Useful Reporting Queries for Your Business

I have rehashed a list of useful reporting queries from my blog which I hope people would find useful for their business or clients. These are not MySQL specific, by any means.

Orders

  • What is the total revenue (by segments)? - You are looking for 'peaks and troughs'. For example, found a peak season? you can plan better for it next year. Found an off-season? perhaps offer some deals for that period.
  • What is the total placed orders, completed orders and abandoned/uncompleted orders?
  • What is the total paid revenue vs outstanding orders? 
  • What is the total number of returned/cancelled orders and lost revenue from them?

Products (Best Sellers)

  • Which are the top 20% best selling products? (businesses love 80/20 comparisons and if you look at reports a lot, you will too)
  • Which are the products that our best customers keep buying from us? - the library example - don't just keep the best selling books, like any supermarket would have. Have a range of books that attract your best customer.
  • Which are the bottom 10-20% worst selling products? - you may want to discontinue these, depending on your top customers
  • Which products generate the most revenue? – (product price * quantity)
  • Which products generate the most profit? – ((product price – product cost) * quantity) 

Customers

  • How many new accounts/customers do we get (each day, week, month, half year or year)?
  • Who are our best customers – that buy the most from us? - The top 20% that make 80% of the profits.
  • What is our attrition rate – the numbers of accounts/customers we lose (each day, week, month, half year or year)? - If your attrition rate is high, the sales team needs to find a lot of new people just to keep things the same.
  • What is the customer satisfaction? – You will probably get this from surveys or customer feedback.
  • What are the top reasons for complaints?
  • How many existing customers referred new customers to your company? – You might need to setup something to collect this data. If you send your customers to do your marketing for you, you can save a lot of money.

Website

  • Which product pages get the most views?
  • Which news/blog articles get the most views?
  • How many minutes does the user spend on the website?
  • What is the percentage of abandoned shopping carts?
  • What is the average amount of time a customer spends in the shopping cart/session?
  • Most important – What is the conversion rate of the visitors coming to the site and the people who purchase a product?
  • Which landing pages are the most viewed?
  • Which landing pages have the highest conversion rate?


Again, I hope these are helpful and if you would like me to add some of your own, please comment below.



Imagine CPUs will not get any faster

http://bit.ly/15fcYLt

I read an article recently, about how mobile apps will probably not get the hardware boost that people are expecting.

This is partially to do with that CPU performance hitting a sort of (heat) wall and cannot improve on their speed. As Linley Gwenapp said “we’ve been falling behind Moore’s Law ever since Intel hit the power wall back in 2005”.


I myself have noticed that on a few occasions when companies decided to buy an expensive machine for their main server, it turned out to perform slower on queries than their previous soon-to-be-updated machine.

In a recent example, a three year old server with CPUs that have 2.66Ghz clock speed was almost twice as fast as a brand new machines with CPUs that have 2.3Ghz clock speed. I'm not exactly sure, but the new machines probably have several times more cache on the CPUs, probably better instruction set and the hosting company swore that it is several times faster than the old machine. However, our results - specifically to MySQL - have been discouraging.


After reading the article, I would like to suggest a thought exercise:
As DBAs, what would happen if CPUs never improve. As in, their clock speed never improves.
They can probably add more cores, fit in more cache, maybe even double the size of the CPU on the motherboard. However, their basic core performance for single threaded applications would not improve.

What would you do?

How would you solve your current company's needs?

How would you solve your future company's needs in the face of issues such as Big Data?


In my opinion, MySQL will need to break up anything that needs to be single-threaded as much as possible. This would probably not be easy. Adding a Map/Reduce layer to MySQL may help this - it works for other commercial database vendors: Infobright, Greenplum, (I think also) Oracle.
(I am not sure if Oracle may be inclined to improve MySQL's processing of large amounts of data as it may hurt profitable parts of their business.)

Sharding can and has helped companies solve this problem. This breaks up the problem by having the single threads process less data per shard. I am not sure about the available and mature solutions there are if you need to group data across several shards.

Regarding hardware, there is certainly room for "SQL" chips (think Kickfire) and other FPGAs.
Hardware compression could help, especially compression that can spread across cores, but the actual processing of the data after decompression would still be single threaded.

Summary tables could very well help for certain workloads as they pre-process large amounts of data for you into more manageable sizes. In addition to using Hadoop and if you have a person that can model data properly, it can be a very long term solution.
Perhaps pre-processing would be a much bigger thing in the future. As in, you speed your queries now by preparing the answers ahead of time and caching them.


I would like to hear more approaches to solve this problem, but I would prefer the solution to lean on the side of 'tried and tested'.
 

Using MySQL to build Big Data Applications


I made a new tutorial for how to use MySQL to develop Big Data Applications.
This is a 'Udacity'/explaining-on-paper style of video which I hope you will enjoy and find helpful.

Please 'like' or retweet if you feel it is informative.