DBMS > Amazon Redshift vs. CrateDB vs. EJDB vs. Oracle Berkeley DB

System Properties Comparison Amazon Redshift vs. CrateDB vs. EJDB vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Distributed Database based on Lucene

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Widely used in-process key-value store

Primary database model

Relational DBMS

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Document store

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.71
Rank	#227	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#10	Vector DBMS

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Website

aws.amazon.com/redshift

cratedb.com

github.com/Softmotions/ejdb

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

Technical documentation

docs.aws.amazon.com/redshift

cratedb.com/docs

github.com/Softmotions/ejdb/blob/master/README.md

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

Developer

Amazon (based on PostgreSQL)

Crate

Softmotions

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2012

2013

2012

1994

Current release

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open Source

GPLv2

Open Source

commercial license available

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

Java

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

Server operating systems

hosted

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

server-less

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

yes

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

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

SQL interfaced based on SQLite is available

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

in-process shared library

Supported programming languages

All languages supporting JDBC/ODBC

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

.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

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions (Javascript)

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Configurable replication on table/partition-level

none

Source-replica 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
Read-after-write consistency on record level

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

ACID

unique row identifiers can be used for implementing an optimistic concurrency control strategy

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

rights management via user accounts

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