Dataclasses are an implementation of a code generator, similar to a Named Tuple. The idea of using a code generator is to save time and reduce wordiness.There are two views about the purpose of Dataclasses:<ol><li>It makes a mutable data holder, in the spirit of named tuples</li><li>It writes boiler-plate code for you, simplifying the process of writing the class</li></ol>These 2 world views are reflected in the name: “Dataclasses”Per the dataclasses PEP, they are roughly a “Mutable named tuple with defaults”They provide an elegant syntax for creating data holder objects

Dataclasses & What are they for

In the beginning there where:<ul><li>dicts for look-ups</li><li>tuples for structures</li><li>hand-written classes for more functionality</li></ul>Later Named Tuples where added as a code generator with the goal of adding names to the fields of a tuple. This was limited by design, hence its nameORMs appeared and pioneered using class attributes to specify rich data structuresIn Python 3.6, it became possible to supply <a href="https://deepstash.com/article/137403/an-introduction-to-python-type-hints">type annotations</a>

A History Lesson

<ul><li class="ql-align-justify">Dataclasses are by default mutable</li><li class="ql-align-justify">Named Tuples have methods which start with an underscore which can be confusing. Ex: obj._replace(f=value)</li><li class="ql-align-justify">Named Tuples have legacy code. Ex: _asdict() returns an OrderedDict</li><li class="ql-align-justify">Named Tuples can be unpacked</li><li class="ql-align-justify">Dataclasses are faster at retrieving data but take up more space</li><li class="ql-align-justify">Dataclasses use functions instead of methods. Ex: asdict() vs nt._asdict()</li><li class="ql-align-justify">Dataclasses are underlying stored as instance dicts</li></ul>

Comparison with Named Tuples

If you have an immutable mind get out!

<ul><li>Dunder methods like init, repr, eq, lt, le, etc.</li><li>Frozen nature by generating dunder methods for __setattr__ and __delattr__</li><li>Class variables for the provided attributes</li></ul>The code generation can be configured by providing keyword arguments to the @dataclass decoratorA complete list of methods and configurations can be found <a href="https://docs.python.org/3/library/dataclasses.html">here</a>

Generated Code Includes

In the image example, the generated code includes:<ul><li>typed attributes</li><li>hidden attributes from __repr__ response</li><li>custom __hash__ dunder with the selected fields</li><li>attribute with default factory value in __init__ constructor</li></ul>

A Complex Example of using Dataclasses

I started using Dataclasses and I think you should too

Speaker: Raymond Hettinger The PEP 557 dataclasses module is available in starting in Python 3.7. It will become an essential part of every Python programmer's toolkit. This talk shows what problem the module solves, explains its key design decisions, and provides practical examples of how to put it to work. Dataclasses are shown to be the next step in a progression of data aggregation tools: tuple, dict, simple class, bunch recipe, named tuples, records, attrs, and then dataclasses. Each builds upon the one that came before, adding expressiveness at the expense of complexity. Dataclasses are unique in that they let you selectively turn-on or turn-off its various capabilities and it lets the user choose the underlying data store (either instance dictionary, instance slots, or an inherited base class). Dataclasses and typing.NamedTuple both use variable annotations which were new in Python 3.6. Slides can be found at: https://speakerdeck.com/pycon2018 and https://github.com/PyCon/2018-slides

computerscience

Computer Science

Speaker: Raymond Hettinger

The PEP 557 dataclasses module is available in starting in Python 3.7.   It will become an essential part of every Python programmer's toolkit.  This talk shows what problem the module solves, explains its key design decisions, and provides practical examples of how to put it to work.

Dataclasses are shown to be the next step in a progression of data aggregation tools: tuple, dict, simple class, bunch recipe, named tuples, records, attrs, and then dataclasses. Each builds upon the one that came before, adding expressiveness at the expense of complexity.

Dataclasses are unique in that they let you selectively turn-on or turn-off its various capabilities and it lets the user choose the underlying data store (either instance dictionary, instance slots, or an inherited base class).

Dataclasses and typing.NamedTuple both use variable annotations which were new in Python 3.6.









Slides can be found at: https://speakerdeck.com/pycon2018 and https://github.com/PyCon/2018-slides

PyCon 2018

Raymond Hettinger - Dataclasses:  The code generator to end all code generators - PyCon 2018

Dataclasses:  The code generator to end all code generators

PEP 484 introduced type hints (<a href="https://docs.python.org/3/library/typing.html">typing</a> module), a.k.a type annotations. While its main focus was function annotations, it also introduced the notion of type comments to annotate variables.Ex: primes = [] #type: List[int]Although type comments work well enough, there are some downsides:<ul><li>text editors highlight comments differently </li><li>one can’t annotate uninitialized variables</li><li>mixing type comments with normal ones is confusing</li></ul>The solution: Variable Annotations

Type Annotations

PEP 526 introduced the syntax for Variable Annotations:var: annotation = valueVariable Annotations don’t affect the Python principle of being a dynamically typed language.Type annotations can’t create errors, they are checked only by third party type checkers like mypy.See image example.

Variable Annotations

Type annotations can also be used to annotate class and instance variables in class bodies and methods.In particular, the value-less notation: a: int allows one to annotate instance variables that should be initialized in __init__ or __new__. See the syntax in the picture. As one knows, defining class member variables is not mandatory but it’s useful for providing autocomplete functionality while coding. The differences between class and instance variables are discussed in the following idea

Class and Instance Variable Annotations 

When one defines a member variable in the class body definition, the interpreter considers it a class variable.class Dog: name: str = ‘Doggo’ # class variableIn the __init__ constructor, one does have access at the ‘name’ class variable using ‘self’. But as self represents the instance of the class, in the constructor scope, ‘self.name’ becomes an instance variable.By deleting the ‘name’ attribute from a Dog instance, one can access the ‘name’ class variable once again.

Class Variables vs. Instance Variables

As I strive to become a better python programmer, I’ve started using type hints and created this intro guide

An introduction to Python Type Hints

It’s a cluster comprised of a group of physical and virtual machines running the Docker Engine, these are called nodes.One of the nodes is elected to be the Leader by using the Raft consensus algorithm. He make all the orchestration decisions for the swarm. In case of failure, a new leader it’s automatically elected.Manager nodes assign tasks to Worker nodes. They can do some other managerial tasks. You want to have an odd number of managers, for reliability purposes. Having to many managers can degrade performance, it’s recommended to have at most 7.

What is Docker Swarm and why You Need to Start using it

Overlay networks manage communications among the Docker nodes participating to the Swarm. Can also be used for service-to-service communication.Ingress network is a special overlay network that facilitates load balancing among the Swarm nodes.docker_gwbridge is a network connecting the containers running on a Docker daemon’s host. Also this network connects the overlay networks to a individual Docker daemon’s physical network

Swarm Service Networks

Just like a manager of a shipping company has to pick which ship is best used for caring a container, a Manager node ensures that container workloads are assigned to run on the most appropriate Worker node for optimal efficiency. The ingress network handles the control and data traffic related to the swarm services and is used by the manager nodes to send workloads to the worker nodes.

You heard about Load-Balancers, Docker Swarm does  it Better

So you started development using a Docker architecture but you don’t know how to scale and deploy?

career

Career

Docker Swarm and deploying like a pro

Docker is an open source containerization toolkit which enables developers to package applications into containers.As the name suggests, Docker is like a container ship which provides means to sort, link, manage and ship containers from a development setup into production.

What Is Docker?

Think like a Virtual Machine but:<ul><li>light weight</li><li>great resource efficiency</li><li>improved developer productivity</li></ul>Where containers differ from VMs is the fact that they take advantage of the Linux kernel which comes with process isolation and virtualization capabilities.These capabilities are control groups(allocating resources), and namespaces(restricting a processes access to other areas of the system).Some real world examples would be the fact that a container has a specific place on the ship and a container lock, which keeps everything secure inside it.

What is a Container

Containers don’t come with the weight of a full OS image and a hypervisor.Containers include only OS processes and dependencies necessary to execute the code inside them.Containers occupy less space(MBs not GBs) which means you can run multiple copies of the same application on the same hardware.Containers are faster and easier to deploy, provision and restart. This make them ideal for CI/CD pipelines and a better fit for Agile development teams.

Why it’s actually not a VM

Docker enhances the native Linux containerization technologies which enables:<ul><li>seamless portability: can run natively across any desktop, data center or cloud development</li><li>granular updates: only one process runs in a container. An application can continue running without a container being taken done</li><li>automatic container creation: based on application source code</li><li>container versioning: tracking image versions and roll backs are fast</li><li>container reuse: base images can be used as templates for nee containers</li><li>shared container libraries: an open source user contributed container images registry</li></ul>

What ACTUALLY is Docker

DockerFile - every container has one and states the instructions for building the container’s image. It consists in command-line instructions;Docker images - contains executable source code, libs and dependencies the code needs; Read-only file; A Docker image can be build from scratch, most developers reuse common ones from repositories;Docker containers - the live and running instance of the Docker image; Ephemeral, users can interact with them, run commands and adjust settings using docker commandsDocker registry - open source distribution system and versioning using git

Docker tools

On the same host:<ul><li>Docker Compose - creates a .yaml which specifies the used containers; can deploy and run containers from a single command; can define persistent volumes for storage, databases and service dependencies;</li></ul>On different hosts:You need container orchestration tools.<ul><li>Docker Swarm</li><li>Kubernetes</li><li>AWS EKS</li><li>OpenShift</li></ul>

Docker deployment

Docker is a very powerful tool for development and code deployment. This is what you’re missing to take it to the next level!

The Power of Docker

Maybe the most recognizable rally era, when the pursuit was the most advanced type of car, revolutionary ideas and speeed

Group B rally

Group B started in the early ’80s, alongside group A, C and N. The purpose of group B was to innovate, the manufacturers were obligated to homologate only 200 examples over a 12 months span. This regulation spawned one of the most iconic special cars from mainstream manufacturers.

The birth

After a failed Escort RS 1700T, started development for a purpose built rally car, the RS200, featuring a plastic-fiberglass composite body, mid-mounted Cosworth engine and four-wheel drive, a winning formula used by many manufacturers. Sadly is didn’t manage more then a third-place because it started a chain of crashes that led FIA to abolish Group B rally.

Ford RS200 Evo

Was the fastest street-legal production car. It was considered the most technologically advanced road car. The production started with the Porsche 953, who won the ‘84 Paris-Dakar Rally with 1st and 6th places. The 959 had a tumultuous run, first started asa a body over the 953 chassis, later fitted with twin-turbochargers. It finished 1st, 2nd and 6th in the ‘86 Paris-Dakar Rally. Unfortunately, when the 959 was ready for production and homologation in 1987, FIA canceled Group B.

Porsche 959, the to be champion

The legendary Quattro, Probably the most famous Rally car, who started the trend for reliable all-wheel drive. It used an inline 5 cylinder with an excess of 500hp and one of the first iterations of a dual clutch automatic gearbox(DSG), which is common these days. The legend had a short life, after a gruesome crash, Audi withdrew from rallying, but the legacy would last forever.

Audi Quattro S1

On 2 May 1986, The drivers of the Lancia Delta S4 left the road, and thus this is know as the las incident in Group B, as FIA canceled this group of racing. After many crashes and incidents, the Group B was deemed to dangerous for both drivers and spectators, but it will be always remembered as the golden age of rallying.

The corner that killed Group B

Unprecedented levels of power, excitement – and controversy. Group B rallying had it all. We take a look under the skin of the famous cars

The anatomy of a Group B rally car - history and tech of rallying's golden era | Evo

If you no longer go for a gap that exists, you’re no longer a racing driver.

To achieve anything in this game, you must be prepared to dabble in the boundary of disaster.

I've been through a lot and I realise the future can't be controlled. I'm not worried. You can always learn to overcome difficulties.

From life lessons to ‘Murrayisms,’ we’ve taken a closer look at the stories behind some of Formula 1’s most iconic quotes.  

Dataclasses: The code generator to end all code generators

An introduction to Python Type Hints

Docker Swarm and deploying like a pro

The Power of Docker

The anatomy of a Group B rally car - history and tech of rallying's golden era | Evo

Pavel-Vlad Mateescu