DBMS > Atos Standard Common Repository vs. Blueflood vs. ClickHouse vs. Riak TS vs. Spark SQL

System Properties Comparison Atos Standard Common Repository vs. Blueflood vs. ClickHouse vs. Riak TS vs. Spark SQL

Editorial information provided by DB-Engines

Name

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

Description

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

Scalable TimeSeries DBMS based on Cassandra

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Document store
Key-value store

Secondary database models

Time Series DBMS

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

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

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

blueflood.io

clickhouse.com

spark.apache.org/sql

Technical documentation

github.com/rax-maas/blueflood/wiki

clickhouse.com/docs

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

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Atos Convergence Creators

Rackspace

Clickhouse Inc.

Open Source, formerly Basho Technologies

Apache Software Foundation

Initial release

2016

2013

2016

2015

2014

Current release

1703

v24.4.1.2088-stable, May 2024

3.0.0, September 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache 2.0

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.

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Implementation language

Java

C++

Erlang

Scala

Server operating systems

Linux

Linux
OS X

FreeBSD
Linux
macOS

Linux
OS X

Linux
OS X
Windows

Data scheme

Schema and schema-less with LDAP views

predefined scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

optional

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

Secondary indexes

yes

restricted

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

yes, limited

SQL-like DML and DDL statements

APIs and other access methods

LDAP

HTTP REST

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

HTTP API
Native Erlang Interface

JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

based on Cassandra

key based and custom

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

links between datasets can be stored

Transaction concepts

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

Atomic execution of specific operations

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

LDAP bind authentication

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

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