DBMS > 4D vs. Ehcache vs. Google BigQuery vs. Splice Machine

System Properties Comparison 4D vs. Ehcache vs. Google BigQuery vs. Splice Machine

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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

Large scale data warehouse service with append-only tables

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

Key-value store

Relational DBMS

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

Score	2.68
Rank	#110	Overall
	#54	Relational DBMS

Score	5.23
Rank	#68	Overall
	#8	Key-value stores

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.54
Rank	#255	Overall
	#116	Relational DBMS

Website

www.4d.com

www.ehcache.org

cloud.google.com/bigquery

splicemachine.com

Technical documentation

developer.4d.com

www.ehcache.org/documentation

cloud.google.com/bigquery/docs

splicemachine.com/how-it-works

Developer

4D, Inc

Terracotta Inc, owned by Software AG

Google

Splice Machine

Initial release

1984

2009

2010

2014

Current release

v20, April 2023

3.10.0, March 2022

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

AGPL 3.0, commercial license available

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

Server operating systems

OS X
Windows

All OS with a Java VM

hosted

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

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

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JCache

RESTful HTTP/JSON API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

4D proprietary IDE
PHP

Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

yes

Java

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

by using Terracotta Server

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

by using Terracotta Server

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles according to SQL-standard

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