I am thinking about designing a system that time slices data and runs tasks like a series of dominos each sequence tied to a new data snapshot isolated from the other dominos. I compare this to a single system trying to reconcile changing data with respect to computational repeatability
That's a really elegant concept—and the domino analogy is spot-on.
What you’re proposing is essentially a time-sliced, immutable snapshot processing pipeline, where:
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Each "domino" is a discrete task or set of tasks triggered by a specific data snapshot at a point in time.
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Once a snapshot is captured, it's immutable—isolated from future changes—ensuring determinism and repeatability of computations tied to that state.
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This contrasts with systems that operate on mutable, real-time data, where the results can vary depending on when and how often computations are run.
This pattern has some big advantages:
Benefits
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Determinism: Each domino runs in a self-contained context, so results are reproducible.
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Traceability: You can trace errors or unexpected behavior to specific snapshots.
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Parallelism: Snapshots can be processed independently, enabling horizontal scaling.
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Debuggability: Easier to debug and replay tasks without affecting the current state.
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Historical Audit: You get a natural log of system behavior over time.
Key Design Elements
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Snapshot Isolation: You’ll need a mechanism to freeze the data view at each slice. This could be:
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Database snapshotting
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Event sourcing
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Time-versioned data stores
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Triggering System: Each domino needs to know when to run—this could be event-based or time-based.
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Snapshot Retention Policy: How long do you keep old snapshots? Tradeoff between cost and replay/debug capabilities.
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State Transition Logging: To reconstruct or analyze transitions across dominos.
Comparative View
| Pattern | Behavior |
|---|---|
| Your design ("Dominos") | Each task is deterministic, isolated, and based on a fixed view of the world. |
| Traditional reactive system | Tasks run on ever-changing state, making them harder to debug and reason about. |
Can you add in the concept of entropy and examples from Newtonian physics
Absolutely—bringing in entropy and Newtonian physics adds a powerful layer of metaphor (and even philosophical grounding) to your "domino snapshot system."
Reframing Your Design Through Entropy and Physics
In thermodynamics, entropy is the measure of disorder or randomness in a system. Left unchecked, all systems tend toward increased entropy (2nd law of thermodynamics). Likewise, in dynamic data systems, continually mutating state introduces uncertainty, inconsistency, and irreproducibility—entropy in action.
Your snapshot-based, domino-style architecture is a mechanism to counteract entropy in a computational system by creating isolated low-entropy environments (snapshots) where repeatable computation is possible.
Analogies from Newtonian Physics
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Snapshots = Newtonian "initial conditions"
In Newtonian mechanics, if you know the initial position and velocity of every particle in a system (initial conditions), you can deterministically predict the future state.
Similarly, your snapshot captures the full state of a system at time T, from which the computation proceeds in a predictable, deterministic way. -
Domino = A Newtonian "step function"
Each domino processes based on fixed initial conditions, like a Newtonian system evolving under known forces—no surprises, no mutations from outside. It represents a low-entropy system, carefully shielded from external chaos. -
Mutable systems = Open thermodynamic systems
Traditional systems are constantly bombarded by external inputs (like heat or particles in physics). They are non-isolated, and their state is in flux, making them resemble systems moving toward maximum entropy—less predictable, harder to reverse.
Entropy in Computing Terms
| Concept | High Entropy | Low Entropy |
|---|---|---|
| Mutable Global State | Data changes unpredictably, results vary | Data is fixed per slice |
| Real-time Analytics | State might change mid-computation | Every computation is isolated |
| Monolithic Task Pipelines | Coupled tasks create cascading changes | Dominoes only trigger the next, not affect each other retroactively |
| Log Replay on Mutable Data | Risk of inconsistency | Logs tied to snapshots are replayable with fidelity |
Summary Analogy
Imagine you’re a physicist trying to run an experiment with Newtonian particles.
In the mutable system, your lab is on a moving truck. The floor shakes, tools shift, the air pressure changes—good luck repeating your result.
In the domino snapshot system, your lab is sealed in a vacuum chamber with fixed walls and instruments calibrated at T=0. You press a button and the chain of events unfolds—just like Newton predicted.
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