Building brighter futures: How YES tackles youth unemployment with Azure Database for MySQL
YES leverages Azure Database for MySQL to power South Africa’s largest youth employment initiative, delivering scalable, reliable systems that connect thousands of young people to jobs and learning opportunities.Exciting things on the horizon for PostgreSQL fans @ Ignite 2025
If you’re passionate about PostgreSQL or just curious about what’s new, you’ll want to join us at Microsoft Ignite 2025. We have a packed lineup, including sessions exploring cutting-edge features and exclusive giveaways at the PostgreSQL on Azure booth. Haven’t registered yet? Now’s the time – sign up for Microsoft Ignite and start building your schedule. Below are the must-see PostgreSQL on Azure activities, with highlights of what you’ll learn at each. Add these to your agenda today. Sessions can fill up fast! Theater sessions: get a first look, fast I know from experience that attention spans can start to wane after hours-long keynotes, content-rich sessions, and conference socializing. Luckily, we have a couple of theater sessions that offer snackable but substantial information in less time than it will take to grab lunch. And they’re located conveniently on the main conference floor. PostgreSQL on Azure: Your launchpad for intelligent apps and agents (THR705) - See how we’re making PostgreSQL AI-aware for developers to drive app and agent innovation. Includes a demo of vector similarity search, semantic operators baked into Postgres, and more! Simplifying scale-out of PostgreSQL for performant multi-tenant apps (THR706) - Discover a smarter, simpler way to scale PostgreSQL using the new Elastic Clusters feature. If your app or service is growing fast (or you want it to!), add this breakout to learn how Azure makes it easier to scale Postgres and keep it reliable. These talks are a great way to sample what’s new and decide where to dive deeper. Plus, they’re fun and demo-heavy, and who doesn’t love a good demo? Breakout sessions: a deep dive into Postgres innovations Led by Azure product leaders and executives from organizations driving innovation backed by PostgreSQL, these breakout sessions will dive into the coolest new capabilities and real-world use cases. If you want rich, technical content and more live demos, these are for you. Build mission-critical apps that scale with PostgreSQL on Azure (BRK127) - Get a closer look at the next generation of PostgreSQL on Azure. Add this session, if you’re curious about how we’re taking Postgres to the next level to support your mission-critical AI workloads. Modern data, modern apps: Innovation with Microsoft Databases (BRK134) - Gain insider knowledge on the latest innovations across open-source, SQL, and NoSQL databases, and understand how Microsoft’s integrated database portfolio supports next-gen innovation. Nasdaq Boardvantage: AI-driven governance on PostgreSQL and AI Foundry (BRK137) - Discover how a Fortune 100 merges trust with cutting-edge AI leveraging Azure’s AI-enriched and enterprise-ready solutions, including Azure Database for PostgreSQL, Azure Database for MySQL, Azure AI Foundry, Azure Kubernetes Service (AKS), and API Management. AI-assisted migration: The path to powerful performance on PostgreSQL (BRK123) - A before and after migration journey from Oracle to Azure Database for PostgreSQL. See how the new AI-assisted migration experience delivers conversion in a few clicks and minimal downtime. The blueprint for intelligent AI agents backed by PostgreSQL (BRK130) - If you’re into AI development, this session will spark ideas on bridging the gap between raw data and AI reasoning. You’ll leave with practical tips to turbocharge your AI agents with PostgreSQL. Each breakout session is 45 minutes with live demos and Q&A, so you’ll get plenty of detail and interaction with Postgres experts. Hands-on lab: experience coding with Azure superpowers Do you learn best by doing? Then our guided workshop, Build advanced AI agents with PostgreSQL (Lab515), is for you. In each 75-minute session, you’ll get to create a fully functional AI-powered application backed by PostgreSQL on Azure with step-by-step guidance and expert insight on the latest innovations enabling intelligent app development. All the tools and instructions you’ll need are provided. Labs have limited capacity, so be sure to reserve your seat for any of the four labs in advance. This lab is a great way to understand how all the pieces come together on Azure. And you’ll gain practical skills you can apply to your own projects, whether it’s customer support bots, intelligent search in your app, or any scenario where PostgreSQL + AI collide. Expert meet-up booth: meet the team, grab some swag If you still want more Postgres (or a little Postgres souvenir), you can stop by the PostgreSQL on Azure Expert Meetup booth in the Ignite Hub. This will be our homebase on the show floor, where you can: Meet the team: I’ll be there in person, along with engineers, program managers, cloud solution architects, and advocates from our team. Whether you have a burning technical question, want to share feedback, or need guidance for your specific use case, come chat with us. Get a quick demo re-run: Sometimes a 5-minute demo is worth a thousand words, especially after you’ve sat through all those words already in a keynote. The booth will have a monitor and a live environment so we can walk you through select use cases if you have questions - no appointment needed. Swag and giveaways: Ah yes, the goodies! We know conference swag is part of the fun, so we’ve got some special PostgreSQL-themed giveaways at the booth. I won’t spoil all the surprises, but rumor has it there are some limited-edition items up for grabs. Network with peers: The expert meet-up area is also a magnet for PostgreSQL enthusiasts. You might bump into other attendees at the booth who are tackling similar projects or challenges. Ignite is about community as much as content, so come by and spark up a conversation. Meet you there? Ignite is our largest event of the year. We love sharing what we’ve been working on and, most of all, hearing from you, the community. So, on behalf of the Azure for PostgreSQL team, thank you for your interest and support. We can’t wait to show you what’s new and to help you continue to succeed with Postgres. See you in San Francisco!
Postgres as a Distributed Cache Unlocks Speed and Simplicity for Modern .NET Workloads
In the world of high-performance, modern software engineering, developers often face a tough tradeoff: how to achieve lightning-fast data retrieval response rates without adding complexity, sacrificing reliability, or getting locked into specialized, external data caching products or platforms. What if you could harness the power and flexibility of your existing Postgres database to solve this challenge? Enter the Microsoft.Extensions.Caching.Postgres library, a new nuget.org package that brings distributed caching to Postgres, unlocking speed, simplicity, and seamless integration for modern .NET workloads. In this article, we’re going to take a closer look at the Postgres caching store, which introduces a new option for .NET developers planning on implementing a distributed cache, such as HybridCache, paired together with a Postgres database to provide distributed backplane operations. One data platform for multiple workloads Postgres’ reputation for reliability, extensibility, and standards compliance has long been respected, with Postgres databases driving some of today’s largest and most popular platforms. Increasingly developers, data engineers, and entrepreneurs alike all rallying to apply these benefits. One of the most compelling aspects of Postgres is its adaptability: it’s a data platform that can simultaneously handle everything from transactional workloads to analytical queries, JSON documents to geospatial data, and even time-series and vectorized AI search. In an era of specialized services, Postgres is proving that one platform can do it all and do it well. Intrepid engineers have also discovered that Postgres is often just as proficient in handling workloads traditionally supported by other very different technology solutions, such as lake-house, pub-sub, message queues, job schedulers, and session store caches. These roles are all now being powered by Postgres databases, while Postgres simultaneously continues to deliver the same scalable, battle-tested, and mission-critical ACID-compliant core relational database operations we’ve all come to expect. When speed matters most Database-backed cache stores are by no means a new concept; the first version of a database cache library for .NET was made available to developers exploring the nuget.org ecosystem (Microsoft.Extensions.Caching.SqlServer) in June 2016. This library included several impressive features, such as expiration policies, serialization, and dependency injection, making it ideal for multi-instance applications requiring shared cache functionality. It was especially useful in environments where Redis or other cache providers were not available. The convenience of leveraging a transactional database’s usefulness to function as a distributed cache comes with some tradeoffs, especially when compared against services such as Redis or Memcached; in a word: speed. All the features which make your database data durable, reliable, and consistent require precious additional clock cycles and I/O operations, and this “overhead” resulted in performance costs when compared to the alternative memory stores and caching system options. What if it was possible to maintain all those familiar and convenient interfaces for connecting to your database, while simultaneously being able to precisely configure specific tables to throw off the burden of crash consistency and replication logging? What if, for only the tables we selected, we could trade this durability for pure speed? Enter Postgres’ UNLOGGED Tables. Postgres' adaptable performance Another compelling aspect of Postgres databases is the ability to significantly speed up write-performance by bypassing the Write Ahead Log (WAL). The WAL is designed to ensure that data is crash-consistent (and replicable), and writing to your database is comprised of a transparent two-step process: your data is written to your database tables, and these changes are also committed to a separate file to guarantee the data’s persistence. It also happens that in some circumstances, the tradeoff to increase performance can be worth the sacrifice to crash-consistency, especially for short-lived, temporary types of data, like when used as a cache store. This table configuration is scoped to individual tables, which allows for combinations of “logged” and “unlogged” table configurations, both operating side-by-side within the same database instance. The net result: Postgres can provide incredibly performant response times when used as a distributed cache, rivaling the performance of other popular cache stores, while also providing the simplicity, familiarity, and consistency that the Postgres engine naturally offers. HybridCache for your .NET solutions It was this capability*combined with the inspiration from the SQL Server library that inspired the creation of the nuget.org Microsoft.Extensions.Caching.Postgres package. As a longtime .NET developer, I have personally witnessed the incredible evolution of the .NET platform and the amazing growth, enhancements, and improvements to the languages, the tooling, runtimes, and the incredible people behind each of these contributions. The recent addition of HybridCache is especially exciting to consider incorporating into your .NET solutions because it dramatically simplifies the steps required to add caching into your project, while simultaneously linking in-memory cache with a second-level tiered cache service. This seamless integration provides your application with the best of both worlds: blazing fast in-memory retrieval paired with a resilient fail-safe and similarly performant backplane in the event an application instance blinks, scales up/out, etc. Don’t just take my word for it, let’s look at some of the benchmarks between a Redis cache and Postgres database. The tests are comprised of synchronous and async operations across three different sized payloads (128, 1024, and 10240 bytes) for read/write, containing both single and concurrent messages, and at fixed and random positions. The tests are further divided into two types of cache expiration windows: absolute/non-sliding and sliding/relative windows. Consider the output from a suite of benchmarks tests, keeping in mind these results are based on microseconds, meaning 1,000 microseconds equals 1 millisecond: What do these results reveal? In certain respects, there aren’t that many surprises. Bespoke memory-based key-value cache systems like Redis continue to outperform relational databases in terms of pure speed and low latency. What is really exciting to see is that Postgres comes very close to Redis performance for more intensive operations! Finding the right fit for your solution I’m excited to make this Postgres package available to everyone considering distributed caching in their solution designs. The combination of HybridCache paired with your choice of backplane will allow you to select the right technologies and tools that are best suited for your solution. Our GitHub repo also contains a variety of sample applications, which demonstrate how to configure and use HybridCache together with the Postgres distributed cache library within a Console app service, as well as an Aspire-based sample Web API. I encourage you to explore these examples and share your thoughts and ideas. I look forward to any feedback you may have to share about the Microsoft.Extensions.Caching.Postgres package. Keep advancing, keep improving, and keep contributing to be a “builder” and an active part of our incredible community! * This package extension is highly configurable, and you can choose whether to enable/disable bypassing the WAL for your cache table, along with several other options that can be adjusted for your particular use case.Lessons Learned #537: Copilot Prompts for Troubleshooting on Azure SQL Database
We had the opportunity to share our experience in several community sessions how SSMS Copilot can help across multiple phases of troubleshooting. In this article, I would like to share a set of prompts we found in those sessions and show how to apply them to an example query. During a performance incident, we captured the following query, generated by PowerBI. SELECT TOP (1000001) * FROM ( SELECT [t2].[Fiscal Month Label] AS [c38], SUM([t5].[Total Excluding Tax]) AS [a0], SUM([t5].[Total Including Tax]) AS [a1] FROM ( SELECT [$Table].[Sale Key] as [Sale Key], [$Table].[City Key] as [City Key], [$Table].[Customer Key] as [Customer Key], [$Table].[Bill To Customer Key] as [Bill To Customer Key], [$Table].[Stock Item Key] as [Stock Item Key], [$Table].[Invoice Date Key] as [Invoice Date Key], [$Table].[Delivery Date Key] as [Delivery Date Key], [$Table].[Salesperson Key] as [Salesperson Key], [$Table].[WWI Invoice ID] as [WWI Invoice ID], [$Table].[Description] as [Description], [$Table].[Package] as [Package], [$Table].[Quantity] as [Quantity], [$Table].[Unit Price] as [Unit Price], [$Table].[Tax Rate] as [Tax Rate], [$Table].[Total Excluding Tax] as [Total Excluding Tax], [$Table].[Tax Amount] as [Tax Amount], [$Table].[Profit] as [Profit], [$Table].[Total Including Tax] as [Total Including Tax], [$Table].[Total Dry Items] as [Total Dry Items], [$Table].[Total Chiller Items] as [Total Chiller Items], [$Table].[Lineage Key] as [Lineage Key] FROM [Fact].[Sale] as [$Table] ) AS [t5] INNER JOIN ( SELECT [$Table].[Date] as [Date], [$Table].[Day Number] as [Day Number], [$Table].[Day] as [Day], [$Table].[Month] as [Month], [$Table].[Short Month] as [Short Month], [$Table].[Calendar Month Number] as [Calendar Month Number], [$Table].[Calendar Month Label] as [Calendar Month Label], [$Table].[Calendar Year] as [Calendar Year], [$Table].[Calendar Year Label] as [Calendar Year Label], [$Table].[Fiscal Month Number] as [Fiscal Month Number], [$Table].[Fiscal Month Label] as [Fiscal Month Label], [$Table].[Fiscal Year] as [Fiscal Year], [$Table].[Fiscal Year Label] as [Fiscal Year Label], [$Table].[ISO Week Number] as [ISO Week Number] FROM [Dimension].[Date] as [$Table] ) AS [t2] ON [t5].[Delivery Date Key] = [t2].[Date] GROUP BY [t2].[Fiscal Month Label] ) AS [MainTable] WHERE ( NOT([a0] IS NULL) OR NOT([a1] IS NULL) ) I structure the investigation in three areas: Analysis – understand the data model, sizes, and relationships. List all tables in the 'Fact' and 'Dimension' schemas with space usage in MB and number of rows. The name of the tables and their relations among them. Please, provide a textual representation for all relations. List all foreign key relationships between tables in the 'Fact' and 'Dimension' schemas, showing the cardinality and referenced columns. Could you please let me know what is the meaning of every table? Describe all schemas in this database, listing the number of tables and views per schema. Create a textual data model (ER-style) representation showing how all Fact and Dimension tables are connected. Maintenance Plan Check – verify statistics freshness, index health/fragmentation, partition layout, and data quality. List all statistics in the database that have not been updated in the last 7 days, showing table name, number of rows, and last update date. List all indexes in the database with fragmentation higher than 30%, including table name, index name, and page count. Please, provide the T-SQL to rebuild all indexes in ONLINE mode and UPDATE STATISTICS for all tables that are automatic statistics. Check for fact table rows that reference dimension keys which no longer exist (broken foreign key integrity). Find queries that perform table scans on large tables where no indexes are used, based on recent execution plans. Performance Improvements – simplify/reshape the query and consider indexed views, columnstore, partitioning, and missing indexes. In this part, I would like to spend more time about these prompts, for example the following ones, help me to understand the performance issue, simplify the query text and also, explains what the query is doing. Identify the longest-running query in the last 24 hours provide the full text of the query Please simplify the query Explain me the query Explain in plain language what the following SQL query does, including the purpose of each subquery and the final WHERE clause. Show a histogram of data distribution for key columns used in joins or filters, such as SaleDate, ProductCategory, or Region. Finally, using this prompt I could find a lot of useful information how to improve the execution of this query: Analyze the following SQL query and provide a detailed performance review tailored for Azure SQL Database Hyperscale and Power BI DirectQuery scenarios. For each recommendation, estimate the potential performance improvement as a percentage (e.g. query runtime reduction, I/O savings, etc.). 1. Could this query benefit from a schemabound indexed view or a materialized view? Estimate the performance gain if implemented. 2. Is there any missing index on the involved tables that would improve join or filter efficiency? Include the suggested index definition and expected benefit. 3. Would using a clustered or nonclustered columnstore index on the main fact table improve performance? Estimate the potential gain in query time or storage. 4. Could partitioning the fact table improve performance by enabling partition elimination? If so, suggest the partition key and scheme, and estimate improvement. 5. Are current statistics sufficient for optimal execution plans? Recommend updates if needed and estimate impact. 6. Does this query preserve query folding when used with Power BI DirectQuery? If not, identify what breaks folding and suggest how to fix it. 7. Recommend any query rewrites or schema redesigns, along with estimated performance improvements for each. I got a lot of improvements suggestions about it: Evaluated a schemabound indexed view that pre‑aggregates by month (see Reference Implementations), then pointed Power BI to the view. Ensured clustered columnstore on Fact.Sale; considered a targeted rowstore NCI on [Delivery Date Key] INCLUDE ([Total Excluding Tax], [Total Including Tax]) when columnstore alone wasn’t sufficient. Verified statistics freshness on join/aggregate columns and enabled incremental stats for partitions. Checked partitioning by date to leverage elimination for common slicers.September 2025 Recap: Azure Database for MySQL
Join us live on our YouTube channel on October 15, 2025 for an exclusive webinar where we’ll dive deeper into the latest Azure Database for MySQL updates and answer your questions! Watch it live here. Support for In-Place Major Version Upgrade from 8.0 to 8.4 We previously announced that Azure Database for MySQL version 8.4 is now generally available. We’re now happy to share that in-place major version upgrade is supported for MySQL servers from 8.0 to 8.4. If you’re currently on MySQL 5.7, you’ll first need to perform an in-place upgrade from 5.7 to 8.0, and then upgrade from 8.0 to 8.4. Learn more. Near-Zero-Downtime Maintenance - General Availability You can now take advantage of near-zero-downtime maintenance for Azure Database for MySQL with high availability (HA) enabled. This capability is now generally available and is supported by our new HA architecture based on a dedicated Azure Standard Load Balancer design. Thanks to the dedicated Standard Load Balancer design, maintenance is now faster and no longer impacted by client-side DNS caching, which previously caused brief connection drops in some scenarios. This enhancement ensures that your mission-critical workloads remain continuously available, even during infrastructure updates, helping you meet strict uptime requirements and maintain operational continuity. Learn more. We would love your feedback We look forward to your feedback as you explore these enhancements and continue building with Azure Database for MySQL. If you have any suggestions or queries about our service, please let us know by emailing us at AskAzureDBforMySQL@service.microsoft.com. To learn more about what's new with Flexible Server, see What's new in Azure Database for MySQL - Flexible Server. Stay tuned for more updates and announcements by following us on social media: YouTube | LinkedIn | X. Thanks for being part of our community!Alta Disponibilidade e Resiliência com App Gateway e Múltiplos APIMs: Uma Arquitetura Estratégica
Resiliência como Pilar do Azure Well-Architected Framework A resiliência é um dos pilares fundamentais do WAF e desempenha papel crítico na construção de arquiteturas modernas em nuvem. Este artigo explora o design de alta disponibilidade (HA) utilizando a oferta Premium Tier do Azure API Management (APIM) em cenários multi-região ou mesmo intra-região, com foco na aplicação de Availability Zones — uma prática recomendada no pilar de Confiabilidade. A configuração Premium permite que as regiões primária e secundária compartilhem a mesma instância de APIM. No entanto, dependendo dos requisitos de negócio e tolerância a falhas, pode ser estratégico operar com duas instâncias separadas, garantindo continuidade de serviço mesmo diante de falhas regionais ou em uma das instâncias. Essa abordagem está alinhada ao modelo de DR Ativo-Ativo, promovendo tolerância a falhas, escalabilidade horizontal e resiliência operacional, conforme descrito no modelo de maturidade de confiabilidade. A imagem acima representa os componentes do APIM nas regiões primária e secundária. Em cenários com instâncias separadas, esses componentes também estariam presentes em cada região. Já em uma configuração com Availability Zones, os elementos podem escalar dentro da mesma região, conforme a demanda — prática que também se conecta ao pilar de excelência operacional, ao permitir automação e monitoramento contínuo. O fluxo de comunicação entre a API e os serviços de backend pode ocorrer por rotas diretas ou por meio de balanceadores de carga, dependendo do caso de uso. Essa arquitetura inspirou uma solução personalizada para um de nossos clientes, que buscava alta disponibilidade com baixa latência, mesmo em cenários de falha regional. Configuração do APIM como gateway de saída para o cluster de aplicações Press enter or click to view image in full size Em alguns casos, por questões de compliance, não é possível operar fora de um regions específica . Ainda assim, essa arquitetura já proporciona ganhos significativos mesmo dentro da mesma região, como maior resiliência, escalabilidade e eficiência operacional. Visão Geral da Arquitetura Componentes principais: Usuário: Inicia a requisição. WAF: Protege contra ameaças e ataques na borda. AKS: Gerencia APIs e aplicações containerizadas. App Gateway (AppGW): Roteia o tráfego para os APIMs. APIM001 e APIM002: Instâncias do Azure API Management, com IPs distintos e potencialmente em regiões diferentes. Sistemas legados (kubernets / VMS): Integração com sistemas on-premises. Essa arquitetura é interessante, pois, neste caso de uso, o APIM atua como ponto de saída do cluster de aplicações — exatamente o caminho crítico que queremos proteger Com duas instâncias de APIM atrás do AppGW, é possível: Realizar manutenções planejadas em uma instância sem impactar os usuários. Redirecionar o tráfego automaticamente para a instância saudável. Garantir zero downtime, mesmo durante atualizações críticas. Essa abordagem é especialmente útil em ambientes com alta exigência de SLA, como bancos, governo e telecom. Testes Blue-Green com Segurança e Controle A arquitetura permite implementar estratégias Blue-Green com facilidade: Uma instância do APIM pode representar o ambiente “Blue” (produção atual). A outra instância representa o ambiente “Green” (nova versão). O AppGW pode direcionar parte do tráfego para o ambiente Green para testes controlados. Após validação, o tráfego pode ser totalmente migrado para o Green, promovendo a nova versão com segurança. Isso reduz riscos de regressão e permite deploys mais confiáveis Redução de Riscos em Caminhos Críticos Ao distribuir o tráfego entre dois APIMs: Reduz-se o risco de ponto único de falha. A arquitetura se torna mais resiliente a falhas regionais ou de serviço. Em caso de falha de uma instância, o AppGW garante continuidade do serviço. Essa redundância é essencial para caminhos críticos de negócio, como autenticação, transações financeiras ou integrações com sistemas legados. Outros Benefícios Estratégicos ✅ Disaster Recovery Ativo-Ativo As duas instâncias do APIM funcionam simultaneamente em modo ativo-ativo. Em caso de falha em uma das instâncias, o tráfego é automaticamente redirecionado. Reduz significativamente o RTO e assegura a continuidade dos serviços.. ✅ Mitigação de Esgotamento de IPs O uso de múltiplas instâncias permite distribuir o consumo de IPs. Evita gargalos de rede e problemas de limitação de recursos. ✅ Escalabilidade Regional A arquitetura possibilita escalabilidade horizontal simplificada. Suporta o crescimento de demanda em diferentes regiões de forma eficiente. Como configurar o APPGW para uma POC de balanceamento entre dois APIMs O Azure Application Gateway é um balanceador de carga de camada 7 (HTTP/HTTPS) que permite gerenciar o tráfego de aplicações web com inteligência, segurança e escalabilidade. Ele opera com base em um conjunto de configurações como Listeners, Routing Rules, Backend Targets entre outras. Além disso, o Application Gateway oferece recursos avançados como WAF (Web Application Firewall), SSL offloading, redirecionamento baseado em caminho e afinidade de sessão, tornando-o ideal para cenários que exigem alta disponibilidade e proteção contra ameaças web. Press enter or click to view image in full size 📦 Descrição de cada componente do diagrama Listener (porta 80): Detecta conexões de entrada na porta especificada (ex: 80 para HTTP). É o ponto inicial onde o tráfego chega ao gateway. Routing Rule: Define como o tráfego será roteado com base em critérios como URL, cabeçalhos ou métodos. É o cérebro da decisão de encaminhamento. Backend Targets: Especifica os destinos finais para o tráfego, como VMs, instâncias de App Service ou containers. 🔁 Relação com Backend Pools: 1 para N — uma regra pode apontar para vários destinos. Backend Setting: Configurações aplicadas ao tráfego, como tempo de timeout, protocolo (HTTP/HTTPS), e afinidade de sessão. 🔁 Relação com Health Probes: 1 para 1 — cada configuração tem uma sonda associada. Backend Pools: Agrupamento lógico dos destinos (targets). Permite distribuir carga entre múltiplas instâncias. 🔁 Relação com Backend Targets: 1 para N — um pool pode conter vários destinos. Health Probes (path:/status-0123456789abcdef) Verifica a saúde dos destinos usando um caminho específico. Se um destino estiver inativo, ele é automaticamente removido do balanceador. Vamos apresentar algumas telas de configuração e destacar pontos de atenção no Application Gateway (AppGW). Um detalhe importante que acabei não mencionando: à frente do Listener existe uma configuração de Frontend IP, que define se o IP do AppGW será público ou privado. Para facilitar os testes iniciais, recomendo começar com o Frontend IP público,alem disso mantenha o Listener configurado na porta 80. Isso evita complicações com certificados SSL durante os testes. Observe a imagem abaixo: o Frontend IP está corretamente vinculado ao Listener1, como indicado. Essa associação é essencial para garantir que o tráfego seja direcionado corretamente, conforme a configuração do IP público ou privado definida no Frontend. Press enter or click to view image in full size O Listener1, configurado na porta 80, está associado à Rule1, que define como o tráfego será roteado para o backend correspondente. Press enter or click to view image in full size A Rule1 está associada às configurações de Backend Settings, que definem como o tráfego será encaminhado para os recursos de backend — incluindo o pool de servidores, o protocolo, a porta e os critérios de saúde. Press enter or click to view image in full size As Backend Settings do Application Gateway incluem configurações importantes, como “Pick host name from backend target” e a associação a um Custom Probe. Por padrão, o AppGW encaminha ao backend o mesmo cabeçalho HTTP Host recebido do cliente. No entanto, se o serviço ou aplicação no backend exigir um valor específico para o cabeçalho Host, é possível sobrescrevê-lo utilizando essa configuração. Com isso, o Application Gateway passa a usar o host do backend para resolver o balanceamento e validar o probe de integridade, garantindo compatibilidade com serviços que dependem de hostname específico para funcionar corretamente. Press enter or click to view image in full size Perceba que o Health Probe também está vinculado às Backend Settings, e utiliza a configuração “Pick host name from backend settings”. Essa associação é fundamental para garantir que o probe de integridade seja executado corretamente, especialmente em cenários onde o backend exige um cabeçalho Host específico. Ao ativar essa opção, o Application Gateway passa a usar o hostname do backend tanto para o roteamento quanto para a validação do probe, assegurando compatibilidade com serviços que dependem dessa configuração. Press enter or click to view image in full size Esse endereço /status-0123456789abcdef é o endereço de sondagem do apim (helth cehck) do APIM Voltando à Rule1, podemos observar que ela também define o Backend Target, que neste caso aponta para o serviço de backend do APIM (Azure API Management). Essa configuração é essencial para garantir que o tráfego roteado pelo Application Gateway seja direcionado corretamente ao endpoint do APIM, respeitando as regras de roteamento, cabeçalhos e probes definidos nas Backend Settings. Press enter or click to view image in full size Nos Backend Pools, temos o pool de backend do APIM, que contém duas instâncias do serviço Azure API Management. Essa configuração permite distribuir o tráfego entre as instâncias, garantindo alta disponibilidade e escalabilidade para os serviços expostos via AppGW. Press enter or click to view image in full size Do lado do APIM Para observar o processo de balanceamento entre instâncias do APIM (por exemplo, APIM A e APIM B), podemos criar uma API de mock que responda de forma simples, identificando qual instância está respondendo. Principais Objetos do APIM Políticas: São regras aplicadas às requisições HTTP, permitindo manipulações como transformação de payloads, controle de acesso, limitação de chamadas (rate limiting), entre outras. API: Representa um conjunto de operações agrupadas sob um único endpoint. Cada API pode conter múltiplas operações. Operações: São as ações HTTP específicas, como os verbos GET, POST, PUT, DELETE, etc. Cada operação define o comportamento de uma rota dentro da API. Como criar um mock no Azure API Management (APIM) Adicionar uma nova API Acesse o menu lateral e clique em “Add API”. Selecione a opção para criar uma API manualmente. Na caixa de diálogo exibida, preencha os dados necessários (nome, URL base, etc.). eu criei uma APIA, com sufixo apim Press enter or click to view image in full size Definir o response da operação Clique em “add operation” e preencha o verbo como get a url como /moq Após criar a operação desejada, clique na aba “Responses”. Clique em “Add response” e selecione o código 200. Em Content type, escolha application/json. No campo Sample, insira um JSON de exemplo para identificar a instância, como: JSON { "name": "APIMA" } Press enter or click to view image in full size Adicionar a política de mock Vá para a aba “Design” da operação. Na seção Inbound processing, clique em “Add policy”. Selecione a política “Mock response”. Verificação Após salvar, o pipeline da requisição exibirá uma tarja amarela, indicando que o mock está ativo. Press enter or click to view image in full size Repita esse processo para a instância dois. Agora já podemos testar o balanceamento de carga pelo Application Gateway Press enter or click to view image in full size Sincronização das Instâncias com o API OPS Conforme mencionado no início deste artigo, a arquitetura “by the book” foi projetada para utilizar o recurso nativo de multi-região do Azure API Management (APIM), o que elimina a necessidade de esforços adicionais para sincronizar configurações entre instâncias. No entanto, ao optarmos por manter duas instâncias separadas na mesma região, com o objetivo de obter os benefícios citados anteriormente, passamos a ter o desafio de manter ambas sincronizadas — ou seja, com as mesmas APIs, endpoints e políticas. Para atender a essa necessidade de sincronização, utilizaremos o API Ops, que automatiza o processo de publicação e atualização das configurações entre as instâncias, garantindo consistência e reduzindo o risco de divergências operacionais. Press enter or click to view image in full size Resumo do Fluxo de API Ops para Sincronização com o APIM Press enter or click to view image in full size Operadores de API executam o pipeline de extração para sincronizar o repositório Git com a instância do API Management, populando o repositório com os objetos no formato necessário. Se houver alterações detectadas na instância do APIM, é criado um Pull Request (PR) para revisão. Após aprovação, os operadores fazem o merge das mudanças no repositório. Desenvolvedores de API clonam o repositório, criam uma branch e definem as APIs usando especificações OpenAPI ou ferramentas de sua preferência. Quando um desenvolvedor envia alterações para o repositório, um novo PR é gerado para revisão. O PR pode ser aprovado automaticamente ou revisado manualmente, conforme o nível de controle exigido. Após a aprovação e o merge, o pipeline de publicação implanta as alterações na instância do API Management. Os operadores também podem criar ou modificar políticas, diagnósticos, produtos e outros objetos relevantes, e então comitar essas alterações. Após o merge, o pipeline publica as mudanças usando o processo de definição de APIs. Conclusão A adoção de uma arquitetura com App Gateway à frente de múltiplos APIMs, integrada com Akamai WAF, Azure Functions, Key Vault e sistemas legados, oferece uma solução robusta, segura e altamente disponível. Essa abordagem não apenas melhora a experiência do usuário, mas também reduz riscos operacionais e facilita a evolução contínua da plataforma. Referencias Estrutura de Well-Architected do Azure — Microsoft Azure Well-Architected Framework | Microsoft Learn Princípios de design de confiabilidade — Microsoft Azure Well-Architected Framework | Microsoft Learn Modelo de maturidade de confiabilidade — Microsoft Azure Well-Architected Framework | Microsoft Learn Links rápidos de excelência operacional — Microsoft Azure Well-Architected Framework | Microsoft Learn Implantações de API automatizadas usando APIOps — Azure Architecture Center | Microsoft Learn
