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2024

  • 3 min read

Microsoft Fabric - Revolutionizing Data Analytics in the AI Era

In today's fast-paced digital world, data is the lifeblood of AI, and the landscape of data and AI tools is vast, with offerings like Hadoop, MapReduce, Spark, and more. As the Chief Information Officer, the last thing you want is to become the Chief Integration Officer, constantly juggling multiple tools and systems. Enter Microsoft Fabric, a game-changing solution designed to simplify and unify data analytics for the era of AI.

From Fragmentation to Unity: The Evolution of Data Analytics

Microsoft Fabric represents a paradigm shift in data analytics, moving from a fragmented landscape of individual components to a unified, integrated stack. It transforms the approach from relying on a single database to harnessing the power of all available data. Most importantly, it evolves from merely incorporating AI as an add-on to embedding generative AI (Gen AI) into the very fabric of the platform.

The Four Core Design Principles of Microsoft Fabric

  1. Complete Analytics Platform: Microsoft Fabric offers a comprehensive solution that is unified, SaaS-fied, secured, and governed, ensuring that all your data analytics needs are met in one place.
  2. Lake Centric and Open: At the heart of Fabric is the concept of "One Lake, One Copy," emphasizing a single data lake that is open at every tier, ensuring flexibility and openness.
  3. Empower Every Business User: The platform is designed to be familiar and intuitive, integrated seamlessly into Microsoft 365, enabling users to turn insights into action effortlessly.
  4. AI Powered: Fabric is turbocharged with AI, from Copilot acceleration to generative AI on your data, providing AI-driven insights to inform decision-making.

The Transition from Synapse to SaaS-fied Fabric

Microsoft Fabric marks a significant evolution from separate products like Azure Data Factory (ADF) and Azure Cosmos DB to a unified, seamless experience. This transition embodies the shift towards a SaaS (Software as a Service) model, characterized by ease of use, cost efficiency, scalability, and accessibility.

OneLake: The OneDrive for Data

OneLake stands as the cornerstone of Microsoft Fabric, offering a single SaaS lake for the entire organization. It is automatically provisioned with the tenant, and all workloads store their data in intuitive workspace folders. OneLake ensures that data is organized, indexed, and ready for discovery, sharing, governance, and compliance, with Delta - parquet as the standard format for all tabular data.

Tailored Experiences for Different Personas

Microsoft Fabric caters to various personas, including data engineers, scientists, analysts, citizens, and stewards, providing optimized experiences for each. From executing tasks faster to making more data-driven decisions, Fabric empowers users across the board.

Copilot: AI Assistance for All

Copilot is a standout feature of Microsoft Fabric, offering AI assistance to enrich, model, analyze, and explore data in notebooks. It helps users understand their data better, create and configure ML models through conversation, write code faster with inline suggestions, and summarize and explain code for enhanced understanding.

Adhering to Design Principles

Microsoft Fabric adheres to key design principles, ensuring a unified SaaS data lake without silos, true data mesh as a service with OneLake, no lock-in with industry-standard APIs and open file formats, and comprehensive security and governance.

In conclusion, Microsoft Fabric is a transformative solution that simplifies and unifies data analytics in the era of AI. With its core design principles, it empowers business users, leverages AI power, and offers a seamless, SaaS-fied experience, making it an essential tool for any organization looking to harness the full potential of their data.

  • in zh
  • 1 min read

微軟 Fabric - 在 AI 時代革新數據分析

在今天的快節奏數位世界中，數據是 AI 的命脈，數據和 AI 工具的景象廣大，如 Hadoop、MapReduce、Spark 等等。作為首席信息官，你最不希望的就是變成首席集成官，不斷地操縱著多種工具和系統。微軟 Fabric 的出現，是一種革命性的解決方案，旨在簡化和統一 AI 時代的數據分析。

從碎片化到統一：數據分析的演變

微軟 Fabric 代表了數據分析的範疇變化，從由個別組件組成的碎片化景象轉變到一個統一、集成的堆疊。它將方法從依賴單一數據庫轉變到利用所有可用數據的力量。最重要的是，它從僅僅作為一種附加裝置將 AI 納入其中，發展到將生成性 AI (Gen AI) 深入到平台的根本中。

微軟 Fabric 的四大核心設計原則

  1. 完整的分析平台：微軟 Fabric 提供完全的解決方案，這是統一的，SaaS 化的，安全的，並受到監管，確保所有您的數據分析需求均在一個地方得到滿足。
  2. 湖心且開放：Fabric 的核心是“一湖、一份”的概念，強調一個在每一階層都開放的單一數據湖，確保靈活性和開放性。
  3. 賦權每一個商業用戶：該平台設計得熟悉且直觀，無縫集成到微軟 365 中，使用者可以毫不費力地將見解轉化為行動。
  4. AI 驅動：Fabric 用 AI 加速，從副駕駛加速到在您的數據上生成 AI，提供 AI 驅動的見解以通報決策。

從 Synapse 到 SaaS 化的 Fabric 的轉變

微軟 Fabric 標誌了從像 Azure Data Factory (ADF) 和 Azure Cosmos DB 這樣的獨立產品向統一，無縫體驗的重大演變。這次轉變體現了朝向 SaaS (Software as a Service) 模型的轉變，其特點是易於使用，成本效益高，可擴展性強和易於取得。

OneLake：數據的 OneDrive

OneLake 是微軟 Fabric 的基石，為整個組織提供單一的 SaaS湖。它自動與租戶一起提供，所有工作負載都將其數據存儲在直觀的工作區文件夾中。OneLake 確保數據組織有序，有索引，並且準備好進行發現，共享，治理和遵守，Delta-parquet 是所有表格數據的標準格式。

為不同人群提供定制的體驗

微軟 Fabric 適合各種人物角色，包括數據工程師，科學家，分析師，公民，和監管者，為每一個都提供優化的體驗。從執行任務更快到作出更多以數據驅動的決策，Fabric 賦權給各種使用者。

副駕：所有人的 AI 幫助

副駕是微軟 Fabric 的一個突出特點，提供 AI 協助來豐富，建模，分析，並在筆記本中探索數據。它幫助用戶更好地理解他們的數據，通過對話創建並配置 ML 模型，更快地寫出代碼，並彙總並解釋代碼以增強理解。

堅持設計原則

微軟 Fabric 遵循關鍵設計原則，確保一個統一的 SaaS 數據湖，無孤島，真正的數據網格作為 OneLake 的服務，無鎖定，具有行業標準 API 和開放文件格式，以及全面的安全性和治理。

總之，微軟 Fabric 是一種改革性的解決方案，大大簡化了 AI 時代的數據分析並加以統一。通過其核心設計原則，它賦權於商業用戶，利用 AI 的力量，並提供無縫的，SaaS 化的體驗，使其成為任何希望充分利用其數據潛力的組織的必須工具。

  • 2 min read

A Pragmatic Approach Towards CDK for Terraform

Infrastructure as Code (IaC) has revolutionized the way we manage and provision resources in the cloud. Terraform, by HashiCorp, has been a leading tool in this space, allowing users to define infrastructure through declarative configuration files. However, with the advent of the Cloud Development Kit for Terraform (CDKTF), developers can now leverage the power of programming languages they are already familiar with, such as TypeScript, Python, Java, C#, and Go, to define their infrastructure.

Building Blocks of CDK for Terraform

CDK for Terraform is built on top of the AWS Cloud Development Kit (CDK) and uses the JSII (JavaScript Interop Interface) to enable publishing of constructs that are usable in multiple programming languages. This polyglot approach opens up new possibilities for infrastructure management.

The foundational classes to build CDKTF applications are:

  • App Class: This is the container for your infrastructure configuration. It initializes the CDK application and acts as the root construct.
  • Stack Class: A stack represents a single deployable unit that contains a collection of related resources.
  • Resource Class: This class represents individual infrastructure components, such as an EC2 instance or an S3 bucket.
  • Constructs: Constructs are the basic building blocks of CDK apps. They encapsulate logic and can be composed to create higher-level abstractions.

When to Use CDK for Terraform

CDK for Terraform is a powerful tool, but it's not always the right choice for every project. Here are some scenarios where CDKTF might be a good fit:

  • Preference for Procedural Languages: If your team is more comfortable with procedural programming languages like Python or TypeScript, CDKTF allows you to define infrastructure using these languages instead of learning a new domain-specific language (DSL) like HCL (HashiCorp Configuration Language).
  • Need for Abstraction: As your infrastructure grows in complexity, creating higher-level abstractions can help manage this complexity. CDKTF enables you to create reusable constructs that encapsulate common patterns.
  • Comfort with Cutting-Edge Tools: CDKTF is a relatively new tool in the Terraform ecosystem. If your team is comfortable adopting new technologies and dealing with the potential for breaking changes, CDKTF can offer a more dynamic and flexible approach to infrastructure as code.

Conclusion

CDK for Terraform offers a pragmatic approach for teams looking to leverage their existing programming skills to define and manage cloud infrastructure. By providing a familiar language interface and enabling the creation of reusable constructs, CDKTF can help streamline the development process and manage complexity in large-scale deployments. However, it's essential to evaluate whether your team is ready to adopt this cutting-edge tool and whether it aligns with your project's needs.

  • in zh
  • 1 min read

對Terraform的CDK採取實用方法

基礎設施即代碼（IaC）已經使我們管理和提供雲端資源的方式進行了革命性的改變。由HashiCorp開發的Terraform在這個領域中一直領先，允許用戶通過聲明性配置文件來定義基礎設施。然而，隨著Terraform的雲端開發套件（CDKTF）的出現，開發者現在可以利用他們已經熟悉的程式設計語言的力量，例如TypeScript、Python、Java、C#和Go，來定義他們的基礎設施。

Terraform CDK的構建塊

Terraform的CDK是建立在AWS的雲端開發套件（CDK）之上的，並使用JSII（JavaScript Interop Interface）來啟用在多種程式設計語言中可用的構建塊的發佈。這種多語言方式為基礎設施管理打開了新的可能性。

構建CDKTF應用的基礎類別包括：

  • 應用類別：這是您的基礎設施配置的容器。它初始化CDK應用並充當根構建塊。
  • 堆棧類別：一個堆棧代表一個包含了一系列相關資源的單一可部署單位。
  • 資源類別：這個類別代表單個基礎設施組件，如EC2實例或S3存储桶。
  • 構建塊：構建塊是CDK應用的基本構建塊。他們封裝邏輯並可以組合創建更高級別的抽象。

何時使用Terraform的CDK

Terraform的CDK是一個強大的工具，但並非每個項目都是最好的選擇。以下是一些CDKTF可能適合的情況：

  • 偏好程序式語言：如果您的團隊更熟悉如Python或TypeScript等程序式程式設計語言，CDKTF允許您使用這些語言而不是學習新的特定領域語言（DSL）如HCL（HashiCorp配置語言）來定義基礎設施。
  • 需要抽象：隨著您的基礎設施變得越來越複雜，創建更高級別的抽象可以幫助管理這種複雜性。CDKTF使您能夠創建封裝常見模式的可重用構建塊。
  • 對前沿工具的熟悉：CDKTF在Terraform生態系統中是一個相對新的工具。如果您的團隊樂於接受新技術並處理可能的重大變化，CDKTF可以提供一種更動態和靈活的基礎設施即代碼方法。

結論

Terraform的CDK為希望利用他們現有程式設計技能來定義和管理雲端基礎設施的團隊提供了一種實用的方法。通過提供熟悉的語言界面並啟用創建可重用構建塊的功能，CDKTF可以幫助簡化開發流程並管理大規模部署中的複雜性。然而，評估您的團隊是否準備好採用這種前沿工具，以及它是否與您的項目需求相符，這是至關重要的。

  • 2 min read

Centralized TLS Certificate Management with HashiCorp Vault PKI and Cert Manager

Embracing Zero Trust Security with HTTPS

In the era of zero-trust security, HTTPS has become a non-negotiable requirement for securing web traffic. It ensures that data transferred between users and websites is encrypted and authenticated, protecting against eavesdropping and man-in-the-middle attacks.

Understanding Public Key Infrastructure (PKI)

PKI is a framework that manages digital certificates and public-key encryption, enabling secure communication over the internet. It involves the creation, distribution, and management of digital certificates, which are used to verify the identity of entities and encrypt data.

Challenges with Traditional PKI Management

Managing PKI manually can be cumbersome and error-prone. The process typically involves:

  1. Generating a key pair and Certificate Signing Request (CSR).
  2. Submitting a support request for certificate issuance, which can take 1-10 days.
  3. Receiving and configuring the service with the returned certificate.
  4. Regularly rotating certificates to maintain security.

This manual approach is not only time-consuming but also increases the risk of misconfigurations and security breaches.

Simplifying PKI with HashiCorp Vault

HashiCorp Vault offers a solution to these challenges by automating the certificate management process. With Vault's PKI Secret Engine, certificates can be automatically requested and updated, streamlining the management of TLS certificates.

Vault PKI Secret Engine Configuration

To set up centralized TLS certificate management using HashiCorp Vault PKI and Cert Manager, follow these steps:

  1. Mount the PKI Secret Engine: Enable the PKI secret engine in Vault to start issuing certificates.

shell vault secrets enable pki

  1. Configure the Root CA: Set up a root Certificate Authority (CA) or an intermediate CA to sign certificates.

shell vault write pki/root/generate/internal \ common_name="example.com" \ ttl=87600h

  1. Enable Kubernetes Authentication: Configure Vault to authenticate Kubernetes service accounts, allowing Cert Manager to interact with Vault.

shell vault auth enable kubernetes

  1. Configure Cert Manager: Set up Cert Manager in your Kubernetes cluster to automatically request and renew certificates from Vault.

yaml apiVersion: cert-manager.io/v1 kind: Issuer metadata: name: vault-issuer spec: vault: path: pki/sign/example-dot-com server: https://vault.example.com auth: kubernetes: role: cert-manager secretRef: name: vault-auth key: token

By integrating HashiCorp Vault PKI with Cert Manager, you can achieve automated and centralized management of TLS certificates, reducing manual effort and enhancing security. This setup ensures that your services are always secured with up-to-date certificates, aligning with zero-trust security principles.

  • in zh
  • 1 min read

使用HashiCorp Vault PKI和Cert Manager進行集中式TLS證書管理

擁抱零信任安全的HTTPS

在零信任安全的時代，HTTPS已成為保護網路流量的必要條件。它確保用戶與網站之間傳輸的數據被加密並被認證，防止監聽和中間人攻擊。

理解公鑰基礎建設 (PKI)

PKI是一個管理數字證書和公鑰加密的架構，使得網路上的通訊更安全。它包含創建、分發和管理數字證書的過程，這些證書用於驗證實體的身份以及加密資料。

傳統PKI管理的挑戰

手動管理PKI可能很繁瑣且容易出錯。該過程通常包括：

  1. 產生一對鍵和證書簽名請求 (CSR)。
  2. 提交支援請求以進行證書發行，這可能需要1到10天。
  3. 接收並配置返回的證書到服務。
  4. 定期旋轉證書以維護安全性。

這種手動方法不僅花費時間，而且增加了配置錯誤和安全違規的風險。

使用HashiCorp Vault簡化PKI

HashiCorp Vault通過自動化證書管理過程，為這些挑戰提供了解決方案。有了Vault的PKI Secret Engine，可以自動請求並更新證書，簡化了TLS證書的管理。

Vault PKI Secret Engine配置

要使用HashiCorp Vault PKI和Cert Manager設置集中式TLS證書管理，請按照以下步驟操作：

  1. 安裝PKI Secret Engine：在Vault中啟用PKI secret engine以開始發行證書。

shell vault secrets enable pki

  1. 配置Root CA：設置一個根證書授權(CA)或一個中間CA來簽證書。

shell vault write pki/root/generate/internal \ common_name="example.com" \ ttl=87600h

  1. 啟用Kubernetes身分驗證：配置Vault以驗證Kubernetes服務帳戶，允許Cert Manager與Vault互動。

shell vault auth enable kubernetes

  1. 配置Cert Manager：在您的Kubernetes集群中設置Cert Manager，以自動請求並更新來自Vault的證書。

yaml apiVersion: cert-manager.io/v1 kind: Issuer metadata: name: vault-issuer spec: vault: path: pki/sign/example-dot-com server: https://vault.example.com auth: kubernetes: role: cert-manager secretRef: name: vault-auth key: token

通過整合HashiCorp Vault PKI和Cert Manager，您可以實現TLS證書的自動和集中管理，減少手工作業並提高安全性。此配置確保您的服務始終使用最新的證書進行保護，符合零信任安全原則。

  • 2 min read

Securing Your Applications Anywhere with F5 and Hashicorp Vault

In today's rapidly evolving digital landscape, the deployment and security of applications have become more crucial than ever. Traditional application deployment methods, which can take weeks or even months, are no longer sufficient. Modern applications require modern solutions that provide consistent security controls and policies regardless of where they are deployed.

The Evolving Security Landscape

The security landscape has been changing dramatically, with the number of Common Vulnerabilities and Exposures (CVEs) found in the last four years surpassing the total of the previous decade. This surge in vulnerabilities has led to increased investments in addressing CVEs, with a significant focus on protecting applications from these threats.

CVEs can have a profound impact on organizations, leading to an increase in alerts, risk analysis, and the need for standby resources. Additionally, they often result in unplanned or out-of-band patches, further straining IT resources and budgets.

Addressing the Challenge with F5 and Hashicorp

To stay ahead of the curve in this evolving landscape, organizations need a robust framework for patch management, golden images, and hardening. This is where F5 and Hashicorp come into play, offering solutions that can address these challenges effectively.

Centralized Management with BIG-IP Next

F5's BIG-IP Next provides centralized management of instances, acting as a Single Source of Truth and enabling control access from anywhere. This simplifies the management of application delivery and security, ensuring consistent policies across all environments.

Enhancing Workflows with Terraform

F5 BIG-IP Solutions for Terraform support customers in their digital transformation journey. However, one challenge is the high domain knowledge required for BIG-IP. By leveraging Terraform, organizations can improve their workflows through automation, using it as a layer of abstraction to simplify the management of BIG-IP configurations.

Dynamic Certificate Management with Vault

Hashicorp Vault plays a crucial role in dynamic certificate management, offering a cloud-agnostic solution that is fully automated. This ensures that there are no downtime or outages caused by expiring certificates. Additionally, Vault enhances security by enabling the use of short-lived certificates, reducing the risk of exposure.

Conclusion

In summary, securing applications in today's ever-changing landscape requires a modern approach. By leveraging the combined strengths of F5 and Hashicorp Vault, organizations can ensure consistent security controls and policies, streamline their workflows, and stay ahead of emerging threats. This not only protects their applications but also supports their digital transformation initiatives.

  • in zh
  • 1 min read

使用F5和Hashicorp Vault在任何地方保護您的應用程序

在今天迅速變化的數字化環境中，應用程序的部署和安全性比以往任何時候都更為重要。傳統的應用程序部署方法，可能需要幾週甚至幾個月的時間，已經不再足夠。現代的應用程序需要提供一致的安全控制和策略的現代解決方案，無論它們部署在何處。

不斷發展的安全景觀

安全性景觀的變化劇烈，過去四年中發現的常見漏洞和暴露（CVE）的數量超過了前十年的總數。這種漏洞的激增已經導致了在處理CVE方面的投資增加，其中重點放在保護應用程序不受這些威脅的影響上。

CVE可能對組織產生深遠的影響，導致警報增加，風險分析和待命資源的需求增加。此外，它們通常導致計劃外或超出頻寬的修補，進一步壓迫IT資源和預算。

使用F5和Hashicorp應對挑戰

為了在這個不斷變化的環境中領先一步，組織需要一個強大的用於修補管理、金像和強化的框架。這就是F5和Hashicorp發揮作用的地方，他們提供的解決方案可以有效地解決這些挑戰。

使用BIG-IP Next進行中心化管理

F5的BIG-IP Next 提供實例的中心化管理，充當唯一真理來源（Single Source of Truth），並能從任何地方控制訪問。這簡化了應用程序交付和安全的管理，確保所有環境的政策一致。

通過Terraform增強工作流程

F5 BIG-IP解決方案支援Terraform為客戶的數字化轉型之旅。然而，一個挑戰是 BIG-IP所需的高領域知識。通過利用Terraform，組織可以通過自動化改善其工作流程，將其作為一種抽象層來簡化BIG-IP配置的管理。

使用Vault進行動態證書管理

Hashicorp Vault在動態證書管理中發揮了關鍵作用，提供了一種完全自動化的，不受雲依賴的解決方案。 這確保了由證書到期引起的停機或損壞都不會發生。此外，Vault通過實現短期證書的使用，增強了安全性，降低了暴露的風險。

結論

總的來說，保護今天不斷變化的環境中的應用程序需要一種現代化的方法。 通過利用F5和Hashicorp Vault的組合優勢，組織可以確保一致的安全性控制和政策，簡化他們的工作流程，並在新的威脅面前保持領先地位。這不僅可以保護他們的應用程序，而且還可以支援他們的數字化轉型計劃。

  • 3 min read

Observability in GraphQL - Navigating the Complexities of Modern APIs

GraphQL has revolutionized the way we build and interact with APIs, offering a more flexible and efficient approach to data retrieval. However, with its advantages come new challenges in ensuring the reliability and performance of our systems. In this blog post, we'll explore the critical role of observability in managing and troubleshooting GraphQL-based architectures, focusing on three common issues: N+1 problems, cyclic queries, and the limitations of API gateways.

The Three Big Challenges of GraphQL

  1. N+1 Problem: This occurs when a single GraphQL query leads to multiple, sequential requests to a database or other data sources, resulting in inefficient data fetching and potential performance bottlenecks.
  2. Cyclic Queries: GraphQL's flexibility allows for complex queries, including those that unintentionally create cycles, leading to infinite loops and server crashes if not properly handled.
  3. API Gateways: While API gateways can provide a layer of security and abstraction, they can also obscure the underlying issues in GraphQL queries. They often return a generic 200 OK status, making it difficult to debug and troubleshoot specific problems.

The Evolution from Monitoring to Observability

Monitoring has traditionally been about answering the "what" - what's happening in our system? However, as our systems grow in complexity, simply knowing what's happening is no longer enough. We need to understand the "why" behind the issues. This is where observability comes in. It's an evolution of monitoring that provides deeper insights into the internal state of our systems, allowing us to diagnose and address problems that we might not have anticipated beforehand.

Leveraging Observability with Telemetry

One of the key components of observability is telemetry, which involves collecting and analyzing data about the operation of a system. OpenTelemetry has emerged as the new open-source standard for exposing observability data, offering a unified approach to collecting traces, metrics, and logs.

Traces in GraphQL

Traces are particularly useful in the context of GraphQL. They allow us to follow a request as it travels through a distributed system, providing a detailed view of how data is fetched and processed. This visibility is crucial for identifying and resolving issues like the N+1 problem or cyclic queries.

The Magic of Context Propagation and Instrumentation

The real magic of observability in GraphQL lies in two concepts: context propagation and instrumentation.

  • Context Propagation: This ensures that the metadata associated with a request is carried throughout the entire processing pipeline, allowing us to maintain a continuous trace of the request's journey.
  • Instrumentation: This involves adding monitoring capabilities to our codebase, enabling us to capture detailed information about the execution of GraphQL queries, including errors and performance metrics.

Instrumenting GraphQL for Error Capture

By instrumenting our GraphQL servers, we can capture errors and log them in a structured format. This data can then be fed into monitoring tools like Prometheus, allowing us to set up alerts and dashboards to track the health of our API.

Leveraging Open Source Tools for Observability

There are several open-source tools available that can enhance the observability of GraphQL systems. Jaeger, for example, is a popular tool for tracing distributed systems. It provides a visual representation of how requests flow through the system, making it easier to diagnose issues and understand the "why" behind the problems.

Conclusion

Observability is crucial for managing the complexities of modern GraphQL-based APIs. By leveraging telemetry, context propagation, and instrumentation, we can gain deeper insights into our systems, allowing us to proactively address issues and ensure the reliability and performance of our APIs. Open-source tools like OpenTelemetry and Jaeger play a vital role in this process, providing the necessary infrastructure to monitor and troubleshoot our systems effectively.

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在GraphQL中的可觀察性 - 瀏覽現代API的複雜性

GraphQL已經徹底改變了我們建立和與API互動的方式，提供了更靈活和高效的數據檢索方法。然而，其優勢也帶來了新的挑戰，以確保我們系統的可靠性和性能。在這篇博客文章中，我們將探討在管理和排除GraphQL基礎架構問題中可觀察性的重要角色，著重於以下三個常見問題：N+1問題，循環查詢，以及API閘道的限制。

GraphQL的三大挑戰

  1. N+1問題：當一個GraphQL查詢導致對資料庫或其他數據源的多個連續請求時，就會發生這種問題，導致數據獲取效率低下並可能產生性能瓶頸。
  2. 循環查詢：GraphQL的靈活性允許複雜的查詢，包括那些無意間創建的循環，如果沒有適當處理，可能會導致無窮迴圈和伺服器崩潰。
  3. API閘道：雖然API閘道可以提供一層安全和抽象的層次，但它們也可能掩蓋GraphQL查詢中的原始問題。它們通常返回一個通用的200 OK狀態，使得難以調試和排出具體的問題。

從監控到可觀察性的演變

監視傳統上是關於回答"什麼"的問題 - 我們的系統發生了什麼？然而，隨著我們的系統變得越來越複雜，僅僅知道發生了什麼已經不再足夠。我們需要理解問題背後的"為什麼"。這就是可觀察性的用途。它是監控的進化，提供了對我們系統內部狀態的深入理解，使我們能夠診斷和解決我們可能事先未能預見的問題。

利用遙測進行可觀察性

可觀察性的一個關鍵組件是遙測，涉及收集和分析系統操作的數據。OpenTelemetry已經成為公開觀察性數據的新開源標準，提供了一種統一的方法來收集追蹤，指標和日誌。

在GraphQL中的追蹤

在GraphQL的上下文中，追蹤特別有用。它們讓我們能夠在分散的系統中跟蹤一個請求，提供了一種關於如何獲取和處理數據的詳細視圖。這種能見度對於識別和解決像N+1問題或循環查詢這類問題至關重要。

上下文傳播和儀器化的魔力

GraphQL中的可觀察性真正的魔力在於兩個概念：上下文傳播和儀器化。

  • 上下文傳播：確保與請求相關的元數據在整個處理流程中被攜帶，使我們能維護對請求旅程的連續追蹤。
  • 儀器化：這涉及向我們的代碼庫添加監控功能，使我們能夠捕獲GraphQL查詢執行的詳細信息，包括錯誤和性能指標。

為錯誤捕獲進行GraphQL的儀器化

通過對我們的GraphQL服務器進行儀器化，我們可以捕捉以結構化格式記錄的錯誤。隨後，這些數據可以餵給像Prometheus之類的監控工具，使我們能設定警告和展板以跟蹤API的健康狀況。

利用開源工具進行可觀察性

有許多開源工具可以增強GraphQL系統的可觀察性。例如，Jaeger是一種用於追蹤分散系統的受歡迎的工具。它提供了一種在系統中請求流動的視覺化表示，使得診斷問題並理解問題背後的"為什麼"變得更為簡單。

結論

可觀察性對於管理現代基於GraphQL的API的複雜性至關重要。通過利用遙測，上下文傳播，以及儀器化，我們可以對我們的系統獲得更深入的理解，使我們能夠主動解決問題並確保我們的API的可靠性和性能。像OpenTelemetry和Jaeger這樣的開源工具在此過程中起著至關重要的角色，提供了監控和有效排除我們系統的必要基礎設施。