# Technical Analysis of TimescaleDB Historian Throughput Bottleneck and Store-and-Forward Risk

**URL:** <https://forum.inductiveautomation.com/t/technical-analysis-of-timescaledb-historian-throughput-bottleneck-and-store-and-forward-risk/116722>\
**Category:** Ignition\
**Tags:** perspective\
**Created:** [August 10, 2026, 7:03am UTC](https://forum.inductiveautomation.com/t/technical-analysis-of-timescaledb-historian-throughput-bottleneck-and-store-and-forward-risk/116722 "2026-08-10T07:03:51Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![narimannejadm](https://avatars.discourse-cdn.com/v4/letter/n/96bed5/32.png) [@narimannejadm](https://forum.inductiveautomation.com/u/narimannejadm)\
**Post date:** [August 10, 2026, 7:03am UTC](https://forum.inductiveautomation.com/t/technical-analysis-of-timescaledb-historian-throughput-bottleneck-and-store-and-forward-risk/116722/1 "2026-08-10T07:03:51Z")

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Problem Statement:

Our current architecture involves 16,000 tags configured with “On Change” history logging. We are utilizing PostgreSQL with the TimescaleDB extension to optimize ingestion rates and minimize memory overhead.

Test Results & Observations:

During a pilot test involving 12,000 simulated tags via the TimescaleDB Historian Module, we identified a critical throughput imbalance. The observed Store Rate is approximately 5,000 datapoints/sec, whereas the Forward Rate is limited to roughly 3,000 datapoints/sec.

Impact Assessment:

This creates a negative net data flow, leading to a continuous accumulation of pending data in the Store-and-Forward (S&F) buffer. In a real-world scenario, if a network outage occurs for 24 hours, the system will be unable to clear the resulting backlog, as the recovery rate is lower than the ingestion rate. This poses a significant risk to data integrity and buffer overflow.