DBMS > Alibaba Cloud AnalyticDB for MySQL vs. Apache IoTDB vs. InfluxDB vs. SQLite vs. TimescaleDB

System Properties Comparison Alibaba Cloud AnalyticDB for MySQL vs. Apache IoTDB vs. InfluxDB vs. SQLite vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

A real-time data warehousing service that can process petabytes of data with high concurrency and low latency. It is fully compatible with the MySQL protocol.

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

DBMS for storing time series, events and metrics

Widely used embeddable, in-process RDBMS

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Document store

Spatial DBMS

with GEO package

Relational DBMS

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

Score	0.85
Rank	#207	Overall
	#96	Relational DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

www.alibabacloud.com/product/analyticdb-for-mysql

iotdb.apache.org

www.influxdata.com/products/influxdb-overview

www.sqlite.org

www.timescale.com

Technical documentation

www.alibabacloud.com/help/doc-detail/93776.htm

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

docs.influxdata.com/influxdb

www.sqlite.org/docs.html

docs.timescale.com

Developer

Alibaba

Apache Software Foundation

Dwayne Richard Hipp

Timescale

Initial release

2018

2013

2000

2017

Current release

1.1.0, April 2023

2.7.6, April 2024

3.45.3 (15 April 2024), April 2024

2.13.0, November 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

MIT-License; commercial enterprise version available

Open Source

Public Domain

Open Source

Apache 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

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

Linux
OS X

through Homebrew

server-less

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

Numeric data and Strings

yes

not rigid because of 'dynamic typing' concept.

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

yes

SQL-like query language

yes

SQL-92 is not fully supported

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
ODBC

JDBC
Native API

HTTP API
JSON over UDP

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

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

Supported programming languages

C#
Java
PHP
Python

C
C#
C++
Go
Java
Python
Scala

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

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)

Sharding

in enterprise version only

none

yes, across time and space (hash partitioning) attributes

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

selectable replication factor

in enterprise version only

none

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

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

Depending on used storage engine

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

simple rights management via user accounts

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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