DBMS > AgensGraph vs. LeanXcale vs. MySQL vs. Spark SQL

System Properties Comparison AgensGraph vs. LeanXcale vs. MySQL vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

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

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

Widely used open source RDBMS

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Graph DBMS
Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

bitnine.net/agensgraph

www.leanxcale.com

www.mysql.com

spark.apache.org/sql

Technical documentation

bitnine.net/documentation

dev.mysql.com/doc

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Bitnine Global Inc.

LeanXcale

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Initial release

2016

2015

1995

2014

Current release

2.1, December 2018

8.3.0, January 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

C and C++

Scala

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

depending on used data model

yes

Typing

predefined data types such as float or date

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

through Apache Derby

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

Cypher Query Language
JDBC

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

Supported programming languages

C
Java
JavaScript
Python

C
Java
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

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

Users with fine-grained authorization concept

no user groups or roles

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