huniq
napkin-math
| huniq | napkin-math | |
|---|---|---|
| 3 | 13 | |
| 230 | 3,031 | |
| - | - | |
| 2.7 | 6.3 | |
| 4 months ago | 20 days ago | |
| Rust | Rust | |
| - | MIT License |
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huniq
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Zet 1.0 is out (compare to uniq and comm)
How does it compare with huniq and runiq?
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I/O is no longer the bottleneck
`sort | uniq` is really slow for this, as it has to sort the entire input first. I use `huniq` which is way faster for this. I'm sure there are many similar options.
https://github.com/koraa/huniq
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What’s your favorite shell one liner?
For better speed, check out https://github.com/koraa/huniq
napkin-math
- capacity planning in system design interviews
- Napkin Math
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S3 Express Is All You Need
Most production storage systems/databases built on top of S3 spend a significant amount of effort building an SSD/memory caching tier to make them performant enough for production (e.g. on top of RocksDB). But it's not easy to keep it in sync with blob...
Even with the cache, the cold query latency lower-bound to S3 is subject to ~50ms roundtrips [0]. To build a performant system, you have to tightly control roundtrips. S3 Express changes that equation dramatically, as S3 Express approaches HDD random read speeds (single-digit ms), so we can build production systems that don't need an SSD cache—just the zero-copy, deserialized in-memory cache.
Many systems will probably continue to have an SSD cache (~100 us random reads), but now MVPs can be built without it, and cold query latency goes down dramatically. That's a big deal
We're currently building a vector database on top of object storage, so this is extremely timely for us... I hope GCS ships this ASAP. [1]
[0]: https://github.com/sirupsen/napkin-math
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Random Read or Sequential Read
Trying to estimate performance using some napkin math based on this: https://github.com/sirupsen/napkin-math
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A CVE has been issued for hyper. Denial of Service possible
So napkin maths time. Typical cross-world bog-standard network speeds for a single TCP channel of ~25MiBps. A single HEADERS+RST pair is likely < 128 bytes (40 for the HEADERS + whatever payload, and 32 for the RST). So 8 pairs per K, 8K pairs per MiB, 200K pairs per 25MiB...
- Index Merges vs Composite Indexes in Postgres and MySQL
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I/O is no longer the bottleneck
Yes, sequential I/O bandwidth is closing the gap to memory. [1] The I/O pattern to watch out for, and the biggest reason why e.g. databases do careful caching to memory, is that _random_ I/O is still dreadfully slow. I/O bandwidth is brilliant, but latency is still disappointing compared to memory.
[1]: https://github.com/sirupsen/napkin-math
- Monthly cost to host server for 1M DAUs?
- Napkin-math: Techniques and numbers for estimating system's performance
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System Design prep?
https://github.com/sirupsen/napkin-math (memorize these)
What are some alternatives?
fzy - :mag: A simple, fast fuzzy finder for the terminal
advisory-database - Security vulnerability database inclusive of CVEs and GitHub originated security advisories from the world of open source software.
RAMCloud - **No Longer Maintained** Official RAMCloud repo
adix - An Adaptive Index Library for Nim
repo
h2 - HTTP 2.0 client & server implementation for Rust.
share-file-systems - Use a Windows/OSX like GUI in the browser to share files cross OS privately. No cloud, no server, no third party.
runiq - An efficient way to filter duplicate lines from input, à la uniq.
simdjson - Parsing gigabytes of JSON per second : used by Facebook/Meta Velox, the Node.js runtime, ClickHouse, WatermelonDB, Apache Doris, Milvus, StarRocks
hn-search - Hacker News Search
Killed by Google - Part guillotine, part graveyard for Google's doomed apps, services, and hardware.