Kubernetes - LINE | Techlist.io

lineJun 24, 20264 min readCurated summary

Flava DBaaS Deep Dive: From Architecture to Migration and Beyond

LY Corporation’s Flava DBaaS is designed to unify the former Verda and YNW cloud platforms on a Kubernetes-based architecture. Its operator pattern separates database business logic from IaaS management, while API servers, managers, and agents divide responsibilities within each DBMS service. The platform expands database support, improves scalability, security, and usability, and treats migration from legacy platforms as part of the DBaaS responsibility. ## Kubernetes Operator-Based Design - Flava DBaaS uses the Kubernetes operator pattern. - Users declare the desired database state through custom resources rather than issuing procedural commands. - Controllers continuously reconcile the actual state with the declared specification. - This approach: - Simplifies troubleshooting through resource status and controller logs. - Handles large database infrastructures efficiently through event-driven processing. - Reuses Kubernetes capabilities for CI/CD and access control. ## Infrastructure Operator Layer - DBaaS must manage IaaS resources such as: - Virtual machines - Storage - Domains and networking - Flava isolates this infrastructure logic in a separate infrastructure operator. - IaaS resources are exposed as Kubernetes custom resources, allowing DBaaS to create infrastructure declaratively without directly calling IaaS APIs. - The resulting layers are: - **DBaaS:** Database-specific business logic - **Infrastructure operator:** Abstraction of IaaS as Kubernetes resources - **IaaS:** Compute, network, and storage services - This separation allows multiple DBMS products to use infrastructure consistently while their developers focus on database operations. ## Custom Resources and DBaaS Components - Each database cluster is represented by a Kubernetes custom resource containing settings such as: - DBMS version - VM size - Storage type and capacity - Replication configuration - These resources are stored in Kubernetes etcd and managed through the Kubernetes API. - Each DBMS implementation consists of three components: - **API server:** Provides REST APIs for creating, modifying, and deleting database resources. Flava UI and IaC tools use these APIs. - **Manager:** Watches resource changes and reconciles the database cluster toward the declared state. - **Agent:** Runs on database VMs and executes local operating-system and database commands. - For example, creating a MySQL cluster causes the API server to create a MySQL custom resource, the manager to provision the required VMs through the infrastructure operator, and the agent to configure replication and database processes inside those VMs. ## Improvements in Flava DBaaS - Flava preserves core DBaaS capabilities such as provisioning, high availability, backup and recovery, scalability, and monitoring. - It combines the DBMS offerings of Verda and YNW, expanding the range of supported database systems. ### Flexible Storage and Scaling - Storage can be configured in 100 GiB increments. - Block-storage-based databases can use up to 5 TiB of storage. - Unlike the legacy platforms, storage is no longer tightly limited by a VM’s local disk capacity. - Custom instance types and separate block storage reduce the need to consider alternatives such as sharding for larger databases. - The 5 TiB limit was selected to cover most analyzed use cases while reducing infrastructure fragmentation. ### Consistent User Experience - All Flava DBaaS products share a common architecture and UI. - Skills learned while changing MySQL server specifications or configuring Cassandra alerts can be applied to other DBMS products. - Users do not need to learn separate operational workflows for each database system. ### Security and Convenience - TDE and TLS are provided as platform-level security features. - Additional features include: - **Custom DB Role:** Reusable database users with configurable permissions. - **Database Parameter Group:** Reusable groups of database configuration parameters. - **Restore backup:** Creation of a new cluster from a selected backup for disaster recovery or realistic performance testing. - Features not yet available for every DBaaS product are planned for broader support. - These improvements reportedly resulted in high internal user-satisfaction scores. ## Migration Responsibilities - A new DBaaS platform is expected to provide migration paths from existing platforms, not merely offer new database clusters. - For migrations between the same DBMS type, the article identifies three general approaches. ### Dump and Restore - Data is backed up from the source database and restored into the destination. - It is the simplest method. - To guarantee consistency, the application generally must be stopped during the migration. ### Replication-Based Migration - The source database is continuously replicated to the destination. - Once replication is caught up, the destination is promoted through failover. - The source database can then be removed. - Data consistency depends on the DBMS’s replication mechanism. - A short application interruption may still occur during primary-node failover. The overall recommendation is to use Flava’s layered, declarative architecture to standardize database operations while continuing to provide practical migration mechanisms from Verda and YNW.

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Kubernetes Infrastructure As Code Mysql Database Migration+4

lineJun 22, 20264 min readCurated summary

From Automation to AI with Infrastructure as Code (IaC): Adopting OpenTofu and ChatOps

LY Corporation’s LINE Plus SRE team migrated Verda cloud infrastructure and IMON monitoring resources from scattered, manual management into an Infrastructure as Code (IaC) and GitOps workflow. Using OpenTofu and Terragrunt, they now manage roughly 1,500 resources across seven services through pull requests, CI/CD, and daily drift detection. The migration required careful automation for importing existing resources, normalizing state, and handling provider limitations and resource dependencies. ## Why IaC Was Needed - Teams previously managed infrastructure through different methods: - Verda’s web dashboard - Scripts - Wiki-based procedures - Personal documents and GitHub repositories - As the number of services and resources grew, this caused: - Inconsistent management practices - Difficult-to-track configuration changes - Greater risk of manual errors - Poor reproducibility and reviewability - The team adopted GitOps so that: - Desired infrastructure state is declared in Git. - All changes go through pull requests. - Infrastructure history is versioned and auditable. - CI/CD applies approved changes automatically. - Their goal was to manage infrastructure with the same engineering standards as application code: reviewable, version-controlled, and reproducible. ## Choosing OpenTofu and Terragrunt - OpenTofu was selected as an open-source Terraform fork. - It retains: - Terraform’s HCL syntax - Provider compatibility - Familiar module and configuration patterns - The team created reusable modules for: - Virtual machines - Load balancers - Monitoring alerts - Modules were versioned so updates could be adopted explicitly rather than affecting every environment immediately. - Terragrunt was added to reduce repetition in environment configuration. - Shared settings are defined once in a parent `root.hcl`. - Individual environments contain only their differing inputs. - OpenTofu reduces duplication in resource definitions, while Terragrunt reduces duplication in environment and backend configuration. ## Planning the Migration - The most difficult part of introducing IaC into an existing environment was importing resources that were already running. - Manual import was considered impractical for hundreds of VMs, load balancers, and DNS records because it would be slow and error-prone. - The migration was split into two phases: - **Phase one:** Automate imports, select one service for a pilot, and establish the complete OpenTofu/Terragrunt pipeline. - **Phase two:** Reuse the validated modules and import scripts to roll the approach out to the remaining services. ## Designing the Import Process - Import scripts were designed to: - Query existing resources - Decide which resources should be managed by IaC - Convert resource data into the desired code structure - Generate Terragrunt configuration - Connect resources to OpenTofu state - Run `plan` to verify that no unintended changes would occur - A key requirement was keeping three representations aligned: - Configuration code - OpenTofu state - Actual cloud resources - Normalization was added because equivalent values could be represented differently—for example, network or image IDs—causing OpenTofu to report misleading differences after import. ## Resource-Specific Import Strategies - Resources could not all be imported using the same procedure. - Different resource types have different identifiers, dependencies, and ownership models: - **VMs:** Imported individually, while distinguishing manually created instances from Kubernetes-managed instances. - **Load balancers:** Imported together with related listeners and pools. - **DNS:** Imported while preserving zone and record relationships. - **Kubernetes:** Structured around clusters and node pools. - **IMON:** Imported according to its hierarchy of teams, alert groups, alert rules, and monitors. - Each resource followed the same broad five-step process, but its implementation was adapted to the resource’s characteristics. ## Problems Discovered During Migration ### Kubernetes-Managed VMs - OpenStack contained both manually created VMs and VMs automatically created by Kubernetes. - Importing Kubernetes-managed VMs into IaC could cause conflicts between OpenTofu and Kubernetes. - The scripts excluded these VMs using naming patterns and metadata. ### IMON’s Hierarchical Structure - IMON alerts are organized as: `Team → Alert Group → Alert Rule → Monitor` - A flat import would lose these relationships. - The team mirrored the hierarchy in the directory structure so ownership and relationships were visible from the file layout. ### Provider and Regional Identifier Issues - The actual cloud platform allowed both hyphens and underscores in load balancer names, but the provider validation logic rejected underscores. - The team fixed this by modifying the provider’s validation logic and contributing the change upstream. - Resource UUIDs such as `flavor_id`, `image_id`, and `network_id` differed by region. - This produced unnecessary changes in `plan`. - The modules added regional mapping logic, allowing users to specify readable names while resolving them to region-specific IDs. The migration demonstrates that successful IaC adoption requires more than writing configuration files: existing infrastructure must be filtered, normalized, modeled according to its dependencies, and validated against real provider behavior. OpenTofu and Terragrunt provided the foundation for scalable GitOps management, while custom import automation and provider improvements made the transition safe for production resources.

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Kubernetes CI/CD Infrastructure As Code Gitops+3

lineMay 22, 20264 min readCurated summary

ODW #7: Reduce Token Consumption by 40% in Three Ways! Context Engineering with ADK

The post explains how LY Corporation’s Orchestration Development Workshop uses context engineering to reduce AI-agent costs and improve accuracy. As internal adoption of tools such as Claude Code, Cline, and ADK grows, excessive token usage, missed instructions, and declining performance in long conversations have become common. The recommended solution is to deliberately select and manage the context sent to an LLM, demonstrated through an ADK-based Jira weekly-report agent. ## Problems Caused by Expanding AI Use - Increased AI adoption has led to unexpectedly high token consumption. - Users often receive incomplete or incorrect results despite providing detailed prompts. - Long-running conversations can cause the model to produce irrelevant answers. - Major causes include: - Trial-and-error prompting - More complex and long-running agents - Expansion from single-agent to multi-agent systems - Tool integrations such as MCP, whose definitions also consume context - Limited awareness of context optimization techniques ## Context Rot and Context Engineering - **Context rot** occurs when long-running agents accumulate conversation history, intermediate results, and irrelevant information. - As the context grows: - The context window becomes pressured. - Relevant information becomes harder to identify. - Noise overwhelms important signals, reducing accuracy. - Context engineering is the deliberate design and management of all information provided during inference, including: - **Static context:** System prompts and tool definitions - **Dynamic context:** User messages, conversation history, and retrieved external data - **Long-term context:** Persistent session state and accumulated information - The core principles are: - Treat tokens as a limited resource and retain the smallest set of high-signal information. - Provide neither too little information, which forces guesswork, nor too much, which wastes tokens and reduces clarity. ## Why Use ADK Google’s open-source Agent Development Kit (ADK) is presented as a practical platform for applying context engineering. - Agents can be designed and shared using team knowledge rather than relying on individual CLI expertise. - ADK includes UI, API-server, evaluation, and multi-agent capabilities. - Its multi-agent architecture naturally supports separating and controlling context. ## ADK Context-Engineering Components The workshop introduces nine key components, including: - **Structured input and output:** JSON or schema-based formats reduce unnecessary text and make agent processing more reliable. - **AgentTool:** Embeds one agent inside another as a tool. The calling agent receives only the final result, preventing internal tools and intermediate context from accumulating. - **MCP Toolset filtering:** The `tool_filter` parameter exposes only required MCP tools, reducing tool-definition tokens and improving model decisions. - The remaining components can be combined with these techniques to control context throughout an agent workflow. ## Jira Weekly Report Example The workshop builds `jira_weekly_report`, an agent that analyzes team Jira tickets and generates a weekly Markdown report. ### Version 1: Single Agent Without Context Engineering - A single agent retrieves the ticket list, fetches each ticket, analyzes it, and builds the report. - All Jira tools are exposed through one MCP toolset. - As the number of tickets increases, detailed ticket contents accumulate in the agent’s context. - This leads to context rot, higher token usage, and declining reliability. ### Version 2: Context-Aware Multi-Agent Design - The workflow is split into: - A root agent that searches Jira tickets and aggregates the final report. - A sub-agent dedicated to analyzing one ticket at a time. - `input_schema` requires a structured `issue_key`. - `output_schema` requires a structured report containing ticket content and progress, including comments. - The sub-agent receives only the `jira_get_issue` MCP tool. - The root agent receives only the `jira_search` tool. - `AgentTool` hides the sub-agent’s internal context and returns only its final report. - The sub-agent is instructed to include facts only and avoid speculation. This design limits each agent’s responsibilities, removes unnecessary tool definitions, and prevents individual ticket details from polluting the root agent’s context. ## Practical Recommendation For production AI agents, treat context as a constrained resource. Use structured schemas, narrowly filtered tools, and specialized sub-agents to pass only the information needed for each step.

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Kubernetes Large Language Models AI Agents Model Context Protocol+3

lineApr 15, 20264 min readCurated summary

ODW #2: Developing Single/Multi-Agents with ADK and Integrating with Internal Systems

AI adoption can improve productivity, but relying on individual developers to create and refine local AI agents leads to knowledge silos, duplicated effort, and uneven results. LY Corporation’s Orchestration Development Workshop addresses this by teaching engineers to build shared single- and multi-agent systems with Google’s Agent Development Kit (ADK). The workshop combines theory with hands-on integration of agents and internal tools such as Jira and Confluence through MCP. ## Organizational Need for AI - Potential applications include pull request reviews, customer support, and internal document search. - Information is difficult to find because company knowledge is distributed across systems such as Jira and Confluence. - LY Corporation aims to double work productivity within three years through AI and continuous innovation. - As tools such as Cline and Claude Code spread, usage remains concentrated among individuals. - This creates: - Productivity gaps between employees - AI knowledge silos - Repeated prompt-development work across teams - Limited awareness of multi-agent approaches - Abandonment of AI when single agents cannot handle complex tasks ## Why a Hands-On Workshop The organizers concluded that organization-wide adoption required practical understanding of three areas: - The strengths and limitations of single-agent and multi-agent systems - A team-based model for building and sharing centralized agents - Integration between AI agents and internal systems through the Model Context Protocol (MCP) Rather than teaching only concepts, the workshop required participants to build working agents with ADK. ## Single-Agent and Multi-Agent Systems - **Single agents** - Use one LLM and are relatively inexpensive and simple to develop. - Work well for straightforward tasks. - Struggle with complex problems requiring multiple specialties. - **Multi-agent systems** - Divide work among multiple specialized LLM-based agents. - Can handle more complex workflows and optimize tasks more effectively. - Require more development effort and token usage. - Must be designed carefully to avoid usage limits and excessive costs. ## Introducing Google ADK - ADK is open-source software for defining agent behavior and building multi-agent systems. - It supports Python, Java, and Go. - Python functions can be exposed as tools that agents invoke. - Teams can build and host shared agents, reducing the need for every employee to independently optimize prompts. ## Building a Single Agent Participants practiced: - Running an ADK web UI and interacting with an agent in a browser - Modifying instructions to change agent behavior - Connecting a prepared Python function as an executable tool The exercises demonstrated that prompts can flexibly control responses and that ordinary Python code can be integrated into an agent with relatively little effort. ## Connecting Agents to Internal Systems with MCP - MCP is an open standard for connecting LLMs to external systems. - It enables agents to actively search sources such as previous inquiries, documentation, Jira, and Confluence. - Participants learned that merely exposing tools is insufficient; the agent also needs clear instructions to use them effectively. - Giving one agent too many tools can enlarge its context, slow responses, and reduce accuracy. - Splitting responsibilities across multiple agents can help isolate context and mitigate these problems. ## Building a Sequential Project Tracker The main exercise created a project-tracking system that analyzes Jira projects and produces translated progress reports. - Four agents execute sequentially: 1. Analyze in-progress tasks 2. Analyze todo or unstarted tasks 3. Generate a consolidated Markdown report 4. Translate the report into the configured language - The first two agents use Jira through MCP. - The report generator synthesizes the preceding analyses. - The translator preserves the report’s formatting and structure. - ADK’s `SequentialAgent` coordinates the workflow and passes results between specialized agents. ## Practical Recommendation Organizations seeking broader AI adoption should move beyond individual experimentation. Shared agents built with ADK, connected to internal systems through MCP, can consolidate expertise, reduce duplicated prompt work, and make multi-agent workflows accessible to entire teams.

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Kubernetes AI Model Context Protocol Python+4

lineApr 3, 20264 min readCurated summary

From Hive to Iceberg: The Secret to 12x Faster Data Reflection

LINE Plus replaced a full-dump ETL pipeline for product data with incremental processing using Apache Iceberg and Apache Flink. The previous HBase/Hive workflow rewrote hundreds of millions of rows for every update, causing high compute costs and delays that left data up to an hour out of date. With the new architecture, update intervals were reduced from 60 minutes to 5 minutes—roughly a 12× improvement—while preserving consistency and fault tolerance. ## Limitations of Full-Data ETL - The existing HBase and Hive pipeline continuously collected CDC data in HDFS but had to merge it with existing data and rewrite the entire table before changes became queryable. - This caused: - High compute and storage costs - Dependence on limited shared Hadoop resources - Delayed updates and stale data - Snapshot-based extraction provides consistency, but large snapshots can take hours and retain old versions through MVCC, increasing system overhead. - Processing only the changed rows would reduce the workload from hundreds of millions of records to tens of thousands, separating update cost from total dataset size. ## Introducing Apache Iceberg - Iceberg manages data through metadata and table snapshots rather than relying solely on directory structures like traditional Hive tables. - It supports row-level `upsert` and `delete` operations. - This allows incremental changes to be written without rewriting the entire table, making much shorter ETL intervals possible. ## Requirements for the Streaming Pipeline The team evaluated Spark and Flink against three essential requirements: - **Data freshness:** Late-arriving compensation or replay data must not overwrite newer records. - **End-to-end exactly-once processing:** Iceberg updates and Kafka status messages must not partially succeed. - **Fault tolerance and state management:** Processing state must survive failures and restarts. A Kafka message indicating that all CDC data through a specific timestamp—such as 13:03—has been applied serves as the signal that a bulk extraction can safely begin. This requires complete confidence that the message accurately represents the Iceberg table’s committed state. ## Why Two-Phase Commit Was Necessary - Iceberg and Kafka are independent systems, so writing to one while failing to write to the other could create inconsistent state. - Two-phase commit (2PC) prevents partial success: - Both systems prepare their writes. - They commit only when all required operations succeed. - Any failure causes the operation to roll back. - Exactly-once processing also prevents duplicate or missing records during retries, network failures, or node restarts. - Together, these guarantees make Kafka status messages a reliable representation of the Iceberg table’s state. ## Choosing Flink over Spark - Spark Structured Streaming uses a micro-batch model, which makes fine-grained event-time and state control more difficult. - Flink provides native event-by-event streaming and better support for the required consistency model. - The team used Flink state to track each record’s `updatedate`: - Older late-arriving events are ignored. - Replayed historical data cannot overwrite newer values. - Flink checkpoints: - Persist streaming state externally. - Enable recovery from the latest consistent point. - Integrate with the Kafka sink’s 2PC mechanism. - Kafka messages remain in a pre-commit state until the Iceberg write and checkpoint both succeed. ## Kubernetes Deployment Options - The team compared: - **Native Kubernetes:** Requires manually configuring roles, service accounts, services, routing, deployments, slots, and jobs. - **Flink Kubernetes Operator:** Represents Flink infrastructure and jobs as custom resources, automating configuration such as routing and the web UI through Helm values. - Although Flink has greater operational complexity and a steeper learning curve than Spark, it was selected because it was the only option that satisfied all three core requirements at the engine level. The recommended architecture is an incremental Iceberg pipeline powered by Flink, with stateful processing, checkpoints, and two-phase commit between Iceberg and Kafka. This approach keeps data current, avoids expensive full-table rewrites, and provides reliable recovery and consistency at a five-minute update interval.

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Kubernetes Database Design Apache Spark Apache Iceberg+4

lineMar 31, 20263 min readCurated summary

Applying Spark on Kubernetes to process large-scale advertising data for LINE services

LINE Ads processes tens of billions of advertising events daily and nearly one hundred billion internal data records. As growing numbers of features increased computational demands, its Spark-on-YARN environment suffered from resource contention, inefficient scaling, and Hadoop dependencies. The team migrated to Spark on Kubernetes to achieve infrastructure independence, containerized execution, flexible scaling, and easier operational automation. ## Large-Scale LINE Ads Data Pipelines - The data pipeline supports: - Real-time advertising-event processing - Abuse and validity checks - Machine-learning systems and model training - Analytics and system integration - Advertiser reporting - The platform must handle hundreds of billions of events per day and hundreds of thousands per second. - It must provide low latency, elastic capacity, minimal service impact during failures, and rapid recovery. - The most heavily used table grew to approximately 2.91 times its December 2022 size by December 2025 as more features were added. ## Limitations of Spark on YARN - Hadoop’s storage and compute resources were colocated, causing Spark workloads to compete with HDFS and other Hadoop components. - Scaling compute required adding Hadoop nodes, even when additional storage was unnecessary, increasing cost and wasting capacity. - JVM and Spark versions were difficult to manage independently, limiting access to newer Spark features. - Applications became tightly coupled to the Hadoop infrastructure. ## How Spark on Kubernetes Works - Kubernetes replaces YARN as the cluster manager. - Spark drivers and executors run as separate Kubernetes pods. - In cluster mode: - `spark-submit` requests a driver pod. - Kubernetes schedules the driver on an appropriate node. - The driver creates a `SparkContext`, builds the DAG, and requests executors. - Executors run as independent pods with individually allocated CPU and memory. - The driver divides the DAG into stages and distributes tasks to executors. - Shuffle data is normally tied to executor-pod lifecycles unless an external shuffle service is configured. ## Advantages over YARN - **Containerized execution:** Docker images package application dependencies, improving reproducibility and CI/CD integration. - **Infrastructure independence:** Spark can use HDFS, S3, GCS, or other storage systems without requiring a Hadoop cluster. - **Simpler autoscaling:** Kubernetes can scale pods and integrate with cloud VM autoscalers. - **Unified platform:** Spark, Airflow, machine-learning workloads, and API servers can share a Kubernetes cluster. - **Governance and isolation:** Namespaces, `ResourceQuota`, and RBAC provide flexible team-level controls. - **Operational automation:** Helm, ArgoCD, GitOps, and rolling updates enable more automated application management. ## LINE Ads’ Kubernetes-Based System The platform is organized into four layers: - **Deployment layer** - GitHub Actions runs CI workflows based on repository events. - ArgoCD monitors desired and deployed states and supports easier rollback and synchronization. - **Compute layer** - Kubeflow’s Spark Operator deploys applications through the `SparkApplication` Kubernetes custom resource. - Apache YuniKorn schedules batch jobs and supports resource coordination and gang scheduling. - LogSender forwards pod logs to OpenSearch. - ClusterMonitoring sends Prometheus metrics to the company’s monitoring system. - **Storage layer** - Kafka provides high-throughput, low-latency storage for real-time advertising actions. - Hadoop remains available for large-scale, long-term analysis. - **Monitoring layer** - Kubernetes workers and Spark applications are monitored through exposed Prometheus metrics and centralized logging. The migration to Spark on Kubernetes is recommended for organizations whose Spark workloads are outgrowing tightly coupled Hadoop environments. It separates compute from storage, improves deployment flexibility, and allows data applications to be managed as cloud-native workloads.

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Kubernetes Database Design Machine Learning Apache Kafka+4

lineMar 13, 20264 min readCurated summary

LY Corporation's Cloud Infrastructure Reorganization: Introducing the Architecture of Flava, a Next-Generation Platform Integrating Two Massive Clouds

LY Corporation is consolidating the former LINE “Verda” and Yahoo Japan “YNW” private clouds into Flava, a next-generation platform designed for large-scale, uninterrupted operations. Its approach assumes failures will occur, prioritizing stateless services, application-led availability, rapid IaC-based recovery, and extensive automation. Flava also restructures the architecture around shared resources, upstream OpenStack, default VPC networking, and user-driven cost optimization. ## Failure-Aware Design and Operations - VM root disks are treated as temporary; persistent data is placed in external storage so instance failures have limited service impact. - Availability is achieved through cooperation between infrastructure and applications rather than excessive infrastructure-side guarantees. - Recovery focuses on maintaining service continuity, rebuilding environments quickly with infrastructure as code, and avoiding lengthy root-cause investigations during incidents. - The company promotes KaaS and PaaS to help developers build resilient services without managing low-level infrastructure. - OS configuration, package installation, networking, and other changes are managed as code through CI/CD. - Deployments are performed by availability zone to limit the blast radius of failures. ## Observability from Fleet-Wide Trends to Root Causes - Prometheus, Grafana, and custom dashboards monitor overall cloud health and long-term trends. - When anomalies appear, engineers investigate at a deeper level using kernel traces, packet captures, and other low-level diagnostics. - This combination of broad monitoring and detailed investigation allows teams to move between “forest” and “tree” perspectives. - The operational model depends not only on tools but also on engineers capable of tracing problems down to their fundamental causes. ## OSS, Software-Defined Infrastructure, and Custom Development - The platform relies heavily on OpenStack, Envoy, Linux kernel technologies such as eBPF/XDP, FRR, and Ceph. - LY contributes patches and new capabilities upstream instead of maintaining long-lived private forks. - It has developed SRv6 BGP functionality required for Flava’s VPCs and contributed related work to FRRouting and the Linux kernel. - Compute, VPC, DNS, and load-balancing services run primarily on commodity x86 servers rather than specialized appliances. - XDP-based data planes, hardware offload, and system tuning are used to achieve near-wire-speed throughput and low latency. - Where OSS cannot meet internal requirements, LY builds systems from scratch, including the Dragon object store, SDN control-plane components, load-balancer health agents, and service discovery tools written in Rust, Go, and Python. ## Autonomous Hardware Operations - With tens of thousands of hypervisors and petabyte-scale storage, hardware failures occur continuously. - Failure detection, requests to data-center technicians, hardware replacement, and cluster reintegration are largely automated. - Some exceptional cases still require engineers, but LY plans to use LLMs to automate more of these operational tasks. ## Flava’s Architectural Improvements ### Shared Resource Pools - Older clouds used many dedicated clusters and resource pools, making capacity planning complex and reducing utilization. - Flava consolidates most products and services into one large shared resource pool. - This reduces planning variables, improves resource efficiency, and accelerates provisioning. ### Upstream-Compatible OpenStack - Excessive customization in the legacy environment made upgrades difficult. - Flava minimizes private patches, follows upstream OpenStack, and contributes necessary improvements back to the project. - This enables regular upgrade cycles and keeps security fixes and features current. ### VPC by Default - VPC networking is the standard security model for multi-tenant workloads. - Logical isolation replaces many cases where dedicated VLANs or firewalls previously required months of preparation. - Equivalent security environments can now be provisioned in minutes. - The VPC data plane is being redesigned with XDP to support the reliability and performance required at company-wide scale. ### Built-In Cost Optimization - Development environments require resource lifetimes, allowing unused “zombie” resources to be deleted automatically. - Object storage offers bucket classes such as “High Performance” and “Scalable.” - Users can change storage classes without changing endpoints, adapting cost and performance as access patterns evolve. ## Remaining Challenges - Flava currently offers only a limited set of products and must expand its capabilities while addressing post-launch bugs and overlooked requirements. - The largest challenge is migrating users from the legacy platforms. - LY is working to provide transparent migration tools and reduce manual effort while shortening the period of duplicate investment in old and new infrastructure. ## Team and Engineering Culture - The team includes specialists ranging from kernel developers to web-front-end engineers. - Engineers are expected to understand and control infrastructure rather than treat it as a black box. - Deep source-level expertise enables upstream OSS contributions and informed negotiations with commercial vendors. - This culture of ownership and technical control is presented as a core reason the platform can evolve at LY’s scale. LY’s experience demonstrates that large private clouds can combine OSS, custom software, commodity hardware, and rigorous automation effectively. The practical recommendation is to design for failure, keep infrastructure reproducible through IaC, contribute changes upstream where possible, and use custom development selectively for requirements that general-purpose platforms cannot satisfy.

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Kubernetes Infrastructure As Code Monitoring Openstack+4

lineMar 9, 20264 min readCurated summary

Building an Enterprise LLM Service Part

FAA achieves a 96.1% response rate by favoring simple, maintainable techniques over complex AI architectures. Its design choices were to use RAG instead of knowledge-focused fine-tuning, retrieve complete documents before cutting them into question-relevant sections, and rely on a basic ReAct agent loop rather than elaborate workflows or multiple agents. The article concludes that improving documentation is more valuable than adding complexity when unanswered questions mainly result from missing source material. ## RAG Instead of Fine-Tuning - Fine-tuning was rejected as the primary method for injecting enterprise knowledge. - Research cited in the article found that fine-tuning was highly effective for changing a model’s style—about 97% success—but achieved only about 11% accuracy when teaching new factual knowledge. - FAA’s experiment with approximately 40 examples showed that the model answered the exact training question correctly but failed when the wording changed slightly. - Maintaining larger fine-tuning datasets would require experts to create, verify, and continuously update training examples whenever product documentation changes. - RAG is better suited to frequently changing product information because only the source documents need to be updated. - Fine-tuning may still be useful for domain-specific terminology or reasoning patterns, but not for keeping FAA’s product knowledge current. ## Retrieving Whole Documents Instead of Pre-Chunking - Conventional RAG systems split documents into small chunks before embedding them, improving semantic search precision. - Pre-chunking can remove essential context, especially when references such as “this case” or “the following settings” are separated from the text they depend on. - FAA’s documents are generally short, well-structured, focused on one product and topic, making whole-document retrieval practical. - Instead of chunking before search, FAA embeds and retrieves complete documents, then splits them after the relevant document is known. - The post-split process has two stages: - Split the document by Markdown headers into meaningful sections. - Use a lightweight LLM to select only the sections relevant to the user’s question. - For a question about creating and deleting a VM, the main model might receive only the “VM creation” and “VM deletion” sections. - This extra filtering call remains inexpensive because the lightweight model outputs only section indexes rather than generating a full response. - The key advantage is that splitting happens after the system understands the question, preserving context while delivering only the necessary information. ## ReAct Instead of Complex Agent Workflows - FAA tested plan-and-execute workflows, in which the model first creates a multi-step plan and then carries it out. - Planning and replanning increased system complexity without producing a noticeable improvement in answer quality. - With well-designed tools and carefully filtered context, the model was able to determine tool order on its own. - FAA therefore uses ReAct: the model reasons, takes an action, observes the result, and decides what to do next. - This approach allowed the agent to handle troubleshooting questions without a separate planning layer. ## Rejecting Multi-Agent Architectures - The team also tested specialized agents, such as separate VM and Kubernetes experts. - Delegating questions and assembling the results required additional LLM calls, increasing response time from roughly 9 seconds to 14 seconds in one test. - Multi-agent routing performed poorly for cross-domain questions, such as moving data from a VM to object storage. - Specialists could miss information outside their assigned domain, whereas a single agent could maintain the complete context. - FAA therefore kept one agent with access to progressively disclosed tools and relevant documentation. ## Documentation as the Main Bottleneck - Analysis of unanswered questions showed that about 50% were caused by a documentation gap: no reference document existed. - Other failures were mostly temporary API issues or questions outside FAA’s intended scope. - This suggests the core retrieval and agent system performs well when documentation is available. - The team shares missing questions with product teams, whose updated documents are then re-embedded and incorporated into future evaluations. The practical recommendation is to start with the simplest architecture that fits the data: use RAG for changing knowledge, preserve document context during retrieval, and let a capable model operate through a ReAct loop. In enterprise systems, improving the underlying documentation may produce greater gains than adopting more sophisticated AI frameworks.

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Kubernetes Database Design React PostgreSQL+4

lineFeb 13, 20263 min readCurated summary

Claude Code Action: Platformizing AI Code

LINE NEXT transformed Claude Code from an individual productivity tool into an organization-wide code review platform integrated with GitHub Actions. The goal was to reduce review-quality variation, standardize policies, and make AI feedback part of the existing pull request workflow. Its central design separates simple repository-level invocation from centrally managed execution, prompts, permissions, and infrastructure. ## Why AI Code Review Needed to Be Platformized - As LINE NEXT’s services and repositories grew, human code review quality varied according to each reviewer’s experience and preferences. - Developers were already using Claude Code locally, but individual usage created several problems: - Inconsistent review criteria and perspectives - No organization-wide quality process - AI feedback disconnected from pull request workflows - Difficulty providing new employees with a consistent review experience - DevOps therefore treated the issue as a decentralized quality-process problem rather than merely a tooling problem. ## Why GitHub Actions and Claude Code - GitHub Actions was already the foundation for CI/CD and automation across LINE NEXT repositories. - It allowed the team to: - Apply a common workflow repository by repository - Centrally manage execution environments and permissions - Avoid requiring each service team to build additional infrastructure - Claude Code Action integrated directly with pull requests: - Developers could trigger reviews with an `@claude` mention. - Results appeared as GitHub comments or PR reviews. - Developers did not need to learn a separate interface. - A shared GitHub App Runner environment provided consistent execution and centralized security controls. ## Centralized Caller–Executor Architecture - Service repositories act as **callers**: - They invoke the standard workflow. - They provide only basic parameters such as service name and review type. - A centrally managed DevOps repository acts as the **executor**: - Stores prompts and review personas - Defines review policies and priorities - Manages permissions and authentication - Contains the actual execution logic - This design makes AI review an organization-wide platform capability rather than a separate configuration maintained by every project. ### Benefits of Central Control - **Consistent quality:** Central prompts and personas ensure common review depth, tone, security checks, stability checks, and priorities. - **Faster adoption:** New repositories need only add the standard workflow and specify a few parameters. - **Improved governance:** GitHub Apps, centrally managed secrets, and shared runners make it possible to track who accessed which code and with what permissions. - **Lower operational overhead:** Service teams use the platform without managing AI infrastructure themselves. ## Handling Fork-Based Pull Requests - The official Claude Code Action initially assumed that a PR branch existed in the base repository’s `origin`. - For pull requests created from forks, this caused failures such as: ```text couldn't find remote ref ``` - The original implementation fetched and checked out the branch by name: ```text git fetch origin <branch> git checkout <branch> ``` - This failed because fork branches exist in the external repository, not necessarily in the base repository. - From a platform perspective, this was a structural limitation because it blocked external contributors and collaboration repositories. - The proposed direction was to redesign the execution flow rather than simply add an exception, using GitHub’s special pull-request reference: ```text refs/pull/<PR number>/head ``` This approach allows the workflow to retrieve the actual pull request head commit regardless of whether the PR originated from the main repository or a fork.

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Kubernetes Database Design CI/CD Claude Code+4

lineFeb 6, 20264 min readCurated summary

Creating the Cloud of the Future

LY Corporation is consolidating Yahoo! JAPAN and LINE’s internal cloud services into Flava, a private cloud for application development. The article outlines how Flava could evolve over the next two to three years through unified developer platforms, stronger yet more usable security, scalable multimedia storage, AI infrastructure, and intelligent cloud management. Its ultimate goal is to make complex infrastructure easier to consume while automating operational work. ## Platform Flavaization - Flava currently focuses on infrastructure, databases, and containers, while other development services are spread across separate internal platforms. - Developers must learn different systems for: - Access control and approvals - Logging, monitoring, metering, and billing - APIs, CLIs, and user interfaces - Multi-region and availability-zone operations - “Flavaization” means offering all development platforms through a consistent cloud experience. - LY expects much of this integration to be completed within the next one to two years. ## Stronger, More Usable Security - Flava incorporates security governance from the architecture and product-planning stages, working with the CISO organization. - Data environments are separated by security level: - Default - Secret - Top secret - Sensitive changes require role-based permissions, organizational reporting, expert review, and formal approval. - The main challenge is usability: - Resources can now be provisioned within minutes, but access may still require around ten workflows, such as VDI and Box account creation, taking up to two months. - VPC ACL controls can add several milliseconds of latency, which may affect latency-sensitive services such as LINE messaging. - Flava must provide “usable security” that preserves strong governance without making development excessively slow or difficult. ## Storage for Growing Multimedia Data - Users continuously generate and retain large volumes of photos, videos, and other multimedia content. - Storage demand can grow even when service traffic remains stable. - Flava needs storage technologies suited to different data lifecycles, balancing: - Cost - Throughput and latency - Searchability - Compression and deduplication - Encryption - Efficient tiered storage will be essential for managing long-lived user data economically. ## AI Operations Platforms - LY is adopting AI tools and agents across its organizations, creating demand for shared AIOps infrastructure. - Potential platform capabilities include: - Approved MCP server development and management - Vector databases - AI observability tools such as Langfuse - AI model management - Because AI systems handle internal data, these platforms must comply with company security and data-processing policies. - Flava aims to rapidly evaluate emerging AI technologies and provide compliant, standardized services across the company. ## Network and Storage Infrastructure for AI - AI workloads process larger datasets while requiring very low network latency and high throughput. - Relevant technologies include: - DPUs - Smart NICs - High-speed NVMe storage - Automated storage tiering - Operating networks and storage at cloud scale introduces major challenges in latency, reliability, fault tolerance, throughput, change management, and security. - Flava’s existing network and storage engineering teams have experience supporting LINE and Yahoo! JAPAN at large scale and will adapt that expertise for AI workloads. ## The Intelligent Cloud - Future users may describe infrastructure requirements in natural language rather than manually configuring resources through consoles, APIs, CLIs, or Terraform. - For example, Flava could translate requirements for image processing, AI-based content labeling, messaging, and tiered storage into an architecture and deployable system. - An intelligent Flava could also: - Generate network diagrams and ACL matrices - Identify vulnerabilities and prioritize remediation - Recommend cost optimizations - Detect underutilized resources - Find unencrypted personal information - Manage OSS vulnerability responses - Chatbots could automate tasks such as identifying low-utilization resources while excluding standby failover servers or proposing cost reductions for them. - Operational campaigns currently requiring substantial engineer participation could increasingly be handled by AI agents. Flava’s recommended direction is to combine a unified cloud experience with practical security, lifecycle-aware storage, AI-ready infrastructure, and natural-language automation. The article argues that building this future cloud requires both deep infrastructure expertise and strong attention to developer and user experience.

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Kubernetes PostgreSQL Cloud Security Infrastructure As Code+4

lineFeb 4, 20264 min readCurated summary

Scaling to Infinity: LY Corporation’s

LY Corporation’s observability team evolved its time-series database to handle rapidly growing infrastructure and Kubernetes workloads. After outgrowing MySQL and OpenTSDB, the team built an engine optimized for high-cardinality metrics, low-latency queries, and seamless API compatibility. Its architecture now combines in-memory, Cassandra, and S3-compatible storage, enabling cost-efficient scaling while supporting trillions of daily metrics. ## Why Time-Series Storage Matters - Metrics record system state as timestamped numerical values. - They support dashboards, threshold-based alerts, and predictive analysis using tools such as ARIMA and Prophet. - Even a small metric record can consume about 280 bytes when timestamps, values, and tags are included. - One CPU metric collected every 15 seconds requires roughly 562 MiB per server annually; across 1,000 servers, this grows to about 548 GiB before adding memory, disk, and network metrics. - High-cardinality cloud environments make both storage cost and query latency critical operational concerns. ## Moving Beyond MySQL and OpenTSDB - MySQL initially became inadequate as the organization moved from SOA to MSA: - Write load increased sharply. - Storage costs and capacity requirements grew. - Query latency worsened for large datasets. - Rigid schemas could not easily represent changing cloud resources. - MySQL sharding provided temporary relief but could not support high-resolution metrics collected at intervals under one minute. - OpenTSDB, introduced in 2016 on Apache HBase, improved write performance but had important limitations: - Tag growth harmed UID-table lookup performance. - Metadata was restricted to a narrow character set. - Large queries required cache warm-up procedures. - These constraints led to the development of an internal database beginning in 2018. ## Building the Internal Time-Series Database - The 2019 engine was designed around: - Flexible protocol support independent of a particular agent. - Linear scalability without downtime. - Low-latency processing of high-resolution metrics. - Strong availability during failures. - Inspired by Meta’s Gorilla research, the team used access patterns in which most queries target recent data. - Frequently accessed metrics were kept in an in-memory database, while colder data was stored in Apache Cassandra. - The new engine enabled metric volumes to grow by more than 200 billion records annually while preserving existing APIs. - Users benefited from the new backend without migration work or code changes. ## Scaling for Kubernetes Workloads - Kubernetes introduced rapidly changing pods, dynamically allocated volumes, and much higher metric churn. - Both major storage layers encountered scaling problems: - IMDB initially required adding identical hardware, limiting expansion options. - Cassandra rebalancing could take tens of hours because of its data volume. - The team improved IMDB with weighted load balancing so nodes with different capacities could be used effectively. - Storage was divided into tiers: - Recent 14-day data remained in Cassandra for high-performance access. - Older data was moved to S3-compatible storage. - This reduced Cassandra dependency, lowered costs, simplified operations, and enabled more flexible hardware and Kubernetes-based deployment. ## Writing and Reading Through S3 - The write path separates data processing from long-term storage: - A Dumper reads metric slots from IMDB. - It converts them into internally defined sub-blocks. - A Block Dumper combines sub-blocks into blocks and writes them to S3. - A Storage Gateway reads the blocks for queries and caches them on local disks. - Disk caching initially caused excessive page-cache use and rapid memory exhaustion. - Direct I/O was considered but withdrawn after the cloud storage team warned that it consumed too much shared bandwidth. - Through cross-team collaboration, the team adopted a B+ tree-based cache that made better use of the kernel page cache without overloading infrastructure. ## Future Direction: From Storage to Intelligence - The team aims to move beyond recording metrics toward prediction and AI-assisted operations. - Achieving this requires consolidating time-series data currently scattered across internal systems. - A key requirement is to perform this integration without imposing migration work or breaking changes on users. - The broader goal is an observability platform that turns unified metrics into predictive and intelligent operational capabilities. The main recommendation is to design time-series platforms around real access patterns, tier storage according to data age, and preserve compatibility while evolving the backend. At extreme scale, careful storage architecture and collaboration across infrastructure teams are as important as raw database performance.

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Kubernetes AI Database Design Observability+4

lineDec 10, 2025Original article

Why an Athenz Engineer Took (opens in new tab)

Security platform engineer Jung-woo Kim details his transition from a specialized Athenz developer to a "Kubestronaut," a prestigious CNCF designation awarded to those who master the entire Kubernetes ecosystem. By systematically obtaining five distinct certifications, he argues that deep, practical knowledge of container orchestration is essential for building secure, scalable access control systems in private cloud environments. His journey demonstrates that moving beyond application-level expertise to master cluster administration and security directly improves architectural design and operational troubleshooting. ## The Kubestronaut Framework * The title is awarded by the Cloud Native Computing Foundation (CNCF) to individuals who pass five specific certification exams: CKA, CKAD, CKS, KCNA, and KCSA. * The CKA (Administrator), CKAD (Application Developer), and CKS (Security Specialist) exams are performance-based, requiring candidates to solve real-world technical problems in a live terminal environment rather than answering multiple-choice questions. * Success in these exams demands a combination of deep technical knowledge, speed, and accuracy, as practitioners must configure clusters and resolve failures under strict time constraints. * The remaining Associate-level exams (KCNA and KCSA) provide a theoretical foundation in cloud-native security and ecosystem standards. ## A Progressive Path to Technical Mastery * **CKAD (Application Developer):** The initial focus was on mastering the deployment of Athenz—an open-source auth system—ensuring it runs efficiently from a developer's perspective. Preparation involved rigorous use of tools like killer.sh to simulate high-pressure environments. * **CKA (Administrator):** To manage multi-cluster environments and understand the underlying components that make Kubernetes function, the author moved to the administrator level, gaining insight into how various services interact within the cluster. * **CKS (Security Specialist):** Given his background in security, this was the most critical and difficult stage, focusing on cluster hardening, vulnerability analysis, and implementing strict network policies to ensure the entire infrastructure remains resilient. ## Organizational Impact and Open Source Governance * Obtaining these certifications provided a clearer understanding of open-source governance, specifically how Special Interest Groups (SIGs) and pull request (PR) workflows drive massive projects like Kubernetes. * This technical depth was applied to a high-stakes project providing Athenz services in a Bare Metal as a Service (BMaaS) environment, allowing for more stable and efficient architecture design. * The learning process was supported by corporate initiatives, including access to Udemy Business for technical training and a hybrid work culture that allowed for consistent, early-morning study habits. To achieve expert-level proficiency in complex systems like Kubernetes, engineers should adopt the "Ubo-cheonri" philosophy—making slow but steady progress. Starting with even one minute of study or a single GitHub commit per day can eventually lead to mastering the highest levels of cloud-native architecture. For those managing enterprise-grade infrastructure, pursuing the Kubestronaut path is highly recommended as it transforms theoretical knowledge into a broad, practical vision for system design.

Kubernetes Microservices Security Cloud Native+3

lineNov 21, 2025Original article

Connecting thousands of LY Corporation services (opens in new tab)

LY Corporation developed a centralized control plane using Central Dogma to manage service-to-service communication across its vast, heterogeneous infrastructure of physical machines, virtual machines, and Kubernetes clusters. By adopting the industry-standard xDS protocol, the new system resolves the interoperability and scaling limitations of their legacy platform while providing a robust GitOps-based workflow. This architecture enables the company to connect thousands of services with high reliability and sophisticated traffic control capabilities. ## Limitations of the Legacy System The previous control plane environment faced several architectural bottlenecks that hindered developer productivity and system flexibility: * **Tight Coupling:** The system was heavily dependent on a specific internal project management tool (PMC), making it difficult to support modern containerized environments like Kubernetes. * **Proprietary Schemas:** Communication relied on custom message schemas, which created interoperability issues between different clients and versions. * **Lack of Dynamic Registration:** The legacy setup could not handle dynamic endpoint registration effectively, functioning more as a static registry than a functional service mesh control plane. * **Limited Traffic Control:** It lacked the ability to perform complex routing tasks, such as canary releases or advanced client-side load balancing, across diverse infrastructures. ## Central Dogma as a Control Plane To solve these issues, the team leveraged Central Dogma, a Git-based repository service for textual configuration, to act as the foundation for a new control plane: * **xDS Protocol Integration:** The new control plane implements the industry-standard xDS protocol, ensuring seamless compatibility with Envoy and other modern data plane proxies. * **GitOps Workflow:** By utilizing Central Dogma’s mirroring features, developers can manage service configurations and traffic policies safely through Pull Requests in external Git repositories. * **High Reliability:** The system inherits Central Dogma’s native strengths, including multi-datacenter replication, high availability, and a robust authorization system. * **Schema Evolution:** The control plane automatically transforms legacy metadata into standard xDS resources, allowing for a smooth transition from old infrastructure to the new service mesh. ## Dynamic Service Discovery and Registration The architecture provides automated ways to manage service endpoints across different environments: * **Kubernetes Endpoint Plugin:** A dedicated plugin watches for changes in Kubernetes services and automatically updates the xDS resource tree in Central Dogma. * **Automated API Registration:** The system provides gRPC and HTTP APIs (e.g., `RegisterLocalityLbEndpoint`) that allow services to register themselves dynamically during the startup process. * **Advanced Traffic Features:** The new control plane supports sophisticated features like zone-aware routing, circuit breakers, automatic retries, and "slow start" mechanisms for new endpoints. ## Evolution Toward Sidecar-less Service Mesh A major focus of the project is improving the developer experience by reducing the operational overhead of the data plane: * **Sidecar-less Options:** The team is working toward providing service mesh benefits without requiring a sidecar proxy for every pod, which reduces resource consumption and simplifies debugging. * **Unified Control:** Central Dogma acts as a single source of truth for both proxy-based and proxyless service mesh configurations, ensuring consistent policy enforcement across the entire organization. For organizations managing large-scale, heterogeneous infrastructure, transitioning to an xDS-compliant control plane backed by a reliable Git-based configuration store is highly recommended. This approach balances the need for high-speed dynamic updates with the safety and auditability of GitOps, ultimately allowing for a more scalable and developer-friendly service mesh.

Kubernetes Gitops Service Mesh Envoy+4

lineAug 7, 2025Original article

Flexible Multi-site Architecture Designed with N (opens in new tab)

LINE NEXT optimized its web server infrastructure by transitioning from fragmented, manual Nginx setups to a centralized native Nginx multi-site architecture. By integrating global configurations and automating the deployment pipeline with Ansible, the team successfully reduced service launch lead times by over 80% while regaining the ability to use advanced features like GeoIP and real client IP tracking. This evolution ensures that the infrastructure can scale to support over 100 subdomains across diverse global services with high reliability and minimal manual overhead. ## Evolution of Nginx Infrastructure * **PMC-based Structure**: The initial phase relied on a Project Management Console using `rsync` via SSH; this created security risks and led to fragmented, siloed configurations that were difficult to maintain. * **Ingress Nginx Structure**: To improve speed, the team moved to Kubernetes-based Ingress using Helm charts, which automated domain and certificate settings but limited the use of native Nginx modules and complicated the retrieval of real client IP addresses. * **Native Nginx Multi-site Structure**: The current hybrid approach utilizes native Nginx managed by Ansible, combining the speed of configuration-driven setups with the flexibility to use advanced modules like GeoIP and Loki for log collection. ## Configuration Integration and Multi-site Management * **Master Configuration Extraction**: Common directives such as `timeouts`, `keep-alive` settings, and `log formats` were extracted into a master Nginx configuration file to eliminate redundancy across services. * **Hierarchical Directory Structure**: Inspired by Apache, the team adopted a `sites-available` structure where individual `server` blocks for different services (alpha, beta, production) are managed in separate files. * **Operational Efficiency**: This integrated structure allows a single Nginx instance to serve multiple sites simultaneously, significantly reducing the time required to add and deploy new service domains. ## Automated Deployment with Ansible * **Standardized Workflow**: The team replaced manual processes with Ansible playbooks that handle everything from cloning the latest configuration from Git to extracting environment-specific files. * **Safety and Validation**: The automated pipeline includes mandatory Nginx syntax verification (`nginx -t`) and process status checks to ensure stability before a deployment is finalized. * **Rolling Deployments**: To minimize service impact, updates are pushed sequentially across servers; the process automatically halts if an error is detected at any stage of the rollout. To effectively manage a rapidly expanding portfolio of global services, infrastructure teams should move toward a "configuration-as-code" model that separates common master settings from service-specific logic. Leveraging automation tools like Ansible alongside a native Nginx multi-site structure provides the necessary balance between rapid deployment and the granular control required for complex logging and security requirements.

Kubernetes Nginx Ansible Infrastructure Automation+4