DBMS > Apache Drill vs. Fauna vs. Riak KV vs. Vertica

System Properties Comparison Apache Drill vs. Fauna vs. Riak KV vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

Distributed, fault tolerant key-value store

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Document store
Relational DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Key-value store

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

Relational DBMS

Column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	1.90
Rank	#126	Overall
	#23	Document stores
	#60	Relational DBMS

Score	1.55
Rank	#143	Overall
	#26	Document stores
	#13	Graph DBMS
	#66	Relational DBMS
	#13	Time Series DBMS

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

drill.apache.org

fauna.com

www.vertica.com

Technical documentation

drill.apache.org/docs

docs.fauna.com

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

vertica.com/documentation

Developer

Apache Software Foundation

Fauna, Inc.

OpenSource, formerly Basho Technologies

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2012

2014

2009

2005

Current release

1.20.3, January 2023

3.2.0, December 2022

12.0.3, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Apache version 2, commercial enterprise edition

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

Erlang

C++

Server operating systems

Linux
OS X
Windows

hosted

Linux
OS X

Linux

Data scheme

schema-free

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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.

Secondary indexes

yes

restricted

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

HTTP API
Native Erlang Interface

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

C++

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

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

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

Server-side scripts

Stored procedures

user defined functions

Erlang

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

pre-commit hooks and post-commit hooks

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

consistent hashing

Sharding

no "single point of failure"

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

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

Depending on the underlying data source

yes

In-memory capabilities

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

Depending on the underlying data source

User concepts

Access control

Depending on the underlying data source

Identity management, authentication, and access control

yes, using Riak Security

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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More resources

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