DBMS > Alibaba Cloud Table Store vs. EJDB vs. Oracle NoSQL vs. PlanetScale

System Properties Comparison Alibaba Cloud Table Store vs. EJDB vs. Oracle NoSQL vs. PlanetScale

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Editorial information provided by DB-Engines

Name

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

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

Primary database model

Wide column store

Document store

Document store
Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.25
Rank	#294	Overall
	#12	Wide column stores

Score	0.13
Rank	#331	Overall
	#46	Document stores

Score	3.07
Rank	#86	Overall
	#15	Document stores
	#11	Key-value stores
	#47	Relational DBMS

Score	1.09
Rank	#178	Overall
	#82	Relational DBMS

Website

www.alibabacloud.com/product/table-store

github.com/Softmotions/ejdb

www.oracle.com/database/nosql/technologies/nosql

planetscale.com

Technical documentation

www.alibabacloud.com/help/en/tablestore

github.com/Softmotions/ejdb/blob/master/README.md

docs.oracle.com/en/database/other-databases/nosql-database/index.html

planetscale.com/docs

Developer

Alibaba

Softmotions

Oracle

PlanetScale

Initial release

2016

2012

2011

2020

Current release

24.1, May 2024

License

Commercial or Open Source

commercial

Open Source

GPLv2

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

commercial

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

Java

Server operating systems

hosted

server-less

Linux
Solaris SPARC/x86

Docker
Linux
macOS

Data scheme

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

optional

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

HTTP API

in-process shared library

RESTful HTTP API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

Java
Python

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C
C#
Go
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

none

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

not for MyISAM storage engine

Transaction concepts

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

Atomic single-row operations

configurable

ACID within a storage node (=shard)

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

table locks or row locks depending on storage engine

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

off heap cache

yes

User concepts

Access control

Access rights based on subaccounts and tokens

Access rights for users and roles

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

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