DBMS > Heroic vs. LMDB vs. OushuDB vs. Quasardb vs. SWC-DB

System Properties Comparison Heroic vs. LMDB vs. OushuDB vs. Quasardb vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Distributed, high-performance timeseries database

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Time Series DBMS

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

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

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

Score	0.04
Rank	#363	Overall
	#154	Relational DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

github.com/spotify/heroic

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

www.oushu.com/product/oushuDB

quasar.ai

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

spotify.github.io/heroic

www.lmdb.tech/doc

www.oushu.com/documentation

doc.quasar.ai/master

Developer

Spotify

Symas

Oushu

quasardb

Alex Kashirin

Initial release

2014

2011

2009

2020

Current release

0.9.32, January 2024

4.0.1, August 2020

3.14.1, January 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

commercial

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

Open Source

GPL V3

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

Java

C++

Server operating systems

Linux
Unix
Windows

Linux

BSD
Linux
OS X
Windows

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

via Elasticsearch

yes

with tags

SQL

Support of SQL

Full-featured ANSI SQL support

SQL-like query language

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC

HTTP API

Proprietary protocol
Thrift

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++

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

C++

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Sharding

consistent hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

User concepts

Access control

Kerberos, SSL and role based access

Cryptographically strong user authentication and audit trail

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