DBMS > Alibaba Cloud AnalyticDB for PostgreSQL vs. Brytlyt vs. Firebase Realtime Database vs. GridDB vs. SQLite

System Properties Comparison Alibaba Cloud AnalyticDB for PostgreSQL vs. Brytlyt vs. Firebase Realtime Database vs. GridDB vs. SQLite

Editorial information provided by DB-Engines

Name

Description

An online MPP (Massively Parallel Processing) data warehousing service based on Greenplum

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

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Scalable in-memory time series database optimized for IoT and Big Data

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Key-value store
Relational DBMS

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

Score	0.79
Rank	#213	Overall
	#101	Relational DBMS

Score	0.27
Rank	#292	Overall
	#132	Relational DBMS

Score	13.60
Rank	#39	Overall
	#6	Document stores

Score	1.91
Rank	#123	Overall
	#10	Time Series DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Website

www.alibabacloud.com/product/hybriddb-postgresql

brytlyt.io

firebase.google.com/products/realtime-database

griddb.net

www.sqlite.org

Technical documentation

www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview

docs.brytlyt.io

firebase.google.com/docs/database

docs.griddb.net

www.sqlite.org/docs.html

Developer

Alibaba / Pivotal Software Inc. / PostgreSQL Global Development Group

Brytlyt

Google

acquired by Google 2014

Toshiba Corporation

Dwayne Richard Hipp

Initial release

2016

2012

2013

2000

Current release

5.0, August 2023

5.1, August 2022

3.46.1 (13 August 2024), August 2024

License

Commercial or Open Source

commercial

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

Public Domain

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, C++ and CUDA

C++

Server operating systems

hosted

Linux
OS X
Windows

hosted

Linux

server-less

Data scheme

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL92, SQL-like TQL (Toshiba Query Language)

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC

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

Android
iOS
JavaScript API
RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
Java
Perl
Python
R

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

Java
JavaScript
Objective-C

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

user defined functions

in PL/pgSQL

limited functionality with using 'rules'

Triggers

yes

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate consistency within container, eventual consistency across containers

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID at container level

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

Access rights for users can be defined per database

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