As your application user base grows, your Heroku dynos will need to scale to keep up with the increased demands. Scaling your dynos at the right time can be a challenging task that involves various considerations.
This article will explore two topics that can help you streamline this process. First, you will learn how to automate scaling of Heroku dynos based on the time of day.
Second, you will examine the assumptions and considerations you need to make when scaling your Heroku dynos.
Automating Scaling of Heroku Dynos based on Time of Day
When running an application, it is not always feasible to have a human manually monitor and adjust the dynos’ scaling. Automating your dyno scaling can save you time and reduce the likelihood of human error.
To automate the scaling of your dynos, we recommend using the APScheduler library. Here is how you can install and use APScheduler:
Installing APScheduler
Installing the APScheduler library in your Python virtual environment is simple. You can use pip, the default package installer for Python, to do this.
In your command line, type:
“`
pip install apscheduler
“`
This will install the latest version of APScheduler in your virtual environment. Now that you have installed APScheduler, let us see how you can use it to automate your dyno scaling.
Scaling Tasks
A task is a function that performs a specific action. We will create a task to handle the scaling of our dynos.
Here is a sample code that scales your dynos at a specific time every day using the Heroku API and requests library:
“`
import os
import requests
from apscheduler.schedulers.blocking import BlockingScheduler
def scale_dynos():
app_name = os.environ[‘APP_NAME’]
api_key = os.environ[‘HEROKU_API_KEY’]
headers = {
“Authorization”: f”Bearer {api_key}”,
“Accept”: “application/vnd.heroku+json; version=3”,
“Content-Type”: “application/json”
}
payload = {
“quantity”: 2 #change the number of dynos you want to scale to for your app
}
url = f”https://api.heroku.com/apps/{app_name}/formation/web”
response = requests.patch(url, headers=headers, json=payload)
if response.status_code != 200:
raise Exception(f”Failed to scale dynos: {response.content}”)
scheduler = BlockingScheduler()
scheduler.add_job(scale_dynos, ‘cron’, hour=’12’, minute=’00’) #adjust the time based on your requirements
scheduler.start()
“`
This code uses the blocking scheduler to execute the `scale_dynos()` function every day at 12:00 PM UTC. The `os.environ` calls retrieve the environment variables needed to authenticate with the Heroku API.
The function then sends a PATCH request to the Heroku Formation API to scale the dynos to a predefined quantity.
Fail-Safe
While automating your dyno scaling can save time and reduce human error, it can also lead to unexpected downtime if not done correctly. To avoid such a scenario, implement a fail-safe mechanism that stops the scaling if an error occurs.
Here is the updated `scale_dynos()` function that includes a fail-safe mechanism:
“`
def scale_dynos():
app_name = os.environ[‘APP_NAME’]
api_key = os.environ[‘HEROKU_API_KEY’]
headers = {
“Authorization”: f”Bearer {api_key}”,
“Accept”: “application/vnd.heroku+json; version=3”,
“Content-Type”: “application/json”
}
payload = {
“quantity”: 2 #change the number of dynos you want to scale to for your app
}
url = f”https://api.heroku.com/apps/{app_name}/formation/web”
response = requests.patch(url, headers=headers, json=payload)
if response.status_code != 200:
raise Exception(f”Failed to scale dynos: {response.content}”)
else:
print(f”Scaled dynos to {payload[‘quantity’]}”)
“`
The updated function includes an `else` statement that prints a confirmation message to the console. This statement helps to signal that the scaling was successful if an error does not occur.
Assumptions and Considerations for Scaling Heroku Dynos
When scaling your Heroku dynos, several assumptions and considerations can affect your decision. Let us explore some of these factors.
Time-of-day Considerations
The time of day when your application sees the highest traffic should influence your scaling decisions. If you know that your application receives the most traffic between 2:00 PM and 6:00 PM every weekday, you can adjust your scaling to match that traffic pattern.
Automating your scaling at these specific times further streamlines the process.
Cost-Savings Analysis
Scaling your dynos can significantly increase your expenses. You should consider implementing cost-saving measures such as scaling down dynos during off-peak hours to reduce your overall cloud compute costs.
It is better to have more dynos available during high traffic periods and fewer dynos during periods of low activity.
Holiday and Weekend Considerations
Finally, you should also consider holidays and weekends when adjusting your dyno scaling. Holidays and weekends often have less traffic than weekdays.
Hence, it may be best to scale down your dynos during such periods to save on cloud compute costs.
Conclusion
The article has explored how to automate scaling of Heroku dynos based on the time of day, considerations, and assumptions to make when scaling dynos. With the right approach to scaling, you can ensure that your application is always available when needed while keeping your cloud compute costs manageable.
By evaluating your application traffic, implementing cost-saving measures such as scaling down dynos during low-traffic periods, and using tools like APScheduler, you can ensure seamless scaling over time.
3) APScheduler and Heroku Platform API
When running an application on Heroku, you may need to automate various tasks, such as scaling your dynos. APScheduler is a powerful library that enables you to schedule these tasks with precision.
Additionally, using the Heroku Platform API, you can send requests to the Heroku platform to control and manipulate your application’s resources, such as scaling your dynos. In this section, we will explore how to use APScheduler and the Heroku Platform API together to scale your dynos efficiently.
Installing APScheduler
To use APScheduler, you will need to install it in your Python environment. To install it using pip, run the following command in your command line:
“`
pip install apscheduler
“`
With APScheduler installed and ready to use, let us look at how you can use it with the Heroku Platform API.
Using Heroku Platform API
To use the Heroku Platform API, you will first need to obtain an API key. You can generate an API key by navigating to your Heroku profile and selecting the “API Key” option.
Once you have your API key, you must authenticate each request you make to the Heroku platform by passing your API key in the header of each request. Here is a sample code that captures how to use the Heroku Platform API to scale your dynos using the requests library:
“`
import os
import requests
app_name = os.environ[‘APP_NAME’]
api_key = os.environ[‘HEROKU_API_KEY’]
headers = {
“Authorization”: f”Bearer {api_key}”,
“Accept”: “application/vnd.heroku+json; version=3”,
“Content-Type”: “application/json”
}
payload = {
“quantity”: 2 #change the number of dynos you want to scale to for your app
}
url = f”https://api.heroku.com/apps/{app_name}/formation/web”
response = requests.patch(url, headers=headers, json=payload)
“`
The above code uses the `os.environ` function to retrieve the environment variables required for authentication with the Heroku Platform API. It then constructs the `headers` and `payload` objects necessary for making the PATCH request that scales your dynos.
The last line of the code sends the PATCH request to the Heroku API to update your dynos’ quantity.
4) Scaling Dynos with APScheduler
In this section, we will explore how to use APScheduler to automate the scaling of your Heroku dynos. Specifically, we will cover the installation of APScheduler, scheduling tasks, and working with the Heroku Platform API.
Installing APScheduler
To use APScheduler, you need to install it in your Python virtual environment. Follow these steps to do so:
1.
Ensure you have a virtual environment set up with Python 3 installed. 2.
Open your command prompt of choice. 3.
Run this command:
“`
pip install apscheduler
“`
Now that you have installed APScheduler let us schedule tasks.
Scheduling Tasks with APScheduler
To schedule tasks with APScheduler, you will use the `add_job()` method. The `add_job()` method takes three arguments: the task to execute, the type of trigger, and the trigger’s arguments.
These arguments enable you to customize how and when your task runs. Here is a sample code to schedule a task that scales your dynos every day at 2:00 PM UTC:
“`
import os
import requests
from apscheduler.schedulers.blocking import BlockingScheduler
def scale_dynos():
app_name = os.environ[‘APP_NAME’]
api_key = os.environ[‘HEROKU_API_KEY’]
headers = {
“Authorization”: f”Bearer {api_key}”,
“Accept”: “application/vnd.heroku+json; version=3”,
“Content-Type”: “application/json”
}
payload = {
“quantity”: 4 #change the number of dynos you want to scale to for your app
}
url = f”https://api.heroku.com/apps/{app_name}/formation/web”
response = requests.patch(url, headers=headers, json=payload)
scheduler = BlockingScheduler()
scheduler.add_job(scale_dynos, ‘cron’, hour=14)
scheduler.start()
“`
This code defines the `scale_dynos()` function that scales your Dynos to four instances. The next step is to add a new job to the APScheduler instance to execute the task daily at a specified time.
We tell APScheduler to execute this task at 2:00 PM UTC every day by passing the `hour=14` parameter.
Conclusion
This expansion of the article has covered how to use APScheduler and Heroku Platform API to automate dynamic scaling of Heroku Dynos specifically. With this, you can scale your dynos to better meet your application’s demands efficiently.
With the right approach to scaling, you can ensure that your application is always available when needed while keeping your cloud compute costs manageable. 5)
Fail-Safe Method for Scaling Dynos
While automating your dyno scaling can save time and reduce human error, unexpected downtime can still occur.
To avoid such a scenario, you should implement a fail-safe mechanism that stops the scaling process if an error occurs. In this section, we will explore a fail-safe method for scaling dynos on Heroku.
Determining Number of Attached Dynos
The first step in implementing a fail-safe method for scaling dynos is to determine the current number of attached dynos. You can achieve this by making a GET request to the Heroku Platform API, as shown below:
“`
import os
import requests
app_name = os.environ[‘APP_NAME’]
api_key = os.environ[‘HEROKU_API_KEY’]
headers = {
“Authorization”: f”Bearer {api_key}”,
“Accept”: “application/vnd.heroku+json; version=3”,
“Content-Type”: “application/json”
}
url = f”https://api.heroku.com/apps/{app_name}/dynos”
response = requests.get(url, headers=headers)
dynos_count = len(response.json())
print(f”Current number of dynos: {dynos_count}”)
“`
The above code captures the current number of dynos attached to your application. Pinging Application for
Fail-Safe Method
The next step in implementing a fail-safe method is to ping your application to check whether it is responsive before scaling.
Here is a sample code that can achieve this:
“`
import requests
def is_application_responsive(url):
response = requests.get(url)
if response.status_code == 200:
return True
return False
“`
With this method, you can check whether your application is responsive before scaling your dynos. If your application is unresponsive, you can halt the scaling process to avoid undesired results.
Scaling Dynos for
Fail-Safe Method
Now that you have mechanisms to retrieve the current number of attached dynos and to check whether your application is responsive, we can incorporate these into our scaling process. Here is an example of a fail-safe scaling method:
“`
import os
import requests
app_name = os.environ[‘APP_NAME’]
api_key = os.environ[‘HEROKU_API_KEY’]
headers = {
“Authorization”: f”Bearer {api_key}”,
“Accept”: “application/vnd.heroku+json; version=3”,
“Content-Type”: “application/json”
}
url = f”https://api.heroku.com/apps/{app_name}/dynos”
response = requests.get(url, headers=headers)
current_dynos_count = len(response.json())
print(f”Current number of dynos: {current_dynos_count}”)
application_url = os.environ[‘APPLICATION_URL’]
if is_application_responsive(application_url):
new_dynos_count = current_dynos_count + 1
payload = {
“quantity”: new_dynos_count
}
response = requests.patch(url, headers=headers, json=payload)
if response.status_code != 200:
raise Exception(f”Failed to scale dynos: {response.content}”)
else:
print(f”Scaled dynos to {new_dynos_count}”)
else:
print(“Application is unresponsive. Scaling is halted.”)
“`
The above code captures the current number of attached dynos using the Heroku Platform API.
We then check whether the application is responsive using the `is_application_responsive()` function. If the application is responsive, we increment the number of dynos and send a PATCH request to the Heroku Platform API to scale up your dynos.
6) Next Steps for Scaling Heroku Dynos
As your application continues to grow, it is essential to keep scaling your dynos. Here we explore some suggestions for the next steps that you can take to improve your dyno scaling.
Autoscaling In
Autoscaling In is an essential feature for dynamically allocating cloud resources based on actual demand.
Autoscaling In reduces the number of dynos in response to a decrease in the application’s demand.
This approach saves on unnecessary cloud compute costs.
Failure Notifications
While scaling your dynos is essential, it is also important to monitor your application’s health. You may experience failures in requests or other application functions when scaling up or down.
Failure notifications can alert you of such failures and prevent them from causing any significant problems.
Response Times for Scaling In
Scaling down takes more time than scaling up in Heroku. This delay is because the Heroku Platform API takes longer to start and scale down dynos.
Consider this delay while adjusting routine scaling schedule, and provide a buffer time. This buffer can help ensure that you have enough available dynos when needed.
Conclusion
This expansion has focused on implementing a fail-safe method when scaling dynos, next steps for scaling Heroku dynos, and included detailed explanations of the processes. Fail-safe mechanisms, such as retrieving the current quantity of dynos, pinging the application, and checking application responsiveness, forms steps for a robust scaling process.
Autoscaling In, failure notifications, and response times when scaling in are all next steps in improving the scaling process to ensure a seamless cloud computing experience. Through building on your initial implementation, you can keep your application highly available and efficient.
In conclusion, this article has provided valuable insights on automating and scaling Heroku dynos efficiently. We learned about using APScheduler and Heroku Platform API to automate the scaling process.
The article also presented a fail-safe method for scaling dynos through retrieving the current quantity of dynos, pinging the application, and checking application responsiveness. The article concluded with next steps for scaling Heroku dynos such as
Autoscaling In, failure notifications, and response times when scaling in were mentioned.
By implementing these techniques, you can ensure