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Azure Application Insights | Learn Application Insights


What is Azure Application Insights?

Application Insights is an Application Performance Management (APM) service for developers to monitor live applications. The anomalies in performance are automatically detected. It also includes powerful analytics tools that help in diagnosing issues. The insights help to understand how users are interacting with the application. With the Application Insights, developers can continuously improve performance and usability.

Application Insights works on applications built with various languages like .NET, Node.js, Java, and Python. The applications can be hosted on-premise or on the cloud, or hybrid. It can integrate with DevOps processes. It also integrates with Visual Studio App Center and can monitor telemetry from mobile apps.

All the data in the Application insights service can be exported to a database or any external tools. Application Insights SDKs are available for web services hosted in ASP.NET servers, Java EE, Azure. They are also available for web clients, desktop apps, mobile devices like Windows Phone, iOS, and Android.

How does it Work?

To monitor your application, all you have to do is enable the Application Insights from the Azure portal or install a small instrumentation package (SDK) in your application. The application will be monitored by this instrumentation package. It will use a unique GUID, which is also known as an Instrumentation Key, to direct the telemetry data to an Application Insights resource. 

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Since we install the instrumentation package in the application, it doesn’t have to be hosted on Azure. The application can run anywhere. We can instrument any background components of an application and the JavaScript in the web pages too. Application Insights can also collect telemetry data from Azure diagnostics, Docker logs, or performance counters when they are integrated into Azure Monitor. 

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What does the Application Insights Monitor?

Application Insights focuses on the performance of an application to ease the work of the development team. It monitors the following constraints,

  • Request rates, response times, and failure rates – It tells us which pages are being visited the most and at what times of the day.
  • Dependency rates, response times, and failure rates – It shows any external sources that might slow the application down.
  • Exceptions – It reports both server and browser exceptions. It gives an aggregate statistics of all the instances. We can further drill down to get statistics of individual instances.
  • It will also monitor the page views and load performance collected from the user’s browser.
  • It monitors AJAX calls from web pages, users, and session counts.
  • It will show the performance of memory, CPU, and network usage.
  • We can get host diagnostics from Docker or Azure.
  • We can correlate events with requests using the diagnostic trace logs of the application.
  • It also shows the custom events or metrics that the developer includes in the code.

Uses of Application Insights

Once we install Application Insights for an application, we can get the following benefits.

  • The load, responsiveness, and the performance of page loads, dependencies, AJAX calls can be known through an intuitive application dashboard.
  • We can identify the slowest requests and determine the requests that are failing often.
  • When a new release of an application is deployed, the statistics of it can be seen through a live stream.
  • If users are affected, we can get an alert so we can check how many users are being affected.
  • If there are any request failures, we can correlate them with the exceptions, dependency calls, and traces.
  • When a new feature of the app has to be deployed, we can measure the effectiveness of it.

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Limitations of Azure Application Insights

Like any other solution, Application Insights has some limitations.

  • If your code uses dynamic SQL, the Application Insights collects the full query into Azure, which might result in uploading sensitive data contained in the query.
  • The reports show up to the server and database level. But it cannot monitor individual SQL queries on how long they are being executed.
  • When you add Application Insights and deploy the application to Azure, it won’t collect the SQL queries unless a site extension is installed for it.
  • It cannot collect first chance exceptions.
  • It cannot show common exceptions across all applications.
  • If you are using ASP.NET for your application, Application Insights does not support asynchronous HttpClient calls.
  • There is no alert severity specified.
  • We cannot configure alerts to go to specific distribution lists based on severity.

Data collection, retention, and storage of Application Insights

When Azure Application Insights SDK is installed in your application, it starts sending telemetry data from your app to the cloud. Each SDK uses different techniques to collect telemetry data from different kinds of applications. You can also include custom telemetry to send your data. Azure runs some processes called availability tests to web applications regularly. The results from the test will be sent back to the Application Insights service.

You can test which data is being sent by the SDK. You can view the data in the output windows of the IDE and browser while testing the application. The data in the Application Insights service can retain up to 730 days. Users can set up a retention duration. The debug snapshots are stored for 15 days in the Application Insights service.

If the SDK is not able to reach the endpoint, the telemetry channels store the data in local storage temporarily by creating temp files. Once the issue is resolved, the new data, along with the persisted data, will be sent to Azure by the telemetry channel.

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Enable Application Insights for your Application

Create Application Insights Service

Navigate to the Azure portal at https://portal.azure.com/ and login to your account. Click on ‘+ New’ from the left side menu. Search for ‘Application Insights’ in the search bar. You can see the service in the search results. Click on it to open the service and click on ‘Create’. Give a name for your service, select your application type from the drop-down menu, and select your subscription. Choose ‘Create new’ for the ‘Resource Group’ field and give the same name that you gave for the service. Select a location and click on ‘Create’.

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Go to the newly created resource group and click on ‘app insights resource’. You will get the details of the resource. Copy the ‘Instrumentation Key’ from the page.

Add the Instrumentation key to the Application

Open Visual Studio and navigate to the appsettings.json file of your application. Add the below code in the file.

"Application Insights": {
"InstrumentationKey": "Your_instrumentation_key"
}

Replace the ‘Your_instrumentation_key’ with the one you copied before. It appears as a NuGet package. Go to the package.json file in your application, and you can see the Application Insights package added. You have successfully configured Application Insights to your application.

View the telemetry data

Launch the application from Visual Studio and play around with it. Stop the application. Right-click on the application, select ‘Application Insights’, and select the ‘Search Debug Session Telemetry’ option. You can see the telemetry data captured by your application. You can also see the details in Application Insights. Right-click on the application, select ‘Application Insights’, and select the ‘Open Application Insights Portal’ option.

The Application Insights portal opens up, and you can see the telemetry data collected from your application. You can drill down to see the page load metrics and more.

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Conclusion

Application Insights is a simple way for developers to detect and diagnose application performance issues of live applications. The SDKs vary for different applications and different platforms. Each SDK component sends different data. So choose one that is suitable for your application and install it. You can also include code in your application to send unhandled exceptions. The Azure Application Insights has a built-in map feature that can be used to identify the performance of dependencies.



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Python Serialization | A Complete Guide on Python Serialization


Python Serialization – Table of Content

Serialization in Python

Serialization in python is a process to serialize data in a species that is user-friendly, human-readable, and easily inspected. There are two very common python serialization libraries that serialize data objects in python. They are ‘HDF5’ and ‘Pickle’ which take dictionaries as well as Tensorflow models for storage purposes and transmission.

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Why Python Serialization?

The serialization process allows the python user to send, receive and save his data alongside maintaining the original structure also. The user finds it very useful to save a certain kind of data in the database so that he can reuse it later whenever it is needed. It can also be used to transmit data on a server network and the user can access it on any system later on.

The process of serialization is also very helpful for projects related to data science. For instance, the process of dataset preprocessing can be very time-consuming, hence preprocessing is done just once that too before saving the data on the disk. It is preferred that the user performs preprocessing each time he uses it. It also eliminates memory limitation problems for big data too which is heavy for loading in the memory as a single piece. So when the data is split into smaller chunks, the user is able to load every single chunk for preprocessing, and he can then save the outputs to the disk, removing all the data chunks from the memory.

Python Serialization: Text Based

The process of textual serialization means serializing the data in some specific format that is easy to understand, human-readable as well as easily inspected. Formats which are text-based are mainly language agnostic and they can be formed with the help of any language related to programming.

JSON is a standard format that is used to exchange data between servers and web clients. JSON is known to serialize the objects in a plain text file format and allow for easy visual identification to the user. JSON stores the objects in the form of key-value pairs, just like a dictionary in Python. JSON is a built-in library in python which makes it a breeze for the user to work with JSON. 

It is very easy to perform JSON serialization just like creating a JSON file and dumping the object. This is done with the help of the dump() method. This method has two arguments which are:  

  • The object user is serializing
  • File which will store the serialized object.

Python JSON has two main functions which it works with:

  • dump(): This function helps to convert a Python object into JSON format
  • Loads(): This function helps to convert the JSON string back into a Python object.

The table below will show the conversion of the python data type into a JSON type:

dict-object
List, tuple- array
str- String
True- true
Int, float- Number
False- false
None- null

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YAML

YAML is not a Markup Language but it is actually a parent set of JSON made in a way to be more comprehensible to the user. The most important and distinguishing feature of YAML is the capacity to create references for other objects in the same file. Another most important advantage is that it is possible to write comments in python. This feature has proved very useful to work with the configuration files also.

Python Serialization: Binary Formats

It is not possible for binary formats in serialization to be human-readable; however they are faster in general and also require much lesser space than text-based counterparts. Let us see some very popular binary formats below:

Pickle

It is a very popular format for python serialization. It is used to serialize almost all the Python object types. Pickle is considered to be an original serialization format used for Python, hence when a user plans to serialize objects in python that he expects to share and he must use with many other languages used for programming, he has to be mindful of the issues such as cross-compatibility. Similarly, pickle works in the same way for various Python versions. The user cannot unpickle a file present in the XXX version, which he picked in the python ZZZ version. So by doing such unnecessary changes, the execution of malicious code gets tough.

Let us see an example below and understand how pickling is performed in python:


import pickle
 
class example_class:
    x_number = 10
    x_string = "Welcome to the tutorial"
    x_list = [10, 20, 30]
    x_dict = {"Heya": "x", "How": 5, "you": [10, 20, 30]}
    x_tuple = (2, 3)
 
my_object = example_class()
 
my_pickled_object = pickle.dumps(my_object)  
print(f"This would be pickled object:\n{my_pickled_object}\n")
 
my_object.a_dict = None
 
my_unpickled_object = pickle.loads(my_pickled_object) 
print(
    f"The dictionary of unpickled object is:\n{my_unpickled_object.a_dict}\n")
 
 Output
This would be pickled object:
b'\x80\x04\x95!\x00\x00\x00\x00\x00\x00\x00\x8c\x08__main__\x94\x8c\rexample_class\x94\x93\x94)\x81\x94.'
 
Traceback (most recent call last):
  File "", line 19, in 
AttributeError: 'example_class' object has no attribute 'a_dict'
> 

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Module Interface for Pickling and Unpickling

The data format is always Python-specific for the pickle module. That is why it is always important to write the essentially required code when the user is performing the process of serialization or deserialization. dumps() is the Python function that is used to serialize an object hierarchy whereas loads() is the function that is used to de-serialize the same.

Pickle Protocols

Protocols in pickle act like the convention measures to deconstruct and construct the python objects. There are in total of 5 protocols that a user can use in pickling. Whenever a user uses a higher protocol version, he will need the latest version of Python to obtain the highly compatible as well as readable pickle.

Protocol version 0: This version is readable by humans. It is compatible to use with data and interfaces from the older python versions.
Protocol version 1: It is known to be an old binary format. Just like protocol version 0, it is also compatible with older python versions.
Protocol version 2: It came into effect during the release of python version 2.3. This version is well known for providing new styles in picking.
Protocol version 3: This version was discovered during the release of python version 3.0. It is famous for supporting byte objects however the major drawback with this version is it gets unpicked by python version 2.0
Protocol version 4: This version was discovered during the release of python version 3.4. This is able to support large objects and various different objects can be picked too. It is also famous for supporting data optimization.

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Numpy

It is a very popular python library used by the user to work with large and multidimensional arrays as well as matrices. It stands for numerical python. They are open source and free to use but slow to process. NumPy arrays can be stored in one continuous place in the memory; however this same is not possible for lists. Processes can therefore access as well as manipulate the arrays very efficiently.

Let us see an example below and understand how the Numpy library is used in python:


import numpy as np
arr = np.array( [[ 10, 20, 30],
[ 40, 20, 50]] )
 
print("The type of array is: ", type(arr))
 
print("The no of dimensions are: ", arr.ndim)
 
print("The shape of the array is: ", arr.shape)
 
print("The size of the array is: ", arr.size)
 
print("Array stores elements of the type: ", arr.dtype)
 
 Output
The type of array is:  <class 'numpy.ndarray'>
The no of dimensions are:  2
The shape of the array is:  (2, 3)
The size of the array is:  6
Array stores elements of the type:  int64
> 

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Conclusion

Serialization is a process that aims at simplifying the data storage methods for a data scientist. Serialization in Python is one of the most important features that ease the data conversion interface of the data. In this article, we have talked about why we need serialization. The serialization process allows the python user to send, receive and save his data alongside maintaining the original structure also. The user finds it very useful to save a certain kind of data in the database so that he can reuse it later whenever it is needed. 

We have also discussed JSON and YAML in python. Then we talked about binary formats of python serialization which are pickle and NumPy. In this sub-topic, we will also have a glance at module instances of pickling and unpickling along with pickle protocols. Now we will be discussing some frequently asked questions by the developers and will give solutions for them.

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