DBMS > BigchainDB vs. Brytlyt vs. GeoMesa vs. Microsoft Azure Cosmos DB vs. Vitess

System Properties Comparison BigchainDB vs. Brytlyt vs. GeoMesa vs. Microsoft Azure Cosmos DB vs. Vitess

Editorial information provided by DB-Engines

Name

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

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.

Globally distributed, horizontally scalable, multi-model database service

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Document store

Relational DBMS

Spatial DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	0.85
Rank	#208	Overall
	#35	Document stores

Score	0.38
Rank	#276	Overall
	#127	Relational DBMS

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

www.bigchaindb.com

brytlyt.io

www.geomesa.org

azure.microsoft.com/services/cosmos-db

vitess.io

Technical documentation

bigchaindb.readthedocs.io/en/latest

docs.brytlyt.io

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

learn.microsoft.com/azure/cosmos-db

vitess.io/docs

Developer

Brytlyt

CCRi and others

Microsoft

The Linux Foundation, PlanetScale

Initial release

2016

2014

2013

Current release

5.0, August 2023

5.0.0, May 2024

15.0.2, December 2022

License

Commercial or Open Source

Open Source

AGPL v3

commercial

Open Source

Apache License 2.0

commercial

Open Source

Apache Version 2.0, commercial licenses available

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

Python

C, C++ and CUDA

Scala

Server operating systems

Linux

Linux
OS X
Windows

hosted

Docker
Linux
macOS

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

CLI Client
RESTful HTTP API

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

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

JavaScript

yes

proprietary syntax

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

depending on storage layer

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

depending on storage layer

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

depending on storage layer

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes

In-memory capabilities

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

depending on storage layer

yes

User concepts

Access control

yes

fine grained access rights according to SQL-standard

yes

depending on the DBMS used for storage

Access rights can be defined down to the item level

Users with fine-grained authorization concept

no user groups or roles

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