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April 2019

Chinar Aliyev's Blog

Chinar Aliyev has recently started to pick up on several of my blog posts regarding Parallel Execution and the corresponding new features introduced in Oracle 12c.

It is good to see that obviously Oracle has since then improved some of these and added new ones as well.

Here are some links to the corresponding posts:

New automatic Parallel Outer Join Null Handling in 18c

Improvements regarding automatic parallel distribution skew handling in 18c

Chinar has also put some more thoughts on the HASH JOIN BUFFERED operation:

Thanks for your feedback Rafael.

Thanks for your feedback Rafael. I think this is 12c drivers but works on lower database versions, excepts some bugs. So better test it…

Parse Calls

When dealing with the library cache / shared pool it’s always worth checking from time to time to see if a new version of Oracle has changed any of the statistics you rely on as indicators of potential problems. Today is also (coincidentally) a day when comments about “parses” and “parse calls” entered my field of vision from two different directions. I’ve tweeted out references to a couple of quirkly little posts I did some years ago about counting parse calls and what a parse call may entail, but I thought I’d finish the day off with a little demo of what the session cursor cache does for you when your client code issues parse calls.

Bitmap Index On Column With 212552698 Distinct Values, What Gives? (I’d Rather Be High)

In my previous post on Indexing The Autonomous Warehouse, I highlighted how it might be necessary to create indexes to improve the performance and scalability of highly selective queries, as it might on any Data Warehouse running on an Exadata platform. In the post, I created a Bitmap Index and showed how improve SQL performance […]

You should set OCSID.CLIENTID

each time you grab an Oracle JDBC connection from the pool

For troubleshooting and monitoring performance, you want to follow what happens from the end-user to the database. It is then mandatory to identify the end-user and application from the database session. With Oracle there are some ‘dbms_application_info’ strings to be set, like MODULE, ACTION and CLIENT_INFO. That’s about the tasks in the application code (like identifying the Java class or method from which the SQL statement is prepared) but that’s not about the end-user.

Not Just the How of AD with Linux VM/SQL 2019, but the WHY

Azure Directory is available with Linux SQL Server 2019 in Preview and as I was setting it up in my Azure environment on a Linux Red Hat 7.3 VM, I was, as many are, happy that they list the commands for the Azure CLI to set up authentication with Azure Directory, but was concerned, that with so many new to Linux, that they didn’t describe in the steps WHY we were running certain commands or setting best practices around Linux database server design.

The setup expects that you already have a Linux VM and SQL 2019 already up and running. The first step they go into is role assignment for the AD login, setting the AD login up as the VM Administrator.

The Late Spring Speaking Gauntlet

There are busy times for everyone and if you speak at conferences, the busy times are March,May and November. I am recovering from the early spring rush, and now it’s time to prepare for the late spring one.

I’ve been fortunate enough to be accepted to speak at the following regional SQL Saturdays and look forward to speaking and meeting new folks, along with catching up with conference friends:

PL/SQL – Don’t mix and match scope

Here’s a simple little PL/SQL block where we call an inner procedure PARAMETER_TESTER from its parent block. Pay particular attention to the parameter we pass to the procedure, and it’s value throughout the execution of that procedure.

V$SQL_CS_HISTOGRAMS: What Are the Buckets’ Thresholds?

The contents of the V$SQL_CS_HISTOGRAM view is used by the SQL engine to decide when a cursor is made bind aware, and therefore, when it should use adaptive cursor sharing. For each child cursor, the view shows three buckets. It is of general knowledge that the first one (BUCKET_ID equal 0) is associated with the executions that process up to and including 1,000 rows, the second one (BUCKET_ID equal 1) with the executions that processes between 1,001 and 1,000,000 rows, and the third one (BUCKET_ID equal 2) with the executions that processes more than 1,000,000 rows. The idea is that after an execution, the SQL engine associates (that is, increments the COUNT column) the execution to one of the three buckets. Then, depending on the distribution, decides whether a cursor has to be made bind aware.

Quick and easy masking

I had a request from a client a while back regarding masking of data. They had an application with sensitive data in the Production environment (where access and audit were very tightly controlled) but the issue was how to respect that sensitivity in non-Production environments whilst still preserving full size data sizes for application testing.

After some conversations about requirements, it turned out that since (even in non-Production environments) all access to application components was logged and audited, the issue was simply protecting against “inadvertent” access to sensitive data. For example, in the application, if I searched for “males with black hair with an interest in technology” I should never see the name “Connor McDonald” on screen in the results, because simply viewing that data could be treated as a breach of privacy.