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В очередной раз наткнулся в сети на статью про эксплуатацию Apache Airflow в среде Kubernetes: What we learned after running Airflow on Kubernetes for 2 years. У автора развёрнут Airflow с 300 дагами и около 5 000 задачами. Преимущественно они запускают лёгкие таски по типу dbt-трансформаций, поэтому при использовании KubernetesExecutor столкнулись с тем, что инициализация Pod занимает больше времени чем выполнение самой задачи. Выбор в итоге пал на CeleryExecutor. В целом статья не столько про нюансы работы Airflow внутри Kubernetes-кластера сколько про опыт эксплуатации инструмента в их конкретном случае. Полезно.

В нашей команде Airflow также является ключевым инструментом для выполнения регулярных и критических задач. Прямо сейчас в нём 5614 дагов, работает это всё на Amazon MWAA (Managed Airflow от AWS). Накидайте 👍🏻, если вам интересно почитать о моём опыте эксплуатации Airflow.

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В очередной раз наткнулся в сети на статью про эксплуатацию Apache Airflow в среде Kubernetes: What we learned after running Airflow on Kubernetes for 2 years. У автора развёрнут Airflow с 300 дагами и около 5 000 задачами. Преимущественно они запускают лёгкие таски по типу dbt-трансформаций, поэтому при использовании KubernetesExecutor столкнулись с тем, что инициализация Pod занимает больше времени чем выполнение самой задачи. Выбор в итоге пал на CeleryExecutor. В целом статья не столько про нюансы работы Airflow внутри Kubernetes-кластера сколько про опыт эксплуатации инструмента в их конкретном случае. Полезно.

В нашей команде Airflow также является ключевым инструментом для выполнения регулярных и критических задач. Прямо сейчас в нём 5614 дагов, работает это всё на Amazon MWAA (Managed Airflow от AWS). Накидайте 👍🏻, если вам интересно почитать о моём опыте эксплуатации Airflow.
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What we learned after running Airflow on Kubernetes for 2 years
Apache Airflow is one of the most important components in our Data Platform, used by different teams inside the business. It powers all of…
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Telegram announces Search Filters

With the help of the Search Filters option, users can now filter search results by type. They can do that by using the new tabs: Media, Links, Files and others. Searches can be done based on the particular time period like by typing in the date or even “Yesterday”. If users type in the name of a person, group, channel or bot, an extra filter will be applied to the searches.

В очередной раз наткнулся в сети на статью про эксплуатацию Apache Airflow в среде Kubernetes: What we learned after running Airflow on Kubernetes for 2 years. У автора развёрнут Airflow с 300 дагами и около 5 000 задачами. Преимущественно они запускают лёгкие таски по типу dbt-трансформаций

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