DBMS > KeyDB vs. Lovefield vs. Oracle Coherence vs. Riak TS vs. Sphinx

System Properties Comparison KeyDB vs. Lovefield vs. Oracle Coherence vs. Riak TS vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Embeddable relational database for web apps written in pure JavaScript

Oracles in-memory data grid solution

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

github.com/Snapchat/KeyDB
keydb.dev

google.github.io/lovefield

www.oracle.com/java/coherence

sphinxsearch.com

Technical documentation

docs.keydb.dev

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.oracle.com/en/middleware/standalone/coherence

www.tiot.jp/riak-docs/riak/ts/latest

sphinxsearch.com/docs

Developer

EQ Alpha Technology Ltd.

Google

Oracle

Open Source, formerly Basho Technologies

Sphinx Technologies Inc.

Initial release

2019

2014

2007

2015

2001

Current release

2.1.12, February 2017

14.1, August 2023

3.0.0, September 2022

3.5.1, February 2023

License

Commercial or Open Source

Open Source

BSD-3

Open Source

Apache 2.0

commercial

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

JavaScript

Java

Erlang

C++

Server operating systems

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

All OS with a Java VM

Linux
OS X

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

by using the Redis Search module

yes

restricted

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

yes, limited

SQL-like query language (SphinxQL)

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protoco

JCache
JPA
RESTful HTTP API

HTTP API
Native Erlang Interface

Proprietary protocol

Supported programming languages

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

JavaScript

.Net
C++
Java

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

Lua

Erlang

Triggers

Using read-only observers

yes

Live Events

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

yes, with selectable consistency level

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong eventual consistency with CRDTs

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Optimistic locking, atomic execution of commands blocks and scripts

ACID

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

optionally

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

yes

User concepts

Access control

simple password-based access control and ACL

authentification to access the cache via certificates or http basic authentication

More information provided by the system vendor

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