DBMS > LeanXcale vs. MySQL vs. NSDb vs. Stardog vs. Teradata

System Properties Comparison LeanXcale vs. MySQL vs. NSDb vs. Stardog vs. Teradata

Editorial information provided by DB-Engines

Name

Description

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

Widely used open source RDBMS

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Graph DBMS
RDF store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

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

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

Technical documentation

Developer

LeanXcale

Oracle

since 2010, originally MySQL AB, then Sun

Stardog-Union

Teradata

Initial release

2015

1995

2017

2010

1984

Current release

8.4.0, April 2024

7.3.0, May 2020

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache Version 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

Java, Scala

Java

Server operating systems

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS

Linux
macOS
Windows

hosted
Linux

Data scheme

yes

schema-free and OWL/RDFS-schema support

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

Import/export of XML data possible

yes

Secondary indexes

yes

all fields are automatically indexed

yes

supports real-time indexing in full-text and geospatial

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

through Apache Derby

yes

with proprietary extensions

SQL-like query language

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

yes

SQL 2016 + extensions

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

ADO.NET
JDBC
ODBC
Proprietary native API

gRPC
HTTP REST
WebSocket

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

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
Scala

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes

proprietary syntax

user defined functions and aggregates, HTTP Server extensions in Java

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

via event handlers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

none

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication in HA-Cluster

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency in HA-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

relationships in graphs

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

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

Using Apache Lucene

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

Users with fine-grained authorization concept

no user groups or roles

Access rights for users and roles

fine grained access rights according to SQL-standard

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