DBMS > 4D vs. Databricks vs. RRDtool vs. Sphinx vs. SQLite

System Properties Comparison 4D vs. Databricks vs. RRDtool vs. Sphinx vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

Open source search engine for searching in data from different sources, e.g. relational databases

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Document store
Relational DBMS

Time Series DBMS

Search engine

Relational DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	1.87
Rank	#136	Overall
	#11	Time Series DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

www.4d.com

www.databricks.com

oss.oetiker.ch/rrdtool

sphinxsearch.com

www.sqlite.org

Technical documentation

developer.4d.com

docs.databricks.com

oss.oetiker.ch/rrdtool/doc

sphinxsearch.com/docs

www.sqlite.org/docs.html

Developer

4D, Inc

Databricks

Tobias Oetiker

Sphinx Technologies Inc.

Dwayne Richard Hipp

Initial release

1984

2013

1999

2001

2000

Current release

v20, April 2023

1.8.0, 2022

3.5.1, February 2023

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Open Source

GPL V2 and FLOSS

Open Source

GPL version 2, commercial licence available

Open Source

Public Domain

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

Implementations in Java (e.g. RRD4J) and C# available

C++

Server operating systems

OS X
Windows

hosted

HP-UX
Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

server-less

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

dynamic column types

Typing

predefined data types such as float or date

yes

Numeric data only

yes

not rigid because of 'dynamic typing' concept.

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

Exporting into and restoring from XML files possible

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

close to SQL 92

with Databricks SQL

SQL-like query language (SphinxQL)

yes

SQL-92 is not fully supported

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
ODBC
RESTful HTTP API

in-process shared library
Pipes

Proprietary protocol

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

4D proprietary IDE
PHP

Python
R
Scala

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

user defined functions and aggregates

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

by using the rrdcached daemon

yes

via file-system locks

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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