DBMS > Amazon Redshift vs. Atos Standard Common Repository vs. HugeGraph vs. Riak TS vs. Rockset

System Properties Comparison Amazon Redshift vs. Atos Standard Common Repository vs. HugeGraph vs. Riak TS vs. Rockset

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

A fast-speed and highly-scalable Graph DBMS

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Primary database model

Relational DBMS

Document store
Key-value store

Graph DBMS

Time Series DBMS

Document store

Secondary database models

Relational DBMS
Search engine

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	0.79
Rank	#211	Overall
	#35	Document stores

Website

aws.amazon.com/redshift

atos.net/en/convergence-creators/portfolio/standard-common-repository

github.com/hugegraph
hugegraph.apache.org

rockset.com

Technical documentation

docs.aws.amazon.com/redshift

hugegraph.apache.org/docs

www.tiot.jp/riak-docs/riak/ts/latest

docs.rockset.com

Developer

Amazon (based on PostgreSQL)

Atos Convergence Creators

Baidu

Open Source, formerly Basho Technologies

Rockset

Initial release

2012

2016

2018

2015

2019

Current release

1703

0.9

3.0.0, September 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Erlang

C++

Server operating systems

hosted

Linux

Linux
macOS
Unix

Linux
OS X

hosted

Data scheme

yes

Schema and schema-less with LDAP views

yes

schema-free

Typing

predefined data types such as float or date

yes

optional

yes

dynamic typing

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

ingestion from XML files supported

Secondary indexes

restricted

yes

also supports composite index and range index

restricted

all fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes, limited

Read-only SQL queries, including JOINs

APIs and other access methods

JDBC
ODBC

LDAP

Java API
RESTful HTTP API
TinkerPop Gremlin

HTTP API
Native Erlang Interface

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC

All languages with LDAP bindings

Groovy
Java
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

asynchronous Gremlin script jobs

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

edges in graph

links between datasets can be stored

Transaction concepts

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

ACID

Atomic execution of specific operations

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

LDAP bind authentication

Users, roles and permissions

Access rights for users and organizations can be defined via Rockset console

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