From IoT and finance to utilities and smart environments, 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 time-series database (TSDB) that can handle it.
Many enterprises initially run their time-series data workflows on traditional general-purpose databases such as MySQL or MongoDB. Although performance is often acceptable in the early stages, 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.
TDengine is an open-source, 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 time-series data operations by 50% or more.
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, ranging from 1.52 to 6.74 times higher than TimescaleDB and 3.01 to 10.63 times higher than InfluxDB. At the same time, in the largest TSBS scenario of 10 million devices, TDengine required only one-fourth the disk space of InfluxDB and less than one-twentieth the disk space of TimescaleDB to store the data.
TDengine also outperformed InfluxDB and TimescaleDB in query response time, especially in more complex scenarios. In extended testing on the TSBS scenario of 4,000 devices, its response times in the double-groupby-5 and double-groupby-all categories were 24 times faster than TimescaleDB and at least 26 times faster than InfluxDB.
In the lastpoint category, its performance was 5 times that of TimescaleDB and 21 times that of InfluxDB, and in the groupby-orderby-limit category, its performance was 8 times that of TimescaleDB and 15 times that of InfluxDB.
For more information about the TSBS evaluation or if you would like to run the comparison testing yourself, download the full report.
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.
To learn more how TDengine can improve the performance of your data infrastructure while reducing cost and complexity, contact us today for a demo customized for your industry.
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