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Month: September 2016

Naming Procedures

Published 2016-09-02 by Kevin Feasel

Aaron Bretrand shares rules that he uses when naming stored procedures:

I’ve talked about this in my live sessions, but this is an extreme case that really happened – a team took over a week to fix a bug in a stored procedure, and the delay was caused solely by poor naming standards. What happened was that the application was calling dbo.Customer_Update, but the team was hunting for the bug in a different procedure, dbo.Update_Customer. While there was no formal convention in place, the real problem was inconsistency – a consultant charged with writing a different application didn’t check for an existing procedure, she just looked for dbo.Update_Customer in the list; when she didn’t find it, she wrote her own. The bug itself wasn’t crucial, but that lost time can never be recovered.

I’ll repeat again that the convention you choose is largely irrelevant, as long as it makes sense to you and your team, and you all agree on it – and abide by it. But I am asked frequently for advice on naming conventions, and for things like tables, I’m not going to get into religious arguments about plural vs. singular, the dreaded tblprefix, or going to great lengths to avoid vowels. But I think I have a pretty sensible standard for stored procedures, and I am always happy to share my biases even though I know not everyone will agree with them. Again, I touched on this in my earlier post, but sometimes these things bear repeating and a little elaboration.

I agree with this:  pick a standard and stick to it.

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Parsing DBCC MEMORYSTATUS

Published 2016-09-02 by Kevin Feasel

Slava Murygin has a script to turn DBCC MEMORYSTATUS output into one result set:

When I wanted to research memory problem on a server and started to dig deeper into “DBCC MEMORYSTATUS” command.Very useful links to understand that command were from Microsoft:
https://support.microsoft.com/en-us/kb/271624
https://support.microsoft.com/en-us/kb/907877

During the research I’ve faced two problems:
1. I had to wait several seconds to get the full result set.
2. I had to scroll down and shuffle 115 different data sets to find the counter I want.

To eliminate both these problem all these different counters have to be in the same table/data set.
That will make research easier and data can be stored or compared with the base line.

Read on for the T-SQL script.

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Stripe Those Azure Disks!

Published 2016-09-01 by Kevin Feasel

Jens Vestergaard shows you how to create striped disks for Azure VMs:

As displayed in above screen shots, the single Azure Standard Storage VHD gives you (as promised) about 500 IOPS. Striping eight (8) of those, will roughly give you eight (8) times the IOPS, but not same magnitude of [MB/s] apparently. Still, the setup is better off, after, rather than before!

Do mind, that there are three levels of storage performance; P10, P20 and P30. For more information, read this.

I did this recently and can confirm that there’s a huge difference between using one virtual disk versus even three or four, and Windows Storage Spaces makes it easy to expose them as one combined mount point.

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Ambari 2.4

Published 2016-09-01 by Kevin Feasel

Jeff Sposetti discusses improvements in Ambari 2.4:

Reduce time to troubleshoot problems. Apache Hadoop components create a lot of log data. Accessing that log data to understand what the component is telling you, especially when issues arise, is critical. Apache Ambari includes a new Log Search service that provides agents for log collection and a delivers a custom UI for searching those logs. This is essential to providing a streamlined approach to searching for stack traces and exceptions across all nodes in the cluster.

I have enjoyed watching Ambari mature as a product.

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Waiting For Rollback

Published 2016-09-01 by Kevin Feasel

Andrea Allred ran into an issue with a long-running job on an Availablity Group:

I panicked. In this situation I would normally pull the database out of the AG and then re-add it.  I didn’t have that option because it is a HUGE database and didn’t have that much time and space to move it around. I knew a large transaction had kicked off (thank you alert email that I created to warn me about such things) but thought that surely the rollback would have cleared quickly.  That lead me to looking for rolling back transactions.

Fortunately, the issue was on a secondary not under heavy use, so she was able to recover in time.

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Flink And Kafka Streams

Published 2016-09-01 by Kevin Feasel

Neha Narkhede and Stephan Ewen compare Apache Flink versus Kafka Streams:

Before Flink, users of stream processing frameworks had to make hard choices and trade off either latency, throughput, or result accuracy. Flink was the first open source framework (and still the only one), that has been demonstrated to deliver (1) throughput in the order oftens of millions of events per second in moderate clusters, (2) sub-second latency that can be as low as few 10s of milliseconds, (3) guaranteed exactly once semantics for application state, as well as exactly once end-to-end delivery with supported sources and sinks (e.g., pipelines from Kafka to Flink to HDFS or Cassandra), and (4) accurate results in the presence of out of order data arrival through its support for event time. Flink is based on a cluster architecture with master and worker nodes. Flink clusters are highly available, and can be deployed standalone or with resource managers such as YARN and Mesos. This architecture is what allows Flink to use a lightweight checkpointing mechanism to guarantee exactly-once results in the case of failures, as well allow easy and correct re-processing via savepoints without sacrificing latency or throughput. Finally, Flink is also a full-fledged batch processing framework, and, in addition to its DataStream and DataSet APIs (for stream and batch processing respectively), offers a variety of higher-level APIs and libraries, such as CEP (for Complex Event Processing), SQL and Table (for structured streams and tables), FlinkML (for Machine Learning), and Gelly (for graph processing). Flink has been proven to run very robustly in production at very large scale by several companies, powering applications that are used every day by end customers.

The upshot is that the two products don’t do exactly the same thing, and there might be room in your organization for the two of them.

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The Joy Of Hyperparameters

Published 2016-09-01 by Kevin Feasel

Koos van Strien shows how to tune hyperparameters using Azure ML:

Today, we’ll focus on tuning the model’s properties. We won’t discuss the details of all properties (you can easily look that up in the docs), instead we’ll look at how to test for different parameter combinations insize Azure ML Studio.

As soon as you click on an untrained model inside your experiment, you’ll be presented with some parameters – or, in ML parlance, hyperparameters – you can tweak.

Parameter tuning is pretty easy using Azure ML.

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Tornado Visual

Published 2016-09-01 by Kevin Feasel

Devin Knight looks at the Tornado chart:

  • The Tornado has a few limitation that should be aware of before using

    • If there’s a legend value it should only have 2 distinct values

    • Each distinct category values is a separate bar with left or right parts

    • Alternatively, you can have two measure values and compare them without  a legend

I’m split on whether I like the tornado or not.  It is intuitive and information-dense, which are two major factors in its favor.  It is, however, difficult to read and compare.  This seems like a useful “big picture” chart, but you’d want to organize the data in a different way when you start drilling down.

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Powershell Workflows

Published 2016-09-01 by Kevin Feasel

Cody Konior has a beef with Powershell workflows:

That’s inexplicable.

One thing which does make it all work is setting $PSRunInProcessPreference which, “If this variable is specified, all activities in the enclosing scope are run in the workflow process.” Unfortunately that doesn’t explain what’s really going on and what the impacts are, so I won’t use it. But here it is turning the original failing script into a working one.

I’ve never used Powershell workflows.  It sounds like potentially an exasperating experience.

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