napkin-math
RAMCloud
| napkin-math | RAMCloud | |
|---|---|---|
| 13 | 1 | |
| 3,031 | 481 | |
| - | 0.4% | |
| 6.3 | 0.0 | |
| 21 days ago | over 4 years ago | |
| Rust | C++ | |
| MIT License | GNU General Public License v3.0 or later |
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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)
RAMCloud
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I/O is no longer the bottleneck
On a related note, John Ousterhout (in the RAMCloud project) was basically betting that the latency of accessing RAM on another computer on a fast local network will eventually become competitive to local RAM access.
https://ramcloud.atlassian.net/wiki/spaces/RAM/overview
What are some alternatives?
huniq - Filter out duplicates on the command line. Replacement for `sort | uniq` optimized for speed (10x faster) when sorting is not needed.
advisory-database - Security vulnerability database inclusive of CVEs and GitHub originated security advisories from the world of open source software.
adix - An Adaptive Index Library for Nim
Killed by Google - Part guillotine, part graveyard for Google's doomed apps, services, and hardware.
h2 - HTTP 2.0 client & server implementation for Rust.
simdjson - Parsing gigabytes of JSON per second : used by Facebook/Meta Velox, the Node.js runtime, ClickHouse, WatermelonDB, Apache Doris, Milvus, StarRocks
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.
fast-sqlite3-inserts - Some bunch of test scripts to generate a SQLite DB with 1B rows in fastest possible way