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

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.

LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values. Keys and values are arbitrary byte arrays. It supports basic operations like Put(key,value), Get(key), Delete(key). Only a single process (possibly multi-threaded) can access a particular database at a time. 

Leveldb - A fast and lightweight key/value database library by Google.

KeyDB is a high performance fork of Redis with a focus on multithreading, memory efficiency, and high throughput. In addition to multithreading, KeyDB also has features only available in Redis Enterprise such as Active Replication, FLASH storage support, and some not available at all such as direct backup to AWS S3.
 
KeyDB maintains full compatibility with the Redis protocol, modules, and scripts. This includes the atomicity guarantees for scripts and transactions. Because KeyDB keeps in sync with Redis development KeyDB is a superset of Redis functionality, making KeyDB a drop in replacement for existing Redis deployments.
 
In addition to supporting LUA, KeyDB also supports JavaScript using the powerful V8 JIT engine. ModJS lets you write functions in javascript that can in turn be called directly by KeyBD.

KeyDB is a high performance fork of Redis with a focus on multithreading, memory efficiency, and high throughput. In addition to multithreading, KeyDB also has features only available in Redis Enterprise such as Active Replication, FLASH storage support, and some not available at all such as direct backup to AWS S3.

Keydb - A Multithreaded Fork of Redis 

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

Oracle Berkeley DB provides the best open source embeddable databases allowing developers the choice of SQL, Key/Value, XML/XQuery or Java Object storage for their data model. At its core is a fast, scalable, transactional database engine with proven reliability and availability. Berkeley DB comes three versions: Berkeley DB, Berkeley DB Java Edition, and Berkeley DB XML.

Berkeley DB - Embeddable Databases to Meet Your Needs

Redis is an advanced key-value store. It is similar to memcached but the dataset is not volatile, and values can be strings, exactly like in memcached, but also lists, sets, and ordered sets. All this data types can be manipulated with atomic operations to push/pop elements, add/remove elements, perform server side union, intersection, difference between sets, and so forth. Redis supports different kind of sorting abilities. 
 It is possible to think at Redis as a data structures server, it is not just another key-value DB. Redis supports operations like atomic push and pop of elements on lists, taking ranges of elements from this lists at once, trimming of lists, server-side intersections of sets and even sorting data.

Redis is an advanced key-value store. It is similar to memcached but the dataset is not volatile, and values can be strings, exactly like in memcached, but also lists, sets, and ordered sets. All this data types can be manipulated with atomic operations to push/pop elements, add/remove elements, perform server side union, intersection, difference between sets, and so forth. Redis supports different kind of sorting abilities.

Redis - Advanced key-value store

Pika is a persistent huge storage service , compatible with the vast majority of redis interfaces (details), including string, hash, list, zset, set and management interfaces. With the huge amount of data stored, redis may suffer for a capacity bottleneck, and pika was born for solving it. Except huge storage capacity, pika also support master-slave mode by slaveof command, including full and partial synchronization.

Pika - nosql compatible with redis, it is developed by Qihoo's DBA and infrastructure team

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

Managing large application state easily, resiliently, and with high performance is one of the hardest problems in the cloud today. The FASTER project offers two artifacts to help tackle this problem. 
 
FASTER Log is a high-performance concurrent persistent recoverable log, iterator, and random reader library in C#. It supports very frequent commit operations at low latency, and can quickly saturate disk bandwidth. It supports both sync and async interfaces, handles disk errors, and supports checksums.
 
FASTER KV is a concurrent key-value store + cache (available in C# and C++) that is designed for point lookups and heavy updates. FASTER supports data larger than memory, by leveraging fast external storage (local or cloud). It also supports consistent recovery using a fast non-blocking checkpointing technique that lets applications trade-off performance for commit latency.

Managing large application state easily, resiliently, and with high performance is one of the hardest problems in the cloud today. The FASTER project offers two artifacts to help tackle this problem. FASTER Log is a high-performance concurrent persistent recoverable log, iterator, and random reader library in C#. FASTER KV is a concurrent key-value store + cache (available in C# and C++) that is designed for point lookups and heavy updates.

FASTER - Fast persistent recoverable log and key-value store + cache, in C# and C++

BuntDB is a low-level, in-memory, key/value store in pure Go. It persists to disk, is ACID compliant, and uses locking for multiple readers and a single writer. It supports custom indexes and geospatial data. It's ideal for projects that need a dependable database and favor speed over data size.The desire to create BuntDB stems from the need for a new embeddable database for Tile38 and SummitDB.

BuntDB - Embeddable, in-memory key/value database for Go with custom indexing and geospatial support

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

ApsaraCache is based on the Redis official release 4.0 and has many features and performance enhancements. ApsaraCache has proven to be very stable and efficient in production environment. ApsaraCache supports two main protocols of Memcached: the classic ASCII, and the newer binary. You can use ApsaraCache just as Memcached, and no client code need to be modified. You can persist your data by using ApsaraCache in Memcached mode just like Redis.

ApsaraCache - Redis branch originated from Alibaba Group. 

Ledisdb is a high-performance NoSQL database library and server written in Go. It's similar to Redis but store data in disk. It supports many data structures including kv, list, hash, zset, set. LedisDB supports multiple different databases as backends. 

LedisDB -  A high performance NoSQL Database Server powered by Go

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

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