DBMS > GBase vs. Ignite vs. KeyDB vs. Sphinx vs. Tkrzw

System Properties Comparison GBase vs. Ignite vs. KeyDB vs. Sphinx vs. Tkrzw

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Name	GBase  Xexclude from comparison	Ignite  Xexclude from comparison	KeyDB  Xexclude from comparison	Sphinx  Xexclude from comparison	Tkrzw Successor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
Description	An analytical database for business intelligence with large customers in China.	Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.	An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols	Open source search engine for searching in data from different sources, e.g. relational databases	A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database model	Relational DBMS	Key-value store Relational DBMS	Key-value store	Search engine	Key-value store
DB-Engines Ranking measures the popularity of database management systems Trend Chart	Score 1.12 Rank #165   Overall #78   Relational DBMS	Score 7.09 Rank #62   Overall #7   Key-value stores #35   Relational DBMS	Score 0.34 Rank #268   Overall #40   Key-value stores	Score 7.23 Rank #61   Overall #8   Search engines	Score 0.00 Rank #363   Overall #57   Key-value stores
Website	www.gbase8a.com	ignite.apache.org	github.com/­JohnSully/­KeyDB keydb.dev	sphinxsearch.com	dbmx.net/­tkrzw
Technical documentation		apacheignite.readme.io/­docs	docs.keydb.dev	sphinxsearch.com/­docs
Developer		Apache Software Foundation	EQ Alpha Technology Ltd.	Sphinx Technologies Inc.	Mikio Hirabayashi
Initial release		2015	2019	2001	2020
Current release	GBase 8a	Apache Ignite 2.6		3.4.1, July 2021	0.9.3, August 2020
License Commercial or Open Source	commercial	Open Source Apache 2.0	Open Source BSD-3	Open Source GPL version 2, commercial licence available	Open Source Apache Version 2.0
Cloud-based only Only available as a cloud service	no	no	no	no	no
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Implementation language		C++, Java, .Net	C++	C++	C++
Server operating systems		Linux OS X Solaris Windows	Linux	FreeBSD Linux NetBSD OS X Solaris Windows	Linux macOS
Data scheme		yes	schema-free	yes	schema-free
Typing predefined data types such as float or date		yes	partial Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes	no	no
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.		yes	no		no
Secondary indexes		yes	yes by using the Redis Search module	yes full-text index on all search fields
SQL Support of SQL		ANSI-99 for query and DML statements, subset of DDL	no	SQL-like query language (SphinxQL)	no
APIs and other access methods		HDFS API Hibernate JCache JDBC ODBC Proprietary protocol RESTful HTTP API Spring Data	Proprietary protocol RESP - REdis Serialization Protoco	Proprietary protocol
Supported programming languages		C# C++ Java PHP Python Ruby Scala	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	C++ unofficial client library Java Perl unofficial client library PHP Python Ruby unofficial client library	C++ Java Python Ruby
Server-side scripts Stored procedures		yes (compute grid and cache interceptors can be used instead)	Lua	no	no
Triggers		yes (cache interceptors and events)	no	no	no
Partitioning methods Methods for storing different data on different nodes		Sharding	Sharding	Sharding Partitioning is done manually, search queries against distributed index is supported	none
Replication methods Methods for redundantly storing data on multiple nodes		yes (replicated cache)	Multi-source replication Source-replica replication	none	none
MapReduce Offers an API for user-defined Map/Reduce methods		yes (compute grid and hadoop accelerator)	no	no	no
Consistency concepts Methods to ensure consistency in a distributed system		Immediate Consistency	Eventual Consistency Strong eventual consistency with CRDTs		Immediate Consistency
Foreign keys Referential integrity		no	no	no	no
Transaction concepts Support to ensure data integrity after non-atomic manipulations of data		ACID	Optimistic locking, atomic execution of commands blocks and scripts	no
Concurrency Support for concurrent manipulation of data		yes	yes	yes	yes
Durability Support for making data persistent		yes	yes Configurable mechanisms for persistency via snapshots and/or operations logs	yes The original contents of fields are not stored in the Sphinx index.	yes
In-memory capabilities Is there an option to define some or all structures to be held in-memory only.		yes	yes		yes using specific database classes
User concepts Access control		Security Hooks for custom implementations	simple password-based access control and ACL	no	no
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	GBase	Ignite	KeyDB	Sphinx	Tkrzw Successor of Tokyo Cabinet and Kyoto Cabinet
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