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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.

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.

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.

SQL Monitor

I’ve mentioned the SQL Monitor report from time to time as a very useful way of reviewing execution plans – the feature is automatically enabled by parallel execution and by queries that are expected to take more than a few seconds to complete, and the inherent overheads of monitoring are less than the impact of enabling the rowsource execution statistics that allow you to use the ‘allstats’ format of dbms_xplan.display_cursor() to get detailed execution information for a query. The drawback to the SQL Monitor feature is that it doesn’t report predicate information.

How to cancel SQL statements in #Oracle 18c

https://uhesse.files.wordpress.com/2015/10/helps.png?w=600&h=558 600w, https://uhesse.files.wordpress.com/2015/10/

Comparing Plans

It can be difficult to find the critical differences when comparing execution plans when you want to find out why the optimizer has changed its choice of plan and what may have happened to cause the change, and even the various diff_plan_xxx() functions in dbms_xplan don’t help very much, so I thought I’d write up an example that appeared recently on the ODC database forum to give people some ideas about how to approach the problem. There is, however, no simple algorithm that you can apply to narrow your focus down to the most probable cause of change, there are simply a few methods that have to be applied with a little flair and imagination.

255 Columns

It’s the gift that keeps on giving – no matter how many problems you find there are always more waiting to be found. It’s been some time since I last wrote about tables with more than 255 columns, and I said then that there was more to come. In the last article I described how adding a few columns to a table, or updating a trailing column in a way that made the table’s used column count exceed 255, could result in some strange row-splitting behaviour – in this article I’m going to look at a critical side effect of that behaviour.

Interval Partition Problem

Assume you’ve got a huge temporary tablespace, there’s plenty of space in your favourite tablespace, you’ve got a very boring, simple table you want to copy and partition, and no-one and nothing is using the system. Would you really expect a (fairly) ordinary “create table t2 as select * from t1” to end with an Oracle error “ORA-1652: unable to extend temp segment by 128 in tablespace TEMP” . That’s the temporary tablespace that’s out of space, not the target tablespace for the copy.

Here’s a sample data set (tested on 11.2.0.4 and 12.1.0.2) to demonstrate the surprise – you’ll need about 900MB of space by the time the entire model has run to completion:

Histogram Threat

Have you ever seen a result like this:


SQL> select sql_id, count(*) from V$sql group by sql_id having count(*) > 1000;

SQL_ID		COUNT(*)
------------- ----------
1dbzmt8gpg8x7	   30516

A client of mine who had recently upgraded to 12.2.0.1 RAC, using DRCP (database resident connection pooling) for an application using PHP was seeing exactly this type of behaviour for a small number of very simple SQL statements and wanted to find out what was going on because they were also seeing an undesirable level of contention in the library cache when the system load increased.

In this note I just want to highlight a particular detail of their problem – with an example – showing how easily histograms can introduce problems if you don’t keep an eye out for the dangers.

One of their queries really was as simple as this:

Case Study – 1

It has been some time since I wrote an article walking through the analysis of information on an AWR report, but a nice example appeared a few weeks ago on Twitter that broke a big AWR picture into a sequence of bite-sized chunks that made a little story so here it is, replayed in sync with my ongoing thoughts. The problem started with the (highly paraphrased) question – “How could I get these headline figures when all the ‘SQL ordered by’ sections of the report show captured SQL account for 0.0% of Total?”.