DBMS > Bangdb vs. GeoMesa vs. GridDB vs. H2GIS vs. Microsoft Azure Synapse Analytics

System Properties Comparison Bangdb vs. GeoMesa vs. GridDB vs. H2GIS vs. Microsoft Azure Synapse Analytics

Editorial information provided by DB-Engines

Name

Description

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

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Scalable in-memory time series database optimized for IoT and Big Data

Spatial extension of H2

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

Primary database model

Document store
Graph DBMS
Time Series DBMS

Secondary database models

Spatial DBMS

Key-value store
Relational DBMS

Relational DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

Score	0.08
Rank	#368	Overall
	#7	Spatial DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Website

azure.microsoft.com/services/synapse-analytics

Technical documentation

docs.bangdb.com

www.geomesa.org/documentation/stable/user/index.html

docs.griddb.net

www.h2gis.org/docs/home

docs.microsoft.com/azure/synapse-analytics

Developer

Sachin Sinha, BangDB

CCRi and others

Toshiba Corporation

CNRS

Microsoft

Initial release

2012

2014

2013

2016

Current release

BangDB 2.0, October 2021

5.0.0, May 2024

5.1, August 2022

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache License 2.0

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

LGPL 3.0

commercial

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

C, C++

Scala

C++

Java

C++

Server operating systems

Linux

hosted

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

yes

numerical, string, blob, geometry, boolean, timestamp

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

SQL

Support of SQL

SQL like support with command line tool

SQL92, SQL-like TQL (Toshiba Query Language)

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Java

C#
Java
PHP

Server-side scripts

Stored procedures

yes

based on H2

Transact SQL

Triggers

yes, Notifications (with Streaming only)

yes

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

depending on storage layer

Sharding

none

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

depending on storage layer

Source-replica replication

yes

based on H2

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

depending on storage layer

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

yes

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

ACID at container level

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

yes, multi-version concurrency control (MVCC)

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

depending on storage layer

yes

User concepts

Access control

yes (enterprise version only)

yes

depending on the DBMS used for storage

Access rights for users can be defined per database

yes

based on H2

yes

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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