DBMS > 4D vs. AlaSQL vs. CockroachDB vs. Microsoft SQL Server vs. Solr

System Properties Comparison 4D vs. AlaSQL vs. CockroachDB vs. Microsoft SQL Server vs. Solr

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

JavaScript DBMS library

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

Microsofts flagship relational DBMS

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Relational DBMS

Document store
Relational DBMS

Relational DBMS

Search engine

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	0.51
Rank	#256	Overall
	#40	Document stores
	#118	Relational DBMS

Score	5.73
Rank	#58	Overall
	#34	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

www.4d.com

alasql.org

www.cockroachlabs.com

www.microsoft.com/en-us/sql-server

solr.apache.org

Technical documentation

developer.4d.com

github.com/AlaSQL/alasql

www.cockroachlabs.com/docs

learn.microsoft.com/en-US/sql/sql-server

solr.apache.org/resources.html

Developer

4D, Inc

Andrey Gershun & Mathias R. Wulff

Cockroach Labs

Microsoft

Apache Software Foundation

Initial release

1984

2014

2015

1989

2006

Current release

v20, April 2023

24.1.0, May 2024

SQL Server 2022, November 2022

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

MIT-License

Open Source

Apache 2.0, commercial license available

commercial

restricted free version is available

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

JavaScript

C++

Java

Server operating systems

OS X
Windows

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

Linux
macOS
Windows

Linux
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

dynamic schema

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

yes

close to SQL 92

Close to SQL99, but no user access control, stored procedures and host language bindings.

yes, wire compatible with PostgreSQL

yes

Solr Parallel SQL Interface

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JavaScript API

JDBC

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Java API
RESTful HTTP/JSON API

Supported programming languages

4D proprietary IDE
PHP

JavaScript

C#
C++
Clojure
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

Multi-source replication using RAFT

yes, but depending on the SQL-Server Edition

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

only for local storage and DOM-storage

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

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

Role-based access control

fine grained access rights according to SQL-standard

yes

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