How to Use MySQL Triggers for Recording History

How to Use MySQL Triggers for Recording History

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MySQL triggers are most commonly used in ecommerce, which isn’t surprising since MySQL is the most popular open-source management tool for web databases. It also can’t hurt that SQL is still the go to language for accessing and managing content in ecommerce databases. Aside from recording history in databases these triggers serve a variety of other useful functions such as:

  • Validating data
  • Accessing system functions
  • Replicating data/data consistency
  • Enforcing business rules
  • Generating unique values for new rows in separate files
  • Write to other files
  • Other logging applications

Being able to record database history is crucial to backing up ecommerce web databases. MySQL triggers can help businesses create another form of backup file. Even though it’s rare, databases can fail. Having a secondary backup or extremely similar table is never a bad idea. A history table is so similar to the main table you can use it to help rebuild a main table, repair harmful changes, or revert back to earlier versions.

What Is Versioning and Why Does It Matter?

Versioning gives ecommerce businesses the option of going back in database history to determine when, where, and who made changes to MySQL relational databases. Using version control in databases also supplies a single, reliable resource to use to recall information as needed. There are several key benefits to using versioning/recording history:

  • Keep an in-depth history of database changes
  • Deploy from or revert to previously known states
  • Use for critical tables that require more than a rolling backup
  • Auditing and troubleshooting databases

Versioning along with relational databases like MySQL for ecommerce applications is beneficial when data needs to be updated, as you only need to update one string of data. For example, if an ecommerce business needs to update client emails they could update that row without having to change other information in the table. MySQL triggers make recalling, reverting, and searching relational database history quick and efficient.

An Introduction to MySQL Triggers

MySQL triggers are database objects associated with a table, that become activated when defined actions take place. To run MySQL triggers users require MySQL SUPERUSER privileges. Examples of MySQL trigger statements in a table include UPDATE, INSERT, and DELETE. These trigger statements can be placed before or after an event in database tables.

Using Triggers to Save Database History

Create a history table that looks exactly like your main database table; size and order need to be identical. At the end of the table you will need to add a CREATE TRIGGER statement. The following examples of MySQL trigger coding are only examples of what programming may look like, there are several ways to create triggers. Below is an example of MySQL syntax for creating a basic CREATE TRIGGER statement in your newly created history table.

CREATE
[DEFINER = user]
TRIGGER trigger_name
trigger_time trigger_event trigger_id
ON history_table FOR EACH ROW
trigger_body

trigger_time { BEFORE | AFTER }
trigger_event { INSERT | UPDATE | DELETE }

This sets the ground for using UPDATE, INSERT, AND DELETE triggers. The following is an example of what a basic MySQL insert trigger looks like.

INSERT INTO history_table SELECT*, ‘update’,
CURRENT_USER(), now(), null FROM
main_table WHERE id=OLD.id;

Delete triggers follow the same exact language only ‘update’ is replaced with ‘delete’. Make sure to update history tables as main tables change, or mismatch errors will show throughout table columns. While some programmers will caution against using * with SELECT to return all columns in a table, it’s one of the fastest ways to recall all the information. Indexes must be attributed to any column you name in the WHERE clause of a SELECT query. The WHERE clause must also be updated if other primary or unique key combinations are in use.

5 Major Advances in Artificial Intelligence in E-commerce

5 Major Advances in Artificial Intelligence in E-commerce

Content Automation

AI has been impacting the online experience since the advent of search way back in 1996 when Sergey Brin and Larry Page founded Google. Smart engines already supply customer recommendations in online shopping (Amazon) and viewing recommendations (YouTube, Netflix). As machine learning evolves, there are countless other ways that applications of artificial intelligence in e-commerce will improve more than just the online shopping experience.

See it, Search, Buy It

Imagine going to a wedding or a special event and falling in love with a friend’s pair of shoes. Visual search helps you find exactly what you want based on something you saw. Take a picture of those shoes, and an AI engine could find multiple samples of something similar. This saves time on the consumer side and increases discoverability for retailers.

Virtual Personal Shoppers

Not everyone loves to spend hours searching for a new jacket online. That’s why companies like, North Face, are introducing artificial intelligence applications that shop for you. Software is faster than humans and may be better at finding specific items based on simple logic rather than emotion. Think about how Waze helps you find the right route based on traffic patterns. It’s using real data to get you there faster, although that route may differ from your habits or assumptions. A virtual shopper could lead buyers to online e-tailers and products they may not have otherwise sought out.

Improved Personalization

There are many ways to gather information about both what you’re shopping for and how you shop. Currently, ad re-targeting reminds users about items that were abandoned in their carts. Eventually, AI could gather even more information about how specific, individual shoppers search for goods, research, and curate information before making a buy. This information, in turn, may lead to smarter and more customized ad platforms and recommendation engines that improve the personal shopping experience.

Integration of AI and CRM

Machines are better equipped to analyze large blocks of data more quickly than humans. All products and brands have to compete on customer service, and AI could help scale customer service more effectively. Imagine that an upset customer is more accurately routed to the right team based on more smarter voice-responsive software. Software could do a better job of helping customers find products and direct them to the right answer to their query or concern.

Using Natural Language Processing for Search

Sometimes, when putting search terms into an engine, it take a few tries before you get the results you want. NLP, or natural language processing, is one of the fastest-growing forms of artificial intelligence in e-commerce because it will help customers get from point A (searching) to point B (conversion) more quickly and with less frustration. NLP enables machines to learn how humans speak and can translate that speech into all types of behavior. Because this is such a wide-ranging field, the applications for this technology will impact everything from shopping to medicine.

CrewMachine is using AI to drive how e-commerce brands create and manage online content and fix problems too great for human teams to solve alone. We’d love to talk more about it, so contact us to set up a demo today.

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