DBMS > Cachelot.io vs. SAP HANA vs. Splice Machine vs. Warp 10

System Properties Comparison Cachelot.io vs. SAP HANA vs. Splice Machine vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

In-memory caching system

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

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Key-value store

Relational DBMS

Time Series 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	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

cachelot.io

www.sap.com/products/hana.html

splicemachine.com

www.warp10.io

Technical documentation

help.sap.com/hana

splicemachine.com/how-it-works

www.warp10.io/content/02_Getting_started

Developer

SAP

Splice Machine

SenX

Initial release

2015

2010

2014

2015

Current release

2.0 SPS07 (April 4, 2023), April 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

Simplified BSD License

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

Apache License 2.0

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

FreeBSD
Linux
OS X

Appliance or cloud-service

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

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

Memcached protocol

JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

SQLScript, R

yes

Java

yes

WarpScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Multi-source replication
Source-replica replication

selectable replication factor

based on HBase

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

none

Immediate Consistency

based on HBase

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

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

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

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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