DBMS > Bangdb vs. EDB Postgres vs. Google Cloud Bigtable vs. HarperDB

System Properties Comparison Bangdb vs. EDB Postgres vs. Google Cloud Bigtable vs. HarperDB

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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

The EDB Postgres Platform is an enterprise-class data management platform based on the open source database PostgreSQL with flexible deployment options and Oracle compatibility features, complemented by tool kits for management, integration, and migration.

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

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Key-value store
Wide column store

Document store

Secondary database models

Spatial DBMS

Document store
Spatial 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	1.91
Rank	#130	Overall
	#60	Relational DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	0.60
Rank	#244	Overall
	#38	Document stores

Website

bangdb.com

www.enterprisedb.com

cloud.google.com/bigtable

www.harperdb.io

Technical documentation

docs.bangdb.com

www.enterprisedb.com/docs

cloud.google.com/bigtable/docs

docs.harperdb.io/docs

Developer

Sachin Sinha, BangDB

EnterpriseDB

Google

HarperDB

Initial release

2012

2005

2015

2017

Current release

BangDB 2.0, October 2021

14, December 2021

3.1, August 2021

License

Commercial or Open Source

Open Source

BSD 3

commercial

BSD for PostgreSQL-components

commercial

free community edition 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

C, C++

Node.js

Server operating systems

Linux

Linux
Windows

hosted

Linux
OS X

Data scheme

schema-free

yes

schema-free

dynamic schema

Typing

predefined data types such as float or date

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

yes

JSON data types

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

SQL-like data manipulation statements

APIs and other access methods

Proprietary protocol
RESTful HTTP API

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

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

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

Supported programming languages

C
C#
C++
Java
Python

.Net
C
C++
Delphi
Java
Perl
PHP
Python
Tcl

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

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

Server-side scripts

Stored procedures

user defined functions

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

Custom Functions

since release 3.1

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

horizontal partitioning

by hash, list or range

Sharding

A table resides as a whole on one (or more) nodes in a cluster

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication

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

yes

the nodes on which a table resides can be defined

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

Atomic execution of specific operations

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

yes, using LMDB

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)

fine grained access rights according to SQL-standard

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

Access rights for users and roles

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