DBMS > Alibaba Cloud MaxCompute vs. Badger vs. Bangdb vs. PouchDB vs. Snowflake

System Properties Comparison Alibaba Cloud MaxCompute vs. Badger vs. Bangdb vs. PouchDB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

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

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Converged and high performance database for device data, events, time series, document and graph

JavaScript DBMS with an API inspired by CouchDB

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS

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

Key-value store

Document store
Graph DBMS
Time Series DBMS

Document store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.77
Rank	#214	Overall
	#98	Relational DBMS

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.alibabacloud.com/product/maxcompute

github.com/dgraph-io/badger

bangdb.com

pouchdb.com

www.snowflake.com

Technical documentation

www.alibabacloud.com/help/en/maxcompute

godoc.org/github.com/dgraph-io/badger

docs.bangdb.com

pouchdb.com/guides

docs.snowflake.net/manuals/index.html

Developer

Alibaba

DGraph Labs

Sachin Sinha, BangDB

Apache Software Foundation

Snowflake Computing Inc.

Initial release

2016

2017

2012

2014

Current release

BangDB 2.0, October 2021

7.1.1, June 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

BSD 3

Open Source

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

C, C++

JavaScript

Server operating systems

hosted

BSD
Linux
OS X
Solaris
Windows

Linux

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

hosted

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

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

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

via views

SQL

Support of SQL

SQL-like query language

SQL like support with command line tool

yes

APIs and other access methods

Fluentd
Flume
MaxCompute Console

Proprietary protocol
RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

CLI Client
JDBC
ODBC

Supported programming languages

Java

C
C#
C++
Java
Python

JavaScript

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Java

View functions in JavaScript

user defined functions

Triggers

yes, Notifications (with Streaming only)

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

with a proxy-based framework, named couchdb-lounge

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

none

selectable replication factor, Knob for CAP (enterprise version only)

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Tunable consistency, set CAP knob accordingly

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

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, run db with in-memory only mode

yes

User concepts

Access control

Access rights for users and roles

yes (enterprise version only)

Users with fine-grained authorization concept, user roles and pluggable authentication

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