DBMS > Amazon Redshift vs. Apache Drill vs. Drizzle vs. etcd

System Properties Comparison Amazon Redshift vs. Apache Drill vs. Drizzle vs. etcd

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

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

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

A distributed reliable key-value store

Primary database model

Relational DBMS

Document store
Relational DBMS

Relational DBMS

Key-value store

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	2.02
Rank	#124	Overall
	#22	Document stores
	#59	Relational DBMS

Score	7.03
Rank	#54	Overall
	#5	Key-value stores

Website

aws.amazon.com/redshift

drill.apache.org

etcd.io
github.com/etcd-io/etcd

Technical documentation

docs.aws.amazon.com/redshift

drill.apache.org/docs

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Drizzle project, originally started by Brian Aker

Initial release

2012

2008

Current release

1.20.3, January 2023

7.2.4, September 2012

3.4, August 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

GNU GPL

Open Source

Apache Version 2.0

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
OS X
Windows

FreeBSD
Linux
OS X

FreeBSD
Linux
Windows

experimental

Data scheme

yes

schema-free

yes

schema-free

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL SELECT statement is SQL:2003 compliant

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

JDBC

gRPC
JSON over HTTP

Supported programming languages

All languages supporting JDBC/ODBC

C++

C
C++
Java
PHP

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

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

Triggers

hooks for callbacks inside the server can be used.

yes, watching key changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

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

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.

yes

Depending on the underlying data source

User concepts

Access control

fine grained access rights according to SQL-standard

Depending on the underlying data source

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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