MySQL Connector/C++ 2.0.4 is the next development milestone of the MySQL

Connector/C++ 2.0 series. Connector/C++ 2.0 can be used to access MySQL
implementing Document Store or in a traditional way, using SQL queries. It
allows writing both C++ applications using X DevAPI or plain C applications
using XAPI.

To learn more about how to write applications using X DevAPI, see X
DevAPI User Guide (http://dev.mysql.com/doc/x-devapi-userguide/en/) and X
DevAPI reference at
https://dev.mysql.com/doc/dev/connector-cpp/devapi_ref.html. For more
information about using plain C XAPI see XAPI reference at
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In this blog, we’ll look at running Percona XtraBackup on Windows via a Docker container.

The question whether Percona XtraBackup is available for Windows comes up every so often. While we are not planning to provide regular releases for Windows, I decided to share a way to run Percona XtraBackup in a Docker container (especially since Docker support for Windows has become more and more stable).

For this exercise, I created a playground Docker image: perconalab/percona-xtrabackup.

First, we need to prepare a few things to make it work:

1. Install Docker on Windows (the current version I am running is 17.03)
2. Enable the sharing of disk C in Docker settings
3. Find out the IP address MySQL is running on …

In this blog post, we’ll look at how Prophet can forecast metrics.

Facebook recently released a forecasting tool called Prophet. Prophet can forecast a particular metric in which we have an interest. It works by fitting time-series data to get a prediction of how that metric will look in the future.

For example, it could be used to:

• Predict how much HTTP traffic we will get, and scale accordingly when needed
• See if a particular feature of our application will have success or if its usage will decline
• Get an approximate date when our database server’s resources will be exhausted
• Forecast new customer’s sign up and resize the staff accordingly
• See what next year’s Black Friday or Cyber Monday will look like, and if we have the resources to handle them
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MySQL Cluster is a highly available, distributed, shared-nothing database with very interesting performance characteristics for some workloads. Among other features, it supports automatic sharding and allows us to bypass the SQL layer if we don’t need it, via the NDB API (which in my eyes, makes it one of the few transactional nosql databases out there).

In this post, I’ll describe how we can set up replication from MySQL Cluster into a standalone MySQL server using Innodb as the storage engine.

Introduction

There are a few reasons to set up replication between MySQL Cluster and a non-NDB based MySQL server. These reasons include (but are not limited to): the need …

This Log Buffer edition covers Oracle, SQL Server and MySQL blog posts with a touch of Cloud.

Oracle:

Provisioning EBS on Oracle Cloud: Latest Enhancements

ORA-54002 when trying to create Virtual Column using REGEXP_REPLACE on Oracle 12cR2

Business rules, common sense and query performance

Problem with V$RECOVERY_AREA_USAGE view and FRA space not being reclaimed

Updated …

Percona announces the release of Percona XtraDB Cluster 5.7.17-27.20 on March 16, 2017. Binaries are available from the downloads section or our software repositories.

NOTE: You can also run Docker containers from the images in the Docker Hub repository.

Percona XtraDB Cluster 5.7.17-27.20 is now the current release, based on the following:

• …

This blog shares some column store database benchmark results, and compares the query performance of MariaDB ColumnStore v. 1.0.7 (based on InfiniDB), Clickhouse and Apache Spark.

I’ve already written about ClickHouse (Column Store database).

The purpose of the benchmark is to see how these three solutions work on a single big server, with many CPU cores and large amounts of RAM. Both systems are massively parallel (MPP) database systems, so they should use many cores for SELECT queries.

For the benchmarks, I chose …

One of the major challenges of query optimizers is to correctly estimate how many rows qualify from each table of a join. If the estimates are wrong, the optimizer may choose a non-optimal join order.

Before MySQL 5.7, the estimated number of rows from a table only took into account the conditions from the WHERE clause that were used to set up the access method (e.g., the size of an index range scan). This often led to row estimates that were far too high, resulting in very wrong cost estimates for join plans. To improve this issue, MySQL 5.7 introduced a cost model that considered the entire WHERE condition when estimating the number of qualifying rows from each table. This model estimates the filtering effect of the table’s conditions.

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Yesterday I was invited to speak at the “Jeudis du Libre” in Mons.

The location was very special as it was in one auditorium of Polytech, the oldest university in the city of Mons.

I presented in French two very hot topics in the MySQL ecosystem:

• MySQL InnoDB Cluster
• MySQL as Document Store with JSON datatype & X plugin

Those are very new technologies illustrating MySQL’s innovation. And of course there is much more to come with MySQL 8 !

Here are the slides if you are interested:

Jeudis du Libre – MySQL InnoDB Cluster from Frederic Descamps

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In this blog post, I will discuss the solutions for monitoring databases (which includes alerting) I have worked with and recommended in the past to my clients. This survey will mostly focus on MySQL solutions. 

One of the most common issues I come across when working with clients is monitoring and alerting. Many times, companies will fall into one of these categories:

• No monitoring or alerting. This means they have no idea what’s going on in their environment whatsoever.
• Inadequate monitoring. Maybe people in this camp are using a platform that just tells them the database is up or connections are happening, but there is no insight into what the database is doing.
• Too much monitoring and alerting. Companies in this camp have tons of dashboards filled with graphs, and their inbox is full of alerts that get promptly ignored. This type of monitoring is just as useful as the first option. Alert …