DBMS > Amazon DocumentDB vs. BigObject vs. PlanetScale vs. Sequoiadb

System Properties Comparison Amazon DocumentDB vs. BigObject vs. PlanetScale vs. Sequoiadb

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Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Analytic DBMS for real-time computations and queries

Scalable, distributed, serverless MySQL database platform built on top of Vitess

NewSQL database with distributed OLTP and SQL

Primary database model

Document store

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Document store
Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.91
Rank	#131	Overall
	#24	Document stores

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	1.49
Rank	#155	Overall
	#72	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Website

aws.amazon.com/documentdb

bigobject.io

planetscale.com

www.sequoiadb.com

Technical documentation

aws.amazon.com/documentdb/resources

docs.bigobject.io

planetscale.com/docs

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

BigObject, Inc.

PlanetScale

Sequoiadb Ltd.

Initial release

2019

2015

2020

2013

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

Server: AGPL; Client: Apache V2

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

C++

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Docker
Linux
macOS

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

SQL-like query language

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

fluentd
ODBC
RESTful HTTP API

ADO.NET
JDBC
MySQL protocol
ODBC

proprietary protocol using JSON

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

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

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

Lua

yes

proprietary syntax

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

none

Multi-source replication
Source-replica replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Eventual Consistency across shards
Immediate Consistency within a shard

Eventual Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

automatically between fact table and dimension tables

yes

not for MyISAM storage engine

Transaction concepts

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

Atomic single-document operations

ACID at shard level

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

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

Access rights for users and roles

Users with fine-grained authorization concept

no user groups or roles

simple password-based access control

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More resources

Recent citations in the news

A hybrid approach for homogeneous migration to an Amazon DocumentDB elastic cluster | Amazon Web Services
4 June 2024, AWS Blog

AWS announces Amazon DocumentDB zero-ETL integration with Amazon OpenSearch Service
16 May 2024, AWS Blog

Use LangChain and vector search on Amazon DocumentDB to build a generative AI chatbot | Amazon Web Services
20 May 2024, AWS Blog

Vector search for Amazon DocumentDB (with MongoDB compatibility) is now generally available | Amazon Web Services
29 November 2023, AWS Blog

AWS announces Amazon DocumentDB I/O-Optimized
21 November 2023, AWS Blog

provided by Google News

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11 March 2024, The Register

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1 June 2024, Yahoo Movies Canada

PlanetScale forks MySQL to add vector support
3 October 2023, TechCrunch

PlanetScale Named to Fortune 2023 Best Small Workplaces
31 August 2023, businesswire.com

How to Migrate to PlanetScale's Serverless Database
14 October 2021, The New Stack

provided by Google News

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