DBMS > RethinkDB vs. Snowflake vs. Spark SQL vs. Splice Machine vs. Transbase

System Properties Comparison RethinkDB vs. Snowflake vs. Spark SQL vs. Splice Machine vs. Transbase

Editorial information provided by DB-Engines

Name

Description

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Cloud-based data warehousing service for structured and semi-structured data

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

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

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Document store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	2.66
Rank	#107	Overall
	#20	Document stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

www.transaction.de/en/products/transbase.html

Technical documentation

rethinkdb.com/docs

docs.snowflake.net/manuals/index.html

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

splicemachine.com/how-it-works

www.transaction.de/en/products/transbase/features.html

Developer

The Linux Foundation

since July 2017

Snowflake Computing Inc.

Apache Software Foundation

Splice Machine

Transaction Software GmbH

Initial release

2009

2014

1987

Current release

2.4.1, August 2020

3.5.0 ( 2.13), September 2023

3.1, March 2021

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache 2.0

Open Source

AGPL 3.0, commercial license available

commercial

free development license

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

C++

Scala

Java

C and C++

Server operating systems

Linux
OS X
Windows

hosted

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

CLI Client
JDBC
ODBC

JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

JavaScript (Node.js)
Python

Java
Python
R
Scala

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

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

user defined functions

yes

Java

yes

Triggers

Client-side triggers through changefeeds

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

range based

yes

yes, utilizing Spark Core

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

none

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

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

Atomic single-document operations

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes

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

yes

users and table-level permissions

Users with fine-grained authorization concept, user roles and pluggable authentication

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

fine grained access rights according to SQL-standard

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