Google launches Cloud Run jobs for containerized and scripted jobs – TechCrunch

During a developer keynote at Google I/O 2022, Google unveiled Cloud Run Jobs, an extension of Google Cloud’s service for developing and deploying containerized applications using languages ​​such as Go, Python and Java. Cloud Run tasks are designed for containers that run to completion and do not respond to requests, such as data processing and administrative tasks, and when multiple copies of a container need to run in parallel.

Cloud Run was launched in 2019, adding to Google Cloud’s then rapidly growing serverless computing stack. As demand for serverless services grows, it looks like extensions like Cloud Run jobs are an attempt to counter rivals like Azure and Amazon Web Services.

Available in preview starting today, Cloud Run jobs can be used to run a script to perform database migrations or other operational tasks, such as sending monthly invoices. Compared to other platforms that support long-running jobs, Cloud Run jobs start quickly after creation, according to Google, with simple containers starting in as little as 10 seconds.

To use Cloud Run jobs, developers create a job that encapsulates all of the configuration needed to run the job, including the container image, region, and environment variables. Then they configure the job to run on a schedule or manually run the job, creating a new job run.

During preview, Cloud Run jobs support up to 50 concurrent runs of the same or different jobs per project per region. Users can view existing jobs, start runs, and monitor run status from the Cloud Console’s Cloud Run Jobs page; Cloud Console currently does not support creating new tasks.

Cloud Run jobs come with an update to Firebase, Google’s popular back-end-as-a-service platform, and AlloyDB, a new fully managed PostgreSQL database service. Arguably the more interesting of the two, AlloyDB offers – as my colleague Frédéric Lardinois writes – a custom caching service based on machine learning to learn a client’s access patterns and then convert the format of Postgres row to an in-memory column format that can be parsed significantly faster.