![]() ![]() ![]() We take into account the structure and content of similar articles that Wikipedia has classified as models – less than one half of one percent of Wikipedia’s published articles. Once the research phase of an assignment is complete, we then craft a draft of a new article. ![]() Ironically, Wikipedia’s “no original research” policy excludes so many potential sources that the need for the right kind of research is especially acute. This branch of Wikipedia policy has generated many hundreds of pages of commentary examining specific stories and sources. What’s more, our researchers have extensive training as to what sources and stories Wikipedia deems “reliable” and what sources it deems “unreliable.” These deceptively simple terms belie tens of thousands of words of often counterintuitive policy. We’re able to track down difficult to find sources (whether technical, historical or just obscure) – and we’re not beyond heading to a research library if necessary. Our Wikipedia agency’s research staff has access to commercial periodical databases and other tools that go far beyond the limits of a simple Google search. Our experts, including former academics and investigative journalists, then conduct intensive research to create the foundation necessary for a successful article. Some clients provide us with punch lists and sources to get us started, while others ask us to do everything ourselves. Once we believe that an article is likely to pass an independent notability by independent, experienced editors, we begin with an in-depth conversation with our clients about the particulars for a new article draft. One can’t judge the qualification of a subject for Wikipedia by looking at similar Wikipedia articles, which might be deeply flawed and likely to be deleted when they are eventually reviewed by experienced editors. Wikipedia “notability” is not synonymous with merit. The full “notability” rule set, however, is dense, long and often counterintuitive, with dozens of special cases exceptions and exclusions. For companies and individuals, this typically requires multiple in-depth stories written about the subject by journalists in independent publications with a reputation for editorial credibility. Qualifying for a new Wikipedia page starts with an assessment of “notability.” We begin by screening whether a proposed page meets Wikipedia’s qualification criteria. I’m also available for consulting if you just don’t have time for that and need to solve performance problems quickly.The most common question we are approached with is: “How do I get a Wikipedia page?” I’m offering a 75% discount to my blog readers if you click from here. If this is the kind of SQL Server stuff you love learning about, you’ll love my training. One limitation of Batch Mode is with Sorts - they are single threaded - a point this particular demo obfuscates, unless they’re a child operator of a Window Aggregate. This isn’t a limitation of Batch Mode generally, though I suspect it has something to do with why we don’t see Repartition Streams show up. Aside from the Scan of the Votes table, only one thread is ever active across the rest of the query. ![]() We have the dreaded serial parallel query. In the Batch Mode plan, that… doesn’t happen. Crazy to think about, but threads dividing work up evenly is, like, a good thing. The number of rows on each thread is even because of this. Sucktownīut in the Row Mode plan, Repartition Streams does exactly what it sounds like it does, and balances things out. Because of that, Bad Things Happen™ Skew Jobīoth plans start out with an unfortunate scan of the Posts table. See that Repartition Streams operator? It literally saves the entire query. The answer to why the Batch Mode plan is 3x slower lies in our Row Mode plan. The Row Mode only plan was 2.6 seconds, and this is 6.8 seconds. Next Up, Hash Hintĭisappointingly, this gets worse. To be fair, it’s really the only one eligible with the trick we used.įorcing this plan to run in parallel, we go back to a 27 second runtime with no operators in Batch Mode. The only operator to run in Batch Mode here is the Sort. Remember than the Hash Join plan in Row Mode ran in 2.6 seconds. Though this plan is ~6 seconds faster than the Merge Join equivalent in the last post, that’s not the kind of improvement I’m shooting for. INDEX c CLUSTERED COLUMNSTORE) First Up, No Hints To do that, we’re going to use an empty temp table with a clustered columnstore index on it. In this post, I’m going to see if Batch Mode will help anything. In the last post, I showed you a query where a bad plan was chosen because of bad guesses. ![]()
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