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Author: Kevin Feasel

Good Practices when Combining Spark with Cassandra

Valerie Parham-Thompson shares some insights for working with Spark and Cassandra together:

Although we are focusing on Cassandra as the data storage in this presentation, other storage sources and destinations are possible. Another frequently used data storage option is Hadoop HDFS. The previously mentioned spark-cassandra-connector has capabilities to write results to Cassandra, and in the case of batch loading, to read data directly from Cassandra.

Native data output formats available include both JSON and Parquet. The Parquet format in particular is useful for writing to AWS S3. See for more information on querying S3 files stored in Parquet format. A good use case for this is archiving data from Cassandra.

Read on for more advice.

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The Either Monad in Scala

Jyoti Sachdeva explains some of the power of Either:

We use Options in scala but why do we want to go for Either?

Either is a better approach in the respect that if something fails we can track down the reason, which in Option None case is not possible.
We simply pass None but what is the reason we got None instead of Some. We will see how to tackle this scenario using Either.

This is a classic functional programming pattern and one of the easier monads to understand.

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ML Services and Resource Governor

I have a post on two gotchas you might run into around Resource Governor throttling SQL Server Machine Learning Services:

By default, SQL Server will grant 20% of available memory to any R or Python scripts running. The purpose of this limit is to prevent you from hurting server performance with expensive external scripts (like, say, training large neural networks on a SQL Server).

Here’s the kicker: this affects you even if you don’t have Resource Governor enabled. If you see out-of-memory exceptions in Python or error messages about memory allocation in R, I’d recommend bumping this max memory percent up above 20, and I have scripts to help you with the job. Of course, making this change assumes that your server isn’t stressed to the breaking point; if it is, you might simply want to offload that work somewhere else.

Click through for the other issue.

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ADF.Procfwk Version 1.8

Paul Andrew has been busy:

Following more great feedback from the Data Platform community the primary goal of this release was to further improve the resilience of the framework processing. These improvements included its restart clean up capabilities and introducing better dependency chain handling between Worker pipelines when failures occur. The latter builds on the existing restart functionality first introduced in release v1.2 and supplements the logic using a new set of pipeline dependency metadata. I’ve created the below visual to conceptually show the new dependency chain behaviour, should you wish to populate and make use of the new metadata handling.

Read on for the full changelog.

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A Note on Distributed Network Names

Allan Hirt provides an explanation around Distributed Network Names when building Windows Server Failover Clusters on Windows Server 2019:

The new Windows Server 2019 DNN functionality does have a side effect that does affect Azure-based configurations. When creating a WSFC, Windows Server 2019 detects that the VM is running in Azure and will use a DNN for the WSFC name. This is the default behavior.

I clipped this paragraph specifically because Allan uses both “affect” and “effect” correctly, and I wanted to call that out. Do read the rest of it as well.

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Understanding the Identity Cache

Jignesh Raiyani explains the purpose of the identity cache and shows how you might disable it if you want to:

In the release of SQL Server 2012, Microsoft has introduced a feature of Identity Cache. The identity jump doesn’t cause any issue neither with the database nor the running tasks; however, this identity value gap is not acceptable in some of the business-oriented scenarios. This Identity counter values of the columns are stored in the system table separately, which is known as sys.identity_columns DMV. This reference object sys.identity_columns does not rely on the transaction status, and it doesn’t matter that the transaction on the user table is committed or rolled back. Now, in case this user transaction is being rolled back, we will find that the identity value has been skipped in the sys.identity_columns table. The reason being is that the value of the Identity column value does not get rolled back. Apart from the above scenario, there could be various reasons or situations where these issues of the skip of the Identity value will be seen, which will be small in number and random. However, when there is an issue of Identity value jump, the amount of the skip value will be prominent and precise in number, depending upon the column data type, which is easy to identify.

My advice is, don’t disable this unless you have a really good reason to. “I don’t want to see numbers skipped” is a bad reason because identity columns never guarantee sequential insertion. You can still have skips due to transaction rollbacks or other errors on insertion, and the identity cache helps with performance problems in cases with lots of writers inserting rows into the table.

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Data Lakes for Smaller Projects

Thomas Spicer explains that your data lake doesn’t need to be enormous to be useful:

We recently wrote an article debunking common myths about data lake architectures, data lake definitions, and data lake analytics. It is called What is a Data Lake? Get A Leg Up Avoiding The Biggest Myths.” In that article, we framed the current conversation about data lakes and how they fit within enterprise data strategies. This topic has historically been confusing and opaque for those wanting to get value from a data lake due to conflicting advice from consultants and vendors.  

One area that can be particularly confusing is the perception that lakes are only for “big data.” If you spend any time reading materials on lakes, you would think there is only one type and it would look like the Capsian Sea (it’s a lake despite “sea” in the name). People describe data lakes as massive, all-encompassing entities, designed to hold all knowledge. The good news is that lakes are not just for “big data” and you have more opportunities than ever to have them be part of your data stack.

Click through for Thomas’s argument.

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The Basics of Autoregressive Models

Holger von Jouanne-Diedrich explains some of the principels of autoregressive models through a demonstration:

Well, this seems to be good news for the sales team: rising sales! Yet, how does this model arrive at those numbers? To understand what is going on we will now rebuild the model. Basically, everything is in the name already: auto-regressive, i.e. a (linear) regression on (a delayed copy of) itself (auto from Ancient Greek self)!

So, what we are going to do is create a delayed copy of the time series and run a linear regression on it. We will use the lm() function from base R for that (see also Learning Data Science: Modelling Basics).

Read on for some additional understanding.

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Tips for Debugging in Visual Studio

Patrick Smacchia has 12 tips for debugging in Visual Studio:

4) Data breakpoint: Break when value changes

If you set a breakpoint to a non-static property setter it will be hit when changing the property value for all objects. The same behavior can be obtained for a single object thanks to the Locals (or Watch) window right click : Break When Value Changes menu.

This facility is illustrated with the animation above. The hit occurs only when obj2.Prop is changed, not when obj1.Prop is changed.

These go a step beyond the basics, so check them out.

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