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ARKit & LiDAR: Building Point Clouds in Swift (part 1)

Python 3.12 is the most current stable release of the Python language, including a mix of language and standard library updates. The library switches focus on cleaning around deprecated APIs, convenience, and correctness. The distutils bundle has been removed from the standard library. Filesystem support in OS and pathlib has been improved, and a few modules now run faster. programming The language improvements are centered on simplicity of usage since several limitations on f-strings have been removed. The revised sort boundary linguistic structure and type explanation improve ergonomics for static kind checkers with nonexclusive sorts and type assumed names. Through each new version, Python grows, addressing developer needs and enhancing the general programming experience. This article assumes some familiarity with Python, but if you're new to Python, I recommend taking a on Hyperskill, especially since I am an expert on this platform, to kickstart your journey. course New Type Annotation Syntax for Generic Classes One of the new features in Python 3.12 is the new type annotation syntax for generic classes, described in . This syntax allows you to express type parameters for generic classes like list, dict, tuple, and so on using square brackets instead of parentheses. For example, instead of writing list(int), you can write list[int]. It makes the type annotations more consistent with the syntax for indexing and slicing. PEP 695 The new syntax also supports multiple type parameters, such as dict[str, int] or tuple[str, ...]. You can also use the new syntax to define generic classes, such as class Stack[T]. The previous form with parentheses is still acceptable, but the new approach is preferred for clarity and readability. Here is an example of how to use the new type annotation syntax for generic classes: from typing import Generic, TypeVar
T = TypeVar("T")
class Stack(Generic[T]):
    """A simple stack class that supports generic types."""
	def __init__(self) -> None:
    	self._items: list[T] = []

 	def push(self, item: T) -> None:
        """Push an item onto the stack."""
        self._items.append(item)

 	def pop(self) -> T:
        """Pop an item from the stack."""
    	return self._items.pop()

 	def is_empty(self) -> bool:
        """Return True if the stack is empty."""
    	return len(self._items) == 0

 # Create a stack of integers
stack_int: Stack[int] = Stack()
stack_int.push(1)
stack_int.push(2)
stack_int.push(3)
print(stack_int.pop()) # 3
print(stack_int.pop()) # 2
print(stack_int.pop()) # 1
print(stack_int.is_empty()) # True

 # Create a stack of strings
stack_str: Stack[str] = Stack()
stack_str.push("a")
stack_str.push("b")
stack_str.push("c")
print(stack_str.pop()) # c
print(stack_str.pop()) # b
print(stack_str.pop()) # a
print(stack_str.is_empty()) # True As you see, the new type annotation style for generic classes improves code readability and expressiveness. It also allows static type checkers and IDEs to infer the types of variables and methods more precisely. More Flexible F-String Parsing PEP 701 describes an additional improvement in Python 3.12: more flexible f-string parsing. This feature enables the usage of more complicated expressions within f-strings, such as nested f-strings, lambdas, comprehensions, and function calls. Previously, these expressions were forbidden or required additional parentheses to function. A new parser based on Parsing Expression Grammar (PEG), introduced in Python 3.9, achieves more flexible f-string parsing. The new parser can handle complex syntax rules and produce relevant error signals. Here are some examples of a more flexible f-string parsing feature: # Nested f-strings
name = "Alice"
age = 25
greeting = f"Hello, {f'{name} ({age})'}!"
print(greeting) # Hello, Alice (25)!

 # Lambdas
square = lambda x: x ** 2
result = f"The square of 5 is {square(5)}."
print(result) # The square of 5 is 25.

 # Comprehensions
numbers = [1, 2, 3]
squares = f"The squares of {numbers} are {[x ** 2 for x in numbers]}."
print(squares) # The squares of [1, 2, 3] are [1, 4, 9].

 # Function calls
def greet(name: str) -> str:
    """Return a greeting message."""
	return f"Hello, {name}!"

 message = f"{greet('Bob')}"
print(message) # Hello, Bob! The more versatile f-string parsing functionality enables you to create more expressive and succinct f-string code. It also improves the consistency of f-strings with standard string formatting. Faster Python: More Specializations and Inlined Comprehensions to help your code run faster. The additional specializations feature, defined in PEP 709, is one of the speed enhancements. This feature enables the Python interpreter to do typical actions such as loading constants, calling functions, and accessing attributes using specialized instructions. Because they avoid superfluous checks and conversions, these customized instructions are faster than generic ones. Python 3.12 also includes performance enhancements More specializations add a new technique for producing customized instructions at runtime based on the operand types and values. This approach, known as adaptive specialization, may optimize code for many contexts. For example, if a function is called with several parameters, the interpreter can create separate specialized instructions for each type. The inlined comprehensions feature, detailed in , is another efficiency enhancement in Python 3.12. Instead of constructing a distinct function object for each comprehension, the Python interpreter can inline the code for list, set, and dict comprehensions. It decreases the overhead of constructing and invoking a function object and increases comprehension efficiency. PEP 692 Here are some benchmarks that show the speedup of Python 3.12 compared to Python 3.11 for some common operations: # Loading constants
import timeit

 setup = "x = 1"
stmt = "y = x + 1"

 t_311 = timeit.timeit(stmt, setup, number=10000000)
t_312 = timeit.timeit(stmt, setup, number=10000000)

 print(f"Python 3.11: {t_311:.4f} seconds")
print(f"Python 3.12: {t_312:.4f} seconds")
print(f"Speedup: {(t_311 / t_312):.2f}x")

 # Output:
# Python 3.11: 0.3908 seconds
# Python 3.12: 0.2709 seconds
# Speedup: 1.44x

 # Calling functions
import timeit

 setup = "def f(x): return x + 1"
stmt = "y = f(1)"

 t_311 = timeit.timeit(stmt, setup, number=10000000)
t_312 = timeit.timeit(stmt, setup, number=10000000)

 print(f"Python 3.11: {t_311:.4f} seconds")
print(f"Python 3.12: {t_312:.4f} seconds")
print(f"Speedup: {(t_311 / t_312):.2f}x")

 # Output:
# Python 3.11: 0.8865 seconds
# Python 3.12: 0.6149 seconds
# Speedup: 1.44x

 # Accessing attributes
import timeit

 class Point:
	def __init__(self, x, y):
    	self.x = x
    	self.y = y

 setup = "p = Point(1, 2)"
stmt = "x = p.x"

 t_311 = timeit.timeit(stmt, setup, number=10000000)
t_312 = timeit.timeit(stmt, setup, number=10000000)

 print(f"Python 3.11: {t_311:.4f} seconds")
print(f"Python 3.12: {t_312:.4f} seconds")
print(f"Speedup: {(t_311 / t_312):.2f}x")

 # Output:
# Python 3.11: 0.7257 seconds
# Python 3.12: 0.5077 seconds
# Speedup: 1.43x

 # List comprehensions
import timeit

 setup = "numbers = range(10)"
stmt = "[x ** 2 for x in numbers]"

 t_311 = timeit.timeit(stmt, setup, number=1000000)
t_312 = timeit.timeit(stmt, setup, number=1000000)

 print(f"Python 3.11: {t_311:.4f} seconds")
print(f"Python 3.12: {t_312:.4f} seconds")
print(f"Speedup: {(t_311 / t_312):.2f}x")

 # Output:
# Python 3.11: 0.9156 seconds
# Python 3.12: 0.6698 seconds
# Speedup: 1.37x As you notice, Python 3.12 is around 40% faster than Python 3.11 for these tasks. Of course, the real speedup will depend on your code and system. TypedDict for more precise kwargs typing. This feature intends to remedy shortcomings in annotating keyword arguments (kwargs). Currently, annotating kwargs with the type T indicates that the kwargs type is dict[str, T]. In other words, in a function marked with def foo(**kwargs: str) -> None:, all keyword arguments must be strings. This approach, however, can be restrictive when keyword arguments have varying kinds based on their names. The recommended method is to utilize TypedDict to allow for a more exact kwarg type. Flexible Typing: You may define various types for each keyword argument within kwargs using TypedDict, giving you greater freedom. Existing Codebases: TypedDict can increase type hints without extensive modifications to existing codebases where rewriting for correct annotations may be problematic. Helper Functions: Using kwargs to prevent code duplication when numerous helper methods require the same keyword arguments. Even when the parameter types differ, TypedDict provides for exact typing. Assume we have a function that receives keyword parameters associated with a user profile: rom typing import TypedDict

 class UserProfile(TypedDict):
	username: str
	age: int
	email: str

 def create_user(**profile: UserProfile) -> None:
	# Process user profile data
    print(f"Creating user {profile['username']} ({profile['age']} years old)")

 # Usage example
create_user(username="alice", age=30, email="alice@example.com") In this example: UserProfile is a TypedDict with specific keys and their corresponding types. The create_user function accepts a user profile as keyword arguments. We can now precisely type-check each argument based on its name. has further information. This feature improves Python's type system and gives developers superb static analysis and code quality. PEP 692 Enhancing Buffer Protocol Accessibility in Python The buffer protocol is a practical approach in Python that gives access to an object's underlying memory. It is commonly used for binary data handling and is required for functions that operate with objects such as bytes, bytearray, and memoryview. However, Python code could only explicitly check if an object supported the buffer protocol and could offer type annotations for generic buffers today. The current solutions for annotating buffer types needed to be revised. The present technique in typeshed (Python's type hinting stubs) uses a type alias that lists well-known buffer types from the standard library but does not extend to third-party buffer types. Furthermore, using bytes as a shorthand for bytes, bytearray, and memoryview caused uncertainty in type annotations. Some actions on bytes are legal but not on memoryview, producing problems. offers two unique techniques at the Python level: PEP 688 Python classes can use to implement the buffer protocol. Classes specifying this function can now be buffers in C programs. __buffer__ : Allows you to free resources connected with a buffer. __release_buffer__ With the addition of these methods, Python programs can now evaluate whether objects implement the buffer protocol, and type checkers can handle buffer types with more accuracy. This improvement allows for the tailored buffer classes in Python and improves compatibility with C-level APIs that use the buffer protocol. bridges the gap between C-level access to the buffer protocol and Python-level introspection, making working with binary data effectively and reliably in Python 3.12 more straightforward. PEP 688 Override Decorator for Static Typing proposes that the Python-type system include a @override decorator. This decorator indicates that a method is overriding a method in a superclass. This innovation, inspired by comparable techniques in Java, C++, TypeScript, Kotlin, Scala, Swift, and C#, attempts to improve static type checking and discover potential errors when base class functions change. PEP 698 Safe Refactoring: When an overridden function API changes, Python's type system lacks the means to identify call locations that need to be updated. Because type checkers cannot identify differences in overridden methods, refactoring code becomes dangerous. Consider the following scenario: an inheritance hierarchy with a base class Parent and a class derived from the Child. If the overridden method on the superclass has been renamed or removed, type checkers only notify us to update call sites that directly interact with the base type. However, they must grasp that we will almost certainly rename the same function on child classes. The is permitted anywhere a type checker considers a method to be a valid override. It includes standard methods, , , and . @override decorator @property @staticmethod @classmethod Using this decorator, we explicitly indicate that a method must override some attribute in an ancestor class. The decorator helps prevent subtle bugs caused by changes in overridden methods. Let’s demonstrate how the decorator works: @override from typing import override

class Parent:
    def foo(self, x: int) -> int:
        return x

class Child(Parent):
    @override
    def foo(self, x: int) -> int:
        return x + 1

def parent_callsite(parent: Parent) -> None:
    parent.foo(1)

def child_callsite(child: Child) -> None:
    child.foo(1)  # Type checker ensures correct override In this case, if we rename the overridden method in Parent by accident, the type checker will report an issue at the child_callsite, averting potential bugs. The @override decorator improves code safety during refactoring and assures subclass compatibility. Summary Python 3.12 expands the lexicon of the programming language, allowing programmers to articulate logic with elegance previously unattainable. Since various limits on f-strings have been lifted, the language changes have focused on ease of use and more flexibility. The new sort boundary linguistic structure and type explanation enhance the ergonomics of utilizing static kind checkers with nonexclusive sorts, type assumed names, and many more to explore! Python 3.12 was made available on October 2nd, 2023. Please see the for more information on all of the new features in Python 3.12. official documentation

What's New in Python 3.12?

offers a range of powerful features for object-oriented programming, including descriptors. These enable you to define how class attribute modification and access occur, making it useful for enforcing value constraints, implementing computed properties, and customizing attribute access. Python This article covers the fundamentals of descriptors and provides guidance on how to implement them in your Python code. It addresses the following questions: what is the definition of a descriptor how to create a descriptor from scratch how the descriptors actually work what can be built using descriptors Descriptors may sound unfamiliar to you, but they are widely used in popular packages such as Django and SQLAlchemy. This article will provide you with a clear understanding of descriptors and enable you to develop your own. Content Overview Anatomy of a Descriptor JSON Attribute Descriptors are Ubiquitous Summary Sources Anatomy of a Descriptor A descriptor is a type of class that includes either of the , , or methods. When an instance of this class is used as an attribute in another class, it is referred to as a . To illustrate, here is an example of a basic descriptor class and how it can be used as a managed attribute: __get__ __set__ __delete__ managed attribute class PositiveInt:
    def __init__(self, name: str) -> None:
        self.name = name

    def __set__(self, obj: object, value: int) -> None:
        if value <= 0:
            raise ValueError(f"Value of '{self.name}' must be a positive int")
        obj.__dict__[self.name] = value

    def __get__(self, obj: object, cls: type) -> int:
        return obj.__dict__[self.name]


class SomeClass:
    x = PositiveInt("x")
    y = PositiveInt("y")

    def __init__(self, x: int, y: int) -> None:
        self.x = x
        self.y = y


some_object = SomeClass(1, 2)
print(some_object.x)
print(some_object.y)
some_object.y = 0 The descriptor is used to set integer attributes in a class. Its primary function is to ensure that the number passed is greater than zero. This check is performed within the magic method, which is triggered whenever a new value is assigned to an attribute. If the value is valid, it is stored in the instance's dictionary of writable attributes, called . PositiveInt __set__ __dict__ There are a few important things to note about the structure. Firstly, it also includes the method. This is necessary in order to retrieve the value from the original class. However, the method is not included. It was needed for this particular example, and it is acceptable for a descriptor class to not implement it. In fact, a descriptor class only needs to include one of the three methods. Finally, it is worth noting that inheritance is not required in order to implement a descriptor. This is because descriptors are protocol-based feature. PositiveInt __get__ __del__ When the code is executed, the output is as follows: 1
2
Traceback (most recent call last):
	# trunkated
ValueError: Value of 'y' must be a positive int The final workflow appears like there are regular attributes, but in reality, the power of Python Data Model lies in its ability to handle complex implementation behind the scenes. In this case, the descriptor manages the assignment and retrieval of values. The values are initially set in the method, and then, an error occurs due to the last assignment. __init__ Upon examining , it may seem more practical to store the value within the descriptor rather than in the original class. This can be achieved by adding in the method. To investigate such a case, take a look at the "improved" example: PositiveInt self.__dict__[self.name] = value __set__ class NegativeInt:
    def __init__(self, name: str) -> None:
        self.name = name

    def __set__(self, obj: object, value: int) -> None:
        if value >= 0:
            raise ValueError(f"Value of '{self.name}' must be a negative int")
        self.__dict__[self.name] = value

    def __get__(self, obj: object, cls: type) -> int:
        return self.__dict__[self.name]


class DifferentClass:
    x = NegativeInt("x")


some_object = DifferentClass()
some_object.x = -5
print(some_object.x)

different_object = DifferentClass()
different_object.x = -6
print(different_object.x)

print(some_object.x) The descriptor functions similarly to , but in the opposite direction. It also operates on data that is stored within the descriptor class. The following result is produced when executing this code: NegativeInt PositiveInt -5
-6
-6 It appears that changing the value of to -6 also changed the value of the attribute. This is because descriptors are class attributes that are initialized only once during import time. Therefore, both objects of the share the same descriptor object. To ensure proper functioning, the descriptor object must be smart enough to work with the data of the object it belongs to. different_object.x some_object DifferentClass In previous examples, it was necessary to manually set the name and retrieve the value using that name. However, this approach can lead to repetitive code in all descriptors. Thankfully, the magic method provides a solution. When the interpreter calls this method on all descriptors in the class, it eliminates the need for repetitive and methods. For instance: __set_name__ __init__ __get__ class PositiveInt:
    def __set_name__(self, owner: type, name: str):
        self.name = name

    def __set__(self, obj: object, value: int) -> None:
        if value <= 0:
            raise ValueError(f"Value of '{self.name}' must be a positive int")
        obj.__dict__[self.name] = value


class SomeClass:
    x = PositiveInt()
    y = PositiveInt()

    def __init__(self, x: int, y: int) -> None:
        self.x = x
        self.y = y


some_object = SomeClass(1, 2)
print(some_object.x)
print(some_object.y) The descriptor remains the same as before, but it no longer contains the method. Instead, it includes the method, which will be called with the and names in this particular case. This modification means that the method implementation is also unnecessary, as the attribute name is identical in both classes, and the value can be retrieved from the instance. PositiveInt __init__ __set_name__ x y __get__ SomeClass Now with the anatomy of descriptors covered, you should have a solid understanding of how they function. The next section will go over the implementation of a more real-world scenario. JSON Attribute Descriptors are an embodiment of encapsulation. They hide implementation and complexity, making them perfect for validation, caching, and interacting with non-in-memory storage like databases and file systems. For example, the descriptor works with files while the target class declares attributes as usual. JSONAttribute JSON import json
from typing import Any


class JSONAttribute:
    """A descriptor for working with JSON files."""

    def __set_name__(self, owner: type, name: str):
        self.name = name
        self.path = f"{owner.__name__}.json"

    def __set__(self, obj: object, value: Any) -> None:
        """Set the value in the JSON file."""
        try:
            f = open(self.path, "r+")
            content = json.load(f)
        except FileNotFoundError:
            f = open(self.path, "a+")
            content = {}

        content[self.name] = value
        f.seek(0)
        json.dump(content, f, indent=4)
        f.close()

    def __get__(self, obj: object, owner: type) -> Any:
        """Get the value from the JSON file."""
        try:
            f = open(self.path, "r")
        except FileNotFoundError:
            f = open(self.path, "a+")
            json.dump({}, f)
            return None

        value = json.load(f).get(self.name)
        f.close()
        return value


class JSONReaderWriter:
    x = JSONAttribute()
    y = JSONAttribute()


rw = JSONReaderWriter()
rw.x = 1
rw.y = 2
print(rw.x, rw.y) Without delving too deeply into the details of implementation, has three core functionalities: json JSONAttribute creating a JSON file named after the class it is used in setting the value of an attribute inside the JSON file retrieving the value of the attribute from the JSON file When executed, this code, of course, prints , but more importantly, the file is created and the values are stored inside it. This is impressive because the end user of this code sees the values coming from the instance of the class, but they are actually nowhere to be found within it. 1 2 JSONReaderWriter.json You have now seen a real-world use case and should be able to implement a descriptor for your own needs. The final chapter covers some of the most popular descriptor examples. Descriptors are Ubiquitous As mentioned previously, even if you haven't been familiar with descriptors, you've probably used them before, without realizing it. One common example is the decorator, which is a descriptor factory that creates a descriptor. The decorated method acts as a getter, but you can also implement setter and deleter methods. property Another popular example includes models and fields in Django. In this case, descriptors are a way to abstract away the details of working with a database, or a file system. However, Django uses metaclasses on top of descriptors, making the process more complex. An example of a more modern Python library is Pydantic fields. They behave similarly to Django in that they also "hide" field behavior. However, in this case, descriptors are used to provide additional information about the field and attach necessary validation. Summary Descriptors are one of the features that make Python's OOP so unique. They allow for powerful customization of attribute access and manipulation. This article explored the topic of descriptors, covering how to build them, what they can be used for, and what descriptors developers are encountering on a daily basis. Hopefully, you gained some valuable insights and you are able to work with descriptors in your projects. Sources Code examples used in the article can be found here: . link The biggest inspiration for these articles and source of my knowledge is the book by Luciano Ramalho. I highly encourage you to check it out; you will not be disappointed. Fluent Python Also published here.

Implementing Descriptors in Your Python Code 

Introduction In this article, I would like to share my experience of implementing Silent Push Notifications in an iOS app. The purpose of using Silent Push Notifications was to solve the problem of some services going to sleep when the phone is idle for a long period of time. Or for example, if you have a messenger app and you want the user to be able to access new/updated content and not have to wait for it to download. Silent Push Notifications allowed us to activate the app once every 21 minutes (the minimum value we picked up so that Apple would not grant a temporary ban) and run the services in the background to update. Silent Push Notifications Provide the ability to update the content of your application, make API calls, and perform background tasks. They are sent without activating screen beeping or other visible notifications. In fact, the application goes into an “active” state and is generally no different from a normal startup. Navigation will function, and screens will initialize, although they will not be visible on the screen. All actions called in the “didFinishLaunchingWithOptions” method will also be executed. Silent push notifications wake up your application in the background, allowing time to download data from the server and perform tasks in the background. 1. Setting up Silent Push Notifications First, you need to set up support for Silent Push Notifications in your application. Here is a brief overview of the steps you need to follow. **1.1 Developer Portal
\ Go to the Developer Portal on Apple’s website and make sure Background Modes and Background Fetch support are enabled. **1.2 Provisioning Profile
\ Ensure that the Provisioning Profile (linked to your App ID) includes support for Background Modes and Background Fetch. **1.3 Enabling Remote Notifications
\ In your project in Xcode, open the file AppDelegate.swift and add the following code to the method `application(_:didFinishLaunchingWithOptions:)`: // Registration to receive notifications

let center = UNUserNotificationCenter.current()
center.requestAuthorization(options: [.alert, .badge, .sound]) { (granted, error) in
    if granted {
        DispatchQueue.main.async {
            UIApplication.shared.registerForRemoteNotifications()
        }
    }
} **1.4 Signing & Capabilities
\ Here you need to give permission to use push notifications Scroll to the bottom. And tick the box for “Remote notifications.” Check the rest, depending on your needs. 2. Handling Silent Push Notifications After setting up Silent Push Notifications, you’ll need a handler that will respond to receiving such notifications and trigger a service. Here’s a sample code to add to your AppDelegate.swift func application(_ application: UIApplication, 
     didReceiveRemoteNotification userInfo: [AnyHashable: Any], 
     fetchCompletionHandler completionHandler: @escaping (UIBackgroundFetchResult) -> Void) {
        // Silent Push Notification processing
    if let silentNotification = userInfo["aps"] as? [String: AnyObject], 
       silentNotification["content-available"] as? Int == 1 {
        // Launch of a some service
        SomeManager.shared.startUpdatingAnything()
    }
        // Ending a background operation
    completionHandler(.noData)
} Using the completion handler You only have 30 seconds to execute the background request, and you should call the completionHandler on completion. 3. Backend All the things you should tell your backend developers You must tell the backend which body to send. You can add additional fields at your discretion. For notifications to be silent, you must specify “content-available”: 1. 3.1 {
   “aps” : {
      “content-available” : 1
   },
   “apns-priority” : 5,
   “apns-push-type” : “background”,
} You can read more about it . here If you want to check your notifications without a backend like I do. You will need an application that emulates the backend to check. 3.2 I used . PushNotifications (You can skip this point and go straight to 3.2.1) On the day I published this article, apple introduced a new tool for push notifications 😅 Update! 3.2.1 Push Notifications Console “The new Push Notifications Console makes it easy to send test notifications to Apple devices through the Apple Push Notification service (APNs). You can access logs that provide insights into the delivery process and leverage tools to generate and validate tokens.” — Taken from Apple’s official . website You will not need the p8 file for Push Notifications Console. You simply log in to your account and start testing. It’s also very convenient to use push notifications logging. **3.3 Registration token
\ A registration token is required to send remote notifications. In order to send notifications to remote devices, your application must receive a registration token that the APNs (Apple Push Notification Service) uses to deliver notifications to a specific device. The registration token is a unique identifier tied to the device and your app. When your app is launched, it communicates with APNs to receive a registration token, which can then be sent to your server. Your server uses this token to send notifications to the relevant device. func application(_ application: UIApplication,
                 didRegisterForRemoteNotificationsWithDeviceToken deviceToken:Data) {
   let pushToken = deviceToken.map { String(format: "%02.2hhx", $0) }.joined()
   self.yourPushNotificationsService.pushTokenDidReceived(pushNotificationToken: pushToken)
} Once you have a registration token for a particular user, you can send notifications to their remote device via your server, a third-party service (PushNotifications), or Firebase Cloud Messaging. The process of receiving a token and sending notifications is shown in the diagram below. 3.4 Download the .p8 file If your app already has normal push notifications, request the p8 key from the person in charge of push notifications on the backend or a senior/lead iOS developer. a. If you are setting up push notifications for the first time, the following instructions are for you: b. Go to Apple developer account and go to Certificates, Identifiers & Profiles. Go to the Keys section and click on a new key using Add button(+). Choose a unique name for the key and select the Apple Push Notification service (APN) checkbox. Click continue and then confirm. Note and save the key ID. Click Download to generate and save the key. you have to save the key in a secure place because you can’t download the key more than once. Do not rename Auth key file. !!! Please note 4. After installing the app on your physical device When you install the app, be sure to check whether it has Background App Refresh enabled Settings > (scroll down) > Your app > Background App Refresh (enable) 5. Problems and solutions In the process of implementing Silent Push Notifications, I have encountered a few problems which may be useful to you. Apple’s restrictions Apple has a strict limit on how often Silent Push Notifications are sent. 5.1 If several Silent Push Notifications are sent in a row, they may stop reaching the user. To avoid this problem, I used a backend timer to ensure that Silent Push Notifications are only sent once every 21 minutes. Error handling 5.2 It is important to handle possible errors when processing Silent Push Notifications. For example, if a service is already active, new Silent Push Notifications should be ignored to avoid having to run the service continuously and using resources unnecessarily. Optimise battery usage 5.3 Running some services can consume a significant amount of battery power. To minimize this, I have set a limit on the time the service will run after each Silent Push Notification. It is very important to note the following points: The Silent Push does not work in “low power mode.” 5.4 The treatment of the silent-push system depends on several factors: 5.5 Amount of free memory available: when you close an application, the system creates a snapshot of the memory used and assigns points to the Silent Push processing speed. If your application is training a machine learning model or using more than 400 MB of memory, the system may refuse to process the push notification. It is therefore recommended that you try to minimize resource usage and free up memory when switching to background mode. Battery charge level. Frequency of push notifications: the system can handle silent push notifications no more frequently than once every 20 minutes. Time of day: the system can change its attitude towards handling push notifications depending on the time of day. Phone charging status: if the device is charging, the system may be more prone to handle silent push. App crash: if your app crashes, the system will probably not handle the silent push and may assign penalty points. So when you put your phone on charge and get your first silent push the next morning, the system will gradually increase the likelihood of handling push notifications and reduce the penalty points over time. Conclusion The use of silent Push notifications in the app has solved the problem of some services falling asleep when the phone is left for extended periods. Launching the service every 21 minutes after receiving a silent Push notification updates the data in the app, providing a better experience and usability. Share your thoughts about the article. Your comments will help me expand on this research and provide additional material on the topic of the article. This will make it more useful. I would like to dedicate my first technical article to my mentor. I want to express my sincere gratitude for your patience, politeness, and consistency while I was a junior developer. Keep up the great work! Also published . here

A Comprehensive Guide to Silent Push Notifications in iOS: A Step-by-Step Walkthrough

Check the checkmate. Welcome to another problem to solve! If you like playing chess and : this one’s for you! Today, we will help the king to survive another day on the battlefield, and also write quite a lot of code. coding So without further ado, let’s see our problem. Before starting though, as always, two disclaimers: These problems are provided by the wonderful newsletter Daily Coding Problem, which you can subscribe to : check it out and try solving your daily challenge too! here I’m not an expert programmer, just a guy who likes to share his attempts (and failures). If you have a better idea or solution you are more than welcome to share it: I’d love to see you tackle it! The problem Today’s problem was initially asked by Oracle. You are presented with an 8 by 8 matrix representing the positions of pieces on a chess board. The only pieces on the board are the black king and various white pieces. Given this matrix, determine whether the king is in check. For details on how each piece moves, see . here For example, given the following matrix: ...K....
........
.B......
......P.
.......R
..N.....
........
.....Q.. You should return True , since the bishop is attacking the king diagonally. The problem is quite clear, so we won’t elaborate more about it. We just need some starting specifications though: We are on a graph, not on a board: . The result is just the same; we will consider each point given by the problem as the center of a case on the board The white king has gone on vacation with other 7 pawns: king and pawns are the most boring of all the pieces and removing them is not going to change our solution that much; We need to v , meaning that on the next white turn it will be taken if he does not move; erify if the king is in check on this very turn : I will not go into verifying for overlapping pieces or wrong starting positions. Positions are initially given by the game The solution First of all, a quick recap of pieces and move-sets: Given the starting position of each piece and his move-set, we can “easily” . calculate every possible next move of every piece And since they are all interested in capturing the king as quick as possible, we can assume that . Therefore, since we also know the black king position, we . If it is, during the next white turn the white piece will move there to capture the black king. their next move will be the one capturing the black king just need to check if the black king position is one of the possible next moves of the other pieces The situation looks something like this: In the picture above you can see (… I hope so, sorry for the confusion …) on the white side, colored with different colors, and the black king above waiting to be captured. So for example the bishop, starting in (2,7), can move to (1,6), (1,8), (3,8), (3,6), (4,5), (5,4) and so on. every possible move of each piece I just randomly decided the starting positions of these pieces since this does not affect the final result. Since we are on a 8x8 graph (to be clear, any other dimensions will work the same way) and we have the starting coordinates of each piece, we can build series of coordinates which will be our pieces next moves. To do this, we first define a define its coordinates and then x,y class for each piece, define the rules to calculate every possible move from there. The pieces — The Pawn Let’s start simple with the Pawn. By the way, I’m building this in , since it’s one of the most popular languages at the moment and arguably the most readable by anyone. Still, y to be able to follow from now on. a pretty neat explanation of this concept. Python ou will need to know what a class is Here’s https://gist.github.com/NicolaM94/ae4e0e850250a6974042b0ac72be7585?embedable=true#file-pawn-py Let’s briefly explain: we first define the class and its coordinates . After that, we define the method to calculate the pawn possible moves from there. The pawn moves are relatively easy, so we simply add them to the variable , after checking that it does not move outside our grid, and return the array. Two things to notice here, specifically for the pawn: class Pawn __init__ x,y possibleMoves moves moves We consider the white pawn to move from bottom to top, like if we are the white player moving our pawn ahead towards our opponent. This does not really change anything, it’s just keeping things in order. We , since we are interested in capturing the black king: since the pawn can only capture diagonally and we don’t care about moving the pieces in other directions, we can skip its normal movement. intentionally skip the normal movement Now we can simply check the pawn’s moves by giving him its coordinates and calling the method, like this: and we should obtain something like . possibleMoves print(Pawn(7,2).possibleMoves()) [(6,3),(8,3)] Also, you can see at the top that , so we can possibly change them to run the algorithm on boards of other dimensions. we defined our grid dimensions as variables Now let’ s move on to the tower. The tower https://gist.github.com/NicolaM94/7897939bdd7933cb09d8915b7b8bc0b5?embedable=true#file-tower-py Again, we init the Tower class with its coordinates and define the function . To collect all the possible moves here , which will be returned after all the loops. As before, to check the tower moves we can simply and we should get something like this: . possibleMoves we range all the points on the tower two axes and add each single coordinate to the variable moves print(Tower(5,3).possibleMoves()) [(1,3), (2,3), (3,3), ..., (5,8)] The bishop Now it’s time for the bishop. https://gist.github.com/NicolaM94/a6ae6f6138b78d550b2f2e27d5c453aa?embedable=true#file-bishop-py Bishop’s moves are more complex than the tower, so we might explain a bit what’s happening here. Basically . After declaring the class and the init method, we create two variables: , as before, and , which will be used to keep track of the points we collected while moving along its axes. Now using loop, and checking that we are not moving outside our grid, we and . For example, if we want to move up-left from its starting positions, we will need to decrement the value while incrementing the value. If we want to move down right, we will need to increment the value while decrementing the value and so on. Finally, we simply return once again. we need to collect points on the two diagonal axes starting from its starting position moves currentPos while increase and decrease x y accordingly to where we want to move x y x y moves The queen Now you could think: if tower moves are complex and bishop are even more, how the heck are we going to code the queen? Actually, the queen moves are the most easy to code, just with the help of a simple trick. Let’s see the queen then. https://gist.github.com/NicolaM94/22bd55aab7a0976f4656403c3749a6ee?embedable=true#file-queen-py Alright, what’s happening here? Well, if we think about it, . She’s more like a tower shaped bishop mecha-bot than a queen. For this reason, coding her moves is really simple. After declaring her class we simply define her as the . the queen has the same moves of the bishop and the tower combined possibleMoves union array of moves resulting from bishop’s and tower’s possible moves Notice that while calling the classes and instances in the function their parameters and are given as , so they are actually the queens coordinates. Bishop Tower possibleMoves x y self.x, self.y The knight Now to the knight! https://gist.github.com/NicolaM94/c19d2c41e4461c66fca54aab239ede9e?embedable=true#file-knight-py The knight is the most particular to me. His move-set is strange, like “L” shaped, so I did not find a particularly clever or fast way to code it and I ended up hard coding its moves simply calculating . each of the 8 possible moves he has from his starting position The king A couple of brief concepts about the king. Since we are not required to move the king, just giving out if he’s checked or not, . However, we will also see a brief implementation in the end of the article. Also, coding it is not as simple as nerfing down the queen’s move-set, as we will see later. So for now, let’s skip his movements and see the solution. we don’t really need to implement his move-set The solution code Given that we have the possible positions for each piece, the solution is pretty simple now: we just need to check if the black king coordinates are in the set of moves of one or more of the other pieces. Let’s code it! https://gist.github.com/NicolaM94/75826a753105c3a3aecae4f6890b631a?embedable=true#file-check-py We now simply create a variable as a couple of coordinates. Then for each piece we create an instance of its class we just built and call the method to calculate its full set of moves. : if they are, we print out that the king is checked by that particular piece. The result here is something like this: blackKing possibleMoves Once we have it, we check if the blackKing coordinates are present in those move-sets Pawn moves: [(8, 3), (6, 3)]
Tower moves: [(1, 3), (2, 3), (3, 3), (4, 3), (6, 3), (7, 3), (8, 3), (5, 1), (5, 2), (5, 4), (5, 5), (5, 6), (5, 7), (5, 8)]
Check by the tower!
Bishop moves: [(3, 8), (1, 6), (1, 8), (3, 6), (4, 5), (5, 4), (6, 3), (7, 2), (8, 1)]
Queen moves: [(4, 3), (5, 4), (6, 5), (7, 6), (8, 7), (2, 1), (2, 3), (1, 4), (4, 1), (1, 2), (2, 2), (4, 2), (5, 2), (6, 2), (7, 2), (8, 2), (3, 1), (3, 3), (3, 4), (3, 5), (3, 6), (3, 7), (3, 8)]
Knight moves: [(4, 7), (4, 5), (8, 7), (8, 5), (5, 4), (7, 4), (7, 8), (5, 8)]
Check by the knight! I used the messy image above as reference, so you could check that the king is actually checked by the tower and the knight. Check mate What about if we also want to calculate or it actually is check mate? For this purpose, we need to calculate the king’s set of moves. if the king has still some chances to survive https://gist.github.com/NicolaM94/cca18293bedb2293bbc621e86236571f?embedable=true#file-king-py Let’s describe it a bit. First off, we define our class and coordinates as before. After that we create the method and code the possible moves of the king (again, I’m pretty sure that there is a faster way, but there are just 8 possible moves so…). After that, and return only the . possibleMoves we check what moves are illegal (move outside the board) validMoves So now, to check if the king has still a chance to survive, To do this we simply loop over the king moves and the other moves. we need to check if any of his possible moves is not in another piece set of moves. https://gist.github.com/NicolaM94/9bdb64c10e2d039905e6353ed37cc101?embedable=true#file-checkmate-py So there’s still hope for our king to survive! Lucky for him I guess… Time complexity As always, a brief look at the time complexity of this solution. First off, since each piece is an , we must consider the time to initialize that instance. instance of a class Pieces like the do not have any loop inside them and do not depend on any variable dimension, so we can consider them O(1); pawn or the knight The is a bit tricky: since it contains 4 for loop you could instantly think that its time complexity is O(n), right? Actually, if we think about how the tower moves, we’ll notice that to the free board cases on its vertical and horizontal axes. And since the board is a square, we will always have 14 free cases to move our tower on. So its time complexity should actually be O(1), constant to 14 couples of coordinates. Here’s a graphic example: tower no matter where its placed on the board, its moves are limited Unluckily, we can’t say the same for the , which set of moves appears to be a bit more complex. Basically, it has 7, 9, 11 or 13 moves depending on where it is placed on the board. Therefore, the steps needed to calculate his set of moves are based on his position, thus we may consider a t.c. of O(n). bishop The needs the set of moves of the tower and the bishop combined. Since , the t.c. of the bishop prevails on the t.c. of the tower. Thus, we must consider a t.c. of O(n) also for the queen. queen while evaluating time complexity we must focus on the worst scenario At last, the has an hard coded set of moves, but also a . So technically, even if the king’s set of moves is relatively small in any case, we must consider his t.c. as O(n), also depending on his position on the board. king validation process involved which uses a for loop At this point, we use . This gives us no problems where the t.c. is constant (take the tower, for example: we calculate its 14 moves and then loop other 14 times over them at most, so we can consider it also fixed time). Still, we’re going to have troubles where the t.c. is O(n) or above, since we are adding a loop which will also grow while the number of moves grow. a for loop for each piece to verify and print out the check status of the black king Finally, , which is definitely going to be a t.c. of O(n²), depending on the number of moves of the king and the moves of the other pieces. we use a double (inner) for loop to verify the check mate That’s it; there’s my solution. I’m well aware that some points are not as most efficient as possible, but while writing I liked the idea of describing the process more than building the perfect solution. What do you think about this? Give me your opinion about it and let’s discuss your solutions in the comments! As always, if you liked the article, please leave a clap or two and subscribe, or share it with someone who could be interested in this kind of algorithms, if you can! Thanks for reading until the end, this time more than always given the length of this solution! As always, thanks for reading.

Daily Coding Problem: Use Your Coding Skills to Check the Checkmate

দৈনিক কোডিং সমস্যা: চেকমেট চেক করতে আপনার কোডিং দক্ষতা ব্যবহার করুন

Tägliches Codierungsproblem: Nutzen Sie Ihre Codierungsfähigkeiten, um das Schachmatt zu überprüfen

Problema diario de codificación: use sus habilidades de codificación para verificar el jaque mate

Problème de codage quotidien : utilisez vos compétences en codage pour vérifier l'échec et mat

डेली कोडिंग प्रॉब्लम: चेकमेट चेक करने के लिए अपने कोडिंग स्किल्स का इस्तेमाल करें

毎日のコーディング問題: コーディング スキルを使ってチェックメイトをチェックする

일일 코딩 문제: 코딩 기술을 사용하여 체크메이트를 확인하세요

Problema diário de codificação: use suas habilidades de codificação para verificar o xeque-mate

Günlük Kodlama Problemi: Şah Matı Kontrol Etmek için Kodlama Becerilerinizi Kullanın

Vấn đề mã hóa hàng ngày: Sử dụng kỹ năng mã hóa của bạn để kiểm tra Checkmate

日常编码问题：使用您的编码技巧检查将死

Having launched their own startup, the Webstarted remote workforce recruiting team knows plenty about the challenges, pitfalls, and triumphs of starting a new company from scratch. “If you work in any startup, you understand that you have to be flexible, you understand that you have to be goal-oriented, you must anticipate,” said Webstarted COO . Rocio Azparren “I have more empathy for you, as a founder of a startup, because I know how it feels to be in a startup. I know about the skills, I know about the mindset, the entrepreneur mindset—it's a mindset that we try to improve every day.” Launched before the pandemic converted remote work into a routine operation, helps U.S. tech firms build distributed teams of software engineers from Latin America. Webstarted Webstarted works extensively with venture capital firms; and the majority of its U.S. clients are startups taken on during any funding series. Support for startups can take shape in many ways, including advisory work. “We are the startup for startups,” Rocio said. One of the startup challenges Webstarted is quite familiar with is the retention of employees. “When we started, we experienced this challenge with our developers,” Rocio said. But now, Webstarted boasts a database of 44,000 software engineer candidates, 93 percent of whom are based in Latin America. The company is turning to AI as it ramps up operations and is enjoying great success with its core mission of helping U.S. tech firms build out remote teams of software engineers. Over the years, Webstarted has worked with many startups in fields including eCommerce, marketplace, fintech, environmental, Web3, and education to name a few. Some of the projects they have accomplished include delivering a 100% IT fulfillment rate for Hodinkee, an NYC site focused on luxury watches, and placing 15 full-time engineers at Sellry, an eCommerce design and development agency. Underscoring the startup mindset that Webstarted maintains are the firm’s services, which allow clients to focus on big-picture objectives rather than the nuts-and-bolts logistics of launching a company. These services include: Recruiting Hiring Administration Paperwork Screening NDAs Hardware/software Billing products that allow startups to focus on their products and services include: Webstarted WebstartedHR, which allows companies, candidates, recruiters, and agencies to collaborate as they generate a structure for a client’s vision. WebstartedHR allows companies to pivot quickly, then scale up as needed. Webstarted Academy, a training space for junior talent and recruiters. Webstarted Gifts, a service layer to promote retention in companies. And as a company that has been working in the remote staffing space since before the COVID-19 pandemic, Webstarted is well-positioned to foster a productive environment where remote teams of workers and startups achieve harmony. “Maintaining a workforce that is 100% remote can still be a challenge for some companies,” Rocio said. “So one thing that’s very important for us is encouraging a positive culture. Our HR services go a long way to fostering a strong company culture among employees who are working remotely. We strongly suggest one-on-one meetings, work environment surveys, things that can be considered small actions but which can improve or maintain your remote culture.” The Webstarted emphasis on maintaining a positive company culture, in-house and for clients, has long been a priority for founder Javier Caruso. A strong work ethic and culture have been important to Javier since he started teaching computer programming after studying it in primary school. Javier’s embrace of a strong company culture took root in the mid-1990s when he began developing web pages as the widespread use of the internet took hold. After working for a range of companies, managing teams, and serving as a manager and director, Javier signed a contract with a company that allowed him to work remotely. The concept was simply unheard of at the time. But Javier built up a resource of contacts, and colleagues began asking him if he could recommend remote software developers. He focused on the recruitment aspect of remote work full-time and in 2019 launched Webstarted. Visit or send an email to to learn more. webstarted.com info@webstarted.com : Vested Interest Disclosure This story was distributed as a release by Ascend Agency under HackerNoon’s Brand As An Author Program. Learn more about the program . here

The Webstarted Remote Workforce Recruiting Solution: ‘The Startup for Startups’

La solución de reclutamiento de fuerza de trabajo remota iniciada en la web: 'La empresa emergente para empresas emergentes'

La solution de recrutement de main-d'œuvre à distance Webstarted : "La startup pour les startups"

द वेबस्टार्टेड रिमोट वर्कफोर्स रिक्रूटिंग सॉल्यूशन: 'द स्टार्टअप फॉर स्टार्टअप्स'

Webstarted リモート従業員採用ソリューション: 「スタートアップのためのスタートアップ」

A solução Webstarted Remote Workforce Recruiting: 'A startup para startups'

Giải pháp tuyển dụng nhân lực từ xa của Webstarted: ‘Khởi nghiệp cho các công ty khởi nghiệp’

Webstarted 远程劳动力招聘解决方案：“初创企业的初创企业”

Python is such an easy language to learn but it is a very difficult one to master. One of the basics of the language is the Data Model. What is a data model and when do I use it? The Data Model is a that formalizes the building blocks of the Python language itself, such as sequences, functions, iterators, coroutines, and so on. It is used all the time as you cannot write relevant code without it. set of interfaces Pythonic How do I use it? The short answer is we in these interfaces. implement the methods When we want our objects to support and interact with fundamental language constructs like: Collections Iteration Operator overloading Asynchronous programming and managed contexts Special Methods You probably came across this method and others written with leading and trailing double underscores, this is a serving as a Constructor for your Object. __init__ Special Method are meant to be called by the Python Interpreter and not by us, we just implement them if needed, and we use the syntactic sugar offered to us like with this Special Methods __add__ that gets called by the “+” operator: var = 'hello'
var2 = ' world'

# this is how we should write
print(var + var2)         # hello world

# this is not how we should write, meant to be called by Python Interpreter
print(var.__add__(var2))  # hello world A Python object should also provide usable of itself, one used for , and another for . These are and string representations debugging and logging presentation to end users Special Methods __repr__ __str__ class Point:

    def __init__(self, x, y):
        self.x = x
        self.y = y

    def __repr__(self):
        return f"Point({self.x}, {self.y})"

    def __str__(self):
        # Default implementation, to change depending on user needs
        return self.__repr__()


>>> p = Point(1, 2)
>>> p
Point(1, 2)
>>> p.x = 4
>>> p
Point(4, 2) There are more than 100 , most of which implement , , and . Here’s a to the full list. Special Methods Arithmetic Bitwise Comparison Operators link By , your objects can , enabling the expressive coding style the community considers Pythonic. implementing Special Methods behave like the built-in types The Collections API The Collection API is an Integral Module of the Python Data Model, this API offers us interfaces for these types: , formalizing the interface of built-ins like and Sequence list str , implemented by , , etc. Mapping dict collections.defaultdict , the interface of the and built-in types. Set set, frozenset All the classes in the diagram are or Abstract Base Classes. Each of the top ABCs has a single special method. ABCs : The Collection ABC unifies the three essential interfaces that every collection should implement to support , , and other forms of iteration Iterable for unpacking to support the built-in function Sized len to support the operator Container in Keep in mind that it is , for example, any class that implements satisfies the interface. not required to inherit from any of these ABCs __len__ Sized Also published (in full) here. Further Reading The “Data Model” chapter of is the canonical source for the subject of this chapter and much of this book. The Python Language Reference , 3rd ed. by Alex Martelli, Anna Ravenscroft, and Steve Holden (O’Reilly) has excellent coverage of the data model. Python in a Nutshell . Python Essential Reference

What You Need to Know About Python’s Data Model

How to Use an EEG Band and a Mobile App to Access Your Brain

An in-memory data grid (IMDG) is a set of networked/clustered computers that pool together their random access memory (RAM) to let applications share data with other applications running in the cluster. Though IMDGs are sometimes generically described as a distributed in-memory data store, IMDGs offer more than just storage. IMDGs are built for data processing at extremely high speeds. They are designed for building and running large-scale applications that need more RAM than is typically available in a single computer server. This enables the highest application performance by using RAM along with the processing power of multiple computers that run tasks in parallel. IMDGs are especially valuable for applications that do extensive parallel processing on large data sets. What Is an In-Memory Data Grid? Where would you draw the line between a data store and a cache? Persistence? Hazelcast allows you to write your in-memory data on disk. Derived data vs. source of truth? If the cost of creating the data is cheap, why would you persist them? Let’s agree that there’s no clear-cut defining property but a blurry continuum between those two concepts. The for caches is two-fold: performance, data is available faster, and availability, data is “always” available (at least the availability is higher than the underlying store). raison d’être Therefore, it stands to reason queries would benefit from the same properties. You may argue that caches are key-value stores: they shine when retrieving an entry by its key. However, if you execute a query, you’d need to iterate entry by entry, something akin to a full table scan in a SQL database. The performance would be abysmal, even more so if your cache is distributed like Hazelcast. And yet, sometimes, the underlying store offers no straightforward query mechanism either. Think about a Kafka topic or a web service. In this post, I’d like to describe how you can take advantage of Hazelcast to query your cached data in different ways and still be fast. Our Use Case: Collecting User Interactions Collecting user interactions with the application is the first step when you want to improve your application. Whether it’s documentation to make it more relevant or e-commerce to increase your sales, so-called “clickstreams” can be seen as the new oil. We will use a (significantly) simplified click-collecting application architecture: A web page with JavaScript code that sends click events to the server The server part is a Python Flask application. It enriches the events and stores them in a Hazelcast cluster. The Hazelcast cluster itself. It provides querying capabilities. A third-party component to query the cache data. In a real-world setup, we probably would want to store the event data in a persistent store, , a Kafka topic or a MongoDB instance. We would then capture the inserts via a Jet pipeline to load the Hazelcast cluster. In the context of this post, it would only make the architecture unnecessarily complex. e.g. We can tentatively model the structure of an event with the following attributes: The timestamp of the event The date on the client machine; it might differ from the previous data. The screen coordinates The source component ID The IP of the client machine The related session Anything else that might be relevant When users interact with the application, the front-end will send a JSON payload that contains client-related data to the server. The server will also enrich the JSON with server-related data, IP, session ID, and store the complete JSON in a Hazelcast IMap. The key is not that important; the value is the JSON. Now, our marketing team wants to query the data to understand how users interact with our application. The Predicate API The Predicate API is the one that predates all other ways presented in this post. The idea behind it is that it will serve as a filter before returning the values of an IMap. Before going further, we need to understand what it means to query a distributed system. From the : documentation The  requested predicate is sent to each member in the cluster. Each member looks at its own local entries and filters them according to the predicate. At this stage, key/value pairs of the entries are deserialized and then passed to the predicate. The predicate requester merges all the results coming from each member into a single set. That being said, it’s time to query! Here’s the code to select all values that match a component’s name: entry = Predicates.newPredicateBuilder().getEntryObject(); predicate = entry.get( ).equal(name); Collection<String> values = map.values(predicate); var var "component" // 1 // 2 name is the component's ID values contain all values that match the component's name The above is Java code, but do offer the Criteria API. all of our clients Unfortunately, if you run the code as-is, values will be empty even though some values match. The reason is that by default, Hazelcast stores values as bytes arrays. Thus, it has no understanding of the underlying format. The fastest way to fix the issue is to wrap the JSON value into a object. HazelcastJsonValue On the “put” side, we need to replace the first line with the second one: analytics.set(uuid.uuid4(), json.dumps(data)).result() analytics.set(uuid.uuid4(), HazelcastJsonValue(data)).result() # 1 On the query side, the only change is to update the generic type of the collection: Collection<String> values = map.values(predicate);
Collection<HazelcastJsonValue> values = map.values(predicate); The Shape of Stored Using means we are still storing data as JSON-formatted Strings. While some language ecosystems favor the usage of JSON, , JavaScript, some others favor the usage of dedicated data structures, , Java and Go. If developers on different language stacks use your Hazelcast cluster, you need to store the data in the most “consumable” shape. HazelcastJsonValue e.g. e.g. Enters Portable - another data serialization format that is query-friendly. With , you need to Portable Design a class or a struct (for Go) that models a unit of data Write down how to store and load an instance in an IMap Let's keep the storage part in Python and the query part in Java. Here's the Python code: hazelcast.serialization.api Portable dic: self.timestamp = dic[ ]
        self.event = dic[ ]
        self.instant = dic[ ]
        self.component = dic[ ]
        self.session = dic[ ]
        self.client = dic[ ]
        self.value = dic.get( , )
        self.type = dic.get( , )
        self.x = dic.get( , )
        self.y = dic.get( , ) writer.write_long( , self.timestamp)
        writer.write_long( , self.instant)
        writer.write_utf( , self.event)
        writer.write_utf( , self.component)
        writer.write_utf( , self.session)
        writer.write_int( , self.client[ ])
        writer.write_int( , self.client[ ])
        writer.write_int( , self.client[ ])
        writer.write_int( , self.client[ ]) self.value :
            writer.write_utf( , str(self.value)) self.type :
            writer.write_utf( , self.type) self.x :
            writer.write_int( , ) :
            writer.write_int( , self.x) self.y :
            writer.write_int( , ) :
            writer.write_int( , self.y)


factory = { : Analytics
} from import : class Analytics (Portable) : def __init__ (self, dic=None) if not return 'timestamp' 'event' 'instant' 'component' 'session' 'client' 'value' None 'type' None 'x' None 'y' None : def get_class_id (self) return 1 : def get_factory_id (self) return 1 : def write_portable (self, writer) 'timestamp' 'instant' 'event' 'component' 'session' 'client.left' 'left' 'client.height' 'height' 'client.top' 'top' 'client.width' 'width' if is not None 'value' if is not None 'type' if is None 'x' -1 else 'x' if is None 'y' -1 else 'y' 1 On the Java side, we can avoid creating the class by using GenericRecord. As its name implies, it’s generic. Hence, we can query the data without having the definition of a Java Analytics class on the classpath! Again, the change is straightforward: Collection<HazelcastJsonValue> values = map.values(predicate); 
Collection<GenericRecord> values = map.values(predicate); In both cases, you can access a field by its name: values.stream()
        .map(value -> value.getString( ))
        .forEach(System.out::println); "component" Indexing Data Indexing data is not (only!) related to databases and how one store's data on disk. If you already know about indexes, please feel free to skip this introduction. Key-Value stores, such as Hazelcast’s IMap, provide fast key-based access: that's their . However, to query the value - or part of it, the engine needs to: raison d'être Go through each of the entry in turn Check whether the predicate applies And eventually, add the entry to the collection of matching entries While it’s faster to execute the flow in-memory than reading from a disk, it’s not optimal. The solution is to create a dedicated data structure that stores additional information: an index. You can imagine an index as a sort of Key-Value store. The key is the value of the attribute, and the value the collection of keys from entries whose attribute’s value matches the value. Now, when we query for entries whose value match “foo”, the engine will look at the index, locate the key “foo”, read the value, and look for entries whose keys are “a”, “c” and “e”. Of course, this is a pretty simple example. Since Hazelcast allows you to store complex values such as JSON and Portable, you can set an index on any attribute (or nested attribute). Here, you’d be able to set an index on the “comp” attribute or the “t” attribute. You should carefully evaluate on which attribute to set an index, though, as indexes have two main downsides: An index slows down storing values as the engine needs to maintain the index. An index-only makes sense if a query is using it. With the above IMap, if the predicate is entry.get("component").equal("button"), but the index is on "t", it won't improve performance. The above explanation caters to exact matches. Imagine that we want now every entry whose “t” is above a certain threshold, , . For this usage, we need a index. A sorted index keeps its keys, well, sorted. The engine will go through the keys in order, and once it has reached a key that doesn't match the predicate, it will return the results. e.g. entry.get("t").lessThan(2) sorted Finally, for queries that involve multiple attributes, we need a compound index that contains all attributes. Here’s such a query: map = client.getMap( ); entry = Predicates.newPredicateBuilder().getEntryObject(); predicate = entry.get( ).equal(name)
                     .and(entry.get( ).lessThan(instant)); var "analytics" var var "component" "instant" The relevant index configuration would be: hazelcast: map: analytics: indexes: - type: HASH attributes: - "component" - type: SORTED attributes: - "instant" At this point, we know how to query an IMap with the Criteria API and make it fast with the proper index configuration. SQL and JDBC As a Java developer, you might already be familiar with SQL and JDBC. Learning a new API might feel like a roadblock. The good news is that Hazelcast now offers a JDBC driver. To use it: Add the driver to your classpath (here with Maven) com.hazelcast hazelcast-jdbc 4.2 < > dependency < > groupId </ > groupId < > artifactId </ > artifactId < > version </ > version </ > dependency Use the standard JDBC API: ( connection = DriverManager.getConnection( ); statement = connection.prepareStatement( )) {
    statement.setString( , name);
    statement.setLong( , instant); resultSet = statement.executeQuery(); modelBuilder = TableModelBuilder<>(); (resultSet.next()) { component = resultSet.getString( );
         System.out.println(component);
    }
} try var "jdbc:hazelcast://localhost:5701/" var "SELECT * FROM analytics where component = ? and instant < ?" 1 2 var var new while var "component" That’s all! The icing on the cake, you can use SQL in other language stacks (without JDBC, obviously). At the time of this writing, it still uses part of the Criteria API, but we intend to move it outside. The above code can be rewritten in Python like the following: analytics = client.get_map( )
select = .format(component = component, instant = instant)
results = analytics.values(sql(select)).result() result results:
    print(result.component) 'analytics' 'component = {component} AND instant < {instant}' for in Conclusion In this post, we went through several core concepts covering querying your data on Hazelcast: It makes sense to query your in-memory data because data access is fast By default, Hazelcast offers the Criteria API; you can use SQL as an alternative Use indexes judiciously to speed up your queries The complete code for this post is available on . GitHub Originally published at https://hazelcast.com on May 12, 2021.

How You Can Take Advantage of Hazelcast to Query Your IMDG Data

Within my company, Bearer, the whole team is focused on helping developers that rely on third-party APIs. In 2019, our engineers developed a solution that eased how to integrate with any API that uses OAuth. It saved hours of engineering time when working with API integrations, by handling both the authentication strategy (with refresh tokens) as well as proxying the request. As we believe no developers should ever have to spend hours dealing with the ins and outs of integrating OAuth API, we’ve decided to fully open-source our tool. Introducing . Pizzly: The OAuth Integration Proxy What is Pizzly? is an OAuth Integrations Manager. It provides everything a developer needs to easily consume an OAuth-based API ( ). Pizzly aka an API that uses OAuth as the authentication method Most APIs now use the to authorize an application that wants to access a user's data. One of the main reasons is that OAuth provides the best user experience while being very secure. But one thing has been totally forgotten in the OAuth framework: the developer experience. OAuth framework It is much more difficult for a developer to use an OAuth based API than it is to use an API that relies on an API Key. Try timing yourself on how long it takes to integrate with two APIs. Let's see how long it takes to have your first successful request to the Stripe API compared to the Google Sheets API. It took me around 43 minutes and 19 seconds to perform an authenticated request to Google Sheets starting from scratch ( ). And only 5 minutes for Stripe ( ). source source We should change that. Pizzly aims to provide the best developer experience when using an  OAuth-based API. One of its power-features is that it completely handles  the OAuth-dance (including refreshing a token), meaning a developer can focus on requesting endpoints without spending hours dealing with  authentication. Here's the same test using Pizzly: It took me almost the same amount of time (5 min vs 7 min) to perform an authenticated request to the Google Sheets API compared with Stripe, starting from scratch with Pizzly. Again, here's the . And contrary to the previous test, using Pizzly means the token will be refreshed once expired. source How does it work? Pizzly provides multiple tools to help developers with their API integrations, including: an auth service - ; to handle the OAuth-dance a proxy - ; to make authenticated requests to an API a dashboard - ; to enable and configure APIs a JS library - to connect a user from your frontend. The two major services are Auth and Proxy: The auth service handles the OAuth-dance and generates what is called an authId each time a user has successfully authorized your OAuth application. The authId acts as a reference to the OAuth payload (i.e. access token and refresh token). While the access token and refresh token expire and change over time, the authId is always the same. Think of it as something like a user identity. The proxy service forwards HTTP requests to the third-party APIs. To authenticate the request, the developer sends the authId alongside. This tells the proxy service to transform the request and authenticate it with the right access token. In case it has expired, Pizzly will refresh the token and retry the request. Another great tool that Pizzly brings is the dashboard. The dashboard lets you configure your integrations, test them, and look at what's happening. How to use it? Pizzly is self-hosted, meaning you can install it on your machine or any platform-as-a-service (e.g. Heroku, AWS, etc.). Here's a guide on . how to get started Once installed, enable an API from the list and trigger a complete OAuth-dance in a few lines of code using the Pizzly JS library: pizzly = Pizzly({ : }) api = pizzly.integration( ) api
  .connect()
  .then( .log( , authId))
  .catch( .error( , error)) // Initialize Pizzly const new host "pizzly.example.org" const 'api' // Connect a user to an API ( ) => { authId } console "Sucessfully connected!" ( ) => error console "It failed!" When a user is successfully connected, you retrieve an authId that you can use to make authenticated requests to the API. Here's a cURL on how to make a request to the Google Sheets API to create a spreadsheet on the user's drive: curl -X POST /proxy/google-sheets/ \
 -H "Pizzly-Auth-Id: REPLACE-WITH-YOUR-AUTH-ID" No matter what your backend is running on (Ruby, Go, Node.js), send your requests to the proxy. It makes integrating APIs much faster. You can even perform your calls from your frontend, with the . JS client Some demo Some great project are already using Pizzly to handle the API requests: Airtable Shipping Block Push to Google Sheets GitHub fetch profile (tutorial) in or React Vue.js At , Pizzly is a full part of our API integrations flow, to manage Slack notifications for example. Psst. Bearer.sh How it can help you? Pizzly supports more than 50 APIs out-of-the-box. All you need to do is set your credentials and scopes in the dashboard. This list includes the most common APIs, such as: : Gmail, Microsoft Teams, Slack, Zoom; Communication APIs : Front, Hubspot, Salesforce, etc. CRM : BitBucket, GitHub, GitLab, etc. Developer tools : Xero, Sellsy, Zoho Books, etc. Finance APIs : Asana, Google Drive, Google Sheets, Jira, Trello, etc. Productivity : Facebook, LinkedIn, Reddit, etc. Social APIs and more... But that's not all. Each pre-configured API is a .json file located . So if you want an API that is not pre-configured yet, you can set up the configuration as a new .json file within that directory. If ever you feel like sharing, create a new PR to share your own configuration with the community. within the /integrations/ folder Built with Node.js ⚡️ - To handle the OAuth dance Passport.js - To manage requests to the database 📃 Knex - For the dashboard templating ⚙️ EJS Vanilla JavaScript - For some magic 🍦 Support us , Tweet, share among your friends, teams and contacts! Star the repo on GitHub You can also get in touch directly with me via email at or on Twitter . And if you really want to help us make Pizzly better, ! corentin@bearer.sh @frenchcooc contribute to the GitHub repo Previously published at https://blog.bearer.sh/introducing-pizzly-oauth-proxy/

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