lasher
Lasher is an embeddable key-value store written in Java. (by soundvibe)
Oak
A Scalable Concurrent Key-Value Map for Big Data Analytics (by yahoo)
Our great sponsors
| lasher | Oak | |
|---|---|---|
| 1 | 2 | |
| 4 | 262 | |
| - | 0.4% | |
| 0.0 | 2.6 | |
| 7 months ago | 12 days ago | |
| Java | Java | |
| Apache License 2.0 | Apache License 2.0 |
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.
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.
lasher
Posts with mentions or reviews of lasher.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2020-12-21.
-
Solution for hash-map with >100M values
Do you need to update the data after initial load? If not, then I would suggest using my Paldb fork , otherwise you could try my lasher library. It's in early stage but first results are very promising, I was testing it with 10-100M elements and the performance was similar to java hashmap.
Oak
Posts with mentions or reviews of Oak.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-10-11.
-
JEP draft: 64 bit object headers
Another to add to your collection, https://github.com/yahoo/Oak
-
Solution for hash-map with >100M values
Consider using an database (e.g. H2 embedded, redis) with an on-heap cache (e.g. Caffeine). Since you say it is a Zipfian distribution, the cache should absorb most of the requests. For an off-heap hashtable, you might try Oak as it is likely a faster implementation.
What are some alternatives?
When comparing lasher and Oak you can also consider the following projects:
MapDB - MapDB provides concurrent Maps, Sets and Queues backed by disk storage or off-heap-memory. It is a fast and easy to use embedded Java database engine.
Chronicle Map - Replicate your Key Value Store across your network, with consistency, persistance and performance.
SmoothieMap - A gulp of low latency Java
java-concurrent-hash-trie-map - Java port of a concurrent trie hash map implementation from the Scala collections library
PalDB - An embeddable key-value store written in Java
time-series-concurrency-example - Time Series Data and CompletableFuture example in Java