DBMS > HEAVY.AI vs. Hive vs. KairosDB vs. RocksDB

System Properties Comparison HEAVY.AI vs. Hive vs. KairosDB vs. RocksDB

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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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Distributed Time Series DBMS based on Cassandra or H2

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS

Time Series DBMS

Key-value store

Secondary database models

Spatial DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.62
Rank	#239	Overall
	#20	Time Series DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

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

hive.apache.org

github.com/kairosdb/kairosdb

rocksdb.org

Technical documentation

docs.heavy.ai

cwiki.apache.org/confluence/display/Hive/Home

kairosdb.github.io

github.com/facebook/rocksdb/wiki

Developer

HEAVY.AI, Inc.

Apache Software Foundation

initially developed by Facebook

Facebook, Inc.

Initial release

2016

2012

2013

Current release

5.10, January 2022

3.1.3, April 2022

1.2.2, November 2018

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++ and CUDA

Java

C++

Server operating systems

Linux

All OS with a Java VM

Linux
OS X
Windows

Linux

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

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
Thrift

Graphite protocol
HTTP REST
Telnet API

C++ API
Java API

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
PHP
Python

Java
JavaScript

Node.js
PHP
Python

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

based on Cassandra

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

based on Cassandra

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

Transaction concepts

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

yes

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

Access rights for users, groups and roles

simple password-based access control

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