Find myself looking for an overview too often. So let’s create a rough overview of common used config for Spark. As a start, create a Spark Session with default config: from pyspark.sql import SparkSession spark = SparkSession.builder \ .master(SPARK_MASTER) \ .appname("app name") \ .getOrCreate() The Spark Context represents the connection to the cluster; communicaties with lower-level API’s and RDDs. Some resource settings on the driver: ... .config("spark.driver.memory", "8g") ... .config("spark.cores.max", "4") .

Through Hackernews I found out about the recent release of Rancher Desktop and I was curious if this would be a good alternative to Docker desktop for the develop of web applications on my local machine. I don’t really have a problem with Docker desktop, just good to try something new every now and then and it is open-source. Running some containers really gets some steam out of my Mac so hopefully it has some improvement there.

Had a complete headache trying to figure out how a decorator as a class can maintain the possible async properties of a method. The solution is actually very simple. When called, use inspect.iscoroutinefunction to check whether it is a coroutine, and return again an async method! The example adds given paths to a registry, import inspect from functools import wraps paths_registry = [] class route(object): def __init__(self, path: str, **kwargs) -> None: self.

This is a simple demonstration of a domain event factory in Python. I assume you are familiar with the Factory Method Pattern. I also use the pydantic package for attribute validation. When implemented, we can use the factory to create immutable domain events with a homogenous data structure across instances of the same type. The metadata is generated by the underlying BaseEvent. In this approach we always produces complete events.

While reading into implementing DDD, there is often a plea for the use of immutable objects. The main motivation is that an object that is initially valid always remains valid; there is no later verification or validation required. Secondly, working with an immutable object cannot cause any side effects. Some data objects in Python are immutable, the dataclasses themselve are not. Let’s have this simple class: class SimpleClass: def __init__(self, attr1: int): self.

One morning you find out your favorite Airflow DAG did not ran that night. Sad… Six months later the task ran twice and now you understand: you scheduled your DAG timezone aware and the clock goes back and forth sometimes because of Daylight Saving Time. For example, in Central European Time (CET) on Sunday 29 March 2020, 02:00, the clocks were turned from “local standard time” forward 1 hour to 03:00:00 “local daylight time”.

With Python, creating a database connection is straightforward. Yet, I often see the following case go wrong, while a simple solution is easily at hand by using the context manager pattern. For database connections, you’ll need at least one secret. Let’s say you get this secret from a secret manager by running the get_secret() method. You also use an utility like JayDeBeApi to setup the connection and you are smart enough to close the connection after querying and deleting the password:

In case you need to provide Spark with resources from a different AWS account, I found that quite tricky to figure out. Let’s assume you have two AWS accounts: the alpha account where you run Python with IAM role alpha-role and access to the Spark cluster; and the beta account where you have the S3 bucket you want to get access to. You could give S3 read access to the alpha-role, but it is more persistent and easier to manage by creating an access-role in the beta account that can be assumed by the alpha-role.

With GitLab CI it is incredibly easy to build a Hugo website (like mine); you can even host it there. But in my case I use AWS S3 and Cloudfront because it is cheap and easy to setup. The CI pipeline to build and upload the static website is also straightforward with the following .gitlab-ci.yml: variables: GIT_SUBMODULE_STRATEGY: recursive stages: - build - upload build: stage: build image: monachus/hugo script: - hugo version - hugo only: - master artifacts: paths: - .

Now that I have a number of pipelines running I would like to deploy these to Kubernetes through a service account. that is quite simple. As an admin user provide resources such as: the namespaces, optionally with limited resources; an isolated service account with restricted access to one namespace; an encoded config file to be used by the Gitlab pipeline. Service Account with permissions The following file serviceaccount.yaml creates the service account, a role, and attach that role to that account: