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Solution for hash-map with >100M values
7 projects | reddit.com/r/java | 21 Dec 2020
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.
We haven't tracked posts mentioning time-series-concurrency-example yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
Oak - A Scalable Concurrent Key-Value Map for Big Data Analytics
SmoothieMap - A gulp of low latency Java
MBassador - Powerful event-bus optimized for high throughput in multi-threaded applications. Features: Sync and Async event publication, weak/strong references, event filtering, annotation driven
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.
PalDB - An embeddable key-value store written in Java
Vert.x - Vert.x is a tool-kit for building reactive applications on the JVM
QuestDB - An open source time-series database for fast ingest and SQL queries
Crate - CrateDB is a distributed SQL database that makes it simple to store and analyze massive amounts of data in real-time. Built on top of Lucene.
Disruptor - High Performance Inter-Thread Messaging Library
java-concurrent-hash-trie-map - Java port of a concurrent trie hash map implementation from the Scala collections library