DBMS > Apache Phoenix vs. BigchainDB vs. Ingres vs. Riak KV vs. TimesTen

System Properties Comparison Apache Phoenix vs. BigchainDB vs. Ingres vs. Riak KV vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

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

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Well established RDBMS

Distributed, fault tolerant key-value store

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

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

Relational DBMS

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

Score	1.90
Rank	#125	Overall
	#59	Relational DBMS

Score	0.76
Rank	#216	Overall
	#36	Document stores

Score	3.63
Rank	#79	Overall
	#43	Relational DBMS

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

phoenix.apache.org

www.bigchaindb.com

www.actian.com/databases/ingres

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

phoenix.apache.org

bigchaindb.readthedocs.io/en/latest

docs.actian.com/ingres

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

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

Apache Software Foundation

Actian Corporation

OpenSource, formerly Basho Technologies

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2014

2016

1974

originally developed at University Berkely in early 1970s

2009

1998

Current release

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

12.0, July 2024

3.2.0, December 2022

Release 22.1

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

AGPL v3

commercial

Open Source

Apache version 2, commercial enterprise edition

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Python

Erlang

Server operating systems

Linux
Unix
Windows

Linux

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux
OS X

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

yes

schema-free

yes

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.

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC

CLI Client
RESTful HTTP API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

HTTP API
Native Erlang Interface

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

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

Go
Haskell
Java
JavaScript
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
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

user defined functions

yes

Erlang

PL/SQL

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

Ingres Replicator

selectable replication factor

Multi-source replication
Source-replica replication

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

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

links between data sets can be stored

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

yes

by means of logfiles and checkpoints

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

yes

fine grained access rights according to SQL-standard

yes, using Riak Security

fine grained access rights according to SQL-standard

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More resources

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11 August 2021, Krishna Maheshwari (sponsor)

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