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blog-examples/ParallelReplicasBench/README.md

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@@ -124,44 +124,6 @@ The script has two layers of cache handling:
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If `DROP_CACHES=false`, caches are not flushed between matrix configurations.
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This means hot runs may become progressively hotter over time, but the cold baseline remains unaffected.
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## Flow: cold vs hot selection (per combination)
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```mermaid
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flowchart TD
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A[Start combo (N nodes, C cores)] --> B{DROP_CACHES?}
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B -- true --> C[Drop FS/Mark/QueryCond caches on cluster]
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B -- false --> D[Skip global cache drop]
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C --> E
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D --> E
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subgraph "Runs 1..R (runs_per_combination)"
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E[Run #1: enable_filesystem_cache=0] --> F[Record time t1, store raw/pretty stats]
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F --> G[Run #2..R: enable_filesystem_cache=1]
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G --> H[Record times t2..tR, store raw/pretty stats]
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end
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H --> I[Aggregate]
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I --> J[cold = t1]
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I --> K[hot = min(t2..tR) (if R>1 else t1)]
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I --> L[hot_avg = avg(t2..tR) (if R>1 else null)]
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I --> M[Compute aggregated stats for cold & hot:
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- avg_replica_pct (incl. initiator)
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- replica_temperature_distribution (incl. initiator)
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- avg_replica_memory_excl_initiator
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- avg_replica_bytes_read (incl. initiator)
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- avg_replica_net_sent_excl_initiator
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- sum_replica_net_sent_excl_initiator
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- initiator_net_recv
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- initiator_rows_per_sec (pretty)
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]
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J --> N[Write per-combo JSON]
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K --> N
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L --> N
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M --> N
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N --> O[Append to consolidated summary]
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```
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## Example usage
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### Parameters
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### License
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MIT
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MIT

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