DBMS > BoltDB vs. HEAVY.AI vs. Oracle Berkeley DB vs. SQLite vs. Warp 10

System Properties Comparison BoltDB vs. HEAVY.AI vs. Oracle Berkeley DB vs. SQLite vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

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

Widely used in-process key-value store

Widely used embeddable, in-process RDBMS

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Key-value store

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

github.com/boltdb/bolt

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

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

www.sqlite.org

www.warp10.io

Technical documentation

docs.heavy.ai

docs.oracle.com/cd/E17076_05/html/index.html

www.sqlite.org/docs.html

www.warp10.io/content/02_Getting_started

Developer

HEAVY.AI, Inc.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Dwayne Richard Hipp

SenX

Initial release

2013

2016

1994

2000

2015

Current release

5.10, January 2022

18.1.40, May 2020

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache Version 2; enterprise edition available

Open Source

commercial license available

Open Source

Public Domain

Open Source

Apache License 2.0

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++ and CUDA

C, Java, C++ (depending on the Berkeley DB edition)

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

server-less

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

dynamic column types

schema-free

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC
Thrift
Vega

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

HTTP API
Jupyter
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

WarpScript

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

Source-replica replication

none

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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