DBMS > Bangdb vs. Brytlyt vs. GeoMesa vs. Hive vs. JaguarDB

System Properties Comparison Bangdb vs. Brytlyt vs. GeoMesa vs. Hive vs. JaguarDB

Editorial information provided by DB-Engines

Name

Description

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

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Performant, highly scalable DBMS for AI and IoT applications

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Spatial DBMS

Relational DBMS

Key-value store
Vector 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	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Website

Technical documentation

docs.bangdb.com

docs.brytlyt.io

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

cwiki.apache.org/confluence/display/Hive/Home

www.jaguardb.com/support.html

Developer

Sachin Sinha, BangDB

Brytlyt

CCRi and others

Apache Software Foundation

initially developed by Facebook

DataJaguar, Inc.

Initial release

2012

2016

2014

2012

2015

Current release

BangDB 2.0, October 2021

5.0, August 2023

4.0.5, February 2024

3.1.3, April 2022

3.3 July 2023

License

Commercial or Open Source

Open Source

BSD 3

commercial

Open Source

Apache License 2.0

Open Source

Apache Version 2

Open Source

GPL V3.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

C, C++ and CUDA

Scala

Java

C++

the server part. Clients available in other languages

Server operating systems

Linux

Linux
OS X
Windows

All OS with a Java VM

Linux

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.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes

SQL-like DML and DDL statements

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC
Thrift

JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

C++
Java
PHP
Python

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

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

yes

user defined functions and integration of map-reduce

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

Replication methods

Methods for redundantly storing data on multiple nodes

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

Source-replica replication

depending on storage layer

selectable replication factor

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

depending on storage layer

Eventual Consistency

Foreign keys

Referential integrity

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

depending on storage layer

User concepts

Access control

yes (enterprise version only)

fine grained access rights according to SQL-standard

yes

depending on the DBMS used for storage

Access rights for users, groups and roles

rights management via user accounts

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