DBMS > RocksDB vs. SAP HANA vs. Splice Machine vs. Ultipa

System Properties Comparison RocksDB vs. SAP HANA vs. Splice Machine vs. Ultipa

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Editorial information provided by DB-Engines

Name

Description

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Key-value store

Relational DBMS

Graph 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	2.84
Rank	#97	Overall
	#16	Key-value stores

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Score	0.11
Rank	#338	Overall
	#32	Graph DBMS

Website

rocksdb.org

www.sap.com/products/hana.html

splicemachine.com

www.ultipa.com

Technical documentation

github.com/facebook/rocksdb/wiki

help.sap.com/hana

splicemachine.com/how-it-works

www.ultipa.com/document

Developer

Facebook, Inc.

SAP

Splice Machine

Ultipa

Initial release

2013

2010

2014

2019

Current release

9.4.0, June 2024

2.0 SPS07 (April 4, 2023), April 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

BSD

commercial

Open Source

AGPL 3.0, commercial license available

commercial

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

C++

Java

Server operating systems

Linux

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

yes

SQL

Support of SQL

yes

APIs and other access methods

C++ API
Java API

JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

RESTful HTTP API

Supported programming languages

C
C++
Go
Java
Perl
Python
Ruby

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

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

SQLScript, R

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes

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

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

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