TechForge’s new guide warns that many fledgling microservices projects end up as “distributed monoliths,” delivering the latency of network calls without any scaling benefits. The piece urges engineering teams to start with a solid monolith and only break it apart when clear scaling or ownership needs arise.
Why teams rush into microservices
The appeal of microservices is obvious: independent services, separate deployments, and the promise of scaling each part of an application on its own terms. Start-up culture and recent success stories have turned the pattern into a badge of modern engineering. Yet splitting a monolith too early often creates a new kind of monolith—dozens of networked components. The cost? Higher latency, harder debugging, and more operational overhead, while the original benefits stay out of reach.
The first mistake: starting with a monolith in name only
Teams often label a system “micro-service-based” while keeping a single codebase and a shared database. The result is a series of tightly coupled modules that still talk to each other over HTTP or RPC. The guide calls this a “distributed monolith.” The pain points match those of a traditional monolith—tight coupling and difficulty changing one part without affecting the rest—plus added latency from network hops.
What to do instead: Build a clean monolith first. Define clear module boundaries, keep the data layer unified, and ensure the application can be tested and deployed as a single unit. Extract a module into its own service only when it needs independent scaling or separate team ownership.
Splitting by technical layer versus business capability
Another frequent error is carving services along technical concerns—UI, business logic, or data access. This forces a request to travel through a chain of services for a single operation, inflating response times and creating a fragile dependency graph.
Better approach: Organize services around business capabilities such as “orders,” “payments,” or “inventory.” Let each capability own its data and its own API, eliminating the need for a request to hop across layers.
Data ownership matters
When two services write to the same database table, they are no longer independent. The guide stresses that a service must never query another service’s tables directly; it should always go through that service’s public API. Sharing a database ties the services together, defeats isolation, and makes schema changes a coordination nightmare.
Synchronous HTTP is not a universal solution
Relying on synchronous HTTP for every interaction makes the whole system vulnerable to a single slow service. If Service A waits for Service B to respond before returning to the client, any slowdown in B propagates to A and ultimately to the user.
Alternative patterns: Use asynchronous messaging for tasks that do not need an immediate answer. Message queues or background jobs let services hand off work and continue processing, keeping the overall system more resilient.
Accepting eventual consistency
Traditional relational databases give you ACID transactions—Atomicity, Consistency, Isolation, Durability. Across service boundaries, those guarantees disappear. Trying to force two-phase commits (a protocol that tries to make distributed transactions behave like local ones) leads to complexity and instability.
The guide recommends sagas (a series of compensating actions) or the outbox pattern (where a service writes events to a local table that are later published). These approaches acknowledge that data may be temporarily out of sync and design the business logic to handle those gaps.
Build for failure from day one
A bug in one service should not bring down the entire system. Implement timeouts to avoid waiting forever, retries with back-off to handle transient failures, and circuit breakers that stop calls to a failing service until it recovers. Adding these safeguards after a production outage is too late; they belong in the initial design.
Observability is non-negotiable
Debugging a distributed system with logs scattered across many containers is near impossible. Centralized logging, aggregated metrics, and request-level correlation IDs let engineers trace a single user request as it moves through multiple services. Tracing tools visualize the call graph, making performance bottlenecks and failures easier to locate.
Keep the infrastructure lightweight at the start
Kubernetes, meskipun kuat, membawa kurva pembelajaran yang curam dan beban operasional yang tinggi. Untuk sejumlah kecil layanan, Docker Compose menyediakan orkestrasi yang cukup untuk menjalankan seluruh stack secara lokal. Hanya ketika pola lalu lintas, frekuensi deployment, atau ukuran tim menuntutnya, platform yang lebih kompleks baru boleh diperkenalkan.
Selaraskan layanan dengan kepemilikan tim
Microservices sebagian diciptakan agar tim kecil yang otonom dapat memiliki siklus hidup layanan secara penuh. Jika satu tim bertanggung jawab atas sepuluh layanan, biaya koordinasi akan meningkat drastis, sehingga mengikis manfaat yang diharapkan. Panduan ini menyarankan bahwa tim dengan kurang dari sepuluh orang mungkin lebih baik menggunakan monolith, guna menjaga kesederhanaan namun tetap memungkinkan pengembangan modular.
Argumen tandingan: kapan microservices bersinar
Panduan ini tidak mengklaim bahwa microservices pada dasarnya buruk. Dalam lingkungan di mana bagian-bagian aplikasi yang berbeda memiliki kebutuhan penskalaan yang sangat berbeda, atau di mana batasan regulasi menuntut isolasi data yang ketat, pola ini dapat memberikan nilai nyata. Organisasi besar dengan berbagai lini produk sering kali menemukan bahwa layanan independen mengurangi gesekan antar-tim dan memungkinkan siklus rilis yang lebih cepat.
Kuncinya adalah kesengajaan. Jika sebuah tim mengadopsi microservices karena mereka perlu menangani jutaan permintaan per detik untuk fitur tertentu, atau karena lini produk baru harus dimiliki oleh unit bisnis terpisah, maka kompleksitas tambahan tersebut dapat dibenarkan. Peringatan dalam panduan ini menargetkan kasus-kasus di mana keputusan didorong oleh hype alih-alih kebutuhan konkret.
Apa yang perlu diperhatikan selanjutnya
Seiring semakin banyaknya perusahaan yang mengadopsi cloud-native stacks, perangkat di sekitar service mesh, distributed tracing, dan automated canary deployments terus berkembang. Kemajuan ini menurunkan hambatan operasional tetapi tidak menghilangkan pilihan desain fundamental yang disorot dalam panduan tersebut. Tim harus memantau evolusi platform observabilitas dan framework messaging async, namun tetap harus memulai dengan justifikasi yang jelas untuk setiap layanan yang mereka jalankan.
Kesimpulan
Microservices adalah sarana untuk mencapai tujuan, bukan tujuan itu sendiri. Mulailah dengan monolith yang terstruktur dengan baik, berikan setiap layanan kepemilikan data yang sesungguhnya, gunakan komunikasi asinkron jika memungkinkan, dan tanamkan ketahanan serta observabilitas sejak baris kode pertama. Ketika alasan bisnis sudah jelas, pisahkan layanan secara sengaja; jika tidak, jaga arsitektur tetap sesederhana yang dibutuhkan oleh masalah tersebut.