DBMS > Alibaba Cloud MaxCompute vs. Bangdb vs. JanusGraph vs. Riak TS

System Properties Comparison Alibaba Cloud MaxCompute vs. Bangdb vs. JanusGraph vs. Riak TS

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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

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

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Relational DBMS

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

Document store
Graph DBMS
Time Series DBMS

Graph DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.84
Rank	#209	Overall
	#98	Relational DBMS

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Website

www.alibabacloud.com/product/maxcompute

bangdb.com

janusgraph.org

Technical documentation

www.alibabacloud.com/help/en/maxcompute

docs.bangdb.com

docs.janusgraph.org

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Alibaba

Sachin Sinha, BangDB

Linux Foundation; originally developed as Titan by Aurelius

Open Source, formerly Basho Technologies

Initial release

2016

2012

2017

2015

Current release

BangDB 2.0, October 2021

0.6.3, February 2023

3.0.0, September 2022

License

Commercial or Open Source

commercial

Open Source

BSD 3

Open Source

Apache 2.0

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

Java

Erlang

Server operating systems

hosted

Linux

Linux
OS X
Unix
Windows

Linux
OS X

Data scheme

yes

schema-free

yes

schema-free

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.

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

restricted

SQL

Support of SQL

SQL-like query language

SQL like support with command line tool

yes, limited

APIs and other access methods

Fluentd
Flume
MaxCompute Console

Proprietary protocol
RESTful HTTP API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

HTTP API
Native Erlang Interface

Supported programming languages

Java

C
C#
C++
Java
Python

Clojure
Java
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in Java

yes

Erlang

Triggers

yes, Notifications (with Streaming only)

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

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

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

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

yes

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable consistency, set CAP knob accordingly

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

links between datasets can be stored

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

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

User concepts

Access control

Access rights for users and roles

yes (enterprise version only)

User authentification and security via Rexster Graph Server

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