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Month: October 2020

Polymorphism in GraphQL

Published 2020-10-12 by Kevin Feasel

Dan Freeman takes us through the concept of polymorphism as it applies to GraphQL:

In APIs (and in domain modeling in general) it’s common to want to represent fields that may point to one (or more) of several different types of object, a.k.a. polymorphism. In GraphQL’s type system, this can be accomplished with either a union or an interface, depending on whether the objects in question are expected to have anything in common.

What’s not always obvious to newcomers to GraphQL, though, is how to best handle that data on the receiving end, when you need to tell what concrete type of object you’re dealing with.

It’s interesting to see how this is handled in GraphQL versus object-oriented languages.

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Passing Parameters from Power Query to SQL Server

Published 2020-10-12 by Kevin Feasel

Gilbert Quevauvilliers has an interesting solution to a common problem:

I had a requirement where the client wanted the capability to decide how much data to load from a SQL Server Query (TSQL). This was so that they could limit the dataset returned, as at times they did not need all the data.

So below I demonstrate how to achieve this.

NOTE: This will be slightly advanced because I had to manually add some code in the Advanced Editor in Power Query.

Maybe it’s because of the number of times I had to do this with Reporting Services, but this seems like it should be a lot easier than it is.

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Validating Data Model Results in Power BI

Published 2020-10-12 by Kevin Feasel

Paul Turley continues a series on doing Power BI the right way:

When designing a new data model, this is typically the first thing I do… For every fact table and for each large dimension table, I create a measure that returns the record count for that table. Users normally think about their data in business terms (like sums, ratios and averages) and not about how many records there are in a table. Record counts are a convenient sanity check for record completeness and distribution across groups; and may also be a good indicator for model size and performance.

Paul takes several passes at the problem, getting a bit deeper into it each time.

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Using Query Store over the Plan Cache

Published 2020-10-12 by Kevin Feasel

Erik Darling has a dream:

I used to think the plan cache was so cool.

– You can find queries that aren’t good there
– Plans are full of details (and XML)
– Supporting DMVs give you extra insights about resource usage

But most of the time now, I’m totally frustrated with it.

It clears out a lot, plans aren’t there for some queries, and the plans that are there can be very misleading.

Can you really tell someone what their worst performing queries are when everything in there is from the last 15 minutes?

No.

Read on for what’s nice about Query Store, as well as a few fixes which need to be there before it’s really useful. I’ve used Query Store in big environments to good effect (though our DBAs had to rewrite the cleanup processes because they’re bad) and I’ve had to turn it off in medium-sized environments running 2016 because it was harming performance. It’s a great concept and reasonable implementation with a few too many sharp edges.

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The Power BI Release Plan

Published 2020-10-12 by Kevin Feasel

Matthew Roche clues us in on what’s coming for Power BI:

The Power BI team at Microsoft publishes a “release plan,” which is essentially the public product roadmap. Anyone can use it to understand what new capabilities and improvements are planned, and when they’re expected to be released.

One challenge with the official release plan comes from the fact that it is a set of online documents, and that for each “release wave” there is a new set of docs – it’s not always clear where to look for the latest information on a given feature.

But thanks to Alex Powers, this is a lot clearer now. Click through to learn how.

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Tips for Optimizing Dedicated SQL Pools in Synpase Analytics

Published 2020-10-09 by Kevin Feasel

Tsuyoshi Matsuzaki shares some tips for improving query performance when using Dedicated SQL Pools in Azure Synapse Analytics:

By above BROADCAST_MOVE operation, the rows in dimension_City table are all copied in a temporary table (called TEMP_ID_3) on all distributed database. (See below.)
Since the size of dimension_City is small, then all rows in this table is duplicated in all database before joining. This time, we join only 2 tables, however, if a lot of tables are needed to join, this data movement will become large overhead for query execution.

The short version is, replicate smaller dimensions and align distribution keys for large tables which get joined together. Both of these minimize the changes of the engine needing to shuffle data between nodes. These sorts of things can make a huge difference when working with Dedicated SQL Pools, cutting query time down by an order of magnitude in some extreme cases.

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Cosmos DB Serverless

Published 2020-10-09 by Kevin Feasel

Hasan Savran takes us through a new approach to Cosmos DB pricing:

There used to be two ways for Azure Cosmos DB to bill you for the services it provides. Those were Manual Provisioned throughput and Auto scale provisioned throughout.          Provisioned throughput is number of request units available for your applications to use per second. For example, Let’s say you picked 400 Request Units. That means your application’s budget is 400 request units per second. Depending on your needs, you can scale up or down. You can set this manually and scale up and down manually if you like. That will be the cheapest option which is 100 Request Units for $0.008 per hour. Your other option is the auto scale option, Cosmos DB scales up and down automatically with this option. All you need to tell Cosmos DB is, what the highest number it can scale up to. This option is 50% higher than the first option.

    These are great options, but they can be still be expensive for what you need.

Read on for what we know about Serverless pricing and which APIs currently support the Serverless model.

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Tips for Monitoring Kubernetes

Published 2020-10-09 by Kevin Feasel

Michael Sorens walks us through some tips for monitoring Kubernetes:

The world begins, of course, with kubectl, the command-line interface to Kubernetes. The commands you start using early on help you examine your Kubernetes resources.

kubectl get . . .

With that command, you can examine your deployments, which rollout your replica sets, which create pods. Then you need services, which are logical sets of pods that provide an interface for external access. What can you examine with kubectl get ?

Use kubectl api-resources to see the list. At the time of this writing, there are 66 different resource types! That number will likely only grow over time. 

Read on for more, including the setup of the Kubernetes UI and third-party tooling.

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