DBMS > EXASOL vs. HEAVY.AI vs. IRONdb vs. RocksDB

System Properties Comparison EXASOL vs. HEAVY.AI vs. IRONdb vs. RocksDB

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Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

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	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

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

Website

www.exasol.com

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

www.circonus.com/solutions/time-series-database/

rocksdb.org

Technical documentation

www.exasol.com/resources

docs.heavy.ai

docs.circonus.com/irondb/category/getting-started

github.com/facebook/rocksdb/wiki

Developer

Exasol

HEAVY.AI, Inc.

Circonus LLC.

Facebook, Inc.

Initial release

2000

2016

2017

2013

Current release

5.10, January 2022

V0.10.20, January 2018

8.11.4, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

BSD

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 and C++

C++

Server operating systems

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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 query language (Circonus Analytics Query Language: CAQL)

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

JDBC
ODBC
Thrift
Vega

HTTP API

C++ API
Java API

Supported programming languages

Java
Lua
Python
R

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

yes, in Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Automatic, metric affinity per node

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

configurable replication factor, datacenter aware

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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