DBMS > LMDB vs. Quasardb vs. SAP HANA vs. Valentina Server

System Properties Comparison LMDB vs. Quasardb vs. SAP HANA vs. Valentina Server

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Editorial information provided by DB-Engines

Name

Description

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

Distributed, high-performance timeseries database

In-memory, column based data store. Available as appliance or cloud service

Object-relational database and reports server

Primary database model

Key-value store

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

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

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

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

quasar.ai

www.sap.com/products/hana.html

www.valentina-db.net

Technical documentation

www.lmdb.tech/doc

doc.quasar.ai/master

help.sap.com/hana

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Symas

quasardb

SAP

Paradigma Software

Initial release

2011

2009

2010

1999

Current release

0.9.32, January 2024

3.14.1, January 2024

2.0 SPS07 (April 4, 2023), April 2023

5.7.5

License

Commercial or Open Source

Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Server operating systems

Linux
Unix
Windows

BSD
Linux
OS X
Windows

Appliance or cloud-service

Linux
OS X
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

HTTP API

JDBC
ODBC

ODBC

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++
Go
Java
JavaScript (Node.js)
PHP
Python
R

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

SQLScript, R

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

consistent hashing

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication with selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

Cryptographically strong user authentication and audit trail

yes

fine grained access rights according to SQL-standard

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