DBMS > Databricks vs. DolphinDB vs. Sphinx vs. SQLite vs. VelocityDB

System Properties Comparison Databricks vs. DolphinDB vs. Sphinx vs. SQLite vs. VelocityDB

Editorial information provided by DB-Engines

Name

Description

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.

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

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

Widely used embeddable, in-process RDBMS

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Document store
Relational DBMS

Time Series DBMS

Search engine

Relational DBMS

Graph DBMS
Object oriented DBMS

Secondary database models

Relational DBMS

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

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

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.05
Rank	#358	Overall
	#36	Graph DBMS
	#16	Object oriented DBMS

Website

Technical documentation

docs.databricks.com

docs.dolphindb.cn/en/help200/index.html

sphinxsearch.com/docs

www.sqlite.org/docs.html

velocitydb.com/UserGuide

Developer

Databricks

DolphinDB, Inc

Sphinx Technologies Inc.

Dwayne Richard Hipp

VelocityDB Inc

Initial release

2013

2018

2001

2000

2011

Current release

v2.00.4, January 2022

3.5.1, February 2023

3.45.3 (15 April 2024), April 2024

7.x

License

Commercial or Open Source

commercial

free community version available

Open Source

GPL version 2, commercial licence available

Open Source

Public Domain

commercial

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

C++

Server operating systems

hosted

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

server-less

Any that supports .NET

Data scheme

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

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

full-text index on all search fields

yes

SQL

Support of SQL

with Databricks SQL

SQL-like query language

SQL-like query language (SphinxQL)

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

Proprietary protocol

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

.Net

Supported programming languages

Python
R
Scala

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

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

.Net

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Triggers

yes

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

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

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

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

Administrators, Users, Groups

Based on Windows Authentication

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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