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# Description `bytes starts-with` converts the input into a `Value` before running .starts_with to find if the binary matches. This has two side effects: it makes the code simpler, only dealing in whole values, and simplifying a lot of input pipeline handling and value transforming it would otherwise have to do. _Especially_ in the presence of a cell path to drill into. It also makes buffers the entire input into memory, which can take up a lot of memory when dealing with large files, especially if you only want to check the first few bytes (like for a magic number). This PR adds a special branch on PipelineData::ExternalStream with a streaming version of starts_with. # User-Facing Changes Opening large files and running bytes starts-with on them will not take a long time. # Tests + Formatting Don't forget to add tests that cover your changes. Make sure you've run and fixed any issues with these commands: - `cargo fmt --all -- --check` to check standard code formatting (`cargo fmt --all` applies these changes) - `cargo clippy --workspace -- -D warnings -D clippy::unwrap_used -A clippy::needless_collect` to check that you're using the standard code style - `cargo test --workspace` to check that all tests pass # Drawbacks Streaming checking is more complicated, and there may be bugs. I tested it with multiple chunks with string data and binary data and it seems to work alright up to 8k and over bytes, though. The existing `operate` method still exists because the way it handles cell paths and values is complicated. This causes some "code duplication", or at least some intent duplication, between the value code and the streaming code. This might be worthwhile considering the performance gains (approaching infinity on larger inputs). Another thing to consider is that my ExternalStream branch considers string data as valid input. The operate branch only parses Binary values, so it would fail. `open` is kind of unpredictable on whether it returns string data or binary data, even when passing `--raw`. I think this can be a problem but not really one I'm trying to tackle in this PR, so, it's worth considering. |
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| .. | ||
| nu_plugin_custom_values | ||
| nu_plugin_example | ||
| nu_plugin_formats | ||
| nu_plugin_gstat | ||
| nu_plugin_inc | ||
| nu_plugin_python | ||
| nu_plugin_query | ||
| nu-cli | ||
| nu-cmd-lang | ||
| nu-color-config | ||
| nu-command | ||
| nu-engine | ||
| nu-explore | ||
| nu-glob | ||
| nu-json | ||
| nu-parser | ||
| nu-path | ||
| nu-plugin | ||
| nu-pretty-hex | ||
| nu-protocol | ||
| nu-system | ||
| nu-table | ||
| nu-term-grid | ||
| nu-test-support | ||
| nu-utils | ||
| README.md | ||
Nushell core libraries and plugins
These sub-crates form both the foundation for Nu and a set of plugins which extend Nu with additional functionality.
Foundational libraries are split into two kinds of crates:
- Core crates - those crates that work together to build the Nushell language engine
- Support crates - a set of crates that support the engine with additional features like JSON support, ANSI support, and more.
Plugins are likewise also split into two types:
- Core plugins - plugins that provide part of the default experience of Nu, including access to the system properties, processes, and web-connectivity features.
- Extra plugins - these plugins run a wide range of different capabilities like working with different file types, charting, viewing binary data, and more.