DBMS > Amazon Redshift vs. Apache Druid vs. Geode vs. Oracle Berkeley DB vs. YDB

System Properties Comparison Amazon Redshift vs. Apache Druid vs. Geode vs. Oracle Berkeley DB vs. YDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Widely used in-process key-value store

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store
Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.35
Rank	#278	Overall
	#43	Document stores
	#127	Relational DBMS

Website

aws.amazon.com/redshift

druid.apache.org

geode.apache.org

www.oracle.com/database/technologies/related/berkeleydb.html

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

docs.aws.amazon.com/redshift

druid.apache.org/docs/latest/design

geode.apache.org/docs

docs.oracle.com/cd/E17076_05/html/index.html

ydb.tech/en/docs

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation and contributors

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Yandex

Initial release

2012

2002

1994

2019

Current release

29.0.1, April 2024

1.1, February 2017

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

commercial license available

Open Source

Apache 2.0; commercial license available

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, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

hosted

Linux
OS X
Unix

All OS with a Java VM

the JDK (8 or later) is also required

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux

Data scheme

yes

schema-less columns are supported

schema-free

Flexible Schema (defined schema, partial schema, 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.

yes

only with the Berkeley DB XML edition

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL for querying

SQL-like query language (OQL)

yes

SQL interfaced based on SQLite is available

SQL-like query language (YQL)

APIs and other access methods

JDBC
ODBC

JDBC
RESTful HTTP/JSON API

Java Client API
Memcached protocol
RESTful HTTP API

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
All JVM based languages
C++
Groovy
Java
Scala

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

Triggers

yes

Cache Event Listeners

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, via HDFS, S3 or other storage engines

Multi-source replication

Source-replica replication

Active-passive shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

yes, on a single node

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights per client and object definable

Access rights defined for Yandex Cloud users

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