Google Search For . (Period)

For today, I wanted to record a quick observation I had while Googling. It’s also a reminder that choosing the correct search terms can drastically change what Google returns to you.

If I Google for the period symbol (.), I get back results for the phrase “full stop punctuation.” I know this because the words “full stop punctuation” are bolded in the returned Google page. Here’s a screenshot in case that changes:

Note that the links aren’t terribly interesting – I don’t see any links to punctuation or style guides, just pages with the words “full stop punctuation.”

Now interestingly, if I search for the words “period punctuation”, I get back a small context box explaining to me what a period is used for in writing, as well as a list of punctuation and writing guides:

The results for a Google search for “period punctuation.”

As you can see, a minor change in search terms dramatically changes what you get, even if both terms mean largely the same thing.

UniSuper and Google Cloud Platform

I know a lot of enterprise cloud customers have been watching the recent incident with Google Cloud (GCP) and UniSuper. For those of you who haven’t seen it: UniSuper is an Australian pension fund firm which had their services hosted on Google Cloud. For some weird reason, their private cloud project was completely deleted. Google’s postmortem of the project is here: . Fascinating reading – in particular what surprises me is that GCP takes full blame for the incident. There must be some very interesting calls occurring with Google and their other enterprise customers.

There’s some fascinating morsels to consider in Google’s postmortem of the incident. Consider this passage:

Data backups that were stored in Google Cloud Storage in the same region were not impacted by the deletion, and, along with third party backup software, were instrumental in aiding the rapid restoration.

Fortunately for UniSuper, the data in Google Cloud Storage didn’t seem to be affected and they were able to restore from there. But it looks like UniSuper also had a another set of data stored with another cloud. The following is from UniSuper’s explanation of the event at: .

UniSuper had backups in place with an additional service provider. These backups have minimised data loss, and significantly improved the ability of UniSuper and Google Cloud to complete the restoration.

Having a full set of backups with another service provider has to be terrifically expensive. I’d be curious to see a discussion of who the additional service provider is and a discussion of the costs. I also wonder if the backup cloud is live-synced with the GCP servers or if there’s a daily/weekly sync of the data to help reduce costs.

The GCP statement seems to say that the restoration was completed with just the data from Google Cloud Storage, while the UniSuper statement is a bit more ambiguous – you could read the statement as either (1) the offsite data was used to complete the restoration or (2) the offsite data was useful but not vital to the restoration effort.

Interestingly, a HN comment indicates that the Australian financial regulator requires this multi-cloud strategy: .

I did a quick dive to figure out where these requirements are coming from, and from the best that I could tell, these requirements come from the APRA’s Prudential Standard CPS 230 – Operational Risk Management document. Here’s some interesting lines from there:

  1. An APRA-regulated entity must, to the extent practicable, prevent disruption to
    critical operations, adapt processes and systems to continue to operate within
    tolerance levels in the event of a disruption and return to normal operations
    promptly once a disruption is over.
  2. An APRA-regulated entity must not rely on a service provider unless it can ensure that in doing so it can continue to meet its prudential obligations in full and effectively manage the associated risks.
Australian Prudential Regulation Authority (APRA) – Prudential Standard CPS 230 Operational Risk Management

I think the “rely on a service provider” is the most interesting text here. I wonder if – by keeping a set of data on another cloud provider – UniSuper can justify to the APRA that it’s not relying on any single cloud provider but instead has diversified its risks.

I couldn’t find any discussion about the maximum amount of downtime allowed, so I’m not sure where the “4 week” tolerance from the HN comment came from. Most likely that is from industry norms. But I did find some text about tolerance levels of disruptive events:

  1. 38. For each critical operation, an APRA-regulated entity must establish tolerance levels for:
    (a) the maximum period of time the entity would tolerate a disruption to the
Australian Prudential Regulation Authority (APRA) – Prudential Standard CPS 230 Operational Risk Management

It’s definitely interesting to see how requirements for enterprise cloud customers grow from their regulators and other interested parties. There’s often some justification underlying every decision (such as duplicating data across clouds) no matter how strange it seems at first.

APRA History On The Cloud

While digging into this subject, I found it quite interesting to trace how the APRA changed its tune about cloud computing over the years. As recently as 2010, the APRA felt the need to, “emphasise the need for proper risk and governance processes for all outsourcing and offshoring arrangements.” Here’s an interesting excerpt from their 2010 letter sent to all APRA-overseen financial companies:

Although the use of cloud computing is not yet widespread in the financial services industry, several APRA-regulated institutions are considering, or already utilising, selected cloud computing based services. Examples of such services include mail (and instant messaging), scheduling (calendar), collaboration (including workflow) applications and CRM solutions. While these applications may seem innocuous, the reality is that they may form an integral part of an institution’s core business processes, including both approval and decision-making, and can be material and critical to the ongoing operations of the institution.
APRA has noted that its regulated institutions do not always recognise the significance of cloud computing initiatives and fail to acknowledge the outsourcing and/or offshoring elements in them. As a consequence, the initiatives are not being subjected to the usual rigour of existing outsourcing and risk management frameworks, and the board and senior management are not fully informed and engaged.

While the letter itself seems rather innocuous, it seems to have had a bit of a chilling effect on Australian banks: this article comments that, “no customers in the finance or government sector were willing to speak on the record for fear of drawing undue attention by regulators“.

An APRA document published on July 6, 2015 seems to be even more critical of the cloud. Here’s a very interesting quote from page 6:

In light of weaknesses in arrangements observed by APRA, it is not readily evident that risk management and mitigation techniques for public cloud arrangements have reached a level of maturity commensurate with usages having an extreme impact if disrupted. Extreme impacts can be financial and/or reputational, potentially threatening the ongoing ability of the APRA-regulated entity to meet its obligations.

Then just three years later, the APRA seems to be much more friendly to cloud computing. A ComputerWorld article entitled “Banking regulator warms to cloud computing” published on September 24, 2018 quotes the APRA chair as acknowledging, “advancements in the safety and security in using the cloud, as well as the increased appetite for doing so, especially among new and aspiring entities that want to take a cloud-first approach to data storage and management.

It’s curious to see the evolution of how organizations consider the cloud. I think UniSuper/GCP’s quick restoration of their cloud projects will result in a much more friendly environment toward the cloud.

How To Waste AdWords Budget: Postie Plugin Edition

Some time ago I was looking for ways to send in posts to my WordPress blog via email, and I found a reference to a WordPress plugin called “Postie.” So I popped that into Google search and what did I get?

The correct answer to this search would be the Postie WordPress plugin hosted here. But apparently there is another company named Postie which manages enterprise mail (hosted at which is a completely separate entity to the WordPress plugin (hosted at As you can see from the screenshot, my search resulted in an ad for the enterprise company.

But I have no interest in enterprise mail. That ad is effectively wasted. Worse yet, the CTR (clickthrough rate, the number of times the ad is clicked on divided by the number of times the ad is shown) of the ad goes down through no fault of the ad itself. But you can see why the ad was shown – the ad’s creator placed ads on the word “postie” and didn’t realize there might be other organizations with the same name.

This is a good example of where negative keywords are used. In short negative keywords are used to find searches to NOT show ads to. In this case, Postie (the enterprise company) should have used negative keywords to exclude the word “plugin” so they’re not confused with Postie Plugin (the WordPress plugin).

Google SEO Update On March 2024: Up 314%

If you’re interested in search optimization, you’ll know about Google’s new search update that released in March 2024. Per Google, the search update is intended to weed out low effort sites, sites with a ton of AI content, affiliate review sites, and so forth. A good outline of what went on in this update is here.

In short, a lot of chaos occurred. Major publications are reporting pretty severe drops in traffic; smaller sites are reporting traffic drops of greater than 90%. Here’s a fun quote:

BBC News, for example, was among the sites that saw the biggest percentage drops, with its site losing 37% of its search visibility having fallen from 24.7 to 15.4 points in a little over six weeks. Its relative decline was second only to Canada-based entertainment site, Screenrant which saw its visibility fall by 40% from 27.6 to 16.7.

There’s a lot of doom and gloom about this update, but I’m really liking it. I’m seeing a lot of very interesting stuff float up on my Google searches that normally would be buried. In particular I’m seeing fewer “top 10 XYZ” type webpages and more links to opinion websites such as Reddit and other forums.

And then there’s this: one of my websites is reporting 314% more clicks from Google search.

I run a small blog (not this one) which is basically a tumblelog-style fan blog for a specific consumer-goods company. It really doesn’t do much except repost funny pictures and interesting articles. The blog typically gets about 100 clicks a month from Google search – which never ceases to amaze me, especially since the site itself is so simple.

With that in mind, I was shocked to suddenly see a burst of emails over the past month congratulating me over a sudden rise in traffic:

A sample of the emails:

What on earth is going on? A quick view of my search console shows the truth:

I’m not making any larger point here, it’s just interesting to see how fast things can change during a search core update.

Task Queue Fun: DeadlineExceeded

I always love pointing out fun errors – where I define “fun error” as an error that is intermittent/happens rarely in the context of regular operation in the application. Those errors are always the most fun to fix.

Today I was poking through some of my toy applications running on Google Cloud when I saw this:

And the text only:

_InactiveRpcError: <_InactiveRpcError of RPC that terminated with:
._end_unary_response_blocking ( /layers/google.python.pip/pip/lib/python3.7/site-packages/grpc/ )	-	Jan 23, 2024	22 hours ago	-

DeadlineExceeded: 504 Deadline Exceeded
.error_remapped_callable ( /layers/google.python.pip/pip/lib/python3.7/site-packages/google/api_core/ )	-	Jan 23, 2024	22 hours ago

Hmm – so an error occurred 22 hours ago, that last reoccurred almost 4 months ago (Jan 23, 2024). Doesn’t sound very important. But just for the laughs, let’s dig in.

Of the two errors, I know that the first one (InactiveRPCError) is most likely due to a connection being unable to complete. Not a giant problem, happens all the time in the cloud – servers get rebooted, VMs get shuffled off to another machine, etc. Not a serious problem. The deadline exceeded one concerns me though because I know this application connects to a bunch of different APIs and does a ton of time consuming operations, and I want to make sure that everything is back to normal.

So here’s the view of the error page:

So I know that the error is somewhere communicating with Google services since the error pops up in the google api core library. Let’s hop on over to logging and find the stack trace – I’ve redacted a line that doesn’t mean anything to the purpose of this post:

Traceback (most recent call last):
  File "/layers/google.python.pip/pip/lib/python3.7/site-packages/flask/", line 2070, in wsgi_app
    response = self.full_dispatch_request()
  File "/layers/google.python.pip/pip/lib/python3.7/site-packages/flask/", line 1515, in full_dispatch_request
    rv = self.handle_user_exception(e)
  File "/layers/google.python.pip/pip/lib/python3.7/site-packages/flask/", line 1513, in full_dispatch_request
    rv = self.dispatch_request()
  File "/layers/google.python.pip/pip/lib/python3.7/site-packages/flask/", line 1499, in dispatch_request
    return self.ensure_sync(self.view_functions[rule.endpoint])(**req.view_args)
  File "/srv/", line 331, in launch_task
    task_creation_results = client.create_task(parent=queue_prop, task=task)
  File "/layers/google.python.pip/pip/lib/python3.7/site-packages/google/cloud/tasks_v2/services/cloud_tasks/", line 2203, in create_task
  File "/layers/google.python.pip/pip/lib/python3.7/site-packages/google/api_core/gapic_v1/", line 131, in __call__
    return wrapped_func(*args, **kwargs)
  File "/layers/google.python.pip/pip/lib/python3.7/site-packages/google/api_core/", line 120, in func_with_timeout
    return func(*args, **kwargs)
  File "/layers/google.python.pip/pip/lib/python3.7/site-packages/google/api_core/", line 81, in error_remapped_callable
    raise exceptions.from_grpc_error(exc) from exc
google.api_core.exceptions.DeadlineExceeded: 504 Deadline Exceeded

If you missed the culprit in the above text, let me help you out: the call to the Google Task Queue service on line 331 of my application ended up exceeding Google’s deadline, and threw up the exception from Google’s end. Perhaps there was a transient infrastructure issue, perhaps task queue was under maintenance, perhaps it was just bad luck.

File "/srv/", line 331, in launch_task
    task_creation_results = client.create_task(parent=queue_prop, task=task)

There’s really nothing to be done here, other than maybe catching the exception and trying again. I will point out that the task queue service is surprisingly resilient: out of tens/hundreds of thousands of task queue calls over the past 5 months that this application has performed, only 2 tasks (one in January 2024, one yesterday) have failed to enqueue. More importantly, my code is functioning as intended and I can mark this issue as Resolved or at least Muted.

Now honestly, this is a my bad sort of situation. If this was a real production app there should be something catching the exception. But since this is a toy application, I absolutely am fine tolerating the random and thankfully very rare failures in task queue.

Coding Fun: Vigenere Cipher Encryption/Decryption

I was working on some fun LeetCode type questions, and I came across a challenge to replicate the Vigenere cipher encryption and decryption in Python.

In short, the Vigenere cipher allows one to encrypt and decrypt a message if the user knows an alphabetic key. It’s notable for being easy to use; encryption and decryption are done by overlaying the key next to the message, then shifting the message letter by the letter number of the overlaid key. For more information, see the Wikipedia page discussing the Vigenere cipher .

The below functions are the “know-your-number-theory” / expected versions of how to encrypt/decrypt, where c is the encrypted message to decrypt, m is the unencrypted text to encrypt, and keyword is the secret encoding key.

def vigenere_decrypt_cipher(c: str, keyword: str) -> str:
    keyword_repeated = (keyword * (len(c) // len(keyword))) + keyword[:len(c) % len(keyword)]
    plaintext = ''
    for i in range(len(c)):
        if c[i].isalpha():
            shift = ord(keyword_repeated[i].upper()) - ord('A')
            if c[i].islower():
                plaintext += chr((ord(c[i]) - ord('a') - shift) % 26 + ord('a'))
                plaintext += chr((ord(c[i]) - ord('A') - shift) % 26 + ord('A'))
            plaintext += c[i]
    return plaintext

def vigenere_encrypt_message(m: str, keyword: str) -> str:
    #filter to kick out spaces and punctuation
    filtered_m = ""
    for toon in m:
        if toon.isalpha():
            filtered_m = filtered_m + toon
    #the rest to process the "real" stuff
    m = filtered_m.upper()
    keyword = keyword.upper()
    encrypted_message = ''
    keyword_repeated = (keyword * (len(m) // len(keyword))) + keyword[:len(m) % len(keyword)]
    for i in range(len(m)):
        char = m[i]
        if char.isalpha():
            shift = ord(keyword_repeated[i].upper()) - ord('A')
            if char.islower():
                encrypted_message += chr((ord(char) - ord('a') + shift) % 26 + ord('a'))
                encrypted_message += chr((ord(char) - ord('A') + shift) % 26 + ord('A'))
            encrypted_message += char
    return encrypted_message.upper()

Honestly, while it was fun to implement, it’s not immediately obvious how Vigenere’s works from the code. So for fun I wrote the functions below, which sort of mimics how Vigenere messages would be coded/decoded by hand:

def look_up_letter_index(letter):
    alphabet = "abcdefghijklmnopqrstuvwxyz".upper()
    return alphabet.find(letter.upper())

def decrypt_vignere(encrypted, key):
    translated = ""
    alphabet = "abcdefghijklmnopqrstuvwxyz".upper()
    count = 0
    alphabet_array = []
    for letter in alphabet:
        single_line = alphabet[count:26] + alphabet[0:count]
        count = count + 1
    overlaid_key = key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    encrypted_count = 0
    for encrypted_letter in encrypted:
        print("encrypted letter" + encrypted_letter)
        print("keyoverlaid letter" + overlaid_key[encrypted_count:encrypted_count + 1])
        encrypted_index = look_up_letter_index(encrypted_letter)
        overlaidkey_index = look_up_letter_index(overlaid_key[encrypted_count:encrypted_count + 1])
        encrypted_count = encrypted_count + 1
        #loop through alphabet array
        single_alphabet_index = 0
        for single_alphabet in alphabet_array:
            single_alphabet_letter_test = single_alphabet[overlaidkey_index:overlaidkey_index + 1]
            if single_alphabet_letter_test == encrypted_letter:
                print(alphabet[single_alphabet_index:single_alphabet_index + 1])
                translated += alphabet[single_alphabet_index:single_alphabet_index + 1]
            single_alphabet_index = single_alphabet_index + 1
    return translated

def encrypt_vignere(message, key):
    alphabet = "abcdefghijklmnopqrstuvwxyz".upper()
    count = 0
    alphabet_array = []
    for letter in alphabet:
        single_line = alphabet[count:26] + alphabet[0:count]
        count = count + 1
    overlaid_key = key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    overlaid_key += key + key + key + key + key + key + key + key + key + key + key + key + key + key
    message_count = 0
    encrypted_message = ""
    for message_letter in message:
        overlaid_key_letter = overlaid_key[message_count:message_count + 1]
        message_letter_index = look_up_letter_index(message_letter)
        overlaid_key_letter_index = look_up_letter_index(overlaid_key_letter)
        translated_letter = alphabet_array[overlaid_key_letter_index][message_letter_index:message_letter_index + 1]
        encrypted_message += translated_letter
        message_count = message_count + 1
    return encrypted_message

print(encrypt_vignere("MESSAGE", "SECRETKEY"))

print(decrypt_vignere("EIUJEZO", "SECRETKEY"))

While these functions are much longer, I think they’re much more readable than the previous set of functions. These versions generate a matrix of 26×26 alphabets; the first row is a to z, the second row is shifted 1 to the right (b to z then a), the third row is shifted 2 to the right (c to z then ab), etc. Then we overlay the secret key and use it along with the message/encrypted message to encrypt/decrypt by finding the appropriate entry in our matrix. Admittedly the code is a little ugly and could be cleaned up, but I thought it would be fun to share.

The database default does not exist for project…

I opened up a new Google Cloud project to act as a staging project and forgot to set up a Firestore database for the project, and got the following error:

google.api_core.exceptions.NotFound: 404 The database default does not exist for project magicalnews Please visit[projectname] to add a Cloud Datastore or Cloud Firestore database

Google Cloud Logging

Obviously to fix this you need to create a Cloud Firestore or Cloud Datastore, but I will say I love the detailed error message and the direct link to fix the problem. I’m noticing a lot of small developer experience fixes and filing down of “sharp edges” – I hope they continue because the developer experience on Google Cloud is just getting better and better.

Python: Changing Timezones In A DateTime

Here’s some quick code samples for shifting a UTC datetime object (created_at is a datetime.utcnow()) to a different timezone. In this first example, we use timedelta to add/remove hours to find the current time at UTC-6:00.

local_time = created_at + timedelta(hours=-6)
final_time =  datetime.strftime(local_time, '%Y-%m-%d %I:%M:%S %p')

In this sample, a datetime (created_at) is declared to be a UTC time, then converted into US/Chicago time and formatted for human presentation:

local_datetime = pytz.timezone('UTC').localize(created_at).astimezone(pytz.timezone('US/Central'))
local_datetime_str = "Created (User Local Time): " +  str(datetime.strftime(local_datetime, '%Y-%m-%d %I:%M:%S %p'))

How To Internet Market: YouTube, Santa, and Canadian Airspace

Merry Christmas and happy holidays to all!

There are a lot of ways to associate your product with a holiday, and if you can successfully do that, the holiday can drive huge amounts of sales. Examples include Elf on a Shelf, eating KFC on Christmas (in Japan, it’s a widespread tradition to eat KFC fried chicken on Christmas), and the Disney parade on Christmas.

But my favorite example of Internet marketing over Christmas is NORAD Tracks Santa, located at NORAD stands for North American Aerospace Defense Command – it’s a joint military command between American and Canadian militaries to protect the skies over both countries. Every year, the website above tracks Santa as he goes around the world delivering presents.

Now you may say: wait a minute, NORAD isn’t selling a product or service, this isn’t an example of marketing. Marketing is far more than just selling a product or service; it also includes burnishing a brand, or building greater awareness of an organization. In this case, I’m using marketing in the context of how NORAD uses NORAD Tracks Santa to build greater public awareness of its mission, and to burnish its reputation. That last part – burnishing reputation – can be helpful for government agencies, especially when asking for funding from Congress.

The NORAD Tracks Santa website is really neat – if you look at it Christmas Eve night, you see an animation of Santa flying over a world map (the world map is provided by Microsoft Bing). Here’s an example screenshot:

A screenshot of the NORAD Tracks Santa page on Christmas Eve.

The reason I love NORAD Tracks Santa as a great example of Internet marketing is how it seamlessly blends marketing, education, and the holidays in one package. For instance, look at this video from the NORAD Tracks Santa page:

A screenshot from one of the Santa-tracking videos on NORAD Tracks Santa. The video embedded on the page is hosted by YouTube. Click on the picture to go to the full video.

The YouTube video embedded on the page goes to here: – go ahead and watch it. Pay close attention to what it says and more importantly, what it does not say.

Here’s a transcript of the video’s narrator if you can’t watch the video:

NORAD is receiving reports that Santa’s sleigh is moving north toward Canadian airspace from the Mid-Atlantic. CF-18 Hornets from the Royal Canadian Air Force are escorting Santa through Canadian airspace. As part of Operation Noble Eagle – NORAD’s mission to safeguard North American skies – CF-18s maintain a constant state of alert, ready to respond immediately to potential threats to the homelands. Santa and his reindeer certainly pose no threat but he can rest easy knowing that the NORAD team has the watch ensuring safe travels across North America.

NORAD Tracks Santa, NTS Santa CAM – Canadian Air Force

Consider how well the marketing is done here. There’s a education element at play (explaining Operation Noble Eagle), a marketing element (associating NORAD with the holidays, which is a positive association) and the entertainment element of watching Santa be escorted by fighter jets.

But also consider what is not said in the video and merely implied. The viewer sees the fighter jets smoothly move into an escort position, implying experience and professionalism in regards to the fighter pilots and the NORAD organization as a whole. The viewer sees the fighters soar across mountainous and ice-covered lands, implying the hard and difficult job of the organization.

Let’s try another example – here is a video of NORAD tracking Santa through Massachusetts:

A screenshot of NORAD Tracks Santa. The video is embedded from YouTube and covers how NORAD tracks Santa through the Massachusetts area. Click the picture to see the full video on YouTube. The red dot at the center of the yellow beam is not a tracking target; it’s Rudolph the Reindeer’s lighted red nose.

The above screenshot embeds the following video, which tracks Santa as he passes over the Cape Cod Air Force Station: . I recommend watching it, but here’s a transcript if you can’t:

NORAD was notified by Air Force Space Command that their PAVE phased-array warning system – early warning radar known as PAVE PAWS at Cape Cod Air Force Station Massachusetts – is tracking Santa on his way from the US to South America. This radar is not only capable of detecting ballistic missile attacks and conducting general Space Surveillance and satellite tracking, but at this time of year the PAVE PAWS station keeps an eye on Santa as he flies over the Atlantic toward the Western Hemisphere.

NTS Santa Cam English Ground Station at Cape Cod

Again, note the educational aspects of the video (what PAVE PAWS stands for and what it does), the marketing aspects of the video (associating NORAD and the Air Force with the holiday season) and the entertainment element of watching Santa.

But again consider what is not said. The video implies professionalism (someone is manning the station at night on a holiday) and security (someone is on the watch for possible threats).

The Takeaway

NORAD Tracks Santa is a masterpiece of marketing done right. Consider adding similar elements to your online marketing strategy, such as a simple game, amusing videos, and educational content discussing your organization’s mission.

Correcting A SQLite Code Example

I’ve been experimenting with filtering and manipulating a large amount of data within a Google Cloud Function. I decided to use an in-memory SQLite database to help manage all the data, so I googled up some code samples. This page came up with some helpful Python code samples.

Unfortunately when I tried to run the sample code, Cloud Functions popped an error. The sample code uses Python 2-style print as a statement instead of as a function call – i.e. the print call is missing the parentheses needed to make it a correct function call. Here’s a sample screenshot:

I’ve placed red arrows next to the erroneous print statements. If you paste this code into Google Cloud Functions, it won’t work because print needs to be a function call, (with parentheses) instead of a statement (missing parentheses). Credit:

Below is a fixed version of the code in the linked page. You can paste it directly into the Google Cloud Functions editor and it’ll work: it sets up an in-memory database, creates a table, adds data, then queries data out of it.

import sqlite3

def hello_world(request):
    """Responds to any HTTP request.
        request (flask.Request): HTTP request object.
        The response text or any set of values that can be turned into a
        Response object using
        `make_response <>`.
    conn = sqlite3.connect(":memory:")
    conn.execute('''CREATE TABLE COMPANY
         NAME           TEXT    NOT NULL,
         AGE            INT     NOT NULL,
         ADDRESS        CHAR(50),
         SALARY         REAL);''')
        VALUES (1, 'Paul', 32, 'California', 20000.00 )");
        VALUES (2, 'Allen', 25, 'Texas', 15000.00 )");
        VALUES (3, 'Teddy', 23, 'Norway', 20000.00 )");
        VALUES (4, 'Mark', 25, 'Rich-Mond ', 65000.00 )");
    print("Records created successfully");
    cursor = conn.execute("SELECT id, name, address, salary from COMPANY")
    for row in cursor:
        print("ID = ", row[0])
        print("NAME = ", row[1])
        print("ADDRESS = ", row[2])
        print("SALARY = ", row[3], "\n")
    request_json = request.get_json()
    if request.args and 'message' in request.args:
        return request.args.get('message')
    elif request_json and 'message' in request_json:
        return request_json['message']
        return f'Hello World!'

Use this code as a starting point to build your own cloud functions and work with data.

I’m pleasantly surprised at how fast SQLite runs within a cloud function – I was worried that the function would run out of memory quickly, but I’ve been manipulating thousands of rows comfortably within a 512MB RAM function.