feat: Add QPS-Latency tradeoff metrics for Streaming Tests

[Akhil-Pathivada]

Summary

This PR implements the feature requested in #638 - exposing the QPS-Latency tradeoff curve for streaming tests. Currently, streaming tests only report a single "Max QPS" value per stage, which provides limited visibility into performance characteristics for capacity planning.

What's New

Backend

• Use HDR Histogram for memory-efficient latency tracking (~20KB fixed memory vs unbounded list growth)
• Collect p99, p95, and average latency at each concurrency level per stage
• Pass latency metrics through the pipeline to results

Frontend

• New "Concurrent Search Performance" section on streaming results page
• Single Stage View: Select a stage to see QPS vs Latency chart and detailed metrics table
• Compare Stages View: Overlay multiple stages to visualize how performance evolves as data grows

Why This Matters

Production workloads typically target a specific latency SLA rather than maximum throughput. With this data, users can now determine:

• What latency to expect at a specific QPS during ingestion
• Where the optimal operating point is before latency spikes
• How performance changes as data volume increases

Files Changed

File	Change
mp_runner.py	HDR Histogram + latency collection per concurrency
read_write_runner.py	Pass latency data through pipeline
metric.py	Add 5 new fields for per-stage latency data
concurrent_detail.py	New - UI component for QPS-Latency visualization
streaming.py	Integrate new UI section
pyproject.toml	Add hdrhistogram>=0.10.1
requirements_py3.11.txt	Add hdrhistogram>=0.10.1 for Docker

Screenshots

Closes #638

[Akhil-Pathivada]

/assign @alwayslove2013

[sre-ci-robot]

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