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google
leveldb
C++2015–2026
google / leveldb

leveldb

LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.

24Distinct people
27Mentions
0Explicit recommenders
39.5KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 5:58:02 AM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year27 dated mentions

Observed mentions: 2015-02-23 18:48:58 UTC to 2026-01-15 09:16:01 UTC.

0123452015-01-01 00:00:00 UTC to 2016-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20152016-01-01 00:00:00 UTC to 2017-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 5 mentions; 0 submissions, 5 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 5 mentions; 0 submissions, 5 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20152015-01-01 00:00:00 UTC2016-01-01 00:00:00 UTC202
20162016-01-01 00:00:00 UTC2017-01-01 00:00:00 UTC101
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC303
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC202
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC202
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC202
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC505
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC505
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC312
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC101
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC110

What Hacker News says

2 sources
Hey, great questions! Query Performance: Right now, we've got test machines deployed with 8 500GB disks for packets + 1 indexing disk (all 15KRPM spinning disks). They keep at 90% full, or roughly 460GB/disk, about 1K files/disk. Querying over the entire corpus (~4TB of packets) for something innocuous like 'port 65432' takes 25 seconds to return ~50K packets (that's after dropping all disk caches). The same query run again takes 1.5 sec, with disk caches in place. Of course, the number of packets returned is a huge factor in this... each packet requires a seek in the packets file. Searching for something that doesn't exist (host 0.0.0.1) takes roughly 5 seconds. Note that time-based queries, like "port 4444 and after 3h ago and before 1h ago" do choose to only query certain files, taking advantage of the fact that we name files by
Read the rest
microsecond timestamp and we flush files every minute. A big part of query performance is actually over-provisioning disks. We see disk throughput of roughly 160-180MB/s. If we write 160MB/s, our read throughput is awful. If we write 100MB/s, it's pretty good. Who would have thought: disks have limited bandwidth, and it's shared between reads and writes. :) We actually don't use LevelDB... we use the SSTables that underly LevelDB. Since we know we're write-once, we use https://github.com/google/leveldb/blob/master/include/leveld... directly for writes (and its Go equivalent for reads). I'm familiar with the file format (they're used extensively inside Google), so it was a simple solution. That said, it's been very successful... we tend to have indexes in the 10s of MBs for 2-4GB files. Of course, index size/compressibility is directly correlated with network traffic: more varied IPs/ports would be harder to compress. The built-in compression of LevelDB tables is also a boon here... we get prefix compression on keys, plus snappy compression on packet seek locations, for free. We currently do no compression of packets. Doing so would definitely increase our CPU usage per packet, and I'm really scared of what it would do to reads. Consider that reading packets in compressed storage would require decompressing each block a packet is in. On the other hand, if someone wanted to store packets REALLY long term, they could easily compress the entire blockfile+index before uploading to more permanent storage. I expect this would be better than having to do it inline. Even if we did build it in, we'd probably do it tiered (initial write uncompressed, then compress later on as possible). AF_PACKET is no better than PF_RING+DNA, but I also don't think it's any worse. They both have very specific trade-offs. The big draw for me for AF_PACKET is that it's already there... any stock Linux machine will already have it built in and working. Thus steno should "just work", while a PF_RING solution has a slightly higher barrier to entry. I think PF_RING+DNA should give similar performance to steno... but libzero currently probably gives better performance because packets can be shared across processes. This is a really interesting problem that I'm wondering if we could also solve with AF_PACKET... but that's a story for another day. Short story: I wanted this to work on stock linux as much as possible.
In Stenographer – A full-packet-capture utility

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