0Pricing
Serverless Backend with AWS Lambda & API Gateway · Pelajaran

Membangun Mesin Status

Rancang dan terapkan berbagai jenis mesin status (Standard, Express) untuk beragam kasus penggunaan dengan integrasi Lambda dan layanan lainnya.

Membangun Mesin Status adalah pelajaran Serverless Backend with AWS Lambda & API Gateway gratis di CoddyKit. Ini adalah pelajaran 2 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar Serverless Backend with AWS Lambda & API Gateway, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus Serverless Backend with AWS Lambda & API Gateway mencakup 4 pelajaran total.

Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.

Build Your First Workflow

Welcome back! In the previous lesson, we learned what AWS Step Functions are. Now, let's dive into building them!

You'll learn how to define different steps (states) and connect them to create powerful, automated workflows. We'll also explore the two main types of workflows: Standard and Express.

Anatomy of a State Machine

A Step Functions state machine is a workflow that defines a series of steps, called states. Each state performs a specific task or makes a decision.

  • States: The individual steps in your workflow.
  • Transitions: Rules that determine which state comes next.
  • Input/Output: Data passed between states.

Think of it like a flowchart, but executed by AWS!

Common State Types

Step Functions offers various state types to build your logic:

  • Task: Performs work by calling a service (e.g., Lambda, ECS).
  • Pass: Passes its input to its output without performing work. Useful for debugging or structuring.
  • Choice: Adds branching logic (if/else).
  • Wait: Pauses the execution for a specified time or until a specific time.
  • Succeed/Fail: Stops the execution successfully or with an error.
  • Parallel: Executes multiple branches in parallel.

Defining a Simple Pass State

Let's start with a simple Pass state. This state simply takes its input and passes it as output. It's often used for testing or structuring your state machine.

State machines are defined using Amazon States Language (ASL), which is a JSON-based structure.

Pass State ASL Example

Here's how a Pass state looks in ASL. It defines the state name, its type, and where to go next.

{ "Comment": "A simple Pass state machine",
  "StartAt": "HelloWorld",
  "States": {
    "HelloWorld": {
      "Type": "Pass",
      "Result": {
        "message": "Hello from Step Functions!"
      },
      "End": true
    }
  }
}

Standard Workflows: The Durable Choice

AWS Step Functions offers two workflow types: Standard and Express.

Standard Workflows are ideal for long-running, durable, and auditable workflows. They can run for up to a year!

  • Durability: Retains full execution history for up to 90 days.
  • Auditability: Easy to track every step and input/output.
  • Use Cases: Order fulfillment, data processing pipelines, human approvals.

Express Workflows: Speed and Scale

Express Workflows are designed for high-volume, event-driven workloads. They are much faster and more cost-effective for short-duration tasks.

  • Speed: Can complete thousands of executions per second.
  • Cost: Priced per execution and duration, often cheaper for short tasks.
  • History: Execution history is limited (up to 90 days in CloudWatch Logs, not directly in Step Functions console).
  • Use Cases: Microservice orchestration, real-time streaming data processing, mobile backend processing.

Integrating Lambda: The Task State

One of the most common ways to perform work in a state machine is using a Task state to invoke an AWS Lambda function.

The Resource field in the ASL specifies the ARN of the Lambda function to call.

Example Lambda for Step Functions

This simple Python Lambda function receives an event (input from Step Functions) and returns a modified message. Step Functions will pass its output to the next state.

import json

def lambda_handler(event, context):
    print(f"Received event: {json.dumps(event)}")
    name = event.get('name', 'World')
    return {
        'statusCode': 200,
        'body': f"Hello, {name}! This is from Lambda."
    }

Workflow Type Challenge

You need to build a workflow for processing thousands of incoming IoT sensor readings every minute. Each reading takes less than 1 second to process.

Recap: Building Workflows

Great job! You've learned the fundamentals of building AWS Step Functions state machines:

  • State machines are defined by a series of states and transitions.
  • Key state types include Task, Pass, Choice, and Wait.
  • Standard Workflows are for long-running, auditable processes.
  • Express Workflows are for high-volume, short-duration tasks.
  • You can integrate Lambda functions using a Task state.

Next, we'll explore orchestrating complex workflows!

Pertanyaan yang Sering Diajukan

Apakah pelajaran “Membangun Mesin Status” gratis?

Ya — teks lengkap “Membangun Mesin Status” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus Serverless Backend with AWS Lambda & API Gateway, upgrade ke CoddyKit PRO. Kursus Serverless Backend with AWS Lambda & API Gateway mencakup 4 pelajaran total.

Apa yang akan aku pelajari di “Membangun Mesin Status”?

Rancang dan terapkan berbagai jenis mesin status (Standard, Express) untuk beragam kasus penggunaan dengan integrasi Lambda dan layanan lainnya. Kamu berlatih Serverless Backend with AWS Lambda & API Gateway dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.

Apakah aku perlu pengalaman untuk memulai Serverless Backend with AWS Lambda & API Gateway?

Tidak diperlukan pengalaman sebelumnya. Serverless Backend with AWS Lambda & API Gateway di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 2 dari 4.

Berapa lama pelajaran “Membangun Mesin Status” memakan waktu?

Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.

Bisakah aku menulis dan menjalankan kode dalam pelajaran Serverless Backend with AWS Lambda & API Gateway ini?

Ya. Setiap pelajaran Serverless Backend with AWS Lambda & API Gateway menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.

Semua pelajaran dalam kursus ini

  1. Pengantar AWS Step Functions
  2. Membangun Mesin Status
  3. Mengatur Alur Kerja Kompleks
  4. Penanganan Kesalahan dan Percobaan Ulang dalam Mesin Status
← Kembali ke Serverless Backend with AWS Lambda & API Gateway