Data Warehouse - Fast Table Scan

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1 - About

Ways to Scan a Fact table Faster

  • Employ Parallelism

If you have the resources, then let divide the table into chunks and use all your system resources to scan it. Unfriendly in an OLTP environment, mind you.

  • Compress Data Segments

Make the table smaller! Block level compression can shrink that data like a plunge into cold water. Very OLTP unfriendly.

  • Reduce Free Space Percent

PCTFREE = very small ⇒ more rows per block ⇒ smaller table. And potentially a higher chance of row migration, of course.

  • Increase Percent Used

PCTUSED = large ⇒ less likely that blocks retain free space following deletes ⇒ more rows per block ⇒ smaller table.

  • Use a Larger Block Size

For significantly long rows you may get reduced empty space, thus a smaller table to scan. Reorder The Columns: If you are commonly interested in just a subset of columns (for example metrics in a fact table) then consider making them the first columns of the table definition - for tables with lots of columns there is measurable overhead in finding the end columns (I’m not talking about row chaining here). Hmmm, maybe I’ll post something else about this.

  • Index Columns of Interest

An index can be treated as a skinny table, and your query might be satisfied by a fast ful or full index scan. The usual comments about NULL values in indexes apply here. Don’t neglect consideraton of index size either - index key compression and use of bitmap indexes provide smaller structures to scan.

  • Materialized Views

Is there anything they’re not good for? This could be a genuine skinny table, or an aggregation of selected columns. Ensure Table Is Spread Over Available Devices: With consequent reduced likelihood of encountering an i/o choke point.

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3 - Documentation / Reference

data/warehouse/fast_scan.txt · Last modified: 2017/09/13 20:36 by gerardnico