DBMS > Heroic vs. IBM Cloudant vs. Splice Machine vs. SwayDB

System Properties Comparison Heroic vs. IBM Cloudant vs. Splice Machine vs. SwayDB

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Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Database as a Service offering based on Apache CouchDB

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Website

github.com/spotify/heroic

www.ibm.com/products/cloudant

splicemachine.com

swaydb.simer.au

Technical documentation

spotify.github.io/heroic

cloud.ibm.com/docs/Cloudant

splicemachine.com/how-it-works

Developer

Spotify

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Splice Machine

Simer Plaha

Initial release

2014

2010

2014

2018

Current release

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

GNU Affero GPL V3.0

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

Java

Erlang

Java

Scala

Server operating systems

hosted

Linux
OS X
Solaris
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

via Elasticsearch

yes

SQL

Support of SQL

yes

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

RESTful HTTP/JSON API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

Java
Kotlin
Scala

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

none

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

atomic operations within a document possible

ACID

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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

Access rights for users can be defined per database

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

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