DBMS > Riak KV vs. SAP HANA vs. Splice Machine

System Properties Comparison Riak KV vs. SAP HANA vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Distributed, fault tolerant key-value store

In-memory, column based data store. Available as appliance or cloud service

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

Primary database model

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

www.sap.com/products/hana.html

splicemachine.com

Technical documentation

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

help.sap.com/hana

splicemachine.com/how-it-works

Developer

OpenSource, formerly Basho Technologies

SAP

Splice Machine

Initial release

2009

2010

2014

Current release

3.2.0, December 2022

2.0 SPS07 (April 4, 2023), April 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache version 2, commercial enterprise edition

commercial

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Erlang

Java

Server operating systems

Linux
OS X

Appliance or cloud-service

Linux
OS X
Solaris
Windows

Data scheme

schema-free

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

restricted

yes

SQL

Support of SQL

yes

APIs and other access methods

HTTP API
Native Erlang Interface

JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

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

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

Server-side scripts

Stored procedures

Erlang

SQLScript, R

yes

Java

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Multi-source 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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between data sets can be stored

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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, using Riak Security

yes

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

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