188,000 Pts/Sec: InfluxDB Data Pipeline Optimization for 188 Million Time-Series Points (Python)

Can you load 15GB of minute-level stock data (188 million points) into a time-series database in under 17 minutes?
This video breaks down the high-performance Python data pipeline we engineered to achieve an average write rate of 188,064 points per second into InfluxDB.
We reveal the critical architectural decisions required for high-throughput time-series data:
1. Specialized Concurrency Model: How to correctly split work between
CPU-bound processes (using Joblib/Multiprocessing) and I/O-bound threads (using ThreadPoolExecutor) to utilize all 18 cores effectively.

2. The Vectorized Line Protocol Secret: A deep dive into the NumPy-backed technique used in run_loading.py to manually construct InfluxDB's Line Protocol, which unlocked an order-of-magnitude performance gain.

3. InfluxDB Schema Optimization: Learn the three-measurement, tag-based schema design required for lightning-fast querying in a quantitative trading system.

This is a masterclass in solving data ingestion bottlenecks for large-scale financial and time-series data.
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#influxdb #timeseriesanalysis #python #dataengineering #code #Multiprocessing #Multithreading