Did you ever try to query DBA_EXTENTS on a very large database with LMT tablespaces? I had to, in the past, in order to find which segment a corrupt block belonged to. The information about extent allocation is stored in the datafiles headers, visible though X$KTFBUE, and queries on it can be very expensive. In addition to that, the optimizer tends to start with the segments and get to this X$KTFBUE for each of them. At this time, I had quickly created a view on the internal dictionary tables, forcing to start by X$KTFBUE with materialized CTE, to replace DBA_EXTENTS.

In the previous post I used a simple oci-curl() function as a Command Line Interface to the Oracle Cloud Infrastructure without installing any client tool or language. It was easy for simple things such as starting and stopping services. But it can also be more powerful because it is simply a wrapper to call the OCI REST API, simplifying the sign-in and authentication, but allowing to run any GET, POST, PUT and DELETE method.

In the previous post I've setup all the environment to be able to easily control the OCI services without bothering with the sign-in headers, and without installing anything. In this post I'll used the oci-curl() function to stop all my Autonomous Database services. In the previous post, I've set the environment variables for the private and public key, and the user, tenant and compartment OCIDs.

As you may have read in the news, CERN is testing some Oracle Cloud services. When a large organisation is using the Cloud Credits, there's a need to control the service resources. This requires automation and then the GUI interface from the Cloud portal is not sufficient. We can control the Oracle Cloud Infrastructure through the REST API, OCI CLI, OCI SDKs, and all those methods require a RSA key for sign-in and some OCI (Oracle Cloud Identifier) to identify the user, the tenant, the compartment, the service,...

How do you manage when you need to gather statistics on some tables in a critical environment? Some queries are too long because of stale statistics. But other queries on the same tables are ok. You cannot leave the inital problem without fixing it. Adding hints or SQL Profiles for the identified queries is not the right solution when you identified that stale statistics are the problem. But you want to reduce the risk of regression on other queries at maximum.

SparkMeasure

The purpose of this post is to explain the need of adding encryption to the network communications between containers, and how to achieve it at application level, creating Transport Layer Security (TLS) certificates with the already provided Kubernetes APIs.

As explained in another blog post, Oracle is becoming a cloud company.

In this post I'm gonna show you how to setup a NFS storage in the Oracle Cloud Infrastructure using Linux Storage Appliance.

In the last year Oracle has changed a lot, moving with determination to the Cloud business. They increased their portfolio with IaaS, PaaS and SaaS solutions. In the context of Openlab collaboration between Oracle and CERN we have been testing some of these cloud solutions. Oracle Cloud Infrastructure ( OCI )  is one of these and in this post I'm gonna show how it is possible to install and run a Kubernetes Cluster in the Oracle Cloud Infrastructure.

Canary deployment is a way to test a new release of a software rolling it only for a small sub set of users. In this post I'll show how at CERN, in the Middleware section of Database group, we configure  our HAProxy setup to work as canary deployment. I'll give a brief introduction on what is a canary deployment and later we will see how to configure HAProxy.

In the modern world where everyone wants to be always connected, High Availability became one of the most important feature for a system. For example if you are running  a system you don't want a failure in one piece of your architecture impacts the  whole system. You have to make all the components of your architecture high available. In this post we will present how, in the Middleware section of Dabatase group at CERN, we setup a High Availability HAProxy based on CentOS 7.

Topic: This post is about techniques and tools for measuring and understanding CPU-bound and memory-bound workloads in Apache Spark. You will find examples applied to studying a simple workload consisting of reading Apache Parquet files into a Spark DataFrame.

Hello everyone,

Now I am back in Norway, and it is time for looking back and reminisce about my amazing
and memorable summer at CERN. So, today I will tell you about my experiences from my
Summer Internship.

This is a short post introducing a notebook that you can use to play with a simple analysis of High Energy Physics (HEP) data using CERN open data and Apache Spark. The idea for this work started with a concept for a technology demonstrator of some recent developments on using Spark for data analysis in the context of HEP.

Topic: In this post you can find a few simple examples illustrating im

Hello there,

Hello there,

Recently I've started to dig into the ORDS authentication and more specifically in how to make it work against my Oracle WebLogic server authenticators.

Topic: This post is about measuring Apache Spark workload metrics for performance investigations.

TOPIC

This post reports performance tests for a few popular data formats and storage engines available in the Hadoop ecosystem: Apache Avro, Apache Parquet, Apache HBase and Apache Kudu. This exercise evaluates space efficiency, ingestion performance, analytic scans and random data lookup for a workload of interest at CERN Hadoop service.

INTRO