DBMS > Heroic vs. Microsoft Azure Cosmos DB vs. Oracle Coherence vs. Riak KV vs. Transwarp Hippo

System Properties Comparison Heroic vs. Microsoft Azure Cosmos DB vs. Oracle Coherence vs. Riak KV vs. Transwarp Hippo

Editorial information provided by DB-Engines

Name

Description

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

Globally distributed, horizontally scalable, multi-model database service

Oracles in-memory data grid solution

Distributed, fault tolerant key-value store

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

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

Vector DBMS

Secondary database models

Spatial DBMS

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

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

Score	0.05
Rank	#386	Overall
	#15	Vector DBMS

Website

github.com/spotify/heroic

azure.microsoft.com/services/cosmos-db

www.oracle.com/java/coherence

www.transwarp.cn/en/subproduct/hippo

Technical documentation

spotify.github.io/heroic

learn.microsoft.com/azure/cosmos-db

docs.oracle.com/en/middleware/standalone/coherence

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

Developer

Spotify

Microsoft

Oracle

OpenSource, formerly Basho Technologies

Initial release

2014

2007

2009

2023

Current release

14.1, August 2023

3.2.0, December 2022

1.0, May 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache version 2, commercial enterprise edition

commercial

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

C++

Server operating systems

hosted

All OS with a Java VM

Linux
OS X

Linux
macOS

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

Vector, Numeric and String

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

All properties auto-indexed by default

restricted

SQL

Support of SQL

SQL-like query language

APIs and other access methods

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

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JCache
JPA
RESTful HTTP API

HTTP API
Native Erlang Interface

RESTful HTTP API

Supported programming languages

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
C++
Java

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
Python

Server-side scripts

Stored procedures

JavaScript

Erlang

Triggers

JavaScript

yes

Live Events

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

no "single point of failure"

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes, with selectable consistency level

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between data sets can be stored

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

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 can be defined down to the item level

authentification to access the cache via certificates or http basic authentication

yes, using Riak Security

Role based access control and fine grained access rights

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