Don't create markRefs unless renderMarks is provided #178
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We have an application that uses react-range under the hood, and we noticed that a range input was taking 2GB of RAM on our machines. I did some investigation and found that regardless of whether the marks functionality was being used, refs were being created for each possible value of the range. We have some fairly huge ranges (we're using the input to scrub a video with potential microsecond accuracy), and can imagine that other people are affected by the previous behavior. This change should allow us to continue using large input ranges without incurring a memory penalty.
wolfd
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wolfd
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May 30, 2023
As mentioned in https://blog.streamlit.io/six-tips-for-improving-your-streamlit-app-performance/ memory usage struggles in the browser if you have large ranges: > Due to implementation details, high-cardinality sliders don't suffer > from the serialization and network transfer delays mentioned earlier, > but they will still lead to a poor user experience (who needs to > specify house prices up to the dollar?) and high memory usage. In my > testing, the example above increased RAM usage by gigabytes until the > web browser eventually gave up (though this is something that should > be solvable on our end. We'll look into it!) This was caused by a bug in react-range, which I fixed last year. tajo/react-range#178 At the time, I had figured it would get picked up by a random yarn upgrade and didn't worry too much about it. But, apparently yarn doesn't really have an easy way of doing upgrades of transitive dependencies (see yarnpkg/yarn#4986)? I took the suggestion of someone in that thread to delete the entry and let yarn regenerate it. Some techinical details about the react-range fix from the original commit message (the "application" is a streamlit app): > We have an application that uses react-range under the hood, and we > noticed that a range input was taking 2GB of RAM on our machines. I > did some investigation and found that regardless of whether the marks > functionality was being used, refs were being created for each > possible value of the range. > We have some fairly huge ranges (we're using the input to scrub a > video with potential microsecond accuracy), and can imagine that > other people are affected by the previous behavior. This change > should allow us to continue using large input ranges without > incurring a memory penalty.
wolfd
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May 30, 2023
As mentioned in https://blog.streamlit.io/six-tips-for-improving-your-streamlit-app-performance/ memory usage struggles in the browser if you have large ranges: > Due to implementation details, high-cardinality sliders don't suffer > from the serialization and network transfer delays mentioned earlier, > but they will still lead to a poor user experience (who needs to > specify house prices up to the dollar?) and high memory usage. In my > testing, the example above increased RAM usage by gigabytes until the > web browser eventually gave up (though this is something that should > be solvable on our end. We'll look into it!) This was caused by a bug in react-range, which I fixed last year. tajo/react-range#178 At the time, I had figured it would get picked up by a random yarn upgrade and didn't worry too much about it. But, apparently yarn doesn't really have an easy way of doing upgrades of transitive dependencies (see yarnpkg/yarn#4986)? I took the suggestion of someone in that thread to delete the entry and let yarn regenerate it. Some technical details about the react-range fix from the original commit message (the "application" is a streamlit app): > We have an application that uses react-range under the hood, and we > noticed that a range input was taking 2GB of RAM on our machines. I > did some investigation and found that regardless of whether the marks > functionality was being used, refs were being created for each > possible value of the range. > We have some fairly huge ranges (we're using the input to scrub a > video with potential microsecond accuracy), and can imagine that > other people are affected by the previous behavior. This change > should allow us to continue using large input ranges without > incurring a memory penalty.
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vdonato
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May 31, 2023
As mentioned in https://blog.streamlit.io/six-tips-for-improving-your-streamlit-app-performance/ memory usage struggles in the browser if you have large ranges: > Due to implementation details, high-cardinality sliders don't suffer > from the serialization and network transfer delays mentioned earlier, > but they will still lead to a poor user experience (who needs to > specify house prices up to the dollar?) and high memory usage. In my > testing, the example above increased RAM usage by gigabytes until the > web browser eventually gave up (though this is something that should > be solvable on our end. We'll look into it!) This was caused by a bug in react-range, which I fixed last year. tajo/react-range#178 At the time, I had figured it would get picked up by a random yarn upgrade and didn't worry too much about it. But, apparently yarn doesn't really have an easy way of doing upgrades of transitive dependencies (see yarnpkg/yarn#4986)? I took the suggestion of someone in that thread to delete the entry and let yarn regenerate it. Some technical details about the react-range fix from the original commit message (the "application" is a streamlit app): > We have an application that uses react-range under the hood, and we > noticed that a range input was taking 2GB of RAM on our machines. I > did some investigation and found that regardless of whether the marks > functionality was being used, refs were being created for each > possible value of the range. > We have some fairly huge ranges (we're using the input to scrub a > video with potential microsecond accuracy), and can imagine that > other people are affected by the previous behavior. This change > should allow us to continue using large input ranges without > incurring a memory penalty.
eric-skydio
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Dec 20, 2023
As mentioned in https://blog.streamlit.io/six-tips-for-improving-your-streamlit-app-performance/ memory usage struggles in the browser if you have large ranges: > Due to implementation details, high-cardinality sliders don't suffer > from the serialization and network transfer delays mentioned earlier, > but they will still lead to a poor user experience (who needs to > specify house prices up to the dollar?) and high memory usage. In my > testing, the example above increased RAM usage by gigabytes until the > web browser eventually gave up (though this is something that should > be solvable on our end. We'll look into it!) This was caused by a bug in react-range, which I fixed last year. tajo/react-range#178 At the time, I had figured it would get picked up by a random yarn upgrade and didn't worry too much about it. But, apparently yarn doesn't really have an easy way of doing upgrades of transitive dependencies (see yarnpkg/yarn#4986)? I took the suggestion of someone in that thread to delete the entry and let yarn regenerate it. Some technical details about the react-range fix from the original commit message (the "application" is a streamlit app): > We have an application that uses react-range under the hood, and we > noticed that a range input was taking 2GB of RAM on our machines. I > did some investigation and found that regardless of whether the marks > functionality was being used, refs were being created for each > possible value of the range. > We have some fairly huge ranges (we're using the input to scrub a > video with potential microsecond accuracy), and can imagine that > other people are affected by the previous behavior. This change > should allow us to continue using large input ranges without > incurring a memory penalty.
zyxue
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Mar 22, 2024
As mentioned in https://blog.streamlit.io/six-tips-for-improving-your-streamlit-app-performance/ memory usage struggles in the browser if you have large ranges: > Due to implementation details, high-cardinality sliders don't suffer > from the serialization and network transfer delays mentioned earlier, > but they will still lead to a poor user experience (who needs to > specify house prices up to the dollar?) and high memory usage. In my > testing, the example above increased RAM usage by gigabytes until the > web browser eventually gave up (though this is something that should > be solvable on our end. We'll look into it!) This was caused by a bug in react-range, which I fixed last year. tajo/react-range#178 At the time, I had figured it would get picked up by a random yarn upgrade and didn't worry too much about it. But, apparently yarn doesn't really have an easy way of doing upgrades of transitive dependencies (see yarnpkg/yarn#4986)? I took the suggestion of someone in that thread to delete the entry and let yarn regenerate it. Some technical details about the react-range fix from the original commit message (the "application" is a streamlit app): > We have an application that uses react-range under the hood, and we > noticed that a range input was taking 2GB of RAM on our machines. I > did some investigation and found that regardless of whether the marks > functionality was being used, refs were being created for each > possible value of the range. > We have some fairly huge ranges (we're using the input to scrub a > video with potential microsecond accuracy), and can imagine that > other people are affected by the previous behavior. This change > should allow us to continue using large input ranges without > incurring a memory penalty.
zyxue
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to zyxue/streamlit
that referenced
this pull request
Apr 16, 2024
As mentioned in https://blog.streamlit.io/six-tips-for-improving-your-streamlit-app-performance/ memory usage struggles in the browser if you have large ranges: > Due to implementation details, high-cardinality sliders don't suffer > from the serialization and network transfer delays mentioned earlier, > but they will still lead to a poor user experience (who needs to > specify house prices up to the dollar?) and high memory usage. In my > testing, the example above increased RAM usage by gigabytes until the > web browser eventually gave up (though this is something that should > be solvable on our end. We'll look into it!) This was caused by a bug in react-range, which I fixed last year. tajo/react-range#178 At the time, I had figured it would get picked up by a random yarn upgrade and didn't worry too much about it. But, apparently yarn doesn't really have an easy way of doing upgrades of transitive dependencies (see yarnpkg/yarn#4986)? I took the suggestion of someone in that thread to delete the entry and let yarn regenerate it. Some technical details about the react-range fix from the original commit message (the "application" is a streamlit app): > We have an application that uses react-range under the hood, and we > noticed that a range input was taking 2GB of RAM on our machines. I > did some investigation and found that regardless of whether the marks > functionality was being used, refs were being created for each > possible value of the range. > We have some fairly huge ranges (we're using the input to scrub a > video with potential microsecond accuracy), and can imagine that > other people are affected by the previous behavior. This change > should allow us to continue using large input ranges without > incurring a memory penalty.
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We have an application that uses
react-rangeunder the hood, and we noticed that a range input was taking 2GB of RAM on our machines. I did some investigation and found that regardless of whether the marks functionality was being used, refs were being created for each possible value of the range.We have some fairly huge ranges (we're using the input to scrub a video with potential microsecond accuracy), and can imagine that other people are affected by the existing behavior. This change should allow us to continue using large input ranges (without marks) without incurring a memory penalty.
Fixes #170