DBMS > Apache Phoenix vs. HEAVY.AI vs. SingleStore vs. VoltDB

System Properties Comparison Apache Phoenix vs. HEAVY.AI vs. SingleStore vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	1.90
Rank	#125	Overall
	#59	Relational DBMS

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	4.02
Rank	#74	Overall
	#39	Relational DBMS

Score	1.38
Rank	#156	Overall
	#72	Relational DBMS

Website

phoenix.apache.org

github.com/heavyai/heavydb
www.heavy.ai

www.singlestore.com

www.voltdb.com

Technical documentation

Developer

Apache Software Foundation

HEAVY.AI, Inc.

SingleStore Inc.

VoltDB Inc.

Initial release

2014

2016

2013

2010

Current release

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

5.10, January 2022

8.5, January 2024

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; enterprise edition available

commercial

free developer edition available

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

C++ and CUDA

C++, Go

Java, C++

Server operating systems

Linux
Unix
Windows

Linux

64 bit version required

Linux
OS X

for development

Data scheme

yes

late-bound, schema-on-read capabilities

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.

Secondary indexes

yes

SQL

Support of SQL

yes

but no triggers and foreign keys

yes

only a subset of SQL 99

APIs and other access methods

JDBC

JDBC
ODBC
Thrift
Vega

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

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

All languages supporting JDBC/ODBC/Thrift
Python

Bash
C
C#
Java
JavaScript (Node.js)
Python

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

yes

Java

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

hash partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

Source-replica replication

stores two copies of each physical data partition on two separate nodes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

yes

Snapshots and command logging

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

fine grained access rights according to SQL-standard

Fine grained access control via users, groups and roles

Users and roles with access to stored procedures

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More resources

DB-Engines blog posts

Cloudera's HBase PaaS offering now supports Complex Transactions
11 August 2021, Krishna Maheshwari (sponsor)

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Turbocharge Your Application Development Using WebAssembly With SingleStoreDB
17 October 2022, Akmal Chaudhri, SingleStore (sponsor)

Cloud-Based Analytics With SingleStoreDB
9 June 2022, Akmal Chaudhri, SingleStore (sponsor)

SingleStore: The Increasing Momentum of Multi-Model Database Systems
14 February 2022, Akmal Chaudhri, SingleStore (sponsor)

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Recent citations in the news

Supercharge SQL on Your Data in Apache HBase with Apache Phoenix
2 June 2016, AWS Blog

Deep dive into Azure HDInsight 4.0
25 September 2018, Microsoft

Hortonworks Starts Hadoop Summit with Data Platform Update
28 June 2016, ADT Magazine

Apache Drill Adds New Data Formats
28 March 2022, iProgrammer

Amazon EMR 4.7.0 – Apache Tez & Phoenix, Updates to Existing Apps
2 June 2016, AWS Blog

provided by Google News

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19 September 2024, Unite.AI

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15 August 2024, Center for Data Innovation

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19 September 2024, Business Wire

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8 July 2024, The Register

SingleStore CEO sees little future for purpose-built vector databases
24 January 2024, VentureBeat

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VoltDB Announces Key New Hires
26 October 2021, citybiz

VoltDB Aims for Fast Big Data Development
29 January 2015, ADT Magazine

Solutions Review Sits Down with VoltDB CEO David Flower
17 April 2018, Solutions Review

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