DBMS > HEAVY.AI vs. JaguarDB vs. KairosDB vs. Warp 10

System Properties Comparison HEAVY.AI vs. JaguarDB vs. KairosDB vs. Warp 10

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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

Performant, highly scalable DBMS for AI and IoT applications

Distributed Time Series DBMS based on Cassandra or H2

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

Primary database model

Relational DBMS

Key-value store
Vector DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	0.09
Rank	#356	Overall
	#53	Key-value stores
	#13	Vector DBMS

Score	0.71
Rank	#230	Overall
	#20	Time Series DBMS

Score	0.08
Rank	#358	Overall
	#35	Time Series DBMS

Website

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

www.jaguardb.com

github.com/kairosdb/kairosdb

www.warp10.io

Technical documentation

docs.heavy.ai

www.jaguardb.com/support.html

kairosdb.github.io

www.warp10.io/content/02_Getting_started

Developer

HEAVY.AI, Inc.

DataJaguar, Inc.

SenX

Initial release

2016

2015

2013

2015

Current release

5.10, January 2022

3.3 July 2023

1.2.2, November 2018

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

GPL V3.0

Open Source

Apache 2.0

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++

the server part. Clients available in other languages

Java

Server operating systems

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

JDBC
ODBC
Thrift
Vega

JDBC
ODBC

Graphite protocol
HTTP REST
Telnet API

HTTP API
Jupyter
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

WarpScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

based on Cassandra

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

based on Cassandra

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

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

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

rights management via user accounts

simple password-based access control

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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More resources

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