DBMS > 4D vs. Alibaba Cloud Table Store vs. Google Cloud Datastore vs. GridDB vs. Heroic

System Properties Comparison 4D vs. Alibaba Cloud Table Store vs. Google Cloud Datastore vs. GridDB vs. Heroic

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Scalable in-memory time series database optimized for IoT and Big Data

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Primary database model

Secondary database models

Key-value store
Relational DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	0.26
Rank	#301	Overall
	#12	Wide column stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Website

www.4d.com

www.alibabacloud.com/product/table-store

cloud.google.com/datastore

griddb.net

github.com/spotify/heroic

Technical documentation

developer.4d.com

www.alibabacloud.com/help/en/tablestore

cloud.google.com/datastore/docs

docs.griddb.net

spotify.github.io/heroic

Developer

4D, Inc

Alibaba

Google

Toshiba Corporation

Spotify

Initial release

1984

2016

2008

2013

2014

Current release

v20, April 2023

5.1, August 2022

License

Commercial or Open Source

commercial

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

Apache 2.0

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++

Java

Server operating systems

OS X
Windows

hosted

Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

yes

numerical, string, blob, geometry, boolean, timestamp

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.

yes

Secondary indexes

yes

via Elasticsearch

SQL

Support of SQL

yes

close to SQL 92

SQL-like query language (GQL)

SQL92, SQL-like TQL (Toshiba Query Language)

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

HTTP API

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Supported programming languages

4D proprietary IDE
PHP

Java
Python

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

using Google App Engine

Triggers

yes

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Implicit feature of the cloud service

Multi-source replication using Paxos

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate consistency within container, eventual consistency across containers

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Atomic single-row operations

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID at container level

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Users and groups

Access rights based on subaccounts and tokens

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

Access rights for users can be defined per database

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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

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