# Deployment > 16 posts about Deployment, summarised, each linking to the original. ## Articles ### [How Ubuntu Is Using Rust to Rebuild Core System Tools](https://yomu.fyi/post/how-ubuntu-is-using-rust-to-rebuild-core-system-tools.md) - Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md) - Author: Irina Mihajlovic - Published: Aug 26, 2026 Canonical is selectively adopting Rust to rewrite core system utilities across Ubuntu releases to improve memory safety, security, and long-term resilience. Starting in Ubuntu 26.04 LTS, uutils coreutils shipped as the default implementation targeting complete GNU compatibility, alongside sudo-rs, which introduced intentional behavior changes like asterisk display on password entry. Canonical is also funding ntpd-rs to consolidate NTP, NTS, and PTP into a single utility, while collaborating on UPKI to bring certificate revocation to system utilities. To manage Rust packaging at distribution scale, Canonical vendors dependencies per package rather than creating individual Debian packages for crates and embeds software bills of materials into binaries using cargo auditable. Future plans include exploring Rust implementations of compression libraries like bzip2-rs, zlib-rs, and zstd-rs. ### [Spring Boot Configuration Management Best Practices](https://yomu.fyi/post/spring-boot-configuration-management-best-practices.md) - Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md) - Author: Siva Katamreddy - Published: Aug 21, 2026 Spring Boot externalizes application configuration across files, environment variables, system properties, and command-line arguments to allow a single artifact to run across multiple environments. A resilient setup classifies values into application defaults, deployment configurations, and externalized secrets stored in dedicated vaults. Binding properties to type-safe Java records via @ConfigurationProperties ensures immutability, supports relaxed property mapping, and eliminates the discovery issues common to scattered @Value annotations. Enforcing validation with @Validated and Jakarta Bean Validation constraints triggers fail-fast startup behavior when required settings are absent or malformed. Property precedence determines the effective runtime values, which must be adapted appropriately whether deploying a monolith, containerized workloads in Kubernetes, or a microservices cluster. ### [Secure all your internal vibe-coded applications — in one click](https://yomu.fyi/post/secure-all-your-internal-vibe-coded-applications-in-one-click.md) - Company: [Cloudflare](https://yomu.fyi/company/cloudflare.md) - Author: Chythra Malapati - Published: Aug 14, 2026 AI-assisted development enables employees to build applications rapidly, but unmanaged deployments can accidentally expose internal company data to the public Internet. Cloudflare introduced direct Cloudflare Access integration for Cloudflare Workers, allowing organizations to enforce authentication policies directly at the account or individual Worker level rather than per hostname. When enabled, incoming requests are authenticated before reaching application code across custom domains, routes, workers.dev subdomains, and preview environments. Developers can access authenticated identity data such as emails and groups directly via the Worker context object without manually validating JSON Web Tokens. This capability was implemented on Cloudflare's Rust-based FL2 proxy, which separated Worker routing from Worker execution so routing runs safely prior to Access policy enforcement. ### [Shipyard: How We Built Slack’s Next-Generation EC2 Platform](https://yomu.fyi/post/shipyard-how-we-built-slack-s-next-generation-ec2-platform.md) - Company: [Slack](https://yomu.fyi/company/slack.md) - Author: Archie Gunasekara - Published: Jul 14, 2026 Slack developed Shipyard to modernize its EC2 infrastructure by replacing continuously updated, mutable instances with immutable deployable artifacts. The platform adopts a layered image architecture starting with a standardized base image called slack-zero, which is built using AWS Image Builder and consumed by downstream service-specific machine images. Rather than running periodic Chef jobs in the background, configuration management is restricted to image baking and initial instance provisioning phases. Fleet management is handled through the Peekaboo inventory system, metric-driven progressive rollouts via Gondola, and automated instance rotation managed by a lifecycle component known as the Reaper. Developers validate cookbook updates prior to merging using Ship Quick, which provisions real infrastructure across dedicated worker fleets to verify provisioning workflows. ### [Enhancing Flink deployment with shadow testing](https://yomu.fyi/post/enhancing-flink-deployment-with-shadow-testing.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Tee Long Lang - Published: May 7, 2026 Production deployments of Apache Flink applications at Grab previously risked up to ten minutes of downtime when new versions failed under live traffic or could not resume from existing checkpoints. To eliminate these deployment failures, Grab's Coban data streaming team introduced an optional shadow testing stage directly into their production Kubernetes deployment pipeline. The shadow pipeline runs new application releases in parallel with the active application for a default observation period of one hour before prompting promotion to main. Dynamic runtime configuration based on an injected environment variable redirects source consumer groups and isolates output to dedicated shadow sinks, metrics, and logs. ### [How GitHub uses eBPF to improve deployment safety](https://yomu.fyi/post/how-github-uses-ebpf-to-improve-deployment-safety.md) - Company: [Github](https://yomu.fyi/company/github.md) - Author: Lawrence Gripper - Published: Apr 16, 2026 Deployment scripts can introduce dangerous circular dependencies when they rely on services or assets from platforms that are currently experiencing outages. Blocking network access at the host level is impractical because stateful nodes continue serving live traffic during rolling deployments. To solve this, GitHub isolates deploy scripts into dedicated Linux cGroups and attaches custom eBPF programs via the cilium/ebpf Go library. The system uses socket-address hooks to redirect DNS queries to a userspace proxy that checks a domain blocklist, while egress packet hooks map DNS transaction IDs to process IDs. This approach successfully prevents deploy-time circular dependencies, provides full command-line audit logs for blocked requests, and speeds up incident recovery. ### [From deployment slop to production reality: How BriX bridges the gap with enterprise-grade AI infrastructure](https://yomu.fyi/post/from-deployment-slop-to-production-reality-how-brix-bridges-the-gap-wi.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Sneh Agrawal - Published: Jan 16, 2026 Internal AI prototypes frequently fail enterprise rollouts due to diverging versions, security oversights, hardcoded credentials, and infrastructure bottlenecks. BriX addresses this deployment gap by turning AI rollout into a configuration-driven platform rather than an engineering rewrite. Built on a synchronous streaming architecture, it routes user prompts through a React frontend using Server-Sent Events, a FastAPI gateway, and LangGraph orchestration. The platform integrates model switching, centralized prompt locks, and standardized Model Context Protocols for governed enterprise data access. ### [Evolution of Catwalk: Model serving platform at Grab](https://yomu.fyi/post/evolution-of-catwalk-model-serving-platform-at-grab.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Vishal Sharma - Published: Oct 1, 2024 Grab developed and scaled Catwalk, an internal machine learning model serving platform, to address operational bottlenecks, low resource utilization, and deployment friction between data scientists and backend engineers. The platform transitioned from an admin-managed TensorFlow Serving setup into a low-code self-service system supporting PyTorch and ONNX, before replacing complex Helm charts with Kubernetes Custom Resource Definitions for declarative, blue-green deployment orchestration. To support complex business workflows and multi-model applications, Grab subsequently introduced Catwalk Orchestrator with bundled deployments that allow individual services to scale independently. Across two years, the orchestrator architecture expanded to 200 deployed applications serving approximately 1,400 production machine learning models. ### [No version left behind: Our epic journey of GitLab upgrades](https://yomu.fyi/post/no-version-left-behind-our-epic-journey-of-gitlab-upgrades.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Saurabh Vajpayee - Published: May 3, 2024 Grab's self-hosted GitLab instance fell approximately 14 months behind official releases, serving thousands of engineers and monorepos reaching up to 39TB in total footprint. To address security updates and system stability, the team established a structured upgrade routine using Terraform, Packer, and Ansible across GitLab's 5,000-user reference architecture. They implemented staged deployments, upgraded stateful Gitaly nodes via in-place rotation, and resolved primary node clustering bottlenecks under Praefect. By utilizing official GitLab Upgrade Paths and parallelizing component deployments, the team caught up on 24 months of releases in 11 months and reduced release lag from 396 days down to 35 days. ### [Safer deployment of streaming applications](https://yomu.fyi/post/safer-deployment-of-streaming-applications.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Shi Kai Ng - Published: May 2, 2023 Stateful stream processing frameworks like Apache Flink present unique deployment challenges because conventional canary and blue-green strategies can cause data inaccuracies or state divergence. Grab's real-time data platform team encountered risks of state loss, manual rollback overhead, and absent health checks in their Kubernetes and Spinnaker deployment pipeline. To resolve these operational issues, the team redesigned the deployment workflow around automated Flink savepointing and programmatic health monitoring. The new pipeline halts existing applications after capturing state snapshots and Kafka offsets, monitors target deployments via API health probes, and executes automated rollbacks using versioned ConfigMaps and replica metadata annotations. This automated process ensures state consistency during upgrades and eliminates manual intervention during deployment failures. ### [Securing GitOps pipelines](https://yomu.fyi/post/securing-gitops-pipelines.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Thang Le - Published: Mar 1, 2023 Grab's real-time data platform team transitioned from an Atlantis-driven Terraform workflow to an in-house GitOps platform called Khone to manage streaming infrastructure resources like Kafka topics and Flink pipelines. The earlier setup suffered from coarse-grained access controls, required manual merge request comments, and lacked flexible validation capabilities within native configuration files. Khone derives environment parameters directly from standardized directory paths and uses Python with the python-hcl2 library to inspect and validate resource definitions before executing Terraform stages in parallel. To prevent configuration tampering in merge requests, CI/CD pipeline definitions and execution scripts are isolated in a separate administrative repository and fetched during job runs using shallow Git clones. ### [Our Journey to Continuous Delivery at Grab (Part 2)](https://yomu.fyi/post/our-journey-to-continuous-delivery-at-grab-part-2.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Sylvain Bougerel - Published: May 10, 2021 Conveyor, an in-house continuous delivery system at Grab, introduces hermetic deployments by tracking application code alongside static and dynamic configuration parameters. This hermeticity guarantees that production releases use combinations of versioned artifacts previously verified in staging, preventing rollback incompatibilities. Conveyor replaces single multi-environment pipelines with decoupled pipelines, while automating cluster locking, deployment slot scheduling, release note generation, and canary monitoring with automated rollbacks. These automations reduced production deployment failure rates from 1.5% to an average of 0.3% over a three-month period. Grab also doubled the volume of production changes between 2018 and 2020 while saving more than 5,000 man-days of engineering effort in 2020. ### [One Small Step Closer to Containerising Service Binaries](https://yomu.fyi/post/one-small-step-closer-to-containerising-service-binaries.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Stan Halka - Published: Feb 23, 2021 Engineering teams at Grab initiated a transition to containerized microservices to standardize environments, enhance security, and decouple services from internal runtime tooling. During this migration, developers noticed that statically-linked Go service binaries were reaching bloated sizes over 100 MB. By analyzing the binaries using the open-source tool go-binsize-viz alongside the Go nm toolchain, the team visualized compiled symbols as interactive treemaps. This analysis revealed that 11 MB of unused message format symbols were being pulled in because a generic interface shared a directory with auto-generated streaming code. Restructuring the packages to isolate interfaces from generated code successfully decreased the binary size down to 78 MB. ### [Our Journey to Continuous Delivery at Grab (Part 1)](https://yomu.fyi/post/our-journey-to-continuous-delivery-at-grab-part-1.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Sylvain Bougerel - Published: Sep 23, 2020 Around the end of 2018, Grab's backend architecture consisted of roughly 270 services managed through fragmented, manual deployment workflows. Engineers copied release parameters between build logs, wiki pages, Slack bots, and multiple Jenkins jobs, leading to high operational friction and an average of 10 business days between production updates for a service. To streamline delivery, Grab built Conveyor, an internal automation platform built on top of open-source Spinnaker. Conveyor introduced a custom user interface focused on pipeline visibility and a pipeline-as-code DSL called Artificer using Jsonnet files in the monorepository. The platform automatically registers build artifacts with commit metadata to eliminate manual parameter entry and automatically provisions integration, staging, and production pipelines. ### [Catwalk: Serving Machine Learning Models at Scale](https://yomu.fyi/post/catwalk-serving-machine-learning-models-at-scale.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Nutdanai Phansooksai - Published: Jul 2, 2019 As machine learning adoption expanded at Grab, individual teams created fragmented model serving solutions that duplicated engineering effort and required data scientists to handle underlying infrastructure. To resolve these inefficiencies, Grab developed Catwalk, a self-service machine learning model serving platform. The system runs TensorFlow Serving containers across a managed Kubernetes cluster integrated with Grab's observability stack. Data scientists deploy or update models simply by saving files using the tf.saved\_model API to dedicated Amazon S3 buckets, while Kubernetes automates orchestration, ingress routing, and pod autoscaling. Catwalk abstracts server management away from data scientists, shortens deployment timelines, and provides high availability during model version rollouts. ### [Deep Dive into iOS Automation at Grab - Continuous Delivery](https://yomu.fyi/post/deep-dive-into-ios-automation-at-grab-continuous-delivery.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Sun Xiangxin - Published: Apr 23, 2017 Grab manages continuous delivery for its iOS applications using four build configurations per target: Adhoc QA, Hot Dogfood, Dogfood, and Testflight. The engineering team moved away from Fastlane in favor of custom bash scripts under 100 lines that execute clean builds and archives via xcodebuild. To eliminate manual logins for build execution, Grab developed a server-side Swift application integrating SlackKit and a bot named Iris. Incoming Slack commands are parsed and scheduled onto a serial DispatchQueue to trigger the appropriate build scripts. Deployment and rollback of updates to Iris and its build scripts are handled through Capistrano.