DBMS > LMDB vs. Microsoft SQL Server vs. RavenDB vs. Spark SQL vs. Stardog

System Properties Comparison LMDB vs. Microsoft SQL Server vs. RavenDB vs. Spark SQL vs. Stardog

Editorial information provided by DB-Engines

Name

Description

A high performant, light-weight, embedded key-value database library

Microsofts flagship relational DBMS

Open Source Operational and Transactional Enterprise NoSQL Document Database

Spark SQL is a component on top of 'Spark Core' for structured data processing

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

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.99
Rank	#125	Overall
	#21	Key-value stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

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

Website

www.symas.com/symas-embedded-database-lmdb

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

ravendb.net

spark.apache.org/sql

www.stardog.com

Technical documentation

www.lmdb.tech/doc

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

ravendb.net/docs

spark.apache.org/docs/latest/sql-programming-guide.html

docs.stardog.com

Developer

Symas

Microsoft

Hibernating Rhinos

Apache Software Foundation

Stardog-Union

Initial release

2011

1989

2010

2014

2010

Current release

0.9.32, January 2024

SQL Server 2022, November 2022

5.4, July 2022

3.5.0 ( 2.13), September 2023

7.3.0, May 2020

License

Commercial or Open Source

Open Source

commercial

restricted free version is available

Open Source

AGPL version 3, commercial license available

Open Source

Apache 2.0

commercial

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

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Scala

Java

Server operating systems

Linux
Unix
Windows

Linux
Windows

Linux
macOS
Raspberry Pi
Windows

Linux
OS X
Windows

Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free

yes

schema-free and OWL/RDFS-schema support

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

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

SQL-like query language (RQL)

SQL-like DML and DDL statements

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

APIs and other access methods

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

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

JDBC
ODBC

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

Supported programming languages

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

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

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
Python
R
Scala

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

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

yes

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

none

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

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, but depending on the SQL-Server Edition

Multi-source replication

none

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

Authorization levels configured per client per database

Access rights for users and roles

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