DBMS > Blueflood vs. MaxDB vs. Spark SQL vs. VoltDB

System Properties Comparison Blueflood vs. MaxDB vs. Spark SQL vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

A robust and reliable RDBMS optimized to run all major SAP solutions

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

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Time Series DBMS

Relational DBMS

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

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	2.26
Rank	#114	Overall
	#55	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

Technical documentation

github.com/rax-maas/blueflood/wiki

maxdb.sap.com/documentation

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

docs.voltdb.com

Developer

Rackspace

SAP, acquired from Software AG (Adabas-D) in 1997

Apache Software Foundation

VoltDB Inc.

Initial release

2013

1984

2014

2010

Current release

7.9.10.12, February 2024

3.5.0 ( 2.13), September 2023

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Limited community edition free

Open Source

Apache 2.0

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Java

C++

Scala

Java, C++

Server operating systems

Linux
OS X

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

Linux
OS X

for development

Data scheme

predefined scheme

yes

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

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

only a subset of SQL 99

APIs and other access methods

HTTP REST

ADO.NET
JDBC
ODBC
WebDAV

JDBC
ODBC

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

.Net
C#
Java
Perl
PHP
Python

Java
Python
R
Scala

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

fine grained access rights according to SQL-standard

Users and roles with access to stored procedures

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