FastAPI vs Spring Boot

 

FastAPI vs Spring Boot: Which One Should You Choose?

In the world of backend development, Spring Boot has been the gold standard for years—especially in enterprise Java applications.
But recently, FastAPI has gained massive popularity in the Python ecosystem for building modern APIs.

So the big question is:

👉 FastAPI vs Spring Boot — which one is better?
👉 Should Java developers care about FastAPI?

Let’s compare them from a real-world, production perspective.

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What is Spring Boot?

Spring Boot is a Java-based framework that simplifies building enterprise-grade applications.

It provides:

• Dependency Injection

• Security

• Transaction management

• Database integration

• Production-ready tooling

Spring Boot is widely used in:

• Banking

• Insurance

• Government systems

• Large enterprises

---

What is FastAPI?

FastAPI is a modern Python framework designed specifically for building high-performance APIs.

It focuses on:

• Speed

• Simplicity

• Automatic validation

• Automatic documentation

FastAPI is widely used in:

• Microservices

• Startups

• AI / ML platforms

• Integration-heavy systems

---

Core Comparison: FastAPI vs Spring Boot

1️⃣ Language & Ecosystem

Aspect	FastAPI	Spring Boot
Language	Python	Java
Ecosystem	Lightweight, fast	Enterprise-grade
Verbosity	Very low	High

Verdict:
Spring Boot is structured and strict. FastAPI is flexible and fast to develop.

---

2️⃣ Development Speed

	FastAPI	Spring Boot
Boilerplate	Minimal	Heavy
API setup time	Minutes	30–60 minutes
Configuration	Very less	Extensive

FastAPI allows teams to ship APIs much faster.

---

3️⃣ Performance & Concurrency

	FastAPI	Spring Boot
Concurrency model	Async (event loop)	Thread-based
Best for	IO-heavy APIs	CPU-heavy logic
Latency	Lower	Slightly higher

FastAPI shines in:

• Webhooks

• Event ingestion

• High-concurrency APIs

---

4️⃣ Validation & API Contracts

	FastAPI	Spring Boot
Request validation	Automatic	Annotations
OpenAPI spec	Auto-generated	Manual setup
Error messages	Clear	Verbose

FastAPI’s validation feels like:

DTO + Validation + Swagger — without extra configuration

---

5️⃣ API Documentation (Major Difference)

FastAPI provides:

• Swagger UI at /docs

• ReDoc at /redoc

No extra setup needed.

In Spring Boot:

• Swagger/OpenAPI requires manual configuration

• Docs often go out of sync

📌 Frontend teams love FastAPI for this reason alone.

---

6️⃣ Async Support

	FastAPI	Spring Boot
Async support	Native	WebFlux
Complexity	Simple	High
Learning curve	Easy	Steep

Many Java teams avoid WebFlux due to complexity, while FastAPI makes async natural.

---

7️⃣ Enterprise Features

Feature	FastAPI	Spring Boot
Security	Good	Excellent
Transactions	Manual	Strong
AOP	❌	✅
Dependency Injection	Basic	Advanced

Spring Boot clearly wins in enterprise governance and safety.

---

8️⃣ Database & ORM Support

	FastAPI	Spring Boot
ORM	SQLAlchemy	Hibernate
MongoDB	Motor	Spring Data Mongo
Maturity	Medium	Very High

Spring Data is more mature for large teams and long-term projects.

---

When to Use FastAPI?

Choose FastAPI if you are building:

• Microservices

• API gateways

• Integration platforms

• Event-driven systems

• AI / ML APIs

• Backend for React / Angular apps

---

When to Use Spring Boot?

Choose Spring Boot if you are building:

• Enterprise systems

• Banking or finance apps

• Complex transactional workflows

• Long-term systems (10–15 years lifespan)

• Large, regulated platforms

---

Real-World Architecture (Very Common)

Many companies use both together:

Spring Boot → Core business logic
FastAPI     → API layer, integrations, ML services

This hybrid approach offers stability + speed.

---

Final Verdict

FastAPI is not a replacement for Spring Boot.
It is a complement.

• Spring Boot = Enterprise backbone

• FastAPI = Fast, modern API layer

For experienced Java developers, learning FastAPI adds a powerful new skill and opens doors to modern architectures and AI-driven systems.

---

Conclusion

If you want:

• Faster development

• Cleaner APIs

• Better API documentation

• Modern async architecture

👉 FastAPI is worth learning.

If you want:

• Enterprise reliability

• Strong governance

• Long-term maintainability

👉 Spring Boot remains unbeatable.

What is FastAPI

 

What is FastAPI? A Beginner-Friendly Guide

In today’s world of web and mobile applications, APIs are the backbone of modern software. If you’re a Python developer—or planning to become one—you’ve probably heard about FastAPI.

But what exactly is FastAPI, and why are companies adopting it so fast?
Let’s break it down in simple terms.

---

What is FastAPI?

FastAPI is a modern Python web framework used to build REST APIs quickly and efficiently.

It is:

• 🚀 Fast (high performance)

• 🧠 Smart (automatic validation & documentation)

• ✨ Easy to use (less boilerplate code)

FastAPI was created to solve common problems developers face with older frameworks like Flask and Django when building APIs at scale.

---

Why is it called FastAPI?

FastAPI is fast because it is built on top of:

• Starlette → for web handling

• Pydantic → for data validation

This combination makes FastAPI one of the fastest Python frameworks, with performance comparable to Node.js and Go for API workloads.

---

A Simple FastAPI Example

Here’s how easy it is to create an API:

from fastapi import FastAPI

app = FastAPI()

@app.get("/hello")
def hello():
    return {"message": "Hello World"}

Run it, and your API is live!

---

Automatic API Documentation (Big Advantage)

One of FastAPI’s biggest strengths is automatic documentation.

Without writing any extra code, you get:

• Swagger UI → /docs

• ReDoc UI → /redoc

This is extremely useful for:

• Frontend developers

• QA teams

• API consumers

Your API docs are always up-to-date.

---

Built-in Data Validation

FastAPI automatically validates incoming requests using Python type hints.

Example:

from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

If incorrect data is sent, FastAPI returns a clear error message—no manual checks needed.

---

Async Support (High Concurrency)

FastAPI supports async / await natively.

This makes it ideal for:

• Webhooks

• Event ingestion

• API integrations

• High traffic systems

Example:

@app.get("/items")
async def get_items():
    return ["item1", "item2"]

---

Where is FastAPI Used in Real Life?

Companies use FastAPI in production for:

• 🔹 Microservices

• 🔹 Backend for React / Angular apps

• 🔹 CRM integrations (Salesforce, Marketo, etc.)

• 🔹 Event processing systems

• 🔹 AI / ML model serving

• 🔹 API gateways

Many startups and even large companies use FastAPI alongside Java Spring Boot systems.

---

FastAPI vs Flask vs Django

Feature	FastAPI	Flask	Django
Performance	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐
Auto Docs	✅	❌	❌
Validation	✅	❌	⚠️
Async	✅	❌	⚠️
Best for	APIs	Small apps	Full web apps

---

When Should You Use FastAPI?

Use FastAPI if you:

• Need to build APIs quickly

• Want high performance

• Work on microservices

• Are building AI/ML APIs

• Want clean and maintainable code

---

When Not to Use FastAPI?

Avoid FastAPI if:

• You need a full server-side HTML framework

• You want heavy built-in admin panels (Django is better here)

---

Final Thoughts

FastAPI has become a game-changer in the Python ecosystem.

It combines:

• ⚡ Speed

• 🧩 Simplicity

• 📄 Excellent documentation

• 🛡️ Strong validation

If you are a backend developer or want to build scalable APIs in Python, FastAPI is absolutely worth learning.

Tableau server enable incremental extracts

To enable incremental extracts on Tableau Server, you need to:

1. Configure the data source for incremental extracts.
2. Publish the data source to Tableau Server.
3. Create an extract refresh schedule.

Configure the data source for incremental extracts

To configure the data source for incremental extracts, open the data source in Tableau Desktop and select Data > Extract Data.

In the Extract Data dialog box, select Incremental refresh and then select a column in the database that will be used to identify new rows. This column should be a unique identifier for each row in the database.

Publish the data source to Tableau Server

Once you have configured the data source for incremental extracts, you need to publish it to Tableau Server. To do this, select File > Publish Data Source.

In the Publish Data Source dialog box, select the appropriate Tableau Server site and project, and then click Publish.

Create an extract refresh schedule

Once the data source has been published to Tableau Server, you need to create an extract refresh schedule. To do this, navigate to the Data Sources page on Tableau Server and click the More actions menu (three dots) next to the data source that you want to create an extract refresh schedule for.

Select Edit Extract Refresh Schedule.

In the Extract Refresh Schedule dialog box, select the Enable extract refresh checkbox.

You can then configure the extract refresh schedule to run on a regular basis, such as every day or every hour.

Once you have configured the extract refresh schedule, click Save.

That's it! You have now enabled incremental extracts on Tableau Server.

Here are some additional tips for using incremental extracts:

• Incremental extracts can only be used with data sources that are connected to a relational database.
• The column that you select for the incremental extract must be a unique identifier for each row in the database.
• If you are using a live connection to the data source, you need to make sure that the data source is configured to allow incremental extracts.
• Incremental extracts can improve the performance of Tableau dashboards and workbooks, but they can also increase the amount of disk space required to store the extract.

If you have any questions about using incremental extracts on Tableau Server, please contact Tableau Support.

Maybe these locations are not writable or multiple nodes were started on the same data path

If Elasticsearch is unable to start due to a data path conflict, it is likely that one of the following is true:

• The data path is not writable. This could be because the directory does not exist, the user running Elasticsearch does not have permission to write to the directory, or the directory is full.
• Multiple nodes are trying to start with the same data path. This is not allowed, as each node must have its own unique data path.

To resolve this issue, you can try the following:

• Make sure that the data path exists and that the user running Elasticsearch has permission to write to the directory.
• Make sure that only one node is trying to start with the given data path.

If you are still having trouble, you can try the following:

• Delete the data directory and start Elasticsearch. Elasticsearch will create a new data directory with the default settings.
• Start Elasticsearch with a different data path.

If you are running Elasticsearch in a production environment, it is important to back up your data before making any changes.

Here are some additional tips for troubleshooting data path conflicts:

• If you are using a cloud-based Elasticsearch service, such as Amazon Elasticsearch Service (AES) or Elasticsearch Service on Google Cloud Platform (GCP), you can usually check the management console for the service to see if there are any errors related to the data path.
• If you are running Elasticsearch on your own infrastructure, you can check the Elasticsearch logs for any errors related to the data path.
• You can also try restarting Elasticsearch to see if that resolves the issue.

If you are still having trouble, you can contact Elastic support for assistance.