DBMS > Databricks vs. H2 vs. Quasardb vs. Riak KV

System Properties Comparison Databricks vs. H2 vs. Quasardb vs. Riak KV

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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.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Distributed, high-performance timeseries database

Distributed, fault tolerant key-value store

Primary database model

Document store
Relational DBMS

Relational DBMS

Time Series DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

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	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

www.databricks.com

www.h2database.com

quasar.ai

Technical documentation

docs.databricks.com

www.h2database.com/html/main.html

doc.quasar.ai/master

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Databricks

Thomas Mueller

quasardb

OpenSource, formerly Basho Technologies

Initial release

2013

2005

2009

Current release

2.2.220, July 2023

3.14.1, January 2024

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Erlang

Server operating systems

hosted

All OS with a Java VM

BSD
Linux
OS X
Windows

Linux
OS X

Data scheme

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

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

restricted

SQL

Support of SQL

with Databricks SQL

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

Python
R
Scala

Java

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions and aggregates

Java Stored Procedures and User-Defined Functions

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

consistent hashing

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

With clustering: 2 database servers on different computers operate on identical copies of a database

Source-replica replication with selectable replication factor

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

yes, using Riak Security

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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