DBMS > AlaSQL vs. Couchbase vs. Elasticsearch vs. Greenplum vs. H2

System Properties Comparison AlaSQL vs. Couchbase vs. Elasticsearch vs. Greenplum vs. H2

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

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

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Primary database model

Document store
Relational DBMS

Document store

Search engine

Relational DBMS

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Document store
Spatial DBMS
Vector DBMS

Document store
Spatial DBMS

Spatial DBMS

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

Score	0.46
Rank	#260	Overall
	#40	Document stores
	#121	Relational DBMS

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	8.37
Rank	#48	Overall
	#30	Relational DBMS

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Website

alasql.org

www.couchbase.com

www.elastic.co/elasticsearch

greenplum.org

www.h2database.com

Technical documentation

github.com/AlaSQL/alasql

docs.couchbase.com

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

docs.greenplum.org

www.h2database.com/html/main.html

Developer

Andrey Gershun & Mathias R. Wulff

Couchbase, Inc.

Elastic

Pivotal Software Inc.

Thomas Mueller

Initial release

2014

2011

2010

2005

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

8.6, January 2023

7.0.0, September 2023

2.2.220, July 2023

License

Commercial or Open Source

Open Source

MIT-License

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

Elastic License

Open Source

Apache 2.0

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

JavaScript

C, C++, Go and Erlang

Java

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

Linux
OS X
Windows

All OS with a Java VM

Linux

All OS with a Java VM

Data scheme

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.

yes

since Version 4.2

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

SQL-like query language

yes

APIs and other access methods

JavaScript API

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

Java API
RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

JavaScript

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

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

C
Java
Perl
Python
R

Java

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

yes

Java Stored Procedures and User-Defined Functions

Triggers

yes

via the TAP protocol

yes

by using the 'percolation' feature

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic Sharding

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

including cross data center replication
Source-replica replication

yes

Source-replica replication

With clustering: 2 database servers on different computers operate on identical copies of a database

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

ES-Hadoop Connector

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

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

yes

only for local storage and DOM-storage

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

yes

In-memory capabilities

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

yes

Ephemeral buckets

Memcached and Redis integration

yes

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

fine grained access rights according to SQL-standard

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