DBMS > H2 vs. HEAVY.AI vs. LevelDB vs. STSdb

System Properties Comparison H2 vs. HEAVY.AI vs. LevelDB vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

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

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

www.h2database.com

github.com/heavyai/heavydb
www.heavy.ai

github.com/google/leveldb

github.com/STSSoft/STSdb4

Technical documentation

www.h2database.com/html/main.html

docs.heavy.ai

github.com/google/leveldb/blob/main/doc/index.md

Developer

Thomas Mueller

HEAVY.AI, Inc.

Google

STS Soft SC

Initial release

2005

2016

2011

Current release

2.2.220, July 2023

5.10, January 2022

1.23, February 2021

4.0.8, September 2015

License

Commercial or Open Source

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

Apache Version 2; enterprise edition available

Open Source

BSD

Open Source

GPLv2, commercial license available

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++ and CUDA

C++

Server operating systems

All OS with a Java VM

Linux

Illumos
Linux
OS X
Windows

Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

JDBC
ODBC
Thrift
Vega

.NET Client API

Supported programming languages

Java

All languages supporting JDBC/ODBC/Thrift
Python

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C#
Java

Server-side scripts

Stored procedures

Java Stored Procedures and User-Defined Functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

With clustering: 2 database servers on different computers operate on identical copies of a database

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

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

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