DBMS > AgensGraph vs. CouchDB vs. Elasticsearch vs. Google BigQuery vs. Graph Engine

System Properties Comparison AgensGraph vs. CouchDB vs. Elasticsearch vs. Google BigQuery vs. Graph Engine

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Primary database model

Graph DBMS
Relational DBMS

Document store

Search engine

Relational DBMS

Graph DBMS
Key-value store

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store
Spatial DBMS
Vector DBMS

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Website

bitnine.net/agensgraph

couchdb.apache.org

www.elastic.co/elasticsearch

cloud.google.com/bigquery

www.graphengine.io

Technical documentation

bitnine.net/documentation

docs.couchdb.org/en/stable

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

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

Developer

Bitnine Global Inc.

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Elastic

Google

Microsoft

Initial release

2016

2005

2010

Current release

2.1, December 2018

3.3.3, December 2023

8.6, January 2023

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache version 2

Open Source

Elastic License

commercial

Open Source

MIT License

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

Erlang

Java

.NET and C

Server operating systems

Linux
OS X
Windows

Android
BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

hosted

.NET

Data scheme

depending on used data model

schema-free

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

yes

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

via views

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

Cypher Query Language
JDBC

RESTful HTTP/JSON API

Java API
RESTful HTTP/JSON API

RESTful HTTP/JSON API

RESTful HTTP API

Supported programming languages

C
Java
JavaScript
Python

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

Server-side scripts

Stored procedures

yes

View functions in JavaScript

yes

user defined functions

in JavaScript

yes

Triggers

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

improved architecture with release 2.0

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

atomic operations within a single document possible

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

Access rights for users can be defined per database

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

Google Cloud Identity & Access Management (IAM)

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