DBMS > 4D vs. Google Cloud Spanner vs. HEAVY.AI vs. MySQL vs. YottaDB

System Properties Comparison 4D vs. Google Cloud Spanner vs. HEAVY.AI vs. MySQL vs. YottaDB

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

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

Widely used open source RDBMS

A fast and solid embedded Key-value store

Primary database model

Relational DBMS

Key/Value like access via memcached API

Key-value store

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

Relational DBMS

using the Octo plugin

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	2.84
Rank	#100	Overall
	#51	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.28
Rank	#306	Overall
	#44	Key-value stores

Website

www.4d.com

cloud.google.com/spanner

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

www.mysql.com

yottadb.com

Technical documentation

developer.4d.com

cloud.google.com/spanner/docs

docs.heavy.ai

dev.mysql.com/doc

yottadb.com/resources/documentation

Developer

4D, Inc

Google

HEAVY.AI, Inc.

Oracle

since 2010, originally MySQL AB, then Sun

YottaDB, LLC

Initial release

1984

2017

2016

1995

2001

Current release

v20, April 2023

5.10, January 2022

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

AGPL 3.0

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.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

C++ and CUDA

C and C++

Server operating systems

OS X
Windows

hosted

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Docker
Linux

Data scheme

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

close to SQL 92

yes

Query statements complying to ANSI 2011

yes

with proprietary extensions

by using the Octo plugin

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
ODBC
Proprietary native API

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

4D proprietary IDE
PHP

Go
Java
JavaScript (Node.js)
Python

All languages supporting JDBC/ODBC/Thrift
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication with 3 replicas for regional instances.

Multi-source replication

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Strict serializable isolation

ACID

not for MyISAM storage engine

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

Users and groups based on OS-security mechanisms

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