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2021-01-25 17:41:20 +03:00
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Multi-process profiles. (#3395) See: [#181837344](https://www.pivotaltracker.com/story/show/181837344). I've separated this PR from some deeper changes I'm making to the profile format, because the changeset was getting too complex. The new APIs and tools in this PR are fully-implemented, except the profile format is too simplistic--it doesn't currently support headers that are needed to determine the relative timings of events from different processes. - Adds basic support for profile files containing data collected by multiple processes. - Implements `api_events_to_profile`, a tool for converting backend message logs (#3392) to the `profiler` format so they can be merged with frontend profiles (currently they can be merged with `cat`, but the next PR will introduce a merge tool). - Introduces `message_beanpoles`, a simple tool that diagrams timing relationships between frontend and backend messages. ### Important Notes - All TODOs introduced here will be addressed in the next PR that defines the new format. - Introduced a new crate, `enso_profiler_enso_data`, to be used by profile consumers that need to refer to Enso application datatypes to interpret metadata. - Introduced a `ProfileBuilder` abstraction for writing the JSON profile format; partially decouples the runtime event log structures from the format definition. - Introducing the conversion performed for `ProfilerBuilder` uncovered that the `.._with_same_start!` low-level `profiler` APIs don't currently work; they return `Started<_>` profilers, but that is inconsistent with the stricter data model that I introduced when I implemented `profiler_data`; they need to return profilers in a created, unstarted state. Low-level async profilers have not been a priority, but once #3382 merges we'll have a way to render their data, which will be really useful because async profilers capture *why* we're doing things. I'll bring up scheduling this in the next performance meeting.
2022-04-21 17:44:03 +03:00
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Multi-process profiles. (#3395) See: [#181837344](https://www.pivotaltracker.com/story/show/181837344). I've separated this PR from some deeper changes I'm making to the profile format, because the changeset was getting too complex. The new APIs and tools in this PR are fully-implemented, except the profile format is too simplistic--it doesn't currently support headers that are needed to determine the relative timings of events from different processes. - Adds basic support for profile files containing data collected by multiple processes. - Implements `api_events_to_profile`, a tool for converting backend message logs (#3392) to the `profiler` format so they can be merged with frontend profiles (currently they can be merged with `cat`, but the next PR will introduce a merge tool). - Introduces `message_beanpoles`, a simple tool that diagrams timing relationships between frontend and backend messages. ### Important Notes - All TODOs introduced here will be addressed in the next PR that defines the new format. - Introduced a new crate, `enso_profiler_enso_data`, to be used by profile consumers that need to refer to Enso application datatypes to interpret metadata. - Introduced a `ProfileBuilder` abstraction for writing the JSON profile format; partially decouples the runtime event log structures from the format definition. - Introducing the conversion performed for `ProfilerBuilder` uncovered that the `.._with_same_start!` low-level `profiler` APIs don't currently work; they return `Started<_>` profilers, but that is inconsistent with the stricter data model that I introduced when I implemented `profiler_data`; they need to return profilers in a created, unstarted state. Low-level async profilers have not been a priority, but once #3382 merges we'll have a way to render their data, which will be really useful because async profilers capture *why* we're doing things. I'll bring up scheduling this in the next performance meeting.
2022-04-21 17:44:03 +03:00
checksum = "9852635589dc9f9ea1b6fe9f05b50ef208c85c834a562f0c6abb1c475736ec2b"
2021-10-30 16:04:07 +03:00
dependencies = [
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2022-05-23 05:16:04 +03:00
[[package]]
name = "enso-build"
version = "0.1.0"
dependencies = [
"anyhow",
"aws-config",
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2022-05-23 05:16:04 +03:00
"aws-sdk-s3",
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2022-05-23 05:16:04 +03:00
"byte-unit",
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2022-05-23 05:16:04 +03:00
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2022-05-23 05:16:04 +03:00
"derivative",
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2022-05-23 05:16:04 +03:00
"dirs",
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Use Enso Font (#7516) Use the new Enso Font; also change the anti-aliasing logic to be based on device pixel ratio, rather than platform. This will improve the clarity of font rendering on Windows/Linux machines with high pixel densities. Design reference: ![image](https://github.com/enso-org/enso/assets/1047859/934ec9ac-52c3-4a81-a9f9-143378ecb658) Tested on various combinations of DPR/platform: OS X, `devicePixelRatio` = 2 (should look similar to how we were already rendering *mplus1* on OS X): <img width="1440" alt="Screenshot 2023-08-07 at 5 46 11 PM" src="https://github.com/enso-org/enso/assets/1047859/2fdf251a-ba5e-426f-b6c4-194347a9cee4"> Windows, `devicePixelRatio` = 1.25 (should look similar to how we were already rendering *mplus1* on this platform/DPR): ![image](https://github.com/enso-org/enso/assets/1047859/55c4a129-4fff-4a9b-8e55-51a5d206e659) Linux, `devicePixelRatio` = 1 (should look similar to how we were already rendering *mplus1* on this platform/DPR): ![image](https://github.com/enso-org/enso/assets/1047859/c5ac61f0-e3c5-43ca-8ee7-e1e04e84d35e) # Important Notes Style changes: - Use the Enso Font for code in Rust, replacing the DejaVu fonts. - Use the Enso Font in HTML: code in documentation, and error visualizations. - Change SpanWidgets from Bold to Extra Bold, to match the design. Implementation improvements: - The new font download is cached (and Github-authenticated); this should eliminate a "rate limit" build failure I've encountered in the past. - Clean up DocSection HTML rendering a bit. - Remove a CSS file that seems to have been superseded.
2023-08-17 21:36:12 +03:00
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"handlebars",
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"ide-ci",
"mime",
"new_mime_guess",
2022-05-23 05:16:04 +03:00
"octocrab",
"path-slash",
2022-05-23 05:16:04 +03:00
"port_check",
"pulldown-cmark",
"regex",
"reqwest",
2022-05-23 05:16:04 +03:00
"scopeguard",
"semver",
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"serde",
"serde_json",
2023-01-27 03:09:09 +03:00
"serde_yaml",
"strum",
"sysinfo",
2022-05-23 05:16:04 +03:00
"tempfile",
"tokio",
2022-05-23 05:16:04 +03:00
"toml",
"tracing",
"url",
"uuid",
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"walkdir",
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2022-05-23 05:16:04 +03:00
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2023-01-27 03:09:09 +03:00
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2023-01-27 03:09:09 +03:00
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Better `release` build time; new maximum-performance `production` profile. (#3498) ### Pull Request Description Using the new tooling (#3491), I investigated the **performance / compile-time tradeoff** of different codegen options for release mode builds. By scripting the testing procedure, I was able to explore many possible combinations of options, which is important because their interactions (on both application performance and build time) are complex. I found **two candidate profiles** that offer specific advantages over the current `release` settings (`baseline`): - `thin16`: Supports incremental compiles in 1/3 the time of `baseline` in common cases. Application runs about 2% slower than `baseline`. - `fat1-O4`: Application performs 13% better than `baseline`. Compile time is almost 3x `baseline`, and non-incremental. (See key in first chart for the settings defining these profiles.) We can build faster or run faster, though not in the same build. Because the effect sizes are large enough to be impactful to developer and user experience, respectively, I think we should consider having it both ways. We could **split the `release` profile** into two profiles to serve different purposes: - `release`: A profile that supports fast developer iteration, while offering realistic performance. - `production`: A maximally-optimized profile, for nightly builds and actual releases. Since `wasm-pack` doesn't currently support custom profiles (rustwasm/wasm-pack#1111), we can't use a Cargo profile for `production`; however, we can implement our own profile by overriding rustc flags. ### Performance details ![perf](https://user-images.githubusercontent.com/1047859/170788530-ab6d7910-5253-4a2b-b432-8bfa0b4735ba.png) As you can see, `thin16` is slightly slower than `baseline`; `fat1-O4` is dramatically faster. <details> <summary>Methodology (click to show)</summary> I developed a procedure for benchmarking "whole application" performance, using the new "open project" workflow (which opens the IDE and loads a complex project), and some statistical analysis to account for variance. To gather this data: Build the application with profiling: `./run.sh ide build --profiling-level=debug` Run the `open_project` workflow repeatedly: `for i in $(seq 0 9); do dist/ide/linux-unpacked/enso --entry-point profile --workflow open_project --save-profile open_project_thin16_${i}.json; done` For each profile recorded, take the new `total_self_time` output of the `intervals` tool; gather into CSV: `echo $(for i in $(seq 0 9); do target/rust/debug/intervals < open_project_thin16_${i}.json | tail -n1 | awk '{print $2}'; do` (Note that the output of intervals should not be considered stable; this command may need modification in the future. Eventually it would be nice to support formatted outputs...) The data is ready to graph. I used the `boxplot` method of the [seaborn](https://seaborn.pydata.org/index.html) package, in order to show the distribution of data. </details> #### Build times ![thin16](https://user-images.githubusercontent.com/1047859/170788539-1578e41b-bc30-4f30-9b71-0b0181322fa5.png) In the case of changing a file in `enso-prelude`, with the current `baseline` settings rebuilding takes over 3 minutes. With the `thin16` settings, the same rebuild completes in 40 seconds. (To gather this data on different hardware or in the future, just run the new `bench-build.sh` script for each case to be measured.)
2022-06-11 01:09:54 +03:00
[[package]]
name = "enso-build-cli"
version = "0.1.0"
dependencies = [
"anyhow",
"byte-unit",
"clap 4.5.4",
Better `release` build time; new maximum-performance `production` profile. (#3498) ### Pull Request Description Using the new tooling (#3491), I investigated the **performance / compile-time tradeoff** of different codegen options for release mode builds. By scripting the testing procedure, I was able to explore many possible combinations of options, which is important because their interactions (on both application performance and build time) are complex. I found **two candidate profiles** that offer specific advantages over the current `release` settings (`baseline`): - `thin16`: Supports incremental compiles in 1/3 the time of `baseline` in common cases. Application runs about 2% slower than `baseline`. - `fat1-O4`: Application performs 13% better than `baseline`. Compile time is almost 3x `baseline`, and non-incremental. (See key in first chart for the settings defining these profiles.) We can build faster or run faster, though not in the same build. Because the effect sizes are large enough to be impactful to developer and user experience, respectively, I think we should consider having it both ways. We could **split the `release` profile** into two profiles to serve different purposes: - `release`: A profile that supports fast developer iteration, while offering realistic performance. - `production`: A maximally-optimized profile, for nightly builds and actual releases. Since `wasm-pack` doesn't currently support custom profiles (rustwasm/wasm-pack#1111), we can't use a Cargo profile for `production`; however, we can implement our own profile by overriding rustc flags. ### Performance details ![perf](https://user-images.githubusercontent.com/1047859/170788530-ab6d7910-5253-4a2b-b432-8bfa0b4735ba.png) As you can see, `thin16` is slightly slower than `baseline`; `fat1-O4` is dramatically faster. <details> <summary>Methodology (click to show)</summary> I developed a procedure for benchmarking "whole application" performance, using the new "open project" workflow (which opens the IDE and loads a complex project), and some statistical analysis to account for variance. To gather this data: Build the application with profiling: `./run.sh ide build --profiling-level=debug` Run the `open_project` workflow repeatedly: `for i in $(seq 0 9); do dist/ide/linux-unpacked/enso --entry-point profile --workflow open_project --save-profile open_project_thin16_${i}.json; done` For each profile recorded, take the new `total_self_time` output of the `intervals` tool; gather into CSV: `echo $(for i in $(seq 0 9); do target/rust/debug/intervals < open_project_thin16_${i}.json | tail -n1 | awk '{print $2}'; do` (Note that the output of intervals should not be considered stable; this command may need modification in the future. Eventually it would be nice to support formatted outputs...) The data is ready to graph. I used the `boxplot` method of the [seaborn](https://seaborn.pydata.org/index.html) package, in order to show the distribution of data. </details> #### Build times ![thin16](https://user-images.githubusercontent.com/1047859/170788539-1578e41b-bc30-4f30-9b71-0b0181322fa5.png) In the case of changing a file in `enso-prelude`, with the current `baseline` settings rebuilding takes over 3 minutes. With the `thin16` settings, the same rebuild completes in 40 seconds. (To gather this data on different hardware or in the future, just run the new `bench-build.sh` script for each case to be measured.)
2022-06-11 01:09:54 +03:00
"derivative",
"enso-build",
"enso-build-base",
"enso-formatter",
Better `release` build time; new maximum-performance `production` profile. (#3498) ### Pull Request Description Using the new tooling (#3491), I investigated the **performance / compile-time tradeoff** of different codegen options for release mode builds. By scripting the testing procedure, I was able to explore many possible combinations of options, which is important because their interactions (on both application performance and build time) are complex. I found **two candidate profiles** that offer specific advantages over the current `release` settings (`baseline`): - `thin16`: Supports incremental compiles in 1/3 the time of `baseline` in common cases. Application runs about 2% slower than `baseline`. - `fat1-O4`: Application performs 13% better than `baseline`. Compile time is almost 3x `baseline`, and non-incremental. (See key in first chart for the settings defining these profiles.) We can build faster or run faster, though not in the same build. Because the effect sizes are large enough to be impactful to developer and user experience, respectively, I think we should consider having it both ways. We could **split the `release` profile** into two profiles to serve different purposes: - `release`: A profile that supports fast developer iteration, while offering realistic performance. - `production`: A maximally-optimized profile, for nightly builds and actual releases. Since `wasm-pack` doesn't currently support custom profiles (rustwasm/wasm-pack#1111), we can't use a Cargo profile for `production`; however, we can implement our own profile by overriding rustc flags. ### Performance details ![perf](https://user-images.githubusercontent.com/1047859/170788530-ab6d7910-5253-4a2b-b432-8bfa0b4735ba.png) As you can see, `thin16` is slightly slower than `baseline`; `fat1-O4` is dramatically faster. <details> <summary>Methodology (click to show)</summary> I developed a procedure for benchmarking "whole application" performance, using the new "open project" workflow (which opens the IDE and loads a complex project), and some statistical analysis to account for variance. To gather this data: Build the application with profiling: `./run.sh ide build --profiling-level=debug` Run the `open_project` workflow repeatedly: `for i in $(seq 0 9); do dist/ide/linux-unpacked/enso --entry-point profile --workflow open_project --save-profile open_project_thin16_${i}.json; done` For each profile recorded, take the new `total_self_time` output of the `intervals` tool; gather into CSV: `echo $(for i in $(seq 0 9); do target/rust/debug/intervals < open_project_thin16_${i}.json | tail -n1 | awk '{print $2}'; do` (Note that the output of intervals should not be considered stable; this command may need modification in the future. Eventually it would be nice to support formatted outputs...) The data is ready to graph. I used the `boxplot` method of the [seaborn](https://seaborn.pydata.org/index.html) package, in order to show the distribution of data. </details> #### Build times ![thin16](https://user-images.githubusercontent.com/1047859/170788539-1578e41b-bc30-4f30-9b71-0b0181322fa5.png) In the case of changing a file in `enso-prelude`, with the current `baseline` settings rebuilding takes over 3 minutes. With the `thin16` settings, the same rebuild completes in 40 seconds. (To gather this data on different hardware or in the future, just run the new `bench-build.sh` script for each case to be measured.)
2022-06-11 01:09:54 +03:00
"futures-util",
"glob",
"humantime",
Better `release` build time; new maximum-performance `production` profile. (#3498) ### Pull Request Description Using the new tooling (#3491), I investigated the **performance / compile-time tradeoff** of different codegen options for release mode builds. By scripting the testing procedure, I was able to explore many possible combinations of options, which is important because their interactions (on both application performance and build time) are complex. I found **two candidate profiles** that offer specific advantages over the current `release` settings (`baseline`): - `thin16`: Supports incremental compiles in 1/3 the time of `baseline` in common cases. Application runs about 2% slower than `baseline`. - `fat1-O4`: Application performs 13% better than `baseline`. Compile time is almost 3x `baseline`, and non-incremental. (See key in first chart for the settings defining these profiles.) We can build faster or run faster, though not in the same build. Because the effect sizes are large enough to be impactful to developer and user experience, respectively, I think we should consider having it both ways. We could **split the `release` profile** into two profiles to serve different purposes: - `release`: A profile that supports fast developer iteration, while offering realistic performance. - `production`: A maximally-optimized profile, for nightly builds and actual releases. Since `wasm-pack` doesn't currently support custom profiles (rustwasm/wasm-pack#1111), we can't use a Cargo profile for `production`; however, we can implement our own profile by overriding rustc flags. ### Performance details ![perf](https://user-images.githubusercontent.com/1047859/170788530-ab6d7910-5253-4a2b-b432-8bfa0b4735ba.png) As you can see, `thin16` is slightly slower than `baseline`; `fat1-O4` is dramatically faster. <details> <summary>Methodology (click to show)</summary> I developed a procedure for benchmarking "whole application" performance, using the new "open project" workflow (which opens the IDE and loads a complex project), and some statistical analysis to account for variance. To gather this data: Build the application with profiling: `./run.sh ide build --profiling-level=debug` Run the `open_project` workflow repeatedly: `for i in $(seq 0 9); do dist/ide/linux-unpacked/enso --entry-point profile --workflow open_project --save-profile open_project_thin16_${i}.json; done` For each profile recorded, take the new `total_self_time` output of the `intervals` tool; gather into CSV: `echo $(for i in $(seq 0 9); do target/rust/debug/intervals < open_project_thin16_${i}.json | tail -n1 | awk '{print $2}'; do` (Note that the output of intervals should not be considered stable; this command may need modification in the future. Eventually it would be nice to support formatted outputs...) The data is ready to graph. I used the `boxplot` method of the [seaborn](https://seaborn.pydata.org/index.html) package, in order to show the distribution of data. </details> #### Build times ![thin16](https://user-images.githubusercontent.com/1047859/170788539-1578e41b-bc30-4f30-9b71-0b0181322fa5.png) In the case of changing a file in `enso-prelude`, with the current `baseline` settings rebuilding takes over 3 minutes. With the `thin16` settings, the same rebuild completes in 40 seconds. (To gather this data on different hardware or in the future, just run the new `bench-build.sh` script for each case to be measured.)
2022-06-11 01:09:54 +03:00
"ide-ci",
"octocrab",
"tempfile",
"tokio",
"toml",
Better `release` build time; new maximum-performance `production` profile. (#3498) ### Pull Request Description Using the new tooling (#3491), I investigated the **performance / compile-time tradeoff** of different codegen options for release mode builds. By scripting the testing procedure, I was able to explore many possible combinations of options, which is important because their interactions (on both application performance and build time) are complex. I found **two candidate profiles** that offer specific advantages over the current `release` settings (`baseline`): - `thin16`: Supports incremental compiles in 1/3 the time of `baseline` in common cases. Application runs about 2% slower than `baseline`. - `fat1-O4`: Application performs 13% better than `baseline`. Compile time is almost 3x `baseline`, and non-incremental. (See key in first chart for the settings defining these profiles.) We can build faster or run faster, though not in the same build. Because the effect sizes are large enough to be impactful to developer and user experience, respectively, I think we should consider having it both ways. We could **split the `release` profile** into two profiles to serve different purposes: - `release`: A profile that supports fast developer iteration, while offering realistic performance. - `production`: A maximally-optimized profile, for nightly builds and actual releases. Since `wasm-pack` doesn't currently support custom profiles (rustwasm/wasm-pack#1111), we can't use a Cargo profile for `production`; however, we can implement our own profile by overriding rustc flags. ### Performance details ![perf](https://user-images.githubusercontent.com/1047859/170788530-ab6d7910-5253-4a2b-b432-8bfa0b4735ba.png) As you can see, `thin16` is slightly slower than `baseline`; `fat1-O4` is dramatically faster. <details> <summary>Methodology (click to show)</summary> I developed a procedure for benchmarking "whole application" performance, using the new "open project" workflow (which opens the IDE and loads a complex project), and some statistical analysis to account for variance. To gather this data: Build the application with profiling: `./run.sh ide build --profiling-level=debug` Run the `open_project` workflow repeatedly: `for i in $(seq 0 9); do dist/ide/linux-unpacked/enso --entry-point profile --workflow open_project --save-profile open_project_thin16_${i}.json; done` For each profile recorded, take the new `total_self_time` output of the `intervals` tool; gather into CSV: `echo $(for i in $(seq 0 9); do target/rust/debug/intervals < open_project_thin16_${i}.json | tail -n1 | awk '{print $2}'; do` (Note that the output of intervals should not be considered stable; this command may need modification in the future. Eventually it would be nice to support formatted outputs...) The data is ready to graph. I used the `boxplot` method of the [seaborn](https://seaborn.pydata.org/index.html) package, in order to show the distribution of data. </details> #### Build times ![thin16](https://user-images.githubusercontent.com/1047859/170788539-1578e41b-bc30-4f30-9b71-0b0181322fa5.png) In the case of changing a file in `enso-prelude`, with the current `baseline` settings rebuilding takes over 3 minutes. With the `thin16` settings, the same rebuild completes in 40 seconds. (To gather this data on different hardware or in the future, just run the new `bench-build.sh` script for each case to be measured.)
2022-06-11 01:09:54 +03:00
"tracing",
]
[[package]]
name = "enso-build-macros"
version = "0.1.0"
dependencies = [
"enso-build-base",
"enso-build-macros-lib",
"itertools",
"proc-macro2",
"syn 1.0.107",
]
[[package]]
name = "enso-build-macros-lib"
version = "0.1.0"
dependencies = [
"convert_case 0.6.0",
2023-01-28 01:39:37 +03:00
"derive_more",
"enso-build-base",
2023-01-28 01:39:37 +03:00
"itertools",
"lazy_static",
"proc-macro2",
"quote",
"regex",
2023-01-27 03:09:09 +03:00
"serde_yaml",
"syn 1.0.107",
]
2023-01-27 03:09:09 +03:00
[[package]]
name = "enso-data-structures"
version = "0.2.0"
2023-01-27 03:09:09 +03:00
dependencies = [
"enso-prelude",
"failure",
"serde",
2023-01-27 03:09:09 +03:00
]
[[package]]
name = "enso-doc-parser"
version = "0.1.0"
dependencies = [
"enso-metamodel",
"enso-metamodel-lexpr",
"enso-parser",
"enso-prelude",
"enso-reflect",
"lexpr",
"pretty_assertions",
"serde",
]
Use Enso Font (#7516) Use the new Enso Font; also change the anti-aliasing logic to be based on device pixel ratio, rather than platform. This will improve the clarity of font rendering on Windows/Linux machines with high pixel densities. Design reference: ![image](https://github.com/enso-org/enso/assets/1047859/934ec9ac-52c3-4a81-a9f9-143378ecb658) Tested on various combinations of DPR/platform: OS X, `devicePixelRatio` = 2 (should look similar to how we were already rendering *mplus1* on OS X): <img width="1440" alt="Screenshot 2023-08-07 at 5 46 11 PM" src="https://github.com/enso-org/enso/assets/1047859/2fdf251a-ba5e-426f-b6c4-194347a9cee4"> Windows, `devicePixelRatio` = 1.25 (should look similar to how we were already rendering *mplus1* on this platform/DPR): ![image](https://github.com/enso-org/enso/assets/1047859/55c4a129-4fff-4a9b-8e55-51a5d206e659) Linux, `devicePixelRatio` = 1 (should look similar to how we were already rendering *mplus1* on this platform/DPR): ![image](https://github.com/enso-org/enso/assets/1047859/c5ac61f0-e3c5-43ca-8ee7-e1e04e84d35e) # Important Notes Style changes: - Use the Enso Font for code in Rust, replacing the DejaVu fonts. - Use the Enso Font in HTML: code in documentation, and error visualizations. - Change SpanWidgets from Bold to Extra Bold, to match the design. Implementation improvements: - The new font download is cached (and Github-authenticated); this should eliminate a "rate limit" build failure I've encountered in the past. - Clean up DocSection HTML rendering a bit. - Remove a CSS file that seems to have been superseded.
2023-08-17 21:36:12 +03:00
[[package]]
name = "enso-enso-font"
version = "0.1.0"
dependencies = [
"enso-font",
"ide-ci",
"owned_ttf_parser",
]
[[package]]
name = "enso-font"
version = "0.1.0"
dependencies = [
"derive_more",
"owned_ttf_parser",
]
2022-03-10 06:47:00 +03:00
[[package]]
name = "enso-formatter"
version = "0.1.0"
dependencies = [
"ide-ci",
2022-03-10 06:47:00 +03:00
"regex",
"tokio",
2022-03-10 06:47:00 +03:00
]
[[package]]
name = "enso-logging"
version = "0.3.1"
dependencies = [
"enso-logging-macros",
"web-sys",
]
[[package]]
name = "enso-logging-macros"
version = "0.1.0"
dependencies = [
"Inflector",
"proc-macro2",
"quote",
"syn 2.0.53",
]
2021-10-30 16:04:07 +03:00
[[package]]
name = "enso-macro-utils"
version = "0.2.0"
dependencies = [
2022-02-11 15:19:02 +03:00
"proc-macro2",
"quote",
"syn 1.0.107",
2021-10-30 16:04:07 +03:00
]
[[package]]
name = "enso-macros"
version = "0.2.7"
dependencies = [
"Inflector",
"enso-macro-utils",
"proc-macro2",
"quote",
"syn 1.0.107",
]
Parser: Transpile Rust AST types to Java types (#3555) Implement generation of Java AST types from the Rust AST type definitions, with support for deserializing in Java syntax trees created in Rust. ### New Libraries #### `enso-reflect` Implements a `#[derive(Reflect)]` macro to enable runtime analysis of datatypes. Macro interface includes helper attributes; **the Rust types and the `reflect` attributes applied to them fully determine the Java types** ultimately produced (by `enso-metamodel`). This is the most important API, as it is used in the subject crates (`enso-parser`, and dependencies with types used in the AST). [Module docs](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/reflect/macros/src/lib.rs). #### `enso-metamodel` Provides data models for data models in Rust/Java/Meta (a highly-abstracted language-independent model--I have referred to it before as the "generic representation", but that was an overloaded term). The high-level interface consists of operations on data models, and between them. For example, the only operations needed by [the binary that drives datatype transpilation](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/main.rs) are: `rust::to_meta`, `java::from_meta`, `java::transform::optional_to_null`, `java::to_syntax`. The low-level interface consists of direct usage of the datatypes; this is used by [the module that implements some serialization overrides](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/serialization.rs) (so that the Java interface to `Code` references can produce `String`s on demand based on serialized offset/length pairs). The serialization override mechanism is based on customizing, not replacing, the generated deserialization methods, so as to be as robust as possible to changes in the Rust source or in the transpilation process. ### Important Notes - Rust/Java serialization is exhaustively tested for structural compatibility. A function [`metamodel::meta::serialization::testcases`](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/metamodel/src/meta/serialization.rs) uses `reflect`-derived data to generate serialized representations of ASTs to use as test cases. Its should-accept cases cover every type a tree can contain; it also produces a representative set of should-reject cases. A Rust `#[test]` confirms that these cases are accepted/rejected as expected, and generated Java tests (see Binaries below) check the generated Java deserialization code against the same test cases. - Deserializing `Code` is untested. The mechanism is in place (in Rust, we serialize only the offset/length of the `Cow`; in Java, during deserialization we obtain a context object holding a buffer for all string data; the accessor generated in Java uses the buffer and the offset/length to return `String`s), but it will be easier to test once we have implemented actually parsing something and instantiating the `Cow`s with source code. - `#[tagged_enum]` [now supports](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/shapely/macros/src/tagged_enum.rs#L36-L51) control over what is done with container-level attributes; they can be applied to the container and variants (default), only to the container, or only to variants. - Generation of `sealed` classes is supported, but currently disabled by `TARGET_VERSION` in `metamodel::java::syntax` so that tests don't require Java 15 to run. (The same logic is run either way; there is a shallow difference in output.) ### Binaries The `enso-parser-generate-java` crate defines several binaries: - `enso-parser-generate-java`: Performs the transpilation; after integration, this will be invoked by the build script. - `java-tests`: Generates the Java code that tests format deserialization; after integration this command will be invoked by the build script, and its Java output compiled and run during testing. - `graph-rust`/`graph-meta`/`graph-java`: Produce GraphViz representations of data models in different typesystems; these are for developing and understanding model transformations. Until integration, a **script regenerates the Java and runs the format tests: `./tools/parser_generate_java.sh`**. The generated code can be browsed in `target/generated_java`.
2022-07-07 05:46:42 +03:00
[[package]]
name = "enso-metamodel"
version = "0.1.0"
dependencies = [
"bincode",
Parser: Transpile Rust AST types to Java types (#3555) Implement generation of Java AST types from the Rust AST type definitions, with support for deserializing in Java syntax trees created in Rust. ### New Libraries #### `enso-reflect` Implements a `#[derive(Reflect)]` macro to enable runtime analysis of datatypes. Macro interface includes helper attributes; **the Rust types and the `reflect` attributes applied to them fully determine the Java types** ultimately produced (by `enso-metamodel`). This is the most important API, as it is used in the subject crates (`enso-parser`, and dependencies with types used in the AST). [Module docs](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/reflect/macros/src/lib.rs). #### `enso-metamodel` Provides data models for data models in Rust/Java/Meta (a highly-abstracted language-independent model--I have referred to it before as the "generic representation", but that was an overloaded term). The high-level interface consists of operations on data models, and between them. For example, the only operations needed by [the binary that drives datatype transpilation](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/main.rs) are: `rust::to_meta`, `java::from_meta`, `java::transform::optional_to_null`, `java::to_syntax`. The low-level interface consists of direct usage of the datatypes; this is used by [the module that implements some serialization overrides](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/serialization.rs) (so that the Java interface to `Code` references can produce `String`s on demand based on serialized offset/length pairs). The serialization override mechanism is based on customizing, not replacing, the generated deserialization methods, so as to be as robust as possible to changes in the Rust source or in the transpilation process. ### Important Notes - Rust/Java serialization is exhaustively tested for structural compatibility. A function [`metamodel::meta::serialization::testcases`](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/metamodel/src/meta/serialization.rs) uses `reflect`-derived data to generate serialized representations of ASTs to use as test cases. Its should-accept cases cover every type a tree can contain; it also produces a representative set of should-reject cases. A Rust `#[test]` confirms that these cases are accepted/rejected as expected, and generated Java tests (see Binaries below) check the generated Java deserialization code against the same test cases. - Deserializing `Code` is untested. The mechanism is in place (in Rust, we serialize only the offset/length of the `Cow`; in Java, during deserialization we obtain a context object holding a buffer for all string data; the accessor generated in Java uses the buffer and the offset/length to return `String`s), but it will be easier to test once we have implemented actually parsing something and instantiating the `Cow`s with source code. - `#[tagged_enum]` [now supports](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/shapely/macros/src/tagged_enum.rs#L36-L51) control over what is done with container-level attributes; they can be applied to the container and variants (default), only to the container, or only to variants. - Generation of `sealed` classes is supported, but currently disabled by `TARGET_VERSION` in `metamodel::java::syntax` so that tests don't require Java 15 to run. (The same logic is run either way; there is a shallow difference in output.) ### Binaries The `enso-parser-generate-java` crate defines several binaries: - `enso-parser-generate-java`: Performs the transpilation; after integration, this will be invoked by the build script. - `java-tests`: Generates the Java code that tests format deserialization; after integration this command will be invoked by the build script, and its Java output compiled and run during testing. - `graph-rust`/`graph-meta`/`graph-java`: Produce GraphViz representations of data models in different typesystems; these are for developing and understanding model transformations. Until integration, a **script regenerates the Java and runs the format tests: `./tools/parser_generate_java.sh`**. The generated code can be browsed in `target/generated_java`.
2022-07-07 05:46:42 +03:00
"derivative",
"derive-where",
Parser: Transpile Rust AST types to Java types (#3555) Implement generation of Java AST types from the Rust AST type definitions, with support for deserializing in Java syntax trees created in Rust. ### New Libraries #### `enso-reflect` Implements a `#[derive(Reflect)]` macro to enable runtime analysis of datatypes. Macro interface includes helper attributes; **the Rust types and the `reflect` attributes applied to them fully determine the Java types** ultimately produced (by `enso-metamodel`). This is the most important API, as it is used in the subject crates (`enso-parser`, and dependencies with types used in the AST). [Module docs](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/reflect/macros/src/lib.rs). #### `enso-metamodel` Provides data models for data models in Rust/Java/Meta (a highly-abstracted language-independent model--I have referred to it before as the "generic representation", but that was an overloaded term). The high-level interface consists of operations on data models, and between them. For example, the only operations needed by [the binary that drives datatype transpilation](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/main.rs) are: `rust::to_meta`, `java::from_meta`, `java::transform::optional_to_null`, `java::to_syntax`. The low-level interface consists of direct usage of the datatypes; this is used by [the module that implements some serialization overrides](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/serialization.rs) (so that the Java interface to `Code` references can produce `String`s on demand based on serialized offset/length pairs). The serialization override mechanism is based on customizing, not replacing, the generated deserialization methods, so as to be as robust as possible to changes in the Rust source or in the transpilation process. ### Important Notes - Rust/Java serialization is exhaustively tested for structural compatibility. A function [`metamodel::meta::serialization::testcases`](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/metamodel/src/meta/serialization.rs) uses `reflect`-derived data to generate serialized representations of ASTs to use as test cases. Its should-accept cases cover every type a tree can contain; it also produces a representative set of should-reject cases. A Rust `#[test]` confirms that these cases are accepted/rejected as expected, and generated Java tests (see Binaries below) check the generated Java deserialization code against the same test cases. - Deserializing `Code` is untested. The mechanism is in place (in Rust, we serialize only the offset/length of the `Cow`; in Java, during deserialization we obtain a context object holding a buffer for all string data; the accessor generated in Java uses the buffer and the offset/length to return `String`s), but it will be easier to test once we have implemented actually parsing something and instantiating the `Cow`s with source code. - `#[tagged_enum]` [now supports](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/shapely/macros/src/tagged_enum.rs#L36-L51) control over what is done with container-level attributes; they can be applied to the container and variants (default), only to the container, or only to variants. - Generation of `sealed` classes is supported, but currently disabled by `TARGET_VERSION` in `metamodel::java::syntax` so that tests don't require Java 15 to run. (The same logic is run either way; there is a shallow difference in output.) ### Binaries The `enso-parser-generate-java` crate defines several binaries: - `enso-parser-generate-java`: Performs the transpilation; after integration, this will be invoked by the build script. - `java-tests`: Generates the Java code that tests format deserialization; after integration this command will be invoked by the build script, and its Java output compiled and run during testing. - `graph-rust`/`graph-meta`/`graph-java`: Produce GraphViz representations of data models in different typesystems; these are for developing and understanding model transformations. Until integration, a **script regenerates the Java and runs the format tests: `./tools/parser_generate_java.sh`**. The generated code can be browsed in `target/generated_java`.
2022-07-07 05:46:42 +03:00
"derive_more",
2023-06-17 17:14:19 +03:00
"enso-zst",
Parser: Transpile Rust AST types to Java types (#3555) Implement generation of Java AST types from the Rust AST type definitions, with support for deserializing in Java syntax trees created in Rust. ### New Libraries #### `enso-reflect` Implements a `#[derive(Reflect)]` macro to enable runtime analysis of datatypes. Macro interface includes helper attributes; **the Rust types and the `reflect` attributes applied to them fully determine the Java types** ultimately produced (by `enso-metamodel`). This is the most important API, as it is used in the subject crates (`enso-parser`, and dependencies with types used in the AST). [Module docs](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/reflect/macros/src/lib.rs). #### `enso-metamodel` Provides data models for data models in Rust/Java/Meta (a highly-abstracted language-independent model--I have referred to it before as the "generic representation", but that was an overloaded term). The high-level interface consists of operations on data models, and between them. For example, the only operations needed by [the binary that drives datatype transpilation](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/main.rs) are: `rust::to_meta`, `java::from_meta`, `java::transform::optional_to_null`, `java::to_syntax`. The low-level interface consists of direct usage of the datatypes; this is used by [the module that implements some serialization overrides](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/serialization.rs) (so that the Java interface to `Code` references can produce `String`s on demand based on serialized offset/length pairs). The serialization override mechanism is based on customizing, not replacing, the generated deserialization methods, so as to be as robust as possible to changes in the Rust source or in the transpilation process. ### Important Notes - Rust/Java serialization is exhaustively tested for structural compatibility. A function [`metamodel::meta::serialization::testcases`](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/metamodel/src/meta/serialization.rs) uses `reflect`-derived data to generate serialized representations of ASTs to use as test cases. Its should-accept cases cover every type a tree can contain; it also produces a representative set of should-reject cases. A Rust `#[test]` confirms that these cases are accepted/rejected as expected, and generated Java tests (see Binaries below) check the generated Java deserialization code against the same test cases. - Deserializing `Code` is untested. The mechanism is in place (in Rust, we serialize only the offset/length of the `Cow`; in Java, during deserialization we obtain a context object holding a buffer for all string data; the accessor generated in Java uses the buffer and the offset/length to return `String`s), but it will be easier to test once we have implemented actually parsing something and instantiating the `Cow`s with source code. - `#[tagged_enum]` [now supports](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/shapely/macros/src/tagged_enum.rs#L36-L51) control over what is done with container-level attributes; they can be applied to the container and variants (default), only to the container, or only to variants. - Generation of `sealed` classes is supported, but currently disabled by `TARGET_VERSION` in `metamodel::java::syntax` so that tests don't require Java 15 to run. (The same logic is run either way; there is a shallow difference in output.) ### Binaries The `enso-parser-generate-java` crate defines several binaries: - `enso-parser-generate-java`: Performs the transpilation; after integration, this will be invoked by the build script. - `java-tests`: Generates the Java code that tests format deserialization; after integration this command will be invoked by the build script, and its Java output compiled and run during testing. - `graph-rust`/`graph-meta`/`graph-java`: Produce GraphViz representations of data models in different typesystems; these are for developing and understanding model transformations. Until integration, a **script regenerates the Java and runs the format tests: `./tools/parser_generate_java.sh`**. The generated code can be browsed in `target/generated_java`.
2022-07-07 05:46:42 +03:00
]
[[package]]
name = "enso-metamodel-lexpr"
version = "0.1.0"
dependencies = [
"bincode",
"derivative",
"enso-metamodel",
"enso-reflect",
"lexpr",
"serde",
]
[[package]]
name = "enso-parser"
version = "0.1.0"
dependencies = [
"bincode",
"enso-data-structures",
"enso-metamodel",
"enso-parser-syntax-tree-visitor",
"enso-prelude",
Parser: Transpile Rust AST types to Java types (#3555) Implement generation of Java AST types from the Rust AST type definitions, with support for deserializing in Java syntax trees created in Rust. ### New Libraries #### `enso-reflect` Implements a `#[derive(Reflect)]` macro to enable runtime analysis of datatypes. Macro interface includes helper attributes; **the Rust types and the `reflect` attributes applied to them fully determine the Java types** ultimately produced (by `enso-metamodel`). This is the most important API, as it is used in the subject crates (`enso-parser`, and dependencies with types used in the AST). [Module docs](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/reflect/macros/src/lib.rs). #### `enso-metamodel` Provides data models for data models in Rust/Java/Meta (a highly-abstracted language-independent model--I have referred to it before as the "generic representation", but that was an overloaded term). The high-level interface consists of operations on data models, and between them. For example, the only operations needed by [the binary that drives datatype transpilation](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/main.rs) are: `rust::to_meta`, `java::from_meta`, `java::transform::optional_to_null`, `java::to_syntax`. The low-level interface consists of direct usage of the datatypes; this is used by [the module that implements some serialization overrides](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/serialization.rs) (so that the Java interface to `Code` references can produce `String`s on demand based on serialized offset/length pairs). The serialization override mechanism is based on customizing, not replacing, the generated deserialization methods, so as to be as robust as possible to changes in the Rust source or in the transpilation process. ### Important Notes - Rust/Java serialization is exhaustively tested for structural compatibility. A function [`metamodel::meta::serialization::testcases`](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/metamodel/src/meta/serialization.rs) uses `reflect`-derived data to generate serialized representations of ASTs to use as test cases. Its should-accept cases cover every type a tree can contain; it also produces a representative set of should-reject cases. A Rust `#[test]` confirms that these cases are accepted/rejected as expected, and generated Java tests (see Binaries below) check the generated Java deserialization code against the same test cases. - Deserializing `Code` is untested. The mechanism is in place (in Rust, we serialize only the offset/length of the `Cow`; in Java, during deserialization we obtain a context object holding a buffer for all string data; the accessor generated in Java uses the buffer and the offset/length to return `String`s), but it will be easier to test once we have implemented actually parsing something and instantiating the `Cow`s with source code. - `#[tagged_enum]` [now supports](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/shapely/macros/src/tagged_enum.rs#L36-L51) control over what is done with container-level attributes; they can be applied to the container and variants (default), only to the container, or only to variants. - Generation of `sealed` classes is supported, but currently disabled by `TARGET_VERSION` in `metamodel::java::syntax` so that tests don't require Java 15 to run. (The same logic is run either way; there is a shallow difference in output.) ### Binaries The `enso-parser-generate-java` crate defines several binaries: - `enso-parser-generate-java`: Performs the transpilation; after integration, this will be invoked by the build script. - `java-tests`: Generates the Java code that tests format deserialization; after integration this command will be invoked by the build script, and its Java output compiled and run during testing. - `graph-rust`/`graph-meta`/`graph-java`: Produce GraphViz representations of data models in different typesystems; these are for developing and understanding model transformations. Until integration, a **script regenerates the Java and runs the format tests: `./tools/parser_generate_java.sh`**. The generated code can be browsed in `target/generated_java`.
2022-07-07 05:46:42 +03:00
"enso-reflect",
"paste",
2022-07-20 17:53:20 +03:00
"rand 0.8.5",
"rand_chacha 0.3.1",
"rand_distr",
Parser: Transpile Rust AST types to Java types (#3555) Implement generation of Java AST types from the Rust AST type definitions, with support for deserializing in Java syntax trees created in Rust. ### New Libraries #### `enso-reflect` Implements a `#[derive(Reflect)]` macro to enable runtime analysis of datatypes. Macro interface includes helper attributes; **the Rust types and the `reflect` attributes applied to them fully determine the Java types** ultimately produced (by `enso-metamodel`). This is the most important API, as it is used in the subject crates (`enso-parser`, and dependencies with types used in the AST). [Module docs](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/reflect/macros/src/lib.rs). #### `enso-metamodel` Provides data models for data models in Rust/Java/Meta (a highly-abstracted language-independent model--I have referred to it before as the "generic representation", but that was an overloaded term). The high-level interface consists of operations on data models, and between them. For example, the only operations needed by [the binary that drives datatype transpilation](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/main.rs) are: `rust::to_meta`, `java::from_meta`, `java::transform::optional_to_null`, `java::to_syntax`. The low-level interface consists of direct usage of the datatypes; this is used by [the module that implements some serialization overrides](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/serialization.rs) (so that the Java interface to `Code` references can produce `String`s on demand based on serialized offset/length pairs). The serialization override mechanism is based on customizing, not replacing, the generated deserialization methods, so as to be as robust as possible to changes in the Rust source or in the transpilation process. ### Important Notes - Rust/Java serialization is exhaustively tested for structural compatibility. A function [`metamodel::meta::serialization::testcases`](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/metamodel/src/meta/serialization.rs) uses `reflect`-derived data to generate serialized representations of ASTs to use as test cases. Its should-accept cases cover every type a tree can contain; it also produces a representative set of should-reject cases. A Rust `#[test]` confirms that these cases are accepted/rejected as expected, and generated Java tests (see Binaries below) check the generated Java deserialization code against the same test cases. - Deserializing `Code` is untested. The mechanism is in place (in Rust, we serialize only the offset/length of the `Cow`; in Java, during deserialization we obtain a context object holding a buffer for all string data; the accessor generated in Java uses the buffer and the offset/length to return `String`s), but it will be easier to test once we have implemented actually parsing something and instantiating the `Cow`s with source code. - `#[tagged_enum]` [now supports](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/shapely/macros/src/tagged_enum.rs#L36-L51) control over what is done with container-level attributes; they can be applied to the container and variants (default), only to the container, or only to variants. - Generation of `sealed` classes is supported, but currently disabled by `TARGET_VERSION` in `metamodel::java::syntax` so that tests don't require Java 15 to run. (The same logic is run either way; there is a shallow difference in output.) ### Binaries The `enso-parser-generate-java` crate defines several binaries: - `enso-parser-generate-java`: Performs the transpilation; after integration, this will be invoked by the build script. - `java-tests`: Generates the Java code that tests format deserialization; after integration this command will be invoked by the build script, and its Java output compiled and run during testing. - `graph-rust`/`graph-meta`/`graph-java`: Produce GraphViz representations of data models in different typesystems; these are for developing and understanding model transformations. Until integration, a **script regenerates the Java and runs the format tests: `./tools/parser_generate_java.sh`**. The generated code can be browsed in `target/generated_java`.
2022-07-07 05:46:42 +03:00
"serde",
"serde_json",
"uuid",
"wasm-bindgen-test",
Parser: Transpile Rust AST types to Java types (#3555) Implement generation of Java AST types from the Rust AST type definitions, with support for deserializing in Java syntax trees created in Rust. ### New Libraries #### `enso-reflect` Implements a `#[derive(Reflect)]` macro to enable runtime analysis of datatypes. Macro interface includes helper attributes; **the Rust types and the `reflect` attributes applied to them fully determine the Java types** ultimately produced (by `enso-metamodel`). This is the most important API, as it is used in the subject crates (`enso-parser`, and dependencies with types used in the AST). [Module docs](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/reflect/macros/src/lib.rs). #### `enso-metamodel` Provides data models for data models in Rust/Java/Meta (a highly-abstracted language-independent model--I have referred to it before as the "generic representation", but that was an overloaded term). The high-level interface consists of operations on data models, and between them. For example, the only operations needed by [the binary that drives datatype transpilation](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/main.rs) are: `rust::to_meta`, `java::from_meta`, `java::transform::optional_to_null`, `java::to_syntax`. The low-level interface consists of direct usage of the datatypes; this is used by [the module that implements some serialization overrides](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/serialization.rs) (so that the Java interface to `Code` references can produce `String`s on demand based on serialized offset/length pairs). The serialization override mechanism is based on customizing, not replacing, the generated deserialization methods, so as to be as robust as possible to changes in the Rust source or in the transpilation process. ### Important Notes - Rust/Java serialization is exhaustively tested for structural compatibility. A function [`metamodel::meta::serialization::testcases`](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/metamodel/src/meta/serialization.rs) uses `reflect`-derived data to generate serialized representations of ASTs to use as test cases. Its should-accept cases cover every type a tree can contain; it also produces a representative set of should-reject cases. A Rust `#[test]` confirms that these cases are accepted/rejected as expected, and generated Java tests (see Binaries below) check the generated Java deserialization code against the same test cases. - Deserializing `Code` is untested. The mechanism is in place (in Rust, we serialize only the offset/length of the `Cow`; in Java, during deserialization we obtain a context object holding a buffer for all string data; the accessor generated in Java uses the buffer and the offset/length to return `String`s), but it will be easier to test once we have implemented actually parsing something and instantiating the `Cow`s with source code. - `#[tagged_enum]` [now supports](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/shapely/macros/src/tagged_enum.rs#L36-L51) control over what is done with container-level attributes; they can be applied to the container and variants (default), only to the container, or only to variants. - Generation of `sealed` classes is supported, but currently disabled by `TARGET_VERSION` in `metamodel::java::syntax` so that tests don't require Java 15 to run. (The same logic is run either way; there is a shallow difference in output.) ### Binaries The `enso-parser-generate-java` crate defines several binaries: - `enso-parser-generate-java`: Performs the transpilation; after integration, this will be invoked by the build script. - `java-tests`: Generates the Java code that tests format deserialization; after integration this command will be invoked by the build script, and its Java output compiled and run during testing. - `graph-rust`/`graph-meta`/`graph-java`: Produce GraphViz representations of data models in different typesystems; these are for developing and understanding model transformations. Until integration, a **script regenerates the Java and runs the format tests: `./tools/parser_generate_java.sh`**. The generated code can be browsed in `target/generated_java`.
2022-07-07 05:46:42 +03:00
]
[[package]]
name = "enso-parser-debug"
version = "0.1.0"
dependencies = [
"enso-metamodel",
"enso-metamodel-lexpr",
"enso-parser",
"enso-reflect",
"lexpr",
"serde",
"serde_json",
]
Parser: Transpile Rust AST types to Java types (#3555) Implement generation of Java AST types from the Rust AST type definitions, with support for deserializing in Java syntax trees created in Rust. ### New Libraries #### `enso-reflect` Implements a `#[derive(Reflect)]` macro to enable runtime analysis of datatypes. Macro interface includes helper attributes; **the Rust types and the `reflect` attributes applied to them fully determine the Java types** ultimately produced (by `enso-metamodel`). This is the most important API, as it is used in the subject crates (`enso-parser`, and dependencies with types used in the AST). [Module docs](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/reflect/macros/src/lib.rs). #### `enso-metamodel` Provides data models for data models in Rust/Java/Meta (a highly-abstracted language-independent model--I have referred to it before as the "generic representation", but that was an overloaded term). The high-level interface consists of operations on data models, and between them. For example, the only operations needed by [the binary that drives datatype transpilation](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/main.rs) are: `rust::to_meta`, `java::from_meta`, `java::transform::optional_to_null`, `java::to_syntax`. The low-level interface consists of direct usage of the datatypes; this is used by [the module that implements some serialization overrides](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/serialization.rs) (so that the Java interface to `Code` references can produce `String`s on demand based on serialized offset/length pairs). The serialization override mechanism is based on customizing, not replacing, the generated deserialization methods, so as to be as robust as possible to changes in the Rust source or in the transpilation process. ### Important Notes - Rust/Java serialization is exhaustively tested for structural compatibility. A function [`metamodel::meta::serialization::testcases`](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/metamodel/src/meta/serialization.rs) uses `reflect`-derived data to generate serialized representations of ASTs to use as test cases. Its should-accept cases cover every type a tree can contain; it also produces a representative set of should-reject cases. A Rust `#[test]` confirms that these cases are accepted/rejected as expected, and generated Java tests (see Binaries below) check the generated Java deserialization code against the same test cases. - Deserializing `Code` is untested. The mechanism is in place (in Rust, we serialize only the offset/length of the `Cow`; in Java, during deserialization we obtain a context object holding a buffer for all string data; the accessor generated in Java uses the buffer and the offset/length to return `String`s), but it will be easier to test once we have implemented actually parsing something and instantiating the `Cow`s with source code. - `#[tagged_enum]` [now supports](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/shapely/macros/src/tagged_enum.rs#L36-L51) control over what is done with container-level attributes; they can be applied to the container and variants (default), only to the container, or only to variants. - Generation of `sealed` classes is supported, but currently disabled by `TARGET_VERSION` in `metamodel::java::syntax` so that tests don't require Java 15 to run. (The same logic is run either way; there is a shallow difference in output.) ### Binaries The `enso-parser-generate-java` crate defines several binaries: - `enso-parser-generate-java`: Performs the transpilation; after integration, this will be invoked by the build script. - `java-tests`: Generates the Java code that tests format deserialization; after integration this command will be invoked by the build script, and its Java output compiled and run during testing. - `graph-rust`/`graph-meta`/`graph-java`: Produce GraphViz representations of data models in different typesystems; these are for developing and understanding model transformations. Until integration, a **script regenerates the Java and runs the format tests: `./tools/parser_generate_java.sh`**. The generated code can be browsed in `target/generated_java`.
2022-07-07 05:46:42 +03:00
[[package]]
name = "enso-parser-generate-java"
version = "0.1.0"
dependencies = [
"derivative",
"enso-metamodel",
"enso-parser",
"enso-prelude",
"enso-reflect",
]
[[package]]
name = "enso-parser-jni"
version = "0.1.0"
dependencies = [
"enso-parser",
"enso-prelude",
"jni",
]
[[package]]
name = "enso-parser-schema"
version = "0.1.0"
dependencies = [
"enso-metamodel",
"enso-parser",
"enso-reflect",
"serde",
"serde_json",
]
[[package]]
name = "enso-parser-syntax-tree-visitor"
version = "0.1.0"
dependencies = [
"enso-macro-utils",
"proc-macro2",
"quote",
"syn 1.0.107",
]
2021-10-30 16:04:07 +03:00
[[package]]
name = "enso-prelude"
version = "0.2.7"
2021-10-30 16:04:07 +03:00
dependencies = [
"boolinator",
"derivative",
"derive_more",
"enso-logging",
"enso-macros",
Parser: Transpile Rust AST types to Java types (#3555) Implement generation of Java AST types from the Rust AST type definitions, with support for deserializing in Java syntax trees created in Rust. ### New Libraries #### `enso-reflect` Implements a `#[derive(Reflect)]` macro to enable runtime analysis of datatypes. Macro interface includes helper attributes; **the Rust types and the `reflect` attributes applied to them fully determine the Java types** ultimately produced (by `enso-metamodel`). This is the most important API, as it is used in the subject crates (`enso-parser`, and dependencies with types used in the AST). [Module docs](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/reflect/macros/src/lib.rs). #### `enso-metamodel` Provides data models for data models in Rust/Java/Meta (a highly-abstracted language-independent model--I have referred to it before as the "generic representation", but that was an overloaded term). The high-level interface consists of operations on data models, and between them. For example, the only operations needed by [the binary that drives datatype transpilation](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/main.rs) are: `rust::to_meta`, `java::from_meta`, `java::transform::optional_to_null`, `java::to_syntax`. The low-level interface consists of direct usage of the datatypes; this is used by [the module that implements some serialization overrides](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/serialization.rs) (so that the Java interface to `Code` references can produce `String`s on demand based on serialized offset/length pairs). The serialization override mechanism is based on customizing, not replacing, the generated deserialization methods, so as to be as robust as possible to changes in the Rust source or in the transpilation process. ### Important Notes - Rust/Java serialization is exhaustively tested for structural compatibility. A function [`metamodel::meta::serialization::testcases`](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/metamodel/src/meta/serialization.rs) uses `reflect`-derived data to generate serialized representations of ASTs to use as test cases. Its should-accept cases cover every type a tree can contain; it also produces a representative set of should-reject cases. A Rust `#[test]` confirms that these cases are accepted/rejected as expected, and generated Java tests (see Binaries below) check the generated Java deserialization code against the same test cases. - Deserializing `Code` is untested. The mechanism is in place (in Rust, we serialize only the offset/length of the `Cow`; in Java, during deserialization we obtain a context object holding a buffer for all string data; the accessor generated in Java uses the buffer and the offset/length to return `String`s), but it will be easier to test once we have implemented actually parsing something and instantiating the `Cow`s with source code. - `#[tagged_enum]` [now supports](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/shapely/macros/src/tagged_enum.rs#L36-L51) control over what is done with container-level attributes; they can be applied to the container and variants (default), only to the container, or only to variants. - Generation of `sealed` classes is supported, but currently disabled by `TARGET_VERSION` in `metamodel::java::syntax` so that tests don't require Java 15 to run. (The same logic is run either way; there is a shallow difference in output.) ### Binaries The `enso-parser-generate-java` crate defines several binaries: - `enso-parser-generate-java`: Performs the transpilation; after integration, this will be invoked by the build script. - `java-tests`: Generates the Java code that tests format deserialization; after integration this command will be invoked by the build script, and its Java output compiled and run during testing. - `graph-rust`/`graph-meta`/`graph-java`: Produce GraphViz representations of data models in different typesystems; these are for developing and understanding model transformations. Until integration, a **script regenerates the Java and runs the format tests: `./tools/parser_generate_java.sh`**. The generated code can be browsed in `target/generated_java`.
2022-07-07 05:46:42 +03:00
"enso-reflect",
2023-06-17 17:14:19 +03:00
"enso-zst",
2021-10-30 16:04:07 +03:00
"serde",
]
Parser: Transpile Rust AST types to Java types (#3555) Implement generation of Java AST types from the Rust AST type definitions, with support for deserializing in Java syntax trees created in Rust. ### New Libraries #### `enso-reflect` Implements a `#[derive(Reflect)]` macro to enable runtime analysis of datatypes. Macro interface includes helper attributes; **the Rust types and the `reflect` attributes applied to them fully determine the Java types** ultimately produced (by `enso-metamodel`). This is the most important API, as it is used in the subject crates (`enso-parser`, and dependencies with types used in the AST). [Module docs](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/reflect/macros/src/lib.rs). #### `enso-metamodel` Provides data models for data models in Rust/Java/Meta (a highly-abstracted language-independent model--I have referred to it before as the "generic representation", but that was an overloaded term). The high-level interface consists of operations on data models, and between them. For example, the only operations needed by [the binary that drives datatype transpilation](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/main.rs) are: `rust::to_meta`, `java::from_meta`, `java::transform::optional_to_null`, `java::to_syntax`. The low-level interface consists of direct usage of the datatypes; this is used by [the module that implements some serialization overrides](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/serialization.rs) (so that the Java interface to `Code` references can produce `String`s on demand based on serialized offset/length pairs). The serialization override mechanism is based on customizing, not replacing, the generated deserialization methods, so as to be as robust as possible to changes in the Rust source or in the transpilation process. ### Important Notes - Rust/Java serialization is exhaustively tested for structural compatibility. A function [`metamodel::meta::serialization::testcases`](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/metamodel/src/meta/serialization.rs) uses `reflect`-derived data to generate serialized representations of ASTs to use as test cases. Its should-accept cases cover every type a tree can contain; it also produces a representative set of should-reject cases. A Rust `#[test]` confirms that these cases are accepted/rejected as expected, and generated Java tests (see Binaries below) check the generated Java deserialization code against the same test cases. - Deserializing `Code` is untested. The mechanism is in place (in Rust, we serialize only the offset/length of the `Cow`; in Java, during deserialization we obtain a context object holding a buffer for all string data; the accessor generated in Java uses the buffer and the offset/length to return `String`s), but it will be easier to test once we have implemented actually parsing something and instantiating the `Cow`s with source code. - `#[tagged_enum]` [now supports](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/shapely/macros/src/tagged_enum.rs#L36-L51) control over what is done with container-level attributes; they can be applied to the container and variants (default), only to the container, or only to variants. - Generation of `sealed` classes is supported, but currently disabled by `TARGET_VERSION` in `metamodel::java::syntax` so that tests don't require Java 15 to run. (The same logic is run either way; there is a shallow difference in output.) ### Binaries The `enso-parser-generate-java` crate defines several binaries: - `enso-parser-generate-java`: Performs the transpilation; after integration, this will be invoked by the build script. - `java-tests`: Generates the Java code that tests format deserialization; after integration this command will be invoked by the build script, and its Java output compiled and run during testing. - `graph-rust`/`graph-meta`/`graph-java`: Produce GraphViz representations of data models in different typesystems; these are for developing and understanding model transformations. Until integration, a **script regenerates the Java and runs the format tests: `./tools/parser_generate_java.sh`**. The generated code can be browsed in `target/generated_java`.
2022-07-07 05:46:42 +03:00
[[package]]
name = "enso-reflect"
version = "0.1.0"
dependencies = [
"enso-metamodel",
"enso-reflect-macros",
]
[[package]]
name = "enso-reflect-macros"
version = "0.1.0"
dependencies = [
"proc-macro2",
"quote",
"syn 1.0.107",
Parser: Transpile Rust AST types to Java types (#3555) Implement generation of Java AST types from the Rust AST type definitions, with support for deserializing in Java syntax trees created in Rust. ### New Libraries #### `enso-reflect` Implements a `#[derive(Reflect)]` macro to enable runtime analysis of datatypes. Macro interface includes helper attributes; **the Rust types and the `reflect` attributes applied to them fully determine the Java types** ultimately produced (by `enso-metamodel`). This is the most important API, as it is used in the subject crates (`enso-parser`, and dependencies with types used in the AST). [Module docs](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/reflect/macros/src/lib.rs). #### `enso-metamodel` Provides data models for data models in Rust/Java/Meta (a highly-abstracted language-independent model--I have referred to it before as the "generic representation", but that was an overloaded term). The high-level interface consists of operations on data models, and between them. For example, the only operations needed by [the binary that drives datatype transpilation](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/main.rs) are: `rust::to_meta`, `java::from_meta`, `java::transform::optional_to_null`, `java::to_syntax`. The low-level interface consists of direct usage of the datatypes; this is used by [the module that implements some serialization overrides](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/parser/generate-java/src/serialization.rs) (so that the Java interface to `Code` references can produce `String`s on demand based on serialized offset/length pairs). The serialization override mechanism is based on customizing, not replacing, the generated deserialization methods, so as to be as robust as possible to changes in the Rust source or in the transpilation process. ### Important Notes - Rust/Java serialization is exhaustively tested for structural compatibility. A function [`metamodel::meta::serialization::testcases`](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/metamodel/src/meta/serialization.rs) uses `reflect`-derived data to generate serialized representations of ASTs to use as test cases. Its should-accept cases cover every type a tree can contain; it also produces a representative set of should-reject cases. A Rust `#[test]` confirms that these cases are accepted/rejected as expected, and generated Java tests (see Binaries below) check the generated Java deserialization code against the same test cases. - Deserializing `Code` is untested. The mechanism is in place (in Rust, we serialize only the offset/length of the `Cow`; in Java, during deserialization we obtain a context object holding a buffer for all string data; the accessor generated in Java uses the buffer and the offset/length to return `String`s), but it will be easier to test once we have implemented actually parsing something and instantiating the `Cow`s with source code. - `#[tagged_enum]` [now supports](https://github.com/enso-org/enso/blob/wip/kw/parser/ast-transpiler/lib/rust/shapely/macros/src/tagged_enum.rs#L36-L51) control over what is done with container-level attributes; they can be applied to the container and variants (default), only to the container, or only to variants. - Generation of `sealed` classes is supported, but currently disabled by `TARGET_VERSION` in `metamodel::java::syntax` so that tests don't require Java 15 to run. (The same logic is run either way; there is a shallow difference in output.) ### Binaries The `enso-parser-generate-java` crate defines several binaries: - `enso-parser-generate-java`: Performs the transpilation; after integration, this will be invoked by the build script. - `java-tests`: Generates the Java code that tests format deserialization; after integration this command will be invoked by the build script, and its Java output compiled and run during testing. - `graph-rust`/`graph-meta`/`graph-java`: Produce GraphViz representations of data models in different typesystems; these are for developing and understanding model transformations. Until integration, a **script regenerates the Java and runs the format tests: `./tools/parser_generate_java.sh`**. The generated code can be browsed in `target/generated_java`.
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Better `release` build time; new maximum-performance `production` profile. (#3498) ### Pull Request Description Using the new tooling (#3491), I investigated the **performance / compile-time tradeoff** of different codegen options for release mode builds. By scripting the testing procedure, I was able to explore many possible combinations of options, which is important because their interactions (on both application performance and build time) are complex. I found **two candidate profiles** that offer specific advantages over the current `release` settings (`baseline`): - `thin16`: Supports incremental compiles in 1/3 the time of `baseline` in common cases. Application runs about 2% slower than `baseline`. - `fat1-O4`: Application performs 13% better than `baseline`. Compile time is almost 3x `baseline`, and non-incremental. (See key in first chart for the settings defining these profiles.) We can build faster or run faster, though not in the same build. Because the effect sizes are large enough to be impactful to developer and user experience, respectively, I think we should consider having it both ways. We could **split the `release` profile** into two profiles to serve different purposes: - `release`: A profile that supports fast developer iteration, while offering realistic performance. - `production`: A maximally-optimized profile, for nightly builds and actual releases. Since `wasm-pack` doesn't currently support custom profiles (rustwasm/wasm-pack#1111), we can't use a Cargo profile for `production`; however, we can implement our own profile by overriding rustc flags. ### Performance details ![perf](https://user-images.githubusercontent.com/1047859/170788530-ab6d7910-5253-4a2b-b432-8bfa0b4735ba.png) As you can see, `thin16` is slightly slower than `baseline`; `fat1-O4` is dramatically faster. <details> <summary>Methodology (click to show)</summary> I developed a procedure for benchmarking "whole application" performance, using the new "open project" workflow (which opens the IDE and loads a complex project), and some statistical analysis to account for variance. To gather this data: Build the application with profiling: `./run.sh ide build --profiling-level=debug` Run the `open_project` workflow repeatedly: `for i in $(seq 0 9); do dist/ide/linux-unpacked/enso --entry-point profile --workflow open_project --save-profile open_project_thin16_${i}.json; done` For each profile recorded, take the new `total_self_time` output of the `intervals` tool; gather into CSV: `echo $(for i in $(seq 0 9); do target/rust/debug/intervals < open_project_thin16_${i}.json | tail -n1 | awk '{print $2}'; do` (Note that the output of intervals should not be considered stable; this command may need modification in the future. Eventually it would be nice to support formatted outputs...) The data is ready to graph. I used the `boxplot` method of the [seaborn](https://seaborn.pydata.org/index.html) package, in order to show the distribution of data. </details> #### Build times ![thin16](https://user-images.githubusercontent.com/1047859/170788539-1578e41b-bc30-4f30-9b71-0b0181322fa5.png) In the case of changing a file in `enso-prelude`, with the current `baseline` settings rebuilding takes over 3 minutes. With the `thin16` settings, the same rebuild completes in 40 seconds. (To gather this data on different hardware or in the future, just run the new `bench-build.sh` script for each case to be measured.)
2022-06-11 01:09:54 +03:00
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Multi-process profiles. (#3395) See: [#181837344](https://www.pivotaltracker.com/story/show/181837344). I've separated this PR from some deeper changes I'm making to the profile format, because the changeset was getting too complex. The new APIs and tools in this PR are fully-implemented, except the profile format is too simplistic--it doesn't currently support headers that are needed to determine the relative timings of events from different processes. - Adds basic support for profile files containing data collected by multiple processes. - Implements `api_events_to_profile`, a tool for converting backend message logs (#3392) to the `profiler` format so they can be merged with frontend profiles (currently they can be merged with `cat`, but the next PR will introduce a merge tool). - Introduces `message_beanpoles`, a simple tool that diagrams timing relationships between frontend and backend messages. ### Important Notes - All TODOs introduced here will be addressed in the next PR that defines the new format. - Introduced a new crate, `enso_profiler_enso_data`, to be used by profile consumers that need to refer to Enso application datatypes to interpret metadata. - Introduced a `ProfileBuilder` abstraction for writing the JSON profile format; partially decouples the runtime event log structures from the format definition. - Introducing the conversion performed for `ProfilerBuilder` uncovered that the `.._with_same_start!` low-level `profiler` APIs don't currently work; they return `Started<_>` profilers, but that is inconsistent with the stricter data model that I introduced when I implemented `profiler_data`; they need to return profilers in a created, unstarted state. Low-level async profilers have not been a priority, but once #3382 merges we'll have a way to render their data, which will be really useful because async profilers capture *why* we're doing things. I'll bring up scheduling this in the next performance meeting.
2022-04-21 17:44:03 +03:00
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Multi-process profiles. (#3395) See: [#181837344](https://www.pivotaltracker.com/story/show/181837344). I've separated this PR from some deeper changes I'm making to the profile format, because the changeset was getting too complex. The new APIs and tools in this PR are fully-implemented, except the profile format is too simplistic--it doesn't currently support headers that are needed to determine the relative timings of events from different processes. - Adds basic support for profile files containing data collected by multiple processes. - Implements `api_events_to_profile`, a tool for converting backend message logs (#3392) to the `profiler` format so they can be merged with frontend profiles (currently they can be merged with `cat`, but the next PR will introduce a merge tool). - Introduces `message_beanpoles`, a simple tool that diagrams timing relationships between frontend and backend messages. ### Important Notes - All TODOs introduced here will be addressed in the next PR that defines the new format. - Introduced a new crate, `enso_profiler_enso_data`, to be used by profile consumers that need to refer to Enso application datatypes to interpret metadata. - Introduced a `ProfileBuilder` abstraction for writing the JSON profile format; partially decouples the runtime event log structures from the format definition. - Introducing the conversion performed for `ProfilerBuilder` uncovered that the `.._with_same_start!` low-level `profiler` APIs don't currently work; they return `Started<_>` profilers, but that is inconsistent with the stricter data model that I introduced when I implemented `profiler_data`; they need to return profilers in a created, unstarted state. Low-level async profilers have not been a priority, but once #3382 merges we'll have a way to render their data, which will be really useful because async profilers capture *why* we're doing things. I'll bring up scheduling this in the next performance meeting.
2022-04-21 17:44:03 +03:00
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2022-06-01 14:44:40 +03:00
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Multi-process profiles. (#3395) See: [#181837344](https://www.pivotaltracker.com/story/show/181837344). I've separated this PR from some deeper changes I'm making to the profile format, because the changeset was getting too complex. The new APIs and tools in this PR are fully-implemented, except the profile format is too simplistic--it doesn't currently support headers that are needed to determine the relative timings of events from different processes. - Adds basic support for profile files containing data collected by multiple processes. - Implements `api_events_to_profile`, a tool for converting backend message logs (#3392) to the `profiler` format so they can be merged with frontend profiles (currently they can be merged with `cat`, but the next PR will introduce a merge tool). - Introduces `message_beanpoles`, a simple tool that diagrams timing relationships between frontend and backend messages. ### Important Notes - All TODOs introduced here will be addressed in the next PR that defines the new format. - Introduced a new crate, `enso_profiler_enso_data`, to be used by profile consumers that need to refer to Enso application datatypes to interpret metadata. - Introduced a `ProfileBuilder` abstraction for writing the JSON profile format; partially decouples the runtime event log structures from the format definition. - Introducing the conversion performed for `ProfilerBuilder` uncovered that the `.._with_same_start!` low-level `profiler` APIs don't currently work; they return `Started<_>` profilers, but that is inconsistent with the stricter data model that I introduced when I implemented `profiler_data`; they need to return profilers in a created, unstarted state. Low-level async profilers have not been a priority, but once #3382 merges we'll have a way to render their data, which will be really useful because async profilers capture *why* we're doing things. I'll bring up scheduling this in the next performance meeting.
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Multi-process profiles. (#3395) See: [#181837344](https://www.pivotaltracker.com/story/show/181837344). I've separated this PR from some deeper changes I'm making to the profile format, because the changeset was getting too complex. The new APIs and tools in this PR are fully-implemented, except the profile format is too simplistic--it doesn't currently support headers that are needed to determine the relative timings of events from different processes. - Adds basic support for profile files containing data collected by multiple processes. - Implements `api_events_to_profile`, a tool for converting backend message logs (#3392) to the `profiler` format so they can be merged with frontend profiles (currently they can be merged with `cat`, but the next PR will introduce a merge tool). - Introduces `message_beanpoles`, a simple tool that diagrams timing relationships between frontend and backend messages. ### Important Notes - All TODOs introduced here will be addressed in the next PR that defines the new format. - Introduced a new crate, `enso_profiler_enso_data`, to be used by profile consumers that need to refer to Enso application datatypes to interpret metadata. - Introduced a `ProfileBuilder` abstraction for writing the JSON profile format; partially decouples the runtime event log structures from the format definition. - Introducing the conversion performed for `ProfilerBuilder` uncovered that the `.._with_same_start!` low-level `profiler` APIs don't currently work; they return `Started<_>` profilers, but that is inconsistent with the stricter data model that I introduced when I implemented `profiler_data`; they need to return profilers in a created, unstarted state. Low-level async profilers have not been a priority, but once #3382 merges we'll have a way to render their data, which will be really useful because async profilers capture *why* we're doing things. I'll bring up scheduling this in the next performance meeting.
2022-04-21 17:44:03 +03:00
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Multi-process profiles. (#3395) See: [#181837344](https://www.pivotaltracker.com/story/show/181837344). I've separated this PR from some deeper changes I'm making to the profile format, because the changeset was getting too complex. The new APIs and tools in this PR are fully-implemented, except the profile format is too simplistic--it doesn't currently support headers that are needed to determine the relative timings of events from different processes. - Adds basic support for profile files containing data collected by multiple processes. - Implements `api_events_to_profile`, a tool for converting backend message logs (#3392) to the `profiler` format so they can be merged with frontend profiles (currently they can be merged with `cat`, but the next PR will introduce a merge tool). - Introduces `message_beanpoles`, a simple tool that diagrams timing relationships between frontend and backend messages. ### Important Notes - All TODOs introduced here will be addressed in the next PR that defines the new format. - Introduced a new crate, `enso_profiler_enso_data`, to be used by profile consumers that need to refer to Enso application datatypes to interpret metadata. - Introduced a `ProfileBuilder` abstraction for writing the JSON profile format; partially decouples the runtime event log structures from the format definition. - Introducing the conversion performed for `ProfilerBuilder` uncovered that the `.._with_same_start!` low-level `profiler` APIs don't currently work; they return `Started<_>` profilers, but that is inconsistent with the stricter data model that I introduced when I implemented `profiler_data`; they need to return profilers in a created, unstarted state. Low-level async profilers have not been a priority, but once #3382 merges we'll have a way to render their data, which will be really useful because async profilers capture *why* we're doing things. I'll bring up scheduling this in the next performance meeting.
2022-04-21 17:44:03 +03:00
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