Tags: on line dating and friendship buddiesHot sex typing chattransgender tween talks datingTeen chat wank no signupgossip girl vampire datingchelsea dating flower show tiscaliSex talk chat lines for free
Thanks also to my colleague Paul Hunt for early brainstorming on this topic.I’m going to use a permanent donor table for now to eliminate any theoretical risk of a binary stats stream from a temp table being incompatible with a permanent table.
Step 4 still updates the stats in series, but is super-fast because it scans only 10m rows vs 19bn in the original.
I will wade through the code in detail below, but I’m sure most of you just want to know the results: I can now update stats on my 22 hour table at the same sample rate in 40 minutes, using proportionately less IO and CPU too.
Sure you can do several tables in parallel, but you can’t update the various stats on a single table in parallel using supported functionality.
So the next step is usually to tinker with sample size, spread the update across multiple maintenance windows, switch older partitions out to an archive table, or perhaps leverage filtered stats (conditions apply) or incremental stats (from SQL 2014).
Disclaimer: This approach is about as safe to use as steroids.
It uses unsupported functionality, and should therefore be applied to production systems with extreme caution, and only by expert programmers who understand their tables and how they are used.
According to the documentation itself ( https://msdn.microsoft.com/en-us/library/ms190397(v=sql.120)) When new partitions are added to a large table, statistics should be updated to include the new partitions.
However the time required to scan the entire table (FULLSCAN or SAMPLE option) might be quite long.
A relatively low risk, future proof (and let’s face it, lazy) approach will reuse rather than reinvent existing stats functionality where possible: At step 3, I’m going to use the same pseudo-random, page-oriented sampling mechanism underlying the standard update stats command.
Extracting this sample of 10m rows from 19bn is hugely time consuming, but now we’re doing it just once rather than 33 times.