1653847680
FileConveyor: Daemon Written in Python to Detect, Process & Sync Files
Description
-----------
File Conveyor is designed to discover new, changed and deleted files via the
operating system's built-in file system monitor. After discovering the files,
they can be optionally be processed by a chain of processors – you can easily
write new ones yourself. After files have been processed, they can also
optionally be transported to a server.
Discovery happens through inotify on Linux (with kernel >= 2.6.13), through
FSEvents on Mac OS X (>= 10.5) and through polling on other operating systems.
Processors are simple Python scripts that can change the file's base name (it
is impossible to change the path) and apply any sort of processing to the
file's contents. Examples are image optimization and video transcoding.
Transporters are simple threaded abstractions around Django storage systems.
For a detailed description of the innards of file conveyor, see my bachelor
thesis text (find it via http://wimleers.com/tags/bachelor-thesis).
This application was written as part of the bachelor thesis [1] of Wim Leers
at Hasselt University [2].
[1] http://wimleers.com/tags/bachelor-thesis
[2] http://uhasselt.be/
<BLINK>IMPORTANT WARNING</BLINK>
--------------------------------
I've attempted to provide a solid enough README to get you started, but I'm
well aware that it isn't superb. But as this is just a bachelor thesis, time
was fairly limited. I've opted to create a solid basis instead of an extremely
rigourously documented piece of software. If you cannot find the answer in the
README.txt, nor the INSTALL.txt, nor the API.txt files, then please look at
my bachelor thesis text instead. If neither of that is sufficient, then please
contact me.
Upgrading
---------
If you're upgrading from a previous version of File Conveyor, please run
upgrade.py.
==============================================================================
| The basics |
==============================================================================
Configuring File Conveyor
-------------------------
The sample configuration file (config.sample.xml) should be self explanatory.
Copy this file to config.xml, which is the file File Conveyor will look for,
and edit it to suit your needs.
For a detailed description, see my bachelor thesis text (look for the
"Configuration file design" section).
Each rule consists of 3 components:
- filter
- processorChain
- destinations
A rule can also be configured to delete source files after they have been
synced to the destination(s).
The filter and processorChain components are optional. You must have at least
one destination.
If you want to use File Conveyor to process files locally, i.e. without
transporting them to a server, then use the Symlink or Copy transporter (see
below).
Starting File Conveyor
----------------------
File Conveyor must be started by starting its arbitrator (which links
everything together; it controls the file system monitor, the processor
chains, the transporters and so on). You can start the arbitrator like this:
python /path/to/fileconveyor/arbitrator.py
Stopping File Conveyor
----------------------
File Conveyor listens to standard signals to know when it should end, like the
Apache HTTP server does too. Send the TERMinate signal to terminate it:
kill -TERM `cat ~/.fileconveyor.pid`
You can configure File Conveyor to store the PID file in the more typical
/var/run location on *nix:
* You can change the PID_FILE setting in settings.py to
/var/run/fileconveyor.pid. However, this requires File Conveyor to be run with
root permissions (/var/run requires root permissions).
* Alternatively, you can create a new directory in /var/run which then no
longer requires root permissions. This can be achieved through these commands:
1. sudo mkdir /var/run/fileconveyor`
2. sudo chown fileconveyor-user /var/run/fileconveyor
3. sudo chown 700 /var/run/fileconveyor
Then, you can change the PID_FILE setting in settings.py to
/var/run/fileconveyor/fileconveyor.pid, and you won't need to run File
Conveyor with root permissions anymore.
File Conveyor's behavior
------------------------
Upon startup, File Conveyor starts the file system monitor and then performs a
"manual" scan to detect changes since the last time it ran. If you've got a
lot of files, this may take a while.
Just for fun, type the following while File Conveyor is syncing:
killall -9 python
Now File Conveyor is dead. Upon starting it again, you should see something like:
2009-05-17 03:52:13,454 - Arbitrator - WARNING - Setup: initialized 'pipeline' persistent queue, contains 2259 items.
2009-05-17 03:52:13,455 - Arbitrator - WARNING - Setup: initialized 'files_in_pipeline' persistent list, contains 47 items.
2009-05-17 03:52:13,455 - Arbitrator - WARNING - Setup: initialized 'failed_files' persistent list, contains 0 items.
2009-05-17 03:52:13,671 - Arbitrator - WARNING - Setup: moved 47 items from the 'files_in_pipeline' persistent list into the 'pipeline' persistent queue.
2009-05-17 03:52:13,672 - Arbitrator - WARNING - Setup: moved 0 items from the 'failed_files' persistent list into the 'pipeline' persistent queue.
As you can see, 47 items were still in the pipeline when File Conveyor was
killed. They're now simply added to the pipeline queue again and they will be
processed once again.
The initial sync
----------------
To get a feeling of File Conveyor's speed, you may want to run it in the console
and look at its output.
Verifying the synced files
--------------------------
Running the verify.py script will open the synced files database and verify
that each synced file actually exists.
==============================================================================
| Processors |
==============================================================================
Addressing processors
---------------------
You can address a specific processor by first specifying its processor module
and then the exact processor name (which is its class name):
- unique_filename.MD5
- image_optimizer.KeepMetadata
- yui_compressor.YUICompressor
- link_updater.CSSURLUpdater
But, it works with third-party processors too! Just make sure the third-party
package is in the Python path and then you can just use this in config.xml:
- MyProcessorPackage.SomeProcessorClass
Processor module: filename
--------------------------
Available processors:
1) SpacesToUnderscores
Changes a filename; replaces spaces by underscores. E.g.:
this is a test.txt --> this_is_a_test.txt
2) SpacesToDashes
Changes a filename; replaces spaces by dashes. E.g.:
this is a test.txt --> this-is-a-test.txt
Processor module: unique_filename
---------------------------------
Available processors:
1) Mtime
Changes a filename based on the file's mtime. E.g.:
logo.gif --> logo_1240668971.gif
2) MD5
Changes a filename based on the file's MD5 hash. E.g.:
logo.gif --> logo_2f0342a2b9aaf48f9e75aa7ed1d58c48.gif
Processor module: image_optimizer
---------------------------------
It's important to note that all metadata is stripped from JPEG images, as that
is the most effective way to reduce the image size. However, this might also
strip copyright information, i.e. this can also have legal consequences.
Choose one of the "keep metadata" classes if you want to avoid this.
When optimizing GIF images, they are converted to the PNG format, which also
changes their filename.
Available processors:
1) Max
optimizes image files losslessly (GIF, PNG, JPEG, animated GIF)
2) KeepMetadata
same as Max, but keeps JPEG metadata
3) KeepFilename
same as Max, but keeps the original filename (no GIF optimization)
4) KeepMetadataAndFilename
same as Max, but keeps JPEG metadata and the original filename (no GIF
optimization)
Processor module: yui_compressor
--------------------------------
Warning: this processor is CPU-intensive! Since you typically don't get new
CSS and JS files all the time, it's still fine to use this. But the initial
sync may cause a lot of CSS and JS files to be processed and thereby cause a
lot of load!
Available processors:
1) YUICompressor
Compresses .css and .js files with the YUI Compressor
Processor module: google_closure_compiler
-----------------------------------------
Warning: this processor is CPU-intensive! Since you typically don't get new
JS files all the time, it's still fine to use this. But the initial sync may
cause a lot of JS files to be processed and thereby cause a lot of load!
Available processors:
1) GoogleClosureCompiler
Compresses .js files with the Google Closure Compiler
Processor module: link_updater
------------------------------
Warning: this processor is CPU-intensive! Since you typically don't get new
CSS files all the time, it's still fine to use this. But the initial sync may
cause a lot of CSS files to be processed and thereby cause a lot of load! Note
that this processor will skip processing a CSS file if not all files that are
referenced from it, have been synced to the CDN yet. Which means the CSS files
may need to parsed over and over again until the images have been synced.
It seems this processor is suited for optimization. It uses the cssutils
Python module, which validates every CSS property. This is an enormous slow-
down: on a 2.66 GHz Core 2 Duo, it causes 100% CPU usage every time it runs.
This module also seems to suffer from rather massive memory leaks. Memory
usage can easily top 30 MB on Mac OS X where it would never go over 17 MB
without this processor!
This processor will replace all URLs in CSS files with references to their
counterparts on the CDN. There are a couple of important gotchas to use this
processor module:
- absolute URLs (http://, https://) are ignored, only relative URLs are
processed
- if a referenced file doesn't exist, its URL will remain unchanged
- if one of the referenced images or fonts is changed and therefor resynced,
and if it is configured to have a unique filename, the CDN URL referenced
from the updated CSS file will no longer be valid. Therefor, when you
update an image file or font file that is referenced by CSS files, you
should modify the CSS files as well. Just modifying the mtime (by using the
touch command) is sufficient.
- it requires the referenced files to be synced to the same server the CSS
file is being synced to. This implies that all the references files must
also be synced to the same server, or the file will never get synced!
Available processors:
1) CSSURLUpdater
Replaces URLs in .css files with their counterparts on the CDN
==============================================================================
| Transporters |
==============================================================================
Addressing transporters
-----------------------
You can address a specific transporter by only specifying its module:
- cf
- ftp
- cloudfiles
- s3
- sftp
- symlink_or_copy
But, it works with third-party transporters too! Just make sure the
third-party package is in the Python path and then you can just use this in
config.xml:
- MyTransporterPackage
Transporter: FTP (ftp)
----------------------
Value to enter: "ftp".
Available settings:
- host
- username
- password
- url
- port
- path
- key
Transporter: SFTP (sftp)
------------------------
Value to enter: "sftp".
Available settings:
- host
- username
- password
- url
- port
- path
Transporter: Amazon S3
----------------------
Value to enter: "s3".
Available settings:
- access_key_id
- secret_access_key
- bucket_name
- bucket_prefix
More than 4 concurrent connections doesn't show a significant speedup.
Transporter: Amazon CloudFront
------------------------------
Value to enter: "cf".
Available settings:
- access_key_id
- secret_access_key
- bucket_name
- bucket_prefix
- distro_domain_name
Transporter: Rackspace Cloud Files
----------------------------------
Value to enter: "cloudfiles".
Available settings:
- username
- api_key
- container
Transporter: Symlink or Copy
----------------------------
Value to enter: "symlink_or_copy".
Available settings:
- location
- url
Transporter: Amazon CloudFront - Creating a CloudFront distribution
-------------------------------------------------------------------
You can either use the S3Fox Firefox add-on to create a distribution or use
the included Python function to do so. In the latter case, do the following:
>>> import sys
>>> sys.path.append('/path/to/fileconveyor/transporters')
>>> sys.path.append('/path/to/fileconveyor/dependencies')
>>> from transporter_cf import create_distribution
>>> create_distribution("access_key_id", "secret_access_key", "bucketname.s3.amazonaws.com")
Created distribution
- domain name: dqz4yxndo4z5z.cloudfront.net
- origin: bucketname.s3.amazonaws.com
- status: InProgress
- comment:
- id: E3FERS845MCNLE
Over the next few minutes, the distribution will become active. This
function will keep running until that happens.
............................
The distribution has been deployed!
==============================================================================
| The advanced stuff |
==============================================================================
Constants in Arbitrator.py
--------------------------
The following constants can be tweaked to change where File Conveyor stores
its files, or to change its behavior.
RESTART_AFTER_UNHANDLED_EXCEPTION = True
Whether File Conveyor should restart itself after it encountered an
unhandled exception (i.e., a bug).
RESTART_INTERVAL = 10
After how much time File Conveyor should restart itself, after it has
encountered an unhandled exception. Thus, this setting only has an effect
when RESTART_AFTER_UNHANDLED_EXCEPTION == True.
LOG_FILE = './fileconveyor.log'
The log file.
PERSISTENT_DATA_DB = './persistent_data.db'
Where to store persistent data (pipeline queue, 'files in pipeline' list and
'failed files' list).
SYNCED_FILES_DB = './synced_files.db'
Where to store the input_file, transported_file_basename, url and server for
each synced file.
WORKING_DIR = '/tmp/fileconveyor'
The working directory.
MAX_FILES_IN_PIPELINE = 50
The maximum number of files in the pipeline. Should be high enough in order
to prevent transporters from idling too long.
MAX_SIMULTANEOUS_PROCESSORCHAINS = 1
The maximum number of processor chains that may be executed simultaneously.
If you've got CPU intensive processors and if you're running File Conveyor
on the web server, you'll want to keep this very low, probably at 1.
MAX_SIMULTANEOUS_TRANSPORTERS = 10
The maximum number of transporters that may be running simultaneously. This
effectively caps the number of simultaneous connections. It can also be used
to have some -- although limited -- control on the throughput consumed by
the transporters.
MAX_TRANSPORTER_QUEUE_SIZE = 1
The maximum of files queued for each transporters. It's recommended to keep
this low enough to ensure files are not unnecessarily waiting. If you set
this too high, no new transporters will be spawned, because all files will
be queued on the existing transporters. Setting this to 0 can only be
recommended in environments with a continuous stream of files that need
syncing. The default of 1 is to ensure each transporter is idling as little
as possible.
QUEUE_PROCESS_BATCH_SIZE = 20
The number of files that will be processed when processing one of the many
queues. Setting this too low will cause overhead. Setting this too high will
cause delays for files that are ready to be processed or transported. See
the "Pipeline design pattern" section in my bachelor thesis text.
CALLBACKS_CONSOLE_OUTPUT = False
Controls whether output will be generated for each callback. (There are
callbacks for the file system monitor, processor chains and transporters.)
CONSOLE_LOGGER_LEVEL = logging.WARNING
Controls the output level of the logging to the console. For a full list of
possibilities, see http://docs.python.org/release/2.6/library/logging.html#logging-levels.
FILE_LOGGER_LEVEL = logging.DEBUG
Controls the output level of the logging to the console. For a full list of
possibilities, see http://docs.python.org/release/2.6/library/logging.html#logging-levels.
RETRY_INTERVAL = 30
Sets the interval in which the 'failed files' list is appended to the
pipeline queue, to retry to sync these failed files.
Understanding persistent_data.db
--------------------------------
We'll go through this by using a sample database I created. You should be able
to reproduce similar output on your persistent_data.db file using the exact
same commands.
Access the database, by using the SQLite console application.
$ sqlite3 persistent_data.db
SQLite version 3.6.11
Enter ".help" for instructions
Enter SQL statements terminated with a ";"
sqlite>
As you can see, there are three tables in the database, one for every
persistent data structure:
sqlite> .table
failed_files_list pipeline_list pipeline_queue
Simple count queries show how many items there are in each persistent data
structure. In this case for example, there are 2560 files waiting to enter the
pipeline, 50 were in the pipeline at the time of stopping File Conveyor (these
will be added to the queue again once we restart File Conveyor) and 0 files
are in the list of failed files. Files end up in there when their processor
chain or (one of) their transporters fails.
sqlite> SELECT COUNT(*) FROM pipeline_queue;
2560
sqlite> SELECT COUNT(*) FROM pipeline_list;
50
sqlite> SELECT COUNT(*) FROM failed_files_list;
0
You can also look at the database schemas of these tables:
sqlite> .schema pipeline_queue
CREATE TABLE pipeline_queue(id INTEGER PRIMARY KEY AUTOINCREMENT, item pickle);
sqlite> .schema pipeline_list
CREATE TABLE pipeline_list(id INTEGER PRIMARY KEY AUTOINCREMENT, item pickle);
sqlite> .schema failed_files_list
CREATE TABLE failed_files_list(id INTEGER PRIMARY KEY AUTOINCREMENT, item pickle);
As you can see, the three tables have identical schemas. the type for the
stored item is 'pickle', which means that you can store any Python object in
there as long as it can be "pickled", which means as much as "convertable to
a string representation". "Serialization" is the term PHP developers have
given to this, although pickling is much more advanced.
The Python object stored in there is the same for all three tables: a tuple of
the filename (as a string) and the event (as an integer). The event is one of
FSMonitor.CREATED, FSMonitor.MODIFIED, FSMonitor.DELETED.
This file is what tracks the curent state of File Conveyor. Thanks to this file,
it is possible for File Conveyor to crash and not lose any data.
Deleting this file would cause File Conveyor to lose all of its current work.
Only new (as in: after the file was deleted) changes in the file system would
be picked up. Changes that still had to be synced, would be forgotten.
Understanding fsmonitor.db
--------------------------
This database has a single table: pathscanner (which is inherited from the
pathscanner module around which the fsmonitor module is built). Its schema is:
sqlite> .schema pathscanner
CREATE TABLE pathscanner(path text, filename text, mtime integer);
This file is what tracks the current state of the directory tree associated
with each source. When an operating system's file system monitor is used, this
database will be updated through its callbacks. When no such file system
monitor is available, it will be updated through polling.
Deleting this file would cause File Conveyor to have to sync all files again.
Understanding synced_files.db
-----------------------------
We'll go through this by using a sample database I created. You should be able
to reproduce similar output on your synced_files.db file using the exact
same commands.
Access the database, by using the SQLite console application.
$ sqlite3 synced_files.db
SQLite version 3.6.11
Enter ".help" for instructions
Enter SQL statements terminated with a ";"
sqlite>
As you can see, there's only one table: synced_files.
sqlite> .table
synced_files
Let's look at the schema. There are 4 fields: input_file,
transported_file_basename, url and server. input_file is the full path.
transported_file_basename is the base name of the file that was transported to
the server. This is stored because the filename might have been altered by the
processors that have been applied to it, but the path cannot change. I use
this to delete the previous version of a file if a file has been modified. The
url field is of course the URL to retrieve the file from the server. Finally,
the server field contains the name you've assigned to the server in the
configuration file. Each file may be synced to multiple servers and this
allows you to check if a file has been synchronized to a specific server.
sqlite> .schema synced_files
CREATE TABLE synced_files(input_file text, transported_file_basename text, url text, server text);
We can again use simple count queries to learn more about the synced files. As
you can see, 845 files have been synced, of which 602 have been synced to a
the server that was named "origin pull cdn" and 243 to the server that was
named "ftp push cdn".
sqlite> SELECT COUNT(*) FROM synced_files;
845
sqlite> SELECT COUNT(*) FROM synced_files WHERE server="origin pull cdn";
602
sqlite> SELECT COUNT(*) FROM synced_files WHERE server="ftp push cdn";
243
License
-------
This application is dual-licensed under the GPL and the UNLICENSE.
Due to the dependencies that were initially included within File Conveyor,
which were all subject to GPL-compatible licenses, it made sense to initially
release the source code under the GPL.
Then, it was decided the UNLICENSE was a better fit.
Author
------
Wim Leers ~ http://wimleers.com/
This application was written as part of the bachelor thesis of Wim Leers at
Hasselt University.Author: wimleers
Source Code: https://github.com/wimleers/fileconveyor
License: Unlicense License
What is GEEK
Buddha Community
1626775355
Why use Python for Software Development
No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.
By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.
Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.
5 Reasons to Utilize Python for Programming Web Apps
Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.
Robust frameworks
Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.
Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.
Simple to read and compose
Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.
The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.
Utilized by the best
Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.
Massive community support
Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.
Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.
Progressive applications
Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.
The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.
Summary
Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.
The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.
#python development services #python development company #python app development #python development #python in web development #python software development
1602666000
How to Remove all Duplicate Files on your Drive via Python
Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.
Intro
In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.
Heres a solution
Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.
But How do we do it?
If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?
The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.
There’s a variety of hashing algorithms out there such as
- md5
- sha1
- sha224, sha256, sha384 and sha512
#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips
1619799996
Deep Learning Project : Real Time Object Detection in Python & Opencv
Real Time Object Detection in Python And OpenCV
Github Link: https://github.com/Chando0185/Object_Detection
Blog Link: https://knowledgedoctor37.blogspot.com/#
I’m on Instagram as @knowledge_doctor.
Follow Me On Instagram :
https://www.instagram.com/invites/contact/?i=f9n3ongbu8ma&utm_content=jresydt
Like My Facebook Page:
https://www.facebook.com/Knowledge-Doctor-Programming-114082097010409/
#python project #object detection #python opencv #opencv object detection #object detection in python #python opencv for object detection
1661577180
Swift Tips: A Collection Useful Tips for The Swift Language
SwiftTips
The following is a collection of tips I find to be useful when working with the Swift language. More content is available on my Twitter account!
Property Wrappers as Debugging Tools
Property Wrappers allow developers to wrap properties with specific behaviors, that will be seamlessly triggered whenever the properties are accessed.
While their primary use case is to implement business logic within our apps, it's also possible to use Property Wrappers as debugging tools!
For example, we could build a wrapper called @History, that would be added to a property while debugging and would keep track of all the values set to this property.
import Foundation
@propertyWrapper
struct History<Value> {
private var value: Value
private(set) var history: [Value] = []
init(wrappedValue: Value) {
self.value = wrappedValue
}
var wrappedValue: Value {
get { value }
set {
history.append(value)
value = newValue
}
}
var projectedValue: Self {
return self
}
}
// We can then decorate our business code
// with the `@History` wrapper
struct User {
@History var name: String = ""
}
var user = User()
// All the existing call sites will still
// compile, without the need for any change
user.name = "John"
user.name = "Jane"
// But now we can also access an history of
// all the previous values!
user.$name.history // ["", "John"]
Localization through String interpolation
Swift 5 gave us the possibility to define our own custom String interpolation methods.
This feature can be used to power many use cases, but there is one that is guaranteed to make sense in most projects: localizing user-facing strings.
import Foundation
extension String.StringInterpolation {
mutating func appendInterpolation(localized key: String, _ args: CVarArg...) {
let localized = String(format: NSLocalizedString(key, comment: ""), arguments: args)
appendLiteral(localized)
}
}
/*
Let's assume that this is the content of our Localizable.strings:
"welcome.screen.greetings" = "Hello %@!";
*/
let userName = "John"
print("\(localized: "welcome.screen.greetings", userName)") // Hello John!
Implementing pseudo-inheritance between structs
If you’ve always wanted to use some kind of inheritance mechanism for your structs, Swift 5.1 is going to make you very happy!
Using the new KeyPath-based dynamic member lookup, you can implement some pseudo-inheritance, where a type inherits the API of another one 🎉
(However, be careful, I’m definitely not advocating inheritance as a go-to solution 🙃)
import Foundation
protocol Inherits {
associatedtype SuperType
var `super`: SuperType { get }
}
extension Inherits {
subscript<T>(dynamicMember keyPath: KeyPath<SuperType, T>) -> T {
return self.`super`[keyPath: keyPath]
}
}
struct Person {
let name: String
}
@dynamicMemberLookup
struct User: Inherits {
let `super`: Person
let login: String
let password: String
}
let user = User(super: Person(name: "John Appleseed"), login: "Johnny", password: "1234")
user.name // "John Appleseed"
user.login // "Johnny"
Composing NSAttributedString through a Function Builder
Swift 5.1 introduced Function Builders: a great tool for building custom DSL syntaxes, like SwiftUI. However, one doesn't need to be building a full-fledged DSL in order to leverage them.
For example, it's possible to write a simple Function Builder, whose job will be to compose together individual instances of NSAttributedString through a nicer syntax than the standard API.
import UIKit
@_functionBuilder
class NSAttributedStringBuilder {
static func buildBlock(_ components: NSAttributedString...) -> NSAttributedString {
let result = NSMutableAttributedString(string: "")
return components.reduce(into: result) { (result, current) in result.append(current) }
}
}
extension NSAttributedString {
class func composing(@NSAttributedStringBuilder _ parts: () -> NSAttributedString) -> NSAttributedString {
return parts()
}
}
let result = NSAttributedString.composing {
NSAttributedString(string: "Hello",
attributes: [.font: UIFont.systemFont(ofSize: 24),
.foregroundColor: UIColor.red])
NSAttributedString(string: " world!",
attributes: [.font: UIFont.systemFont(ofSize: 20),
.foregroundColor: UIColor.orange])
}
Using switch and if as expressions
Contrary to other languages, like Kotlin, Swift does not allow switch and if to be used as expressions. Meaning that the following code is not valid Swift:
let constant = if condition {
someValue
} else {
someOtherValue
}
A common solution to this problem is to wrap the if or switch statement within a closure, that will then be immediately called. While this approach does manage to achieve the desired goal, it makes for a rather poor syntax.
To avoid the ugly trailing () and improve on the readability, you can define a resultOf function, that will serve the exact same purpose, in a more elegant way.
import Foundation
func resultOf<T>(_ code: () -> T) -> T {
return code()
}
let randomInt = Int.random(in: 0...3)
let spelledOut: String = resultOf {
switch randomInt {
case 0:
return "Zero"
case 1:
return "One"
case 2:
return "Two"
case 3:
return "Three"
default:
return "Out of range"
}
}
print(spelledOut)
Avoiding double negatives within guard statements
A guard statement is a very convenient way for the developer to assert that a condition is met, in order for the execution of the program to keep going.
However, since the body of a guard statement is meant to be executed when the condition evaluates to false, the use of the negation (!) operator within the condition of a guard statement can make the code hard to read, as it becomes a double negative.
A nice trick to avoid such double negatives is to encapsulate the use of the ! operator within a new property or function, whose name does not include a negative.
import Foundation
extension Collection {
var hasElements: Bool {
return !isEmpty
}
}
let array = Bool.random() ? [1, 2, 3] : []
guard array.hasElements else { fatalError("array was empty") }
print(array)
Defining a custom init without loosing the compiler-generated one
It's common knowledge for Swift developers that, when you define a struct, the compiler is going to automatically generate a memberwise init for you. That is, unless you also define an init of your own. Because then, the compiler won't generate any memberwise init.
Yet, there are many instances where we might enjoy the opportunity to get both. As it turns out, this goal is quite easy to achieve: you just need to define your own init in an extension rather than inside the type definition itself.
import Foundation
struct Point {
let x: Int
let y: Int
}
extension Point {
init() {
x = 0
y = 0
}
}
let usingDefaultInit = Point(x: 4, y: 3)
let usingCustomInit = Point()
Implementing a namespace through an empty enum
Swift does not really have an out-of-the-box support of namespaces. One could argue that a Swift module can be seen as a namespace, but creating a dedicated Framework for this sole purpose can legitimately be regarded as overkill.
Some developers have taken the habit to use a struct which only contains static fields to implement a namespace. While this does the job, it requires us to remember to implement an empty private init(), because it wouldn't make sense for such a struct to be instantiated.
It's actually possible to take this approach one step further, by replacing the struct with an enum. While it might seem weird to have an enum with no case, it's actually a very idiomatic way to declare a type that cannot be instantiated.
import Foundation
enum NumberFormatterProvider {
static var currencyFormatter: NumberFormatter {
let formatter = NumberFormatter()
formatter.numberStyle = .currency
formatter.roundingIncrement = 0.01
return formatter
}
static var decimalFormatter: NumberFormatter {
let formatter = NumberFormatter()
formatter.numberStyle = .decimal
formatter.decimalSeparator = ","
return formatter
}
}
NumberFormatterProvider() // ❌ impossible to instantiate by mistake
NumberFormatterProvider.currencyFormatter.string(from: 2.456) // $2.46
NumberFormatterProvider.decimalFormatter.string(from: 2.456) // 2,456
Using Never to represent impossible code paths
Never is quite a peculiar type in the Swift Standard Library: it is defined as an empty enum enum Never { }.
While this might seem odd at first glance, it actually yields a very interesting property: it makes it a type that cannot be constructed (i.e. it possesses no instances).
This way, Never can be used as a generic parameter to let the compiler know that a particular feature will not be used.
import Foundation
enum Result<Value, Error> {
case success(value: Value)
case failure(error: Error)
}
func willAlwaysSucceed(_ completion: @escaping ((Result<String, Never>) -> Void)) {
completion(.success(value: "Call was successful"))
}
willAlwaysSucceed( { result in
switch result {
case .success(let value):
print(value)
// the compiler knows that the `failure` case cannot happen
// so it doesn't require us to handle it.
}
})
Providing a default value to a Decodable enum
Swift's Codable framework does a great job at seamlessly decoding entities from a JSON stream. However, when we integrate web-services, we are sometimes left to deal with JSONs that require behaviors that Codable does not provide out-of-the-box.
For instance, we might have a string-based or integer-based enum, and be required to set it to a default value when the data found in the JSON does not match any of its cases.
We might be tempted to implement this via an extensive switch statement over all the possible cases, but there is a much shorter alternative through the initializer init?(rawValue:):
import Foundation
enum State: String, Decodable {
case active
case inactive
case undefined
init(from decoder: Decoder) throws {
let container = try decoder.singleValueContainer()
let decodedString = try container.decode(String.self)
self = State(rawValue: decodedString) ?? .undefined
}
}
let data = """
["active", "inactive", "foo"]
""".data(using: .utf8)!
let decoded = try! JSONDecoder().decode([State].self, from: data)
print(decoded) // [State.active, State.inactive, State.undefined]
Another lightweight dependency injection through default values for function parameters
Dependency injection boils down to a simple idea: when an object requires a dependency, it shouldn't create it by itself, but instead it should be given a function that does it for him.
Now the great thing with Swift is that, not only can a function take another function as a parameter, but that parameter can also be given a default value.
When you combine both those features, you can end up with a dependency injection pattern that is both lightweight on boilerplate, but also type safe.
import Foundation
protocol Service {
func call() -> String
}
class ProductionService: Service {
func call() -> String {
return "This is the production"
}
}
class MockService: Service {
func call() -> String {
return "This is a mock"
}
}
typealias Provider<T> = () -> T
class Controller {
let service: Service
init(serviceProvider: Provider<Service> = { return ProductionService() }) {
self.service = serviceProvider()
}
func work() {
print(service.call())
}
}
let productionController = Controller()
productionController.work() // prints "This is the production"
let mockedController = Controller(serviceProvider: { return MockService() })
mockedController.work() // prints "This is a mock"
Lightweight dependency injection through protocol-oriented programming
Singletons are pretty bad. They make your architecture rigid and tightly coupled, which then results in your code being hard to test and refactor. Instead of using singletons, your code should rely on dependency injection, which is a much more architecturally sound approach.
But singletons are so easy to use, and dependency injection requires us to do extra-work. So maybe, for simple situations, we could find an in-between solution?
One possible solution is to rely on one of Swift's most know features: protocol-oriented programming. Using a protocol, we declare and access our dependency. We then store it in a private singleton, and perform the injection through an extension of said protocol.
This way, our code will indeed be decoupled from its dependency, while at the same time keeping the boilerplate to a minimum.
import Foundation
protocol Formatting {
var formatter: NumberFormatter { get }
}
private let sharedFormatter: NumberFormatter = {
let sharedFormatter = NumberFormatter()
sharedFormatter.numberStyle = .currency
return sharedFormatter
}()
extension Formatting {
var formatter: NumberFormatter { return sharedFormatter }
}
class ViewModel: Formatting {
var displayableAmount: String?
func updateDisplay(to amount: Double) {
displayableAmount = formatter.string(for: amount)
}
}
let viewModel = ViewModel()
viewModel.updateDisplay(to: 42000.45)
viewModel.displayableAmount // "$42,000.45"
Getting rid of overabundant [weak self] and guard
Callbacks are a part of almost all iOS apps, and as frameworks such as RxSwift keep gaining in popularity, they become ever more present in our codebase.
Seasoned Swift developers are aware of the potential memory leaks that @escaping callbacks can produce, so they make real sure to always use [weak self], whenever they need to use self inside such a context. And when they need to have self be non-optional, they then add a guard statement along.
Consequently, this syntax of a [weak self] followed by a guard rapidly tends to appear everywhere in the codebase. The good thing is that, through a little protocol-oriented trick, it's actually possible to get rid of this tedious syntax, without loosing any of its benefits!
import Foundation
import PlaygroundSupport
PlaygroundPage.current.needsIndefiniteExecution = true
protocol Weakifiable: class { }
extension Weakifiable {
func weakify(_ code: @escaping (Self) -> Void) -> () -> Void {
return { [weak self] in
guard let self = self else { return }
code(self)
}
}
func weakify<T>(_ code: @escaping (T, Self) -> Void) -> (T) -> Void {
return { [weak self] arg in
guard let self = self else { return }
code(arg, self)
}
}
}
extension NSObject: Weakifiable { }
class Producer: NSObject {
deinit {
print("deinit Producer")
}
private var handler: (Int) -> Void = { _ in }
func register(handler: @escaping (Int) -> Void) {
self.handler = handler
DispatchQueue.main.asyncAfter(deadline: .now() + 1.0, execute: { self.handler(42) })
}
}
class Consumer: NSObject {
deinit {
print("deinit Consumer")
}
let producer = Producer()
func consume() {
producer.register(handler: weakify { result, strongSelf in
strongSelf.handle(result)
})
}
private func handle(_ result: Int) {
print("🎉 \(result)")
}
}
var consumer: Consumer? = Consumer()
consumer?.consume()
DispatchQueue.main.asyncAfter(deadline: .now() + 2.0, execute: { consumer = nil })
// This code prints:
// 🎉 42
// deinit Consumer
// deinit Producer
Solving callback hell with function composition
Asynchronous functions are a big part of iOS APIs, and most developers are familiar with the challenge they pose when one needs to sequentially call several asynchronous APIs.
This often results in callbacks being nested into one another, a predicament often referred to as callback hell.
Many third-party frameworks are able to tackle this issue, for instance RxSwift or PromiseKit. Yet, for simple instances of the problem, there is no need to use such big guns, as it can actually be solved with simple function composition.
import Foundation
typealias CompletionHandler<Result> = (Result?, Error?) -> Void
infix operator ~>: MultiplicationPrecedence
func ~> <T, U>(_ first: @escaping (CompletionHandler<T>) -> Void, _ second: @escaping (T, CompletionHandler<U>) -> Void) -> (CompletionHandler<U>) -> Void {
return { completion in
first({ firstResult, error in
guard let firstResult = firstResult else { completion(nil, error); return }
second(firstResult, { (secondResult, error) in
completion(secondResult, error)
})
})
}
}
func ~> <T, U>(_ first: @escaping (CompletionHandler<T>) -> Void, _ transform: @escaping (T) -> U) -> (CompletionHandler<U>) -> Void {
return { completion in
first({ result, error in
guard let result = result else { completion(nil, error); return }
completion(transform(result), nil)
})
}
}
func service1(_ completionHandler: CompletionHandler<Int>) {
completionHandler(42, nil)
}
func service2(arg: String, _ completionHandler: CompletionHandler<String>) {
completionHandler("🎉 \(arg)", nil)
}
let chainedServices = service1
~> { int in return String(int / 2) }
~> service2
chainedServices({ result, _ in
guard let result = result else { return }
print(result) // Prints: 🎉 21
})
Transform an asynchronous function into a synchronous one
Asynchronous functions are a great way to deal with future events without blocking a thread. Yet, there are times where we would like them to behave in exactly such a blocking way.
Think about writing unit tests and using mocked network calls. You will need to add complexity to your test in order to deal with asynchronous functions, whereas synchronous ones would be much easier to manage.
Thanks to Swift proficiency in the functional paradigm, it is possible to write a function whose job is to take an asynchronous function and transform it into a synchronous one.
import Foundation
func makeSynchrone<A, B>(_ asyncFunction: @escaping (A, (B) -> Void) -> Void) -> (A) -> B {
return { arg in
let lock = NSRecursiveLock()
var result: B? = nil
asyncFunction(arg) {
result = $0
lock.unlock()
}
lock.lock()
return result!
}
}
func myAsyncFunction(arg: Int, completionHandler: (String) -> Void) {
completionHandler("🎉 \(arg)")
}
let syncFunction = makeSynchrone(myAsyncFunction)
print(syncFunction(42)) // prints 🎉 42
Using KeyPaths instead of closures
Closures are a great way to interact with generic APIs, for instance APIs that allow to manipulate data structures through the use of generic functions, such as filter() or sorted().
The annoying part is that closures tend to clutter your code with many instances of {, } and $0, which can quickly undermine its readably.
A nice alternative for a cleaner syntax is to use a KeyPath instead of a closure, along with an operator that will deal with transforming the provided KeyPath in a closure.
import Foundation
prefix operator ^
prefix func ^ <Element, Attribute>(_ keyPath: KeyPath<Element, Attribute>) -> (Element) -> Attribute {
return { element in element[keyPath: keyPath] }
}
struct MyData {
let int: Int
let string: String
}
let data = [MyData(int: 2, string: "Foo"), MyData(int: 4, string: "Bar")]
data.map(^\.int) // [2, 4]
data.map(^\.string) // ["Foo", "Bar"]
Bringing some type-safety to a userInfo Dictionary
Many iOS APIs still rely on a userInfo Dictionary to handle use-case specific data. This Dictionary usually stores untyped values, and is declared as follows: [String: Any] (or sometimes [AnyHashable: Any].
Retrieving data from such a structure will involve some conditional casting (via the as? operator), which is prone to both errors and repetitions. Yet, by introducing a custom subscript, it's possible to encapsulate all the tedious logic, and end-up with an easier and more robust API.
import Foundation
typealias TypedUserInfoKey<T> = (key: String, type: T.Type)
extension Dictionary where Key == String, Value == Any {
subscript<T>(_ typedKey: TypedUserInfoKey<T>) -> T? {
return self[typedKey.key] as? T
}
}
let userInfo: [String : Any] = ["Foo": 4, "Bar": "forty-two"]
let integerTypedKey = TypedUserInfoKey(key: "Foo", type: Int.self)
let intValue = userInfo[integerTypedKey] // returns 4
type(of: intValue) // returns Int?
let stringTypedKey = TypedUserInfoKey(key: "Bar", type: String.self)
let stringValue = userInfo[stringTypedKey] // returns "forty-two"
type(of: stringValue) // returns String?
Lightweight data-binding for an MVVM implementation
MVVM is a great pattern to separate business logic from presentation logic. The main challenge to make it work, is to define a mechanism for the presentation layer to be notified of model updates.
RxSwift is a perfect choice to solve such a problem. Yet, some developers don't feel confortable with leveraging a third-party library for such a central part of their architecture.
For those situation, it's possible to define a lightweight Variable type, that will make the MVVM pattern very easy to use!
import Foundation
class Variable<Value> {
var value: Value {
didSet {
onUpdate?(value)
}
}
var onUpdate: ((Value) -> Void)? {
didSet {
onUpdate?(value)
}
}
init(_ value: Value, _ onUpdate: ((Value) -> Void)? = nil) {
self.value = value
self.onUpdate = onUpdate
self.onUpdate?(value)
}
}
let variable: Variable<String?> = Variable(nil)
variable.onUpdate = { data in
if let data = data {
print(data)
}
}
variable.value = "Foo"
variable.value = "Bar"
// prints:
// Foo
// Bar
Using typealias to its fullest
The keyword typealias allows developers to give a new name to an already existing type. For instance, Swift defines Void as a typealias of (), the empty tuple.
But a less known feature of this mechanism is that it allows to assign concrete types for generic parameters, or to rename them. This can help make the semantics of generic types much clearer, when used in specific use cases.
import Foundation
enum Either<Left, Right> {
case left(Left)
case right(Right)
}
typealias Result<Value> = Either<Value, Error>
typealias IntOrString = Either<Int, String>
Writing an interruptible overload of forEach
Iterating through objects via the forEach(_:) method is a great alternative to the classic for loop, as it allows our code to be completely oblivious of the iteration logic. One limitation, however, is that forEach(_:) does not allow to stop the iteration midway.
Taking inspiration from the Objective-C implementation, we can write an overload that will allow the developer to stop the iteration, if needed.
import Foundation
extension Sequence {
func forEach(_ body: (Element, _ stop: inout Bool) throws -> Void) rethrows {
var stop = false
for element in self {
try body(element, &stop)
if stop {
return
}
}
}
}
["Foo", "Bar", "FooBar"].forEach { element, stop in
print(element)
stop = (element == "Bar")
}
// Prints:
// Foo
// Bar
Optimizing the use of reduce()
Functional programing is a great way to simplify a codebase. For instance, reduce is an alternative to the classic for loop, without most the boilerplate. Unfortunately, simplicity often comes at the price of performance.
Consider that you want to remove duplicate values from a Sequence. While reduce() is a perfectly fine way to express this computation, the performance will be sub optimal, because of all the unnecessary Array copying that will happen every time its closure gets called.
That's when reduce(into:_:) comes into play. This version of reduce leverages the capacities of copy-on-write type (such as Array or Dictionnary) in order to avoid unnecessary copying, which results in a great performance boost.
import Foundation
func time(averagedExecutions: Int = 1, _ code: () -> Void) {
let start = Date()
for _ in 0..<averagedExecutions { code() }
let end = Date()
let duration = end.timeIntervalSince(start) / Double(averagedExecutions)
print("time: \(duration)")
}
let data = (1...1_000).map { _ in Int(arc4random_uniform(256)) }
// runs in 0.63s
time {
let noDuplicates: [Int] = data.reduce([], { $0.contains($1) ? $0 : $0 + [$1] })
}
// runs in 0.15s
time {
let noDuplicates: [Int] = data.reduce(into: [], { if !$0.contains($1) { $0.append($1) } } )
}
Avoiding hardcoded reuse identifiers
UI components such as UITableView and UICollectionView rely on reuse identifiers in order to efficiently recycle the views they display. Often, those reuse identifiers take the form of a static hardcoded String, that will be used for every instance of their class.
Through protocol-oriented programing, it's possible to avoid those hardcoded values, and instead use the name of the type as a reuse identifier.
import Foundation
import UIKit
protocol Reusable {
static var reuseIdentifier: String { get }
}
extension Reusable {
static var reuseIdentifier: String {
return String(describing: self)
}
}
extension UITableViewCell: Reusable { }
extension UITableView {
func register<T: UITableViewCell>(_ class: T.Type) {
register(`class`, forCellReuseIdentifier: T.reuseIdentifier)
}
func dequeueReusableCell<T: UITableViewCell>(for indexPath: IndexPath) -> T {
return dequeueReusableCell(withIdentifier: T.reuseIdentifier, for: indexPath) as! T
}
}
class MyCell: UITableViewCell { }
let tableView = UITableView()
tableView.register(MyCell.self)
let myCell: MyCell = tableView.dequeueReusableCell(for: [0, 0])
Defining a union type
The C language has a construct called union, that allows a single variable to hold values from different types. While Swift does not provide such a construct, it provides enums with associated values, which allows us to define a type called Either that implements a union of two types.
import Foundation
enum Either<A, B> {
case left(A)
case right(B)
func either(ifLeft: ((A) -> Void)? = nil, ifRight: ((B) -> Void)? = nil) {
switch self {
case let .left(a):
ifLeft?(a)
case let .right(b):
ifRight?(b)
}
}
}
extension Bool { static func random() -> Bool { return arc4random_uniform(2) == 0 } }
var intOrString: Either<Int, String> = Bool.random() ? .left(2) : .right("Foo")
intOrString.either(ifLeft: { print($0 + 1) }, ifRight: { print($0 + "Bar") })
If you're interested by this kind of data structure, I strongly recommend that you learn more about Algebraic Data Types.
Asserting that classes have associated NIBs and vice-versa
Most of the time, when we create a .xib file, we give it the same name as its associated class. From that, if we later refactor our code and rename such a class, we run the risk of forgetting to rename the associated .xib.
While the error will often be easy to catch, if the .xib is used in a remote section of its app, it might go unnoticed for sometime. Fortunately it's possible to build custom test predicates that will assert that 1) for a given class, there exists a .nib with the same name in a given Bundle, 2) for all the .nib in a given Bundle, there exists a class with the same name.
import XCTest
public func XCTAssertClassHasNib(_ class: AnyClass, bundle: Bundle, file: StaticString = #file, line: UInt = #line) {
let associatedNibURL = bundle.url(forResource: String(describing: `class`), withExtension: "nib")
XCTAssertNotNil(associatedNibURL, "Class \"\(`class`)\" has no associated nib file", file: file, line: line)
}
public func XCTAssertNibHaveClasses(_ bundle: Bundle, file: StaticString = #file, line: UInt = #line) {
guard let bundleName = bundle.infoDictionary?["CFBundleName"] as? String,
let basePath = bundle.resourcePath,
let enumerator = FileManager.default.enumerator(at: URL(fileURLWithPath: basePath),
includingPropertiesForKeys: nil,
options: [.skipsHiddenFiles, .skipsSubdirectoryDescendants]) else { return }
var nibFilesURLs = [URL]()
for case let fileURL as URL in enumerator {
if fileURL.pathExtension.uppercased() == "NIB" {
nibFilesURLs.append(fileURL)
}
}
nibFilesURLs.map { $0.lastPathComponent }
.compactMap { $0.split(separator: ".").first }
.map { String($0) }
.forEach {
let associatedClass: AnyClass? = bundle.classNamed("\(bundleName).\($0)")
XCTAssertNotNil(associatedClass, "File \"\($0).nib\" has no associated class", file: file, line: line)
}
}
XCTAssertClassHasNib(MyFirstTableViewCell.self, bundle: Bundle(for: AppDelegate.self))
XCTAssertClassHasNib(MySecondTableViewCell.self, bundle: Bundle(for: AppDelegate.self))
XCTAssertNibHaveClasses(Bundle(for: AppDelegate.self))
Many thanks Benjamin Lavialle for coming up with the idea behind the second test predicate.
Small footprint type-erasing with functions
Seasoned Swift developers know it: a protocol with associated type (PAT) "can only be used as a generic constraint because it has Self or associated type requirements". When we really need to use a PAT to type a variable, the goto workaround is to use a type-erased wrapper.
While this solution works perfectly, it requires a fair amount of boilerplate code. In instances where we are only interested in exposing one particular function of the PAT, a shorter approach using function types is possible.
import Foundation
import UIKit
protocol Configurable {
associatedtype Model
func configure(with model: Model)
}
typealias Configurator<Model> = (Model) -> ()
extension UILabel: Configurable {
func configure(with model: String) {
self.text = model
}
}
let label = UILabel()
let configurator: Configurator<String> = label.configure
configurator("Foo")
label.text // "Foo"
Performing animations sequentially
UIKit exposes a very powerful and simple API to perform view animations. However, this API can become a little bit quirky to use when we want to perform animations sequentially, because it involves nesting closure within one another, which produces notoriously hard to maintain code.
Nonetheless, it's possible to define a rather simple class, that will expose a really nicer API for this particular use case 👌
import Foundation
import UIKit
class AnimationSequence {
typealias Animations = () -> Void
private let current: Animations
private let duration: TimeInterval
private var next: AnimationSequence? = nil
init(animations: @escaping Animations, duration: TimeInterval) {
self.current = animations
self.duration = duration
}
@discardableResult func append(animations: @escaping Animations, duration: TimeInterval) -> AnimationSequence {
var lastAnimation = self
while let nextAnimation = lastAnimation.next {
lastAnimation = nextAnimation
}
lastAnimation.next = AnimationSequence(animations: animations, duration: duration)
return self
}
func run() {
UIView.animate(withDuration: duration, animations: current, completion: { finished in
if finished, let next = self.next {
next.run()
}
})
}
}
var firstView = UIView()
var secondView = UIView()
firstView.alpha = 0
secondView.alpha = 0
AnimationSequence(animations: { firstView.alpha = 1.0 }, duration: 1)
.append(animations: { secondView.alpha = 1.0 }, duration: 0.5)
.append(animations: { firstView.alpha = 0.0 }, duration: 2.0)
.run()
Debouncing a function call
Debouncing is a very useful tool when dealing with UI inputs. Consider a search bar, whose content is used to query an API. It wouldn't make sense to perform a request for every character the user is typing, because as soon as a new character is entered, the result of the previous request has become irrelevant.
Instead, our code will perform much better if we "debounce" the API call, meaning that we will wait until some delay has passed, without the input being modified, before actually performing the call.
import Foundation
func debounced(delay: TimeInterval, queue: DispatchQueue = .main, action: @escaping (() -> Void)) -> () -> Void {
var workItem: DispatchWorkItem?
return {
workItem?.cancel()
workItem = DispatchWorkItem(block: action)
queue.asyncAfter(deadline: .now() + delay, execute: workItem!)
}
}
let debouncedPrint = debounced(delay: 1.0) { print("Action performed!") }
debouncedPrint()
debouncedPrint()
debouncedPrint()
// After a 1 second delay, this gets
// printed only once to the console:
// Action performed!
Providing useful operators for Optional booleans
When we need to apply the standard boolean operators to Optional booleans, we often end up with a syntax unnecessarily crowded with unwrapping operations. By taking a cue from the world of three-valued logics, we can define a couple operators that make working with Bool? values much nicer.
import Foundation
func && (lhs: Bool?, rhs: Bool?) -> Bool? {
switch (lhs, rhs) {
case (false, _), (_, false):
return false
case let (unwrapLhs?, unwrapRhs?):
return unwrapLhs && unwrapRhs
default:
return nil
}
}
func || (lhs: Bool?, rhs: Bool?) -> Bool? {
switch (lhs, rhs) {
case (true, _), (_, true):
return true
case let (unwrapLhs?, unwrapRhs?):
return unwrapLhs || unwrapRhs
default:
return nil
}
}
false && nil // false
true && nil // nil
[true, nil, false].reduce(true, &&) // false
nil || true // true
nil || false // nil
[true, nil, false].reduce(false, ||) // true
Removing duplicate values from a Sequence
Transforming a Sequence in order to remove all the duplicate values it contains is a classic use case. To implement it, one could be tempted to transform the Sequence into a Set, then back to an Array. The downside with this approach is that it will not preserve the order of the sequence, which can definitely be a dealbreaker. Using reduce() it is possible to provide a concise implementation that preserves ordering:
import Foundation
extension Sequence where Element: Equatable {
func duplicatesRemoved() -> [Element] {
return reduce([], { $0.contains($1) ? $0 : $0 + [$1] })
}
}
let data = [2, 5, 2, 3, 6, 5, 2]
data.duplicatesRemoved() // [2, 5, 3, 6]
Shorter syntax to deal with optional strings
Optional strings are very common in Swift code, for instance many objects from UIKit expose the text they display as a String?. Many times you will need to manipulate this data as an unwrapped String, with a default value set to the empty string for nil cases.
While the nil-coalescing operator (e.g. ??) is a perfectly fine way to a achieve this goal, defining a computed variable like orEmpty can help a lot in cleaning the syntax.
import Foundation
import UIKit
extension Optional where Wrapped == String {
var orEmpty: String {
switch self {
case .some(let value):
return value
case .none:
return ""
}
}
}
func doesNotWorkWithOptionalString(_ param: String) {
// do something with `param`
}
let label = UILabel()
label.text = "This is some text."
doesNotWorkWithOptionalString(label.text.orEmpty)
Encapsulating background computation and UI update
Every seasoned iOS developers knows it: objects from UIKit can only be accessed from the main thread. Any attempt to access them from a background thread is a guaranteed crash.
Still, running a costly computation on the background, and then using it to update the UI can be a common pattern.
In such cases you can rely on asyncUI to encapsulate all the boilerplate code.
import Foundation
import UIKit
func asyncUI<T>(_ computation: @autoclosure @escaping () -> T, qos: DispatchQoS.QoSClass = .userInitiated, _ completion: @escaping (T) -> Void) {
DispatchQueue.global(qos: qos).async {
let value = computation()
DispatchQueue.main.async {
completion(value)
}
}
}
let label = UILabel()
func costlyComputation() -> Int { return (0..<10_000).reduce(0, +) }
asyncUI(costlyComputation()) { value in
label.text = "\(value)"
}
Retrieving all the necessary data to build a debug view
A debug view, from which any controller of an app can be instantiated and pushed on the navigation stack, has the potential to bring some real value to a development process. A requirement to build such a view is to have a list of all the classes from a given Bundle that inherit from UIViewController. With the following extension, retrieving this list becomes a piece of cake 🍰
import Foundation
import UIKit
import ObjectiveC
extension Bundle {
func viewControllerTypes() -> [UIViewController.Type] {
guard let bundlePath = self.executablePath else { return [] }
var size: UInt32 = 0
var rawClassNames: UnsafeMutablePointer<UnsafePointer<Int8>>!
var parsedClassNames = [String]()
rawClassNames = objc_copyClassNamesForImage(bundlePath, &size)
for index in 0..<size {
let className = rawClassNames[Int(index)]
if let name = NSString.init(utf8String:className) as String?,
NSClassFromString(name) is UIViewController.Type {
parsedClassNames.append(name)
}
}
return parsedClassNames
.sorted()
.compactMap { NSClassFromString($0) as? UIViewController.Type }
}
}
// Fetch all view controller types in UIKit
Bundle(for: UIViewController.self).viewControllerTypes()
I share the credit for this tip with Benoît Caron.
Defining a function to map over dictionaries
Update As it turns out, map is actually a really bad name for this function, because it does not preserve composition of transformations, a property that is required to fit the definition of a real map function.
Surprisingly enough, the standard library doesn't define a map() function for dictionaries that allows to map both keys and values into a new Dictionary. Nevertheless, such a function can be helpful, for instance when converting data across different frameworks.
import Foundation
extension Dictionary {
func map<T: Hashable, U>(_ transform: (Key, Value) throws -> (T, U)) rethrows -> [T: U] {
var result: [T: U] = [:]
for (key, value) in self {
let (transformedKey, transformedValue) = try transform(key, value)
result[transformedKey] = transformedValue
}
return result
}
}
let data = [0: 5, 1: 6, 2: 7]
data.map { ("\($0)", $1 * $1) } // ["2": 49, "0": 25, "1": 36]
A shorter syntax to remove nil values
Swift provides the function compactMap(), that can be used to remove nil values from a Sequence of optionals when calling it with an argument that just returns its parameter (i.e. compactMap { $0 }). Still, for such use cases it would be nice to get rid of the trailing closure.
The implementation isn't as straightforward as your usual extension, but once it has been written, the call site definitely gets cleaner 👌
import Foundation
protocol OptionalConvertible {
associatedtype Wrapped
func asOptional() -> Wrapped?
}
extension Optional: OptionalConvertible {
func asOptional() -> Wrapped? {
return self
}
}
extension Sequence where Element: OptionalConvertible {
func compacted() -> [Element.Wrapped] {
return compactMap { $0.asOptional() }
}
}
let data = [nil, 1, 2, nil, 3, 5, nil, 8, nil]
data.compacted() // [1, 2, 3, 5, 8]
Dealing with expirable values
It might happen that your code has to deal with values that come with an expiration date. In a game, it could be a score multiplier that will only last for 30 seconds. Or it could be an authentication token for an API, with a 15 minutes lifespan. In both instances you can rely on the type Expirable to encapsulate the expiration logic.
import Foundation
struct Expirable<T> {
private var innerValue: T
private(set) var expirationDate: Date
var value: T? {
return hasExpired() ? nil : innerValue
}
init(value: T, expirationDate: Date) {
self.innerValue = value
self.expirationDate = expirationDate
}
init(value: T, duration: Double) {
self.innerValue = value
self.expirationDate = Date().addingTimeInterval(duration)
}
func hasExpired() -> Bool {
return expirationDate < Date()
}
}
let expirable = Expirable(value: 42, duration: 3)
sleep(2)
expirable.value // 42
sleep(2)
expirable.value // nil
I share the credit for this tip with Benoît Caron.
Using parallelism to speed-up map()
Almost all Apple devices able to run Swift code are powered by a multi-core CPU, consequently making a good use of parallelism is a great way to improve code performance. map() is a perfect candidate for such an optimization, because it is almost trivial to define a parallel implementation.
import Foundation
extension Array {
func parallelMap<T>(_ transform: (Element) -> T) -> [T] {
let res = UnsafeMutablePointer<T>.allocate(capacity: count)
DispatchQueue.concurrentPerform(iterations: count) { i in
res[i] = transform(self[i])
}
let finalResult = Array<T>(UnsafeBufferPointer(start: res, count: count))
res.deallocate(capacity: count)
return finalResult
}
}
let array = (0..<1_000).map { $0 }
func work(_ n: Int) -> Int {
return (0..<n).reduce(0, +)
}
array.parallelMap { work($0) }
🚨 Make sure to only use parallelMap() when the transform function actually performs some costly computations. Otherwise performances will be systematically slower than using map(), because of the multithreading overhead.
Measuring execution time with minimum boilerplate
During development of a feature that performs some heavy computations, it can be helpful to measure just how much time a chunk of code takes to run. The time() function is a nice tool for this purpose, because of how simple it is to add and then to remove when it is no longer needed.
import Foundation
func time(averagedExecutions: Int = 1, _ code: () -> Void) {
let start = Date()
for _ in 0..<averagedExecutions { code() }
let end = Date()
let duration = end.timeIntervalSince(start) / Double(averagedExecutions)
print("time: \(duration)")
}
time {
(0...10_000).map { $0 * $0 }
}
// time: 0.183973908424377
Running two pieces of code in parallel
Concurrency is definitely one of those topics were the right encapsulation bears the potential to make your life so much easier. For instance, with this piece of code you can easily launch two computations in parallel, and have the results returned in a tuple.
import Foundation
func parallel<T, U>(_ left: @autoclosure () -> T, _ right: @autoclosure () -> U) -> (T, U) {
var leftRes: T?
var rightRes: U?
DispatchQueue.concurrentPerform(iterations: 2, execute: { id in
if id == 0 {
leftRes = left()
} else {
rightRes = right()
}
})
return (leftRes!, rightRes!)
}
let values = (1...100_000).map { $0 }
let results = parallel(values.map { $0 * $0 }, values.reduce(0, +))
Making good use of #file, #line and #function
Swift exposes three special variables #file, #line and #function, that are respectively set to the name of the current file, line and function. Those variables become very useful when writing custom logging functions or test predicates.
import Foundation
func log(_ message: String, _ file: String = #file, _ line: Int = #line, _ function: String = #function) {
print("[\(file):\(line)] \(function) - \(message)")
}
func foo() {
log("Hello world!")
}
foo() // [MyPlayground.playground:8] foo() - Hello world!
Comparing Optionals through Conditional Conformance
Swift 4.1 has introduced a new feature called Conditional Conformance, which allows a type to implement a protocol only when its generic type also does.
With this addition it becomes easy to let Optional implement Comparable only when Wrapped also implements Comparable:
import Foundation
extension Optional: Comparable where Wrapped: Comparable {
public static func < (lhs: Optional, rhs: Optional) -> Bool {
switch (lhs, rhs) {
case let (lhs?, rhs?):
return lhs < rhs
case (nil, _?):
return true // anything is greater than nil
case (_?, nil):
return false // nil in smaller than anything
case (nil, nil):
return true // nil is not smaller than itself
}
}
}
let data: [Int?] = [8, 4, 3, nil, 12, 4, 2, nil, -5]
data.sorted() // [nil, nil, Optional(-5), Optional(2), Optional(3), Optional(4), Optional(4), Optional(8), Optional(12)]
Safely subscripting a Collection
Any attempt to access an Array beyond its bounds will result in a crash. While it's possible to write conditions such as if index < array.count { array[index] } in order to prevent such crashes, this approach will rapidly become cumbersome.
A great thing is that this condition can be encapsulated in a custom subscript that will work on any Collection:
import Foundation
extension Collection {
subscript (safe index: Index) -> Element? {
return indices.contains(index) ? self[index] : nil
}
}
let data = [1, 3, 4]
data[safe: 1] // Optional(3)
data[safe: 10] // nil
Easier String slicing using ranges
Subscripting a string with a range can be very cumbersome in Swift 4. Let's face it, no one wants to write lines like someString[index(startIndex, offsetBy: 0)..<index(startIndex, offsetBy: 10)] on a regular basis.
Luckily, with the addition of one clever extension, strings can be sliced as easily as arrays 🎉
import Foundation
extension String {
public subscript(value: CountableClosedRange<Int>) -> Substring {
get {
return self[index(startIndex, offsetBy: value.lowerBound)...index(startIndex, offsetBy: value.upperBound)]
}
}
public subscript(value: CountableRange<Int>) -> Substring {
get {
return self[index(startIndex, offsetBy: value.lowerBound)..<index(startIndex, offsetBy: value.upperBound)]
}
}
public subscript(value: PartialRangeUpTo<Int>) -> Substring {
get {
return self[..<index(startIndex, offsetBy: value.upperBound)]
}
}
public subscript(value: PartialRangeThrough<Int>) -> Substring {
get {
return self[...index(startIndex, offsetBy: value.upperBound)]
}
}
public subscript(value: PartialRangeFrom<Int>) -> Substring {
get {
return self[index(startIndex, offsetBy: value.lowerBound)...]
}
}
}
let data = "This is a string!"
data[..<4] // "This"
data[5..<9] // "is a"
data[10...] // "string!"
Concise syntax for sorting using a KeyPath
By using a KeyPath along with a generic type, a very clean and concise syntax for sorting data can be implemented:
import Foundation
extension Sequence {
func sorted<T: Comparable>(by attribute: KeyPath<Element, T>) -> [Element] {
return sorted(by: { $0[keyPath: attribute] < $1[keyPath: attribute] })
}
}
let data = ["Some", "words", "of", "different", "lengths"]
data.sorted(by: \.count) // ["of", "Some", "words", "lengths", "different"]
If you like this syntax, make sure to checkout KeyPathKit!
Manufacturing cache-efficient versions of pure functions
By capturing a local variable in a returned closure, it is possible to manufacture cache-efficient versions of pure functions. Be careful though, this trick only works with non-recursive function!
import Foundation
func cached<In: Hashable, Out>(_ f: @escaping (In) -> Out) -> (In) -> Out {
var cache = [In: Out]()
return { (input: In) -> Out in
if let cachedValue = cache[input] {
return cachedValue
} else {
let result = f(input)
cache[input] = result
return result
}
}
}
let cachedCos = cached { (x: Double) in cos(x) }
cachedCos(.pi * 2) // value of cos for 2π is now cached
Simplifying complex conditions with pattern matching
When distinguishing between complex boolean conditions, using a switch statement along with pattern matching can be more readable than the classic series of if {} else if {}.
import Foundation
let expr1: Bool
let expr2: Bool
let expr3: Bool
if expr1 && !expr3 {
functionA()
} else if !expr2 && expr3 {
functionB()
} else if expr1 && !expr2 && expr3 {
functionC()
}
switch (expr1, expr2, expr3) {
case (true, _, false):
functionA()
case (_, false, true):
functionB()
case (true, false, true):
functionC()
default:
break
}
Easily generating arrays of data
Using map() on a range makes it easy to generate an array of data.
import Foundation
func randomInt() -> Int { return Int(arc4random()) }
let randomArray = (1...10).map { _ in randomInt() }
Using @autoclosure for cleaner call sites
Using @autoclosure enables the compiler to automatically wrap an argument within a closure, thus allowing for a very clean syntax at call sites.
import UIKit
extension UIView {
class func animate(withDuration duration: TimeInterval, _ animations: @escaping @autoclosure () -> Void) {
UIView.animate(withDuration: duration, animations: animations)
}
}
let view = UIView()
UIView.animate(withDuration: 0.3, view.backgroundColor = .orange)
Observing new and old value with RxSwift
When working with RxSwift, it's very easy to observe both the current and previous value of an observable sequence by simply introducing a shift using skip().
import RxSwift
let values = Observable.of(4, 8, 15, 16, 23, 42)
let newAndOld = Observable.zip(values, values.skip(1)) { (previous: $0, current: $1) }
.subscribe(onNext: { pair in
print("current: \(pair.current) - previous: \(pair.previous)")
})
//current: 8 - previous: 4
//current: 15 - previous: 8
//current: 16 - previous: 15
//current: 23 - previous: 16
//current: 42 - previous: 23
Implicit initialization from literal values
Using protocols such as ExpressibleByStringLiteral it is possible to provide an init that will be automatically when a literal value is provided, allowing for nice and short syntax. This can be very helpful when writing mock or test data.
import Foundation
extension URL: ExpressibleByStringLiteral {
public init(stringLiteral value: String) {
self.init(string: value)!
}
}
let url: URL = "http://www.google.fr"
NSURLConnection.canHandle(URLRequest(url: "http://www.google.fr"))
Achieving systematic validation of data
Through some clever use of Swift private visibility it is possible to define a container that holds any untrusted value (such as a user input) from which the only way to retrieve the value is by making it successfully pass a validation test.
import Foundation
struct Untrusted<T> {
private(set) var value: T
}
protocol Validator {
associatedtype T
static func validation(value: T) -> Bool
}
extension Validator {
static func validate(untrusted: Untrusted<T>) -> T? {
if self.validation(value: untrusted.value) {
return untrusted.value
} else {
return nil
}
}
}
struct FrenchPhoneNumberValidator: Validator {
static func validation(value: String) -> Bool {
return (value.count) == 10 && CharacterSet(charactersIn: value).isSubset(of: CharacterSet.decimalDigits)
}
}
let validInput = Untrusted(value: "0122334455")
let invalidInput = Untrusted(value: "0123")
FrenchPhoneNumberValidator.validate(untrusted: validInput) // returns "0122334455"
FrenchPhoneNumberValidator.validate(untrusted: invalidInput) // returns nil
Implementing the builder pattern with keypaths
With the addition of keypaths in Swift 4, it is now possible to easily implement the builder pattern, that allows the developer to clearly separate the code that initializes a value from the code that uses it, without the burden of defining a factory method.
import UIKit
protocol With {}
extension With where Self: AnyObject {
@discardableResult
func with<T>(_ property: ReferenceWritableKeyPath<Self, T>, setTo value: T) -> Self {
self[keyPath: property] = value
return self
}
}
extension UIView: With {}
let view = UIView()
let label = UILabel()
.with(\.textColor, setTo: .red)
.with(\.text, setTo: "Foo")
.with(\.textAlignment, setTo: .right)
.with(\.layer.cornerRadius, setTo: 5)
view.addSubview(label)
🚨 The Swift compiler does not perform OS availability checks on properties referenced by keypaths. Any attempt to use a KeyPath for an unavailable property will result in a runtime crash.
I share the credit for this tip with Marion Curtil.
Storing functions rather than values
When a type stores values for the sole purpose of parametrizing its functions, it’s then possible to not store the values but directly the function, with no discernable difference at the call site.
import Foundation
struct MaxValidator {
let max: Int
let strictComparison: Bool
func isValid(_ value: Int) -> Bool {
return self.strictComparison ? value < self.max : value <= self.max
}
}
struct MaxValidator2 {
var isValid: (_ value: Int) -> Bool
init(max: Int, strictComparison: Bool) {
self.isValid = strictComparison ? { $0 < max } : { $0 <= max }
}
}
MaxValidator(max: 5, strictComparison: true).isValid(5) // false
MaxValidator2(max: 5, strictComparison: false).isValid(5) // true
Defining operators on function types
Functions are first-class citizen types in Swift, so it is perfectly legal to define operators for them.
import Foundation
let firstRange = { (0...3).contains($0) }
let secondRange = { (5...6).contains($0) }
func ||(_ lhs: @escaping (Int) -> Bool, _ rhs: @escaping (Int) -> Bool) -> (Int) -> Bool {
return { value in
return lhs(value) || rhs(value)
}
}
(firstRange || secondRange)(2) // true
(firstRange || secondRange)(4) // false
(firstRange || secondRange)(6) // true
Typealiases for functions
Typealiases are great to express function signatures in a more comprehensive manner, which then enables us to easily define functions that operate on them, resulting in a nice way to write and use some powerful API.
import Foundation
typealias RangeSet = (Int) -> Bool
func union(_ left: @escaping RangeSet, _ right: @escaping RangeSet) -> RangeSet {
return { left($0) || right($0) }
}
let firstRange = { (0...3).contains($0) }
let secondRange = { (5...6).contains($0) }
let unionRange = union(firstRange, secondRange)
unionRange(2) // true
unionRange(4) // false
Encapsulating state within a function
By returning a closure that captures a local variable, it's possible to encapsulate a mutable state within a function.
import Foundation
func counterFactory() -> () -> Int {
var counter = 0
return {
counter += 1
return counter
}
}
let counter = counterFactory()
counter() // returns 1
counter() // returns 2
Generating all cases for an Enum
⚠️ Since Swift 4.2,
allCasescan now be synthesized at compile-time by simply conforming to the protocolCaseIterable. The implementation below should no longer be used in production code.
Through some clever leveraging of how enums are stored in memory, it is possible to generate an array that contains all the possible cases of an enum. This can prove particularly useful when writing unit tests that consume random data.
import Foundation
enum MyEnum { case first; case second; case third; case fourth }
protocol EnumCollection: Hashable {
static var allCases: [Self] { get }
}
extension EnumCollection {
public static var allCases: [Self] {
var i = 0
return Array(AnyIterator {
let next = withUnsafePointer(to: &i) {
$0.withMemoryRebound(to: Self.self, capacity: 1) { $0.pointee }
}
if next.hashValue != i { return nil }
i += 1
return next
})
}
}
extension MyEnum: EnumCollection { }
MyEnum.allCases // [.first, .second, .third, .fourth]
Using map on optional values
The if-let syntax is a great way to deal with optional values in a safe manner, but at times it can prove to be just a little bit to cumbersome. In such cases, using the Optional.map() function is a nice way to achieve a shorter code while retaining safeness and readability.
import UIKit
let date: Date? = Date() // or could be nil, doesn't matter
let formatter = DateFormatter()
let label = UILabel()
if let safeDate = date {
label.text = formatter.string(from: safeDate)
}
label.text = date.map { return formatter.string(from: $0) }
label.text = date.map(formatter.string(from:)) // even shorter, tough less readable
📣 NEW 📣 Swift Tips are now available on YouTube 👇
Summary
- #57 Property Wrappers as Debugging Tools
- #56 Localization through
Stringinterpolation - #55 Implementing pseudo-inheritance between
structs - #54 Composing
NSAttributedStringthrough a Function Builder - #53 Using
switchandifas expressions - #52 Avoiding double negatives within
guardstatements - #51 Defining a custom
initwithout loosing the compiler-generated one - #50 Implementing a namespace through an empty
enum - #49 Using
Neverto represent impossible code paths - #48 Providing a default value to a
Decodableenum - #47 Another lightweight dependency injection through default values for function parameters
- #46 Lightweight dependency injection through protocol-oriented programming
- #45 Getting rid of overabundant
[weak self]andguard - #44 Solving callback hell with function composition
- #43 Transform an asynchronous function into a synchronous one
- #42 Using KeyPaths instead of closures
- #41 Bringing some type-safety to a
userInfoDictionary - #40 Lightweight data-binding for an MVVM implementation
- #39 Using
typealiasto its fullest - #38 Writing an interruptible overload of
forEach - #37 Optimizing the use of
reduce() - #36 Avoiding hardcoded reuse identifiers
- #35 Defining a union type
- #34 Asserting that classes have associated NIBs and vice-versa
- #33 Small footprint type-erasing with functions
- #32 Performing animations sequentially
- #31 Debouncing a function call
- #30 Providing useful operators for
Optionalbooleans - #29 Removing duplicate values from a
Sequence - #28 Shorter syntax to deal with optional strings
- #27 Encapsulating background computation and UI update
- #26 Retrieving all the necessary data to build a debug view
- #25 Defining a function to map over dictionaries
- #24 A shorter syntax to remove
nilvalues - #23 Dealing with expirable values
- #22 Using parallelism to speed-up
map() - #21 Measuring execution time with minimum boilerplate
- #20 Running two pieces of code in parallel
- #19 Making good use of #file, #line and #function
- #18 Comparing Optionals through Conditional Conformance
- #17 Safely subscripting a Collection
- #16 Easier String slicing using ranges
- #15 Concise syntax for sorting using a KeyPath
- #14 Manufacturing cache-efficient versions of pure functions
- #13 Simplifying complex condition with pattern matching
- #12 Easily generating arrays of data
- #11 Using @autoclosure for cleaner call sites
- #10 Observing new and old value with RxSwift
- #09 Implicit initialization from literal values
- #08 Achieving systematic validation of data
- #07 Implementing the builder pattern with keypaths
- #06 Storing functions rather than values
- #05 Defining operators on function types
- #04 Typealiases for functions
- #03 Encapsulating state within a function
- #02 Generating all cases for an Enum
- #01 Using map on optional values
Tips
Download Details:
Author: vincent-pradeilles
Source code: https://github.com/vincent-pradeilles/swift-tips
License: MIT license
#swift
1602968400
Python Tricks Every Developer Should Know
Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?
In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.
Let’s get started
Swapping value in Python
Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead
>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName
>>> print(FirstName, LastName)
('Jordan', 'kalebu')
#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development