Press "Enter" to skip to content

Author: Kevin Feasel

Spark SQL Bucketing and Query Tuning

Published 2021-12-15 by Kevin Feasel

Tomaz Kastrun continues a series on Apache Spark. Part 13 looks at bucketing and partitioning in Spark SQL:

Partitioning and Bucketing in Hive are used to improve performance by eliminating table scans when dealing with a large set of data on a Hadoop file system (HDFS). The major difference between them is how they split the data.

Part 14 covers query hints:

This hint instructs Spark to use the hinted strategy on specified relation when joining tables together. When BROADCASTJOIN hint is used on Data1 table with Data2 table and overrides the suggested setting of statistics from configuration spark.sql.autoBroadcastJoinThreshold.

Spark also prioritise the join strategy, and also when different JOIN strategies are used, Spark SQL will always prioritise them.

Be sure to check those out.

Comments closed

Hypothetical Indexes in SQL Server

Published 2021-12-15 by Kevin Feasel

Eitan Blumin explains what hypothetical indexes are and why they might be useful:

Using Hypothetical Indexes, you can generate an estimated execution plan for a given query, that would essentially assume the existence of a “hypothetical” index as if it actually exists as a real index. Compare that estimated execution plan to its counterpart without the hypothetical index, and you’ll be able to determine whether creating this index for real is worth the time and effort.

Hypothetical Indexes are actually nothing new in SQL Server. It existed since SQL Server version 2005. However, its use is still not widespread to this day. Most likely because it’s not very easy to use and the relevant commands are undocumented.

Click through to see how to use them and an important warning if you try it in production.

Comments closed

Query Variation in QDS Toolbox

Published 2021-12-15 by Kevin Feasel

Jared Poche looks at a procedure to track query variation over time:

Since Query Store stores statistics long term, we can use it to look for trends or major changes in performance. The Query Variation component of the QDS Toolbox helps us review changes and has a lot of options to allow us to select the kinds of changes we are interested in.

So, let’s review our options and go through some examples.

Click through for some examples of how to use the query variation procedure.

Comments closed

Get the Previous Non-NULL Value in DAX

Published 2021-12-15 by Kevin Feasel

Kristyna Hughes calculates the lagged value in DAX:

If you are an avid report designer or user, you may have wanted to see the percent difference between one row and the previous one. Sounds easy right? Well let’s break it down. Percent difference follows a consistent formula: (current value – previous value)/previous value. Current value? Easy peasy. Previous value? That’s where this blog comes into play. It’s fairly simple to get the previous month value or previous date period value because Power BI can tell it exactly what the filter would be (September minus one month is always August ). However, there may not have been any invoices in August, so how can we compare September with the last month that had an invoice?

Click through to see how.

Comments closed

Preventing Concurrent Pipeline Execution in Azure Data Factory

Published 2021-12-15 by Kevin Feasel

Dave Ruijter and Laura de Bruin want to prevent concurrent runs of a pipeline:

For scheduled triggers, there is nothing out-of-the-box that can help you to prevent concurrent pipeline runs. For tumbling window triggers there is a maxConcurrency property, but keep in mind that this will create a queue/backlog of pipeline runs. It will not cancel any pipeline runs. It depends on your use case if you really want that behavior. 

Instead, the two look at a pair of designs and this post is all about the first one.

Comments closed

Spark DataFrames

Published 2021-12-14 by Kevin Feasel

Tomaz Kastrun continues a series on working with Apache Spark. Part 10 looks at the DataFrame construct:

We have looked in datasets and seen that a dataset is distributed collection of data. A dataset can be constructer from JVM object and later manipulated with transformation operations (e.g.: filter(), Map(),…). API for these datasets are also available in Scala and in Java. But in both cases of Python and R, you can also access the columns or rows from datasets.

On the other hand, dataframe is organised dataset with named columns. It offers much better optimizations and computations and still resembles a typical table (as we know it from database world). Dataframes can be constructed from arrays or from matrices from variety of files, SQL tables, and datasets (RDDs). Dataframe API is available in all flavours: Java, Scala, R and Python and hence it’s popularity.

Part 11 looks at external R and Python packages and DataFrame support:

When you install Spark, the extension of not only languages but also other packages, systems is huge. For example with R, not only that you can harvest the capabilities of distributed and parallel computations, you can also extend the use of R language.

Part 12 gets into Spark SQL:

Spark SQL is a one of the Spark modules for structured data processing and analysing. Spark provides Spark SQL and also API for execution of SQL queries. Spark SQL can read data from Hive instance, but also from datasets and dataframe. The communication between Spark SQL and execution engine will always result in a dataset or datafrane.

These formats are interchangeable. So interacting with SQL against result from a different API is possible, respectively. Plugging in the Java JDBD or standard ODBC drivers will also give your SQL interface access to different sources. This unification means that developers can easily switch back and forth between different APIs based on which provides the most natural way to express a given transformation.

With API unification, user can access Spark SQL using Scala spark-shell, using Python pyspark or using R sparkR shell.

DataFrames are so popular that they’ve become the de facto standard for working with data in Spark, and .NET languages only work with DataFrames, not with the raw RDDs.

Comments closed

Learning Experiences from Transactional Replication

Published 2021-12-14 by Kevin Feasel

Ned Otter shares war stories:

I’ve dealt with SQL replication for decades, and in a sense, not a lot has changed. I mean this from a basic configuration and troubleshooting perspective, though it has in some ways been extended a bit through the years, for new SQL Server features (like In-Memory OLTP, Azure, etc.).

Many refer to replication as the the Swiss Army Knife of SQL Server, and I can understand why, but with this “extreme flexibility” comes “extreme shortcomings”, and this post will delve into some of the issues you should be aware of.

Click through for plenty of useful tips.

Comments closed

Finding SQL Servers with the MAP Toolkit

Published 2021-12-14 by Kevin Feasel

Lee Markum goes to active sonar:

A full tutorial on using the MAP Toolkit is available here on Microsoft Learn. MAP Toolkit installation is fairly straight forward but is also included in the  tutorial from Microsoft that I linked to above. You can install it on your own desktop and then scan Active Directory for SQL Servers. Please be sure to notify your System Administrators BEFORE you run the scan as it will set off intrusion detection alarms.

Sure, it’s no nmap sweep, but any blog post which includes a warning like that is worth the read.

Comments closed

Strongly Type those Parameters

Published 2021-12-14 by Kevin Feasel

Erik Darling has a recommendation:

When working with ORMs, care has to be taken to strongly type your parameters to match the data type, length, precision, and scale of the columns those parameters will be compared to. Time and time again, I see the same patterns with string parameters:

– They’re unnecessarily typed as Unicode/nvarchar

– They’re not defined with an appropriate length

– They’re used as catch-all parameters for temporal types (dates, etc.)

Spoiler: these aren’t benefits.

Comments closed

Give Only Table Creators Access to Tables

Published 2021-12-14 by Kevin Feasel

Ronen Ariely takes us to crazy town:

From time to time someone come to the forum with an interview questions which are not a real scenario on live servers and in other cases the requirement on live servers should be implemented and you better re-design your system so you will not need this recruitment. What ever the reason is, you might want to know how the task can be done and this is what we have in the following request on stackoverflow question.

So.. if someone ask, let’s provide the answer…

I find this interesting in a macabre fashion. I’d really hate to be in a position where the information is useful, though.

Comments closed