DBMS > Elasticsearch vs. IBM Db2 warehouse vs. Oracle Berkeley DB vs. Snowflake vs. YDB

System Properties Comparison Elasticsearch vs. IBM Db2 warehouse vs. Oracle Berkeley DB vs. Snowflake vs. YDB

Editorial information provided by DB-Engines

Name

Description

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Cloud-based data warehousing service

Widely used in-process key-value store

Cloud-based data warehousing service for structured and semi-structured data

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

Search engine

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Relational DBMS

Document store
Relational DBMS

Secondary database models

Document store
Spatial DBMS
Vector DBMS

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

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

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

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

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

Website

www.elastic.co/elasticsearch

www.ibm.com/products/db2/warehouse

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

www.snowflake.com

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

Technical documentation

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

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

docs.snowflake.net/manuals/index.html

ydb.tech/en/docs

Developer

Elastic

IBM

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Snowflake Computing Inc.

Yandex

Initial release

2010

2014

1994

2014

2019

Current release

8.6, January 2023

18.1.40, May 2020

License

Commercial or Open Source

Open Source

Elastic License

commercial

Open Source

commercial license available

commercial

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

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

All OS with a Java VM

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

hosted

Linux

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

Import/export of XML data possible

yes

only with the Berkeley DB XML edition

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

SQL interfaced based on SQLite is available

yes

SQL-like query language (YQL)

APIs and other access methods

Java API
RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
OLE DB

CLI Client
JDBC
ODBC

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

Java
JavaScript (Node.js)
Perl
PHP
Python
R
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

JavaScript (Node.js)
Python

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

yes

PL/SQL, SQL PL

user defined functions

Triggers

yes

by using the 'percolation' feature

yes

only for the SQL API

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

Active-passive shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Immediate Consistency

Foreign keys

Referential integrity

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

In-memory capabilities

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

Memcached and Redis integration

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

Access rights defined for Yandex Cloud users

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