DBMS > RocksDB vs. SQream DB vs. Teradata Aster

System Properties Comparison RocksDB vs. SQream DB vs. Teradata Aster

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Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

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

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

Platform for big data analytics on multistructured data sources and types

Primary database model

Key-value store

Relational DBMS

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

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Score	0.73
Rank	#228	Overall
	#105	Relational DBMS

Website

rocksdb.org

sqream.com

Technical documentation

github.com/facebook/rocksdb/wiki

docs.sqream.com

Developer

Facebook, Inc.

SQream Technologies

Teradata

Initial release

2013

2017

2005

Current release

8.11.4, April 2024

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux

Data scheme

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

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.

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

C++ API
Java API

.Net
JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C
C++
Go
Java
Perl
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

user defined functions in Python

R packages

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

horizontal and vertical partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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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