DBMS > BoltDB vs. Drizzle vs. HEAVY.AI vs. Kdb vs. SQLite

System Properties Comparison BoltDB vs. Drizzle vs. HEAVY.AI vs. Kdb vs. SQLite

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

An embedded key-value store for Go.

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

High performance Time Series DBMS

Widely used embeddable, in-process RDBMS

Primary database model

Key-value store

Relational DBMS

Time Series DBMS
Vector DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	0.76
Rank	#224	Overall
	#32	Key-value stores

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	7.70
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Score	116.01
Rank	#10	Overall
	#7	Relational DBMS

Website

github.com/boltdb/bolt

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

kx.com

www.sqlite.org

Technical documentation

docs.heavy.ai

code.kx.com

www.sqlite.org/docs.html

Developer

Drizzle project, originally started by Brian Aker

HEAVY.AI, Inc.

Kx Systems, a division of First Derivatives plc

Dwayne Richard Hipp

Initial release

2013

2008

2016

2000

kdb was released 2000, kdb+ in 2003

2000

Current release

7.2.4, September 2012

5.10, January 2022

3.6, May 2018

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

MIT License

Open Source

GNU GPL

Open Source

Apache Version 2; enterprise edition available

commercial

free 32-bit version

Open Source

Public Domain

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++

C++ and CUDA

Server operating systems

BSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X

Linux

Linux
OS X
Solaris
Windows

server-less

Data scheme

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

Secondary indexes

yes

table attribute 'grouped'

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

SQL-like query language (q)

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC

JDBC
ODBC
Thrift
Vega

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
C++
Java
PHP

All languages supporting JDBC/ODBC/Thrift
Python

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

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

user defined functions

Triggers

hooks for callbacks inside the server can be used.

yes

with views

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

Multi-source replication

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

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

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

rights management via user accounts

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
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Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
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Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
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Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

upon request
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