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certainly! "programming distributed systems" by mae milano provides insights into the design and implementation of distributed systems. below, i will summarize key concepts and provide a simple code example to illustrate some of these principles.

overview of distributed systems

distributed systems consist of multiple independent components (often called nodes) that communicate and coordinate with each other to achieve a common goal. they are characterized by the following properties:

1. **concurrency**: multiple processes can run simultaneously, leading to potential race conditions.
2. **scalability**: systems can grow by adding more nodes.
3. **fault tolerance**: the system should continue to operate even when some components fail.
4. **transparency**: users should be unaware of the distribution of components.

fundamentals of distributed systems

1. **communication**: nodes communicate over a network. common protocols include http, rpc (remote procedure call), and message queues.
2. **consistency**: ensuring that all nodes see the same data at the same time. this can be challenging due to network latency and partitioning.
3. **coordination**: processes need to coordinate actions, often using algorithms like consensus (e.g., raft, paxos).

example: building a simple distributed key-value store

we'll create a simple distributed key-value store using python's flask for the web api and the `requests` library for inter-node communication. this example will demonstrate basic concepts of a distributed system.

step 1: setup

you need to install flask and requests:

```bash
pip install flask requests
```

step 2: create the key-value store

here’s a simple implementation of a distributed key-value store with two nodes.

```python
node.py
from flask import flask, request, jsonify
import requests

app = flask(__name__)
data_store = {}

endpoint to get a value by key
@app.route('/get/key', methods=['get'])
def get_value(key):
return jsonify({key: data_store. ...

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distributed systems
programming
MAE Milano
concurrency
scalability
fault tolerance
microservices
message passing
data consistency
system architecture
cloud computing
high availability
load balancing
distributed algorithms
network protocols