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.
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.
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.
In the database team at CERN, we have developed a general-purpose metrics monitor, a missing part in our next generation monitoring infrastructure.
In the implemented metrics monitor, metrics can come from several sources like Apache Kafka, new metrics can be defined combining other metrics, different analysis can be applied, notifications, configuration can be updated without restarting, it can detect missing metrics, ...
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.
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