DBMS > Datomic vs. eXtremeDB vs. HugeGraph vs. Microsoft Azure Synapse Analytics vs. NSDb

System Properties Comparison Datomic vs. eXtremeDB vs. HugeGraph vs. Microsoft Azure Synapse Analytics vs. NSDb

Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

A fast-speed and highly-scalable Graph DBMS

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Graph DBMS

Relational DBMS

Time Series DBMS

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

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

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Website

www.datomic.com

www.mcobject.com

github.com/hugegraph
hugegraph.apache.org

azure.microsoft.com/services/synapse-analytics

nsdb.io

Technical documentation

docs.datomic.com

www.mcobject.com/docs/extremedb.htm

hugegraph.apache.org/docs

docs.microsoft.com/azure/synapse-analytics

nsdb.io/Architecture

Developer

Cognitect

McObject

Baidu

Microsoft

Initial release

2012

2001

2018

2016

2017

Current release

1.0.7075, December 2023

8.2, 2021

0.9

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2.0

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, Clojure

C and C++

Java

C++

Java, Scala

Server operating systems

All OS with a Java VM

AIX
HP-UX
Linux
macOS
Solaris
Windows

Linux
macOS
Unix

hosted

Linux
macOS

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

also supports composite index and range index

yes

all fields are automatically indexed

SQL

Support of SQL

yes

with the option: eXtremeSQL

yes

SQL-like query language

APIs and other access methods

RESTful HTTP API

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

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
ODBC

gRPC
HTTP REST
WebSocket

Supported programming languages

Clojure
Java

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

Groovy
Java
Python

C#
Java
PHP

Java
Scala

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

asynchronous Gremlin script jobs

Transact SQL

Triggers

By using transaction functions

yes

by defining events

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

horizontal partitioning / sharding

yes

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

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

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

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

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

Using Apache Lucene

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

Users, roles and permissions

yes

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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