DBMS > Amazon DocumentDB vs. Bangdb vs. Google Cloud Bigtable vs. PostgreSQL

System Properties Comparison Amazon DocumentDB vs. Bangdb vs. Google Cloud Bigtable vs. PostgreSQL

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Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Primary database model

Document store

Document store
Graph DBMS
Time Series DBMS

Key-value store
Wide column store

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

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

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	645.54
Rank	#4	Overall
	#4	Relational DBMS

Website

aws.amazon.com/documentdb

bangdb.com

cloud.google.com/bigtable

www.postgresql.org

Technical documentation

aws.amazon.com/documentdb/resources

docs.bangdb.com

cloud.google.com/bigtable/docs

www.postgresql.org/docs

Developer

Sachin Sinha, BangDB

Google

PostgreSQL Global Development Group

www.postgresql.org/developer

Initial release

2019

2012

2015

1989

1989: Postgres, 1996: PostgreSQL

Current release

BangDB 2.0, October 2021

16.3, May 2024

License

Commercial or Open Source

commercial

Open Source

BSD 3

commercial

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C, C++

Server operating systems

hosted

Linux

hosted

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

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

standard with numerous extensions

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

Proprietary protocol
RESTful HTTP API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

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

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C
C#
C++
Java
Python

C#
C++
Go
Java
JavaScript (Node.js)
Python

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

none

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

Sharding

partitioning by range, list and (since PostgreSQL 11) by hash

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

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

Internal replication in Colossus, and regional replication between two clusters in different zones

Source-replica replication

other methods possible by using 3rd party extensions

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable consistency, set CAP knob accordingly

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

ACID

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

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

User concepts

Access control

Access rights for users and roles

yes (enterprise version only)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

More information provided by the system vendor

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