Sentiment Analysis with Python

Bruno Stecanella shows us how to use MonkeyLearn to perform sentiment analysis in Python:

Sentiment analysis is a set of Natural Language Processing (NLP) techniques that takes a text (in more academic circles, a document) written in natural language and extracts the opinions present in the text.

In a more practical sense, our objective here is to take a text and produce a label (or labels) that summarizes the sentiment of this text, e.g. positiveneutral, and negative.

For example, if we were dealing with hotel reviews, we would want the sentence ‘The staff were lovely‘ to be labeled as Positive, and the sentence ‘The shared bathroom was absolutely disgusting‘ labeled as Negative.

Click through for a demo.

Callout Lines

Lisa Charlotte Rost shows different sorts of callout lines and how they can affect the way a person views your chart:

After Defne’s awesome monster Weekly Charts in the last 14 days (here’s number 1, here’s number 2), this weeks’ Weekly Chart keeps it small. Today we launched callout lines for our locator maps (they are lines connecting marker and label), and I wrote a few words about how to create them and when – so I will simply refer you to this announcement blog post and explain the importance of our new feature with this map:

In moderation, they can be quite useful for indicating and explaining important but subtle points you’d like to make.

Forcing Plans with Table Variables

Grant Fritchey takes us through an interesting scenario around Query Store:

This weekend I was in Stockholm in Sweden, talking Query Store and plan forcing with Steinar Anderson, when he mentioned the problems he had while forcing plans that had table variables in them.

Don’t panic. Of course you can force a plan with a table variable, most of the time. Steinar had a fairly focused problem. Before I go on to explain the issue, let me be really clear, Steinar figured out the issue all on his own. When he outlined the problem, I saw immediately what his conclusion was going to be. What’s spurring this blog post is that Steinar said he’d searched on the internet and no one had talked about the issue yet. So, let’s talk about it.

Read on for the problem as well as solution.

Learning About Big Data Clusters

Kevin Chant shares resources for getting started with SQL Server Big Data Clusters:

In a previous post I shared current SQL Server 2019 learning resources, which you can view in detail here.

However, SQL Server 2019 Big Data Clusters are very involved. So, I thought I better dedicate a whole post to further learning resources for it.

Because some people have different learning methods I have included references to both documents and videos in this post. In addition, I have created the below links in case somebody wants to go directly to a specific section.

Kevin’s put together quite a few useful links here.

T-SQL Tips Regarding Subqueries

Itzik Ben-Gan provides quality information on working with subqueries in SQL Server:

In this plan you see a Nested Loops (Left Semi Join) operator, with a scan of the clustered index on Customers as the outer input and a seek in the index on the customerid column in the Orders as the inner input. You also see an outer reference (correlated parameter) based on the custid column in Customers, and the seek predicate Orders.customerid = Customers.custid.

So why are you getting the plan in Figure 1 and not the one in Figure 2? If you haven’t figured it out yet, look closely at the definitions of both tables—specifically the column names—and at the column names used in the query. You will notice that the Customers table holds customer IDs in a column called custid, and that the Orders table holds customer IDs in a column called customerid. However, the code uses custid in both the outer and inner queries. 

Itzik covers three specific scenarios, all of which can cause trouble to database developers who haven’t been burned yet. And sometimes even those who have.

NT AUTHORITY\ANONYMOUS Error Editing Procedures

Kenneth Fisher takes us through a security issue:

If you have to deal with linked servers then you probably have or will run into the following error:

Login failed for user ‘NT AUTHORITY\ANONYMOUS LOGON’

But I’m not trying to use the linked server. I’m trying to create/alter a stored procedure.

Kenneth explains why you might get this even when you’re just editing a procedure and not directly hitting a linked server, as well as a few tips for fixing the issue.

Persisting SQL Server Databases on Kubernetes

Anthony Nocentino walks us through persistent volumes and Kubernetes:

One of the key principals of Kubernetes is the ephemerality of Pods. No Pod is every redeployed, a completely new Pod is created. If a Pod dies, for whatever reason, a new Pod is created in its place there is no continuity in the state of that Pod. The newly created Pod will go back to the initial state of the container image defined in the Pod’s spec. This is very valuable for stateless workloads, not so much for stateful workloads like SQL Server.

This means that for a stateful workload like SQL Server we need to store both configuration and data externally from the Pod to maintain state through the recreation of a Pod. Kubernetes give us constructs two constructs to do that, environment variables and Persistent Volumes. 

Read on for a good bit of background and a few scripts to help you get started.

Load Testing Databases

Ed Elliott shares some tips on load testing a database:

Testing database performance is hard and takes a great deal of work to probably not even do particularly well. Instead of thinking about how you can load test a database think about how you can drive the application.

For instance, if you have a web app then use JMeter to simulate load. If you have a “fat app”, then you might need to write some code to call specific workflows through the application.

Click through for good advice, particularly around what you should not do.

Categories

May 2019
MTWTFSS
« Apr  
 12345
6789101112
13141516171819
20212223242526
2728293031