Probably not well known but quite an important optimization was introduced in MySQL 5.6 – reduced overhead for “read only transactions”. While usually by a “transaction” we mean a query or a group of queries that change data, with transaction engines like InnoDB, every data read or write operation is a transaction.

Now, as a non-locking read operation obviously has less impact on the data, it does not need all the instrumenting overhead a write transaction has. The main thing that can be avoided, as described by documentation, is the transaction ID. So, since MySQL 5.6, a read only transaction does not have a transaction ID. Moreover, such a transaction is not visible in the SHOW ENGINE INNODB STATUS output, though I will not go deeper on what really that means under the hood in this article. The fact is that this optimization …

The release of MySQL 8.0 has brought a lot of bold implementations that touched on things that have been avoided before, such as added support for common table expressions and window functions. Another example is the change in how AUTO_INCREMENT (autoinc) sequences are persisted, and thus replicated.

This new implementation carries the fix for bug #73563 (Replace result in auto_increment value less or equal than max value in row-based), which we’ve only found about recently. The surprising part is that the use case we were analyzing is a somewhat common one; this must be affecting a good number of people out there.

Understanding the bug

The business logic of the use case is such the UNIQUE column found in a table whose id is managed by an AUTO_INCREMENT sequence needs to be updated, and this is done with a …

When deciding on your backup strategy, one of the key components for Point In Time Recovery (PITR) will be the binary logs. Thankfully, the mysqlbinlog command allows you to easily take binary log backups, including those that would otherwise be encrypted on disk using encrypt_binlog=ON.

When

mysqlbinlog

  is used with

--raw --read-from-remote-server --stop-never --verify-binlog-checksum

  then it will retrieve binary logs from whichever master it is pointed to, and store …

Some time ago, a customer had a performance issue with an internal process. He was comparing, finding, and reporting the rows that were different between two tables. This is simple if you use a LEFT JOIN and an 

IS NULL

  comparison over the second table in the WHERE clause, but what if the column could be null? That is why he used UNION, GROUP BY and a HAVING clauses, which resulted in poor performance.

The challenge was to be able to compare each row using a LEFT JOIN over NULL values.

The challenge in more detail

I’m not going to use the customer’s real table. Instead, I will be comparing two sysbench tables with the same structure:

CREATE TABLE `sbtest1` (
  `id` int(10) unsigned NOT NULL AUTO_INCREMENT,
  `k` int(10) unsigned DEFAULT NULL,
  `c` char(120) DEFAULT NULL,
  `pad` char(60) DEFAULT NULL, …

Following my post from a year ago https://www.percona.com/blog/2017/07/06/clickhouse-one-year/, I wanted to review what happened in ClickHouse during this year.
There is indeed some interesting news to share.

1. ClickHouse in DB-Engines Ranking. It did not quite get into the top 100, but the gain from position 174 to 106 is still impressive. Its DB-Engines Ranking score tripled from 0.54 last September to 1.57 this September

And indeed, in my conversation with customers and partners, the narrative has changed from: “ClickHouse, what is …

Starting with PMM 1.13,  PMM uses Prometheus 2 for metrics storage, which tends to be heaviest resource consumer of CPU and RAM.  With Prometheus 2 Performance Improvements, PMM can scale to more than 1000 monitored nodes per instance in default configuration. In this blog post we will look into PMM scaling and capacity planning—how to estimate the resources required, and what drives resource consumption.

We have now tested PMM with up to 1000 nodes, using a virtualized system with 128GB of memory, 24 virtual cores, and SSD storage. We found PMM scales pretty linearly with the available memory and CPU cores, and we believe that a higher number of nodes could be …

In my previous blog post, I showed how to deploy Percona Monitoring and Management (PMM) on Linode manually. It is pretty simple, but with a little coding it can be done even more easily using StackScripts

Here’s how:

1. Click on the “Add a Linode” and pick a Linode type you want to deploy.

2. Click on the deployed Linode and then click on the “Rebuild” Link

3. Click on Deploy Using StackScripts

4. On the resulting page search for “PMM” and pick PMMServer from PerconaLab.

5. Provide the host name for new Linode, pick the root password and click on “Rebuild”

6. Boot the server.

7.  You’re done. Wait for about 5 minutes for the installation to complete, then you can see PMM interface by going to this Linode …

Please join Percona’s Senior Consultant, Jervin Real, as he presents Migrating to AWS Aurora: A Checklist for Success. The event will take place on Tuesday, September 11th, 2018, at 11:00 AM PDT (UTC-7) / 2:00 PM EDT (UTC-4).

Register Now

In the last few weeks, we have shown you how to successfully migrate from on-premise MySQL installations to AWS Aurora. What comes next is how to successfully ensure that your Aurora cluster performs and operates as you expect it to.

While …

One of the common ways to classify database workloads is whether it is  “read intensive” or “write intensive”. In other words, whether the workload is dominated by reads or writes.

Why should you care? Because recognizing if the workload is read intensive or write intensive will impact your hardware choices, database configuration as well as what techniques you can apply for performance optimization and scalability.

This question looks trivial on the surface, but as you go deeper—complexity emerges. There are different “levels” of reads and writes for you to consider. You can also choose to look at event counts or at the time it takes to do operations. These can provide very different responses, especially as the cost difference between a single read and a single write can be an order of magnitude.

Let’s examine the TPC-C Benchmark from this point of view, or more specifically its …

Percona Monitoring and Management (PMM) provides an excellent solution for system monitoring. Sometimes, though, you’ll have the need for a metric that’s not present in the list of node_exporter metrics out of the box. In this post, we introduce a simple method and show how to extend the list of available metrics without modifying the node_exporter code. It’s based on the textfile collector.

Enable the textfile collector in pmm-client

This collector is not enabled by default in the latest version of pmm-client. So, first let’s enable the textfile collector.

# pmm-admin rm linux:metrics
OK, removed system pmm-client-hostname from monitoring.
# pmm-admin …