DBMS > Elasticsearch vs. Google BigQuery vs. Kinetica vs. Memcached vs. Oracle Rdb

System Properties Comparison Elasticsearch vs. Google BigQuery vs. Kinetica vs. Memcached vs. Oracle Rdb

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

Large scale data warehouse service with append-only tables

Fully vectorized database across both GPUs and CPUs

In-memory key-value store, originally intended for caching

Primary database model

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Spatial DBMS
Time Series DBMS

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

Score	132.83
Rank	#7	Overall
	#1	Search engines

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	18.08
Rank	#32	Overall
	#4	Key-value stores

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Website

www.elastic.co/elasticsearch

cloud.google.com/bigquery

www.kinetica.com

www.memcached.org

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

Technical documentation

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

cloud.google.com/bigquery/docs

docs.kinetica.com

github.com/memcached/memcached/wiki

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

Elastic

Google

Kinetica

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2010

2012

2003

1984

Current release

8.6, January 2023

7.1, August 2021

1.6.27, May 2024

7.4.1.1, 2021

License

Commercial or Open Source

Open Source

Elastic License

commercial

Open Source

BSD license

commercial

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, C++

Server operating systems

All OS with a Java VM

hosted

Linux

FreeBSD
Linux
OS X
Unix
Windows

HP Open VMS

Data scheme

schema-free

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

yes

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.

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

Java API
RESTful HTTP/JSON API

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

Proprietary protocol

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

user defined functions

Triggers

yes

by using the 'percolation' feature

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

Repcached, a Memcached patch, provides this functionallity

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

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

yes, on a single node

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

GPU vRAM or System RAM

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles on table level

yes

using SASL (Simple Authentication and Security Layer) protocol

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