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Power BI 101 – Log Files and Tracing

Knowing where log files are and how to turn on debugging is an essential part of any technical job and this goes for Power BI, too.  Remember, as I learn, so does everyone else….Come on, pretty please?

Power BI Desktop

Log files and traces can be accessed one of two ways-

  • Via the Power BI Application
  • Via File Explorer

In the Power BI application, go to File –> Options and Settings –> Options –> Diagnostics.

Quiz Night

Because it’s been a long time since the last quiz night.  Here’s a question prompted by a recent thread on the ODevCom database forum – how many rows will Oracle sorts (assuming you have enough rows to start with in all_objects) for the final query, and how many sort operations will that take ?

drop table t1 purge;

create table t1 nologging as select * from all_objects where rownum < 50000;

select owner, count(distinct object_type), count(distinct object_name) from t1 group by owner;

Try to resist the temptation of doing a cut-n-paste and running the code until after you’ve thought about the answer.

Complex materialized views and fast refresh

Just a quick discovery that came across the AskTOM “desk” recently. We have an outstanding bug in some instances of fast refresh materialized views when the definition of the materialized view references a standard view.

Here’s a simple demo of the issue – I’ll use a simplified version of the EMP and DEPT tables, linked by a foreign key in the usual way:

Lighty for PostgreSQL

If you follow this blog, you should know how I like Orachrome Lighty for Oracle, for its efficiency to monitor database performance statistics. Today Orachrome released the beta version of Lighty for Postgres:
The Cloud is perfect to do short tests with more resources than my laptop, especially the predictability of performance, then I started a Bitnami Postgres Compute service on the Oracle Cloud and did some tests with pgbench and pgio.

The installation is easy:

When WHEN went faster

Yeah…try saying that blog post title 10 times in a row as fast as you can Smile

But since we’re talking about doing things fast, this is just a quick post about a conversation I had a twitter yesterday about the WHEN clause in a trigger.



That is an easy benchmark to whip up – I just need a couple of tables, each with a simple a trigger differing only by their usage of the WHEN clause.  Here is my setup:

Power BI and the Speed(ier) Desktop

I can be an extremely impatient person about anything I think should be faster.


In a recent ODC thread someone had a piece of SQL that was calling dbms_random.string(‘U’,20) to generate random values for a table of 100,000,000 rows. The thread was about how to handle the ORA-30009 error (not enough memory for operation) that is almost inevitable when you use the “select from dual connect by level <= n” strategy for generating very large numbers of rows, but this example of calling dbms_random.string() so frequently prompted me to point out an important CPU saving , and then publicise through this blog a little known fact (or deduction) about the dbms_random.string() function.

Massive Delete

The question of how to delete 25 million rows from a table of one billion came up on the ODC database forum recently. With changes in the numbers of rows involved it’s a question that keeps coming back and I wrote a short series for AllthingsOracle a couple of years ago that discusses the issue. This is note is just a catalogue of links to the articles:

“Call me!” Many many times!

Some readers might recall that classic Blondie track “Call me”.  Of course, some readers might be wishing that I wouldn’t harp on about great songs from the 80’s. But bear with me, there is a (very tenuous) link to this post. If you haven’t heard the song, you can jump to the chorus right here.  Go on, I’ll wait until you get back. Smile

This Week in PostgreSQL – May 31

Since last October I’ve been periodically writing up summaries of interesting content I see on the internet related to PostgreSQL (generally blog posts). My original motivation was just to learn more about PostgreSQL – but I’ve started sharing them with a few colleagues and received positive feedback.  Thought I’d try posting one of these digests here on the Ardent blog – who knows, maybe a few old readers will find it interesting? Here’s the update that I put together last week – let me know what you think!

Hello from California!

Part of my team is here in Palo Alto and I’m visiting for a few days this week. You know… for all the remote work I’ve done over the years, I still really value this in-person, face-to-face time. These little trips from Seattle to other locations where my teammates physically sit are important to me.