52833efda58f5ada19d8532bdb9b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Dapena=20Paz?= Date: Tue, 18 Jul 2023 13:32:58 +0200 Subject: [PATCH 20/27] speeding-up-heap-snapshots.md: apply suggestions from Joyee code review Co-authored-by: Joyee Cheung --- src/blog/speeding-up-v8-heap-snapshots.md | 30 +++++++++++------------ 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index 9d548f7e1..e32faf427 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -21,13 +21,13 @@ A common technique to debug a routine memory leak scenario like this is to captu require('v8').writeHeapSnapshot(); ``` -They wanted to capture several snapshots at different points in the application's life, so that DevTools Memory viewer could be used to show the difference between the heaps at different times. The problem was that capturing a single full-size (500MB) snapshot was taking **over 30 minutes** alone! +They wanted to capture several snapshots at different points in the application's life, so that DevTools Memory viewer could be used to show the difference between the heaps at different times. The problem was that capturing a single full-size (500MB) snapshot was taking **over 30 minutes**! It was this slowness in the memory analysis workflow that we needed to solve. ## Narrowing the Problem -Then, Bloomberg engineers started investigating the issue using some V8 parameters. As described in the previous post, V8 has some nice command line parameters that can help with that. These options were used to create the heap snapshots, simplify the reproduction, and improve observability: +Then, Bloomberg engineers started investigating the issue using some V8 parameters. As described in the previous post, Node.js and V8 have some nice command line parameters that can help with that. These options were used to create the heap snapshots, simplify the reproduction, and improve observability: `--max-old-space-size=100` : This limits the heap to 100 megabytes and helps to reproduce the issue much faster. @@ -41,7 +41,7 @@ Then, Bloomberg engineers started investigating the issue using some V8 paramete `--interpreted-frames-native-stack` : This flag is used in combination with tools like ETW, WPA & xperf to see the native stack when profiling. (available in Node.js v20+). -When V8 is close to the heap limit, it forces a garbage collection to reduce the memory usage and avoid hitting the limit. It also notifies the embedder that the heap is about to reach the memory limit. The `--heapsnapshot-near-heap-limit` flag in Node.js dumps a new heap snapshot upon notification. In the test case, the memory usage decreases, but, after several iterations, garbage collection ultimately can not free up enough space and so the application is terminated with an *Out-Of-Memory* error. +When the size of the V8 heap is approaching the limit, V8 forces a garbage collection to reduce the memory usage. It also notifies the embedder about this. The `--heapsnapshot-near-heap-limit` flag in Node.js generates a new heap snapshot upon notification. In the test case, the memory usage decreases, but, after several iterations, garbage collection ultimately can not free up enough space and so the application is terminated with an *Out-Of-Memory* error. They took recordings using Windows Performance Analyzer (see below) in order to narrow down the issue. This revealed that most CPU time was being spent within the V8 Heap Explorer. Specifically, it took around 30 minutes just to walk through the heap to visit each node and collect the name. This didn’t seem to make much sense - why would recording the name of each property take so long? @@ -53,9 +53,9 @@ The first step was adding support in V8 to better understand where time is spent Using this flag, we learned some interesting things! -First, we could observe the exact time the CPU was using for each snapshot. In our reduced test case, the first took 5 minutes, the second took 8 minutes, and each subsequent snapshot kept on taking longer and longer. Nearly all of this time was spent in the generation phase. +First, we could observe the exact amount of time V8 was spending on generating each snapshot. In our reduced test case, the first took 5 minutes, the second took 8 minutes, and each subsequent snapshot kept on taking longer and longer. Nearly all of this time was spent in the generation phase. -We also identified other widely-used JavaScript applications that demonstrated the same slowdown, in particular, running ESLint on TypeScript. The problem was not app-specific. +This also allowed us to quantify the time spent on snapshot generation with a trivial overhead, which helped us isolate and identify similar slowdowns in other widely-used JavaScript applications - in particular, ESLint on TypeScript. So we know the problem was not app-specific. Furthermore, we found the problem happened on both Windows and Linux. The problem was also not platform-specific. @@ -96,13 +96,13 @@ Because this was run with a release build, the information of the inlined functi So over 30% of the snapshot generation time was spent on `ComputeStringHash()`, but why? -Let’s first talk about `StringsStorage`. Its purpose is to store a unique copy of all the strings that will be used in the heap snapshot. For fast access and avoiding duplicates, this class uses a flatmap: a hashmap backed by an array, where collisions are handled by storing elements in the next position of the array. +Let’s first talk about `StringsStorage`. Its purpose is to store a unique copy of all the strings that will be used in the heap snapshot. For fast access and avoiding duplicates, this class uses a flatmap: a hashmap backed by an array, where collisions are handled by storing elements in the next free location in the array. -I started to suspect that the problem could be caused by collisions, which could lead to long searches in the array. But I needed to prove it. So I added exhaustive logs to see the generated hash keys and, on insertion, see how far it was between the expected position calculated from the hash key and the actual position the entry ended up in due to collisions. +I started to suspect that the problem could be caused by collisions, which could lead to long searches in the array. So I added exhaustive logs to see the generated hash keys and, on insertion, see how far it was between the expected position calculated from the hash key and the actual position the entry ended up in due to collisions. In the logs, things were… not right: the offset of many items was over 20, and in the worst case, in the order of thousands! -Part of the problem was caused by the scripts using strings for lots of numbers - and especially a big range of numbers from 1 to several hundreds without gaps. The hash key algorithm had two implementations, one for numbers and another for other strings. While the string hash function was quite classical, the implementation for the numbers would basically return the value of the number prefixed by the number of digits: +Part of the problem was caused by numeric strings - especially strings for a wide range of consecutive numbers. The hash key algorithm had two implementations, one for numeric strings and another for other strings. While the string hash function was quite classical, the implementation for the numeric strings would basically return the value of the number prefixed by the number of digits: ``` ValueBits := 24 @@ -125,7 +125,7 @@ This function was problematic. Some examples of problems with this hash function - Once we inserted a string whose hash key value was a small number, we would run into collisions when we tried to store another number in that location, and there would be similar collisions if we tried to store subsequent numbers consecutively. - Or even worse: if there were already a lot of consecutive numbers stored in the map, and we wanted to insert a string whose hash key value was in that range, we had to move the entry along all the occupied locations to find a free location. -What did I do to fix it? As the problem comes mostly from numbers represented as strings that would fall in consecutive positions, I modified the hash function so we would rotate the resulting hash value 2 positions to the left. +What did I do to fix it? As the problem comes mostly from numbers represented as strings that would fall in consecutive positions, I modified the hash function so we would rotate the resulting hash value 2 bits to the left. ``` NewHash(x) := OriginalHash(x) << 2 @@ -140,7 +140,7 @@ NewHash(x) := OriginalHash(x) << 2 | 10 | `0x200000a` | `0x8000028` | | 11 | `0x200000b` | `0x800002c` | -So, for each number, we would introduce 3 free positions. Why rotating 2 bits? Empirical testing across several work-sets showed this number was the best choice to minimize collisions. +So for each pair of consecutive numbers, we would introduce 3 free positions in between. This modification was chosen because empirical testing across several work-sets showed that it worked best for minimizing collisions. [This hashing fix](https://chromium-review.googlesource.com/c/v8/v8/+/4428811) has landed in V8. @@ -148,17 +148,17 @@ So, for each number, we would introduce 3 free positions. Why rotating 2 bits? E After fixing the hashing, we re-profiled and found a further optimization opportunity that would reduce a significant part of the overhead. -For each allocation in the V8 heap, the heap snapshot tries to record the call stack responsible for the allocation. Therefore, for each stack frame, it needs to know the function name and its source location (filename, line number, column number). It was the fetching of this information that turned out to be super slow! +When generating a heaps snapshot, for each function in the heap, V8 tries to record its start position in a pair of line and column numbers. This information can be used by the DevTools to display a link to the source code of the function. During usual compilation, however, V8 only stores the start position of each function in the form of a linear offset from the beginning of the script. To calculate the line and column numbers based on the linear offset, V8 needs to traverse the whole script and record where the line breaks are. This calculation turns out to be very expensive. What was happening? It was something similar to [what I fixed in the ETW stack walk](https://chromium-review.googlesource.com/c/v8/v8/+/3669698) and that I explained in [this post](https://blogs.igalia.com/dape/2022/12/21/native-call-stack-profiling-3-3-2022-work-in-v8/). -To compute the source code lines of a function, V8 knows the linear position of the function in the script. But, to find the source line, it needs to traverse the whole script to identify where each newline occurs. This is expensive. +V8 usually only stores where the the function starts in the form of linear offsets in the script. To map that linear offset into a pair of line and column number within the script, V8 needs to traverse the whole script and record where the line breaks are. -When requesting line information, V8 already implements caching of the source line positions per-script in method `Script::InitLineEnds()`. That information is stored in each script using a new heap object. Unfortunately, the snapshot implementation cannot modify the heap when traversing it, so the newly calculated line information cannot be cached. +Normally, after V8 finishes calculating the offsets of line breaks in a script, it caches them in a newly allocated array attached to the script. Unfortunately, the snapshot implementation cannot modify the heap when traversing it, so the newly calculated line information cannot be cached. -The solution? Before generating the heap snapshot, we now iterate over all the scripts in the V8 context to compute and cache the source line positions. As this is not done when we traverse the heap for heap snapshot generation, it is still possible to modify the heap and store the source line positions as a cache. +The solution? Before generating the heap snapshot, we now iterate over all the scripts in the V8 context to compute and cache the offsets of the line breaks. As this is not done when we traverse the heap for heap snapshot generation, it is still possible to modify the heap and store the source line positions as a cache. -[This source position caching fix](https://chromium-review.googlesource.com/c/v8/v8/+/4538766) has also landed in V8. +[The fix for the caching of line break offsets](https://chromium-review.googlesource.com/c/v8/v8/+/4538766) has also landed in V8. ## Did we make it fast? From 3785dbe8b57dd79b3cd7f2ce76b250e7c26aa7b5 Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Tue, 18 Jul 2023 12:38:39 +0200 Subject: [PATCH 21/27] Update speeding-up-v8-heap-snapshot.md: replace obscure reference to previous post Originally the blog post was part of a series until it was proposed to be an independent post in v8.dev. Make the reference to the previous post independent of the fact by referring to the link of the previous post. --- src/blog/speeding-up-v8-heap-snapshots.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index e32faf427..268f8817c 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -27,7 +27,7 @@ It was this slowness in the memory analysis workflow that we needed to solve. ## Narrowing the Problem -Then, Bloomberg engineers started investigating the issue using some V8 parameters. As described in the previous post, Node.js and V8 have some nice command line parameters that can help with that. These options were used to create the heap snapshots, simplify the reproduction, and improve observability: +Then, Bloomberg engineers started investigating the issue using some V8 parameters. As described in the [this post](https://blogs.igalia.com/dape/2023/05/18/javascript-memory-profiling-with-heap-snapshot/), Node.js and V8 have some nice command line parameters that can help with that. These options were used to create the heap snapshots, simplify the reproduction, and improve observability: `--max-old-space-size=100` : This limits the heap to 100 megabytes and helps to reproduce the issue much faster. From c6037863890e19c7774393f0689184bf59b8e021 Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Tue, 18 Jul 2023 17:03:59 +0200 Subject: [PATCH 22/27] speeding-up-v8-snapshots.md: following recommendations from Joyee, simplified writing Several changes to reduce redundancies and simplify reading the post: - Remove step-by-step guide of using Windows Performance Recording, referring to upstream documentation now. - Snippets are now almost C++ code instead of pseudocode. - Some typos. - Simplified line break caching explanation, and removed reference to equivalent ETW fix. --- src/blog/speeding-up-v8-heap-snapshots.md | 34 ++++++++++------------- 1 file changed, 14 insertions(+), 20 deletions(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index 268f8817c..f49a32aea 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -63,16 +63,9 @@ Furthermore, we found the problem happened on both Windows and Linux. The proble As the problem was initially reported on a Windows platform, I used [Windows Performance Toolkit](https://learn.microsoft.com/en-us/windows-hardware/test/wpt/), based on [ETW](https://learn.microsoft.com/en-us/windows-hardware/drivers/devtest/event-tracing-for-windows--etw-), for analysis. This is a powerful low-level expert tool to find out exactly what a program is doing on Windows. -To record the session, I followed these steps: +To analyze the performance of heap snapshot generation, I followed [these steps](https://learn.microsoft.com/en-us/windows-hardware/test/wpt/wpr-how-to-topics#start-a-recording) to run the Windows Performance Recorder with the failing script, with `NODE_OPTIONS="--max-old-space-size=100 --heapsnapshot-near-heap-limit=10 --profile-heap-snapshot`. I had to modify Node.js to accept `--profile-heap-snapshot` in `NODE_OPTIONS`, as it uses an allowlist to filter V8 flags that can be configured through the environment variable. -1. Opened [Windows Performance Recorder](https://learn.microsoft.com/en-us/windows-hardware/test/wpt/windows-performance-recorder) and selected CPU profiling, verbose detail level, general scenario, and file logging mode. - - ![](/_img/speeding-up-v8-heap-snapshots/img1.png){.no-darkening} - - -2. After that, I started the recording session (pressing the Start button). -3. Then, I executed the failing script with `NODE_OPTIONS="--max-old-space-size=100 --heapsnapshot-near-heap-limit=10 --profile-heap-snapshot`. I had to modify Node.js to accept `--profile-heap-snapshot` in `NODE_OPTIONS`, as it uses an allowlist to filter V8 flags that can be configured through the environment variable. -4. I just let it run to generate a couple of heap snapshots (it would already take over 10 minutes!) and then I stopped the recording. +I just let it run to generate a couple of heap snapshots (it would already take over 10 minutes!) and then I stopped the recording. ## First Optimization: Improved StringsStorage hashing @@ -104,10 +97,13 @@ In the logs, things were… not right: the offset of many items was over 20, and Part of the problem was caused by numeric strings - especially strings for a wide range of consecutive numbers. The hash key algorithm had two implementations, one for numeric strings and another for other strings. While the string hash function was quite classical, the implementation for the numeric strings would basically return the value of the number prefixed by the number of digits: -``` -ValueBits := 24 -Mask := (1 << ValueBits) - 1 /* 0xffffff */ -OriginalHash(x) := (digits(x) << ValueBits) | (x & Mask) +```cpp +int32_t OriginalHash(const std::string& numeric_string) { + int kValueBits = 24; + + int32_t mask = (1 << kValueBits) - 1; /* 0xffffff */ + return (numeric_string.length() << kValueBits) | (numeric_string & mask); +} ``` | x | OriginalHash(x) | @@ -127,8 +123,10 @@ This function was problematic. Some examples of problems with this hash function What did I do to fix it? As the problem comes mostly from numbers represented as strings that would fall in consecutive positions, I modified the hash function so we would rotate the resulting hash value 2 bits to the left. -``` -NewHash(x) := OriginalHash(x) << 2 +```cpp +int32_t NewHash(const std::string& numeric_string) { + return OriginalHash(numeric_string) << 2; +} `````` | x | OriginalHash(x) | NewHash(x) | @@ -148,11 +146,7 @@ So for each pair of consecutive numbers, we would introduce 3 free positions in After fixing the hashing, we re-profiled and found a further optimization opportunity that would reduce a significant part of the overhead. -When generating a heaps snapshot, for each function in the heap, V8 tries to record its start position in a pair of line and column numbers. This information can be used by the DevTools to display a link to the source code of the function. During usual compilation, however, V8 only stores the start position of each function in the form of a linear offset from the beginning of the script. To calculate the line and column numbers based on the linear offset, V8 needs to traverse the whole script and record where the line breaks are. This calculation turns out to be very expensive. - -What was happening? It was something similar to [what I fixed in the ETW stack walk](https://chromium-review.googlesource.com/c/v8/v8/+/3669698) and that I explained in [this post](https://blogs.igalia.com/dape/2022/12/21/native-call-stack-profiling-3-3-2022-work-in-v8/). - -V8 usually only stores where the the function starts in the form of linear offsets in the script. To map that linear offset into a pair of line and column number within the script, V8 needs to traverse the whole script and record where the line breaks are. +When generating a heap snapshot, for each function in the heap, V8 tries to record its start position in a pair of line and column numbers. This information can be used by the DevTools to display a link to the source code of the function. During usual compilation, however, V8 only stores the start position of each function in the form of a linear offset from the beginning of the script. To calculate the line and column numbers based on the linear offset, V8 needs to traverse the whole script and record where the line breaks are. This calculation turns out to be very expensive. Normally, after V8 finishes calculating the offsets of line breaks in a script, it caches them in a newly allocated array attached to the script. Unfortunately, the snapshot implementation cannot modify the heap when traversing it, so the newly calculated line information cannot be cached. 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zvViBHWi$Q3l6d}0TtT&uC$Vc>R^TG}*nLLhe5NCi$6s^*_jG$r{4fAA0?eLjrvN--V=^X*Cs)=6tO6BXHC%>b zL;@g-=8|3&*r`vPKMTgpq6|RiRN4m#WVjv@Rnl5}K z%62Q#0#Y&@#0TDtwYDWWJ{~gRa8xKe^%T+D_^bdnS^#8x`6IaZmFfx1MD=08Ot>u5&)0&%J4DByset)!4lH=@b}Y{rJ*Se1?%JX?Qx zbR2sq<|FYPzr&?xY=S1}Y%`2FXM zNJhrMmL14Pm0|kQ)t*VK`S((j^VwFSHozaR=$pq3jKHWzI&*edQWX2Oz$gjX{%y7K z2->7E-xwXy{Z=#f|DfQxO(!Z7b9{9U~3fSSvU;kx?cFjczDbU}*{Gt2W&v+sL zyonU$G2+|1^^l>?RUOp7!218G8U)5t1Lw^o3qT8r{RYfjTp@sc`(Lu^zj>V$Imf6^ z%m@G5h(~=fCL(~Yk;}Lt8Cu%@|L#Y|XAxYx75vjAGygWp^%sOfdCYEsLNPG#dtN{i MqOu}oA9Z~H7g4_53jhEB From ca5bf1979cc497ac3b0a938899bfacb12a374330 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Dapena=20Paz?= Date: Wed, 19 Jul 2023 16:53:36 +0200 Subject: [PATCH 24/27] speeding-up-v8-heap-snapshots.md: apply suggestions from @syg Co-authored-by: Shu-yu Guo --- src/blog/speeding-up-v8-heap-snapshots.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index f49a32aea..68d24b2ea 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -76,7 +76,7 @@ When I opened the recording with [Windows Performance Analyzer](https://learn.mi One third of the samples was spent in `v8::internal::StringsStorage::GetEntry`: -```javascript +```cpp 181 base::HashMap::Entry* StringsStorage::GetEntry(const char* str, int len) { 182 uint32_t hash = ComputeStringHash(str, len); 183 return names_.LookupOrInsert(const_cast(str), hash); @@ -89,7 +89,7 @@ Because this was run with a release build, the information of the inlined functi So over 30% of the snapshot generation time was spent on `ComputeStringHash()`, but why? -Let’s first talk about `StringsStorage`. Its purpose is to store a unique copy of all the strings that will be used in the heap snapshot. For fast access and avoiding duplicates, this class uses a flatmap: a hashmap backed by an array, where collisions are handled by storing elements in the next free location in the array. +Let’s first talk about `StringsStorage`. Its purpose is to store a unique copy of all the strings that will be used in the heap snapshot. For fast access and avoiding duplicates, this class uses a hashmap backed by an array, where collisions are handled by storing elements in the next free location in the array. I started to suspect that the problem could be caused by collisions, which could lead to long searches in the array. So I added exhaustive logs to see the generated hash keys and, on insertion, see how far it was between the expected position calculated from the hash key and the actual position the entry ended up in due to collisions. From bdd5b961b218f2b0b55199a6fe6ead8ee90f1a15 Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Wed, 19 Jul 2023 17:00:54 +0200 Subject: [PATCH 25/27] speeding-up-v8-heap-snapshots.md: manually apply @syg proposed changes --- src/blog/speeding-up-v8-heap-snapshots.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index 68d24b2ea..6490e9c73 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -127,7 +127,7 @@ What did I do to fix it? As the problem comes mostly from numbers represented as int32_t NewHash(const std::string& numeric_string) { return OriginalHash(numeric_string) << 2; } -`````` +``` | x | OriginalHash(x) | NewHash(x) | | -: | -: | -: | @@ -137,6 +137,7 @@ int32_t NewHash(const std::string& numeric_string) { | 3 | `0x1000003` | `0x400000c` | | 10 | `0x200000a` | `0x8000028` | | 11 | `0x200000b` | `0x800002c` | +| 100 | `0x3000064` | `0xc000190` | So for each pair of consecutive numbers, we would introduce 3 free positions in between. This modification was chosen because empirical testing across several work-sets showed that it worked best for minimizing collisions. From 8f083a8b1bbb881822402c5664751513faff6765 Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Wed, 19 Jul 2023 17:01:17 +0200 Subject: [PATCH 26/27] speeding-up-v8-heap-snapshots.md: update publication date --- src/blog/speeding-up-v8-heap-snapshots.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index 6490e9c73..65db228da 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -2,7 +2,7 @@ title: 'Speeding up V8 heap snapshots' description: 'This post about V8 heap snapshots presents some performance problems found by Bloomberg engineers, and how we fixed them to make JavaScript memory analysis faster than ever.' author: 'Jose Dapena Paz' -date: 2023-06-23 +date: 2023-07-27 tags: - memory - tools From b065c4340503d42ec9d19483c020e24f702000e7 Mon Sep 17 00:00:00 2001 From: Jose Dapena Paz Date: Wed, 26 Jul 2023 16:27:45 +0200 Subject: [PATCH 27/27] speeding-up-v8-heap-snapshots.md: drop WPA session --- src/blog/speeding-up-v8-heap-snapshots.md | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/src/blog/speeding-up-v8-heap-snapshots.md b/src/blog/speeding-up-v8-heap-snapshots.md index 65db228da..8d476cc3f 100644 --- a/src/blog/speeding-up-v8-heap-snapshots.md +++ b/src/blog/speeding-up-v8-heap-snapshots.md @@ -59,16 +59,10 @@ This also allowed us to quantify the time spent on snapshot generation with a tr Furthermore, we found the problem happened on both Windows and Linux. The problem was also not platform-specific. -## Windows Performance Analyzer to the rescue - -As the problem was initially reported on a Windows platform, I used [Windows Performance Toolkit](https://learn.microsoft.com/en-us/windows-hardware/test/wpt/), based on [ETW](https://learn.microsoft.com/en-us/windows-hardware/drivers/devtest/event-tracing-for-windows--etw-), for analysis. This is a powerful low-level expert tool to find out exactly what a program is doing on Windows. - -To analyze the performance of heap snapshot generation, I followed [these steps](https://learn.microsoft.com/en-us/windows-hardware/test/wpt/wpr-how-to-topics#start-a-recording) to run the Windows Performance Recorder with the failing script, with `NODE_OPTIONS="--max-old-space-size=100 --heapsnapshot-near-heap-limit=10 --profile-heap-snapshot`. I had to modify Node.js to accept `--profile-heap-snapshot` in `NODE_OPTIONS`, as it uses an allowlist to filter V8 flags that can be configured through the environment variable. - -I just let it run to generate a couple of heap snapshots (it would already take over 10 minutes!) and then I stopped the recording. - ## First Optimization: Improved StringsStorage hashing +To identify what was causing the excesive delay I profiled the failing script using [Windows Performance Toolkit](https://learn.microsoft.com/en-us/windows-hardware/test/wpt/). + When I opened the recording with [Windows Performance Analyzer](https://learn.microsoft.com/en-us/windows-hardware/test/wpt/windows-performance-analyzer), this was what I found: ![](/_img/speeding-up-v8-heap-snapshots/img2.png){.no-darkening}