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

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

Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

Scalable TimeSeries DBMS based on Cassandra

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

Document store
Graph DBMS
Time Series DBMS

Time Series DBMS

Graph DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	0.06
Rank	#353	Overall
	#34	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

bangdb.com

blueflood.io

github.com/hugegraph
hugegraph.apache.org

www.snowflake.com

spark.apache.org/sql

Technical documentation

docs.bangdb.com

github.com/rax-maas/blueflood/wiki

hugegraph.apache.org/docs

docs.snowflake.net/manuals/index.html

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

Developer

Sachin Sinha, BangDB

Rackspace

Baidu

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2012

2013

2018

2014

Current release

BangDB 2.0, October 2021

0.9

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache 2.0

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

C, C++

Java

Scala

Server operating systems

Linux

Linux
OS X

Linux
macOS
Unix

hosted

Linux
OS X
Windows

Data scheme

schema-free

predefined scheme

yes

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

yes

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

also supports composite index and range index

SQL

Support of SQL

SQL like support with command line tool

yes

SQL-like DML and DDL statements

APIs and other access methods

Proprietary protocol
RESTful HTTP API

HTTP REST

Java API
RESTful HTTP API
TinkerPop Gremlin

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

Groovy
Java
Python

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

user defined functions

Triggers

yes, Notifications (with Streaming only)

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

based on Cassandra

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, Knob for CAP (enterprise version only)

selectable replication factor

based on Cassandra

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

Tunable consistency, set CAP knob accordingly

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, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

Users, roles and permissions

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

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