Categories
Data & Analytics

1st Birthday MySQL Data Analysis With DBeaver & Python

In this post, I celebrate amazonwebshark’s 1st birthday with an analysis of my site’s MySQL data using DBeaver, Python and Matplotlib.

Table of Contents

Introduction

amazonwebshark is one year old today!

PXL 20221230 170232698 800600

To mark the occasion, I decided to examine the MySQL database that WordPress uses to run amazonwebshark, and see what it could tell me about the past twelve months.

In addition, I’ve been trying out DataCamp and have recently finished their Intermediate Python course. It introduced me to Matplotlib, and this post is a great chance to try out those new skills!

Timeline

I’ll start by answering a question. Why is amazonwebshark’s birthday on January 09 when my first post’s publication date is December 02 2021?

In response, here’s a brief timeline of how amazonwebshark came to be:

09 January 2022 was the first day that everything was fully live, so I view that as amazonwebshark’s birthday.

The three LinkedIn posts were added here on 19 January 2022, but without Introducing amazonwebshark.com they would never have left LinkedIn!

WordPress Database

In this section, I take a closer look at amazonwebshark’s MySQL database and the ways I can access it.

Database Schema

A WordPress site has lots to keep track of, like logins, plugins and posts. For this, it uses the MySQL database management system.

A standard WordPress installation creates twelve MySQL tables. WordPress describes them in its documentation, which includes this entity relationship diagram:

Additionally, DeliciousBrains have produced an Ultimate Developer’s Guide to the WordPress Database which gives a full account of each table’s columns and purpose.

SiteGround Portal Database Access

WordPress databases are usually accessed with phpMyAdmin – a free tool for MySQL admin over the Internet.

WPBeginner has a Beginner’s Guide To WordPress Database Management With phpMyAdmin, covering topics including restoring backups, optimisation and password resets.

While phpMyAdmin is great for basic maintenance, it’s not very convenient for data analysis:

  • There are no data tools like schema visualization or query plans.
  • It lacks scripting tools like IntelliSense or auto-complete.
  • Accessing it usually involves accessing the web host’s portal first.

Ideally, I’d prefer to access my database remotely using a SQL client instead. While this needs some additional config, SiteGround makes this very simple!

Remote Database Access

By default, SiteGround denies any remote access requests. Many people will never use this feature, so disabling it is a good security measure. Remote access is enabled in the SiteGround portal, which grants immediate remote access for specified IPs and hostnames.

After doing that, I created a new database user with read-only (SELECT) access to the MySQL database:

2023 01 01 SiteGroundMySQLPermissions

This isn’t strictly necessary, but the default user has unlimited access and violates the principle of least privilege in this situation.

The following SiteGround video demonstrates enabling remote access:

Analysis Tools

In this section, I examine the various tools I’ll use for amazonwebshark’s 1st birthday data analysis.

DBeaver

DBeaver is a free database tool and SQL client. It is multi-platform, open-source and supports a variety of databases including Microsoft SQL Server, Amazon Athena and MySQL.

DBeaver’s features include:

This VK Tech 360 video demonstrates connecting DBeaver to a local MySQL database:

mysql-connector-python

mysql-connector-python is a free Python driver for communicating with MySQL.

This Telusko video shows mysql-connector-python being used to access a local MySQL database:

mysql-connector-python is on PyPi and is installable via pip:

pip install mysql-connector-python

After mysql-connector-python is installed and imported, a connection to a MySQL database can be made using the mysql.connector.connect() function with the following arguments:

  • host: Hostname or IP address of the MySQL server.
  • database: MySQL database name.
  • user: User name used to authenticate with the MySQL server.
  • password: Password to authenticate the user with the MySQL server.

This opens a connection to the MySQL server and creates a connection object. I store this in the variable conn_mysql:

import mysql.connector

conn_mysql = mysql.connector.connect(
	host = var.MYSQL_HOST,
	database = var.MYSQL_DATABASE,
	user = var.MYSQL_USER,
	password = var.MYSQL_PASSWORD
	)

I have my credentials stored in a separate Python script that is imported as var. This means I can protect them via .gitignore until I get something better in place!

After that, I need a cursor for running my SQL queries. I create this using the cursor() method of my conn_mysql connection object and store the whole thing as cursor:

cursor = conn_mysql.cursor()

I’m now in a position to start running SQL queries from Python. I import a SQL query from my var script (in this case my Category query) and store it as query:

query = var.QUERY_CATEGORY

I then run my query using the execute() method. I get the results as a list of tuples using the fetchall() method, which I store as results:

cursor.execute(query)
results = cursor.fetchall()

Finally, I disconnect my cursor and MySQL connection with the close() method:

cursor.close()
conn_mysql.close()

Matplotlib

Matplotlib is a library for creating visualizations in Python. It can produce numerous plot types and has a large gallery of examples.

This BlondieBytes video shows a short demo of Matplotlib inside a Jupyter Notebook:

Matplotlib is on PyPi and is installable via pip:

pip install matplotlib

To view Matplotlib’s charts in Visual Studio Code I had to use an interactive window. Visual Studio Code has several options for this. Here I used the Run Current File In Interactive Window option, which needed the IPyKernel package to be installed in my Python virtual environment first.

Categories Analysis

In this section, I begin amazonwebshark’s 1st birthday data analysis by writing a SQL query for amazonwebshark’s categories and analysing the results with Python.

Categories Analysis: SQL Query

For my Category SQL query, I’ll be using the terms and term_taxonomy tables:

2023 01 07 WordPressTables

WordPress has a taxonomy system for content organization. Individual taxonomy items are called terms, and they are stored in the terms table. Terms for amazonwebshark include Data & Analytics and Security & Monitoring.

The term_taxonomy table links a term_id to a taxonomy, giving context for each term. Common taxonomies are category, post_tag and nav_menu.

If I join these tables on term_id then I can map terms to taxonomies. In the following query results, the first two columns are from terms and the rest are from term_taxonomy:

2023 01 07 DBeaverQuery

My final query keeps the join, cleans up terms.name, returns all categories with at least one use and orders the results by term_taxonomy.count and terms.name:

SELECT
	REPLACE (t.name, '&', '&') AS name,
	tt.count
FROM
	term_taxonomy AS tt
INNER JOIN terms AS t ON
	tt.term_id = t.term_id
WHERE
	tt.taxonomy = 'category'
	AND tt.count > 0
ORDER BY
	tt.count ASC ,
	t.name DESC
2023 01 04 DBeaverCategoryResults

In future, I’ll need to limit the results of this query to a specific time window. Here, I want all the results so no further filtering is needed.

Categories Analysis: Python Script

Currently, my results variable contains the results of the var.QUERY_CATEGORY SQL query. When I run print(results), I get this list of tuples:

[('Training & Community', 1), ('DevOps & Infrastructure', 1), ('AI & Machine Learning', 1), ('Internet Of Things & Robotics', 2), ('Architecture & Resilience', 2), ('Security & Monitoring', 3), ('Me', 4), ('Data & Analytics', 5), ('Developing & Application Integration', 8)]

So how do I turn this into a graph? Firstly, I need to split results up into what will be my X-axis and Y-axis. For this, I create two empty lists called name and count:

name = []
count = []

After that, I populate the lists by looping through result. For each tuple, the first item is appended to name and the second is appended to count:

for result in results:
    name.append(result[0])
    count.append(result[1])

When I print the lists now, name and count contain the names and counts from the SQL query in the same order as the original results:

print(f'name = {name}')
name = ['Training & Community', 'DevOps & Infrastructure', 'AI & Machine Learning', 'Internet Of Things & Robotics', 'Architecture & Resilience', 'Security & Monitoring', 'Me', 'Data & Analytics', 'Developing & Application Integration']
print(f'count = {count}')
count = [1, 1, 1, 2, 2, 3, 4, 5, 8]

I then use these lists with Matplotlib, imported as plt:

plt.bar(name, count)
plt.xlabel("category name")
plt.ylabel("category count")
plt.title("amazonwebshark categories")
plt.show()
  • bar sets the visual’s type as a bar chart. The X-axis is name and the Y-axis is count.
  • xlabel labels the X-axis as category name
  • ylabel labels the Y-axis as category count.
  • title names the chart as amazonwebshark categories
  • show shows the graph.

The following chart is produced:

sharkbirth category bar

However, the X-axis labels are unreadable. I can fix this by changing my script:

plt.barh(name, count)
plt.xlabel("count")
plt.ylabel("name")
plt.title("amazonwebshark categories")
plt.show()

I use barh to change the graph to a horizontal bar chart, and then swap the xlabel and ylabel strings around. This time the chart is far easier to read:

sharkbirth category barh

Tags Analysis

In this section, I continue amazonwebshark’s 1st birthday data analysis by writing a SQL query for amazonwebshark’s tags and analysing the results with Python.

Tags Analysis: SQL Query

My Tags SQL query is almost the same as my Categories one. This time, my WHERE clause is filtering on post_tag:

SELECT
	REPLACE (t.name, '&', '&') AS name,
	tt.count
FROM
	term_taxonomy AS tt
INNER JOIN terms t ON
	tt.term_id = t.term_id
WHERE
	tt.taxonomy = 'post_tag'
	AND tt.count > 0
ORDER BY
	tt.count ASC ,
	t.name DESC

There are more results this time. While I try to limit my use of categories, I’m currently using 44 tags:

2023 01 04 DBeaverTagsResults

Tags Analysis: Python Script

My Tags Python script is (also) almost the same as my Categories one. This time, the query variable has a different value:

query = var.QUERY_TAG

So print(results) returns a new list of tuples:

[('Running', 1), ('Read The Docs', 1), ('Raspberry Pi Zero', 1), ('Raspberry Pi 4', 1), ('Python: Pandas', 1), ('Python: NumPy', 1), ('Python: Boto3', 1), ('Presto', 1), ('Postman', 1), ('Microsoft Power BI', 1), ('Linux', 1), ('Gardening', 1), ('DNS', 1), ('AWS IoT Core', 1), ('Amazon RDS', 1), ('Amazon EventBridge', 1), ('Amazon EC2', 1), ('Amazon DynamoDB', 1), ('Agile', 1), ('Academia', 1), ('WordPress', 2), ('PowerShell', 2), ('OAuth2', 2), ('Microsoft SQL Server', 2), ('Microsoft Azure', 2), ('AWS Data Wrangler', 2), ('AWS CloudTrail', 2), ('Apache Parquet', 2), ('Amazon SNS', 2), ('Amazon Route53', 2), ('Amazon CloudWatch', 2), ('T-SQL Tuesday', 3), ('Strava', 3), ('Certifications', 3), ('Amazon Athena', 3), ('WordPrompt', 4), ('Project: iTunes Export Data Pipeline (2022-2023)', 4), ('Music', 4), ('GitHub', 4), ('AWS Billing And Cost Management', 4), ('Visual Studio Code', 5), ('Amazon S3', 5), ('Python', 6), ('Amazon Web Services', 16)]

Matplotlib uses these results to produce another horizontal bar chart with a new title:

plt.barh(name, count)
plt.xlabel("count")
plt.ylabel("name")
plt.title("amazonwebshark tags")
plt.show()

But this chart has a different problem – the Y-axis is unreadable because of the number of tags returned by my SQL query:

sharkbirth tag all

To fix this, I reduce the number of rows returned by changing my SQL WHERE clause from:

WHERE
	tt.taxonomy = 'post_tag'
	AND tt.count > 0

to:

WHERE
	tt.taxonomy = 'post_tag'
	AND tt.count > 2

This returns a smaller list of tuples:

[('T-SQL Tuesday', 3), ('Strava', 3), ('Certifications', 3), ('Amazon Athena', 3), ('WordPrompt', 4), ('Project: iTunes Export Data Pipeline (2022-2023)', 4), ('Music', 4), ('GitHub', 4), ('AWS Billing And Cost Management', 4), ('Visual Studio Code', 5), ('Amazon S3', 5), ('Python', 6), ('Amazon Web Services', 16)]

I then update the title to reflect the new results and use yticks to reduce the Y-axis label font size to 9:

plt.barh(name, count)
plt.xlabel("tag count")
plt.ylabel("tag name")
plt.title("amazonwebshark tags (most assignments)")
plt.yticks(fontsize = 9)
plt.show()

The chart is now more useful and easier to read:

sharkbirth tag most

Views Analysis

I also planned to analyse my post views here. But, as I mentioned in my last post, some of this data is missing! So any chart will be wrong.

I haven’t had time to look at this yet, so stay tuned!

Summary

In this post, I celebrated amazonwebshark’s 1st birthday with an analysis of my site’s MySQL data using DBeaver, Python and Matplotlib.

I had fun researching and writing this! It can be tricky to find a data source that isn’t contrived or overused. Having access to the amazonwebshark database gives me data that I’m personally invested in, and an opportunity to practise writing MySQL queries.

I’ve also been able to improve my Python, and meaningfully experiment with Matplotlib to get charts that will be useful going forward. For example, I used the very first Tags chart to prune some unneeded tags from my WordPress taxonomy.

If this post has been useful, please feel free to follow me on the following platforms for future updates:

Thanks for reading ~~^~~

Categories
DevOps & Infrastructure

Migrating amazonwebshark To SiteGround

In this post, I examine the process of migrating amazonwebshark to SiteGround and give an overview of the processes involved.

Table of Contents

Introduction

When I started amazonwebshark I had to make some infrastructure decisions. I registered the domain name with Amazon Route 53, and then needed to choose a blog hosting platform.

In December 2021 I took advantage of a Bluehost offer and paid £31.90 for their Basic WordPress Hosting package. This included a variety of services including:

My Bluehost renewal came through earlier this month, priced at £107.76. I’ve had great service from Bluehost and have no complaints, but that price was quite a leap. So, before I accepted it, I decided to do some research and see what my alternatives were.

Hosting Alternatives

In this section, I go through the results of my research into alternative hosting platforms.

Amazon Lightsail

I started by looking at Amazon Lightsail. Essentially, Lightsail is a simplified way of deploying AWS services like EC2, EBS and Elastic Load Balancing.

Lightsail pricing differs from most AWS services. Instead of the common Pay-As-You-Go pricing model, Lightsail has set monthly pricing. For example, a Linux server with similar memory, processing and storage to my Bluehost server currently costs $3.50 a month.

There is an important difference though. While Bluehost has teams of people responsible for tasks like server maintenance, database recovery, hard disk failures and security patches, with Lightsail the infrastructure would become my responsibility. I would save money over Bluehost, but at the cost of doing my own systems admin.

And the above list isn’t even exhaustive! It doesn’t include the setup and maintenance of services like CDN, SSL certificates and email accounts, all of which come with their own extra requirements and costs.

At this point, Bluehost was still on top.

SiteGround

SiteGround is in the same business as Bluehost. It offers a variety of hosting solutions for an array of use cases and has good standing in the industry.

SiteGround also had a great Black Friday offer this year! It was offering pretty much the same deal as Bluehost at £1.99 a month:

2022 11 25 SitegroundOfferScroll

This is INSANELY cheap, especially considering how much all this infrastructure costs to run!

SiteGround has also developed a free WordPress plugin to automate migrations from other hosting platforms. While this isn’t unique to them, a combination of good reviews, extensive services, low hassle and a great price was more than enough to get me on board.

Migrate What Exactly?

Before continuing, I thought it best to go into a bit of detail about what exactly is being migrated. I’ve mentioned servers, databases and domains, but what gets moved where? And why?

Well, because I’m moving things around on the Internet, I need to talk about the Domain Name System (DNS).

Wait! Come back!

Explain DNS Like I’m 5

What follows is a very simple introduction to DNS. There’s far more to DNS than this, but that’s beyond the scope of this post.

Let’s say I want to phone The Shark Trust. I can’t type “The Shark Trust” into my handset – I need their phone number. So I open my phone book, turn to the S section and find The Shark Trust. Next to this entry is a phone number: 01752 672008. I type that number into the handset and get through to their office.

DNS is like the Internet’s phone book. Websites are held on servers, and the ‘phone numbers’ for those servers are IP addresses. When I request a website like amazonwebshark.com, my web browser needs to know the IP address for the server holding the site’s data, for example 34.91.95.18.

Explain DNS With Pictures

This WebDeasy diagram show DNS at a high level:

WebDeasy: How the Domain Name System (DNS) works – Basics

When a URL is entered into a web browser, a query is sent to a DNS server. Using the phone book analogy, the web browser is asking the DNS server for the amazonwebshark.com phone number.

DNS servers don’t have any IP addresses, but they know which ‘phone book’ to look in. These ‘phone books’ are called name servers. The DNS server finds and contacts the right name server, which matches the amazonwebshark.com domain name to an IP address.

The DNS server then returns this IP address to the web browser, which uses it to contact the server hosting the amazonwebshark.com resources.

In the diagram, the DNS-Server represents Route 53. Route 53 holds DNS records for the amazonwebshark.com domain name, and knows where to find the name servers that have the amazonwebshark.com IP address.

The webdeasy.de server represents the Bluehost name servers. These servers can answer a variety of DNS queries, and are considered the ground truth for initial site visits and browser caching.

amazonwebshark’s DNS Setup

At the start of December 2022 the amazonwebshark.com domain name was hosted by Route 53, with an NS record pointing at the Bluehost name servers:

2022 12 02 Route53Bluehost

The basic infrastructure looked like this, with outbound requests in blue and inbound responses in green:

2022 12 27 amazonwebsharkDNSdiagram

And that’s it! To further explore DNS core concepts, this DNSimple comic is well worth a read and this Fireship video gives a solid, if a little more technical, account:

Data Migration

In this section, I start migrating my amazonwebshark data from Bluehost to SiteGround.

SiteGround has an automated migrator plugin that copies existing WordPress sites from other hosting platforms. And it’s very good! The process boils down to:

The process can also be seen in this Avada video:

The plugin copies all the amazonwebshark server files, scripts and database objects in a process that takes about five minutes. SiteGround then provides a temporary URL for testing and performance checks:

2022 12 02 SiteGroundMigratonComplete

After completely migrating amazonwebshark to SiteGround, the next step involves telling the amazonwebshark domain where to find the new server. Time for some DNS!

DNS Migration

In this section, I update the amazonwebshark DNS records with the SiteGround name servers.

I repointed the existing amazonwebshark NS record from Bluehost to SiteGround by updating the values in Route 53 from this:

2022 12 02 Route53Bluehost

To this:

2022 12 02 Route53SiteGround

My change then needed to propagate through the Internet. Internet Service Providers update their records at different rates, so changes can take up to 72 hours to complete worldwide.

Free DNS checking tools like WhatIsMyDNS can perform global checks on a domain name’s IP address and DNS record information. The check below was done after around 30 hours, by which time most of the servers were returning SiteGround IPs:

2022 12 06 DNSPropagationCheck

Any Problems?

First, the good news. There was no downtime while migrating amazonwebshark to SiteGround! During the migration, DNS queries were resolved by either Bluehost’s or SiteGround’s name servers. Both platforms had amazonwebshark data, so both could answer DNS queries.

Additionally, as I set a change freeze on amazonwebshark until the migration was over, there was no lost or orphaned content.

I did lose some WPStatistics hit statistics data though. There is no data for December 03 and December 04:

2022 12 18 WPStatisticsHits

This was my fault. The DNS propagation took longer than it should have because of a misunderstanding on my part!

So why was data lost? WPStatistics stores data in tables in the site’s MySQL database. When I first migrated my data on December 02, the Bluehost and SiteGround tables were the same. After that point, Bluehost continued to serve amazonwebshark until December 05, and wrote its statistics in the Bluehost MySQL tables.

It was only after I corrected my DNS mistake that SiteGround could start serving content and writing statistics on the SiteGround MySQL tables. So SiteGround didn’t record anything for December 03 and December 04, and as no additional data migration was done the statistics that Bluehost recorded never made it to the SiteGround tables.

I can recover this if I want to though. I took a full backup of my Bluehost data before ending the contract. That included a full backup of the Bluehost MySQL database with the WPStatistics tables. I’ll take a look at the tables at some point, see how the data is arranged and decide from there.

Future Plans

I’m considering moving amazonwebshark to a serverless architecture in 2023. While the migration was a success, servers still have inherent problems:

  • Servers can break or go offline.
  • They can be hacked.
  • They can be over or under-provisioned.

Serverless infrastructure could remove those pain points. I don’t use any WordPress enterprise features, and amazonwebshark could exist very well as an event-driven static website. Tools like Hugo and Jekyll are designed for the job and documented well, and people like Kendra Little and Chrissy LeMaire have successfully transitioned their blogs to serverless infrastructures.

The biggest challenge here isn’t architectural. If I moved to a serverless architecture, I would want something similar to the Yoast SEO analysis plugin. This plugin has really helped me improve my posts, and by extension has made them more enjoyable to write.

I’ve seen lots of serverless tooling for migrating resources and serving content, but not so much for SEO guidance and proofreading. Any amazonwebshark serverless migration would be contingent on finding something decent along these lines. After all, if the blog becomes a pain to write for then what’s the point?

Summary

In this post, I examined the process of migrating amazonwebshark to SiteGround and gave an overview of the processes involved.

I’m very happy with how things went overall! The heavy lifting was done for me, both companies were open and professional throughout and what could have been a daunting process was made very simple!

If this post has been useful, please feel free to follow me on the following platforms for future updates:

Thanks for reading ~~^~~

Categories
Developing & Application Integration

Production Code Qualities

In this post, I respond to November 2022’s T-SQL Tuesday #156 Invitation and give my thoughts on some production code qualities.

tsql tuesday

Table of Contents

Introduction

This month, Tomáš Zíka’s T-SQL Tuesday invitation was as follows:

Which quality makes code production grade?

Please be as specific as possible with your examples and include your reasoning.

Good question!

In each section, I’ll use a different language. Firstly I’ll create a script, and then show a problem the script could encounter in production. Finally, I’ll show how a different approach can prevent that problem from occurring.

I’m limiting myself to three production code qualities to keep the post at a reasonable length, and so I can show some good examples.

Precision

In this section, I use T-SQL to show how precise code in production can save a data pipeline from unintended failure.

Setting The Scene

Consider the following SQL table:

USE [amazonwebshark]
GO

CREATE TABLE [2022].[sharkspecies](
	[shark_id] [int] IDENTITY(1,1) NOT NULL,
	[name_english] [varchar](100) NOT NULL,
	[name_scientific] [varchar](100) NOT NULL,
	[length_max_cm] [int] NULL,
	[url_source] [varchar](1000) NULL
)
GO

This table contains a list of sharks, courtesy of the Shark Foundation.

Now, let’s say that I have a data pipeline that uses data in amazonwebshark.2022.sharkspecies for transformations further down the pipeline.

No problem – I create a #tempsharks temp table and insert everything from amazonwebshark.2022.sharkspecies using SELECT *:

When this script runs in production, I get two tables with the same data:

2022 11 02 SQLResults1

What’s The Problem?

One day a new last_evaluated column is needed in the amazonwebshark.2022.sharkspecies table. I add the new column and backfill it with 2019:

ALTER TABLE [2022].sharkspecies
ADD last_evaluated INT DEFAULT 2019 WITH VALUES
GO

However, my script now fails when trying to insert data into #tempsharks:

2022 11 02 SQLResults2Sharp
(1 row affected)

(4 rows affected)

Msg 213, Level 16, State 1, Line 17
Column name or number of supplied values does not match table definition.

Completion time: 2022-11-02T18:00:43.5997476+00:00

#tempsharks has five columns but amazonwebshark.2022.sharkspecies now has six. My script is now trying to insert all six sharkspecies columns into the temp table, causing the msg 213 error.

Doing Things Differently

The solution here is to replace row 21’s SELECT * with the precise columns to insert from amazonwebshark.2022.sharkspecies:

While amazonwebshark.2022.sharkspecies now has six columns, my script is only inserting five of them into the temp table:

2022 11 02 SQLResults3Sharp

I can add the last_evaluated column into #tempsharks in future, but its absence in the temp table isn’t causing any immediate problems.

Works The Same In Other Environments

In this section, I use Python to show the value of production code that works the same in non-production.

Setting The Scene

Here I have a Python script that reads data from an Amazon S3 bucket using a boto3 session. I pass my AWS_ACCESSKEY and AWS_SECRET credentials in from a secrets manager, and create an s3bucket variable for the S3 bucket path:

When I deploy this script to my dev environment it works fine.

What’s The Problem?

When I deploy this script to production, s3bucket will still be s3://dev-bucket. The potential impact of this depends on the AWS environment setup:

Different AWS account for each environment:

  • dev-bucket doesn’t exist in Production. The script fails.

Same AWS account for all environments:

  • Production IAM roles might not have any permissions for dev-bucket. The script fails.
  • Production processes might start using a dev resource. The script succeeds but now data has unintentionally crossed environment boundaries.

Doing Things Differently

A solution here is to dynamically set the s3bucket variable based on the ID of the AWS account the script is running in.

I can get the AccountID using AWS STS. I’m already using boto3, so can use it to initiate an STS client with my AWS credentials.

STS then has a GetCallerIdentity action that returns the AWS AccountID linked to the AWS credentials. I capture this AccountID in an account_id variable, then use that to set s3bucket‘s value:

More details about get_caller_identity can be found in the AWS Boto3 documentation.

For bonus points, I can terminate the script if the AWS AccountID isn’t defined. This prevents undesirable states if the script is run in an unexpected account.

Speaking of which…

Prevents Undesirable States

In this section, I use PowerShell to demonstrate how to stop production code from doing unintended things.

Setting The Scene

In June I started writing a PowerShell script to upload lossless music files from my laptop to one of my S3 buckets.

I worked on it in stages. This made it easier to script and test the features I wanted. By the end of Version 1, I had a script that dot-sourced its variables and wrote everything in my local folder $ExternalLocalSource to my S3 bucket $ExternalS3BucketName:

#Load Variables Via Dot Sourcing
. .\EDMTracksLosslessS3Upload-Variables.ps1


#Upload File To S3
Write-S3Object -BucketName $ExternalS3BucketName -Folder $ExternalLocalSource -KeyPrefix $ExternalS3KeyPrefix -StorageClass $ExternalS3StorageClass

What’s The Problem?

NOTE: There were several problems with Version 1, all of which were fixed in Version 2. In the interests of simplicity, I’ll focus on a single one here.

In this script, Write-S3Object will upload everything in the local folder $ExternalLocalSource to the S3 bucket $ExternalS3BucketName.

Problem is, the $ExternalS3BucketName S3 bucket isn’t for everything! It should only contain lossless music files!

At best, Write-S3Object will upload everything in the local folder to S3 whether it’s music or not.

At worst, if the script is pointing at a different folder it will start uploading everything there instead! PowerShell commonly defaults to C:\Windows, so this could cause all kinds of problems.

Doing Things Differently

I decided to limit the extensions that the PowerShell script could upload.

Firstly, the script captures the extensions for each file in the local folder $ExternalLocalSource using Get-ChildItem and [System.IO.Path]::GetExtension:

$LocalSourceObjectFileExtensions = Get-ChildItem -Path $ExternalLocalSource | ForEach-Object -Process { [System.IO.Path]::GetExtension($_) }

Then it checks each extension using a ForEach loop. If an extension isn’t in the list, PowerShell reports this and terminates the script:

ForEach ($LocalSourceObjectFileExtension In $LocalSourceObjectFileExtensions) 

{
If ($LocalSourceObjectFileExtension -NotIn ".flac", ".wav", ".aif", ".aiff") 
{
Write-Output "Unacceptable $LocalSourceObjectFileExtension file found.  Exiting."
Start-Sleep -Seconds 10
Exit
}

So now, if I attempt to upload an unacceptable .log file, PowerShell raises an exception and terminates the script:

**********************
Transcript started, output file is C:\Files\EDMTracksLosslessS3Upload.log

Checking extensions are valid for each local file.
Unacceptable .log file found.  Exiting.
**********************

While an acceptable .flac file will produce this message:

**********************
Transcript started, output file is C:\Files\EDMTracksLosslessS3Upload.log

Checking extensions are valid for each local file.
Acceptable .flac file.
**********************

To see the code in full, as well as the other problems I solved, please check out my post from June.

Summary

In this post, I responded to November 2022’s T-SQL Tuesday #156 Invitation and gave my thoughts on some production code qualities. I gave examples of each quality and showed how they could save time and prevent unintended problems in a production environment.

Thanks to Tomáš for this month’s topic! My previous T-SQL Tuesday posts are here.

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Thanks for reading ~~^~~