DBMS > Alibaba Cloud MaxCompute vs. Apache Druid vs. Faircom DB vs. HugeGraph

System Properties Comparison Alibaba Cloud MaxCompute vs. Apache Druid vs. Faircom DB vs. HugeGraph

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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

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

A fast-speed and highly-scalable Graph DBMS

Primary database model

Relational DBMS

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

Relational DBMS
Time Series DBMS

Key-value store
Relational DBMS

Graph DBMS

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

Score	0.77
Rank	#214	Overall
	#98	Relational DBMS

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Website

www.alibabacloud.com/product/maxcompute

druid.apache.org

www.faircom.com/products/faircom-db

github.com/hugegraph
hugegraph.apache.org

Technical documentation

www.alibabacloud.com/help/en/maxcompute

druid.apache.org/docs/latest/design

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

hugegraph.apache.org/docs

Developer

Alibaba

Apache Software Foundation and contributors

FairCom Corporation

Baidu

Initial release

2016

2012

1979

2018

Current release

29.0.1, April 2024

V12, November 2020

0.9

License

Commercial or Open Source

commercial

Open Source

Apache license v2

commercial

Restricted, free version available

Open Source

Apache Version 2.0

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

ANSI C, C++

Java

Server operating systems

hosted

Linux
OS X
Unix

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux
macOS
Unix

Data scheme

yes

schema-less columns are supported

schema free, schema optional, schema required, partial schema,

yes

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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

also supports composite index and range index

SQL

Support of SQL

SQL-like query language

SQL for querying

yes, ANSI SQL with proprietary extensions

APIs and other access methods

Fluentd
Flume
MaxCompute Console

JDBC
RESTful HTTP/JSON API

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

Java API
RESTful HTTP API
TinkerPop Gremlin

Supported programming languages

Java

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

Groovy
Java
Python

Server-side scripts

Stored procedures

user defined functions

in Java

yes

.Net, JavaScript, C/C++

asynchronous Gremlin script jobs

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

manual/auto, time-based

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

yes

depending on used storage backend, e.g. Cassandra and HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes, via HDFS, S3 or other storage engines

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

depending on used storage backend, e.g. Cassandra and HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

tunable from ACID to Eventually Consistent

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

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

Access rights for users and roles

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Fine grained access rights according to SQL-standard with additional protections for files

Users, roles and permissions

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