DBMS > AlaSQL vs. Couchbase vs. Manticore Search vs. Postgres-XL

System Properties Comparison AlaSQL vs. Couchbase vs. Manticore Search vs. Postgres-XL

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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

Multi-storage database for search, including full-text search.

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

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

Time Series DBMS

using the Manticore Columnar Library

Document store
Spatial DBMS

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

Score	0.42
Rank	#264	Overall
	#42	Document stores
	#122	Relational DBMS

Score	16.74
Rank	#33	Overall
	#5	Document stores

Score	0.23
Rank	#301	Overall
	#21	Search engines

Score	0.43
Rank	#260	Overall
	#119	Relational DBMS

Website

Technical documentation

github.com/AlaSQL/alasql

docs.couchbase.com

manual.manticoresearch.com

www.postgres-xl.org/documentation

Developer

Andrey Gershun & Mathias R. Wulff

Couchbase, Inc.

Manticore Software

Initial release

2014

2011

2017

2014

since 2012, originally named StormDB

Current release

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

6.0, February 2023

10 R1, October 2018

License

Commercial or Open Source

Open Source

MIT-License

Open Source

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

Open Source

GPL version 2

Open Source

Mozilla 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

C++

Server operating systems

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

Linux
OS X
Windows

FreeBSD
Linux
macOS
Windows

Linux
macOS

Data scheme

schema-free

Fixed schema

yes

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

XML type, but no XML query functionality

Secondary indexes

yes

full-text index on all search fields

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

distributed, parallel query execution

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

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

JavaScript

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

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

user defined functions

Triggers

yes

via the TAP protocol

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic Sharding

Sharding

Partitioning is done manually, search queries against distributed index is supported

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

including cross data center replication
Source-replica replication

Synchronous replication based on Galera library

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

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

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

yes

The original contents of fields are not stored in the Manticore index.

yes

In-memory capabilities

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

yes

Ephemeral buckets

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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