DBMS > eXtremeDB vs. HBase vs. Snowflake vs. Spark SQL vs. STSdb

System Properties Comparison eXtremeDB vs. HBase vs. Snowflake vs. Spark SQL vs. STSdb

Editorial information provided by DB-Engines

Name

Description

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

Cloud-based data warehousing service for structured and semi-structured data

Spark SQL is a component on top of 'Spark Core' for structured data processing

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Relational DBMS
Time Series DBMS

Wide column store

Relational DBMS

Key-value store

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

Score	0.74
Rank	#223	Overall
	#103	Relational DBMS
	#18	Time Series DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

github.com/STSSoft/STSdb4

Technical documentation

www.mcobject.com/docs/extremedb.htm

hbase.apache.org/book.html

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

McObject

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Snowflake Computing Inc.

Apache Software Foundation

STS Soft SC

Initial release

2001

2008

2014

2011

Current release

8.2, 2021

2.3.4, January 2021

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

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

C and C++

Java

Scala

Server operating systems

AIX
HP-UX
Linux
macOS
Solaris
Windows

Linux
Unix
Windows

using Cygwin

hosted

Linux
OS X
Windows

Windows

Data scheme

yes

schema-free, schema definition possible

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

yes

primitive types and user defined types (classes)

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

yes

Secondary indexes

yes

SQL

Support of SQL

yes

with the option: eXtremeSQL

yes

SQL-like DML and DDL statements

APIs and other access methods

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

Java API
RESTful HTTP API
Thrift

CLI Client
JDBC
ODBC

JDBC
ODBC

.NET Client API

Supported programming languages

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

JavaScript (Node.js)
Python

Java
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

yes

Coprocessors in Java

user defined functions

Triggers

yes

by defining events

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning / sharding

Sharding

yes

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

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
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

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 (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Users with fine-grained authorization concept, user roles and pluggable authentication

More information provided by the system vendor

Specific characteristics

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

eXtremeDB databases can be modeled relationally or as objects and can utilize SQL...
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Typical application scenarios

IoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
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Key customers

Schneider Electronics, F5 Networks, TNS, Boeing, Northrop Grumman, GoPro, ViaSat,...
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Market metrics

With hundreds of customers and over 30 million devices/applications using the product...
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Licensing and pricing models

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