Minimize Entropy while maximizing Caching.

Furthering my conversation with ChatGPT  I bring the previous concepts of hot swapping containers into a pod.

Prompt:

Specifically for this Time-Sliced topic. I want to meld in the idea of Hot Swapping containers in each domino.. This time with the additional possibility of sharing reusing results from previous domino in calculations where the underlying data did not change. I was planning on using a time series database as a daemonset on a node where pods could query the database for data and then persist their results. The key to this database would be its locality. The idea was to have a network or nodes performing operations and syncing their databases with debezium

Output:

This is a really compelling evolution of the time-sliced system—now you're introducing hot-swappable compute units (containers) with localized, node-bound time series data, plus selective caching based on data immutability. Let’s unpack how this system flows, now incorporating these layers:

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🔁 Conceptual Flow Overview

1. Immutable Snapshot Trigger:
   A new data snapshot is created at a fixed interval or event (e.g., via Kafka + Debezium).

2. Domino Activation:
   A container (domino) is hot-swapped in (ephemeral pod) to process that snapshot.

3. Local State Access:
   The domino queries the node-local time series DB DaemonSet, getting:

   • Current snapshot input

   • Cached outputs from previous dominos (if applicable)

4. Memoization Check:
   If dependencies haven't changed (verified via a fingerprint or hash of prior snapshot input), the domino reuses the cached result instead of recomputing.

5. Result Persistence & Sync:

   • The result is persisted to the local DB.

   • The node's DB syncs changes with others via Debezium, ensuring global visibility without compromising local speed.

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⚙️ Technical Building Blocks

Component	Role
DaemonSet TSDB (e.g., VictoriaMetrics or Prometheus TSDB)	Local, fast access; stores snapshot data + domino outputs
Hot-swapped containers (pods)	Each domino = isolated compute container
Debezium + Kafka	CDC pipeline for syncing base snapshot data across nodes
Hash-based memoization	Avoid recomputation if snapshot input hasn't changed
Kubernetes node affinity	Ensures dominos schedule close to the local DB for low-latency access

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🔬 Physics & Entropy Tie-In (Expanded)

This system creates localized low-entropy pockets—small, stable labs where Newtonian determinism rules:

• Hot-swapping containers mirrors particles with finite lifespans governed by deterministic rules.

• Shared results across dominos reflect conservation laws—energy (computational work) is neither created nor destroyed, just reused.

• Debezium syncing is like thermodynamic equilibrium slowly being restored across the system.

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🧠 Optional Enhancements

• Bloom filter or Merkle Tree to quickly detect if inputs to a domino have changed.

• Sidecar pattern for lightweight cache/memo layer attached to each hot-swapped container.

• Snapshot tagging using Git-style hashes, so every domino’s input and output can be traced back.

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