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Oracle 12c in-memory option and IO

This article is about the Oracle 12c in-memory option, and specifically looks at how the background worker processes do IO to populate the in-memory column store.

Hardware: Apple Macbook with VMWare Fusion 7.1.3.
Operating system: Oracle Linux 6.7, kernel: 3.8.13-118.el6uek.x86_64.
Database version: Oracle
Patch: opatch lspatches
19392604;OCW PATCH SET UPDATE : (19392604)
19303936;Database Patch Set Update : (19303936)

But first things first, let’s setup the in-memory option first with a test table. The first thing to consider is to create the in-memory area to store the objects. I only want a single table stored in the in-memory area, so I can very simply look at the size of object:

Demos do fail.

I am an ardent believer of “show me how it works” principle and usually, I have demos in my presentation. So, I was presenting “Tools for advanced debugging in Solaris and Linux” with demos in IOUG Collaborate 2015 in Las Vegas on April 13 and my souped-up laptop (with 32G of memory, SSD drives, and an high end video processor etc ) was not responding when I tried to access folder to open my presentation files.

Sometimes, demos do fail. At least, I managed to complete the demos with zero slides </p />

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Presentation material & E-learning videos – In-Memory Column Store Workshop with Maria Colgan

You can now download and have another look at the presentations used during the In-Memory…

Oracle Database In-Memory Test Drive Workshop: Canberra 28 April 2015

I’ll be running a free Oracle Database In-Memory Test Drive Workshop locally here in Canberra on Tuesday, 28th April 2015. Just bring a laptop with at least 8G of RAM and I’ll supply a VirtualBox image with the Oracle Database 12c In-Memory environment. Together we’ll go through a number of hands-on labs that cover: Configuring the Product Easily […]

Exadata & In-Memory Real World Performance Artikel (German)

Heute wurde auf "" ein aktueller Artikel von mir veröffentlicht. Es geht darin um die Analyse eines Falles bei einem meiner Kunden, der auf Exadata nicht die erwartete Performance erreicht hat.

In dem Artikel werden unterschiedliche Abfrage-Profile analysiert und erklärt, wie diese unterschiedlichen Profile die speziellen Features von Exadata und In-Memory beeinflussen.

Teil 1 des Artikels
Teil 2 des Artikels

In-memory DB

A recent thread on the OTN database forum supplied some code that seemed to show that In-memory DB made no difference to performance when compared with the traditional row-store mechanism and asked why not.  (It looked as if the answer was that almost all the time for the tests was spent returning the 3M row result set to the SQL*Plus client 15 rows at a time.)

The responses on the thread led to the question:  Why would the in-memory (column-store) database be faster than simply having the (row-store) data fully cached in the buffer cache ?

New Version Of XPLAN_ASH Utility - In-Memory Support

A new version 4.21 of the XPLAN_ASH utility is available for download. I publish this version because it will be used in the recent video tutorials explaining the Active Session History functionality of the script.

As usual the latest version can be downloaded here.

This is mainly a maintenance release that fixes some incompatibilities of the 4.2 version with less recent versions (10.2 and

As an extra however, this version now differentiates between general CPU usage and in-memory CPU usage (similar to Real-Time SQL Monitoring). This is not done in all possible sections of the output yet, but the most important ones are already covered.

Interesting Oracle Syntax Error

As shared by a well known Oracle and Big Data performance geek!

SQL> ALTER SYSTEM SET inmemory_size = 5T SCOPE=spfile;
ALTER SYSTEM SET inmemory_size = 5T SCOPE=spfile
ERROR at line 1:
ORA-32005: error while parsing size specification [5T]

SQL> ALTER SYSTEM SET inmemory_size = 5120G SCOPE=spfile;

System altered.

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In-memory pre-population speed

While presenting at Oaktable World 2014 in San Fransisco, I discussed the in-memory pre-population speed and indicated that it takes about 30 minutes to 1 hour to load ~300GB of tables. Someone asked me “Why?” and that was a fair question. So, I profiled the in-memory pre-population at startup.

Profiling methods

I profiled all in-memory worker sessions using Tanel’s snapper script and also profiled the processes in OS using Linux perf tool with 99Hz sample rate. As there is no other activity in the database server, it is okay to sample everything in the server. Snapper output will indicate where the time is spent; if the time is spent executing in CPU, then the perf report output will tell us the function call stack executing at that CPU cycle. Data from these two profiling methods will help us to understand the root cause of slowness.