Incremental refresh #41
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Also added a function to calculate the time bucket using the Continuous Aggregate bucket function configuration. Changed the API to define the number of the buckets to split ranges. The range size is based on the bucket width of the CAgg.
zilder
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Jan 2, 2025
utils/incremental_refresh/util.sql
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| FROM | ||
| ranges, | ||
| -- Split ranges with 5 times the bucket width | ||
| LATERAL generate_series(ranges.global_start, ranges.global_end, (bucket_width * nbuckets)) AS start |
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I've been looking through the changes, and I think one thing is missing from my original code. The user has this weird data points in year 1917 or so (probably inserted by mistake). And apparently they want to keep it that way. If we generate ranges with this query it would create all the ranges between 1917 and 2024, while they only have few data points in pre-2020. In my original code I had this join to only generate the ranges that intersect with existing tiered chunks:
FROM timescaledb_osm.tiered_chunks ch
JOIN _timescaledb_additional.generate_increments(start_t, end_t, increment_size) AS i
ON tstzrange(i.incr_start, i.incr_end, '[)') && tstzrange(ch.range_start, ch.range_end, '[)')
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Not for merge. The current scripts we are planning to use to refresh the CAggs, that have been materialized with
timescaledb.enable_tiered_readsdisabled (SDC). Based on #40 but adapted specifically to discover CAggs built on tiered data and to insert an invalidation record to_timescaledb_catalog.continuous_aggs_materialization_invalidation_logto allow data re-materialization.