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## Performance
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Valkey-Search achieves high performance by storing vectors in-memory and applying optimizations throughout the stack to efficiently use host resources, such as:
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Valkey-Search achieves high performance by storing vectors in-memory and applying optimizations throughout the stack to efficiently utilize the host resources, such as:
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-**Parallelism:** Threading model that enables lock-free execution in the read path.
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-**CPU Cache Efficiency:** Designed to promote efficient use of CPU cache.
For development purposes, it is recommended to use <b>VSCode</b>, which is already configured to run within a Docker container and is integrated with clang-tidy and clang-format. Follow these steps to set up your environment:
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1. <b>Install VSCode Extensions:</b>
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- Install the `Dev Containers` extension by Microsoft in VSCode.
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- Note: Building the code may take some time, and it is important to use a host with decent CPU capacity. If you prefer, you can use a remote host. In that case, also install the following extensions:
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-`Remote - SSH` by Microsoft
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-`Remote Explorer` by Microsoft
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2. <b>Run the dev container setup script</b>
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- Issue the following command from the cloned repo root directory:
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```sh
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.devcontainer/setup.sh
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```
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3. <b>Open the Repository in VSCode:</b>
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- On your local machine, open the root directory of the cloned valkey-search repository in VSCode.
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- If the repository is located on a remote host:
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1. Press Ctrl+Shift+P (Windows/Linux) or Cmd+Shift+P (macOS) to open the Command Palette.
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2. Type Remote-SSH: Connect to Host and selectit.
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3. Choose the appropriate host and provide any necessary authentication details.
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Once connected, VSCode will open the repository in the context of the remote host.
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For detailed development instructions, please refer to the [Developer Guide](DEVELOPER.md).
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