DBMS > 4D vs. Ehcache vs. GBase vs. Google BigQuery vs. TimescaleDB

System Properties Comparison 4D vs. Ehcache vs. GBase vs. Google BigQuery vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

A widely adopted Java cache with tiered storage options

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Large scale data warehouse service with append-only tables

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Relational DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

www.4d.com

www.ehcache.org

www.gbase.cn

cloud.google.com/bigquery

www.timescale.com

Technical documentation

developer.4d.com

www.ehcache.org/documentation

cloud.google.com/bigquery/docs

docs.timescale.com

Developer

4D, Inc

Terracotta Inc, owned by Software AG

General Data Technology Co., Ltd.

Google

Timescale

Initial release

1984

2009

2004

2010

2017

Current release

v20, April 2023

3.10.0, March 2022

GBase 8a, GBase 8s, GBase 8c

2.15.0, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

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

Java

C, Java, Python

Server operating systems

OS X
Windows

All OS with a Java VM

Linux

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

Standard with numerous extensions

yes

full PostgreSQL SQL syntax

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JCache

ADO.NET
C API
JDBC
ODBC

RESTful HTTP/JSON API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

4D proprietary IDE
PHP

Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

by using Terracotta Server

horizontal partitioning (by range, list and hash) and vertical partitioning

none

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

by using Terracotta Server

yes

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

supports JTA and can work as an XA resource

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

yes

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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.