DBMS > EJDB vs. GeoMesa vs. Microsoft SQL Server vs. PouchDB vs. SingleStore

System Properties Comparison EJDB vs. GeoMesa vs. Microsoft SQL Server vs. PouchDB vs. SingleStore

Editorial information provided by DB-Engines

Name

Description

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

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Microsofts flagship relational DBMS

JavaScript DBMS with an API inspired by CouchDB

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

Secondary database models

Document store
Graph DBMS
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	0.78
Rank	#214	Overall
	#4	Spatial DBMS

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	2.18
Rank	#114	Overall
	#21	Document stores

Score	4.02
Rank	#74	Overall
	#39	Relational DBMS

Website

github.com/Softmotions/ejdb

www.geomesa.org

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

pouchdb.com

www.singlestore.com

Technical documentation

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

www.geomesa.org/documentation/stable/user/index.html

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

pouchdb.com/guides

docs.singlestore.com

Developer

Softmotions

CCRi and others

Microsoft

Apache Software Foundation

SingleStore Inc.

Initial release

2012

2014

1989

2012

2013

Current release

5.0.1, July 2024

SQL Server 2022, November 2022

7.1.1, June 2019

8.5, January 2024

License

Commercial or Open Source

Open Source

GPLv2

Open Source

Apache License 2.0

commercial

restricted free version is available

Open Source

commercial

free developer edition available

Cloud-based only

Only available as a cloud service

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Implementation language

Scala

C++

JavaScript

C++, Go

Server operating systems

server-less

Linux
Windows

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

Linux

64 bit version required

Data scheme

schema-free

yes

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.

yes

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

but no triggers and foreign keys

APIs and other access methods

in-process shared library

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

HTTP REST

only for PouchDB Server
JavaScript API

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

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

JavaScript

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

Server-side scripts

Stored procedures

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

View functions in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

depending on storage layer

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

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

depending on storage layer

yes, but depending on the SQL-Server Edition

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

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

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

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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.

depending on storage layer

yes

User concepts

Access control

yes

depending on the DBMS used for storage

fine grained access rights according to SQL-standard

Fine grained access control via users, groups and roles

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