Handling Errors in Kafka Connect

Robin Moffatt shows us some techniques for handling errors in your Kafka topics:

We’ve seen how setting errors.tolerance = all will enable Kafka Connect to just ignore bad messages. When it does, by default it won’t log the fact that messages are being dropped. If you do set errors.tolerance = all, make sure you’ve carefully thought through if and how you want to know about message failures that do occur. In practice that means monitoring/alerting based on available metrics, and/or logging the message failures.

The most simplistic approach to determining if messages are being dropped is to tally the number of messages on the source topic with those written to the output:

Read on for a few different tactics and how you can implement them.

Unit Testing R Code

Kevin Feasel

2019-03-15

R, Testing

John Mount points out that you don’t need special infrastructure to perform unit testing in R:

There seems to be a general (false) impression among non R-core developers that to run tests, R package developers need a test management system such as RUnit or testthat. And a further false impression that testthat is the only R test management system. This is in fact not true, as R itself has a capable testing facility in “R CMD check” (a command triggering R checks from outside of any given integrated development environment).

By a combination of skimming the R-manuals ( https://cran.r-project.org/manuals.html ) and running a few experiments I came up with a description of how R-testing actually works. And I have adapted the available tools to fit my current preferred workflow. This may not be your preferred workflow, but I have and give my reasons below.

Food for thought for any R developer.

Investigating Azure Data Explorer

Kevin Feasel

2019-03-15

Cloud

James Serra digs into how you can use Azure Data Explorer:

Azure Data Explorer (ADX) was announced as generally available on Feb 7th.  In short, ADX is a fully managed data analytics service for near real-time analysis on large volumes of data streaming (i.e. log and telemetry data) from such sources as applications, websites, or IoT devices.  ADX makes it simple to ingest this data and enables you to perform complex ad-hoc queries on the data in seconds – ADX has speeds of up to 200MB/sec per node (currently up to 3 nodes) and queries across a billion records take less than a second.  A typical use case is when you are generating terabytes of data from which you need to understand quickly what that data is telling you, as opposed to a traditional database that takes longer to get value out of the data because of the effort to collect the data and place it in the database before you can start to explore it.

It’s a tool for speculative analysis of your data, one that can inform the code you build, optimizing what you query for or helping build new models that can become part of your machine learning platform.  It can not only work on numbers but also does full-text search on semi-structured or un-structured data.  One of my favorite demo’s was watching a query over 6 trillion log records, counting the number of critical errors by doing a full-text search for the word ‘alert’ in the event text that took just 2.7 seconds.  Because of this speed, ADX can be a replacement for search and log analytics engines such as elasticsearch or Splunk.  One way I heard it described that I liked was to think of it as an optimized cache on top of a data lake.

Click through for James’s explanation and where you might want to use ADX.

L-Diversity versus K-Anonymity

Duncan Greaves explains the concepts behind l-diversity:

There are problems with K-anonymous datasets, namely the homogeneous pattern attack, and the background knowledge attack, details of which are in my original post. A slightly different approach to anonymising public datasets comes in the form of ℓ -diversity, a way of introducing further entropy/diversity into a dataset.

A sensitive data record is made of the following microdata types: the ID; any Key Attributes; and the confidential outcome attribute(s). ℓ -diversity seeks to extend the equivalence classes that we created using K-anonymity by generalisation and masking of the quasi-identifiers (the QI groups) to the confidential attributes in the record as well. The ℓ -diversity principle demands that, in each QI-group, at most 1/ ℓ of its tuples can have an identical sensitive attribute value.

L-diversity is not perfect either, but Duncan gives a good explanation of the topic.

Fixing High VLF Counts

Ajay Dwivedi shares a technique for optimizing VLF counts on log files:

DBAs! I guess everyone know that huge number of Virtual Log Files (VLFs) in SQL Server can cause Backup/Restore & Database Recovery process slow. Even in rare cases, it can introduce slowness at transaction level.
https://sqlperformance.com/2013/02/system-configuration/transaction-log-configuration

Even we all are aware of it, it has been still a challenge to remove High VLF counts from SQL Server log files since it involved log of manual effort with Shrinking and re-growing the log files.

This is where my ‘Space-Capacity-Automation‘ open source project comes to your rescue. It has a parameter option @optimizeLogFiles that can help you optimize your log files by below below tasks:-

Click through to see Ajay’s technique.

Bugs and Pitfalls with Non-Determinism

Kevin Feasel

2019-03-15

T-SQL

Itzik Ben-Gan has started a new series on T-SQL bugs, pitfalls, and best practices. The first post in this series is on non-deterministic behavior:

A nondeterministic function is a function that given the same inputs, can return different results in different executions of the function. Classic examples are SYSDATETIME, NEWID, and RAND (when invoked without an input seed). The behavior of nondeterministic functions in T-SQL can be surprising to some, and could result in bugs and pitfalls in some cases.

Many people assume that when you invoke a nondeterministic function as part of a query, the function gets evaluated separately per row. In practice, most nondeterministic functions get evaluated once per reference in the query. 

This is the start to a great series.

Diving Into SQL Notebooks

Rob Sewell tries out Azure Data Studio’s SQL notebooks, currently in preview:

OK, so now that we have the dependencies installed we can create a notebook. I decided to use the ValidationResults database that I use for my dbachecks demos and describe here. I need to restore it from my local folder that I have mapped as a volume to my container. Of course, I use dbatools for this 

Click through to see how to install and use SQL notebooks.

Building Temporal Tables From Existing Tables

Mala Mahadevan shows how you can convert an existing SQL Server table into a history table using temporal tables in SQL Server 2016:

SQL Server has had many different methods to track changes to data. There has been old-fashioned trigger-based logging, Change Data Capture, and Change Tracking. All of these features allow you to see how data has changed with varying degrees of how far you can go back historically to see the changes. However, there has never been the ability to see how the entire table looked at any given point in time. That is what Temporal Tables do – they log every change that happens to the table. When you want to query it for any specific period of time, SQL Server will do the hard work of getting the snapshot of how the data in the entire table looked at that time.

A great introduction on how to set up temporal tables in different ways with various limitations can be found here. In this article, you will learn how to set up versioning when creating a new table and how to convert a table with an existing history to a system-versioned temporal table.

Read on to learn how to use temporal tables.

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