DBMS > Alibaba Cloud MaxCompute vs. EJDB vs. Google Cloud Bigtable vs. JSqlDb vs. PostgreSQL

System Properties Comparison Alibaba Cloud MaxCompute vs. EJDB vs. Google Cloud Bigtable vs. JSqlDb vs. PostgreSQL

Editorial information provided by DB-Engines

Name

JSqlDB seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

MaxCompute (previously known as ODPS) is a general purpose, fully managed, multi-tenancy data processing platform for large-scale data warehousing

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

JavaScript Query Language Database, stores JavaScript objects and primitives

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Primary database model

Relational DBMS

a graph-processing framework is available with the graphs being stored in tables

Document store

Key-value store
Wide column store

Document store
Object oriented DBMS

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	0.77
Rank	#214	Overall
	#98	Relational DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	645.54
Rank	#4	Overall
	#4	Relational DBMS

Website

www.alibabacloud.com/product/maxcompute

github.com/Softmotions/ejdb

cloud.google.com/bigtable

jsqldb.org (offline)

www.postgresql.org

Technical documentation

www.alibabacloud.com/help/en/maxcompute

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

cloud.google.com/bigtable/docs

www.postgresql.org/docs

Developer

Alibaba

Softmotions

Google

Konrad von Backstrom

PostgreSQL Global Development Group

www.postgresql.org/developer

Initial release

2016

2012

2015

2018

1989

1989: Postgres, 1996: PostgreSQL

Current release

0.8, December 2018

16.3, May 2024

License

Commercial or Open Source

commercial

Open Source

GPLv2

commercial

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

Server operating systems

hosted

server-less

hosted

Linux
macOS
Windows

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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.

yes

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

standard with numerous extensions

APIs and other access methods

Fluentd
Flume
MaxCompute Console

in-process shared library

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

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

Supported programming languages

Java

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

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

JavaScript

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

user defined functions

in Java

functions in JavaScript

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Sharding

none

partitioning by range, list and (since PostgreSQL 11) by hash

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

none

Internal replication in Colossus, and regional replication between two clusters in different zones

none

Source-replica replication

other methods possible by using 3rd party extensions

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

using RocksDB

yes

In-memory capabilities

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

User concepts

Access control

Access rights for users and roles

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

More information provided by the system vendor

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