DBMS > Databricks vs. HEAVY.AI vs. IRONdb vs. Lovefield vs. SQLite

System Properties Comparison Databricks vs. HEAVY.AI vs. IRONdb vs. Lovefield vs. SQLite

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

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.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Embeddable relational database for web apps written in pure JavaScript

Widely used embeddable, in-process RDBMS

Primary database model

Document store
Relational DBMS

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial 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	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

www.databricks.com

github.com/heavyai/heavydb
www.heavy.ai

www.circonus.com/solutions/time-series-database/

google.github.io/lovefield

www.sqlite.org

Technical documentation

docs.databricks.com

docs.heavy.ai

docs.circonus.com/irondb/category/getting-started

github.com/google/lovefield/blob/master/docs/spec_index.md

www.sqlite.org/docs.html

Developer

Databricks

HEAVY.AI, Inc.

Circonus LLC.

Google

Dwayne Richard Hipp

Initial release

2013

2016

2017

2014

2000

Current release

5.10, January 2022

V0.10.20, January 2018

2.1.12, February 2017

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

Apache 2.0

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

C++ and CUDA

C and C++

JavaScript

Server operating systems

hosted

Linux

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

tested with Chrome, Firefox, IE, Safari

server-less

Data scheme

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

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like query language

via JavaScript builder pattern

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Python
R
Scala

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

JavaScript

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

user defined functions and aggregates

yes, in Lua

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Automatic, metric affinity per node

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

configurable replication factor, datacenter aware

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

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

via file-system locks

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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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