DBMS > Apache Druid vs. Blueflood vs. Datastax Enterprise vs. Riak TS

System Properties Comparison Apache Druid vs. Blueflood vs. Datastax Enterprise vs. Riak TS

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Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Scalable TimeSeries DBMS based on Cassandra

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

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

Primary database model

Relational DBMS
Time Series DBMS

Time Series DBMS

Wide column store

Time Series DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

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

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	5.80
Rank	#60	Overall
	#4	Wide column stores

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

druid.apache.org

blueflood.io

www.datastax.com/products/datastax-enterprise

Technical documentation

druid.apache.org/docs/latest/design

github.com/rax-maas/blueflood/wiki

docs.datastax.com

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

Developer

Apache Software Foundation and contributors

Rackspace

DataStax

Open Source, formerly Basho Technologies

Initial release

2012

2013

2011

2015

Current release

29.0.1, April 2024

6.8, April 2020

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache 2.0

commercial

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Erlang

Server operating systems

Linux
OS X
Unix

Linux
OS X

Data scheme

yes

schema-less columns are supported

predefined scheme

schema-free

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

restricted

SQL

Support of SQL

SQL for querying

SQL-like DML and DDL statements (CQL); Spark SQL

yes, limited

APIs and other access methods

JDBC
RESTful HTTP/JSON API

HTTP REST

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

HTTP API
Native Erlang Interface

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

based on Cassandra

Sharding

no "single point of failure"

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

selectable replication factor

based on Cassandra

configurable replication factor, datacenter aware, advanced replication for edge computing

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

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

Atomicity and isolation are supported for single 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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights for users can be defined per object

More information provided by the system vendor

Specific characteristics

DataStax Enterprise is scale-out data infrastructure for enterprises that need to...
» more

Competitive advantages

Supporting the following application requirements: Zero downtime - Built on Apache...
» more

Typical application scenarios

Applications that must be massively and linearly scalable with 100% uptime and able...
» more

Key customers

Capital One, Cisco, Comcast, eBay, McDonald's, Microsoft, Safeway, Sony, UBS, and...
» more

Market metrics

Among the Forbes 100 Most Innovative Companies, DataStax is trusted by 5 of the top...
» more

Licensing and pricing models

Annual subscription
» more

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