Tag: Amazon Web Services

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 200 fully-featured services from data centres globally.

More information is available on the Amazon Web Services website.

Categories
DevOps & Infrastructure

Migrating amazonwebshark To SiteGround

BlogMigration

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

2022 11 08 ProductionCodeQualities

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.

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

Thanks for reading ~~^~~

Categories
Data & Analytics

Connecting Athena To Power BI With Simba Athena

In this post, I use Simba Athena to create a secure connection between my iTunes data in Amazon Athena and Microsoft Power BI.

Table of Contents

Introduction

In my recent posts, I’ve been transforming an iTunes Export CSV file using Python and AWS.

Firstly, in July I built a Python ETL that extracts data from my iTunes CSV into a Pandas DataFrame and transforms some columns.

Next, I updated my ETL script at the start of August. It now uploads the changed data to S3 as a Parquet file. Then I made my data available in an Athena table so I could use some of Athena’s benefits:

  • My data now has high availability at low cost.
  • My data can be queried faster from Athena than from the CSV.
  • I can limit what data is accessed, as opposed to all-or-nothing.

Now I want to start analysing my data. There are many business intelligence (BI) tools available to help me with this. I will be using the latest version of Power BI on my Windows 10 laptop.

But wait. If Power BI is on my laptop and my data is in Athena, how can Power BI access my data? Do I need to make my AWS resources publically accessible? Do I need to download the data to my laptop?

Fortunately not! Welcome to the world of data connectors. Meet Simba Athena.

Simba Athena

In this section, I will look at how Simba Athena bridges the gap between my locally-installed BI tool and my data in AWS.

What Is Simba Athena?

Simba Athena is an Open Database Connectivity (ODBC) driver built for Athena. The history of Simba dates back to 1992 when Simba Technologies co-developed the first standards-based ODBC driver with Microsoft. Magnitude acquired Simba Technologies in 2016.

Simba offers numerous data connectors that all work in roughly the same way:

odbc open data base connectivity
Source: What is ODBC – Open Database Connectivity

Relating this diagram to Athena and Power BI:

  • The user sends a query to Power BI.
  • Power BI passes the query to Simba Athena via the ODBC Device Manager.
  • Simba Athena queries Athena and gets the results.
  • Simba Athena passes the results to Power BI via the ODBC Device Manager.
  • Power BI shows the results to the user.

Features Of Simba Athena

Simba Athena has several features that make it a great partner for Athena:

  • Simba Athena works with Windows, macOS and Linux. Just as Athena supports multiple operating systems, Simba Athena is also OS-agnostic.
  • Numerous applications support Simba Athena including Excel, Tableau and Power BI.

Speaking of Power BI…

Microsoft Power BI

In this section, I will examine Power BI and explain why I chose to use it.

What Is Power BI?

Microsoft Power BI is a data visualization solution with a primary focus on BI. At the time of writing, Power BI’s main components are:

  • Power BI Desktop: a free locally-installed application designed for connecting to, transforming, and visualizing data.
  • Power BI Service: a cloud-based SaaS supporting the deployment and sharing of dashboards.
  • Power BI Mobile: a mobile app platform for Windows, iOS, and Android devices.

So what makes Power BI a good choice here?

Choosing Power BI

My decision to use Power BI came down to three factors:

  • Prior Experience. I’ve used Power BI many times over the years, and have become very familiar with it. This will let me deliver results quickly.
  • Support: Both Microsoft and AWS have rich documentation for Simba Athena. This gives me confidence in setting it up and reduces the chance of any blockers.
2022 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms

So now I’ve talked about Simba Athena and Power BI, let’s get them working together.

Setting Up Simba Athena

In this section, I will install and configure Simba Athena on my laptop. I will then attempt to extract data from Athena using Power BI.

The remainder of this post will focus on the Windows version of Simba Athena. AWS offers download links for Windows, Linux and macOS, and provides installation instructions in the Simba Athena Documentation.

Downloading Simba Athena

The first step is to download the Simba Athena ODBC driver provided by AWS. The options vary depending on platform and bitness.

The installation process mainly focuses on the end-user license agreement and destination folder selection. Once Simba Athena is installed, it can be configured.

Configuring Simba Athena

Simba Athena’s configuration settings are available via the Windows ODBC Data Source Administrator. This can be found in the Start Bar’s Windows Administrative Tools folder, or by running a Windows search for ODBC.

Accessing this and selecting the System DSN tab shows Simba Athena as a System Data Source:

2022-08-28-SimbaSystemDSN

From here, selecting Configure shows a setup screen with a few familiar fields:

Of these, Catalog, Schema and Workgroup are pre-populated with Athena defaults and Metadata Retrieval Method is set to Auto.

That leaves the Data Source Name and Description to identify the data source, and the AWS Region containing the Athena data.

In Output Options, I can state my S3 Output Location and Encryption Options. The output location is Athena’s Query Result Location, and the encryption options should mirror the S3 bucket’s encryption settings.

If the S3 Output Location is left blank, this will cause an error when Power BI tries to connect to Athena:

Details: "ODBC: ERROR [HY000] [Simba][Athena] (1040) An error has been thrown from the AWS Athena client. Athena Error No: 130, HTTP Response Code: 400, Exception Name: InvalidRequestException, Error Message: outputLocation is null or empty

Simba Athena’s remaining settings are out of scope for this post, although there’s one I definitely need to mention – Authentication Options:

This is how Simba Athena authenticates its requests to AWS. As mentioned earlier, there are several options here. Depending on the authentication type selected, Simba Athena can store Access Keys, Session Tokens, TenantIDs and any other required credentials.

That’s all the Simba Athena configuration I’m going to do here. For full details on all of Simba Athena’s features, please refer to the Simba Athena Documentation.

Now let’s use Simba Athena to get Athena and Power BI talking to each other!

Using Simba Athena

The Athena documentation has a great section about using the Athena Power BI connector. After launching Power BI and selecting Amazon Athena as a data source, Power BI will need to know which DSN to use.

This is the Simba Athena DSN in the System DSN tab:

The Navigator screen then shows my Athena data catalog, my blog_amazonwebshark database, and my basic_itunes_python_etl table with a sample of the data it contains:

That’s everything! My basic_itunes_python_etl Athena table is now available in Power BI.

Summary

In this post, I used Simba Athena to create a secure connection between my iTunes data in Amazon Athena and Microsoft Power BI.

This post was originally part of a larger post that is still being written. But after I’d finished my Simba Athena section it made sense to have a separate post for it!

Finally, in other news, this post’s featured image is a DALL·E 2 creation. This was by far the best image it gave me for pixel art baby lion and shark – I’m sure it’ll improve soon!

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

Thanks for reading ~~^~~