Techniques for microsoft sql database design and optimization _ hacker news

1. H2 database SET STATISTICS IO ON. Nexus 5 data recovery This is much more useful than setting time on imo. Data recovery uk This will give you physical / logical reads per table as well as how specifically they were fetched. Database collation Usually I don’t care if the query took 11 seconds, I want to know what took those 11 seconds.

2. Database yugioh The % numbers that everyone relies on when skimming execution plans are total BS. Top 10 data recovery software free No one seems to realize this, but the percentage values are based off of the estimated query plan even when you’re running the actual execution plan.


Data recovery vancouver bc Use the plan to determine which operators were chosen, disregard the % values. 7 data recovery suite crack To get the true amount of work done, look at stats IO above. Database normalization definition If your underlying issues is missing or stale stats for example, that incorrect data will pass through to your execution plan and that plan will lie to you.

3. Data recovery wizard Try to determine why a plan is getting generated. Data recovery video Don’t just keep trying wacky code changes until you can get the correct plan (once..), find out what the optimizer is seeing and why it’s doing what it’s doing. Database query example You may know what operation is best in the current moment (“this was much faster as a nested loop”) but instead of using a join hint, force the optimizer’s hand by correcting whatever underlying issue is making it think that the merge/hash is a better route. Database migration This will save future-you hours of head-bashing when, inevitably, that join hint that was added three years ago causes a big production issue.

Programmers avoid working with DBAs because programmers think of SQL and databases as an annoyance that gets in the way of just shoving data into a hole somewhere. Data recovery free This is why MongoDB et al are so popular; they’ve provided cover for programmers to drop any pretense that organized data is useful. Database vs spreadsheet The end result is a continuous stream of data catastrophe after data catastrophe, and thousands of wasted man-hours trying to figure out how to do something with Mongo/Hadoop/whatever that is simple with a SQL server (and often reaching incorrect conclusions anyway).

I’m not saying there’s never a good reason to use a doc DB like Mongo or a “Big Data” implementation like Hadoop, but programmers don’t choose to use these things for good reasons 95% of the time. Database name sql They choose to use them a) because they’re new, and they want the resume points; and b) because they allow them to shed the pesky DBA and the pesky database engine, always in their hair about the “Right Way” to do things so that the data can be recalled and analyzed quickly, easily, and repeatably.

IMO the heart of the issue is the bifurcation in roles. Database management Both DBAs and traditional coders are programmers/developers. Data recovery miami Both should be trained in the others’ concerns and expected to help out in both parts of the system, and the role distinction of 100% DBA or 100% non-DBA should not exist. H2 database viewer This keeps balance in the incentives on both sides.

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