DBMS > GridDB vs. KeyDB vs. Sadas Engine vs. XTDB

System Properties Comparison GridDB vs. KeyDB vs. Sadas Engine vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Scalable in-memory time series database optimized for IoT and Big Data

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

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Secondary database models

Key-value store
Relational DBMS

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

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	0.70
Rank	#229	Overall
	#32	Key-value stores

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

griddb.net

github.com/Snapchat/KeyDB
keydb.dev

www.sadasengine.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.griddb.net

docs.keydb.dev

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

www.xtdb.com/docs

Developer

Toshiba Corporation

EQ Alpha Technology Ltd.

SADAS s.r.l.

Juxt Ltd.

Initial release

2013

2019

2006

2019

Current release

5.1, August 2022

8.0

1.19, September 2021

License

Commercial or Open Source

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

BSD-3

commercial

free trial version available

Open Source

MIT License

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++

Clojure

Server operating systems

Linux

AIX
Linux
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

partial

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

yes

yes, extensible-data-notation format

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

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Proprietary protocol

RESP - REdis Serialization Protoco

JDBC
ODBC
Proprietary protocol

HTTP REST
JDBC

Supported programming languages

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

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

.Net
C
C#
C++
Groovy
Java
PHP
Python

Clojure
Java

Server-side scripts

Stored procedures

Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID at container level

Optimistic locking, atomic execution of commands blocks and scripts

ACID

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

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

managed by 'Learn by Usage'

User concepts

Access control

Access rights for users can be defined per database

simple password-based access control and ACL

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

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