The sheer volume and complex nature of time-series data have given rise to the purpose-built time series database (TSDB). While performance is crucial for all databases, the size and complexity of time-series data make speed, precision, and scalability especially important.
The performance of your TSDB doesn’t just impact your ability to ingest, store, and analyze large amounts of data; it directly affects your total cost of ownership (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 high performance, we evaluated the platform against the latest InfluxDB 3 Core as well as InfluxDB 1.8.
Objective Evaluation via the Time Series Benchmark Suite
To ensure an even playing field, we used the open-source Time Series Benchmark Suite (TSBS) framework and ran the tests on identical systems. TSBS is designed for objective database evaluations and generates datasets for a range of recommended ingestion and query scenarios. TSBS is used by other database providers, including VictoriaMetrics and Timescale, to perform evaluations similar to the one described here.
TSBS is an open and independent framework, meaning the test procedures and datasets generated aren’t designed to benefit any database platform, and allows anyone to conduct an objective evaluation.
This evaluation applied the TSBS DevOps and IoT datasets, which include small and large-scale scenarios. The DevOps scenarios each have ten metrics, ten tags, and a microsecond-precision timestamp, while the IoT scenarios are more complex, including out-of-order and missing data. For detailed information, see the official TSBS repository in GitHub.
Performance Comparison
Ingestion Performance
Time series databases need to ingest massive amounts of data, and TDengine achieves the fastest ingestion speeds across all TSBS scenarios, ranging from 3.1 to 42.8 times the speed of InfluxDB 1.8, and 4.4 to 14 times the speed of InfluxDB 3 Core. Interestingly, while InfluxDB 3 Core outperforms InfluxDB 1.8 in the largest datasets, its ingestion performance is actually lower in the first three scenarios. This indicates that while the cardinality issues that plagued earlier versions of InfluxDB may have been resolved, performance still leaves much to be desired.
Query Performance
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.
TDengine returned results for all simpler queries in under 20 milliseconds, while InfluxDB 1.8 took 1.1 to 8.2 times as long to retrieve the same information, and InfluxDB 3 Core was at least 10x slower. More complex queries allowed TDengine to show off its processing power, reaching 15x the performance of InfluxDB 1.8 and 66x the performance of InfluxDB 3 Core in the groupby-orderby-limit scenario. This demonstrates that TDengine is best prepared to handle the most performance-intensive queries without slowing down.
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.
Conclusion
Across all key test metrics for ingestion and querying, TDengine clearly emerges as the highest-performing time series database.
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Ingestion: Although InfluxDB 3 Core’s performance is more stable than older versions when handling larger datasets, its ingestion rates still lag behind modern time-series databases like TDengine. InfluxDB 1.8 ingests smaller datasets faster than InfluxDB 3 Core, but as the cardinality increases, ingestion performance declines rapidly.
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Queries: TDengine has the fastest query response time across all scenarios. InfluxDB 1.8 offers competitive latency in some simpler scenarios, but is still 3.7 to 15x slower than TDengine in more complex queries. InfluxDB 3 Core returned results significantly slower than TDengine and InfluxDB 1.8 in all scenarios and failed to run several IoT queries due to incompatibility.
The performance advantages shown by this evaluation indicate that TDengine excels at time-series data processing, especially with larger datasets and more complex queries. These advantages, combined with its comprehensive feature set and ease of use, make TDengine the best option for growing enterprises to scale their data pipelines.
