In addition to the charts that follow, you might want to consider the Frequently Asked Questions section for a selection of common questions about MongoDB.
The following table presents the MySQL/Oracle executables and the corresponding MongoDB executables.
| // github.com/nu7hatch/gouuid | |
| BenchmarkV4 1000000 1426 ns/op | |
| BenchmarkV5 2000000 910 ns/op | |
| // github.com/streadway/simpleuuid | |
| BenchmarkV5 5000000 689 ns/op | |
| // github.com/tux21b/gocql/tree/master/uuid | |
| BenchmarkRandomUUID 1000000 1470 ns/op |
Author: Chris Lattner
| name old time/op new time/op delta | |
| BinaryTree17 5.44s ± 2% 3.27s ± 2% -39.90% (p=0.000 n=20+19) | |
| Fannkuch11 4.95s ± 2% 2.68s ± 2% -45.87% (p=0.000 n=20+20) | |
| FmtFprintfEmpty 142ns ± 2% 49ns ± 3% -65.39% (p=0.000 n=20+18) | |
| FmtFprintfFloat 765ns ± 2% 260ns ± 2% -66.02% (p=0.000 n=20+20) | |
| FmtFprintfInt 341ns ± 2% 95ns ± 2% -72.08% (p=0.000 n=19+20) | |
| FmtFprintfIntInt 554ns ± 2% 150ns ± 1% -72.95% (p=0.000 n=20+19) | |
| FmtFprintfPrefixedInt 497ns ± 3% 178ns ± 3% -64.12% (p=0.000 n=20+20) | |
| FmtFprintfString 466ns ± 2% 86ns ± 3% -81.54% (p=0.000 n=20+20) | |
| FmtManyArgs 2.23µs ± 2% 0.59µs ± 1% -73.46% (p=0.000 n=20+17) |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
