Voldemort is a distributed key-value storage system. Data is automatically replicated over multiple servers. Data is automatically partitioned so each server contains only a subset of the total data. 
Server failure is handled transparently. It is used at LinkedIn for certain high-scalability storage problems where simple functional partitioning is not sufficient.

Project-voldemort - A distributed database, Clone of Amazon's Dynamo

etcd is a distributed, consistent key-value store for shared configuration and service discovery. It is simple, secure, fast and reliable. it uses the Raft consensus algorithm to manage a highly-available replicated log.


etcd - Distributed reliable key-value store for the most critical data of a distributed system

BadgerDB is an embeddable, persistent, simple and fast key-value (KV) store, written purely in Go. It's meant to be a performant alternative to non Go based key-value stores like RocksDB. It is written out of frustration with existing KV stores which are either written in pure Go and slow, or fast but require usage of Cgo. Badger aims to provide an equal or better speed compared to industry leading KV stores (like RocksDB), while maintaining the entire code base in pure Go.

BadgerDB - Fast key-value store in Go.

Ehcache is an open source, standards-based cache used to boost performance, offload the database and simplify scalability. Ehcache is robust, proven and full-featured and this has made it the most widely-used Java-based cache. Its features include: 
 <UL>
	<LI>Fast and Light Weight</LI>
	<LI>Scalable to hundreds of caches</LI>
	<LI>Multiple CacheManagers per virtual machine</LI>
	<LI>Distributed Caching</LI>
	<LI>Provides LRU, LFU and FIFO cache eviction policies</LI>
	<LI>Supports Object or Serializable caching</LI>
	<LI>Full implementation of JSR107 JCACHE API</LI>
	<LI>Extensible</LI>
	<LI>Persistence, Flush to disk on demand</LI>
	<LI>Supports as standalone server, SOAP cache server, RESTful cache server</LI>
	<LI>Works with Hibernate, Google App Engine </LI>
 </UL>

Ehcache is an open source, standards-based cache used to boost performance, offload the database and simplify scalability. Ehcache is robust, proven and full-featured and this has made it the most widely-used Java-based cache.

Ehcache

OrientDB has the flexibility of the Document databases and the power of the Graph databases to manage relationships. It can work in schema-less mode, schema-full or a mix of both. It can store up to 150,000 records per second on common hardware. OrientDB has been designed to be very fast. It inherits the best features and concepts from the Object Databases, Graph DBMS and the modern NoSQL engines. 
 Source code location: <A HREF="http://code.google.com/p/orient/" target="_blank">http://code.google.com/p/orient/</A>

OrientDB has the flexibility of the Document databases and the power of the Graph databases to manage relationships. It can work in schema-less mode, schema-full or a mix of both. It can store up to 150,000 records per second on common hardware. OrientDB has been designed to be very fast. It inherits the best features and concepts from the Object Databases, Graph DBMS and the modern NoSQL engines.

OrientDB - The NoSQL Graph-Document DBMS

FoundationDB is a distributed database designed to handle large volumes of structured data across clusters of commodity servers. It organizes data as an ordered key-value store and employs ACID transactions for all operations. It is especially well-suited for read/write workloads but also has excellent performance for write-intensive workloads. Users interact with the database using API language binding.
 
FoundationDB is multi-model, meaning you can store many types data in a single database. All data is safely stored, distributed, and replicated in the Key-Value Store component. It provides amazing performance on commodity hardware, allowing you to support very heavy loads at low cost.

FoundationDB is a distributed database designed to handle large volumes of structured data across clusters of commodity servers. It organizes data as an ordered key-value store and employs ACID transactions for all operations. It is especially well-suited for read/write workloads but also has excellent performance for write-intensive workloads. Users interact with the database using API language binding. 

FoundationDB - Distributed, Transactional Key-value store

kumofs is a scalable and highly available distributed key-value store. 
Data is replicated over multiple servers. 
Data is partitioned over multiple servers. 
Extreme single node performance; comparable with memcached. 
Both read and write performance got improved as servers added. 
Servers can be added without stopping the system. 
Servers can be added without changing the client applications. 
The system does not stop even if one or two servers crashed. 
The system does not stop to recover crashed servers. 
Scalable from 2 to 60 servers. (more than 60 servers has not be tested yet) 
Optimized for storing a large amount of small data. 
memcached protocol support. (get, set and delete only; expiration time and flags must be 0)

kumofs is a scalable and highly available distributed key-value store.

Kumofs

JCS is a distributed caching system written in java. It is intended to speed up applications by providing a means to manage cached data of various dynamic natures. Its feature include Memory management, Element grouping, Data expiration, UDP Discovery of other caches, Key pattern matching retrieval, Remote server chaining (or clustering) and failover, Remote synchronization, Region data separation and configuration and lot more.

Apache Commons JCS - Java Caching System

Caffeine is a high performance, near optimal caching library based on Java 8. It provides an in-memory cache using a Google Guava inspired API.
 
<ul>
<li><a href="https://github.com/ben-manes/caffeine/wiki/Population">automatic loading of entries</a> into the cache, optionally asynchronously</li>
<li><a href="https://github.com/ben-manes/caffeine/wiki/Eviction#size-based">size-based eviction</a> when a maximum is exceeded based on <a href="https://github.com/ben-manes/caffeine/wiki/Efficiency">frequency and recency</a></li>
<li><a href="https://github.com/ben-manes/caffeine/wiki/Eviction#time-based">time-based expiration</a> of entries, measured since last access or last write</li>
<li><a href="https://github.com/ben-manes/caffeine/wiki/Refresh">asynchronously refresh</a> when the first stale request for an entry occurs</li>
<li>keys automatically wrapped in <a href="https://github.com/ben-manes/caffeine/wiki/Eviction#reference-based">weak references</a></li>
<li>values automatically wrapped in <a href="https://github.com/ben-manes/caffeine/wiki/Eviction#reference-based">weak or soft references</a></li>
<li><a href="https://github.com/ben-manes/caffeine/wiki/Removal">notification</a> of evicted (or otherwise removed) entries</li>
<li><a href="https://github.com/ben-manes/caffeine/wiki/Writer">writes propagated</a> to an external resource</li>
<li>accumulation of cache access <a href="https://github.com/ben-manes/caffeine/wiki/Statistics">statistics</a></li>
</ul>

Caffeine is a high performance, near optimal caching library based on Java 8. It provides an in-memory cache using a Google Guava inspired API.

Caffeine -  A high performance caching library for Java 8

DiskCache is a disk and file backed cache library, written in pure-Python, and compatible with Django. The cloud-based computing of 2021 puts a premium on memory. Gigabytes of empty space is left on disks as processes vie for memory. Among these processes is Memcached (and sometimes Redis) which is used as a cache. It is good to use empty disk space for caching.
 
DiskCache efficiently makes gigabytes of storage space available for caching. By leveraging rock-solid database libraries and memory-mapped files, cache performance can match and exceed industry-standard solutions. There's no need for a C compiler or running another process. Performance is a feature and testing has 100% coverage with unit tests and hours of stress.

DiskCache is a disk and file backed cache library, written in pure-Python, and compatible with Django. The cloud-based computing of 2021 puts a premium on memory. Gigabytes of empty space is left on disks as processes vie for memory. Among these processes is Memcached (and sometimes Redis) which is used as a cache. It is good to use empty disk space for caching.

DiskCache - Python disk backed cache (Django-compatible). Faster than Redis and Memcached. Pure-Python

Ristretto is a fast, concurrent cache library built with a focus on performance and correctness. Its features include Concurrency, High Hit Ratios, Fast Throughput, Cost-Based Eviction, Metrics support.

Ristretto - A high performance memory-bound Go cache

The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system. Apache Accumulo is based on Google's BigTable design and is built on top of Apache Hadoop, Zookeeper, and Thrift. Apache Accumulo features a few novel improvements on the BigTable design in the form of cell-based access control and a server-side programming mechanism that can modify key/value pairs at various points in the data management process.

Apache Accumulo - Key Value Store based on Google BigTable

ArangoDB is a multi-purpose open-source database with flexible data model for documents, graphs, and key-values. Build high performance application using a convenient sql-like query language or JavaScript/Ruby extensions. Its key features are  Schema-free, Convenient querying using AQL, Extendable through JS, Space efficiency, Supports modern storage hardware, like SSD and large caches and lot more.

ArangoDB - The Multi-purpose NoSQL DB

Apache Ignite In-Memory Data Fabric is a high-performance, integrated and distributed in-memory platform for computing and transacting on large-scale data sets in real-time, orders of magnitude faster than possible with traditional disk-based or flash technologies. 
 
Apache Ignite has advanced compute and clustering capabilities including Dynamic Sub-Clusters, Clustered Lambda Execution, Clustered ForkJoin Tasks.

Apache Ignite In-Memory Data Fabric is a high-performance, integrated and distributed in-memory platform for computing and transacting on large-scale data sets in real-time, orders of magnitude faster than possible with traditional disk-based or flash technologies. 


Apache Ignite - High performance in-memory data grid

Dynomite, inspired by Dynamo whitepaper, is a thin, distributed dynamo layer for different storages and protocols. Dynomite is a sharding and replication layer. Dynomite can make existing non distributed datastores, such as Redis or Memcached, into a fully distributed & multi-datacenter replicating datastore.

Dynomite - Making Non-Distributed Databases, Distributed 

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