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Orchestrating Atomic Operations Across Multiple Services

Distributed transactions are crucial for maintaining data consistency when operations need to span multiple independent services or databases. Imagine an e-commerce platform where placing an order involves updating inventory, processing payment, and creating a shipment record, each potentially managed by a separate microservice. Ensuring all these steps succeed or fail together is paramount to prevent inconsistent states. This challenge will guide you in implementing a mechanism to achieve this atomicity.

Problem Description

Your task is to implement a simplified two-phase commit (2PC) protocol in Python to manage distributed transactions. You will simulate multiple independent services (e.g., databases, payment gateways) that need to participate in a single, atomic operation.

What needs to be achieved: Implement a coordinator that orchestrates a distributed transaction across multiple participants. The coordinator should ensure that either all participants commit their changes or all participants abort their changes.

Key requirements:

  1. Coordinator: Implement a Coordinator class responsible for initiating, managing, and concluding the transaction.
  2. Participants: Simulate multiple Participant classes, each representing an independent service that can prepare to commit and then either commit or abort based on the coordinator's instruction.
  3. Two-Phase Commit (2PC) Logic:
    • Phase 1 (Prepare): The coordinator requests each participant to prepare for commit. A participant either succeeds in preparing (returns True) or fails (returns False).
    • Phase 2 (Commit/Abort):
      • If all participants successfully prepare, the coordinator instructs them to commit.
      • If any participant fails to prepare, the coordinator instructs all participants to abort.
  4. State Management: Each participant should maintain a state (e.g., "prepared", "committed", "aborted").
  5. Error Simulation: Allow for simulating failures during the prepare phase for participants.

Expected behavior: When the Coordinator initiates a transaction:

  • If all participants successfully prepare, all participants will eventually be instructed to commit.
  • If even one participant fails to prepare, all participants (including those that might have prepared successfully) will be instructed to abort.

Important edge cases to consider:

  • What happens if a participant fails to respond to the prepare or commit/abort requests? (For this challenge, assume synchronous responses and no network failures, but acknowledge this in your design thinking).
  • Handling scenarios with zero participants.

Examples

Example 1: Successful Transaction

class MockParticipant:
    def __init__(self, name):
        self.name = name
        self.state = "initial"
        self.prepared_data = None

    def prepare(self, transaction_data):
        print(f"Participant {self.name}: Preparing with data '{transaction_data}'...")
        # Simulate successful preparation
        self.prepared_data = transaction_data
        self.state = "prepared"
        print(f"Participant {self.name}: Preparation successful.")
        return True

    def commit(self):
        if self.state == "prepared":
            print(f"Participant {self.name}: Committing changes for data '{self.prepared_data}'.")
            self.state = "committed"
            print(f"Participant {self.name}: Commit successful.")
        else:
            print(f"Participant {self.name}: Cannot commit, current state is '{self.state}'.")

    def abort(self):
        if self.state in ["prepared", "initial"]:
            print(f"Participant {self.name}: Aborting changes for data '{self.prepared_data}'.")
            self.state = "aborted"
            print(f"Participant {self.name}: Abort successful.")
        else:
            print(f"Participant {self.name}: Cannot abort, current state is '{self.state}'.")

    def get_state(self):
        return self.state

class Coordinator:
    def __init__(self):
        self.participants = []

    def add_participant(self, participant):
        self.participants.append(participant)

    def execute_transaction(self, transaction_data):
        print("\n--- Starting Distributed Transaction ---")
        
        # Phase 1: Prepare
        preparations_successful = True
        for participant in self.participants:
            if not participant.prepare(transaction_data):
                preparations_successful = False
                print(f"Transaction aborted: Participant {participant.name} failed to prepare.")
                break

        # Phase 2: Commit or Abort
        if preparations_successful:
            print("All participants prepared. Proceeding to commit.")
            for participant in self.participants:
                participant.commit()
        else:
            print("Some participants failed to prepare. Aborting transaction for all.")
            for participant in self.participants:
                participant.abort()
        
        print("--- Distributed Transaction Finished ---")

# Scenario
participant_a = MockParticipant("A")
participant_b = MockParticipant("B")
participant_c = MockParticipant("C")

coordinator = Coordinator()
coordinator.add_participant(participant_a)
coordinator.add_participant(participant_b)
coordinator.add_participant(participant_c)

transaction_details = "Order #123: Product X, Quantity 5"
coordinator.execute_transaction(transaction_details)

# Expected Output Snippet (order of print statements may vary slightly):
# --- Starting Distributed Transaction ---
# Participant A: Preparing with data 'Order #123: Product X, Quantity 5'...
# Participant A: Preparation successful.
# Participant B: Preparing with data 'Order #123: Product X, Quantity 5'...
# Participant B: Preparation successful.
# Participant C: Preparing with data 'Order #123: Product X, Quantity 5'...
# Participant C: Preparation successful.
# All participants prepared. Proceeding to commit.
# Participant A: Committing changes for data 'Order #123: Product X, Quantity 5'.
# Participant A: Commit successful.
# Participant B: Committing changes for data 'Order #123: Product X, Quantity 5'.
# Participant B: Commit successful.
# Participant C: Committing changes for data 'Order #123: Product X, Quantity 5'.
# Participant C: Commit successful.
# --- Distributed Transaction Finished ---

Example 2: Transaction Aborted Due to Participant Failure

class FailingMockParticipant(MockParticipant):
    def __init__(self, name, fail_prepare=False):
        super().__init__(name)
        self.fail_prepare = fail_prepare

    def prepare(self, transaction_data):
        if self.fail_prepare:
            print(f"Participant {self.name}: Simulating preparation failure.")
            self.state = "failed_to_prepare"
            return False
        return super().prepare(transaction_data)

# Scenario
participant_x = MockParticipant("X")
participant_y = FailingMockParticipant("Y", fail_prepare=True) # This participant will fail preparation
participant_z = MockParticipant("Z")

coordinator_fail = Coordinator()
coordinator_fail.add_participant(participant_x)
coordinator_fail.add_participant(participant_y)
coordinator_fail.add_participant(participant_z)

transaction_details_fail = "Transfer Funds: $100 from Account P to Account Q"
coordinator_fail.execute_transaction(transaction_details_fail)

# Expected Output Snippet:
# --- Starting Distributed Transaction ---
# Participant X: Preparing with data 'Transfer Funds: $100 from Account P to Account Q'...
# Participant X: Preparation successful.
# Participant Y: Simulating preparation failure.
# Transaction aborted: Participant Y failed to prepare.
# Some participants failed to prepare. Aborting transaction for all.
# Participant X: Aborting changes for data 'Transfer Funds: $100 from Account P to Account Q'.
# Participant X: Abort successful.
# Participant Y: Aborting changes for data 'None'. # Note: prepared_data might be None if prepare returned False early
# Participant Y: Abort successful.
# Participant Z: Aborting changes for data 'Transfer Funds: $100 from Account P to Account Q'.
# Participant Z: Abort successful.
# --- Distributed Transaction Finished ---

Example 3: Transaction with No Participants

# Scenario
coordinator_empty = Coordinator()
transaction_details_empty = "Empty Transaction"
coordinator_empty.execute_transaction(transaction_details_empty)

# Expected Output Snippet:
# --- Starting Distributed Transaction ---
# All participants prepared. Proceeding to commit.
# --- Distributed Transaction Finished ---

Constraints

  • Participants and the coordinator will be implemented as Python classes.
  • The simulation will be synchronous; no asynchronous operations or network latency are required.
  • Focus on the logic of the 2PC protocol, not robust error handling for network partitions or participant crashes.
  • The transaction_data can be any Python object that can be passed between methods.

Notes

  • This challenge simplifies distributed transactions by omitting complex failure recovery mechanisms (like timeouts, log-based recovery, or handling coordinator failures). Real-world distributed transaction management is significantly more complex.
  • Consider how you would represent the "work" being done by each participant within their prepare, commit, and abort methods. For this challenge, simply printing messages and changing internal states is sufficient.
  • Think about how the coordinator tracks the success or failure of each participant's preparation phase.
  • The MockParticipant and FailingMockParticipant classes provided in the examples are for illustration. You should implement your own Participant and Coordinator classes as part of your solution.
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