Press "Enter" to skip to content

Author: Kevin Feasel

Monitoring InfluxDB Using Telegraf

Published 2018-05-01 by Kevin Feasel

Tracy Boggiano continues her performance metrics series by showing how to monitor the monitoring solution:

We need to generate a special config file for the Linux machine to capture the CPU and disk metrics. This config file will be in your /etc/telegraf folder.

After you run this you will need to open it with your favorite editor, mine is vim:

Once the file the is open you can type the letter i to be able to edit the file. Then type /influxdb to find the outputs.influxdb section of the file and edit the same sections we edited for Windows. Type /inputs.outfluxdb to jump down and edit the InFluxdb section. After you have finished editing the file in vim you can type Esc then :wq! and the hit Enter for it save the config file.

Click through to check out her solution.

Comments closed

Notes On Automating Automatic Indexing

Published 2018-05-01 by Kevin Feasel

Grant Fritchey shares with us some of his findings with automatic indexing on Azure SQL Database:

What you’ll notice is that several of the queries are filtering on the FirstName column. There’s no good index there. If you look at the execution plans for those queries you’ll also note the Missing Index suggestion. That suggestion is a necessary part of the automatic indexing. Yeah, missing indexes. I know. They’re not always accurate. It’s just a suggestion. Blah, blah, blah. I hear you.

The magic is not supplied by missing indexes. The magic is supplied by lots of data. Microsoft can take advantage of three things. Yes, missing index suggestions is first. Then, they can use the query metrics gathered in Query Store to see the behavior of your queries over time. Finally, they can use machine learning algorithms to determine if indexes will be helpful and measure how helpful they’ve been if one gets added. It’s great stuff. Go and read on it.

Click through for more notes, as well as a Powershell script you can use to replicate his findings.

Comments closed

Graph Algorithms Supported In Neo4j

Published 2018-04-30 by Kevin Feasel

Amy Hodler gives us a quick summary of fifteen separate algorithms for traversing a graph in Neo4j:

6. PageRank

What it does: Estimates a current node’s importance from its linked neighbors and then again from their neighbors. A node’s rank is derived from the number and quality of its transitive links to estimate influence. Although popularized by Google, it’s widely recognized as a way of detecting influential nodes in any network.

How it’s used: PageRank is used in quite a few ways to estimate importance and influence. It’s used to suggest Twitter accounts to follow and for general sentiment analysis.

PageRank is also used in machine learning to identify the most influential features for extraction. In biology, it’s been used to identify which species extinctions within a food web would lead to biggest chain reaction of species death.

If you are interested in getting into graph databases, it’s useful to know these algorithms.

Comments closed

Demos Using Amazon QuickSight

Published 2018-04-30 by Kevin Feasel

Karthik Kumar Odapally and Pranabesh Mandal have several example visuals that you can generate using Amazon QuickSight:

Typical Amazon QuickSight workflow

When you create an analysis, the typical workflow is as follows:

  1. Connect to a data source, and then create a new dataset or choose an existing dataset.

  2. (Optional) If you created a new dataset, prepare the data (for example, by changing field names or data types).

  3. Create a new analysis.

  4. Add a visual to the analysis by choosing the fields to visualize. Choose a specific visual type, or use AutoGraph and let Amazon QuickSight choose the most appropriate visual type, based on the number and data types of the fields that you select.

  5. (Optional) Modify the visual to meet your requirements (for example, by adding a filter or changing the visual type).

  6. (Optional) Add more visuals to the analysis.

  7. (Optional) Add scenes to the default story to provide a narrative about some aspect of the analysis data.

  8. (Optional) Publish the analysis as a dashboard to share insights with other users.

It’s interesting to see how Amazon is trying to move this functionality from third-party tools (Power BI, Tableau, etc.) and notebooks right into the set of AWS offerings.  Contrast this with the way that Microsoft is building in Jupyter with Azure Notebooks.

Comments closed

What’s Deprecated In SSAS 2017?

Published 2018-04-30 by Kevin Feasel

Chris Webb looks at the deprecated and discontinued features in Analysis Services 2017:

Here’s the link to the official documentation:

https://docs.microsoft.com/en-us/sql/analysis-services/analysis-services-backward-compatibility-sql2017?view=sql-analysis-services-2017

…and here are the definitions of ‘deprecated’ and ‘discontinued’:

A deprecated feature will be discontinued from the product in a future release, but is still supported and included in the current release to maintain backward compatibility. It’s recommended you discontinue using deprecated features in new and existing projects to maintain compatibility with future releases.

A discontinued feature was deprecated in an earlier release. It may continue to be included in the current release, but is no longer supported. Discontinued features may be removed entirely in a future release or update.

Read on for the lists of deprecated and discontinued features.

Comments closed

Essential SQL Server Tools

Published 2018-04-30 by Kevin Feasel

Jens Vestergaard shares the most recent T-SQL Tuesday round-up:

Tuesday 3rd of this Month I invited people in the SQL Server community to share which tools are essential to their daily work. I was really overwhelmed by the number of stories that the topic triggered. 22 in total took the time to write down and share which tools they use for their work chores.
Going through 22 posts and aggregating them has been taking more time than I had hoped for, since my trusted laptop broke down – blinking codes are well and alive I tell you!

Click through for the 22 submissions as well as Jens’s set of links to the tools people mentioned.

Comments closed

Using Crosstabs To Learn About Categorical Variable Relationships

Published 2018-04-30 by Kevin Feasel

Stacia Varga shows one way of learning about the relationships between categorical variables in Power BI:

A common way to review categorical variable relationships is to create a cross tab, also known as a matrix, to evaluate the counts for each resulting combination.

For example, in my current data set, I can create a matrix to compare the number of players in two teams, say the Knights and the Sharks, by position and by handedness.

In descriptive analytics, I’m not trying to prove anything by looking at these values. I’m just reporting them. (Although I do find it interesting that there is a preponderance of lefties in these two teams.)

In the business world, I might do something similar by placing product categories on rows and customer geography (country or state) on columns.

Stacia also gives her explanation of descriptive analytics, so check that out too.

Comments closed

Introducing Azure Notebooks

Published 2018-04-30 by Kevin Feasel

Zach Stagers has an introductory post to Azure Notebooks:

No installation, no maintenance

As with any PaaS solution, Azure Notebooks makes it far quicker and easier to get up and running, as there’s no download or installation required. Microsoft handles all the maintenance for you too!

I’m working on a fairly big project using Azure Notebooks.  It’s very helpful getting 1GB of space, so I can include all of my data, images, etc. from a fairly large number of notebooks.  The big downside is that the server running these notebooks is pretty slow—even for a fairly simple ARIMA model, I had it sitting there for 10 minutes at 100% CPU.  So don’t expect to run a heavy workload against it.

Comments closed

Deleting A Small Number Of Rows From A Big Table

Published 2018-04-30 by Kevin Feasel

Brent Ozar has a tip for deleting a relatively small percentage of data from a very large table:

Say you’ve got a table with millions or billions of rows, and you need to delete some rows. Deleting ALL of them is fast and easy – just do TRUNCATE TABLE – but things get much harder when you need to delete a small percentage of them, say 5%.

It’s especially painful if you need to do regular archiving jobs, like deleting the oldest 30 days of data from a table with 10 years of data in it.

The trick is making a view that contains the top, say, 1,000 rows that you want to delete:

Read on for a demo.

Comments closed

Using Powershell To Find Linked Server References

Published 2018-04-30 by Kevin Feasel

Drew Furgiuele shows us how we can use the sqlserver Powershell module to find linked server references in code:

SQL Search is doing exactly what it’s designed to do here: it’s finding every object that matches that string, HumanResources. It’s unfortunate that it also happens to be the name of my linked server and a schema in my database, but such is life, right? Its returning every object it hits a match on, which includes a bunch of views, plus one of those views actually contains my linked server reference.

I love SQL Search

Let me be clear: I’m not here to gang up on the fine folks at Redgate. This tool is beautiful and I love it. Otherwise, how else could we quickly search for objects in our databases? The alterative would be either querying system views for object definitions or using cursors to call sp_helptext over and over, and then trying to do pattern matching. Same as what SQL Search does.

There’s no easy way to sort this wheat from chaff, is there? This might be a starting point; let’s narrow down the search the objects we might need to look at. Then, we’ll manually script each one out, one at a time. That sounds an awful lot like a manual process. “If only there was a way to automate this checking”, he asked, sarcastically.

Click through for a very interesting cmdlet.

Comments closed