Klib VS Redis

Compare Klib vs Redis and see what are their differences.

Redis

Redis is an in-memory database that persists on disk. The data model is key-value, but many different kind of values are supported: Strings, Lists, Sets, Sorted Sets, Hashes, Streams, HyperLogLogs, Bitmaps. (by redis)
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Klib Redis
23 311
3,996 64,235
- 1.0%
4.3 9.7
4 days ago 2 days ago
C C
MIT License GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Klib

Posts with mentions or reviews of Klib. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-11-10.
  • Factor is faster than Zig
    11 projects | news.ycombinator.com | 10 Nov 2023
    In my example the table stores the hash codes themselves instead of the keys (because the hash function is invertible)

    Oh, I see, right. If determining the home bucket is trivial, then the back-shifting method is great. The issue is just that it’s not as much of a general-purpose solution as it may initially seem.

    “With a different algorithm (Robin Hood or bidirectional linear probing), the load factor can be kept well over 90% with good performance, as the benchmarks in the same repo demonstrate.”

    I’ve seen the 90% claim made several times in literature on Robin Hood hash tables. In my experience, the claim is a bit exaggerated, although I suppose it depends on what our idea of “good performance” is. See these benchmarks, which again go up to a maximum load factor of 0.95 (Although boost and Absl forcibly grow/rehash at 0.85-0.9):

    https://strong-starlight-4ea0ed.netlify.app/

    Tsl, Martinus, and CC are all Robin Hood tables (https://github.com/Tessil/robin-map, https://github.com/martinus/robin-hood-hashing, and https://github.com/JacksonAllan/CC, respectively). Absl and Boost are the well-known SIMD-based hash tables. Khash (https://github.com/attractivechaos/klib/blob/master/khash.h) is, I think, an ordinary open-addressing table using quadratic probing. Fastmap is a new, yet-to-be-published design that is fundamentally similar to bytell (https://www.youtube.com/watch?v=M2fKMP47slQ) but also incorporates some aspects of the aforementioned SIMD maps (it caches a 4-bit fragment of the hash code to avoid most key comparisons).

    As you can see, all the Robin Hood maps spike upwards dramatically as the load factor gets high, becoming as much as 5-6 times slower at 0.95 vs 0.5 in one of the benchmarks (uint64_t key, 256-bit struct value: Total time to erase 1000 existing elements with N elements in map). Only the SIMD maps (with Boost being the better performer) and Fastmap appear mostly immune to load factor in all benchmarks, although the SIMD maps do - I believe - use tombstones for deletion.

    I’ve only read briefly about bi-directional linear probing – never experimented with it.

  • A simple hash table in C
    7 projects | news.ycombinator.com | 13 Jun 2023
  • So what's the best data structures and algorithms library for C?
    8 projects | /r/C_Programming | 15 Mar 2023
    It could be that the cost of the function calls, either directly or via a pointer, is drowned out by the cost of the one or more cache misses inevitably invoked with every hash table lookup. But I don't want to say too much before I've finished my benchmarking project and published the results. So let me just caution against laser-focusing on whether the comparator and hash function are/can be inlined. For example stb_ds uses a hardcoded hash function that presumably gets inlined, but in my benchmarking (again, I'll publish it here in coming weeks) shows it to be generally a poor performer (in comparison to not just CC, the current version of which doesn't necessarily inline those functions, but also STC, khash, and the C++ Robin Hood hash tables I tested).
  • Generic dynamic array in 60 lines of C
    4 projects | news.ycombinator.com | 28 Feb 2023
    Not an entirely uncommon idea. I've written one.

    There's also a well-known one here, in klib: https://github.com/attractivechaos/klib/blob/master/kvec.h

  • C_dictionary: A simple dynamically typed and sized hashmap in C - feedback welcome
    10 projects | /r/C_Programming | 23 Jan 2023
  • Inside boost::unordered_flat_map
    11 projects | /r/cpp | 18 Nov 2022
  • The New Ghostscript PDF Interpreter
    4 projects | news.ycombinator.com | 31 Jul 2022
    Code reuse is achievable by (mis)using the preprocessor system. It is possible to build a somewhat usable API, even for intrusive data structures. (eg. the linux kernel and klib[1])

    I do agree that generics are required for modern programming, but for some, the cost of complexity of modern languages (compared to C) and the importance of compatibility seem to outweigh the benefits.

    [1]: http://attractivechaos.github.io/klib

  • C LIBRARY
    2 projects | /r/C_Programming | 10 Jul 2022
  • boost::unordered map is a new king of data structures
    10 projects | /r/cpp | 30 Jun 2022
    Unordered hash map shootout CMAP = https://github.com/tylov/STC KMAP = https://github.com/attractivechaos/klib PMAP = https://github.com/greg7mdp/parallel-hashmap FMAP = https://github.com/skarupke/flat_hash_map RMAP = https://github.com/martinus/robin-hood-hashing HMAP = https://github.com/Tessil/hopscotch-map TMAP = https://github.com/Tessil/robin-map UMAP = std::unordered_map Usage: shootout [n-million=40 key-bits=25] Random keys are in range [0, 2^25). Seed = 1656617916: T1: Insert/update random keys: KMAP: time: 1.949, size: 15064129, buckets: 33554432, sum: 165525449561381 CMAP: time: 1.649, size: 15064129, buckets: 22145833, sum: 165525449561381 PMAP: time: 2.434, size: 15064129, buckets: 33554431, sum: 165525449561381 FMAP: time: 2.112, size: 15064129, buckets: 33554432, sum: 165525449561381 RMAP: time: 1.708, size: 15064129, buckets: 33554431, sum: 165525449561381 HMAP: time: 2.054, size: 15064129, buckets: 33554432, sum: 165525449561381 TMAP: time: 1.645, size: 15064129, buckets: 33554432, sum: 165525449561381 UMAP: time: 6.313, size: 15064129, buckets: 31160981, sum: 165525449561381 T2: Insert sequential keys, then remove them in same order: KMAP: time: 1.173, size: 0, buckets: 33554432, erased 20000000 CMAP: time: 1.651, size: 0, buckets: 33218751, erased 20000000 PMAP: time: 3.840, size: 0, buckets: 33554431, erased 20000000 FMAP: time: 1.722, size: 0, buckets: 33554432, erased 20000000 RMAP: time: 2.359, size: 0, buckets: 33554431, erased 20000000 HMAP: time: 0.849, size: 0, buckets: 33554432, erased 20000000 TMAP: time: 0.660, size: 0, buckets: 33554432, erased 20000000 UMAP: time: 2.138, size: 0, buckets: 31160981, erased 20000000 T3: Remove random keys: KMAP: time: 1.973, size: 0, buckets: 33554432, erased 23367671 CMAP: time: 2.020, size: 0, buckets: 33218751, erased 23367671 PMAP: time: 2.940, size: 0, buckets: 33554431, erased 23367671 FMAP: time: 1.147, size: 0, buckets: 33554432, erased 23367671 RMAP: time: 1.941, size: 0, buckets: 33554431, erased 23367671 HMAP: time: 1.135, size: 0, buckets: 33554432, erased 23367671 TMAP: time: 1.064, size: 0, buckets: 33554432, erased 23367671 UMAP: time: 5.632, size: 0, buckets: 31160981, erased 23367671 T4: Iterate random keys: KMAP: time: 0.748, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 CMAP: time: 0.627, size: 23367671, buckets: 33218751, repeats: 8, sum: 4465059465719680 PMAP: time: 0.680, size: 23367671, buckets: 33554431, repeats: 8, sum: 4465059465719680 FMAP: time: 0.735, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 RMAP: time: 0.464, size: 23367671, buckets: 33554431, repeats: 8, sum: 4465059465719680 HMAP: time: 0.719, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 TMAP: time: 0.662, size: 23367671, buckets: 33554432, repeats: 8, sum: 4465059465719680 UMAP: time: 6.168, size: 23367671, buckets: 31160981, repeats: 8, sum: 4465059465719680 T5: Lookup random keys: KMAP: time: 0.943, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 CMAP: time: 0.863, size: 23367671, buckets: 33218751, lookups: 34235332, found: 29040438 PMAP: time: 1.635, size: 23367671, buckets: 33554431, lookups: 34235332, found: 29040438 FMAP: time: 0.969, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 RMAP: time: 1.705, size: 23367671, buckets: 33554431, lookups: 34235332, found: 29040438 HMAP: time: 0.712, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 TMAP: time: 0.584, size: 23367671, buckets: 33554432, lookups: 34235332, found: 29040438 UMAP: time: 1.974, size: 23367671, buckets: 31160981, lookups: 34235332, found: 29040438
  • C++ containers but in C
    8 projects | /r/C_Programming | 8 Mar 2022

Redis

Posts with mentions or reviews of Redis. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-27.

What are some alternatives?

When comparing Klib and Redis you can also consider the following projects:

Redis - 🚀 A robust, performance-focused, and full-featured Redis client for Node.js.

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

RabbitMQ - Open source RabbitMQ: core server and tier 1 (built-in) plugins

Polly - Polly is a .NET resilience and transient-fault-handling library that allows developers to express policies such as Retry, Circuit Breaker, Timeout, Bulkhead Isolation, and Fallback in a fluent and thread-safe manner. From version 6.0.1, Polly targets .NET Standard 1.1 and 2.0+.

celery - Distributed Task Queue (development branch)

Riak - Riak is a decentralized datastore from Basho Technologies.

cockroach - CockroachDB - the open source, cloud-native distributed SQL database.

Apache HBase - Apache HBase

RedisJSON - RedisJSON - a JSON data type for Redis

ArangoDB - 🥑 ArangoDB is a native multi-model database with flexible data models for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

CouchDB - Seamless multi-master syncing database with an intuitive HTTP/JSON API, designed for reliability

Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing