DBMS > eXtremeDB vs. Heroic vs. TigerGraph

System Properties Comparison eXtremeDB vs. Heroic vs. TigerGraph

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Editorial information provided by DB-Engines

Name

Description

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Relational DBMS
Time Series DBMS

Time Series DBMS

Graph DBMS

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	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

www.mcobject.com

github.com/spotify/heroic

www.tigergraph.com

Technical documentation

www.mcobject.com/docs/extremedb.htm

spotify.github.io/heroic

docs.tigergraph.com

Developer

McObject

Spotify

Initial release

2001

2014

2017

Current release

8.2, 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

C and C++

Java

C++

Server operating systems

AIX
HP-UX
Linux
macOS
Solaris
Windows

Linux

Data scheme

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.

support of XML interfaces available

Secondary indexes

yes

via Elasticsearch

SQL

Support of SQL

yes

with the option: eXtremeSQL

SQL-like query language (GSQL)

APIs and other access methods

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

HQL (Heroic Query Language, a JSON-based language)
HTTP API

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

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

C++
Java

Server-side scripts

Stored procedures

yes

Triggers

yes

by defining events

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning / sharding

Sharding

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

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

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

Role-based access control

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

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More resources

Recent citations in the news

McObject Announces the Release of eXtremeDB/rt 1.2
23 May 2023, Embedded Computing Design

With eXtremeDB Database, Spreadbrokers Targets Real-Time Trading
27 March 2012, GlobeNewswire

Latest embedded DBMS supports asymmetric multiprocessing systems
24 May 2023, Embedded

McObject’s new eXtremeDB Cluster provides distributed database solution for real-time apps
20 July 2011, Embedded

Schneider Electric to collaborate with McObject
14 October 2015, Construction Week Online

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30 April 2024, TechTarget

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20 June 2023, SiliconANGLE News

TigerGraph Bolsters DB for Enterprise Graph Workloads
1 November 2023, Datanami

provided by Google News

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