Collecting Registry Data

Slava Murygin shows how to use Powershell to read the Registry:

Do you use third party tools to document state of your SQL Server?
If not, that script is for you!

At first, you will know what is your SQL Server is up to.
At second, that might be your baseline document, to which you can compare a current SQL Server state over the time.
At third, that is a priceless piece of documentation!!! (I mean FREE!!!) which you can put in a folder and report to your boss.

Registry settings are a good part of a baseline, particularly a security baseline.

Finding Destinations In SSISDB

Bill Fellows has a script to figure out the name of that table throwing errors upon insertion:

There is a rich set of tables and views available in the SSISDB that operate as a flight recorder for SSIS packages as they execute. Markus Ehrenmüller (t) had a great question in Slack. In short, can you figure out what table is being used as a destination and I took a few minutes to slice through the tables to see if I could find it.

If it’s going to be anywhere, it looks like you can find it in catalog.event_message_context

If someone is using an OLE DB Destination and uses “Table or view” or “Table or View – fast load” settings, the name of the table will be the event message_context table. If they are using a variable name, then it’s going to be trickier.

Read on for the script.

OR Filters With Excel Cube Functions

Chris Webb shows how to build an OR filter in Power Pivot:

Now, imagine that you want a report with the Sales Amount measure on columns and Years on rows, and you want to filter the data so that you only see values for Mondays in July or Wednesdays in September. Using the Fields, Items and Sets functionality you could filter the data to only show the day/month combinations you need for each year, but since you can’t put a named set into the Filter area of a PivotTable you would have to use Excel formulas to sum up the combinations to get the totals you need

I’ve never used Excel’s Cube Functions.  Looks like something to add to the to-learn pile.

ExecuteSQL In Biml

Andy Leonard shows how to create an Integration Services ExecuteSQL task using Biml:

You do not yet have an SSIS package. You have the Biml template of a package. I can hear you thinking, “So how does this become an SSIS package, Andy?” I’m glad you asked!

We now need to execute the BimlScript and then build the output.

Follow along with the images.

Plan Explorer Pro Is Now Free

Greg Gonzalez with the announcement of the week:

Which brings me to today, one of the most exciting days in the history of SQL Sentry. I would like to introduce to you the new unified Plan Explorer v3! This edition includes all features of Plan Explorer PRO, plus two new and incredibly powerful features: Index Analysis and Performance Profiling.

These new features were originally going to be part of yet a 3rd edition called Plan Explorer ULTIMATE, and at an even higher price point. However, we ultimately came to our senses and realized that this would be counter to the mission, and that the only way ahead was to make all features of Plan Explorer freely available to everyone.

Andy Mallon goes in-depth with two of the new features:

If you’ve used Live Query Profile in Plan Explorer (or Live Query Statistics in SSMS), then you’re familiar with seeing your query plan play live as it executes. Performance Profiling adds some special sauce to that feature. Its like a DVR for the live query profile, and also displays the performance metrics as a graph below the plan diagram. The graph gives performance metrics by time, rather than by operator. In complex plans, you might have many operators doing work at the same time, and you’ll be able to see that better by looking at the graphs. Adding a time axis to the plan is a pretty ingenious way to give a new way to look at performance.

You can then replay the “DVRed” query, and re-watch the profile without re-running the query. If you save the plan as a .pesession file, the DVR’ed query with performance profile is included in that file. You can even change the speed to watch in slow-mo or fast-forward.

If you don’t already use Plan Explorer, you’re missing out on the single best query tuning add-in.

Constraints Or Unique Indexes

Kendra Little discusses unique constraints versus unique indexes:

Unique constraints and unique nonclustered indexes have a lot in common: unique constraints are implemented with a unique index behind the scenes.

While unique nonclustered indexes have a couple of additional features, I can still think of a scenario where it might be best to use unique constraints.

The value I see in unique constraints over unique indexes is semantic:  it says to everyone, “I’m supposed to be unique by virtue of my existence.”  They’re part of the data model, whereas indexes are performance optimizations to handle common data paths.

Query Store And Better Plans

Grant Fritchey asks, what happens when you force a Query Store plan and later a better plan becomes available?

Let’s answer a different question first. What happens when the plan gets invalidated, when the index being used gets dropped or some other structural change occurs so that the plan is no longer valid? I answered that question in this blog post. The plan being forced, after the object is dropped, becomes invalid, so that plan can no longer be used. The Query Store still attempts to apply the plan during any recompile or compile event of the query in question, but it fails and a proper plan is used. All this means, I think, the Query Store is going to ignore the new index, since a new index doesn’t invalidate an existing plan. A new index just makes new plans possible. However, when I was asked this question, this wasn’t something I had tested, so I gave a speculative, best guess, answer with plenty of caveats and the promise to provide a tested answer ASAP. Here we go.

Read on for the full answer.

Pulsar

Kevin Feasel

2016-09-08

Hadoop

Alex Woodie reports that Yahoo is open sourcing Pulsar:

Pulsar uses Apache Bookkeeper (committed by Yahoo to open source in 2011) as its durable storage mechanism. “With Bookkeeper, applications can create many independent logs, called ledgers,” Pulsar’s project page on GitHub says. “A ledger is an append-only data structure with a single writer that is assigned to multiple storage nodes (or bookies) and whose entries are replicated to multiple of these nodes.”

Pulsar uses brokers to serve topics. Each topic is assigned to a broker, and an individual broker can serve thousands of topics, Yahoo says. “The broker accepts messages from writers, commits them to a durable store, and dispatches them to readers,” Yahoo says.

My biases would lead me to still go with Kafka over Pulsar, but it’d be interesting to see a good comp between the two.

Power BI And Impala

Justin Kestelyn describes the Impala Connector for Power BI Desktop:

Note that the connector currently only supports Import mode, which requires downloading the query output data to the local data model. In future updates, we will enhance the connector with DirectQuery capabilities, as well as with support for refresh scenarios via the Power BI Gateway. [Ed. Note: As of the August 2016 update, the Impala Connector also supports DirectQuery mode, which means you are always viewing the most up-to-date data. The functionality for both periodic refreshes and DirectQuery mode require the Power BI Gateway running either on-premise or in Microsoft Azure.]

Enabling Power BI connectivity to Impala has been a very frequently requested capability from our customers. We encourage you to give it a try and share with us any feedback or issues that you encounter via the “Send a Frown” feature in Power BI Desktop.

Good stuff.

Nested Loop Estimation

Kendra Little describes the nested loop operator in execution plans:

One of the first things we often learn when we’re looking at plans is that SQL Server uses estimates. And sometimes, those estimates are wrong. At first glance, this looks really wrong– it estimated 11.5 rows, and actually got 20,825 rows!

Read the whole thing to see how nested loop estimates which seem way off might not actually be.

Categories

September 2019
MTWTFSS
« Aug  
 1
2345678
9101112131415
16171819202122
23242526272829
30