# Cloud Run Cloud Run is a serverless application-hosting platform. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Temporal expands its Google Cloud Partnership with Gemini integration and pay-as-you-go pricing on Google Cloud Marketplace DevFeed: [Temporal expands its Google Cloud Partnership with Gemini integration and pay-as-you-go pricing on Google Cloud Marketplace]() Original publisher: [Read original article]() Author: Jay Sivachelvan Published: 2026-08-31T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Google Cloud Platform (GCP)](), [SDKs](), [Google](), [reliability](), [Artificial Intelligence](), [Cloud Run](), [Resilience](), [API](), [Serverless]() Tags: [agent](), [agent-development-kit](), [agent-framework](), [announcements](), [api](), [billing](), [cloud-marketplace](), [cloud-run](), [execution](), [gemini](), [google](), [google-cloud](), [integration](), [launch](), [partnership](), [pricing]() ### AI overview Temporal announces expanded Google Cloud integrations, including Public Preview support for the Google Gen AI Python SDK, durable execution for Gemini-based workflows, and pre-release Serverless Workers for Google Cloud Run. Temporal Cloud is also available on Google Cloud Marketplace with pay-as-you-go pricing. ### Source excerpt Temporal expands its Google Cloud partnership with Gemini integration, durable execution, and pay-as-you-go pricing on Google Cloud Marketplace. ## Instrument serverless apps with agentic onboarding DevFeed: [Instrument serverless apps with agentic onboarding]() Original publisher: [Read original article]() Author: Rohan Agarwal; Piyali Banerjee Published: 2026-08-10T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Serverless](), [AWS Lambda](), [azure container apps](), [Cloud Run](), [Command-line interface](), [MCP Server](), [Artificial Intelligence](), [Claude Code](), [cursor](), [Node.js](), [Terraform]() Tags: [agentic](), [ai](), [aws](), [aws-lambda](), [azure](), [azure-container-apps](), [claude-code](), [cli](), [cursor](), [google-cloud](), [google-cloud-run](), [mcp-server](), [node](), [serverless](), [serverless-monitoring](), [terraform]() ### AI overview Datadog's agentic onboarding helps instrument serverless applications across AWS Lambda, Google Cloud Run, and Azure Container Apps. Developers can use an AI assistant connected through the Datadog MCP Server or run the AI Setup CLI to inspect projects, adapt setup to existing deployment tools and runtimes, and prepare configuration changes for review. ### Source excerpt Use Datadog agentic onboarding to instrument AWS Lambda, Google Cloud Run, and Azure Container Apps from an AI assistant or CLI. ## Q2 2026 Product Update: Harness Continuous Delivery & GitOps DevFeed: [Q2 2026 Product Update: Harness Continuous Delivery & GitOps]() Original publisher: [Read original article]() Author: Vishal Vishwaroop Published: 2026-08-06T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [CI/CD](), [GitOps](), [Deployment](), [Kubernetes](), [AI Agent](), [Cloud Run]() Tags: [2026](), [agent](), [cloud-run](), [continuous-delivery](), [deployment](), [gitops](), [kubernetes](), [product]() ### AI overview Harness reviews its Q2 2026 product updates, covering 38 enhancements across Continuous Delivery, Verification, and GitOps. The update includes progressive Kubernetes canary rollouts, native AI agent deployments, staged Cloud Run traffic, enhanced verification controls, and GitOps rollback and RBAC improvements. ### Source excerpt Progressive canary rollouts, native AI agent deployments, staged Cloud Run traffic, smarter verification controls, and 12 GitOps improvements -- Q2 2026 in revie | Blog ## Durable Digest: July highlights DevFeed: [Durable Digest: July highlights]() Original publisher: [Read original article]() Author: Temporal Technologies Published: 2026-07-30T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [releases](), [distributed-systems](), [Cloud Run](), [Langgraph](), [datadog](), [API]() Tags: [announcements](), [api](), [cloud](), [cloud-run](), [datadog](), [distributed-systems](), [explore](), [langgraph](), [releases]() ### AI overview Temporal's July Durable Digest summarizes product releases and updates, including pre-release Serverless Workers for GCP Cloud Run, public-preview LangGraph and LangSmith integrations, generally available billing and metrics APIs, Datadog and Vantage integrations, and improvements to the Worker Status UI. ### Source excerpt Highlights from July include major product releases that make it easier to build and operate durable applications, and enhanced visibility into your Workers. ## Announcing Serverless Workers for Google Cloud Run DevFeed: [Announcing Serverless Workers for Google Cloud Run]() Original publisher: [Read original article]() Author: Brandon Chavis Published: 2026-07-30T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Cloud Run](), [autoscaling](), [Serverless](), [Google Cloud Platform (GCP)](), [Deployment](), [container](), [IAM]() Tags: [autoscaling](), [cloud-run](), [deployment](), [docker](), [google-cloud-run](), [iam](), [product-news](), [serverless]() ### AI overview Temporal announces Serverless Workers support for Google Cloud Run. The service integrates with Cloud Run Worker Pools and dynamically adjusts capacity, including scaling to zero when appropriate, reducing the infrastructure planning and autoscaling work required from users. ### Source excerpt Temporal now supports Serverless Workers on Google Cloud Run, autoscaling Worker Pools for you so you never manage infrastructure or pay for idle compute. ## Building Svitgrid, a Solar Monitoring Platform for Home Systems DevFeed: [Building Svitgrid, a Solar Monitoring Platform for Home Systems]() Original publisher: [Read original article]() Author: Ivan Ursul Published: 2026-05-21T00:00:00Z Content type: article Language: en Sources: [Ivan Ursul]() Topics: [Monitoring](), [dashboards](), [real-time](), [Fastify](), [Flutter](), [Modbus](), [TypeScript](), [Cloud Run](), [ESP-IDF](), [ESP32-S3](), [Firestore](), [Mobile]() Tags: [android](), [battery](), [cloud-run](), [energy-monitoring](), [esp-idf](), [esp32](), [esp32-s3](), [firestore](), [flutter](), [gcp](), [iot](), [java](), [migrations](), [mobile](), [modbus](), [mongodb](), [monitoring](), [real-time](), [side-project](), [solar](), [typescript](), [web]() ### AI overview This article explains how Svitgrid, a solar monitoring platform for residential systems, was built. It covers the product's goals, cross-platform clients, cloud backend, ESP32-S3 edge hardware, inverter data paths, and support for multiple inverter systems over Modbus TCP. ### Source excerpt Svitgrid dashboard: real-time power flow from PV to battery, home, and grid ## How I Actually Use AI as a Developer (And When I Don't) DevFeed: [How I Actually Use AI as a Developer (And When I Don't)]() Original publisher: [Read original article]() Author: Brian Jenney Published: 2025-12-06T17:14:53Z Content type: opinion Language: en Sources: [Brian Jenney]() Topics: [Artificial Intelligence](), [Code](), [Python](), [Cloud Run](), [cloud-tasks](), [SQL](), [API]() Tags: [ai](), [cloud-run](), [cloud-tasks](), [code](), [developer](), [python](), [sql](), [workflow]() ### AI overview A developer describes a practical workflow for using AI-assisted coding under startup delivery pressure. The article covers making unfamiliar Python code understandable, evaluating architecture and debugging options across local and Cloud Run environments, and running rapid experiments involving batching, SQL queries, API calls, and logging. The author presents this as a personal workflow rather than a universal prescription. ### Source excerpt The workflow that helped me "make the impossible possible" in my first 3 months at a startup. ## Data Connect: Event Triggers and Generated Admin SDKs DevFeed: [Data Connect: Event Triggers and Generated Admin SDKs]() Original publisher: [Read original article]() Author: Andrea Wu Published: 2025-11-17T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [data](), [SDKs](), [Cloud Functions](), [Firestore](), [Cloud Run](), [Messaging]() Tags: [admin-sdk](), [cloud-functions](), [cloud-run](), [firebase](), [firestore](), [graphql](), [messaging](), [news](), [payload](), [sdks](), [sql-connect](), [updates]() ### AI overview Firebase Data Connect adds event triggers that run server-side code after mutations, along with generated type-safe admin SDKs for privileged environments. The article describes integrations with Cloud Functions for Firebase, Cloud Run, App Hosting, Firestore, external APIs, email, and Firebase Cloud Messaging. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Building for an Open Future - our new partnership with Google Cloud DevFeed: [Building for an Open Future - our new partnership with Google Cloud]() Original publisher: [Read original article]() Author: Jeff Boudier; Simon Pagezy Published: 2025-11-13T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [hugging face](), [Cloud](), [Open Source](), [Cloud Run](), [inference-endpoints](), [Low-Latency Inference](), [Cache](), [xet](), [data](), [Deployment]() Tags: [announcement](), [artificial-intelligence](), [cache](), [cloud](), [data](), [datasets](), [deployment](), [google](), [google-cloud](), [inference-endpoints](), [networking](), [open](), [open-source](), [partnership](), [partnerships](), [storage](), [vertex](), [vertex-ai](), [xet]() ### AI overview Hugging Face and Google Cloud announce a strategic partnership focused on making open AI models easier to use, customize, deploy, and govern. The article describes integrations across Vertex AI, GKE AI/ML, Cloud Run GPUs, and other Google Cloud infrastructure, plus a planned CDN Gateway using Hugging Face Xet and Google Cloud storage and networking to accelerate model and dataset downloads and improve supply-chain robustness. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Building Arc: An AI Messenger Powered by Firebase, Flutter, and Vertex AI DevFeed: [Building Arc: An AI Messenger Powered by Firebase, Flutter, and Vertex AI]() Original publisher: [Read original article]() Author: TAKAYUKI MIYANO Published: 2025-10-27T07:49:55Z Content type: article Language: en Sources: [Firebase Developers - Medium]() Topics: [App](), [Architecture & Design](), [Firebase](), [Flutter](), [Cloud Run](), [Cloud Functions](), [Retrieval Augmented Generation (RAG)](), [Messaging](), [Microservices](), [Security](), [Resilience](), [networking]() Tags: [architecture](), [cloud-functions](), [cloud-run](), [firebase](), [flutter](), [gemini](), [messenger](), [microservices](), [resilience](), [retrieval-augmented-generation](), [security](), [vertex-ai]() ### AI overview This article describes the architecture of Arc, a private messaging app built with Flutter and Firebase. It covers ephemeral messaging, mesh networking for resilience, Cloud Run microservices, Cloud Functions for event-driven processing, and a RAG architecture using Vertex AI Search and Gemini models. It also discusses security, privacy, and GenAI companions. ### Source excerpt Introduction: The Vision of Arc Our journey at Arc began with a singular vision: to redefine private communication in the digital age. We envisioned a world of ephemeral, intimate conversations -- a space where privacy isn't an afterthought, but the very foundation. This philosophy gave birth to Arc, a next-generation messaging app where every message, even in group chats, is designed to disappear over time. Our vision goes beyond just ephemerality. We also saw a critical need to build resilience, especially in regions with unstable internet infrastructure and in a world where disasters are a constant threat. This led us to develop our mesh networking technology, a potential lifeline when traditional communication fails. Our mission is to transform our app into a vital piece of social infrastructure that protects people's safety and gives them peace of mind. However, a vision of this scale demands an architecture that can compete on a global level. For us, true "optimization" is a comprehensive concept built on four pillars: Stability, Speed, Lightweight Design, and High Security. In this article, we'll explore the architectural decisions that shaped Arc. We'll detail how we integrated Flutter as our client, used Firebase's managed services as our foundation, and combined them with Cloud Run for scalable microservices, Cloud Functions for event-driven processing, and an advanced RAG (Retrieval-Augmented Generation) architecture using Vertex AI Search for grounding and Gemini models. This is the story of how we built a platform that is not only secure and high-performance but also intelligent and ready for future evolution. The Challenge: Going Beyond a Standard Messenger To make Arc a reality, we faced numerous technical hurdles that went beyond conventional architectures. Our goal wasn't just to build another messaging app; it was to create a new conversational experience and a resilient communication platform. Our app also includes GenAI companions (AI Characters) ## TRMNL DevFeed: [TRMNL]() Original publisher: [Read original article]() Author: Adam Langley Published: 2025-07-27T00:00:00Z Content type: opinion Language: en Sources: [ImperialViolet]() Topics: [e-ink](), [Microcontroller](), [Cloud Run](), [Go Language](), [JSON](), [HTTP]() Tags: [battery](), [e-ink](), [firmware](), [http](), [program](), [usb](), [wi-fi]() ### AI overview A review of the TRMNL, an 800x600, 1-bit e-ink display powered by a battery and microcontroller. The article describes its firmware and server customization, tile-based display service, Liquid templating, and a Go program on Cloud Run that displays family calendar events. It also notes refresh-scheduling difficulties and the device's relatively high cost. ### Source excerpt The TRMNL is an 800x600, 1-bit e-ink display connected to a battery and a microcontroller, all housed in a nice but unremarkable plastic case. Because the microcontroller spends the vast majority of the time sleeping, and because e-ink displays don't require power unless they're updating, the battery can last six or more months. It charges over USB-C. When the microcontroller wakes up, it connects to a Wi-Fi network and communicates with a pre-configured server to fetch an 800x600 image to display, and the duration of the next sleep. You can flash your own firmware on the device, or point the standard firmware at a custom server. The company provides an example server, although you can implement the (HTTP-based) protocol in whatever way you wish. I considered running my own server, but thought I would give the easy path a try first to see if it would suffice. The default service lets you split the display into several tiles, and there are a number of pre-built and community-built things that can display in each. None of them worked well for me, but that's okay because you can create your own private ones. They get data either by polling a given URL, or by having data posted to a webhook. The layout is rendered using the Liquid templating system, which I had not used before, but it's reasonably straightforward. I wrote a Go program hosted on Cloud Run which fetches the family shared calendar and converts events from the next week into a JSON format designed to make it trivial to render in the templating system. With a 3D-printed holder, super glue, and some magnets, it's now happily stuck to the fridge where it displays the current date and the family events for the next week. The most awkward part of the default service is managing the refreshes. The device has a sleep schedule, and so do the tiles, which are only updated periodically. So the combination can easily leave the wrong day showing. It would be helpful if the service told you when the device would next up ## Deploying a Kotlin-based remote MCP Server to Google Cloud Run DevFeed: [Deploying a Kotlin-based remote MCP Server to Google Cloud Run]() Original publisher: [Read original article]() Published: 2025-07-26T23:00:00Z Content type: tutorial Language: en Sources: [John O'Reilly]() Topics: [MCP Server](), [Cloud Run](), [Kotlin](), [Deployment](), [Google Cloud Platform (GCP)]() Tags: [cloud-run](), [deployment](), [google-cloud](), [how-to](), [kotlin](), [mcp](), [mcp-server]() ### AI overview A tutorial on deploying a Kotlin-based MCP Server from the ClimateTraceKMP sample to Google Cloud Run. It covers building and publishing a container with the Jib Gradle Plugin, configuring the server for Cloud Run, and connecting to Claude Desktop, Claude mobile apps, and MCP Inspector. ### Source excerpt I wrote a previous post about how to develop an MCP Server using the Kotlin MCP SDK. That was primarily based on local deployment (using stdio protocol)...a setup that Claude Desktop for example could only support at the time. With the announcement that Claude now supports access to remote MCP Servers (from desktop and mobile) I thought I'd take a look at deploying the MCP Server in the ClimateTraceKMP sample to Google Cloud Run. ## What web frameworks does Firebase App Hosting support? DevFeed: [What web frameworks does Firebase App Hosting support?]() Original publisher: [Read original article]() Author: Jeff Huleatt Published: 2025-06-05T00:00:00Z Content type: article Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [hosting](), [Framework](), [Angular](), [Next.js](), [Node.js](), [Nuxt.js](), [Cloud Run](), [Astro](), [Serverless](), [Web app](), [Server-side rendering]() Tags: [angular](), [app-hosting](), [astro](), [cloud-run](), [firebase](), [frameworks](), [hosting](), [javascript](), [next-js](), [node-js](), [nuxt](), [react](), [serverless](), [ssr](), [svelte](), [vite](), [web](), [web-app]() ### AI overview Firebase App Hosting provides built-in build and deployment support for Angular and Next.js, while community adapters enable additional JavaScript frameworks such as Nuxt, TanStack Start, SolidStart, and Analog. Astro support is described as a proof of concept, and unrecognized frameworks can use the Node.js Cloud buildpack. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Deploy Angular & Next.js apps with App Hosting, now GA! DevFeed: [Deploy Angular & Next.js apps with App Hosting, now GA!]() Original publisher: [Read original article]() Author: Julia Reid; Jeff Huleatt Published: 2025-04-09T06:01:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [hosting](), [Angular](), [Next.js](), [CI/CD](), [Cloud Run](), [GitHub](), [Google Cloud Platform (GCP)]() Tags: [angular](), [api-keys](), [app-hosting](), [apps](), [ci-cd](), [cloud-next](), [cloud-run](), [domain](), [firebase](), [github](), [google-cloud-platform](), [hosting](), [javascript](), [launch](), [next-js](), [security](), [ssr](), [web]() ### AI overview Firebase App Hosting has reached General Availability. The service provides managed hosting for Angular and Next.js web apps, including automated CI/CD, Cloud Run deployment for server-rendered content, global CDN caching, domain migration, and API-key secret management. ### Source excerpt Ready to host your production server-rendered web apps on Google Cloud. ## Deploying Temporal Workers to Google Cloud Run DevFeed: [Deploying Temporal Workers to Google Cloud Run]() Original publisher: [Read original article]() Author: Brandon Chavis Published: 2025-01-06T04:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Cloud Run](), [autoscaling](), [Google Cloud Platform (GCP)](), [container](), [Deployment](), [event driven](), [Kubernetes]() Tags: [autoscaling](), [cloud-run](), [container](), [deployment](), [event-driven](), [how-to](), [kubernetes](), [sidecar](), [temporal-concepts]() ### AI overview This tutorial explains how to deploy Temporal Workers on Google Cloud Run using Worker Pools, a sidecar container for metrics, and Cloud Run External Metrics Autoscaling (CREMA) based on Temporal Task Queue backlog. ### Source excerpt Learn how to deploy Temporal Workers on Google Cloud Run with a sidecar container for metrics collection, scaling, and efficient task processing. ## Migrating Chainguard's Serving Infrastructure to Cloud Run DevFeed: [Migrating Chainguard's Serving Infrastructure to Cloud Run]() Original publisher: [Read original article]() Published: 2024-12-10T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Cloud Run](), [Kubernetes](), [chainguard](), [autoscaling]() Tags: [autoscaling](), [chainguard](), [cloud](), [cloud-run](), [github-actions](), [gke](), [go](), [istio](), [kubernetes](), [security](), [serving-architecture]() ### AI overview Chainguard migrated its serving platform from two regional GKE clusters running on Kubernetes to Cloud Run. The article previews the previous architecture and the operational pressures that motivated the change, including infrastructure complexity, unused development clusters, rising costs, slow node autoscaling, traffic spikes, and user-visible errors. ### Source excerpt Chainguard has migrated its serving platform from Kubernetes to Cloud Run. Take a peek at how we did it, and how it makes Chainguard a more secure place. ## Zero CVE metrics on Cloud Run DevFeed: [Zero CVE metrics on Cloud Run]() Original publisher: [Read original article]() Published: 2024-09-03T00:00:00Z Content type: tutorial Language: en Sources: [Chainguard: Unchained]() Topics: [Cloud Run](), [OpenTelemetry](), [chainguard images](), [Dockerfile](), [Terraform](), [Monitoring]() Tags: [chainguard-images](), [cloud-run](), [cve](), [monitoring](), [opentelemetry](), [prometheus](), [terraform]() ### AI overview This tutorial explains how Chainguard uses an OpenTelemetry sidecar on Cloud Run to expose metrics and send them to managed Prometheus and Stackdriver. It describes using a curated sidecar image, configuring it through an environment variable, and deploying the setup with Terraform to reduce CVEs. ### Source excerpt Achieve Zero CVEs on Cloud Run with OpenTelemetry sidecar. Learn how to leverage Chainguard Images for secure and efficient monitoring. ## Introducing Firebase App Hosting DevFeed: [Introducing Firebase App Hosting]() Original publisher: [Read original article]() Author: Julia Reid; Jeff Huleatt Published: 2024-05-14T02:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [hosting](), [Serverless](), [Angular](), [Next.js](), [Cloud Run](), [GitHub](), [Back end](), [Front end]() Tags: [ai](), [angular](), [app-hosting](), [cloud-run](), [firebase](), [github](), [launch](), [next-js](), [performance](), [serverless](), [ssr](), [web](), [web-hosting]() ### AI overview Firebase introduces the public preview of Firebase App Hosting, a secure serverless hosting product for server-rendered web apps. It manages builds, deployment, CDN delivery, and server-side rendering, with deployment support for Angular and Next.js through GitHub. ### Source excerpt The next generation of serverless web hosting with Google ## Blog: Integrate Runtime Security into Your Environment with Falcosidekick DevFeed: [Blog: Integrate Runtime Security into Your Environment with Falcosidekick]() Original publisher: [Read original article]() Published: 2023-10-24T00:00:00Z Content type: article Language: en Sources: [Falco - Falco](), [Falco - The Falco blog]() Topics: [Falco](), [Security](), [threat detection](), [Kubernetes](), [Cloud Native Ecosystem](), [Open Source](), [Tooling](), [AWS Lambda](), [Cloud Functions](), [Cloud Run](), [Kafka](), [RabbitMQ]() Tags: [aws-lambda](), [cloud-functions](), [cloud-native](), [cloud-run](), [falco](), [falcosidekick](), [integrations](), [kafka](), [kubernetes](), [notifications](), [rabbitmq](), [runtime-security](), [security](), [tool]() ### AI overview This article explains how Falcosidekick extends Falco's limited default output options by forwarding runtime-security events to services such as Slack, PagerDuty, email, AWS Lambda, cloud functions, and message queues. It describes configurable notifications and automated responses for suspicious activity in cloud, container, and Kubernetes environments. ### Source excerpt If you're looking to integrate runtime security into your existing environment, Falco is an obvious choice. Falco is a Cloud Native Computing Foundation backed open source project that provides real-time threat detection for cloud, container, and Kubernetes workloads. With over 80 million downloads Falco has been adopted by some of the largest companies in the world. However, what many Falco users discover early on is that Falco's default event output is rather limited. Out of the box, Falco can only send output to five different endpoints: syslog, stdout, stderr, and gRPC or HTTPS endpoints. While these outputs might be enough to get you started, most practitioners want to integrate Falco with the tooling they already use. This is where Falcosidekick comes in. Falcosidekick is a companion (i.e. a side-kick ;)) project for Falco that allows Falco events to be forwarded to 60 different services (with more being added all the time) allowing practitioners to monitor and react to Falco events with the tools they are already using. For example, if you'd like to receive immediate notifications of suspicious activity you can forward Falco events to chat programs such as Slack or Telegram, alerting platforms like PagerDuty or AlertManager, or, of course, email. In order to minimize noise, you can expressly set the level on which to notify, for example, warning-level events might be delivered via email, while critical or higher-level events are sent via chat or directed to your alerting platform. If you want to programmatically address certain events, Falcosidekick integrates with a bunch of different services including functions as a service platforms like AWS Lambda, GCP Cloud Run and Cloud Functions, or Knative. Alerts can also be sent to message queues like Amazon SNS, Apache Kafka, or RabbitMQ. These integrations offer almost endless possibilities for building out response systems for events. For instance, let's say you're running Falco on your Kubernetes cluster, and F ## Publishing Kotlin Multiplatform Swift Packages Using Google Cloud Storage and Cloud Run DevFeed: [Publishing Kotlin Multiplatform Swift Packages Using Google Cloud Storage and Cloud Run]() Original publisher: [Read original article]() Author: Nate Ebel Published: 2023-10-18T05:28:28Z Content type: tutorial Language: en Sources: [Engineering at Premise - Medium]() Topics: [Kotlin Multiplatform](), [Cloud Run](), [Gradle](), [iOS](), [kotlin-multiplatform-libraries](), [Swift](), [Cloud]() Tags: [cloud](), [cloud-run](), [github](), [google-cloud-platform](), [google-cloud-run](), [gradle](), [ios](), [kotlin](), [kotlin-multiplatform](), [kotlin-multiplatform-libraries](), [mobile-app-development](), [multiplatform](), [swift]() ### AI overview This tutorial describes Premise's approach to publishing and consuming Kotlin Multiplatform Swift Packages. It uses a custom Gradle plugin to publish XCFrameworks to Google Cloud Storage and a Cloud Run service to download the Swift Package binaries requested by Xcode, reducing the need to store large binaries in GitHub. ### Source excerpt By Nate Ebel, Android developer In this post, we'll detail our solution for publishing, and consuming, Kotlin Multiplatform Swift Packages. Our solution leverages a custom Gradle plugin publishing XCFrameworks to Google Cloud Storage and a Google Cloud Run service to download Swift Package binaries requested by XCode. With this solution in place, we've been able to more efficiently serve multiple Kotlin Multiplatform libraries to our iOS application. This is a part of an ongoing series on our usage of Kotlin Multiplatform at Premise: Part 1: Kotlin Multiplatform at Premise Part 2: Kotlin Multiplatform Project Structure for Integrating with Brownfield Applications Part 3: Building a CI Pipeline for Kotlin Multiplatform Mobile Using GitHub Actions Part 4: Publishing Kotlin Multiplatform Swift Packages Using Google Cloud Storage and Cloud Run -- This Post Part 5: Generating BuildConfig Files for a Kotlin Multiplatform Library -- Coming Soon Part 6: Optimizing Local Build Times for Kotlin Multiplatform Mobile Projects -- Coming Soon Premise and Kotlin Multiplatform Swift Packages We've been using Kotlin Multiplatform in production since early 2021 in the form of our mobile-shared project. During that time, we've consumed our shared code as a Swift Package within our iOS application. The integration of that Swift Package has gone through several iterations. v1: Use the multiplatform-swiftpackage plugin to build the Swift Package and store the XCFramework binary in GitHub v2: Use our own custom Gradle plugin to build the Swift Package and store the XCFramework binary in GitHub These two solutions were very similar. Build the XCFramework. Generate the Package.swiftfile. Check both into git with the desired version tag. These approaches worked fine for a while, but eventually we started to pay the price for our simple initial solution. An XCFramework binary can be pretty large. Ours were in the ballpark of 100MB. So checking 2-3 of these into each commit (1 for each iOS archit ## Cloud Functions for Firebase 2nd gen goes GA and adds support for Python DevFeed: [Cloud Functions for Firebase 2nd gen goes GA and adds support for Python]() Original publisher: [Read original article]() Author: Chris Gill; Jeff Huleatt Published: 2023-07-31T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Cloud Functions](), [Firebase](), [Python](), [Cloud Run]() Tags: [announce](), [cloud-functions](), [cloud-run](), [concurrency](), [firebase](), [google](), [launch](), [preview](), [python](), [web]() ### AI overview Cloud Functions for Firebase 2nd gen is generally available for production apps. The release improves efficiency through concurrency, adds new EventArc-powered triggers, and introduces public-preview support for writing functions in Python. ### Source excerpt Cloud Functions for Firebase 2nd gen, now Generally Available, features more efficient functions, new triggers, and support for Python. ## Tutorial: Low Usage Alerting On Slack for Google Cloud Platform (GCP) DevFeed: [Tutorial: Low Usage Alerting On Slack for Google Cloud Platform (GCP)]() Original publisher: [Read original article]() Author: Mauricio Martinez Published: 2023-04-24T15:57:09Z Content type: tutorial Language: en Sources: [Engineering at Premise - Medium]() Topics: [Google Cloud Platform (GCP)](), [Cloud Run](), [Python](), [Monitoring](), [Slack]() Tags: [cloud-computing](), [cloud-run](), [cloudrun](), [cost-savings](), [deploy](), [gcp](), [github](), [google-cloud](), [metrics](), [monitoring](), [python](), [slack]() ### AI overview This tutorial explains how to deploy a Python-based Cloud Function that monitors weekly CPU and memory usage for Google Cloud services, including Cloud Run, and sends Slack notifications to service owners when a service may be safely downscaled to reduce costs. It covers creating MQL queries, retrieving monitoring metrics, checking minimum thresholds, and deploying the scheduled function. ### Source excerpt Google Cloud Platform (GCP) is a powerful cloud computing platform that allows businesses to run their applications and workloads with ease. However, as the number of services and applications increases, it becomes challenging to keep track of the usage of each service and ensure they are cost optimized. To address this issue, we can deploy a Python-based Cloud Function that will monitor the low usage of GCP services and notify service owners weekly via Slack that their service can be safely downscaled to minimize costs. This tutorial will walk you through setting up this entire flow in a few quick and easy steps, while allowing you to easily customize the service thresholds, Slack message ui, and the cron schedule. The code for this tutorial can be found on our gcp-tutorials GitHub repository. ArchitectureGet CPU/Memory Usage Metrics We can use the Metrics Explorer UI to build a MQL query to retrieve the desired data from the monitoring metrics API. In this tutorial we will monitor Cloud Run services, but you can monitor other resources by using a different MQL query. In our example we selected the CPU and Memory Cloud Run metrics: We group by service name and location and select the alignment for the 99th percentage to get the max usage over the 1 week duration. Then click on CODE EDITOR to generate a sample MQL query: fetch cloud_run_revision | metric 'run.googleapis.com/container/cpu/utilizations' | group_by 1w, [value_utilizations_percentile: percentile(value.utilizations, 99)] | every 1w | group_by [resource.service_name, resource.location], [value_utilizations_percentile_max: max(value_utilizations_percentile)]fetch cloud_run_revision | metric 'run.googleapis.com/container/memory/utilizations' | group_by 1w, [value_utilizations_percentile: percentile(value.utilizations, 99)] | every 1w | group_by [resource.service_name, resource.location], [value_utilizations_percentile_max: max(value_utilizations_percentile)]Code Once we have our MQL queries we will use the ## Hosting a fully Serverless Web-Based Postgres Admin Client on GCP using Pgweb, Cloud Run, & IAP DevFeed: [Hosting a fully Serverless Web-Based Postgres Admin Client on GCP using Pgweb, Cloud Run, & IAP]() Original publisher: [Read original article]() Author: Austen Novis Published: 2023-03-06T13:17:20Z Content type: tutorial Language: en Sources: [Engineering at Premise - Medium]() Topics: [Cloud Run](), [PostgreSQL](), [Google Cloud Platform (GCP)](), [Serverless](), [Authentication](), [Command-line interface](), [container]() Tags: [authentication](), [cli](), [cloud-computing](), [cloud-run](), [cloud-sql](), [cloudrun](), [container](), [deploy](), [gcp](), [google-cloud-platform](), [identity-aware-proxy](), [pgweb](), [postgresql](), [serverless](), [vpc]() ### AI overview A tutorial explains how to deploy Pgweb, a Go-based PostgreSQL administration client, as a serverless web application on Google Cloud. It uses Cloud Run, CloudSQL, a CloudSQL connector, Secret Manager, Artifact Registry, and optionally an HTTP load balancer with Identity-Aware Proxy for authentication. ### Source excerpt By Austen Novis, Staff Software Engineer Pgweb example screenshot There are a number of quality Postgres open source administration tools such as pgAdmin or DBeaver, but these tools require a persistent server to run. This means that each user needs to install the tool locally and setup their connections, or you need to host the tool in a cloud server, which can get expensive. A much cheaper alternative is to use Pgweb, a lightweight web-based database explorer for PostgreSQL written in Go, and deploy to Cloud Run. This allows you to utilize a web-based tool, for minimal cost, that can scale for any number of users. We will leverage several Google Cloud Platform services for the complete setup starting with a http load balancer to allow for a custom DNS name as well as enable Identity Aware Proxy (IAP), which is what we will be using for authentication. We will host the service in Cloud Run and connect directly to CloudSQL using a CloudSQL connector. You can bypass the load balancer and IAP if you would like to use username and passwords for authentication. Cloud componentsCloud Setup The first step is setting up your CloudSQL instance. This can be done via terraform or in the GCP console. The main requirement is that you enable a public ip address, which will allow us to use the CloudSQL Auth proxy to connect our Cloud Run instance to our CloudSQL instance. If this is not possible you can still connect to your CloudSQL instance to Cloud Run through a VPC Connector instead. Next we will need to save our database connection credentials in GCP's Secret Manger by creating a new secret called PGWEB_DATABASE_URL in the format of postgres:///DB_NAME?host=/cloudsql/PROJECT:REGION:INSTANCE_NAME&user=DB_USER&password=DB_PASSWORD . Now that we have our CloudSQL instance and connection secret we will deploy our Cloud Run instance using the gcloud cli. First we will need to push the desired Pgweb container to GCP's Artifact Registry, which we can do using the following commands ## Tutorial: Connecting Cloudrun and Cloudfunctions to Redis and other Private Services using Goblet DevFeed: [Tutorial: Connecting Cloudrun and Cloudfunctions to Redis and other Private Services using Goblet]() Original publisher: [Read original article]() Author: Qua Jones Published: 2023-02-13T16:38:05Z Content type: tutorial Language: en Sources: [Engineering at Premise - Medium]() Topics: [Tutorial](), [Cloud Run](), [Redis](), [VPC](), [Google Cloud Platform (GCP)](), [Serverless](), [Command-line interface](), [Deployment](), [Python](), [Caching]() Tags: [caching](), [cli](), [cloud-computing](), [cloud-run](), [deployment](), [gcp](), [google-cloud-run](), [python](), [redis](), [serverless](), [tutorial](), [vpc]() ### AI overview A tutorial showing how to deploy a Cloud Run service that privately connects to a Redis instance through a VPC Connector. It explains how Goblet can provision the connector and inject the required configuration during deployment, using Google Cloud services and Python prerequisites. ### Source excerpt By Qua Jones, Senior Software Engineer Photo by israel palacio on Unsplash Google Cloud Platform's (GCP) Private Services Access allows you to connect to GCP services without an external IP being assigned. Redis is an in-memory DB that is used for caching and is a service that supports private access. In order to take advantage of this feature, a private connection is necessary. For serverless environments, this can be done using a VPC Connector. VPC Connectors allow serverless environments to connect to VPC Networks by handling traffic between the network and environment, restricting requests to use only internal IP addresses. This can be beneficial for applications that do not require exposure to the public internet or if latency for requests across services needs to be improved. Currently you can provision a connector using the console, cli, or terraform but you will still need to configure your serverless environment to utilize this connector. Instead of manually going through the segmented process of creating a connector and updating your environment, you can leverage Goblet to provision your connector and inject needed configuration values into your deployment. This tutorial will walk you through the steps of deploying a Cloud Run service that privately connects to a Redis Instance using a VPC Connector. Prerequisites: GCP Account Python Environment (>= 3.7) Gcloud CLI You will also need to have goblet and redis installed which can be done by running pip install goblet-gcp && pip install redis Getting Started: Ensure you have credentials configured by running gcloud auth login and sign in to the desired project. Make sure to have the correct services enabled in your GCP project which include: Cloud Run Cloud Build Artifact Registry Serverless VPC Access Memorystore for Redis To get started, clone the code from the gcp-tutorials repository and navigate to the directory. This can be done with the Github CLI by running: gh repo clone premisedata/gcp-tutorials/014- [Next page]()