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IMPORTANT NOTE: Timing of sessions and room locations are subject to change.
For time series data processing, there are two popular data models: a tag set data model like those adopted by Prometheus, OpenTSDB and InfluxDB, and a relational data model like those adopted by TimescaleDB and QuestDB. For both models, there are pros and cons. TDengine proposed a new data model based on the relational data model, namely "one table for one data collection point". This model retains the advantages of the relational model while significantly improving data ingestion rate, query performance, and storage efficiency. This talk will discuss how the model works, including why its data compression ratio is much higher, why its data ingestion rate is 2 to 5 times higher, and why its query latency is even 2 to 30 times lower than other models. It will also address the challenges for this new data model, particularly the difficulty of managing the large number of tables that it requires, which can reach millions or even one billion for large-scale deployments. The concept of the "supertable" will be introduced to solve this problem. Additionally, to provide hard data proving that this new data model is effective, the talk will include a benchmark report based on the widely used TSBS test suite, including the step-by-step procedure to reproduce the benchmark.
Jeff Tao has a stellar background as a technologist and serial entrepreneur. Early in his career he conducted research and development on mobile Internet at Motorola, 3Com and other companies in Chicago. This was followed by founding two successful startups. In his latest venture... Read More →
