DBMS > AlaSQL vs. AnzoGraph DB vs. GridGain vs. MySQL vs. PlanetScale

System Properties Comparison AlaSQL vs. AnzoGraph DB vs. GridGain vs. MySQL vs. PlanetScale

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

GridGain is an in-memory computing platform, built on Apache Ignite

Widely used open source RDBMS

Scalable, distributed, serverless MySQL database platform built on top of Vitess

Primary database model

Document store
Relational DBMS

Graph DBMS
RDF store

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	0.51
Rank	#256	Overall
	#40	Document stores
	#118	Relational DBMS

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	1.49
Rank	#155	Overall
	#72	Relational DBMS

Website

alasql.org

cambridgesemantics.com/anzograph

www.gridgain.com

www.mysql.com

planetscale.com

Technical documentation

github.com/AlaSQL/alasql

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

www.gridgain.com/docs/index.html

dev.mysql.com/doc

planetscale.com/docs

Developer

Andrey Gershun & Mathias R. Wulff

Cambridge Semantics

GridGain Systems, Inc.

Oracle

since 2010, originally MySQL AB, then Sun

PlanetScale

Initial release

2014

2018

2007

1995

2020

Current release

2.3, January 2021

GridGain 8.5.1

8.4.0, April 2024

License

Commercial or Open Source

Open Source

MIT-License

commercial

free trial version available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

JavaScript

Java, C++, .Net

C and C++

Server operating systems

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

Linux

Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Docker
Linux
macOS

Data scheme

schema-free

Schema-free and OWL/RDFS-schema support

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

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

SPARQL and SPARQL* as primary query language. Cypher preview.

ANSI-99 for query and DML statements, subset of DDL

yes

with proprietary extensions

yes

with proprietary extensions

APIs and other access methods

JavaScript API

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

JavaScript

C++
Java
Python

C#
C++
Java
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

user defined functions and aggregates

yes (compute grid and cache interceptors can be used instead)

yes

proprietary syntax

yes

proprietary syntax

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic sharding

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication in MPP-Cluster

yes (replicated cache)

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency in MPP-Cluster

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not needed in graphs

yes

not for MyISAM storage engine

yes

not for MyISAM storage engine

Transaction concepts

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

yes

only for local storage and DOM-storage

ACID

not for MyISAM storage engine

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

table locks or row locks depending on storage engine

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

Access rights for users and roles

Security Hooks for custom implementations

Users with fine-grained authorization concept

no user groups or roles

Users with fine-grained authorization concept

no user groups or roles

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