The supplied text does not include the tech blog post itself. It contains Datadog navigation links and a promotional banner announcing its recognition as a Leader in the 2026 Gartner Magic Quadrant for Observability Platforms, but no article body or technical sections.
## Available content
- Datadog promotes observability products covering:
- Infrastructure and Kubernetes monitoring
- Application performance monitoring
- Logs and database monitoring
- Security
- Digital experience monitoring
- Software delivery and CI visibility
- Service management
- AI-powered investigation and monitoring
- The page links to an engineering article at:
- `/blog/engineering/agent-go-binaries/`
- No technical explanation, examples, conclusions, or section content from that article is included.
Please provide the blog post’s full text or relevant excerpt for a substantive summary.
The Datadog Agent’s Linux artifact grew from 428 MiB in version 7.16.0 to 1.22 GiB in 7.60.0, creating problems for serverless, IoT, and containerized environments. Rather than remove features, Datadog reduced Go binary sizes by up to 77% between versions 7.60.0 and 7.68.0. The effort combined dependency analysis, targeted code refactoring, and renewed use of Go linker optimizations.
## Why the Agent Became So Large
- The Agent supports many operating systems, architectures, distributions, and deployment environments.
- Its codebase contains hundreds of dependencies, including cloud SDKs, container runtimes, and security tools.
- Build tags and dependency injection determine which features are included in each binary.
- The compressed Linux amd64 Debian package grew from 126 MiB to 265 MiB.
- Its uncompressed size increased from 428 MiB to 1,248 MiB—a 192% increase over five years.
- Go binaries represented a substantial portion of that growth and became the primary optimization target.
## How Go Selects Dependencies
- Go compiles required packages individually before the linker combines them into a binary.
- Files are included only when they:
- Are not test files ending in `_test.go`
- Match the current operating system, architecture, and build tags
- Satisfy other constraints such as CGO settings, compiler version, or architecture features
- Starting from the main package, Go transitively includes imported packages and the runtime required by every Go binary.
- Unnecessary dependencies can be excluded by:
- Adding a build tag to the file that imports them
- Moving dependency-using symbols into a separate package imported only by relevant binaries
## Analyzing Imports and Dependencies
- `go list` reveals all packages used for a specific OS, architecture, and set of build tags.
- `goda` generates dependency graphs, including indirect imports.
- `goda` can also show only the paths leading to a particular target package using its `reach` function.
- These tools account for `GOOS`, `GOARCH`, and build constraints, making them useful for examining platform-specific builds.
## Why Package Lists Are Not Enough
- A package’s presence does not directly indicate its binary size impact.
- The linker removes symbols that are not reachable from the program’s entry points.
- The same package can therefore contribute different amounts of code depending on how it is used.
- Importing a package can still have significant side effects:
- `init` functions execute.
- Global variables are initialized.
- These behaviors may force otherwise unnecessary symbols to remain in the binary.
- Certain uses of reflection can also limit linker optimizations.
- Datadog used `go-size-analyzer` to measure the contribution of individual dependencies more accurately than import graphs alone.
## Overall Optimization Strategy
- Datadog systematically audited dependencies rather than removing product capabilities.
- The work focused on restructuring imports, isolating optional functionality, and restoring linker optimizations that had been disabled or undermined over time.
- The resulting improvements brought artifact sizes close to levels from roughly five years earlier.
- Some compiler and linker behaviors uncovered during the effort led to improvements benefiting other large Go projects, including Kubernetes.
The practical lesson is to treat binary size as an ongoing dependency and architecture concern: analyze actual symbol reachability, isolate optional features behind build constraints or packages, and verify each build variant independently.
Pinterest developed **Auto Memory Retries** to reduce Spark out-of-memory failures without permanently assigning oversized executors to every task. The system detects OOM failures and retries affected tasks with progressively larger resource profiles, reducing both on-call incidents and wasted compute. Instead of tuning every job for its peak memory demand, Pinterest can size jobs around typical usage while handling exceptional tasks elastically.
## Pinterest’s Spark Environment
- Pinterest processes more than **90,000 Spark jobs daily** across tens of thousands of nodes.
- Its infrastructure includes:
- Kubernetes clusters
- Spark 3.2, with Spark 3.5 adoption underway
- Apache Celeborn for shuffle
- Apache YuniKorn for scheduling
- Apache Gluten and Meta’s Velox for acceleration
- Archer, Pinterest’s internal submission service
- More than **4.6% of job failures** were caused by OOM errors.
## Why Manual Memory Tuning Was Insufficient
- Pinterest’s clusters are memory-bound, so simply increasing executor sizes is expensive and difficult.
- Automatic tuning generally reduces executor memory to match historical usage and improve resource efficiency.
- Manual tuning can work, but requires substantial expertise because:
- Different stages perform different operations.
- Individual tasks may have very different memory needs because of data skew.
- Configurations that work for most tasks may fail for a small number of high-memory tasks.
- Auto Memory Retries allow jobs to target approximately their **P90 memory usage**, while automatically giving unusually demanding tasks more capacity.
## How Spark Executor Memory Works
- An executor’s memory and CPU capacity determine how many tasks can run concurrently.
- By default, each CPU core provides a task slot.
- For example, with `spark.task.cpus=2`, an executor with two usable task slots and 8 GB of memory provides roughly 4 GB per task on average.
- Memory is shared, so one task may temporarily use more than its average allocation if another uses less.
- An OOM occurs when the combined memory usage of concurrent tasks exceeds the executor’s available memory.
## Auto Memory Retries Design
Pinterest modified Spark’s scheduling loop so individual tasks can use resource profiles different from their parent `TaskSet`.
- Each task can store an optional `taskRpId` identifying its retry resource profile.
- Pinterest creates immutable retry profiles at **2x, 3x, and 4x** the base profile.
- If off-heap memory is enabled, it is scaled as well.
- Retries use a hybrid strategy:
- **First retry:** Double `cpus per task`, allowing the task to run on an existing executor with fewer concurrent tasks.
- **Later retry:** Launch a physically larger executor if the task still fails or already requires the entire executor.
- The approach prioritizes reusing existing executors before provisioning larger ones.
## Changes to Spark Internals
Pinterest extended core Spark components through Pinterest-specific subclasses rather than using a listener-only implementation.
- **Task**
- Stores the optional task resource profile ID.
- **TaskSetManager**
- Tracks tasks with non-default profiles.
- Assigns the next larger retry profile after an OOM.
- **TaskSchedulerImpl**
- Allows tasks with increased CPU requirements to run on standard executors.
- **ExecutorAllocationManager**
- Tracks pending tasks by retry profile.
- Requests larger executors when physical memory is required.
- The feature-specific classes are loaded only when Auto Memory Retries is enabled.
- The Spark UI was updated to display each task’s resource profile ID.
## Handling Tasks After an OOM
- When a task fails on an executor with more than one core, its first retry doubles `spark.task.cpus`.
- Other tasks in the same stage or future stages are unaffected.
- Spark cannot reliably determine which concurrent task caused the executor-level OOM.
- As a result, Pinterest treats **all tasks running on the terminated executor** as having failed due to OOM and routes them to retries that do not share the executor with other tasks.
## Practical Conclusion
Pinterest’s approach makes executor sizing elastic at the task level: configure jobs for normal memory usage, then progressively increase resources only for tasks that need them. This can reduce OOM-related failures and operational load while avoiding the cost of running every task on oversized executors.
AWS’s February 16, 2026 roundup highlights the launch of Amazon EC2 M8azn instances, which deliver substantial performance gains for compute-intensive workloads. It also covers expanded Amazon Bedrock model and networking support, improved observability in EKS Auto Mode, more efficient OpenSearch Serverless capacity management, and configurable RDS backup settings during snapshot restoration. The post concludes with upcoming AWS conferences, summits, and community events.
## Amazon EC2 M8azn Instances
- Powered by fifth-generation AMD EPYC processors with a maximum frequency of 5 GHz.
- Compared with M5zn instances, they provide:
- Up to 2× compute performance
- 4.3× higher memory bandwidth
- 10× larger L3 cache
- Up to 2× networking throughput
- Up to 3× EBS throughput
- Built on the AWS Nitro System with sixth-generation Nitro Cards.
- Available in nine sizes, from 2 to 96 vCPUs and up to 384 GiB of memory, including two bare-metal options.
- Designed for high-performance workloads such as financial analytics, high-frequency trading, CI/CD, gaming, simulations, and HPC.
## New Open-Weight Models in Amazon Bedrock
- Bedrock now supports six fully managed models:
- DeepSeek V3.2
- MiniMax M2.1
- GLM 4.7
- GLM 4.7 Flash
- Kimi K2.5
- Qwen3 Coder Next
- The models target reasoning, agentic intelligence, autonomous coding, and cost-efficient production deployments.
- They use Project Mantle and support OpenAI-compatible APIs.
- DeepSeek V3.2, MiniMax 2.1, and Qwen3 Coder Next are also available in Kiro.
## Amazon Bedrock PrivateLink Support
- AWS PrivateLink now supports the `bedrock-mantle` endpoint in addition to `bedrock-runtime`.
- Project Mantle provides serverless inference, quality-of-service controls, automated capacity management, and OpenAI API compatibility.
- PrivateLink support for OpenAI-compatible endpoints is available in 14 AWS Regions.
## EKS Auto Mode Logging
- EKS Auto Mode now supports CloudWatch Vended Logs for managed capabilities such as:
- Compute autoscaling
- Block storage
- Load balancing
- Pod networking
- Logs can be delivered to CloudWatch Logs, Amazon S3, or Amazon Data Firehose.
- The feature includes AWS authentication and authorization and is offered at a lower price than standard CloudWatch Logs.
## OpenSearch Serverless Collection Groups
- Collection Groups allow multiple collections to share OpenSearch Compute Units while retaining separate KMS keys and access controls.
- Shared capacity can reduce OCU costs.
- Administrators can define both minimum and maximum OCU limits, ensuring baseline capacity for latency-sensitive applications.
## RDS Snapshot Restore Improvements
- RDS now lets users view and configure backup retention periods and preferred backup windows before or during snapshot restoration.
- Restored databases no longer need post-restore backup configuration changes.
- The feature supports all major RDS engines, Aurora editions, commercial AWS Regions, and GovCloud at no additional cost.
## Upcoming AWS Events
- AWS Summits in Paris, London, and Bengaluru during April 2026.
- AWS AI and Data Conference in Ireland on March 12, focusing on Bedrock, SageMaker, QuickSight, agent deployment, data integration, and governance.
- AWS Community Days in Ahmedabad, Slovakia, and Pune.
Overall, the announcements emphasize faster specialized compute, broader managed AI model access, stronger private connectivity, and improved operational controls across AWS services.
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.
Toss Payments transformed its security infrastructure from a vulnerable, single-layered legacy system into a robust "Defense in Depth" architecture spanning hybrid IDC and AWS environments. By integrating advanced perimeter defense, internal server monitoring, and container runtime security, the team established a comprehensive framework that prioritizes visibility and continuous verification. This four-year journey demonstrates that modern security requires moving beyond simple boundary protection toward a proactive, multi-layered strategy that assumes breaches can occur.
### Perimeter Defense and SSL/TLS Visibility
* Addressed the critical visibility gap in legacy systems by implementing dedicated SSL/TLS decryption tools, allowing the team to analyze encrypted traffic for hidden malicious payloads.
* Established a hybrid security architecture using a combination of physical DDoS protection, IPS, and WAF in IDC environments, complemented by AWS WAF and AI-based GuardDuty in the cloud.
* Developed a collaborative merchant response process that moves beyond simple IP blocking; the system automatically detects malicious traffic from partners and provides them with detailed vulnerability reports and remediation guides (e.g., specific SQL injection points).
### Internal Network Security and "Assume Breach" Monitoring
* Implemented **Wazuh**, an open-source security platform, in IDC environments to monitor lateral movement, collect centralized logs, and perform file integrity checks across diverse operating systems.
* Leveraged **AWS GuardDuty** for intelligent threat detection in the cloud, focusing on malware scanning for EC2 instances and monitoring for suspicious process activities.
* Established automated detection for privilege escalation and unauthorized access to sensitive system files, such as tracking instances where root privileges are obtained to modify the `/etc/passwd` file.
### Container Runtime Security as the Final Defense
* Adopted **Falco**, a CNCF-hosted runtime security tool, to protect Kubernetes environments by monitoring system calls (syscalls) in real-time.
* Configured specific security rules to detect "container escape" attempts, unauthorized access to sensitive files like `/etc/shadow`, and the execution of new or suspicious binaries within running containers.
* Integrated **Falco Sidekick** to manage security events efficiently, ensuring that anomalous behaviors at the container level are instantly routed to the security team for response.
### Zero Trust and Continuous Verification
* Shifted toward a Zero Trust model for the internal work network to ensure that all users and devices are continuously verified regardless of their location.
* Focused on implementing dynamic access control and the principle of least privilege to minimize the potential impact of credential theft or device compromise.
Organizations operating in hybrid cloud environments should move away from relying on a single perimeter and instead adopt a multi-layered defense strategy. True security resilience is achieved by gaining deep visibility into encrypted traffic and maintaining granular monitoring at the server and container levels to intercept threats that inevitably bypass initial defenses.
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.
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.
Toss Payments modernized its inherited legacy infrastructure by building an OpenStack-based private cloud to operate alongside public cloud providers in an Active-Active hybrid configuration. By overcoming extreme technical debt—including servers burdened with nearly 2,000 manual routing entries—the team achieved a cloud-agnostic deployment environment that ensures high availability and cost efficiency. The transformation demonstrates how a small team can successfully implement complex open-source infrastructure through automation and the rigorous technical internalization of Cluster API and OpenStack.
### The Challenge of Legacy Networking
- The inherited infrastructure relied on server-side routing rather than network equipment, meaning every server carried its own routing table.
- Some legacy servers contained 1,997 individual routing entries, making manual management nearly impossible and preventing efficient scaling.
- Initial attempts to solve this via public cloud (AWS) faced limitations, including rising costs due to exchange rates, lack of deep visibility for troubleshooting, and difficulties in disaster recovery (DR) configuration between public and on-premise environments.
### Scaling OpenStack with a Two-Person Team
- Despite having only two engineers with no prior OpenStack experience, the team chose the open-source platform to maintain 100% control over the infrastructure.
- The team internalized the technology by installing three different versions of OpenStack dozens of times and simulating various failure scenarios.
- Automation was prioritized using Ansible and Terraform to manage the lifecycle of VMs and load balancers, enabling new instance creation in under 10 seconds.
- Deep technical tuning was applied, such as modifying the source code of the Octavia load balancer to output custom log formats required for their specific monitoring needs.
### High Availability and Monitoring Strategy
- To ensure reliability, the team built three independent OpenStack clusters operating in an Active-Active configuration.
- This architecture allows for immediate traffic redirection if a specific cluster fails, minimizing the impact on service availability.
- A comprehensive monitoring stack was implemented using Zabbix, Prometheus, Mimir, and Grafana to collect and visualize every essential metric across the private cloud.
### Managing Kubernetes with Cluster API
- To replicate the convenience of Public Cloud PaaS (like EKS), the team implemented Cluster API to manage the Kubernetes lifecycle.
- Cluster API treats Kubernetes clusters themselves as resources within a management cluster, allowing for standardized and rapid deployment across the private environment.
- This approach ensures that developers can deploy applications without needing to distinguish between the underlying cloud providers, fulfilling the goal of "cloud-agnostic" infrastructure.
### Practical Recommendation
For organizations dealing with massive technical debt or high public cloud costs, the Toss Payments model suggests that a "Private-First" hybrid approach is viable even with limited headcount. The key is to avoid proprietary black-box solutions and instead invest in the technical internalization of open-source tools like OpenStack and Cluster API, backed by a "code-as-infrastructure" philosophy to ensure scalability and reliability.
Toss Payments manages thousands of API and batch server configurations that handle trillions of won in transactions, where a single typo in a JVM setting can lead to massive financial infrastructure failure. To solve the risks associated with manual "copy-paste" workflows and configuration duplication, the team developed a sophisticated system that treats configuration as code. By implementing layered architectures and dynamic templates, they created a testable, unified environment capable of managing complex hybrid cloud setups with minimal human error.
## Overlay Architecture for Hierarchical Control
* The team implemented a layered configuration system consisting of `global`, `cluster`, `phase`, and `application` levels.
* Settings are resolved by priority, where lower-level layers override higher-level defaults, allowing servers to inherit common settings while maintaining specific overrides.
* This structure allows the team to control environment-specific behaviors, such as disabling canary deployments in development environments, from a single centralized directory.
* The directory structure maps files 1:1 to their respective layers, ensuring that naming conventions drive the CI/CD application process.
## Solving Duplication with Template Patterns
* Standard YAML overlays often fail when dealing with long strings or arrays, such as `JVM_OPTION`, because changing a single value usually requires redefining the entire block.
* To prevent the proliferation of nearly identical environment variables, the team introduced a template pattern using placeholders like `{{MAX_HEAP}}`.
* Developers can modify specific parameters at the application layer while the core string remains defined at the global layer, significantly reducing the risk of typos.
* This approach ensures that critical settings, like G1GC parameters or heap region sizes, remain consistent across the infrastructure unless explicitly changed.
## Dynamic and Conditional Configuration Logic
* The system allows for "evolutionary" configurations where Python scripts can be injected to generate dynamic values, such as random JMX ports or data fetched from remote APIs.
* Advanced conditional logic was added to handle complex deployment scenarios, enabling environment variables to change their values automatically based on the target cluster name (e.g., different profiles for AWS vs. IDC).
* By treating configuration as a living codebase, the team can adapt to new infrastructure requirements without abandoning their core architectural principles.
## Reliable Batch Processing through Simplicity
* For batch operations handling massive settlement volumes, the team prioritized "appropriate technology" and simplicity to minimize failure points.
* They chose Jenkins for its low learning curve and reliability, despite its lack of native GitOps support.
* To address inconsistencies in manual UI entries and varying Java versions across machines, they standardized the batch infrastructure to ensure that high-stakes financial calculations are executed in a controlled, predictable environment.
The most effective way to manage large-scale infrastructure is to transition from static, duplicated configuration files to a dynamic, code-centric system. By combining an overlay architecture for hierarchy and a template pattern for granular changes, organizations can achieve the flexibility needed for hybrid clouds while maintaining the strict safety standards required for financial systems.
The provided text does not include the blog post’s article body. It contains Datadog’s navigation menu and a link to an engineering post titled around “eBPF workload protection lessons,” so there is not enough source material to accurately summarize its technical arguments or conclusions.
## Available information
- The page is hosted by Datadog’s engineering blog.
- The linked topic concerns workload protection built with eBPF.
- Datadog’s broader product areas include infrastructure monitoring, application performance monitoring, security, logs, and AI.
- The excerpt itself does not describe:
- The eBPF implementation
- Design challenges or trade-offs
- Performance considerations
- Security detection methods
- Lessons learned or recommendations
Please provide the article text or a fuller extract for a substantive summary.
This blog post by the Daangn (Karrot) search platform team details their journey in optimizing Elasticsearch operations on Kubernetes (ECK). While their initial migration to ECK reduced deployment times, the team faced critical latency spikes during rolling restarts due to "cold caches" and high traffic volumes. To achieve a "deploy anytime" environment, they developed a data node warm-up system to ensure nodes are performance-ready before they begin handling live search requests.
## Scaling Challenges and Operational Constraints
- Over two years, Daangn's search infrastructure expanded from a single cluster to four specialized clusters, with peak traffic jumping from 1,000 to over 10,000 QPS.
- The initial strategy of "avoiding peak hours" for deployments became a bottleneck, as the window for safe updates narrowed while total deployment time across all clusters exceeded six hours.
- Manual monitoring became a necessity rather than an option, as engineers had to verify traffic conditions and latency graphs before and during every ArgoCD sync.
## The Hazards of Rolling Restarts in Elasticsearch
- Standard Kubernetes rolling restarts are problematic for stateful systems because a "Ready" Pod does not equate to a "Performant" Pod; Elasticsearch relies heavily on memory-resident caches (page cache, query cache, field data cache).
- A version update in the Elastic Operator once triggered an unintended rolling restart that caused a 60% error rate and 3-second latency spikes because new nodes had to fetch all data from disk.
- When a node restarts, the cluster enters a "Yellow" state where remaining replicas must handle 100% of the traffic, creating a single point of failure and increasing the load on the surviving nodes.
## Strategy for Reliable Node Warm-up
- The primary goal was to reach a state where p99 latency remains stable during restarts, regardless of whether the deployment occurs during peak traffic hours.
- The solution involves a "Warm-up System" designed to pre-load frequently accessed data into the filesystem and Elasticsearch internal caches before the node is allowed to join the load balancer.
- By executing representative search queries against a newly started node, the system ensures that the necessary segments are already in the page cache, preventing the disk I/O thrashing that typically follows a cold start.
## Implementation Goals
- Automate the validation of node readiness beyond simple health checks to include performance readiness.
- Eliminate the need for human "eyes-on-glass" monitoring during the 90-minute deployment cycles.
- Maintain high availability and consistent user experience even when shards are being reallocated and replicas are temporarily unassigned.
To maintain a truly resilient search platform on Kubernetes, it is critical to recognize that for stateful applications, "available" is not the same as "ready." Implementing a customized warm-up controller or logic is a recommended practice for any high-traffic Elasticsearch environment to decouple deployment schedules from traffic patterns.
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.
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.
Datadog was named a Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms. The announcement positions Datadog as a provider of broad, integrated monitoring across infrastructure, applications, logs, security, digital experiences, software delivery, and AI. The supplied content does not include Gartner’s evaluation details or the blog post’s supporting arguments.
## Recognition and Positioning
- Datadog highlights its designation as a Leader in the Gartner Magic Quadrant for Observability Platforms.
- The announcement emphasizes Datadog’s unified observability platform rather than a single monitoring product.
## Breadth of the Platform
- **Infrastructure:** infrastructure, container, network, serverless, GPU, storage, and cloud-cost monitoring.
- **Applications and data:** APM, database monitoring, continuous profiling, data-stream monitoring, and job monitoring.
- **Logs and observability operations:** log management, sensitive-data scanning, audit trails, and observability pipelines.
- **Security:** cloud security, SIEM, workload protection, code security, vulnerability management, and application/API protection.
- **Digital experience:** browser and mobile RUM, session replay, synthetic monitoring, product analytics, and error tracking.
- **Software delivery and service management:** CI visibility, testing, feature flags, incident response, SLOs, workflow automation, and case management.
- **AI capabilities:** agent observability, GPU monitoring, AI integrations, Bits AI agents, and investigation tools.
Overall, the available material presents Datadog’s Gartner Leader recognition and extensive product coverage, but it does not provide enough article text to summarize the specific reasoning behind the designation.
