DBMS > Cachelot.io vs. Google Cloud Datastore vs. Riak KV vs. SAP HANA vs. STSdb

System Properties Comparison Cachelot.io vs. Google Cloud Datastore vs. Riak KV vs. SAP HANA vs. STSdb

Editorial information provided by DB-Engines

Name

Description

In-memory caching system

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Distributed, fault tolerant key-value store

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Key-value store

Document store

Key-value store

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

Relational DBMS

Key-value store

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	0.04
Rank	#388	Overall
	#62	Key-value stores

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

cachelot.io

cloud.google.com/datastore

www.sap.com/products/hana.html

github.com/STSSoft/STSdb4

Technical documentation

cloud.google.com/datastore/docs

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

help.sap.com/hana

Developer

Google

OpenSource, formerly Basho Technologies

SAP

STS Soft SC

Initial release

2015

2008

2009

2010

2011

Current release

3.2.0, December 2022

2.0 SPS07 (April 4, 2023), April 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Simplified BSD License

commercial

Open Source

Apache version 2, commercial enterprise edition

commercial

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

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++

Erlang

Server operating systems

FreeBSD
Linux
OS X

hosted

Linux
OS X

Appliance or cloud-service

Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

yes

primitive types and user defined types (classes)

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

restricted

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

APIs and other access methods

Memcached protocol

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

HTTP API
Native Erlang Interface

JDBC
ODBC

.NET Client API

Supported programming languages

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

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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#
Java

Server-side scripts

Stored procedures

using Google App Engine

Erlang

SQLScript, R

Triggers

Callbacks using the Google Apps Engine

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

no "single point of failure"

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using Paxos

selectable replication factor

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

links between data sets can be stored

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

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 based on Google Cloud Identity and Access Management (IAM)

yes, using Riak Security

yes

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