DBMS > LMDB vs. Sadas Engine vs. Splice Machine vs. TimesTen

System Properties Comparison LMDB vs. Sadas Engine vs. Splice Machine vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

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

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

In-Memory RDBMS compatible to Oracle

Primary database model

Key-value store

Relational DBMS

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

Score	2.09
Rank	#121	Overall
	#20	Key-value stores

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

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

www.sadasengine.com

splicemachine.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

www.lmdb.tech/doc

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

splicemachine.com/how-it-works

docs.oracle.com/database/timesten-18.1

Developer

Symas

SADAS s.r.l.

Splice Machine

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2011

2006

2014

1998

Current release

0.9.32, January 2024

8.0

3.1, March 2021

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

commercial

free trial version available

Open Source

AGPL 3.0, commercial license available

commercial

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++

Java

Server operating systems

Linux
Unix
Windows

AIX
Linux
Windows

Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC
ODBC
Proprietary protocol

JDBC
Native Spark Datasource
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

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

.Net
C
C#
C++
Groovy
Java
PHP
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

Java

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

managed by 'Learn by Usage'

yes

User concepts

Access control

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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