DBMS > Splice Machine vs. SQream DB vs. Trafodion vs. WakandaDB

System Properties Comparison Splice Machine vs. SQream DB vs. Trafodion vs. WakandaDB

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Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

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

Transactional SQL-on-Hadoop DBMS

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Relational DBMS

Object oriented DBMS

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

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

Technical documentation

splicemachine.com/how-it-works

docs.sqream.com

trafodion.apache.org/documentation.html

wakanda.github.io/doc

Developer

Splice Machine

SQream Technologies

Apache Software Foundation, originally developed by HP

Wakanda SAS

Initial release

2014

2017

2014

2012

Current release

3.1, March 2021

2022.1.6, December 2022

2.3.0, February 2019

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

AGPL 3.0, commercial license available

commercial

Open Source

Apache 2.0

Open Source

AGPLv3, extended commercial license available

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++, CUDA, Haskell, Java, Scala

C++, Java

C++, JavaScript

Server operating systems

Linux
OS X
Solaris
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC
Native Spark Datasource
ODBC

.Net
JDBC
ODBC

ADO.NET
JDBC
ODBC

RESTful HTTP API

Supported programming languages

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C++
Java
JavaScript (Node.js)
Python

All languages supporting JDBC/ODBC/ADO.Net

JavaScript

Server-side scripts

Stored procedures

yes

Java

user defined functions in Python

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Shared Nothhing Auto-Sharding, Columnar Partitioning

horizontal and vertical partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

yes, via HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

yes

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More resources

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24 June 2020, VentureBeat

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