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For this benchmark, weĬreated multiple data sets with the following scale and the resulting We had a cardinality of 15,552,000 for all systems. The tests we ran for our previous benchmarks all used a scale of 4000, meaning Well-known topic for developers and users of databases. Thought this would be interesting to share with readers as high-cardinality is a We wanted to explore this topic in moreĭetail to see how QuestDB can handle different degrees of cardinality. That tested the performance of our new ingestion subsystem, but we didn't touch
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#Dbschema show cardinality series
Scale_val x 3888 Copy Exploring high-cardinality in a time series database benchmark # For measuring the performance of QuestDB, weĬreate data in InfluxDB line protocol format, which consists of ten 'tags' and Time Series Benchmark Suite, a collection of Go programs that generate metricsįrom multiple simulated systems. How can I measure database performance using high-cardinality data? #Ī popular way of measuring the throughput of time series databases is to use the each indexed column contains many unique values.High-cardinality boils down to the following two conditions: With each new tag orĬategory we add to our data set, cardinality grows exponentially. Insights on more kinds of information about the devices, such as applicationĮrrors, device state, metadata, configuration and so on. In these scenarios, experience shows that we will want to eventually get Tracking a new firmware version for the devices would increase the set to This can quickly get unmanageable in some cases, as even adding and The cardinality of this set is 500,000 ( 1000 x 20 Locations, they're running one of 5 firmware versions, and report input from 5 In theĬontext of a time series database (TSDB), rows will usually have columns thatĬategorize the data and act like tags. What is high-cardinality data? #Ĭardinality typically refers to the number of elements in a set's size. Still, a solid understanding of this concept helps when planning general-purposeĭatabase schemas and understanding common factors that can influence database Monitoring are use cases where high-cardinality is more likely to be a concern. Intimidating topic if you're unfamiliar with it, but this article explains whatĬardinality is and why it crops up often with databases of all types. High-cardinality or ran into issues relating to it. If you're working with large amounts of data, you've likely heard about