DBMS > Galaxybase vs. HEAVY.AI vs. IBM Db2 Event Store vs. PouchDB

System Properties Comparison Galaxybase vs. HEAVY.AI vs. IBM Db2 Event Store vs. PouchDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Scalable, ACID-compliant native distributed parallel graph platform

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Distributed Event Store optimized for Internet of Things use cases

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Graph DBMS

Relational DBMS

Event Store
Time Series DBMS

Document store

Secondary database models

Spatial DBMS

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

Score	0.00
Rank	#385	Overall
	#40	Graph DBMS

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	0.18
Rank	#315	Overall
	#2	Event Stores
	#26	Time Series DBMS

Score	2.18
Rank	#114	Overall
	#21	Document stores

Website

galaxybase.com

github.com/heavyai/heavydb
www.heavy.ai

www.ibm.com/products/db2-event-store

pouchdb.com

Technical documentation

docs.heavy.ai

www.ibm.com/docs/en/db2-event-store

pouchdb.com/guides

Developer

Chuanglin(Createlink) Technology Co., Ltd 浙江创邻科技有限公司

HEAVY.AI, Inc.

IBM

Apache Software Foundation

Initial release

2017

2016

2017

2012

Current release

Nov 20, November 2021

5.10, January 2022

2.0

7.1.1, June 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

free developer edition available

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C and Java

C++ and CUDA

C and C++

JavaScript

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

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

Data scheme

Strong typed schema

yes

schema-free

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

via views

SQL

Support of SQL

yes

through the embedded Spark runtime

APIs and other access methods

Browser interface
console (shell)
Graph API (Gremlin)
OpenCypher
Proprietary native API

JDBC
ODBC
Thrift
Vega

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

Go
Java
Python

All languages supporting JDBC/ODBC/Thrift
Python

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

JavaScript

Server-side scripts

Stored procedures

user defined procedures and functions

yes

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Active-active shard replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

by using IndexedDB, WebSQL or LevelDB as backend

In-memory capabilities

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

yes

User concepts

Access control

Role-based access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

show all

Recent citations in the news

Dr. Mike Flaxman, VP or Product Management at HEAVY.AI – Interview Series
19 September 2024, Unite.AI

5 Q’s for Mike Flaxman, Vice President of Heavy.AI
15 August 2024, Center for Data Innovation

HEAVY.AI Accelerates Big Data Analytics with Vultr's High-Performance GPU Cloud Infrastructure
11 September 2024, insideBIGDATA

HEAVY.AI Accelerates Big Data Analytics with Vultr’s High-Performance GPU Cloud Infrastructure
11 September 2024, insideBIGDATA

Meta delivers strong earnings, but weak guidance and heavy AI spending prompt investors to bail
24 April 2024, SiliconANGLE News

provided by Google News

Mudhakar Srivatsa
11 December 2023, IBM Research

Best cloud databases of 2022
4 October 2022, ITPro

Why a robust data management strategy is essential today | IBM HDM
19 September 2019, Express Computer

How IBM Is Turning Db2 into an ‘AI Database’
3 June 2019, Datanami

provided by Google News

Create Offline Web Apps Using Service Workers & PouchDB
7 March 2017, SitePoint

Getting Started with PouchDB Client-Side JavaScript Database
7 September 2016, SitePoint

Building an Offline First App with PouchDB
10 March 2014, SitePoint

provided by Google News

Share this page