DBMS > EJDB vs. GigaSpaces vs. Microsoft SQL Server vs. PlanetScale vs. SingleStore

System Properties Comparison EJDB vs. GigaSpaces vs. Microsoft SQL Server vs. PlanetScale vs. SingleStore

Editorial information provided by DB-Engines

Name

Description

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Microsofts flagship relational DBMS

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

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Primary database model

Document store

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Secondary database models

Graph DBMS
Search engine

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	0.13
Rank	#331	Overall
	#46	Document stores

Score	0.91
Rank	#197	Overall
	#33	Document stores
	#7	Object oriented DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	1.09
Rank	#178	Overall
	#82	Relational DBMS

Score	4.02
Rank	#74	Overall
	#39	Relational DBMS

Website

github.com/Softmotions/ejdb

www.gigaspaces.com

www.microsoft.com/en-us/sql-server

planetscale.com

www.singlestore.com

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

docs.gigaspaces.com/latest/landing.html

learn.microsoft.com/en-US/sql/sql-server

planetscale.com/docs

docs.singlestore.com

Developer

Softmotions

Gigaspaces Technologies

Microsoft

PlanetScale

SingleStore Inc.

Initial release

2012

2000

1989

2020

2013

Current release

15.5, September 2020

SQL Server 2022, November 2022

8.5, January 2024

License

Commercial or Open Source

Open Source

GPLv2

Open Source

Apache Version 2; Commercial licenses available

commercial

restricted free version is available

commercial

free developer edition available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java, C++, .Net

C++

C++, Go

Server operating systems

server-less

Linux
macOS
Solaris
Windows

Linux
Windows

Docker
Linux
macOS

Linux

64 bit version required

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

with proprietary extensions

yes

but no triggers and foreign keys

APIs and other access methods

in-process shared library

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

ADO.NET
JDBC
MySQL protocol
ODBC

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

.Net
C++
Java
Python
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

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

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

proprietary syntax

yes

Triggers

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

Source-replica replication

stores two copies of each physical data partition on two separate nodes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

table locks or row locks depending on storage engine

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

In-memory capabilities

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

yes

User concepts

Access control

Role-based access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

Fine grained access control via users, groups and roles

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