DBMS > Apache Impala vs. Databricks vs. Lovefield vs. mSQL

System Properties Comparison Apache Impala vs. Databricks vs. Lovefield vs. mSQL

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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.

Embeddable relational database for web apps written in pure JavaScript

mSQL (Mini SQL) is a simple and lightweight RDBMS

Primary database model

Relational DBMS

Document store
Relational DBMS

Relational DBMS

Secondary database models

Document store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

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

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Website

impala.apache.org

www.databricks.com

google.github.io/lovefield

hughestech.com.au/products/msql

Technical documentation

impala.apache.org/impala-docs.html

docs.databricks.com

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Databricks

Google

Hughes Technologies

Initial release

2013

2014

1994

Current release

4.1.0, June 2022

2.1.12, February 2017

4.4, October 2021

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache 2.0

commercial

free licenses can be provided

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

JavaScript

Server operating systems

Linux

hosted

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

tested with Chrome, Firefox, IE, Safari

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, 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

SQL-like DML and DDL statements

with Databricks SQL

SQL-like query language

via JavaScript builder pattern

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Python
R
Scala

JavaScript

C
C++
Delphi
Java
Perl
PHP
Tcl

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions and aggregates

Triggers

Using read-only observers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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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