[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-1394":3},{"id":4,"name":5,"fullName":6,"owner":5,"repo":5,"description":7,"homepage":8,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":9,"rankLanguage":9,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":22,"hasPages":22,"topics":24,"createdAt":9,"pushedAt":9,"updatedAt":30,"readmeContent":31,"aiSummary":32,"trendingCount":15,"starSnapshotCount":15,"syncStatus":33,"lastSyncTime":34,"discoverSource":35},1394,"duckdb","duckdb\u002Fduckdb","DuckDB is an analytical in-process SQL database management system","http:\u002F\u002Fwww.duckdb.org",null,"C++",38734,3311,267,438,0,9,131,654,63,118,"MIT License",false,"main",[25,26,27,28,29],"analytics","database","embedded-database","olap","sql","2026-06-12 04:00:09","\u003Cdiv align=\"center\">\n  \u003Cpicture>\n    \u003Csource media=\"(prefers-color-scheme: light)\" srcset=\"logo\u002FDuckDB_Logo-horizontal.svg\">\n    \u003Csource media=\"(prefers-color-scheme: dark)\" srcset=\"logo\u002FDuckDB_Logo-horizontal-dark-mode.svg\">\n    \u003Cimg alt=\"DuckDB logo\" src=\"logo\u002FDuckDB_Logo-horizontal.svg\" height=\"100\">\n  \u003C\u002Fpicture>\n\u003C\u002Fdiv>\n\u003Cbr>\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fduckdb\u002Fduckdb\u002Factions\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fduckdb\u002Fduckdb\u002Factions\u002Fworkflows\u002FMain.yml\u002Fbadge.svg?branch=main\" alt=\"Github Actions Badge\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fdiscord.gg\u002FtcvwpjfnZx\">\u003Cimg src=\"https:\u002F\u002Fshields.io\u002Fdiscord\u002F909674491309850675\" alt=\"discord\" \u002F>\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fduckdb\u002Fduckdb\u002Freleases\u002F\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fv\u002Frelease\u002Fduckdb\u002Fduckdb?color=brightgreen&display_name=tag&logo=duckdb&logoColor=white\" alt=\"Latest Release\">\u003C\u002Fa>\n\u003C\u002Fp>\n\n## DuckDB\n\nDuckDB is a high-performance analytical database system. It is designed to be fast, reliable, portable, and easy to use. DuckDB provides a rich SQL dialect with support far beyond basic SQL. DuckDB supports arbitrary and nested correlated subqueries, window functions, collations, complex types (arrays, structs, maps), and [several extensions designed to make SQL easier to use](https:\u002F\u002Fduckdb.org\u002Fdocs\u002Fcurrent\u002Fsql\u002Fdialect\u002Ffriendly_sql.html).\n\nDuckDB is available as a [standalone CLI application](https:\u002F\u002Fduckdb.org\u002Fdocs\u002Fcurrent\u002Fclients\u002Fcli\u002Foverview) and has clients for [Python](https:\u002F\u002Fduckdb.org\u002Fdocs\u002Fcurrent\u002Fclients\u002Fpython\u002Foverview), [R](https:\u002F\u002Fduckdb.org\u002Fdocs\u002Fcurrent\u002Fclients\u002Fr), [Java](https:\u002F\u002Fduckdb.org\u002Fdocs\u002Fcurrent\u002Fclients\u002Fjava), [Wasm](https:\u002F\u002Fduckdb.org\u002Fdocs\u002Fcurrent\u002Fclients\u002Fwasm\u002Foverview), etc., with deep integrations with packages such as [pandas](https:\u002F\u002Fduckdb.org\u002Fdocs\u002Fguides\u002Fpython\u002Fsql_on_pandas) and [dplyr](https:\u002F\u002Fduckdb.org\u002Fdocs\u002Fcurrent\u002Fclients\u002Fr#duckplyr-dplyr-api).\n\nFor more information on using DuckDB, please refer to the [DuckDB documentation](https:\u002F\u002Fduckdb.org\u002Fdocs\u002Fcurrent\u002F).\n\n## Installation\n\nIf you want to install DuckDB, please see [our installation page](https:\u002F\u002Fduckdb.org\u002Fdocs\u002Finstallation\u002F) for instructions.\n\n## Data Import\n\nFor CSV files and Parquet files, data import is as simple as referencing the file in the FROM clause:\n\n```sql\nSELECT * FROM 'myfile.csv';\nSELECT * FROM 'myfile.parquet';\n```\n\nRefer to our [Data Import](https:\u002F\u002Fduckdb.org\u002Fdocs\u002Fcurrent\u002Fdata\u002Foverview) section for more information.\n\n## SQL Reference\n\nThe documentation contains a [SQL introduction and reference](https:\u002F\u002Fduckdb.org\u002Fdocs\u002Fcurrent\u002Fsql\u002Fintroduction).\n\n## Development\n\nFor development, DuckDB requires [CMake](https:\u002F\u002Fcmake.org), Python 3 and a `C++17` compliant compiler. In the root directory, run `make` to compile the sources. For development, use `make debug` to build a non-optimized debug version. You should run `make unit` and `make allunit` to verify that your version works properly after making changes. To test performance, you can run `BUILD_BENCHMARK=1 BUILD_TPCH=1 make` and then perform several standard benchmarks from the root directory by executing `.\u002Fbuild\u002Frelease\u002Fbenchmark\u002Fbenchmark_runner`. The details of benchmarks are in our [Benchmark Guide](benchmark\u002FREADME.md).\n\nPlease also refer to our [Build Guide](https:\u002F\u002Fduckdb.org\u002Fdocs\u002Fcurrent\u002Fdev\u002Fbuilding\u002Foverview) and [Contribution Guide](CONTRIBUTING.md).\n\n## Support\n\nSee the [Support Options](https:\u002F\u002Fduckdblabs.com\u002Fsupport\u002F) page and the dedicated [`endoflife.date`](https:\u002F\u002Fendoflife.date\u002Fduckdb) page.\n","DuckDB 是一个高性能的分析型嵌入式SQL数据库管理系统。它支持丰富的SQL方言，包括任意和嵌套的相关子查询、窗口函数、排序规则以及复杂类型（数组、结构体、映射）等高级功能。DuckDB 采用C++开发，具有快速、可靠、便携的特点，并且易于使用。该系统适用于需要在应用程序内部进行高效数据分析处理的场景，如数据科学项目或实时分析应用。通过其提供的多种客户端接口（Python、R、Java等），DuckDB能够与pandas、dplyr等流行的数据处理库无缝集成，极大地简化了数据分析工作流程。",2,"2026-06-11 02:43:30","top_all"]