DBMS > Apache Phoenix vs. Cassandra vs. Fujitsu Enterprise Postgres vs. HugeGraph

System Properties Comparison Apache Phoenix vs. Cassandra vs. Fujitsu Enterprise Postgres vs. HugeGraph

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Name

Description

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

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

A fast-speed and highly-scalable Graph DBMS

Primary database model

Secondary database models

Vector DBMS

starting with release V5

Document store
Spatial DBMS

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	0.31
Rank	#285	Overall
	#129	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Website

phoenix.apache.org

cassandra.apache.org

www.postgresql.fastware.com

github.com/hugegraph
hugegraph.apache.org

Technical documentation

phoenix.apache.org

cassandra.apache.org/doc/latest

www.postgresql.fastware.com/product-manuals

hugegraph.apache.org/docs

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

Baidu

Initial release

2014

2008

2018

Current release

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

4.1.3, July 2023

Fujitsu Enterprise Postgres 14, January 2022

0.9

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache version 2

commercial

Open Source

Apache Version 2.0

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.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

Server operating systems

Linux
Unix
Windows

BSD
Linux
OS X
Windows

Linux
Windows

Linux
macOS
Unix

Data scheme

yes

late-bound, schema-on-read capabilities

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.

Secondary indexes

yes

restricted

only equality queries, not always the best performing solution

yes

also supports composite index and range index

SQL

Support of SQL

yes

SQL-like SELECT, DML and DDL statements (CQL)

yes

APIs and other access methods

JDBC

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

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

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Groovy
Java
Python

Server-side scripts

Stored procedures

user defined functions

asynchronous Gremlin script jobs

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

partitioning by range, list and by hash

yes

depending on used storage backend, e.g. Cassandra and HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

Representation of geographical distribution of servers is possible

Source-replica replication

yes

depending on used storage backend, e.g. Cassandra and HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

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

Access rights for users can be defined per object

fine grained access rights according to SQL-standard

Users, roles and permissions

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
» more

100% compatible with community PostgreSQL
» more

Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
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Built-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
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Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
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Transactional payments applications, reporting and mixed workloads.
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Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
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Market metrics

Cassandra is used by 40% of the Fortune 100.
» more

Over 30 years experience in database technology. Over 20 years in Postgres development...
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Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
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Core based licensing
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