DBMS > Alibaba Cloud MaxCompute vs. Apache IoTDB vs. Graph Engine vs. MySQL vs. TerarkDB

System Properties Comparison Alibaba Cloud MaxCompute vs. Apache IoTDB vs. Graph Engine vs. MySQL vs. TerarkDB

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 IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Widely used open source RDBMS

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Relational DBMS

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

Time Series DBMS

Graph DBMS
Key-value store

Relational DBMS

Key/Value like access via memcached API

Key-value store

Secondary database models

Document store
Spatial DBMS

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

Score	0.77
Rank	#214	Overall
	#98	Relational DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

www.alibabacloud.com/product/maxcompute

iotdb.apache.org

www.graphengine.io

www.mysql.com

github.com/bytedance/terarkdb

Technical documentation

www.alibabacloud.com/help/en/maxcompute

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

www.graphengine.io/docs/manual

dev.mysql.com/doc

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Alibaba

Apache Software Foundation

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

ByteDance, originally Terark

Initial release

2016

2018

2010

1995

2016

Current release

1.1.0, April 2023

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

MIT License

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

.NET and C

C and C++

C++

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

.NET

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

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

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

Fluentd
Flume
MaxCompute Console

JDBC
Native API

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

C++ API
Java API

Supported programming languages

Java

C
C#
C++
Go
Java
Python
Scala

C#
C++
F#
Visual Basic

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C++
Java

Server-side scripts

Stored procedures

user defined functions

in Java

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

horizontal partitioning

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

yes

Users with fine-grained authorization concept

no user groups or roles

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