DBMS > Amazon Aurora vs. Manticore Search vs. PouchDB vs. SQLite vs. TerarkDB

System Properties Comparison Amazon Aurora vs. Manticore Search vs. PouchDB vs. SQLite vs. TerarkDB

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

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

JavaScript DBMS with an API inspired by CouchDB

Widely used embeddable, in-process RDBMS

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Secondary database models

Document store

Time Series DBMS

using the Manticore Columnar Library

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

Score	7.91
Rank	#50	Overall
	#32	Relational DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

aws.amazon.com/rds/aurora

manticoresearch.com

pouchdb.com

www.sqlite.org

github.com/bytedance/terarkdb

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

manual.manticoresearch.com

pouchdb.com/guides

www.sqlite.org/docs.html

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Amazon

Manticore Software

Apache Software Foundation

Dwayne Richard Hipp

ByteDance, originally Terark

Initial release

2015

2017

2012

2000

2016

Current release

6.0, February 2023

7.1.1, June 2019

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Open Source

GPL version 2

Open Source

Public Domain

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

JavaScript

C++

Server operating systems

hosted

FreeBSD
Linux
macOS
Windows

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

server-less

Data scheme

yes

Fixed schema

schema-free

yes

dynamic column types

schema-free

Typing

predefined data types such as float or date

yes

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

yes

not rigid because of 'dynamic typing' concept.

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

Can index from XML

Secondary indexes

yes

full-text index on all search fields

yes

via views

yes

SQL

Support of SQL

yes

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

ADO.NET
JDBC
ODBC

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

HTTP REST

only for PouchDB Server
JavaScript API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

C++ API
Java API

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

JavaScript

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C++
Java

Server-side scripts

Stored procedures

yes

user defined functions

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

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

Sharding

with a proxy-based framework, named couchdb-lounge

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Synchronous replication based on Galera library

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

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

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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