DBMS > Badger vs. LeanXcale vs. Microsoft SQL Server vs. TerminusDB

System Properties Comparison Badger vs. LeanXcale vs. Microsoft SQL Server vs. TerminusDB

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

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

Microsofts flagship relational DBMS

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Key-value store

Key-value store
Relational DBMS

Relational DBMS

Graph DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
RDF store

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Website

github.com/dgraph-io/badger

www.leanxcale.com

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

terminusdb.com

Technical documentation

godoc.org/github.com/dgraph-io/badger

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

terminusdb.github.io/terminusdb/#

Developer

DGraph Labs

LeanXcale

Microsoft

DataChemist Ltd.

Initial release

2017

2015

1989

2018

Current release

SQL Server 2022, November 2022

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

restricted free version is available

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Prolog, Rust

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
Windows

Linux

Data scheme

schema-free

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

SQL-like query language (WOQL)

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

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

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

C
Java
Scala

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

JavaScript
Python

Server-side scripts

Stored procedures

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

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

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

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, but depending on the SQL-Server Edition

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory journaling

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

Role-based access control

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