MySQL is an outstanding database for online transaction processing. With suitable hardware, it is easy to execute more than 1M queries per second and handle tens of thousands of simultaneous connections. Many of the most demanding web applications on the planet are built on MySQL. With capabilities like that, why would MySQL users need anything else?  

Well, analytic queries for starters. Analytic queries answer important business questions like finding the number of unique visitors to a website over time or figuring out how to increase online purchases. They scan large volumes of data and compute aggregates, including sums, averages, and much more complex …

In this article I will show you how to properly configure Apache Superset in order to take advantage of a high performance, in-memory query accelerator: MySQL HeatWave.

The post Explore & visualize your MySQL HeatWave data with Superset first appeared on dasini.net - Diary of a MySQL expert.

    Pandas is a Python software library for data analysis and manipulation.  It allows you to import data from CSV files, SQL, and Excel.  And it is open source.

    Pandas works well with the MySQL Shell.  After installing Pandas with pip install pandas on my Fedora test system, I was able to load Pandas.  The documentation is excellent at pandas.pydata.org   and I was quickly able to get one of their examples working from within mysqlsh.

Did I mention SQL as a source of data? Reading from MySQL is very easy.  

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Rolling sum and average query results are possible by combining the aggregate functions SUM() or AVG() with the OVER() clause, making for powerful analytic queries. I recently learned how to compute a rolling average or sum of values by using the Windowing option of the OVER() clause, applying the concepts to a data set I am familiar with. I wanted to share my learning with any readers who might be interested…

Image by Steve Buissinne from …

Recently one of our client approach Mydbops with Query slowness on a MySQL environment . They deployed the new code for generate the huge reports for the year end analytics data . After the deployment the queries were extremely slow and they struggled lot , then they approached us for the solution. After the analysis, their OLAP database as expected it was IO bound with 100% disk IOPS utilised during the report generation. So, the queries were starving for the Disk IO slows the process .

Problem statement :

• Reports are majorly focused on two larger log tables ( emp_Report_model , emp_details ) .
• The report generator (procedure) is using the count(*) statement to stimulate the aggregated data on each call. It is required for their business purpose .
• Count(*) is terribly slow in MySQL ( Using MySQL 5.7 ) as it …

Monitoring your MySQL database performance in real-time helps you immediately identify problems and other factors that could be causing issues now or in the future. It’s also a good way to determine which components of the database can be enhanced or optimized to increase your efficiency and performance. This is usually done through monitoring software and tools either built-in to the database management software or installed from third-party providers.

Prometheus is an open-source software application used for event monitoring and alerting. It can be used along with a visualization tool like Grafana to easily create and edit dashboards, query, visualize, alert on, and understand your metrics. ScaleGrid provides full admin access to your MySQL deployments – this makes it …

Ready to transition from a commercial database to open source, and want to know which databases are most popular in 2019? Wondering whether an on-premise vs. public cloud vs. hybrid cloud infrastructure is best for your database strategy? Or, considering adding a new database to your application and want to see which combinations are most popular? We found all the answers you need at the Percona Live event last month, and broke down the insights into the following free trends reports:

• Top Databases Used: Open Source vs. Commercial
• Cloud Infrastructure Analysis: Public Cloud vs. On-Premise vs. Hybrid Cloud
• Polyglot Persistence Trends: …

PALO ALTO, Calif., June 6, 2019 – ScaleGrid, the Database-as-a-Service (DBaaS) leader in the SQL and NoSQL space, has announced the expansion of their fully managed MySQL Hosting services to support Amazon Web Services (AWS) cloud. The platform allows MySQL AWS administrators to automate their time-consuming database operations in the cloud and improve their performance with high availability, disaster recovery, polyglot persistence, and advanced monitoring and analytics.

Over the years, migrating data to the cloud has become a top priority for organizations looking to modernize their infrastructure for improved security, performance, and …

MySQL Adventures: Metabase Sync May Eat Your InnoDB Buffer Pool

To continue our adventures on MySQL, today we investigated one more issue about MySQL’s memory consumptions. It's not a very big deal but looks something interesting/warning if you are using Metabase.

You may like these articles: …

Windowing Functions can get quite complex very quickly when you start taking advantage of the frame clause. Ranges and rows can get confusing.  So for review lets look at how the specification looks:

Window_spec:
   [window name] [partition clause] [order clause] [frame clause]

That looks simple. And them come terms like UNBOUNDED PRECEDING that could put a knot in your gut.  The manual is not exactly written to help novices in this area get up to speed.  But don't panic.  If you work through the examples that follow (and please do the preceding part of this series before trying these examples) you will have a better appreciation of what is going on with window function.

The Frame Clause
So the frame clause is optional in the window function.  A frame is considered a subset of the current partition and defines that subset.  Frames are determined with …