From manufacturing and energy to connected cars and infrastructure, the amount of data being generated in all industries is increasing faster than ever before. Data at this scale can offer astounding new insights into business processes—but requires a high performance industrial data platform that can handle it.
Industry leaders have realized that the key to high performance in industrial data processing is the deployment of a time series database (TSDB). While some attempt to run their time-series data workflows on traditional general-purpose databases such as MySQL or MongoDB, these enterprises find that as their business grows, the scale of their data increases exponentially, and their data infrastructure quickly becomes overwhelmed. Their costs skyrocket and their performance suffers as they are forced constantly to upgrade the hardware of their general-purpose systems just to keep up with the data that they generate.
The TDengine industrial data platform includes a high performance, cloud native time series database that enables efficient ingestion, processing, and monitoring of petabytes of data per day, generated by billions of sensors and data collectors. Thanks to its data model that takes full advantage of the characteristics of time-series data, TDengine delivers more than ten times the performance of general-purpose platforms while requiring only one-fifth the storage space. By migrating data workflows to TDengine, enterprises not only enjoy faster data ingestion and query response times—they can also reduce the TCO of their industrial data operations by 50% or more.
Proven High Performance in Ingestion and Querying
In addition, TSBS benchmark results show that TDengine has far superior performance than other time-series database products in both ingesting and querying big data—while using far fewer CPU and storage resources.
According to a publicly available TSBS evaluation, the ingestion performance of TDengine exceeded that of TimescaleDB and InfluxDB in all five scenarios of the IoT use case. At the same time, in the largest TSBS scenario of 10 million devices, TDengine required only one-third the disk space of InfluxDB and one-twelfth the disk space of TimescaleDB to store the data.
TDengine outperformed InfluxDB and TimescaleDB in query response time, especially in more complex scenarios. In extended testing on the TSBS scenario of 4,000 devices, TDengine displayed 87.1 times the performance of TimescaleDB in the long-daily-sessions scenario and 132 times the performance of InfluxDB in the stationary-trucks scenario.
In addition to the IoT use case, TDengine also delivered superior performance in the TSBS DevOps CPU-only use case.
In this use case, TDengine ingested the test data between 1.5 to 6.7 times faster than TimescaleDB, and 3.0 to 10.6 times faster than InfluxDB, with significantly lower CPU overhead. For complex queries, TDengine responded up to 21.1x faster than InfluxDB and 8.4x faster than TimescaleDB.
TDengine is able to deliver this high performance due to its unique storage architecture and design, including the concept of creating one table per device, and by introducing the supertable to enable aggregation operations across tables. These along with its distributed design ensure that TDengine provides optimal performance even with high-cardinality data.