DBMS > Blueflood vs. eXtremeDB vs. HugeGraph vs. Snowflake vs. Spark SQL

System Properties Comparison Blueflood vs. eXtremeDB vs. HugeGraph vs. Snowflake vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

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

A fast-speed and highly-scalable Graph DBMS

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

Primary database model

Time Series DBMS

Relational DBMS
Time Series DBMS

Graph DBMS

Relational DBMS

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

blueflood.io

www.mcobject.com

github.com/hugegraph
hugegraph.apache.org

www.snowflake.com

spark.apache.org/sql

Technical documentation

github.com/rax-maas/blueflood/wiki

www.mcobject.com/docs/extremedb.htm

hugegraph.apache.org/docs

docs.snowflake.net/manuals/index.html

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

Developer

Rackspace

McObject

Baidu

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2013

2001

2018

2014

Current release

8.2, 2021

0.9

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

Java

Scala

Server operating systems

Linux
OS X

AIX
HP-UX
Linux
macOS
Solaris
Windows

Linux
macOS
Unix

hosted

Linux
OS X
Windows

Data scheme

predefined scheme

yes

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

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

yes

Secondary indexes

yes

also supports composite index and range index

SQL

Support of SQL

yes

with the option: eXtremeSQL

yes

SQL-like DML and DDL statements

APIs and other access methods

HTTP REST

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

Java API
RESTful HTTP API
TinkerPop Gremlin

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

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

Groovy
Java
Python

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

asynchronous Gremlin script jobs

user defined functions

Triggers

yes

by defining events

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

horizontal partitioning / sharding

yes

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

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

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

Users, roles and permissions

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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