DBMS > Apache Phoenix vs. eXtremeDB vs. Google Cloud Spanner vs. TimesTen

System Properties Comparison Apache Phoenix vs. eXtremeDB vs. Google Cloud Spanner vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

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

Natively in-memory DBMS with options for persistency, high-availability and clustering

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

In-Memory RDBMS compatible to Oracle

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	0.80
Rank	#214	Overall
	#99	Relational DBMS
	#18	Time Series DBMS

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

phoenix.apache.org

www.mcobject.com

cloud.google.com/spanner

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

Technical documentation

phoenix.apache.org

www.mcobject.com/docs/extremedb.htm

cloud.google.com/spanner/docs

docs.oracle.com/database/timesten-18.1

Developer

Apache Software Foundation

McObject

Google

Oracle, TimesTen Performance Software, HP

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

Initial release

2014

2001

2017

1998

Current release

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

8.2, 2021

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

Apache Version 2.0

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

C and C++

Server operating systems

Linux
Unix
Windows

AIX
HP-UX
Linux
macOS
Solaris
Windows

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

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.

support of XML interfaces available

Secondary indexes

yes

SQL

Support of SQL

yes

with the option: eXtremeSQL

yes

Query statements complying to ANSI 2011

yes

APIs and other access methods

JDBC

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

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

.Net
C
C#
C++
Java
Lua
Python
Scala

Go
Java
JavaScript (Node.js)
Python

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

user defined functions

yes

PL/SQL

Triggers

yes

by defining events

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning / sharding

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

Multi-source replication with 3 replicas for regional instances.

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

ACID

Strict serializable isolation

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

yes

Durability

Support for making data persistent

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

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

eXtremeDB is an in-memory and/or persistent database system that offers an ultra-small...
» more

Competitive advantages

eXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
» more

Typical application scenarios

IoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
» more

Key customers

Schneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
» more

Market metrics

With hundreds of customers and over 30 million devices/applications using the product...
» more

Licensing and pricing models

For server use cases, there is a simple per-server license irrespective of the number...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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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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