DBMS > Bangdb vs. HugeGraph vs. IBM Db2 Event Store vs. Microsoft Azure Synapse Analytics

System Properties Comparison Bangdb vs. HugeGraph vs. IBM Db2 Event Store vs. Microsoft Azure Synapse Analytics

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Editorial information provided by DB-Engines

Name

Description

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

A fast-speed and highly-scalable Graph DBMS

Distributed Event Store optimized for Internet of Things use cases

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

Primary database model

Document store
Graph DBMS
Time Series DBMS

Graph DBMS

Event Store
Time Series 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	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Website

bangdb.com

github.com/hugegraph
hugegraph.apache.org

www.ibm.com/products/db2-event-store

azure.microsoft.com/services/synapse-analytics

Technical documentation

docs.bangdb.com

hugegraph.apache.org/docs

www.ibm.com/docs/en/db2-event-store

docs.microsoft.com/azure/synapse-analytics

Developer

Sachin Sinha, BangDB

Baidu

IBM

Microsoft

Initial release

2012

2018

2017

2016

Current release

BangDB 2.0, October 2021

0.9

2.0

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache Version 2.0

commercial

free developer edition available

commercial

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

C and C++

C++

Server operating systems

Linux

Linux
macOS
Unix

Linux

Linux, macOS, Windows for the developer addition

hosted

Data scheme

schema-free

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.

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

also supports composite index and range index

yes

SQL

Support of SQL

SQL like support with command line tool

yes

through the embedded Spark runtime

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

Groovy
Java
Python

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

C#
Java
PHP

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

Transact SQL

Triggers

yes, Notifications (with Streaming only)

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

yes

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

Sharding

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

yes

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

Active-active shard replication

yes

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

Immediate 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 concurrency control

yes

No - written data is immutable

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

fine grained access rights according to SQL-standard

yes

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