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Oracle database processes and waiting long in the supermarket

Hopefully I got your interest by the weird name of this blogpost. This blogpost is about sensible usage of an Oracle database. Probably, there are a lot of blog posts like this, but I need to get this off my chest.

A quote any starwars fan would recognise is ‘I sense a disturbance in the force’. I do, and I have felt it for a long time. This disturbance is the usage of the number of connections for a database. Because of my profession, this is the oracle database, but this really applies to the server-side of any client/server server processor running on at least (but probably not limited to) intel Xeon processors.

The disturbance is the oversubscription or sometimes even excessive oversubscription of database connections from application servers, or any other means of database processes that acts as clients. This means this does not exclude parallel query processes, in other words: this applies to parallel query processes too.

Recovery in the ☁ with Google Cloud SQL (PostgreSQL)

By Franck Pachot

In a previous post I started this series of “Recovery in the ☁” with the Oracle Autonomous database. My goal is to explain the recovery procedures, especially the Point-In-Time recovery procedures because there is often confusion, which I tried to clarify in What is a database backup (back to the basics). And the terms used in managed cloud services or documentation is not very clear, not always the same, and sometimes misleading.

Using Grafana Loki to be able to search and view all logs

This post is about how to make your log files being aggregated in a single place and easy searchable via a convenient web interface.

Some myths about PostgreSQL vs. Oracle

By Franck Pachot

I originally wrote this as a comment on the following post that you may find on internet:
but my comment was not published (many links in it… I suppose it has been flagged as spam?) so I put it there.

You should never take any decision on what you read on the internet without verifying. It is totally valid to consider a move to Open Source databases, but doing it without good understanding is a risk for your migration project success.

AWS Aurora vs. RDS PostgreSQL on frequent commits

This post is the second part of where I’ve run row-by-row inserts on AWS Aurora with different size of intermediate commit. Without surprise the commit-each-row anti-pattern has a negative effect on performance. And I mentioned that this is even worse in Aurora where the session process sends directly the WAL to the network storage and waits, at commit, that it is acknowledged by at least 4 out of the 6 replicas. An Aurora specific wait event is sampled on these waits: XactSync. At the end of the post I have added some CloudWatch statistics about the same running in RDS but with the EBS-based PostgreSQL rather than the Aurora engine. The storage is then an EBS volume mounted on the EC2 instance.

AWS Aurora IO:XactSync is not a PostgreSQL wait event

By Franck Pachot

In AWS RDS you can run two flavors of the PostgreSQL managed service: the real PostgreSQL engine, compiled from the community sources, and running on EBS storage mounted by the database EC2 instance, and the Aurora which is proprietary and AWS Cloud only, where the upper layer has been taken from the community PostgreSQL. The storage layer in Aurora is completely different.

ysql_bench: the YugaByteDB version of pgbench

By Franck Pachot

This follows the previous post on testing YugaByteDB 2.1 performance with pgbench:
A distributed database needs to reduce inter-node synchronization latency and then replaces two-phase pessimistic locking by optimistic concurrency control in many places. This means more serialization errors where a transaction may have to be re-tried. But the PostgreSQL pgbench does not have this possibility and this makes benchmarking distributed database very hard. For example when CERN tested CoackroachDB the conclusion was: “comparative benchmarking of CockroachDB was not possible with the current tools used”.


By Franck Pachot

I love databases and, rather than trying to compare and rank them, I like to understand their difference. Sometimes, you make a mistake and encounter an error. Let’s take the following example:
create table DEMO (n int);
begin transaction;
insert into DEMO values (0);
select n "after insert" from DEMO;
update DEMO set n=1/n;
select n "after error" from DEMO;
select n "after commit" from DEMO;

The “begin transaction” is not valid syntax in all databases because transactions may be started implicitly, but the other statements are valid syntax in all the common SQL databases. They all raise an error in the update execution because there’s one row with N=0 and then we cannot calculate 1/N as it is a math error. But, what about the result of the last select?