digoal
2021-07-15
PostgreSQL , Hyperfunctions , timescaledb
Today, we’re excited to launch TimescaleDB hyperfunctions, a series of SQL functions within TimescaleDB that make it easier to manipulate and analyze time-series data in PostgreSQL with fewer lines of code. You can use hyperfunctions to calculate percentile approximations of data, compute time-weighted averages, downsample and smooth data, and perform faster COUNT DISTINCT queries using approximations. Moreover, hyperfunctions are “easy” to use: you call a hyperfunction using the same SQL syntax you know and love.
At Timescale, our mission is to enable every software developer to store, analyze, and build on top of their time-series data, so that they can measure what matters in their world: IoT devices, IT systems, marketing analytics, user behavior, financial metrics, and more. (For example, we’ve built a free multi-node, petabyte-scale, time-series database; a multi-cloud, fully-managed service for time-series data; and Promscale, an open-source analytics platform for Prometheus monitoring data.)
https://docs.timescale.com/api/latest/hyperfunctions/
TimescaleDB hyperfunctions are a series of SQL functions within TimescaleDB that make it easier to manipulate and analyze time-series data in PostgreSQL with fewer lines of code. You can use hyperfunctions to easily aggregate data into consistent buckets of time, calculate percentile approximations of data, compute time-weighted averages, downsample and smooth data, and perform faster COUNT DISTINCT queries using approximations.
Hyperfunctions are “easy” to use: you call a hyperfunction using the same SQL syntax you know and love.
- approximate_row_count
- first
- last
- histogram
- time_bucket
- Gapfilling and Interpolation
- time_bucket_gapfill
- locf
- interpolate
- Percentile Approximation
- percentile_agg
- approx_percentile
- approx_percentile_rank
- rollup
- max_val
- mean
- error
- min_val
- num_vals
- Advanced Aggregation Methods
- uddsketch
- tdigest
- Time Weighted Averages
- time_weight
- rollup
- average
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