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  • in zh
  • 1 min read

平衡網絡安全與用戶體驗——企業實用指南

在當今數位化的環境中，企業越來越意識到網絡安全的重要性。保護客戶數據、確保合規性以及管理聲譽風險只是企業大力投資網絡安全措施的一部分原因。然而，挑戰在於如何在維護強大安全性的同時，保持無縫的用戶體驗，並避免對業務運營造成干擾。

以下是企業用於實現此平衡的一些實用策略。

1. 通過有效的風險管理優先處理風險

並非所有風險都具有相同的重要性。企業需要採用結構化的方法來識別、評估和優先處理基於潛在影響的網絡安全威脅。這種方法使企業能夠有效分配資源，並避免系統因無法提供顯著效益的安全措施而過載。

  • 基於風險的方法： 通過聚焦高影響、高可能性的風險，企業可以實施針對性的安全措施。這可防止業務運營和用戶工作流程因不必要的控製措施而受到干擾。

  • 適應性安全框架： 採用能隨威脅環境變化而調整的適應性安全框架，是動態管理風險的一種有效方式。例如，實時威脅檢測和響應系統可以幫助企業根據威脅類型比例適當地做出反應，而不需要採用可能阻礙日常運營的僵化安全規則。

2. 設計以用戶為中心的安全方法

一個有效的網絡安全策略應優先考慮不僅是數據和系統的保護，還包括用戶體驗。通過將安全性融入用戶旅程，企業可以避免令人生厭或過於複雜的措施，從而減少對用戶的困擾。

  • 無縫的身份驗證選項： 像多因素身份驗證（MFA）這樣的安全流程對於保護敏感數據至關重要，但它們不應破壞用戶的流暢體驗。用戶友好的MFA選項，例如生物識別身份驗證或一鍵驗證，可以以最小的摩擦提供強大的保護。

  • 行為分析用於異常檢測： 利用行為分析可以通過分析用戶行為（例如登錄時間和IP地址）來識別可疑活動。這種方法使企業能夠在不需要用戶頻繁輸入或增加額外步驟的情況下檢測和緩解威脅。

  • 用戶教育： 安全措施在用戶知識淵博和警惕時最有效。通過簡單、可訪問的培訓和持續的溝通，公司可以使用戶成為其安全姿態的重要組成部分。受過教育的用戶更可能遵循安全實踐，從而減少對限制性安全措施的需求。

3. 接受安全措施的持續改進

網絡安全策略不應是靜態的。隨著新威脅和技術的出現，適應和發展以有效保護數據和用戶體驗至關重要。

  • 用戶中心安全的反饋迴路： 企業可以創建反饋迴路來評估安全措施對用戶的影響，並確定改進的領域。定期收集用戶對安全流程的反饋有助於公司調整和定製安全協議，以平衡用戶需求和保護。

  • 敏捷、迭代的安全更新： 與其實施可能擾亂業務運營的大規模更新，敏捷的網絡安全方法允許企業進行漸進式改進。較小的更新還可以幫助企業保持靈活性，並更快地適應新威脅，而不會對用戶體驗或生產力產生重大影響。

結論

在當今，平衡網絡安全和用戶體驗是企業的一項複雜但必要的任務。通過實施基於風險的方法、設計以用戶為中心的安全措施以及接受持續改進，企業可以創建一種既能保護其資產，又不會損害用戶滿意度或運營效率的網絡安全策略。

在這個用戶體驗與數據保護同等重要的時代，能夠掌握這種平衡的企業將更能建立信任、保留客戶，並在快速變化的數位化世界中安全運營。

  • 4 min read

The Role of Cybersecurity in Digital Transformation - Building, Buying, and Balancing Value vs. Cost

As organizations accelerate their digital transformation journeys, cybersecurity has moved from a supporting role to a critical pillar of success. Digital transformation initiatives can increase data exposure, expand attack surfaces, and amplify vulnerabilities in new technology stacks, all of which underscore the need for robust cybersecurity. A well-executed cybersecurity strategy not only protects against threats but also builds customer trust and regulatory compliance, supporting sustainable digital growth. In this post, we explore the cybersecurity capabilities needed for digital transformation, the debate between building versus buying solutions, and how to balance value and cost.

Core Cybersecurity Capabilities Essential for Digital Transformation

Before diving into how to source cybersecurity capabilities, let’s outline the key functions needed to secure a digitally transformed organization:

  1. Identity and Access Management (IAM): Proper IAM controls access to digital resources through mechanisms like multi-factor authentication (MFA) and single sign-on (SSO), minimizing unauthorized access risks.

  2. Threat Intelligence and Detection: With digital transformation, real-time threat detection, AI-based anomaly analysis, and actionable threat intelligence are essential to quickly identify and neutralize threats.

  3. Cloud Security: Digital transformation often involves cloud migration. Cloud security includes secure configurations, data protection, and access controls to ensure that cloud infrastructure and applications remain secure.

  4. Data Protection and Encryption: Encrypting sensitive data at rest and in transit is crucial, especially as digital transformation efforts involve collecting, storing, and processing more data than ever before.

  5. Endpoint Security: Digital transformation increases reliance on mobile devices, IoT, and other endpoints, which can introduce security vulnerabilities. Endpoint security extends protection across all devices connected to the network.

  6. Compliance and Risk Management: Ensuring regulatory compliance (e.g., GDPR, CCPA, APPI) is crucial to avoid fines and build trust with customers.

  7. Incident Response and Recovery: In case of a security breach, a well-planned incident response and disaster recovery strategy are essential to minimize downtime and financial impact.

Building In-House vs. Buying Cybersecurity Solutions

When deciding between building in-house cybersecurity solutions or outsourcing, it’s essential to consider organizational needs, budget, and long-term goals.

Build In-House

Advantages: - Customization: In-house solutions are highly tailored to an organization’s unique requirements, industry regulations, and architecture. - Full Control: An in-house team offers complete control over cybersecurity data, practices, and responses. - Scalable Expertise: Building in-house expertise allows the organization to adapt its cybersecurity posture proactively as digital initiatives expand.

Disadvantages: - High Initial Investment: Establishing and maintaining in-house cybersecurity is resource-intensive, requiring significant budgets for hiring, training, and technology. - Ongoing Training: Cybersecurity demands continuous education to stay ahead of emerging threats, a challenge in-house teams must prioritize. - Slower Deployment: Developing capabilities in-house may take longer compared to ready-made solutions.

Best For: Larger companies with complex, industry-specific security needs, or those with regulatory or privacy requirements that necessitate close control over data and security processes.

Buy (Outsource)

Advantages: - Rapid Deployment: Outsourced solutions can be implemented faster, meeting immediate security needs for organizations with limited time or in-house talent. - Access to Advanced Technology: Vendors bring cutting-edge tools, threat intelligence, and expertise, often surpassing what an internal team could provide. - Reduced Upfront Costs: SaaS or managed security services reduce the need for upfront infrastructure investments and lower initial setup costs.

Disadvantages: - Less Customization: External solutions may be less tailored to an organization’s specific architecture or compliance requirements. - Data Privacy Concerns: Outsourcing involves entrusting third parties with sensitive data, potentially increasing risk in areas like data residency and compliance. - Integration Challenges: Integrating outsourced solutions with existing systems can be challenging, requiring compatibility with the organization’s tech stack and processes.

Best For: Smaller organizations or those needing rapid implementation of advanced cybersecurity capabilities without substantial in-house resources.

Value vs. Cost: What’s the Right Approach?

Digital transformation demands that cybersecurity be viewed not as a mere line item but as a strategic asset that enhances value.

The Value Approach: Cybersecurity as an Investment

Organizations that prioritize value in cybersecurity understand it as an essential investment that supports digital transformation. This approach emphasizes building customer trust, securing intellectual property, and ensuring uninterrupted service—all of which contribute to a competitive advantage.

  • Long-Term Benefits: By focusing on long-term value, organizations gain greater agility, enhanced brand reputation, and improved operational resilience.
  • Proactive Measures: A value-focused approach enables continuous investment in threat detection, incident response, and compliance, protecting the organization from costly breaches and compliance issues.
The Cost Approach: Cybersecurity as an Expense

The cost-focused mindset prioritizes minimizing cybersecurity spend, focusing on compliance at the minimum level required to avoid fines and sanctions. While this approach reduces initial expenses, it often results in reactive cybersecurity measures that may not fully protect against sophisticated attacks.

  • Risks of Cost-Cutting: A purely cost-based approach can lead to gaps in threat detection, incident response delays, and brand damage in case of a breach.
  • Short-Term View: Organizations focusing solely on cost might miss out on opportunities to build a strong security foundation, leading to higher expenses when breaches occur.

Conclusion

For successful digital transformation, cybersecurity capabilities are indispensable. The decision to build in-house or buy outsourced solutions depends on factors like organizational size, budget, and specific security needs. Large organizations with custom needs may benefit from in-house solutions, while smaller firms or those seeking quick deployment may prefer outsourcing.

Ultimately, viewing cybersecurity as an investment rather than a cost yields greater long-term value. A proactive, value-driven approach to cybersecurity supports a sustainable digital transformation journey, empowering organizations to innovate securely, build customer trust, and maintain regulatory compliance. Balancing between building or buying, and focusing on value over cost, lays a strong foundation for cybersecurity in an ever-evolving digital landscape.

  • in zh
  • 1 min read

網絡安全在數位轉型中的角色 - 建設、自購，以及價值與成本之間的平衡

隨著組織加速數位轉型之旅，網絡安全從支援角色轉變為成功的關鍵支柱。數位轉型計劃可能增加數據暴露面，擴大攻擊面，並放大新技術堆棧中的漏洞，這些都凸顯了強大網絡安全需求的重要性。一個執行良好的網絡安全策略不僅能防範威脅，還能建立客戶信任並實現法規遵從，支援可持續的數位增長。本文探討了數位轉型所需的網絡安全能力、建設與購買解決方案之間的爭論，以及如何在價值與成本之間取得平衡。

數位轉型核心網絡安全能力

在探討如何獲取網絡安全能力之前，讓我們概述一下保護數位轉型組織所需的關鍵功能：

  1. 身份和訪問管理（IAM）： 通過多因素身份驗證（MFA）和單一登入（SSO）等機制適當管理對數位資源的訪問，將未經授權的訪問風險降到最低。

  2. 威脅情報與檢測： 隨著數位轉型的推進，即時威脅檢測、基於AI的異常分析和可行的威脅情報成為快速識別和中和威脅的必要手段。

  3. 雲安全： 數位轉型通常涉及雲遷移。雲安全包括安全配置、數據保護和訪問控制，以確保雲基礎設施和應用程序的安全。

  4. 數據保護與加密： 尤其是隨著數位轉型的推進，對靜態和傳輸中的敏感數據進行加密至關重要。

  5. 端點安全： 數位轉型增加了對移動設備、物聯網（IoT）和其他端點的依賴，這可能會帶來安全漏洞。端點安全將保護擴展到所有連接到網絡的設備。

  6. 合規性與風險管理： 確保法規遵從（例如GDPR、CCPA、APPI）對於避免罰款和建立客戶信任至關重要。

  7. 事件響應與恢復： 在發生安全漏洞的情況下，精心規劃的事件響應和災後恢復策略對於將停機時間和財務影響降到最低至關重要。

自建與購買網絡安全解決方案的選擇

在決定是自建網絡安全解決方案還是外包時，需要考慮組織需求、預算和長期目標。

自建

優勢： - 定制化： 自建解決方案可以高度針對組織的獨特需求、行業法規和架構進行定制。 - 完全控制： 自建團隊可完全控制網絡安全數據、實踐和響應。 - 專業技能的擴展： 自建專業技能允許組織隨著數位計劃的擴展主動調整其網絡安全防禦。

劣勢： - 初期投資高昂： 建立和維護內部網絡安全資源密集，需要在招聘、培訓和技術上投入大量資金。 - 持續培訓需求： 網絡安全需要持續的教育來應對新興威脅，這是內部團隊必須優先考慮的挑戰。 - 部署速度較慢： 與即用型解決方案相比，內部開發能力可能需要更長的時間。

購買（外包）

優勢： - 快速部署： 外包解決方案能夠更快地實施，滿足資源有限或內部技術人才缺乏組織的即時需求。 - 訪問先進技術： 供應商提供的尖端工具、威脅情報和專業知識通常超過內部團隊能夠提供的水平。 - 降低前期成本： SaaS或託管安全服務減少了基礎設施的前期投資需求，並降低了初期設置成本。

劣勢： - 定制化較少： 外部解決方案可能不太針對組織的具體架構或合規要求。 - 數據隱私問題： 外包涉及將敏感數據交付給第三方，可能會增加如數據駐留和合規等領域的風險。 - 整合挑戰： 將外包解決方案與現有系統整合可能具有挑戰性，需要與組織的技術堆棧和流程兼容。

價值與成本的取捨

成功的數位轉型要求將網絡安全視為戰略資產，而非僅僅是支出項目。

價值導向：網絡安全作為投資

強調價值的網絡安全策略認為其是支持數位轉型的必要投資。這種方法強調建立客戶信任、保護知識產權和確保服務連續性，這些都能增強競爭優勢。

成本導向：網絡安全作為開支

成本導向的心態優先考慮減少支出，僅追求最低限度的法規遵從。這種方法雖然降低了初始費用，但可能導致對複雜攻擊的保護不充分。

結論

對於成功的數位轉型，網絡安全能力不可或缺。建設與購買的選擇取決於組織規模、預算和具體需求。將網絡安全視為投資而非成本，能帶來更大的長期價值。

  • 3 min read

Embracing Failure - The Pathway to Success

“Losing billions of dollars is no big deal.” At first glance, this statement might seem shocking, but it holds a powerful truth about the relationship between risk, failure, and success. To truly understand this mindset, we must recognize that extraordinary growth and innovation come from bold risks, embracing uncertainty, and being unafraid of failure.

Risk: The Fuel for Growth

True growth doesn’t happen by playing it safe. It happens when you step out of your comfort zone, take risks, and create opportunities to fail. It’s through these daring decisions that real strength and depth of thought are forged. Without risk, there’s no progress, and without failure, there’s no learning.

While many people and organizations see failure as something to avoid at all costs, this belief only holds them back. Playing it safe might protect your finances and reputation in the short term, but it will also keep you from ever achieving the kind of breakthrough success you dream of. The truth is, if you avoid failure, you avoid growth.

Turning Failure into Success

Failure isn’t the end. In fact, it’s often the very beginning of success. The key is how you respond to failure. "Successful failure" isn’t about celebrating mistakes—it’s about taking the lessons from those setbacks and applying them in ways that propel you forward. Every failure contains valuable insights, and those insights can make the difference between stagnation and transformation.

Those who are willing to experiment, make mistakes, and learn from them are the ones who will innovate and lead. True innovation requires testing the unknown, and if you already know something will succeed, it’s not really an experiment. This mindset—embracing the unknown and being open to failure—is what drives the most groundbreaking advances.

Learning from Setbacks: The Apollo 1 Example

One powerful example of failure leading to future success comes from space exploration. The tragedy of the "Apollo 1" disaster shook the world, but it also provided critical lessons that would ensure the success of future space missions. What seemed like a devastating failure at the time became the foundation for safer, more successful missions that followed.

In the same way, any failure—whether in business or personal life—can become a stepping stone to success if you’re willing to learn from it. Failures offer valuable data, insights, and experience that can shape your next steps, help you avoid repeated mistakes, and lead to greater achievements in the future. The only real failure is failing to learn.

Invention and Failure: A Dynamic Duo

To innovate, you must be willing to fail. It’s as simple as that. The process of invention is messy, unpredictable, and often fraught with setbacks. But without those failures, true breakthroughs would never happen. If you’re unwilling to take risks, you’ll never create anything new or revolutionary. As they say, if you know something is going to work, it’s not an experiment—it's routine. But to transform, you must break away from routine and embrace the unknown.

Many of the world’s greatest successes are built not just on smart decisions, but on the insights gained from countless wrong ones. Every misstep adds to your knowledge, experience, and resilience, making you stronger and better prepared for the future.

Conclusion: Fail Forward

The road to success is paved with failures, but those failures are not something to be feared—they are to be embraced. Each failure is a lesson, a stepping stone, a necessary part of the journey toward innovation and greatness.

Don’t fear failure; fear staying in your comfort zone. The greatest breakthroughs happen when you push boundaries, take risks, and open yourself up to the possibility of failure. Because in the end, it’s not about how many times you fall—it’s about how many times you get back up, ready to apply what you’ve learned.

Every setback is just a setup for your next leap forward. Failure is not the opposite of success—it’s the foundation of it.

  • in zh
  • 1 min read

擁抱失敗 - 通往成功的道路

「損失數十億美元並不算什麼大事。」這句話乍看之下可能令人震驚，但它揭示了一個關於風險、失敗與成功之間關係的重要真相。要真正理解這種思維模式，我們必須認識到，非凡的成長和創新來自於大膽的冒險、擁抱不確定性，以及無懼失敗。

風險：成長的燃料

真正的成長並非來自於墨守成規，而是當你踏出舒適區，勇於冒險並創造失敗的機會時才會發生。正是通過這些大膽的決策，真正的力量和深度思考得以塑造。沒有風險，就沒有進步；沒有失敗，就沒有學習。

許多人和組織將失敗視為必須避免的事，但這種信念只會限制他們的發展。表面上看，墨守成規或許能在短期內保護你的財務和聲譽，但同時也會讓你無法實現夢想中的突破性成功。事實是，如果你避免失敗，就等於避免成長。

將失敗轉化為成功

失敗不是終點。事實上，它往往是成功的起點。關鍵在於你如何應對失敗。「成功的失敗」並不是在慶祝錯誤，而是將那些挫折中的教訓應用於推動你前進的方向。每次失敗都蘊藏著寶貴的見解，這些見解可能成為停滯與變革之間的分水嶺。

那些願意嘗試、犯錯並從中學習的人，才是最終能夠創新和領先的人。真正的創新需要測試未知，如果你已經知道某件事情會成功，那它就不是真正的實驗。這種心態——擁抱未知並對失敗保持開放——驅動著最具突破性的進展。

從挫折中學習：「阿波羅1號」的例子

太空探索中的一個強有力的例子是「阿波羅1號」災難。這場悲劇震驚了世界，但同時也提供了關鍵的教訓，保證了後續太空任務的成功。當時看似令人心碎的失敗，最終成為未來更安全、更成功任務的基石。

同樣地，無論是在商業還是個人生活中，任何失敗都可以成為通向成功的墊腳石，只要你願意從中學習。失敗提供了寶貴的數據、見解和經驗，這些可以塑造你的下一步行動，幫助你避免重蹈覆轍，並在未來實現更大的成就。唯一真正的失敗是未能從中學習。

創新與失敗：動態雙贏

要創新，就必須願意失敗。這是簡單的道理。發明的過程是混亂的、不可預測的，並且經常充滿挫折。但如果沒有這些失敗，真正的突破就不會發生。如果你不願冒險，就永遠無法創造任何新的或革命性的東西。正如他們所說，如果你知道某件事情一定會成功，那就不是實驗——而是例行公事。但要轉型，你必須打破常規，擁抱未知。

世界上許多最偉大的成功不僅建立在明智的決策之上，更基於從無數錯誤中獲得的見解。每一次失誤都增加了你的知識、經驗和韌性，使你變得更強大、更能應對未來。

結論：向前失敗

通往成功的道路佈滿了失敗，但這些失敗並非值得害怕——它們值得被擁抱。每一次失敗都是一個教訓、一個墊腳石，是邁向創新和偉大的必要部分。

不要害怕失敗；害怕待在你的舒適區。 最大的突破發生在你突破邊界、冒險並向失敗敞開心扉的時候。因為最終，這不是關於你跌倒了多少次——而是關於你多少次站起來，準備應用你所學到的。

每一次挫折都是為你的下一次飛躍做準備。失敗不是成功的對立面——它是成功的基石。

  • 5 min read

Understanding Logging in Kubernetes - From Containers to Nodes

Logging is an essential component of monitoring and maintaining applications, particularly in a complex environment like Kubernetes. Logs provide valuable insights into how an application behaves, identifying errors, performance issues, and security threats. However, logging in Kubernetes is challenging due to the dynamic and distributed nature of the platform. This blog post will explain where logs originate within Kubernetes, the importance of log collectors, and compare popular logging solutions such as Fluentd, Fluent Bit, and AWS CloudWatch Container Insights.

Where Do Logs Come From in Kubernetes?

In Kubernetes, logs are generated at various layers, including:

  • Containers: Each container in a Kubernetes pod generates its own logs. These logs are written to the container's standard output (stdout) and standard error (stderr). The container runtime (such as Docker or containerd) manages these logs.

  • Pods: Since a pod can have multiple containers, it aggregates logs from all its containers. However, Kubernetes does not automatically store or forward pod logs. They are ephemeral and typically vanish when a pod is terminated or restarted.

  • Nodes: Each Kubernetes node has a logging agent that collects logs from all the pods running on the node. These logs are stored locally on the node, but like pod logs, they are temporary and could be lost if the node fails or is replaced.

Why Not Just Use AWS CloudWatch for EKS?

AWS CloudWatch is a powerful tool for monitoring and logging in AWS environments, including Elastic Kubernetes Service (EKS). While it may seem convenient to use CloudWatch for EKS logging, it has limitations when managing the full spectrum of log collection and processing needs.

Limitations of AWS CloudWatch for Kubernetes Logging:

  • Lack of Flexibility: CloudWatch works well for simple, centralized logging, but it may not offer the flexibility needed to manage complex Kubernetes environments. It doesn't support advanced log parsing, enrichment, or filtering natively, which are often required in real-world applications.

  • Cost Management: CloudWatch pricing is based on the volume of logs ingested and stored. In a Kubernetes environment where log volumes can grow exponentially, this can lead to unexpectedly high costs without offering enough control over data retention and processing.

  • Multi-cluster Aggregation: Kubernetes often runs across multiple clusters. CloudWatch isn't designed to natively support cross-cluster log aggregation, which can make it challenging to get a unified view of your logs.

Given these challenges, many teams opt for specialized log collectors to gain better control over their logging infrastructure.

The Need for a Log Collector

A log collector is a tool designed to aggregate, process, and forward logs from different parts of the Kubernetes infrastructure. Instead of relying solely on CloudWatch, a log collector allows you to:

  • Process Logs Efficiently: Filter and transform logs in real time, only forwarding the necessary information to CloudWatch or other logging backends.
  • Enhance Log Enrichment: Enrich logs with additional metadata like pod labels, namespace, or node name, making it easier to analyze and search through logs.
  • Optimize Cost: Reduce the volume of logs sent to CloudWatch by filtering irrelevant logs, thus minimizing cost.
  • Centralized Aggregation: Collect logs from multiple clusters, enabling better observability across environments.

There are several tools available for collecting and managing logs in Kubernetes, including Fluentd, Fluent Bit, and AWS CloudWatch Container Insights. Each tool has its own advantages and trade-offs.

Fluentd

  • Overview: Fluentd is a full-fledged, open-source data collector designed to unify log data. It offers a wide range of plugins to integrate with various systems like Elasticsearch, S3, and CloudWatch.

  • Pros:

  • Highly customizable with over 500 plugins.
  • Supports advanced log processing, filtering, and transformation.

  • Works well in large, complex environments with heavy log processing needs.

  • Cons:

  • Heavier in terms of resource consumption due to its more extensive feature set.

  • Requires more configuration and tuning, which can be complex.

  • Use Case: Best suited for large-scale Kubernetes clusters where complex log management and advanced processing are needed.

Fluent Bit

  • Overview: Fluent Bit is a lightweight, fast log processor and forwarder that is part of the Fluentd ecosystem. It shares much of Fluentd's functionality but with a lower resource footprint, making it ideal for environments with limited resources.

  • Pros:

  • Lightweight and fast, ideal for resource-constrained environments.
  • Supports many of the same plugins as Fluentd, including integration with AWS services.

  • Less configuration overhead than Fluentd.

  • Cons:

  • Limited advanced processing capabilities compared to Fluentd.

  • Not as feature-rich, which may limit its use in more complex log aggregation pipelines.

  • Use Case: Ideal for lightweight logging needs, edge devices, or smaller Kubernetes clusters where resource efficiency is a priority.

AWS CloudWatch Container Insights

  • Overview: AWS CloudWatch Container Insights is a managed service provided by AWS to collect, aggregate, and visualize logs and metrics from your containerized applications on EKS.

  • Pros:

  • Seamless integration with AWS services, no need for additional setup.
  • Provides built-in visualizations and monitoring for Kubernetes metrics and logs.

  • Simplifies log collection for AWS-native Kubernetes environments.

  • Cons:

  • Limited customization and flexibility compared to Fluentd and Fluent Bit.
  • Can become expensive as log volume increases.

  • Primarily focuses on AWS, lacking multi-cloud or on-premise integration options.

  • Use Case: Best suited for teams fully committed to the AWS ecosystem and those looking for a managed logging service with minimal setup.

Conclusion

Logging in Kubernetes requires more than just capturing container output; it involves orchestrating logs across multiple layers of the platform. AWS CloudWatch can handle basic logging, but to get the most out of your logs while optimizing costs, a dedicated log collector is often necessary. Fluentd, Fluent Bit, and AWS CloudWatch Container Insights each provide unique benefits depending on your environment's scale and complexity.

  • Fluentd: Best for complex environments requiring extensive log processing and integration.
  • Fluent Bit: Lightweight and efficient for smaller clusters or environments where resource usage is a concern.
  • AWS CloudWatch Container Insights: An excellent option for those who want AWS-native integration with minimal setup but may not need the flexibility of the other solutions.

By choosing the right log collection strategy, you can ensure better observability and performance in your Kubernetes clusters while keeping costs under control.

  • in zh
  • 2 min read

瞭解 Kubernetes 中的日誌記錄 - 從容器到節點

日誌記錄是監控和維護應用程式的重要組成部分，尤其是在像 Kubernetes 這樣複雜的環境中。日誌能夠提供應用程式行為的寶貴見解，有助於識別錯誤、性能問題和安全威脅。然而，由於 Kubernetes 平台的動態和分散式特性，日誌記錄面臨著諸多挑戰。本篇文章將解釋 Kubernetes 中日誌的來源、日誌收集器的重要性，並比較 Fluentd、Fluent Bit 和 AWS CloudWatch Container Insights 等流行的日誌記錄解決方案。

Kubernetes 中的日誌從哪裡來？

在 Kubernetes 中，日誌產生於多個層次，包括：

  • 容器: 每個 Kubernetes pod 中的容器都會生成自己的日誌，這些日誌寫入到容器的標準輸出 (stdout) 和標準錯誤 (stderr) 中。容器執行環境（如 Docker 或 containerd）負責管理這些日誌。

  • Pod: Pod 可以包含多個容器，因此會聚合來自所有容器的日誌。然而，Kubernetes 並不會自動儲存或轉發 pod 的日誌。這些日誌是臨時的，通常會在 pod 終止或重啟時消失。

  • 節點: 每個 Kubernetes 節點都有一個日誌代理，負責收集該節點上運行的所有 pod 的日誌。這些日誌儲存在節點本地，但與 pod 日誌類似，如果節點故障或被替換，這些日誌也可能丟失。

為什麼不直接使用 AWS CloudWatch 來處理 EKS 的日誌？

AWS CloudWatch 是一款功能強大的工具，用於在 AWS 環境（包括 Elastic Kubernetes Service，簡稱 EKS）中進行監控和日誌記錄。雖然在 EKS 上使用 CloudWatch 似乎很方便，但在處理全面的日誌收集和處理需求時，它有一定的限制。

AWS CloudWatch 在 Kubernetes 日誌記錄中的局限性：

  • 靈活性不足: CloudWatch 對於簡單的集中式日誌記錄非常有用，但在管理複雜的 Kubernetes 環境時可能缺乏所需的靈活性。它不原生支持高級的日誌解析、豐富化或過濾，這些功能在實際應用中經常需要。

  • 成本管理: CloudWatch 的定價基於日誌的攝取量和儲存量。在 Kubernetes 環境中，日誌量可能呈指數級增長，這可能導致成本出乎意料地高昂，並且缺乏對數據保留和處理的足夠控制。

  • 多集群聚合: Kubernetes 通常運行於多個集群之上。CloudWatch 沒有為跨集群日誌聚合設計原生支持，這可能使得獲得統一的日誌視圖變得困難。

鑑於這些挑戰，許多團隊選擇使用專門的日誌收集器來更好地控制其日誌基礎架構。

日誌收集器的必要性

日誌收集器是一種專門設計用於聚合、處理和轉發來自 Kubernetes 基礎設施中不同部分的日誌的工具。相比完全依賴 CloudWatch，日誌收集器能夠讓你：

  • 高效處理日誌: 實時過濾和轉換日誌，只將必要的信息轉發至 CloudWatch 或其他日誌後端。
  • 增強日誌豐富化: 通過添加 pod 標籤、命名空間或節點名稱等額外元數據來豐富日誌，讓日誌分析和搜尋變得更加容易。
  • 優化成本: 通過過濾掉不相關的日誌來減少發送至 CloudWatch 的日誌量，從而降低成本。
  • 集中聚合: 從多個集群收集日誌，實現更好的環境觀察能力。

流行日誌收集器的比較：Fluentd、Fluent Bit 和 AWS CloudWatch Container Insights

以下是幾款 Kubernetes 日誌記錄工具的優劣比較：

Fluentd

  • 概述: Fluentd 是一款全功能的開源數據收集器，旨在統一日誌數據。它提供了多種插件來與 Elasticsearch、S3 和 CloudWatch 等系統集成。

  • 優點:

  • 擁有超過 500 個插件，功能非常強大。
  • 支持高級日誌處理、過濾和轉換。

  • 適用於大型、複雜的環境，特別是需要大量日誌處理的場景。

  • 缺點:

  • 資源消耗較大，因為其功能更為全面。

  • 配置和調整可能比較複雜。

  • 適用場景: 適用於需要複雜日誌管理和高級處理的大型 Kubernetes 集群。

Fluent Bit

  • 概述: Fluent Bit 是 Fluentd 生態系統的一部分，是一個輕量級且快速的日誌處理和轉發工具。它與 Fluentd 功能相似，但資源佔用更低，適用於資源有限的環境。

  • 優點:

  • 輕量且快速，非常適合資源有限的環境。
  • 支持與 AWS 服務集成的多種插件。

  • 配置簡單，操作門檻低。

  • 缺點:

  • 與 Fluentd 相比，進階處理能力有限。

  • 功能不如 Fluentd 豐富，因此可能無法滿足複雜的日誌處理需求。

  • 適用場景: 適合輕量級日誌需求的場景，例如資源受限的 Kubernetes 集群或邊緣設備。

AWS CloudWatch Container Insights

  • 概述: AWS CloudWatch Container Insights 是 AWS 提供的一項管理服務，用於從 EKS 上的容器化應用收集、聚合和可視化日誌及指標。

  • 優點:

  • 與 AWS 服務無縫集成，無需額外配置。
  • 提供內建的 Kubernetes 日誌及指標可視化功能。

  • 簡化了 AWS 原生 Kubernetes 環境的日誌收集。

  • 缺點:

  • 與 Fluentd 和 Fluent Bit 相比，定制性和靈活性不足。
  • 隨著日誌量增加，成本可能變得高昂。

  • 主要針對 AWS，缺乏多雲或本地部署的集成選項。

  • 適用場景: 適用於完全依賴 AWS 生態系統的團隊，或者需要最少配置的托管日誌服務。

結論

在 Kubernetes 中進行日誌記錄不僅僅是捕獲容器輸出，還需要協調來自平台多層的日誌。AWS CloudWatch 能夠處理基本日誌記錄，但若要最大化日誌的價值，同時控制成本，通常需要專門的日誌收集器。Fluentd、Fluent Bit 和 AWS CloudWatch Container Insights 根據環境的規模和複雜性提供不同的優勢：

  • Fluentd: 適用於需要廣泛日誌處理和集成的複雜環境。
  • Fluent Bit: 適合資源受限的集群或需要高效日誌記錄的小型環境。
  • AWS CloudWatch Container Insights: 適合希望最小化配置的 AWS 原生集成團隊。

選擇正確的日誌收集策略，可以確保 Kubernetes 集群的更佳可觀察性和性能，同時控制成本。

  • 3 min read

Mastering Long-Term Thinking - How to Build a Resilient and Innovative Organization

In a remote mountain in West Texas, a unique project is taking shape. A clock, known as the 10,000-Year Clock, is being built to last and tick for 10,000 years. Its century hand moves once every 100 years, and its cuckoo emerges once every millennium. The clock is a symbol of long-term thinking, highlighting the value of considering the future—both in business and in life.

The commitment to long-term thinking is critical to success in many areas. When organizations focus only on short-term results, they often find themselves competing in crowded spaces. But extending the time horizon allows for innovation, growth, and endeavors that short-term thinking could never support.

The 10,000-Year Vision Applied to the Digital Era

So, how does this principle of long-term thinking apply to the digital era? In today's fast-paced world, many organizations struggle to balance speed with sustainability. Digital strategies are often focused on quick wins, but a different approach is needed: thinking long-term, even in a rapidly changing environment. Key elements to avoid stagnation include customer obsession, skepticism about proxies, adopting external trends, and making high-velocity decisions. These essentials aren’t tied to financial or market goals—they are cultural elements that leaders can control.

This approach defends against becoming what some call a "Day 2" company—a company that is slow, reactive, and focused more on maintaining the status quo than on innovating. Organizations need to be cautious not to let processes dominate outcomes. Instead, they should constantly ask, "Do we own the process, or does the process own us?"

Innovation through Trial and Error

Innovation is born from a culture of "forward failure"—the idea that failure is a necessary step toward success. Many successful projects began as small experiments, nurtured over time, driven by a set of guiding principles. These principles create a framework for a culture that embraces trial and error. High-judgment failure—where an initiative was worth trying but didn’t work—should lead to learning and adapting. The cycle of failure, learning, and trying again drives the most important successes.

This iterative process allows organizations to build momentum and discover what works. It’s about selecting people who are dissatisfied with the status quo, people who notice small inefficiencies and want to fix them. Innovation, in this context, is not about avoiding failure but learning from it and moving forward with greater insight.

Avoiding Bureaucracy and Embracing Speed

One of the biggest threats to innovation is bureaucracy. Bureaucracy slows down decision-making and stifles creativity. High-performing individuals often hate bureaucracy, while underperformers tend to hide behind it, creating the kind of friction that slows progress. Strong processes with measurable outcomes can help eliminate bureaucracy, exposing underperformers and allowing top talent to excel.

Recognizing bureaucracy isn’t always easy. It often manifests when rules can’t be explained, when they don’t benefit the customer, or when there is no clear path for resolving issues. When these symptoms arise, bureaucracy is likely creeping in. High standards and attention to detail are essential to avoiding this pitfall, ensuring that processes serve the business rather than becoming burdensome.

Conclusion: The Key to Long-Term Success

The philosophy is clear: long-term thinking, a culture of innovation, and a resistance to bureaucracy are essential to staying competitive in the digital age. Whether you’re running a small startup or a global enterprise, these principles can help build a resilient organization that thrives on change and embraces the future. By focusing on long-term goals, fostering a culture of experimentation, and eliminating unnecessary bureaucracy, you can set the stage for sustained success, just like the 10,000-Year Clock—built to last.

  • in zh
  • 1 min read

掌握長期思維：如何打造具有韌性與創新精神的組織

在美國德克薩斯州西部的偏遠山區，一個獨特的項目正在成形。一座名為「一萬年鐘」的時鐘正被建造，設計目標是能持續運行並計時一萬年。這座時鐘的世紀指針每百年移動一次，而千年鳥則每千年出現一次。這座時鐘象徵著長期思維，強調在商業與生活中考慮未來的價值。

對於長期思維的承諾在許多領域都是成功的關鍵。當組織僅關注短期結果時，往往會發現自己在擁擠的市場中競爭。但延長時間視野則可以帶來創新、增長以及短期思維所無法支持的努力。

一萬年願景在數位時代的應用

那麼，長期思維的原則如何應用於數位時代呢？在當今這個節奏快速的世界中，許多組織都在努力平衡速度與可持續性。數位策略往往著眼於快速成果，但一種不同的方式是必要的：在快速變化的環境中思考長遠。避免停滯的關鍵元素包括：以客戶為中心、對代理指標保持懷疑、採納外部趨勢，以及進行高速度決策。這些要素並不依附於財務或市場目標，而是領導者可以掌控的文化層面。

這種方法能防止組織淪為所謂的「第二天公司」——一個遲鈍、被動，並且更關注維持現狀而非創新的公司。組織需要謹慎，不能讓流程凌駕於結果之上。相反地，他們應該不斷問自己：「我們是擁有流程，還是被流程所掌控？」

通過試驗與錯誤實現創新

創新的誕生源於一種「向前失敗」的文化——失敗是通往成功的必要步驟。許多成功的項目始於小型實驗，在時間的滋養下成長，由一套指導原則驅動。這些原則為一種接受試驗與錯誤的文化建立了框架。高判斷力失敗——即值得嘗試但未成功的倡議——應引導出學習與調整。這種失敗、學習與再嘗試的循環推動了最重要的成功。

這種迭代過程使組織能夠建立動能並發現有效的方法。它關乎選擇那些對現狀不滿意的人——那些注意到小問題並希望改進的人。在這種語境中，創新不是關於避免失敗，而是從中學習，並以更深刻的洞察力前進。

避免官僚主義並擁抱速度

創新的最大威脅之一是官僚主義。官僚主義會拖慢決策過程並扼殺創造力。高績效個體通常討厭官僚主義，而低績效者則傾向於躲在其中，造成減緩進展的摩擦。透過具有可衡量結果的強大流程可以幫助消除官僚主義，暴露低績效者，並讓頂尖人才得以發揮。

認識到官僚主義並不總是容易的。它往往表現在規則無法解釋、規則不利於客戶，或問題解決路徑不明確時。當這些症狀出現時，官僚主義很可能正在滲入。高標準與對細節的關注是避免這種陷阱的關鍵，確保流程為業務服務，而非成為負擔。

結論：長期成功的關鍵

理念非常清楚：長期思維、創新文化，以及抵制官僚主義是保持競爭力的必要條件。無論您經營的是一家小型初創公司還是全球企業，這些原則都有助於打造一個在變化中茁壯成長並擁抱未來的韌性組織。通過專注於長期目標、培養實驗文化以及消除不必要的官僚主義，您可以為持續的成功奠定基礎，就像一萬年鐘一樣——為長久而建。

  • 3 min read

Understanding Kubernetes Autoscaling - Speed and Traffic Capacity

Autoscaling is a powerful feature in Kubernetes that ensures your applications scale dynamically to handle increasing or decreasing traffic. However, one common question is: How fast can Kubernetes scale out, and how much traffic can it handle?

Two Levels of Horizontal Scaling

In Kubernetes, autoscaling operates on two levels: Pod-level autoscaling and Node-level autoscaling.

1. Pod-level Autoscaling (Horizontal Pod Autoscaler - HPA)

The Horizontal Pod Autoscaler (HPA) monitors the resource usage of your pods, such as CPU or memory, and automatically scales the number of replicas up or down based on demand. Here's what you need to know:

  • Scaling Speed: Pod-level autoscaling is generally fast, typically scaling out in less than a minute depending on how the cluster is configured. However, certain configurations can make scaling even faster:
  • PriorityClass: Pods can have different priorities based on their importance. Critical pods with higher priority can be scheduled faster during scaling events. This ensures that important workloads are prioritized when resources are constrained.

  • Pinned and Pre-scaled HPA: You can configure the HPA to pre-scale pods if you anticipate spikes in traffic. This allows the system to respond quicker to traffic surges without waiting for resource thresholds to be breached.

  • Traffic Capacity: The amount of traffic your pods can handle depends on the resource allocation (e.g., CPU, memory) for each pod. If each pod can handle a fixed number of requests per second, scaling out additional pods ensures that the overall system can manage larger traffic loads. By carefully configuring pod resource limits and HPA thresholds, you can optimize the system to balance resource efficiency and traffic capacity.

2. Node-level Autoscaling (Cluster Autoscaler or Karpenter)

When scaling pods isn't enough, Kubernetes can also scale nodes (virtual machines) in the cluster to accommodate more pods.

  • Scaling Speed: Scaling nodes can take longer than scaling pods because it involves provisioning new instances from your cloud provider (AWS, GCP, etc.). Typically, scaling out nodes can take a few minutes, depending on the cloud provider's infrastructure and the size of the instance. To optimize node-level scaling:
  • Karpenter: A newer alternative to Cluster Autoscaler, Karpenter optimizes node scaling by efficiently provisioning nodes with the exact resources required. It is often faster than the traditional autoscaler and can bring up nodes in seconds.

  • Over-provisioning: To mitigate the time it takes to scale nodes, you can "over-provision" nodes. This means keeping a small buffer of idle nodes that are ready to handle a sudden surge in traffic. This approach ensures that your system can scale instantly without waiting for new nodes to spin up.

  • Traffic Capacity: At the node level, the capacity to handle traffic is related to how many pods can be scheduled on the available nodes. By scaling out nodes, you increase the cluster's total resource pool, allowing for more pods and thus more traffic handling capability.

Conclusion

Kubernetes autoscaling is highly dynamic, with two distinct layers working together to ensure your application scales as needed.

  • Pod-level scaling is rapid, generally happening in less than a minute, especially when pre-scaled or with proper PriorityClass settings.
  • Node-level scaling may take a few minutes, but tools like Karpenter and over-provisioning can help speed up the process.

By effectively managing both pod and node autoscaling, you can ensure that your application can handle large traffic surges while maintaining efficiency.