DBMS > Google Cloud Bigtable vs. HarperDB vs. jBASE vs. Microsoft Azure Cosmos DB

System Properties Comparison Google Cloud Bigtable vs. HarperDB vs. jBASE vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

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.

A robust multi-value DBMS comprising development tools and middleware

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Key-value store
Wide column store

Document store

Multivalue DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

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

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	1.49
Rank	#156	Overall
	#3	Multivalue DBMS

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

Website

cloud.google.com/bigtable

www.harperdb.io

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-jbase

azure.microsoft.com/services/cosmos-db

Technical documentation

cloud.google.com/bigtable/docs

docs.harperdb.io/docs

docs.rocketsoftware.com/bundle?labelkey=jbase_5.9

learn.microsoft.com/azure/cosmos-db

Developer

Google

HarperDB

Rocket Software (formerly Zumasys)

Microsoft

Initial release

2015

2017

1991

2014

Current release

3.1, August 2021

5.7

License

Commercial or Open Source

commercial

free community edition available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Node.js

Server operating systems

hosted

Linux
OS X

AIX
Linux
Windows

hosted

Data scheme

schema-free

dynamic schema

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

optional

yes

JSON 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

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like data manipulation statements

Embedded SQL for jBASE in BASIC

SQL-like query language

APIs and other access methods

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

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API

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

Supported programming languages

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

.Net
Basic
Jabbascript
Java

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

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

yes

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

the nodes on which a table resides can be defined

yes

Implicit feature of the cloud service

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

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-row operations

Atomic execution of specific operations

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

yes

In-memory capabilities

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

yes

User concepts

Access control

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

Access rights for users and roles

Access rights can be defined down to the item level

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