What is Causal Ordering in Distributed Systems?
Causal ordering is a principle in distributed systems that ensures operations are executed in an order consistent with their cause-and-effect relationships. In simple terms, if one operation depends on or is influenced by another, the system ensures that the dependent operation is not processed before its cause.
Key Concepts
1. Causality in Events
If an event A causes or influences another event B, A should be processed before B.
Example: If Alice sends a message to Bob, and Bob replies, the system should ensure that Bob’s reply is processed only after Alice’s original message.
2. Causal Relationships
Causal ordering captures the happens-before relationship (denoted as →) between events
- If B happens before B, then A→B.
- If two events A and B are independent, they are considered concurrent.
3. Preserving Causal Order
In systems that preserve causal order, all participants in the system see events in the same causally consistent order, even if events occur at different times or locations.
Why is Causal Ordering Important?
Causal ordering ensures logical correctness in distributed systems. Without it, systems may exhibit unexpected or incorrect behaviors:
Example of Violating Causality
- Alice revokes Bob’s access to a file.
- Bob accesses the file shortly after the revocation because the system processes the access request before the revocation.
- This violates the causal order: the revocation should precede any access attempts.
How is Causal Ordering Achieved?
1. Logical Clocks
- Logical clocks, such as Lamport Timestamps, are used to assign a timestamp to each event. Events with smaller timestamps are considered to have occurred earlier.
- Limitations: Lamport timestamps ensure causality but do not track concurrent events explicitly.
2. Vector Clocks
Vector clocks improve on Lamport timestamps by maintaining an array of counters, one for each process. This allows systems to distinguish between causally related and concurrent events.
3. Database Techniques
Systems like Zanzibar use databases like Google Spanner, which supports causal ordering via its TrueTime API:
- TrueTime assigns each operation a globally unique, causally ordered timestamp.
- Updates respect this ordering, ensuring consistency across globally distributed replicas.
Examples of Causal Ordering in Real Life
- Messaging Applications:
If Alice sends two consecutive messages, they should appear in the correct order for Bob. - Version Control Systems:
When a developer pushes code changes, subsequent changes should respect the order of commits. - Access Control:
In Zanzibar, if a user is removed from a group and then new content is shared with the group, the removed user should not gain access to the new content.
Key Challenges in Causal Ordering
- Latency: Enforcing causal order in a distributed system often requires communication between nodes, which can introduce delays.
- Concurrency: Distinguishing between concurrent and causally related events requires careful tracking and coordination.
- Scalability: As the number of nodes and events increases, maintaining causal ordering becomes computationally expensive.
Takeaway
Causal ordering is a foundational concept for building consistent and predictable distributed systems. It ensures that operations occur in a logical sequence, reflecting the true dependencies between events. Systems like Zanzibar leverage causal ordering to provide robust, globally consistent authorization decisions.
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This article is part of the Paper “Zanzibar: Google’s Consistent, Global Authorization System” that I am reading currently. More contents from this paper are going to come.
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