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Validating System Executions with TLA+

• 0:00:02 Introduction: The talk focuses on "closing the spec to code gap" using TLA+ for validating system executions, particularly in distributed systems.

• 0:00:43 Core Idea (Trace Validation): The fundamental approach involves collecting logs from individual nodes, combining them into a global log (ordered by time/causality), and creating a TLA+ "trace specification" that represents the behaviors observed in the log.

• 0:01:59 Comparison with Specs: This trace specification is then compared to the handwritten (or reverse-engineered) system specification. If the intersection of their behaviors is non-empty, the execution is considered valid according to the high-level spec.

• 0:02:25 Prior Work: The concept isn't new; it builds upon work from over 20 years ago, including efforts by Lamport's group, and more recent applications at MongoDB and Atomix.

• 0:04:06 Experience Reports: The speaker presents experience with seven systems. The first five were developed using spec-driven development with trace validation, and include examples like a multi-producer/multi-consumer queue, a distributed termination detection algorithm (EWD 998), a two-phase commit implementation, a key-value store, and a Vanilla Raft implementation. Trace validation found bugs in all of these.

• 0:07:05 etcd: A larger system, etcd, was also analyzed. Trace validation was added to vanilla etcd, finding an inefficiency (though not a safety bug). This is being used to validate a new "witness support" feature.

• 0:09:54 Confidential Consortium Framework (CCF): A graph-inspired, crash fault-tolerant consensus system used in Azure. Reverse-engineering the spec and applying trace validation revealed two data loss issues, even starting from passing tests.

• 0:14:51 Iterative Process: Trace validation is part of a larger TLA+ engineering process. It involves iteratively updating the high-level spec (or documenting divergences) until traces validate, then continuing with full-scale verification, which can reveal spec-level bugs.

• 0:16:19 Counter Example Handling: Manually turned counter examples into new test that confirmed the existance of bugs in the implementations.

• 0:16:53 Mini-Tutorial (EWD 998): A detailed walkthrough of applying trace validation to the EWD 998 termination detection algorithm.

• 0:19:53 Execution Example: Illustrates actions (send, receive, deactivate, initiate token, pass token) and how they relate to asynchronous message delivery and synchronous token passing.

• 0:20:27 Logging Strategy: Logging is done at two key points: when a node sends a message and when it receives a message. JSON is used as the logging format, but this is arbitrary.

• 0:21:11 Trace Specification: Shows a simplified TLA+ trace specification that reads the log file and translates it into TLA+ behaviors. It extends the original spec and uses helper modules (Json, VectorClock).

• 0:21:59 Actions and State: Actions in the trace spec (e.g., isSentMessage) check log entries and map them to actions in the high-level spec, updating variables accordingly. Constraints help prune the state space.

• 0:24:29 Dealing with Mismatches: Demonstrates how to handle mismatches between the log and the spec. For instance, asynchronous token passing in the log versus atomic token passing in the spec is addressed by adding stuttering steps (using isReceiveToken).

• 0:26:53 Handling Missing Information: Another issue: the spec requires nodes to be inactive to send tokens, but this isn't logged. This is solved using action composition (deactivate composed with passToken).

• 0:27:48 Validation Success: After these adjustments, the trace validates.

• 0:28:02 Verification Property: Discusses what is actually verified. It's not a simple safety or liveness property. Ideally, it's a CTL property (existence of a path where the log is fully processed), but this can't be directly expressed in TLA+. A workaround using liveness and checking the graph's diameter is used.

• 0:31:20 Debugger Help: The TLA+ debugger can help pinpoint where validation fails.

• 0:31:53 Logging Overhead (etcd & CCF): In etcd, logging was added in 11 places; in CCF, 15 places. It's essentially just using STD আউট. No complex logging library or deterministic hypervisor is needed.

• 0:33:04 Logging Guidelines: Log when messages are sent/received, and ideally, log observable node-local state changes. Include primed/unprimed variables for matching. Only log constant-size variables.

• 0:34:08 Clock Synchronization: Use a centralized clock or, preferably, a logical distributed clock (vector clock or Lamport clock).

• 0:35:10 Tooling: Mentions the VectorClock module and the Shiviz tool for visualization.

• 0:35:18 Wrap-up: Emphasizes that TLA+ tools are mature enough to bridge the spec-to-code gap. Trace validation found divergences in all seven systems and helped in reverse-engineering CCF.

• 0:36:31 Future Work: Considers generalizing trace validation, refining specs, adding a true depth-first search mode to TLC, and creating a companion "model-based testing" approach to generate diverse behaviors and translate counter-examples to system tests.

• 0:39:10 Q&A Importance level: Markus confirms the approach is effective, and that it had a high return of investment.

• 0:40:06 Q&A Trace validation vs. Piglet/Generating Implementation: Trace validation is useful for brownfield projects (existing implementations). Greenfield projects might benefit more from code generation (e.g., Piglet).

• 0:41:37 Q&A: Jepson, Chaos Engineering and Antithesis: Markus agrees that it is beneficial to combine the work with other tools, such as fuzzing tools, chaos engineering, and deterministic hypervisors.

• 0:43:57 Q&A: Shared Memory vs. Message Passing Specification: If a specification deviates in its message passing assumptions from the implementation, trace validation requires more adaptation, such as employing stuttering steps and action composition to achieve alignment.