DBMS > Blueflood vs. HugeGraph vs. IBM Db2 vs. Snowflake vs. Spark SQL

System Properties Comparison Blueflood vs. HugeGraph vs. IBM Db2 vs. Snowflake vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

A fast-speed and highly-scalable Graph DBMS

Common in IBM host environments, 2 different versions for host and Windows/Linux

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

Graph DBMS

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

blueflood.io

github.com/hugegraph
hugegraph.apache.org

www.ibm.com/products/db2

www.snowflake.com

spark.apache.org/sql

Technical documentation

github.com/rax-maas/blueflood/wiki

hugegraph.apache.org/docs

www.ibm.com/docs/en/db2

docs.snowflake.net/manuals/index.html

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

Developer

Rackspace

Baidu

IBM

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2013

2018

1983

host version

2014

Current release

0.9

12.1, October 2016

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

free version is available

commercial

Open Source

Apache 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

C and C++

Scala

Server operating systems

Linux
OS X

Linux
macOS
Unix

AIX
HP-UX
Linux
Solaris
Windows
z/OS

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.

yes

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

HTTP REST

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

Groovy
Java
Python

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

yes

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

Sharding

only with Windows/Unix/Linux Version

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes

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

yes

with separate tools (MQ, InfoSphere)

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

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

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

fine grained access rights according to SQL-standard

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

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