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DBMS > Apache Phoenix vs. EsgynDB vs. Google Cloud Bigtable vs. HarperDB vs. SQL.JS

System Properties Comparison Apache Phoenix vs. EsgynDB vs. Google Cloud Bigtable vs. HarperDB vs. SQL.JS

Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

HarperDB takes the "stack" out of "tech stack" by combining an ultra-fast document-style data store, in-memory cache, real-time message broker, and your application components into a single distributed technology.

Port of SQLite to JavaScript

Primary database model

Relational DBMS

Relational DBMS

Key-value store
Wide column store

Relational DBMS

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

Score	1.90
Rank	#125	Overall
	#59	Relational DBMS

Score	0.15
Rank	#325	Overall
	#144	Relational DBMS

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	0.55
Rank	#243	Overall
	#38	Document stores

Score	0.53
Rank	#246	Overall
	#115	Relational DBMS

Website

phoenix.apache.org

cloud.google.com/bigtable

www.harperdb.io

Technical documentation

phoenix.apache.org

cloud.google.com/bigtable/docs

docs.harperdb.io/docs

sql.js.org/documentation/index.html

Developer

Apache Software Foundation

Esgyn

Google

HarperDB

Alon Zakai

enhancements implemented by others

Initial release

2014

2015

2015

2017

2012

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

3.1, August 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

commercial

commercial

free community edition available

Open Source

Cloud-based only

Only available as a cloud service

no

no

yes

no

no

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++, Java

Node.js

JavaScript

Server operating systems

Linux
Unix
Windows

Linux

hosted

Linux
OS X

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

yes

late-bound, schema-on-read capabilities

yes

schema-free

dynamic schema

yes

Typing

predefined data types such as float or date

yes

yes

no

yes

JSON data 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.

no

no

no

no

no

Secondary indexes

yes

yes

no

yes

SQL

Support of SQL

yes

yes

no

SQL-like data manipulation statements

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC

ADO.NET
JDBC
ODBC

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

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JavaScript API

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

All languages supporting JDBC/ODBC/ADO.Net

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

JavaScript

Server-side scripts

Stored procedures

user defined functions

Java Stored Procedures

no

Custom Functions

since release 3.1

no

Triggers

no

no

no

no

no

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication between multi datacenters

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

yes

the nodes on which a table resides can be defined

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

yes

no

no

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

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

Immediate Consistency

none

Foreign keys

Referential integrity

no

yes

no

no

yes

Transaction concepts

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

ACID

ACID

Atomic single-row operations

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes

yes

yes

Durability

Support for making data persistent

yes

yes

yes

yes, using LMDB

no

except by serializing a db to a file

In-memory capabilities

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

yes

no

no

yes

yes

User concepts

Access control

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

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

no

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