DBMS > Apache Phoenix vs. Coveo vs. InfinityDB vs. Riak TS

System Properties Comparison Apache Phoenix vs. Coveo vs. InfinityDB vs. Riak TS

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Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

A Java embedded Key-Value Store which extends the Java Map interface

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	2.28
Rank	#114	Overall
	#11	Search engines

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

phoenix.apache.org

www.coveo.com

boilerbay.com

Technical documentation

phoenix.apache.org

docs.coveo.com

boilerbay.com/infinitydb/manual

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

Developer

Apache Software Foundation

Coveo

Boiler Bay Inc.

Open Source, formerly Basho Technologies

Initial release

2014

2012

2002

2015

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

4.0

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

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

Server operating systems

Linux
Unix
Windows

hosted

All OS with a Java VM

Linux
OS X

Data scheme

yes

late-bound, schema-on-read capabilities

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

restricted

SQL

Support of SQL

yes

yes, limited

APIs and other access methods

JDBC

RESTful HTTP API

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

HTTP API
Native Erlang Interface

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

C#
Java
JavaScript
Python

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

Server-side scripts

Stored procedures

user defined functions

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

none

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

Eventual Consistency

Foreign keys

Referential integrity

yes

manual creation possible, using inversions based on multi-value capability

links between datasets can be stored

Transaction concepts

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

ACID

yes

ACID

Optimistic locking for transactions; no isolation for bulk loads

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

granular access controls, API key management, content filters

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More resources

DB-Engines blog posts

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11 August 2021, Krishna Maheshwari (sponsor)

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