3 个仓库
Awesome GitHub RepositoriesLazy Stream Processing
On-demand processing pipelines for mapping, filtering, and reducing data streams.
Distinct from Lazy Stream Sources: Candidates focus on specific target streaming or DOM events; this is a general functional programming stream utility.
Explore 3 awesome GitHub repositories matching data & databases · Lazy Stream Processing. Refine with filters or upvote what's useful.
Awesome Lazy Stream Processing GitHub Repositories
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- duke-git/lancet
duke-git/lancet
5,295在 GitHub 上查看Lancet 是 Go 标准库的全面扩展，提供了一系列可复用的函数和数据结构，旨在减少应用中的样板代码。它是一个跨多个领域的通用工具包，包括并发、安全、网络和函数式逻辑。 该项目的特色在于针对 Go 并发的专用工具包（如键控锁和基于通道的流处理），以及支持柯里化和函数组合的专用函数式编程套件。它还包含一个使用 AES、RSA 和 SM 标准实现对称和非对称加密的专用密码学库。 该库涵盖了广泛的功能领域，包括 LRU 缓存和二叉搜索树等高级数据结构、代数和统计数学计算，以及用于进程和文件管理的操作系统集成。它还提供了用于 HTTP 网络、日期和时间操作，以及集合代数和惰性流求值等高级数据处理的实用程序。
Creates lazy streams from slices or channels to perform mapping, filtering, and reduction.
Gogenericsgogolang - multiprocessio/dsq
multiprocessio/dsq
3,866在 GitHub 上查看dsq is a command-line utility that enables SQL-based analysis of local files by treating them as relational database tables. It allows users to execute standard SQL queries against heterogeneous data formats, including JSON, CSV, Excel, and Parquet, without requiring a formal database import process. The tool distinguishes itself by providing a persistent interactive shell for iterative data exploration and schema inspection. It supports complex operations such as joining data across multiple disparate files and converting between structured formats by applying SQL transformations to the inpu
Reads and processes data records incrementally from source files to maintain low memory usage during large dataset operations.
Go - ys-l/csvlens
YS-L/csvlens
3,861在 GitHub 上查看Csvlens is a command-line utility designed for the inspection and navigation of large structured tabular files. It provides a terminal-based interface that allows users to view, sort, and filter datasets directly within the command line without the memory overhead associated with loading entire files into spreadsheet applications. The tool utilizes memory-mapped file access and lazy-loading data streams to maintain a constant memory footprint, regardless of the total size of the source document. By identifying field boundaries during an initial scan, it enables efficient column-specific opera
Processes input files as sequential streams to maintain a constant memory footprint regardless of the total size of the source document.
Rust
