DBMS > AlaSQL vs. Brytlyt vs. Couchbase vs. Manticore Search

System Properties Comparison AlaSQL vs. Brytlyt vs. Couchbase vs. Manticore Search

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Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

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.

Primary database model

Document store
Relational DBMS

Relational DBMS

Document store

Search engine

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

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	0.27
Rank	#292	Overall
	#132	Relational DBMS

Score	16.74
Rank	#33	Overall
	#5	Document stores

Score	0.23
Rank	#301	Overall
	#21	Search engines

Website

Technical documentation

github.com/AlaSQL/alasql

docs.brytlyt.io

docs.couchbase.com

manual.manticoresearch.com

Developer

Andrey Gershun & Mathias R. Wulff

Brytlyt

Couchbase, Inc.

Manticore Software

Initial release

2014

2016

2011

2017

Current release

5.0, August 2023

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

6.0, February 2023

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

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

Open Source

GPL version 2

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++ and CUDA

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

Data scheme

schema-free

yes

schema-free

Fixed schema

Typing

predefined data types such as float or date

yes

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

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

specific XML-type available, but no XML query functionality.

Can index from XML

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

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

yes

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

SQL-like query language

APIs and other access methods

JavaScript API

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

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

Supported programming languages

JavaScript

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

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

user defined functions

Triggers

yes

via the TAP protocol

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

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

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

Immediate Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

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

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.

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

fine grained access rights according to SQL-standard

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

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