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Month: April 2017

Advanced Report Design

Published 2017-04-03 by Kevin Feasel

Paul Turley excerpts a chapter from his new Reporting Services book:

With respect to page layout, reports have two sizing modes: interactive and printable. When users run a report in their web browser and use it interactively, they typically don’t care that much about the page size. This is particularly true with reports that have wide content like a matrix region that can dynamically grow horizontally with the data. When a report is printed or rendered to a print- able format like a PDF or Word file, we need to be mindful about fitting the content on pages.

The report designer does not make page sizing and dimensions particularly obvious so it’s an easy thing to miss. Fortunately, the science behind page sizing is pretty simple. Page dimension properties are grouped into two objects that you can select in the designer; these are shown in Figure 7-1. With the Properties window visible, click outside the report body to show properties for the report. Here you will see the InteractiveSize and PageSize properties. Expand these to see the individual Width and Height properties for each group.

Read on to get the better part of a full chapter’s worth of material.

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Introduction To Amazon Kinesis

Published 2017-04-03 by Kevin Feasel

Jen Underwood describes Amazon Kinesis:

Amazon Kinesis is a fully managed service for real-time processing of streaming data at massive scale. Amazon Kinesis is ideal for Internet of Things (IoT) use cases. It can collect and process hundreds of terabytes of data per hour from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, Raspberry Pi gadgets, devices, social media, operational logs, metering data and more.

With Amazon Kinesis, you can build real-time dashboards, capture exceptions, execute algorithms, and generate alerts. With point-and-click menus, you can ingest data, query it and then send output to a variety of destinations including but not limited to Amazon S3, Amazon EMR, Amazon DynamoDB, or Amazon Redshift.

Kinesis is powerful, especially if you’re already locked into the AWS platform.  My preference is Apache Kafka, but Kinesis is definitely worth learning about.

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Create Index With Drop_Existing Bug

Published 2017-04-03 by Kevin Feasel

Kendra Little describes a bug that she encountered in discussions with a reader:

My first thought was that perhaps there is some process that runs against the production system and the test system that goes to sleep with an open transaction, holding an X or an IX lock against this table. If the index create can’t get its shared lock, then it could be part of a blocking chain.

So I asked first if the index create was the head of the blocking chain, or if it was perhaps blocked by something else. The answer came back that no, the index create was NOT blocked. It was holding the shared lock for a long time.

My new friend even sent a screenshot of the index create running against the test instance in sp_WhoIsActive with blocking_session_id null.

Read on for the full story and keep those systems patched.

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Aggregate Predicate Pushdown And Data Types

Published 2017-04-03 by Kevin Feasel

Niko Neugebauer shows an example of how a slightly different data type can cause columnstore queries to be much faster:

Even though they are estimated to cost the same (50% for each one) with the estimated cost of 0.275286 to be more precise in this sense.
To be more precise in the reality you will notice the Aggregate Predicate Pushdown taking place on the first query, while the second query is using the Storage Engine to read out all of the 2 million rows from the table and filter it in the Hash Match iterator.

Actual Number of Locally Aggregated Rows is the one property on the Columnstore Index Scan iterator that will give you an insight on what happened within the Columnstore Index Scan, since the Aggregate Predicate Pushdown is not shown as a filter on the property. This is not the most fortunate solution as far as I am concerned, but since the 0 rows flowing out of the Columnstore Index Scan will serve as a good indication that Aggregate Predicate Pushdown took place, but if you want to be sure of all the details you will need to check the properties of the involved iterators.

Definitely worth reading.

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