DBMS > HEAVY.AI vs. RocksDB vs. SQream DB

System Properties Comparison HEAVY.AI vs. RocksDB vs. SQream DB

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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

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

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Relational DBMS

Key-value store

Relational DBMS

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	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

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

rocksdb.org

sqream.com

Technical documentation

docs.heavy.ai

github.com/facebook/rocksdb/wiki

docs.sqream.com

Developer

HEAVY.AI, Inc.

Facebook, Inc.

SQream Technologies

Initial release

2016

2013

2017

Current release

5.10, January 2022

8.11.4, April 2024

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

BSD

commercial

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++

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

SQL

Support of SQL

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

C++ API
Java API

.Net
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Go
Java
Perl
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

horizontal partitioning

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

ACID

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

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