Databases at CERN blog

Backups in Data Guard environment

Physical standby databases seem to be ideal candidates for offloading backups from primary ones. Instead of "wasting" resources (unless you're already using Active Data Guard for example), you could avoid affecting primary performance while backing up your database, especially if your storage is under heavy load even during normal (user- or application-generated) workload. So, if you're seeking for good reasons to convince your boss/finance department/etc.

Scaling up Cardinality Estimates in

Topic: Counting the number of distinct values (NDV) for a table column has important applications in the database domain, ranging from query optimization to optimizing reports for large data warehouses. However the legacy SQL method of using SELECT COUNT (DISTINCT <COL>) can be very slow. This is a well known problem and Oracle provides a new function APPROX_COUNT_DISTINCT implemented with a new-generation algorithm to address this issue by providing fast and scalable cardinality estimates.



My experience testing the Oracle In-Memory Column Store

Oracle patch set has been released and with it comes an important new feature: the In-Memory option. CERN has been involved in the testing of this feature since an early stage so I'd like to take the occasion to share my experience with you!

What is it?

It is a new static pool in the System Global Area, keeping a copy of the data  stored In-Memory in Columnar format:

How to verify if archived log deletion policy is correctly applied?

What is the best way to handle archived logs deletion in environments with standby and downstream capture databases? One could use own scripts, to delete for example all backed up archived logs, older than n days. But better way, will be to set RMAN archived log deletion policy, because then, additional options could be specified, to delete archived logs which are not only backed up n times, but also applied or shipped to other databases in the environment.

Avoid commiting database passwords in your version control system.

Having the datasource password in my version control systems is an issue that has run after me since the beginning of the time. It is  classic that you are always postponing it in the development process untill somebody from the security team comes to your office and tells you "what the @#$ are these passwords doing in the svn/git???" To avoid this embarrasing situation you have different choices:

ASM Metadata, Internals and Diagnostic Utilities

If you use ASM on a regular basis, you may find that knowledge of the internals can be very useful for advanced troubleshooting and in general to acquire familiarity with the technology. In particular this is even more relevant when using ASM capabilities for data redundancy/mirroring (i.e. when deploying disk groups with normal or high redundancy).

Recent Updates of OraLatencyMap and PyLatencyMap

Topic: This post introduces the latest changes to OraLatencyMap and PyLatencyMap, two custom tools for collecting and displaying Oracle wait event latency details using heatmaps.

OraLatencyMap is a SQL*Plus tool, with a core written in PL/SQL, aimed at studying Oracle random I/O by displaying the latency drill-down of the wait event 'db file sequential read' using heatmaps. The tool can also be used to collect and display event latency histograms for any other Oracle wait event, for example: log file sync to study commit time latency.


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