DBMS > AgensGraph vs. AlaSQL vs. GridDB vs. LeanXcale vs. SWC-DB

System Properties Comparison AgensGraph vs. AlaSQL vs. GridDB vs. LeanXcale vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

JavaScript DBMS library

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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

A high performance, scalable Wide Column DBMS

Primary database model

Graph DBMS
Relational DBMS

Document store
Relational DBMS

Time Series DBMS

Key-value store
Relational DBMS

Wide column store

Secondary database models

Key-value store
Relational DBMS

Time Series DBMS

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

Score	0.16
Rank	#333	Overall
	#30	Graph DBMS
	#146	Relational DBMS

Score	0.49
Rank	#259	Overall
	#40	Document stores
	#120	Relational DBMS

Score	2.02
Rank	#132	Overall
	#11	Time Series DBMS

Score	0.35
Rank	#283	Overall
	#41	Key-value stores
	#128	Relational DBMS

Score	0.01
Rank	#387	Overall
	#13	Wide column stores

Website

bitnine.net/agensgraph

alasql.org

griddb.net

www.leanxcale.com

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

bitnine.net/documentation

github.com/AlaSQL/alasql

docs.griddb.net

Developer

Bitnine Global Inc.

Andrey Gershun & Mathias R. Wulff

Toshiba Corporation

LeanXcale

Alex Kashirin

Initial release

2016

2014

2013

2015

2020

Current release

2.1, December 2018

5.1, August 2022

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

MIT-License

Open Source

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

commercial

Open Source

GPL V3

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

JavaScript

C++

Server operating systems

Linux
OS X
Windows

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

Linux

Data scheme

depending on used data model

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

SQL

Support of SQL

yes

Close to SQL99, but no user access control, stored procedures and host language bindings.

SQL92, SQL-like TQL (Toshiba Query Language)

yes

through Apache Derby

SQL-like query language

APIs and other access methods

Cypher Query Language
JDBC

JavaScript API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Proprietary protocol
Thrift

Supported programming languages

C
Java
JavaScript
Python

JavaScript

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

C
Java
Scala

C++

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Source-replica replication

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

none

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

only for local storage and DOM-storage

ACID at container level

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

yes

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users can be defined per database

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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