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DBMS > EsgynDB vs. Google Cloud Bigtable vs. HarperDB vs. Hazelcast vs. SQL.JS

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

Editorial information provided by DB-Engines

Name

Description

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.

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 widely adopted in-memory data grid

Port of SQLite to JavaScript

Primary database model

Relational DBMS

Key-value store
Wide column store

Key-value store

Relational DBMS

Secondary database models

JSON support with IMDG 3.12

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

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

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.53
Rank	#252	Overall
	#116	Relational DBMS

Website

cloud.google.com/bigtable

www.harperdb.io

Technical documentation

cloud.google.com/bigtable/docs

docs.harperdb.io/docs

hazelcast.org/imdg/docs

sql.js.org/documentation/index.html

Developer

Esgyn

Google

HarperDB

Hazelcast

Alon Zakai

enhancements implemented by others

Initial release

2015

2015

2017

2008

2012

Current release

3.1, August 2021

5.3.6, November 2023

License

Commercial or Open Source

commercial

commercial

commercial

free community edition available

Open Source

Apache Version 2; commercial licenses available

Open Source

Cloud-based only

Only available as a cloud service

no

yes

no

no

no

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++, Java

Node.js

Java

JavaScript

Server operating systems

Linux

hosted

Linux
OS X

All OS with a Java VM

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

Data scheme

yes

schema-free

dynamic schema

schema-free

yes

Typing

predefined data types such as float or date

yes

no

yes

JSON data types

yes

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

yes

the object must implement a serialization strategy

no

Secondary indexes

yes

no

yes

yes

SQL

Support of SQL

yes

no

SQL-like data manipulation statements

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

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

JCache
JPA
Memcached protocol
RESTful HTTP API

JavaScript API

Supported programming languages

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

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

JavaScript

Server-side scripts

Stored procedures

Java Stored Procedures

no

Custom Functions

since release 3.1

yes

Event Listeners, Executor Services

no

Triggers

no

no

no

yes

Events

no

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

Replicated Map

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes

no

yes

no

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

none

Foreign keys

Referential integrity

yes

no

no

no

yes

Transaction concepts

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

ACID

Atomic single-row operations

Atomic execution of specific operations

one or two-phase-commit; repeatable reads; read commited

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes

yes

yes

Durability

Support for making data persistent

yes

yes

yes, using LMDB

yes

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.

no

no

yes

yes

yes

User concepts

Access control

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

Role-based access control

no

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More resources

DB-Engines blog posts

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