Tag: Technical Blog

In this post, I’ll present the statspack_top_sqls.sql script, which returns SQL performance statistics by mining the Statspack repository. In a previous blog post, I presented the statspack_load_trends.sql and the AWR_load_trends.sql scripts, which provide a high-level overview of workload dynamics on…

There are various ways that we can approach the analysis of a past database performance problem. The initial steps usually differ based on their scope. Is it limited to a certain SQL/process/task, or is it perceived as a database-wide slowdown?…

The buzz from OpenWorld this year was the permanently free Oracle Cloud offering, the new Exadata X8M (reportedly capable of 12 million I/O per second!), Oracle Autonomous Cloud at Customer, and the new Blockchain table. Here are three interesting or…

What’s Airflow? Apache Airflow is an open source scheduler built on Python. It uses a topological sorting mechanism, called a DAG (Directed Acyclic Graph) to generate dynamic tasks for execution according to dependency, schedule, dependency task completion, data partition and/or…

Some time ago I was doing a maintenance on an OVM and noticed that it had significant number of disks without mapping to any virtual machine (I need to mention that the OVM cluster was a home for more than…

Gleb Otochkin, Principal Consultant and Certified Oracle Expert at Pythian, discusses Oracle’s recently released new beta 12.1.0.2.6 version for the Orachk utility, and why Oracle DBAs should be using it.

Based on previous findings on CPU counting in public clouds, we look at an example where it makes a big difference: software licensing, particularly Oracle. Additional licensing costs resulting from CPU counting can dwarf any cost savings on infrastructure. We talk about how this happens, and show an example.

After looking at how virtual CPUs are handled in Amazon Web Services and Microsoft Azure, we explore CPU definitions and CPU performance under the Google Compute Engine platform.

Oracle Grid Infrastructure 12c uses a considerable amount of RAM and CPU resources; here are a few tips to shoehorn it into a 2GB virtual machine.

Normally when using Avro files as input or output to a MapReduce job, you write a Java main[] method to set up the Job using AvroJob. That documentation page does a good job of explaining where to use AvroMappers, AvroReducers,…