{"id":25743,"date":"2025-06-05T14:20:14","date_gmt":"2025-06-05T21:20:14","guid":{"rendered":"https:\/\/tdengine.com\/?p=25743"},"modified":"2025-11-03T08:07:24","modified_gmt":"2025-11-03T16:07:24","slug":"performance-summary-influxdb-vs-tdengine","status":"publish","type":"post","link":"https:\/\/tdengine.com\/performance-summary-influxdb-vs-tdengine\/","title":{"rendered":"Performance Summary: InfluxDB 3.0 vs. TDengine"},"content":{"rendered":"\n

The performance of your TSDB doesn\u2019t just impact your ability to ingest, store, and analyze large amounts of data; it directly affects your total cost of ownership<\/a> (TCO). Better ingestion rates, query response times and compression ratios mean your system consumes fewer resources to process the same amount of data. To demonstrate TDengine’s robust performance<\/a>, we evaluated the platform against the recently released InfluxDB 3 in DevOps and IoT scenarios.<\/p>\n\n\n\n

Performance Comparison<\/h2>\n\n\n\n

Ingestion Performance<\/h3>\n\n\n\n

Time series databases need to ingest massive amounts of data, and TDengine achieves the fastest ingestion speeds across all datasets, ranging from 12 to 20 times the speed of InfluxDB in the DevOps use case and 6 to 11 times in the IoT use case.<\/p>\n\n\n\n

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TDengine ingests data significantly faster than InfluxDB in both TSBS use cases<\/figcaption>\n<\/figure>\n\n\n\n

Resource Consumption<\/h3>\n\n\n\n

In addition, TDengine uses less processing power than InfluxDB to ingest the datasets. While TDengine does require higher CPU usage, it completes all ingestion and compression tasks within 5 minutes while InfluxDB takes more than 5 times longer, meaning that the total resource usage of TDengine is lower.<\/p>\n\n\n\n

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InfluxDB demands processing resources over a 25 minute period while TDengine finishes its work in under 5 minutes<\/figcaption>\n<\/figure>\n\n\n\n

Query Performance<\/h3>\n\n\n\n

As performance can differ based on a number of factors, the TSBS framework covers a wide range of query types. TDengine provided the fastest query response across all scenarios, confirming that organizations dependent on real-time analytics are best served with this purpose-built platform.<\/p>\n\n\n\n

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TDengine returns results more quickly for simple and complex queries across both use cases<\/figcaption>\n<\/figure>\n\n\n\n

Notably, six of the twelve IoT query sets failed to run at all in InfluxDB 3 Core, either throwing an error or returning no results. This indicates that InfluxQL compatibility is still an issue for InfluxDB 3, and it is hoped that more complete results can be obtained once this is resolved.<\/p>\n\n\n\n

Disk Storage<\/h3>\n\n\n\n

TDengine required less disk space to store the TSBS datasets in all scenarios and use cases. Its compression performance ranged from 2.6 to 25 times better than InfluxDB in the DevOps use case and 2.3 to 7.4 times better in the IoT use case, with significantly higher efficiency in the 100,000 devices and smaller-scale categories.<\/p>\n\n\n\n

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TDengine consumes less disk space than InfluxDB to store the TSBS datasets<\/figcaption>\n<\/figure>\n\n\n\n

Conclusion<\/h2>\n\n\n\n

Across all key test metrics for ingestion, compression, and querying, TDengine clearly emerges as the highest-performing time series database<\/em>.<\/p>\n\n\n\n