Fast, concurrent, evicting in-memory cache written to keep big number of entries without impact on performance. BigCache keeps entries on heap but omits GC for them. To achieve that operations on bytes arrays take place, therefore entries (de)serialization in front of the cache will be needed in most use cases. 

bigcache - Efficient cache for gigabytes of data written in Go.

BaasBox is an Open Source project that aims to provide a backend for mobile and web apps. It helps to manage the content, A document belongs to a Collection. It can create, read, update and delete a document, provided user have access to it. Additional to that it has support for File management, Backup and Restore, API access control, User and Profile management and lot more.

BaasBox - Backend for Mobile and Web Application

Redisson - distributed Java objects and services (Set, Multimap, SortedSet, Map, List, Queue, BlockingQueue, Deque, BlockingDeque, Semaphore, Lock, AtomicLong, Map Reduce, Publish / Subscribe, Bloom filter, Spring Cache, Executor service, Tomcat Session Manager, Scheduler service, JCache API) on top of Redis server. Rich Redis client.
 
Its features include: 
<ul>
<li>Replicated servers mode (also supports <a href="http://docs.aws.amazon.com/AmazonElastiCache/latest/UserGuide/Replication.html">AWS ElastiCache</a> and <a href="https://azure.microsoft.com/en-us/services/cache/">Azure Redis Cache</a>):
<ol>
<li>automatic master server change discovery</li>
</ol>
</li>
<li>Cluster servers mode (also supports <a href="http://docs.aws.amazon.com/AmazonElastiCache/latest/UserGuide/Clusters.html">AWS ElastiCache Cluster</a> and <a href="https://azure.microsoft.com/en-us/services/cache/">Azure Redis Cache</a>):
<ol>
<li>automatic master and slave servers discovery</li>
<li>automatic status and topology update</li>
<li>automatic slots change discovery</li>
</ol>
</li>
<li>Sentinel servers mode:
<ol>
<li>automatic master, slave and sentinel servers discovery</li>
<li>automatic status and topology update</li>
</ol>
</li>
<li>Master with Slave servers mode</li>
<li>Single server mode</li>
<li>Thread-safe implementation</li>
<li><a href="https://github.com/redisson/redisson/wiki/3.-operations-execution#32-reactive-way">Reactive Streams</a> API</li>
<li><a href="https://github.com/redisson/redisson/wiki/3.-operations-execution#31-async-way">Asynchronous</a> API</li>
<li>Asynchronous connection pool</li>
<li>Lua scripting</li>
<li><a href="https://github.com/redisson/redisson/wiki/6.-Distributed-objects">Distributed objects</a> 
Object holder, Binary stream holder, Geospatial holder, BitSet, AtomicLong, AtomicDouble, PublishSubscribe,
Bloom filter, HyperLogLog</li>
<li><a href="https://github.com/redisson/redisson/wiki/7.-Distributed-collections">Distributed collections</a> 
Map, Multimap, Set, List, SortedSet, ScoredSortedSet, LexSortedSet, Queue, Deque, Blocking Queue, Bounded Blocking Queue, Blocking Deque, Delayed Queue, Priority Queue, Priority Deque</li>
<li><a href="https://github.com/redisson/redisson/wiki/8.-Distributed-locks-and-synchronizers">Distributed locks and synchronizers</a> 
Lock, FairLock, MultiLock, RedLock, ReadWriteLock, Semaphore, PermitExpirableSemaphore, CountDownLatch</li>
<li><a href="https://github.com/redisson/redisson/wiki/9.-distributed-services">Distributed services</a> 
Remote service, Live Object service, Executor service, Scheduler service, MapReduce service</li>
<li><a href="https://github.com/redisson/redisson/wiki/14.-Integration%20with%20frameworks/#141-spring-cache">Spring Cache</a> implementation &nbsp;</li>
<li><a href="https://github.com/redisson/redisson/wiki/14.-Integration%20with%20frameworks/#142-hibernate-cache">Hibernate Cache</a> implementation</li>
<li><a href="https://github.com/redisson/redisson/wiki/14.-Integration%20with%20frameworks/#143-jcache-api-jsr-107-implementation">JCache API (JSR-107)</a> implementation</li>
<li><a href="https://github.com/redisson/redisson/wiki/14.-Integration%20with%20frameworks#144-tomcat-redis-session-manager">Tomcat Session Manager</a> implementation</li>
<li><a href="https://github.com/redisson/redisson/wiki/14.-Integration%20with%20frameworks/#145-spring-session">Spring Session</a> implementation</li>
<li><a href="https://github.com/redisson/redisson/wiki/10.-additional-features#102-execution-batches-of-commands">Redis pipelining</a> (command batches)</li>
<li>Supports Android platform</li>
<li>Supports auto-reconnection</li>
<li>Supports failed to send command auto-retry</li>
<li>Supports OSGi</li>
<li>Supports SSL</li>
<li>Supports many popular codecs (<a href="https://github.com/FasterXML/jackson">Jackson JSON</a>, <a href="http://avro.apache.org/">Avro</a>, <a href="http://wiki.fasterxml.com/SmileFormatSpec">Smile</a>, <a href="http://cbor.io/">CBOR</a>, <a href="http://msgpack.org/">MsgPack</a>, <a href="https://github.com/EsotericSoftware/kryo">Kryo</a>, <a href="https://amzn.github.io/ion-docs/">Amazon Ion</a>, <a href="https://github.com/RuedigerMoeller/fast-serialization">FST</a>, <a href="https://github.com/jpountz/lz4-java">LZ4</a>, <a href="https://github.com/xerial/snappy-java">Snappy</a> and JDK Serialization)</li>
<li>With over 1000 unit tests</li>
</ul>

Redisson - distributed Java objects and services (Set, Multimap, SortedSet, Map, List, Queue, BlockingQueue, Deque, BlockingDeque, Semaphore, Lock, AtomicLong, Map Reduce, Publish / Subscribe, Bloom filter, Spring Cache, Executor service, Tomcat Session Manager, Scheduler service, JCache API) on top of Redis server. Rich Redis client.

Redisson - Redis based In-Memory Data Grid for Java

MapDB is an embedded database engine. It provides Maps and other collections backed by disk or memory storage. It offers excellent performance comparable to java collections, but is not limited by GC overhead. It is also a full database engine with storage backends, transactions, cache algorithms, expiration and many other options. MapDB provides concurrent Maps, Sets and Queues backed by disk storage or off-heap memory. 

MapDB - Embedded Database Engine

cache2k focuses on providing a well engineered in-memory object cache implementation for Java applications. It has fastest access times, due to non blocking and wait free access of cached values. It is thread safe, with a complete set of atomic operations. It supports automatic expiry and refresh: duration or point in time, variable expiry per entry, delta calculations.

Cache2k –  Lightweight, high performance Java caching 

Apache Mnemonic is a non-volatile hybrid memory storage oriented library, it proposed a non-volatile/durable Java object model and durable computing service that bring several advantages to significantly improve the performance of massive real-time data processing/analytics. developers are able to use this library to design their cache-less and SerDe-less high performance applications.
<ul>
<li>In-place data storage on local non-volatile memory</li>
<li>Durable Object Model (DOM)</li>
<li>Durable Native Computing Model (DNCM)</li>
<li>Object graphs lazy loading &amp; sharing</li>
<li>Auto-reclaim memory resources and Mnemonic objects</li>
<li>Hierarchical cache pool for massive data caching</li>
<li>Extensible memory services for new device adoption and allocation optimization</li>
<li>Durable data structure collection(WIP)</li>
<li>Durable computing service</li>
<li>Minimize memory footprint of on-heap</li>
<li>Reduce GC Overheads as the following chart shown (collected from Apache Spark experiments)</li>
<li>Drop-in Hadoop MapReduce support</li>
<li>Drop-in Hadoop Spark support</li>
</ul>

Apache Mnemonic is a non-volatile hybrid memory storage oriented library, it proposed a non-volatile/durable Java object model and durable computing service that bring several advantages to significantly improve the performance of massive real-time data processing/analytics. developers are able to use this library to design their cache-less and SerDe-less high performance applications.

Apache Mnemonic -  Non-volatile hybrid memory storage oriented library

Apache Geode  is distributed, in-memory database for scale-out applications. All data is stored in-memory for low latency. Performance scales linearly as nodes are added. Data is distributed automatically between nodes to optimize performance. Clusters fail-over to other nodes in case of failures, and rebalance remaining resources. Geode servers can be configured to talk memcached protocol. 

Apache Geode - Distributed, In-memory Database for Scale-Out Applications

A library for managing large off-heap arrays that can hold more than 2G (2^31) entries in Java and Scala. Notably LArray is disposable by calling LArray.free or you can let GC automatically release the memory. LArray also can be used to create an mmap (memory-mapped file) whose size is more than 2GB

LArray - Large off-heap arrays for Java/Scala

Para was designed to be a simple and modular backend framework for object persistence and retrieval. It helps you build applications faster by taking care of the backend. It works on three levels - objects are stored in a NoSQL data store or any old relational database, then automatically indexed by a search engine and finally, cached. Para is also multitenant and has a clean and easy-to-use JSON API.

Para - The backend for busy developers

Off-Heap concurrent hash map intended to store GBs of serialized data. It has optional per entry or default TTL/expireAt, Entry eviction and expiration without a separate thread, Capable of maintaining huge amounts of cache memory, Suitable for tiny/small entries with low overhead using the chunked implementation.
 
OHC provides two implementations for different cache entry characteristics: - The _linked_ implementation allocates off-heap memory for each entry individually and works best for medium and big entries. - The _chunked_ implementation allocates off-heap memory for each hash segment as a whole and is intended for small entries.

Off-Heap concurrent hash map intended to store GBs of serialized data. It has optional per entry or default TTL/expireAt, Entry eviction and expiration without a separate thread, Capable of maintaining huge amounts of cache memory, Suitable for tiny/small entries with low overhead using the chunked implementation.

OHC - Java large off heap cache

Corfu is a consistency platform designed around the abstraction of a shared log. CorfuDB objects are in-memory, highly available data structures providing linearizable read/write operations and strictly serializable transactions. 
 
Corfu is built upon the abstraction of a distributed shared log. The Corfu infrastructure provides this log to clients, which use the log for coordination, communication and storage. The log is a highly available, dynamic and high performance scalable fabric: it is resilient to failures and can be reconfigured at any time.

Corfu is a consistency platform designed around the abstraction of a shared log. CorfuDB objects are in-memory, highly available data structures providing linearizable read/write operations and strictly serializable transactions

CorfuDB - A cluster consistency platform

A high performance in-memory key-value item storage engine written in golang. The storage engine is based lock-free data structures and scales well with multicore CPUs.

nitro - A high performance in-memory index storage engine

Caffeine is an awesome Java caching library. It has an impressive performance and a neat Java 8 API. However the API does not play very well with Scala. So this is the thinner wrapper we can came with to make Caffeine easy and idiomatic to use in Scala.

scaffeine - Thin Scala wrapper for Caffeine

A small library that provide a disk backed map implementation for storing large number of key value pairs. The map implementations (HashMap, HashTable) max out around 3-4Million keys/GB of memory for very simple key/value pairs and in most cases the limit is much lower. DiskBacked map on the other hand can store betweeen 16Million (64bit JVM) to 20Million(32bit JVM) keys/GB, regardless the size of the key/value pairs.

Disk-backed-map - A simple java disk backed map 

JetCache is a Java cache abstraction which provides consistent use for various caching solutions. Presently it support Redis and Alibaba Tair and Caffeine cache as embedded cache.
 
It simplifies cache operation with these key features:
like declarative caching using annotation with TTL(Time To Live) and two level caching support, Policy of key generation and value serialization can be custom, Detailed stat of each cache area, Asynchronous API and lot more.

JetCache is a Java cache abstraction which provides consistent use for various caching solutions. Presently it support Redis and Alibaba Tair and Caffeine cache as embedded cache.

 JetCache - Java cache abstraction which provides consistent use for various caching solutions

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