As promised, here is the follow up of the MySQL 8.0-dev Progress teaser ;-)
so, yes, we expect to see the most hot contentions gone, as it's seen from the following graph : Observations :

• the graph is representing the spin waits / spin rounds events happening during the same Sysbench Update-NoKEY workload
• the load is progressing from 8 concurrent users to 16, 32, .. 512
• 3 engines are tested one after one : MySQL 5.7, MySQL 8.0 (current DMR), MySQL 8.0-dev (current prototype)
• first all 3 engines are running on 22cores-HT only (1 CPU socket)
• then, the second time : on 44cores-HT (2 CPU sockets)
• and the most important thing to retain about this graph is that on 8.0-dev we see all main hot contentions gone ;-)

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In this blog post, I’ll review how a hung MySQL transaction can cause the InnoDB history length to grow and negatively affect MySQL performance.

Recently I was helping a customer discover why SELECT queries were running slower and slower until the server restarts (which got things back to normal). It took some time to get from that symptom to a final diagnosis. Please follow me on the journey of chasing this strange MySQL behavior!

Symptoms

Changes in the query response time can mean tons of things. We can check everything from the query plan to the disk performance. However, fixing it with a restart is less common. After looking at “show engine innodb status”, I noticed some strange lines:

Trx read view will not see trx with id >= 41271309593, sees < 41268384363
---TRANSACTION 41271309586, ACTIVE 766132 sec
2 lock struct(s), heap size 376, 0 row lock(s), undo log entries 1 …

InnoDB Cluster was released as GA a few weeks ago. I remember the initial announcement of the product at OOW 2016, promising a seamless solution for replication and high availability with great ease of use. I was a bit disappointed to see that, at GA release time, the InnoDB Cluster is a patchwork of three separate products (Group Replication, MySQL Router, MySQL Shell) which the users have to collect and install separately.

Given this situation, I was very pleased when Matthew Lord published Docker-InnoDB-Cluster, an image for Docker that contains everything you need to get the system up and running. The …

I heard a frequent question at last week’s Percona Live conference regarding Percona Monitoring and Management (PMM): How much disk space should I allocate for PMM Server?

First, let’s review the three components of Percona Monitoring and Management that consume non-negligible disk space:

1. Prometheus data source for the time series metrics
2. Query Analytics (QAN) which uses Percona Server XtraDB (Percona’s enhanced version of the InnoDB storage engine)
3. Orchestrator, also backed by Percona Server XtraDB

Of these, you’ll find that Prometheus is generally your largest consumer of disk space. Prometheus hits a steady state of disk utilization once you reach the defined storage.local.retention period. If you deploy Percona Monitoring and Management …

From everyone at Percona and Percona Live 2017, we’d like to send a big thank you to all our sponsors, exhibitors, and attendees at this year’s conference.

This year’s conference was an outstanding success! The event brought the open source database community together, with a technical emphasis on the core topics of MySQL, MariaDB, MongoDB, PostgreSQL, AWS, RocksDB, time series, monitoring and other open source database technologies.

We will be posting tutorial and session presentation slides at the Percona Live site, and all of them should be available shortly. 

Highlights This Year:

• Informative tutorials on day one, including …

Welcome to the third (and final) day of the Percona Live Open Source Database Conference 2017, and the third (and final) set of Percona Live keynotes! The enthusiasm hasn’t waned here at Percona Live, and we had a full house on Thursday morning!

Day three of the conference kicked off with three keynotes talks, and ended with the Community Awards Ceremony:

Spinaltap: Airbnb’s Change Data Capture System

Xinyao Hu (AirBnB)

In this talk, Xinyao introduced Airbnb’s change data change system, Spinaltap. He briefly covered its design, and focused on various use cases inside Airbnb. These use cases covered both online …

Welcome to the second day of the Percona Live Open Source Database Conference 2017, and the second set of Percona Live keynotes! It’s a bit rainy outside today, but that isn’t bothering the Percona Live attendees (we’re all indoors learning about new open source technologies)!

Day two of the conference kicked off with another four keynote talks, all of which discussed issues and technologies that are addressed by open source solutions:

The Open Source Database Business Model is Under Siege

Paul Dix (InfluxData)

Paul Dix’s keynote may have ruffled a few feathers, as he looked at possible futures for the open …

Welcome to the first day of the Percona Live Open Source Database Conference: Percona Live 2017 tutorials day! While technically the first day of the conference, this day focused on provided hands-on tutorials for people interested in learning directly how to use open source tools and technologies.

Today attendees went to training sessions taught by open source database experts and got first-hand experience configuring, working with, and experimenting with various open source technologies and software.

The first full day (which includes opening keynote speakers and breakout sessions) starts Tuesday 4/25 at 9:00 am.

Some of the …

Consider the scenario that you execute a query. You expect it to be fast - typically subsecond - but now it take not return until after 50 seconds (innodb_lock_wait_timeout seconds) and then it returns with an error:

mysql> UPDATE world.City SET Population = Population + 999 WHERE ID = 130;
ERROR 1205 (HY000): Lock wait timeout exceeded; try restarting transaction

You continue to investigate the issue using the sys.innodb_lock_waits view or the underlying Information Schema tables (INNODB_TRX, INNODB_LOCKS and INNODB_LOCK_WAITS).

Note: …

MySQL 8.0.1 introduces two new features which allow you to better manage situations where you have tables with hot row contention.  This issue frequently presents itself in scenarios such as worker threads all accessing the same tables trying to find new work, and ecommerce websites trying to keep accurate inventory counts.…