Creating a confirmation box c#

I am having a bit of a problem. I am still new to C# however I am progressing slowly and learning new things.

However I am confused. I am trying to create a confirmation box. However it doesn't seem to function the way it is intended to.

Here is the code:

private void exitToolStripMenuItem_Click(object sender, EventArgs e)
{
    MessageBox.Show("Are you sure you want to exit off the application", "Are you sure?", MessageBoxButtons.YesNoCancel); //Gets users input by showing the message box
if (DialogResult == DialogResult.Yes) //Creates the yes function
{
    this.Close(); //Exits off the application
}

else if (DialogResult == DialogResult.No)
{
    //Does nothing
}

#c-sharp

What is GEEK

Buddha Community

PyTumblr: A Python Tumblr API v2 Client

PyTumblr

Installation

Install via pip:

$ pip install pytumblr

Install from source:

$ git clone https://github.com/tumblr/pytumblr.git
$ cd pytumblr
$ python setup.py install

Usage

Create a client

A pytumblr.TumblrRestClient is the object you'll make all of your calls to the Tumblr API through. Creating one is this easy:

client = pytumblr.TumblrRestClient(
    '<consumer_key>',
    '<consumer_secret>',
    '<oauth_token>',
    '<oauth_secret>',
)

client.info() # Grabs the current user information

Two easy ways to get your credentials to are:

1. The built-in interactive_console.py tool (if you already have a consumer key & secret)
2. The Tumblr API console at https://api.tumblr.com/console
3. Get sample login code at https://api.tumblr.com/console/calls/user/info

Supported Methods

User Methods

client.info() # get information about the authenticating user
client.dashboard() # get the dashboard for the authenticating user
client.likes() # get the likes for the authenticating user
client.following() # get the blogs followed by the authenticating user

client.follow('codingjester.tumblr.com') # follow a blog
client.unfollow('codingjester.tumblr.com') # unfollow a blog

client.like(id, reblogkey) # like a post
client.unlike(id, reblogkey) # unlike a post

Blog Methods

client.blog_info(blogName) # get information about a blog
client.posts(blogName, **params) # get posts for a blog
client.avatar(blogName) # get the avatar for a blog
client.blog_likes(blogName) # get the likes on a blog
client.followers(blogName) # get the followers of a blog
client.blog_following(blogName) # get the publicly exposed blogs that [blogName] follows
client.queue(blogName) # get the queue for a given blog
client.submission(blogName) # get the submissions for a given blog

Post Methods

Creating posts

PyTumblr lets you create all of the various types that Tumblr supports. When using these types there are a few defaults that are able to be used with any post type.

The default supported types are described below.

• state - a string, the state of the post. Supported types are published, draft, queue, private
• tags - a list, a list of strings that you want tagged on the post. eg: ["testing", "magic", "1"]
• tweet - a string, the string of the customized tweet you want. eg: "Man I love my mega awesome post!"
• date - a string, the customized GMT that you want
• format - a string, the format that your post is in. Support types are html or markdown
• slug - a string, the slug for the url of the post you want

We'll show examples throughout of these default examples while showcasing all the specific post types.

Creating a photo post

Creating a photo post supports a bunch of different options plus the described default options * caption - a string, the user supplied caption * link - a string, the "click-through" url for the photo * source - a string, the url for the photo you want to use (use this or the data parameter) * data - a list or string, a list of filepaths or a single file path for multipart file upload

#Creates a photo post using a source URL
client.create_photo(blogName, state="published", tags=["testing", "ok"],
                    source="https://68.media.tumblr.com/b965fbb2e501610a29d80ffb6fb3e1ad/tumblr_n55vdeTse11rn1906o1_500.jpg")

#Creates a photo post using a local filepath
client.create_photo(blogName, state="queue", tags=["testing", "ok"],
                    tweet="Woah this is an incredible sweet post [URL]",
                    data="/Users/johnb/path/to/my/image.jpg")

#Creates a photoset post using several local filepaths
client.create_photo(blogName, state="draft", tags=["jb is cool"], format="markdown",
                    data=["/Users/johnb/path/to/my/image.jpg", "/Users/johnb/Pictures/kittens.jpg"],
                    caption="## Mega sweet kittens")

Creating a text post

Creating a text post supports the same options as default and just a two other parameters * title - a string, the optional title for the post. Supports markdown or html * body - a string, the body of the of the post. Supports markdown or html

#Creating a text post
client.create_text(blogName, state="published", slug="testing-text-posts", title="Testing", body="testing1 2 3 4")

Creating a quote post

Creating a quote post supports the same options as default and two other parameter * quote - a string, the full text of the qote. Supports markdown or html * source - a string, the cited source. HTML supported

#Creating a quote post
client.create_quote(blogName, state="queue", quote="I am the Walrus", source="Ringo")

Creating a link post

• title - a string, the title of post that you want. Supports HTML entities.
• url - a string, the url that you want to create a link post for.
• description - a string, the desciption of the link that you have

#Create a link post
client.create_link(blogName, title="I like to search things, you should too.", url="https://duckduckgo.com",
                   description="Search is pretty cool when a duck does it.")

Creating a chat post

Creating a chat post supports the same options as default and two other parameters * title - a string, the title of the chat post * conversation - a string, the text of the conversation/chat, with diablog labels (no html)

#Create a chat post
chat = """John: Testing can be fun!
Renee: Testing is tedious and so are you.
John: Aw.
"""
client.create_chat(blogName, title="Renee just doesn't understand.", conversation=chat, tags=["renee", "testing"])

Creating an audio post

Creating an audio post allows for all default options and a has 3 other parameters. The only thing to keep in mind while dealing with audio posts is to make sure that you use the external_url parameter or data. You cannot use both at the same time. * caption - a string, the caption for your post * external_url - a string, the url of the site that hosts the audio file * data - a string, the filepath of the audio file you want to upload to Tumblr

#Creating an audio file
client.create_audio(blogName, caption="Rock out.", data="/Users/johnb/Music/my/new/sweet/album.mp3")

#lets use soundcloud!
client.create_audio(blogName, caption="Mega rock out.", external_url="https://soundcloud.com/skrillex/sets/recess")

Creating a video post

Creating a video post allows for all default options and has three other options. Like the other post types, it has some restrictions. You cannot use the embed and data parameters at the same time. * caption - a string, the caption for your post * embed - a string, the HTML embed code for the video * data - a string, the path of the file you want to upload

#Creating an upload from YouTube
client.create_video(blogName, caption="Jon Snow. Mega ridiculous sword.",
                    embed="http://www.youtube.com/watch?v=40pUYLacrj4")

#Creating a video post from local file
client.create_video(blogName, caption="testing", data="/Users/johnb/testing/ok/blah.mov")

Editing a post

Updating a post requires you knowing what type a post you're updating. You'll be able to supply to the post any of the options given above for updates.

client.edit_post(blogName, id=post_id, type="text", title="Updated")
client.edit_post(blogName, id=post_id, type="photo", data="/Users/johnb/mega/awesome.jpg")

Reblogging a Post

Reblogging a post just requires knowing the post id and the reblog key, which is supplied in the JSON of any post object.

client.reblog(blogName, id=125356, reblog_key="reblog_key")

Deleting a post

Deleting just requires that you own the post and have the post id

client.delete_post(blogName, 123456) # Deletes your post :(

A note on tags: When passing tags, as params, please pass them as a list (not a comma-separated string):

client.create_text(blogName, tags=['hello', 'world'], ...)

Getting notes for a post

In order to get the notes for a post, you need to have the post id and the blog that it is on.

data = client.notes(blogName, id='123456')

The results include a timestamp you can use to make future calls.

data = client.notes(blogName, id='123456', before_timestamp=data["_links"]["next"]["query_params"]["before_timestamp"])

Tagged Methods

# get posts with a given tag
client.tagged(tag, **params)

Using the interactive console

This client comes with a nice interactive console to run you through the OAuth process, grab your tokens (and store them for future use).

You'll need pyyaml installed to run it, but then it's just:

$ python interactive-console.py

and away you go! Tokens are stored in ~/.tumblr and are also shared by other Tumblr API clients like the Ruby client.

Running tests

The tests (and coverage reports) are run with nose, like this:

python setup.py test

Author: tumblr
Source Code: https://github.com/tumblr/pytumblr
License: Apache-2.0 license

#python #api 

Exploring Mutable and Immutable in Python

In this Python article, let's learn about Mutable and Immutable in Python. 

Mutable and Immutable in Python

Mutable is a fancy way of saying that the internal state of the object is changed/mutated. So, the simplest definition is: An object whose internal state can be changed is mutable. On the other hand, immutable doesn’t allow any change in the object once it has been created.

Both of these states are integral to Python data structure. If you want to become more knowledgeable in the entire Python Data Structure, take this free course which covers multiple data structures in Python including tuple data structure which is immutable. You will also receive a certificate on completion which is sure to add value to your portfolio.

Mutable Definition

Mutable is when something is changeable or has the ability to change. In Python, ‘mutable’ is the ability of objects to change their values. These are often the objects that store a collection of data.

Immutable Definition

Immutable is the when no change is possible over time. In Python, if the value of an object cannot be changed over time, then it is known as immutable. Once created, the value of these objects is permanent.

List of Mutable and Immutable objects

Objects of built-in type that are mutable are:

• Lists
• Sets
• Dictionaries
• User-Defined Classes (It purely depends upon the user to define the characteristics) 

Objects of built-in type that are immutable are:

• Numbers (Integer, Rational, Float, Decimal, Complex & Booleans)
• Strings
• Tuples
• Frozen Sets
• User-Defined Classes (It purely depends upon the user to define the characteristics)

Object mutability is one of the characteristics that makes Python a dynamically typed language. Though Mutable and Immutable in Python is a very basic concept, it can at times be a little confusing due to the intransitive nature of immutability.

Objects in Python

In Python, everything is treated as an object. Every object has these three attributes:

• Identity – This refers to the address that the object refers to in the computer’s memory.
• Type – This refers to the kind of object that is created. For example- integer, list, string etc. 
• Value – This refers to the value stored by the object. For example – List=[1,2,3] would hold the numbers 1,2 and 3

While ID and Type cannot be changed once it’s created, values can be changed for Mutable objects.

Check out this free python certificate course to get started with Python.

Mutable Objects in Python

I believe, rather than diving deep into the theory aspects of mutable and immutable in Python, a simple code would be the best way to depict what it means in Python. Hence, let us discuss the below code step-by-step:

#Creating a list which contains name of Indian cities  

cities = [‘Delhi’, ‘Mumbai’, ‘Kolkata’]

# Printing the elements from the list cities, separated by a comma & space

for city in cities:
		print(city, end=’, ’)

Output [1]: Delhi, Mumbai, Kolkata

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(cities)))

Output [2]: 0x1691d7de8c8

#Adding a new city to the list cities

cities.append(‘Chennai’)

#Printing the elements from the list cities, separated by a comma & space 

for city in cities:
	print(city, end=’, ’)

Output [3]: Delhi, Mumbai, Kolkata, Chennai

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(cities)))

Output [4]: 0x1691d7de8c8

The above example shows us that we were able to change the internal state of the object ‘cities’ by adding one more city ‘Chennai’ to it, yet, the memory address of the object did not change. This confirms that we did not create a new object, rather, the same object was changed or mutated. Hence, we can say that the object which is a type of list with reference variable name ‘cities’ is a MUTABLE OBJECT.

Let us now discuss the term IMMUTABLE. Considering that we understood what mutable stands for, it is obvious that the definition of immutable will have ‘NOT’ included in it. Here is the simplest definition of immutable– An object whose internal state can NOT be changed is IMMUTABLE.

Again, if you try and concentrate on different error messages, you have encountered, thrown by the respective IDE; you use you would be able to identify the immutable objects in Python. For instance, consider the below code & associated error message with it, while trying to change the value of a Tuple at index 0. 

#Creating a Tuple with variable name ‘foo’

foo = (1, 2)

#Changing the index[0] value from 1 to 3

foo[0] = 3
	
TypeError: 'tuple' object does not support item assignment 

Immutable Objects in Python

Once again, a simple code would be the best way to depict what immutable stands for. Hence, let us discuss the below code step-by-step:

#Creating a Tuple which contains English name of weekdays

weekdays = ‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’

# Printing the elements of tuple weekdays

print(weekdays)

Output [1]:  (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’)

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(weekdays)))

Output [2]: 0x1691cc35090

#tuples are immutable, so you cannot add new elements, hence, using merge of tuples with the # + operator to add a new imaginary day in the tuple ‘weekdays’

weekdays  +=  ‘Pythonday’,

#Printing the elements of tuple weekdays

print(weekdays)

Output [3]: (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’, ‘Pythonday’)

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(weekdays)))

Output [4]: 0x1691cc8ad68

This above example shows that we were able to use the same variable name that is referencing an object which is a type of tuple with seven elements in it. However, the ID or the memory location of the old & new tuple is not the same. We were not able to change the internal state of the object ‘weekdays’. The Python program manager created a new object in the memory address and the variable name ‘weekdays’ started referencing the new object with eight elements in it.  Hence, we can say that the object which is a type of tuple with reference variable name ‘weekdays’ is an IMMUTABLE OBJECT.

Also Read: Understanding the Exploratory Data Analysis (EDA) in Python

Where can you use mutable and immutable objects:

Mutable objects can be used where you want to allow for any updates. For example, you have a list of employee names in your organizations, and that needs to be updated every time a new member is hired. You can create a mutable list, and it can be updated easily.

Immutability offers a lot of useful applications to different sensitive tasks we do in a network centred environment where we allow for parallel processing. By creating immutable objects, you seal the values and ensure that no threads can invoke overwrite/update to your data. This is also useful in situations where you would like to write a piece of code that cannot be modified. For example, a debug code that attempts to find the value of an immutable object.

Watch outs:  Non transitive nature of Immutability:

OK! Now we do understand what mutable & immutable objects in Python are. Let’s go ahead and discuss the combination of these two and explore the possibilities. Let’s discuss, as to how will it behave if you have an immutable object which contains the mutable object(s)? Or vice versa? Let us again use a code to understand this behaviour–

#creating a tuple (immutable object) which contains 2 lists(mutable) as it’s elements

#The elements (lists) contains the name, age & gender 

person = (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the tuple

print(person)

Output [1]: (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(person)))

Output [2]: 0x1691ef47f88

#Changing the age for the 1st element. Selecting 1st element of tuple by using indexing [0] then 2nd element of the list by using indexing [1] and assigning a new value for age as 4

person[0][1] = 4

#printing the updated tuple

print(person)

Output [3]: (['Ayaan', 4, 'Male'], ['Aaradhya', 8, 'Female'])

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(person)))

Output [4]: 0x1691ef47f88

In the above code, you can see that the object ‘person’ is immutable since it is a type of tuple. However, it has two lists as it’s elements, and we can change the state of lists (lists being mutable). So, here we did not change the object reference inside the Tuple, but the referenced object was mutated.

Also Read: Real-Time Object Detection Using TensorFlow

Same way, let’s explore how it will behave if you have a mutable object which contains an immutable object? Let us again use a code to understand the behaviour–

#creating a list (mutable object) which contains tuples(immutable) as it’s elements

list1 = [(1, 2, 3), (4, 5, 6)]

#printing the list

print(list1)

Output [1]: [(1, 2, 3), (4, 5, 6)]

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(list1)))

Output [2]: 0x1691d5b13c8	

#changing object reference at index 0

list1[0] = (7, 8, 9)

#printing the list

Output [3]: [(7, 8, 9), (4, 5, 6)]

#printing the location of the object created in the memory address in hexadecimal format

print(hex(id(list1)))

Output [4]: 0x1691d5b13c8

As an individual, it completely depends upon you and your requirements as to what kind of data structure you would like to create with a combination of mutable & immutable objects. I hope that this information will help you while deciding the type of object you would like to select going forward.

Before I end our discussion on IMMUTABILITY, allow me to use the word ‘CAVITE’ when we discuss the String and Integers. There is an exception, and you may see some surprising results while checking the truthiness for immutability. For instance:
#creating an object of integer type with value 10 and reference variable name ‘x’ 

x = 10
 

#printing the value of ‘x’

print(x)

Output [1]: 10

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(x)))

Output [2]: 0x538fb560

#creating an object of integer type with value 10 and reference variable name ‘y’

y = 10

#printing the value of ‘y’

print(y)

Output [3]: 10

#Printing the location of the object created in the memory address in hexadecimal format

print(hex(id(y)))

Output [4]: 0x538fb560

As per our discussion and understanding, so far, the memory address for x & y should have been different, since, 10 is an instance of Integer class which is immutable. However, as shown in the above code, it has the same memory address. This is not something that we expected. It seems that what we have understood and discussed, has an exception as well.

Quick check – Python Data Structures

Immutability of Tuple

Tuples are immutable and hence cannot have any changes in them once they are created in Python. This is because they support the same sequence operations as strings. We all know that strings are immutable. The index operator will select an element from a tuple just like in a string. Hence, they are immutable.

Exceptions in immutability

Like all, there are exceptions in the immutability in python too. Not all immutable objects are really mutable. This will lead to a lot of doubts in your mind. Let us just take an example to understand this.

Consider a tuple ‘tup’.

Now, if we consider tuple tup = (‘GreatLearning’,[4,3,1,2]) ;

We see that the tuple has elements of different data types. The first element here is a string which as we all know is immutable in nature. The second element is a list which we all know is mutable. Now, we all know that the tuple itself is an immutable data type. It cannot change its contents. But, the list inside it can change its contents. So, the value of the Immutable objects cannot be changed but its constituent objects can. change its value.

FAQs

1. Difference between mutable vs immutable in Python?

2. What are the mutable and immutable data types in Python?

• Some mutable data types in Python are:

list, dictionary, set, user-defined classes.

• Some immutable data types are: 

int, float, decimal, bool, string, tuple, range.

3. Are lists mutable in Python?

Lists in Python are mutable data types as the elements of the list can be modified, individual elements can be replaced, and the order of elements can be changed even after the list has been created.
(Examples related to lists have been discussed earlier in this blog.)

4. Why are tuples called immutable types?

Tuple and list data structures are very similar, but one big difference between the data types is that lists are mutable, whereas tuples are immutable. The reason for the tuple’s immutability is that once the elements are added to the tuple and the tuple has been created; it remains unchanged.

A programmer would always prefer building a code that can be reused instead of making the whole data object again. Still, even though tuples are immutable, like lists, they can contain any Python object, including mutable objects.

5. Are sets mutable in Python?

A set is an iterable unordered collection of data type which can be used to perform mathematical operations (like union, intersection, difference etc.). Every element in a set is unique and immutable, i.e. no duplicate values should be there, and the values can’t be changed. However, we can add or remove items from the set as the set itself is mutable.

6. Are strings mutable in Python?

Strings are not mutable in Python. Strings are a immutable data types which means that its value cannot be updated.

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Original article source at: https://www.mygreatlearning.com

#python 

Rufus Scheduler: Job Scheduler for Ruby (at, Cron, in and Every Jobs)

rufus-scheduler

Job scheduler for Ruby (at, cron, in and every jobs).

It uses threads.

Note: maybe are you looking for the README of rufus-scheduler 2.x? (especially if you're using Dashing which is stuck on rufus-scheduler 2.0.24)

Quickstart:

# quickstart.rb

require 'rufus-scheduler'

scheduler = Rufus::Scheduler.new

scheduler.in '3s' do
  puts 'Hello... Rufus'
end

scheduler.join
  #
  # let the current thread join the scheduler thread
  #
  # (please note that this join should be removed when scheduling
  # in a web application (Rails and friends) initializer)

(run with ruby quickstart.rb)

Various forms of scheduling are supported:

require 'rufus-scheduler'

scheduler = Rufus::Scheduler.new

# ...

scheduler.in '10d' do
  # do something in 10 days
end

scheduler.at '2030/12/12 23:30:00' do
  # do something at a given point in time
end

scheduler.every '3h' do
  # do something every 3 hours
end
scheduler.every '3h10m' do
  # do something every 3 hours and 10 minutes
end

scheduler.cron '5 0 * * *' do
  # do something every day, five minutes after midnight
  # (see "man 5 crontab" in your terminal)
end

# ...

Rufus-scheduler uses fugit for parsing time strings, et-orbi for pairing time and tzinfo timezones.

non-features

Rufus-scheduler (out of the box) is an in-process, in-memory scheduler. It uses threads.

It does not persist your schedules. When the process is gone and the scheduler instance with it, the schedules are gone.

A rufus-scheduler instance will go on scheduling while it is present among the objects in a Ruby process. To make it stop scheduling you have to call its #shutdown method.

related and similar gems

• Whenever - let cron call back your Ruby code, trusted and reliable cron drives your schedule
• ruby-clock - a clock process / job scheduler for Ruby
• Clockwork - rufus-scheduler inspired gem
• Crono - an in-Rails cron scheduler
• PerfectSched - highly available distributed cron built on Sequel and more

(please note: rufus-scheduler is not a cron replacement)

note about the 3.0 line

It's a complete rewrite of rufus-scheduler.

There is no EventMachine-based scheduler anymore.

I don't know what this Ruby thing is, where are my Rails?

I'll drive you right to the tracks.

notable changes:

• As said, no more EventMachine-based scheduler
• scheduler.every('100') { will schedule every 100 seconds (previously, it would have been 0.1s). This aligns rufus-scheduler with Ruby's sleep(100)
• The scheduler isn't catching the whole of Exception anymore, only StandardError
• The error_handler is #on_error (instead of #on_exception), by default it now prints the details of the error to $stderr (used to be $stdout)
• Rufus::Scheduler::TimeOutError renamed to Rufus::Scheduler::TimeoutError
• Introduction of "interval" jobs. Whereas "every" jobs are like "every 10 minutes, do this", interval jobs are like "do that, then wait for 10 minutes, then do that again, and so on"
• Introduction of a lockfile: true/filename mechanism to prevent multiple schedulers from executing
• "discard_past" is on by default. If the scheduler (its host) sleeps for 1 hour and a every '10m' job is on, it will trigger once at wakeup, not 6 times (discard_past was false by default in rufus-scheduler 2.x). No intention to re-introduce discard_past: false in 3.0 for now.
• Introduction of Scheduler #on_pre_trigger and #on_post_trigger callback points

getting help

So you need help. People can help you, but first help them help you, and don't waste their time. Provide a complete description of the issue. If it works on A but not on B and others have to ask you: "so what is different between A and B" you are wasting everyone's time.

"hello", "please" and "thanks" are not swear words.

Go read how to report bugs effectively, twice.

Update: help_help.md might help help you.

on Gitter

You can find help via chat over at https://gitter.im/floraison/fugit. It's fugit, et-orbi, and rufus-scheduler combined chat room.

Please be courteous.

issues

Yes, issues can be reported in rufus-scheduler issues, I'd actually prefer bugs in there. If there is nothing wrong with rufus-scheduler, a Stack Overflow question is better.

faq

• It doesn't work...
• I want a refund
• Passenger and rufus-scheduler
• Passenger and rufus-scheduler (2)
• Passenger in-depth spawn methods
• Passenger in-depth spawn methods (smart spawning)
• The scheduler comes up when running the Rails console or a Rake task
• The job triggers twice
• I don't get any of this, I just want it to work in my Rails application
• [I get "zotime.rb:41:in `initialize': cannot determine timezone from nil"](#i-get-zotimerb41in-initialize-cannot-determine-timezone-from-nil)

scheduling

Rufus-scheduler supports five kinds of jobs. in, at, every, interval and cron jobs.

Most of the rufus-scheduler examples show block scheduling, but it's also OK to schedule handler instances or handler classes.

in, at, every, interval, cron

In and at jobs trigger once.

require 'rufus-scheduler'

scheduler = Rufus::Scheduler.new

scheduler.in '10d' do
  puts "10 days reminder for review X!"
end

scheduler.at '2014/12/24 2000' do
  puts "merry xmas!"
end

In jobs are scheduled with a time interval, they trigger after that time elapsed. At jobs are scheduled with a point in time, they trigger when that point in time is reached (better to choose a point in the future).

Every, interval and cron jobs trigger repeatedly.

require 'rufus-scheduler'

scheduler = Rufus::Scheduler.new

scheduler.every '3h' do
  puts "change the oil filter!"
end

scheduler.interval '2h' do
  puts "thinking..."
  puts sleep(rand * 1000)
  puts "thought."
end

scheduler.cron '00 09 * * *' do
  puts "it's 9am! good morning!"
end

Every jobs try hard to trigger following the frequency they were scheduled with.

Interval jobs trigger, execute and then trigger again after the interval elapsed. (every jobs time between trigger times, interval jobs time between trigger termination and the next trigger start).

Cron jobs are based on the venerable cron utility (man 5 crontab). They trigger following a pattern given in (almost) the same language cron uses.

#schedule_x vs #x

schedule_in, schedule_at, schedule_cron, etc will return the new Job instance.

in, at, cron will return the new Job instance's id (a String).

job_id =
  scheduler.in '10d' do
    # ...
  end
job = scheduler.job(job_id)

# versus

job =
  scheduler.schedule_in '10d' do
    # ...
  end

# also

job =
  scheduler.in '10d', job: true do
    # ...
  end

#schedule and #repeat

Sometimes it pays to be less verbose.

The #schedule methods schedules an at, in or cron job. It just decides based on its input. It returns the Job instance.

scheduler.schedule '10d' do; end.class
  # => Rufus::Scheduler::InJob

scheduler.schedule '2013/12/12 12:30' do; end.class
  # => Rufus::Scheduler::AtJob

scheduler.schedule '* * * * *' do; end.class
  # => Rufus::Scheduler::CronJob

The #repeat method schedules and returns an EveryJob or a CronJob.

scheduler.repeat '10d' do; end.class
  # => Rufus::Scheduler::EveryJob

scheduler.repeat '* * * * *' do; end.class
  # => Rufus::Scheduler::CronJob

(Yes, no combination here gives back an IntervalJob).

schedule blocks arguments (job, time)

A schedule block may be given 0, 1 or 2 arguments.

The first argument is "job", it's simply the Job instance involved. It might be useful if the job is to be unscheduled for some reason.

scheduler.every '10m' do |job|

  status = determine_pie_status

  if status == 'burnt' || status == 'cooked'
    stop_oven
    takeout_pie
    job.unschedule
  end
end

The second argument is "time", it's the time when the job got cleared for triggering (not Time.now).

Note that time is the time when the job got cleared for triggering. If there are mutexes involved, now = mutex_wait_time + time...

"every" jobs and changing the next_time in-flight

It's OK to change the next_time of an every job in-flight:

scheduler.every '10m' do |job|

  # ...

  status = determine_pie_status

  job.next_time = Time.now + 30 * 60 if status == 'burnt'
    #
    # if burnt, wait 30 minutes for the oven to cool a bit
end

It should work as well with cron jobs, not so with interval jobs whose next_time is computed after their block ends its current run.

scheduling handler instances

It's OK to pass any object, as long as it responds to #call(), when scheduling:

class Handler
  def self.call(job, time)
    p "- Handler called for #{job.id} at #{time}"
  end
end

scheduler.in '10d', Handler

# or

class OtherHandler
  def initialize(name)
    @name = name
  end
  def call(job, time)
    p "* #{time} - Handler #{name.inspect} called for #{job.id}"
  end
end

oh = OtherHandler.new('Doe')

scheduler.every '10m', oh
scheduler.in '3d5m', oh

The call method must accept 2 (job, time), 1 (job) or 0 arguments.

Note that time is the time when the job got cleared for triggering. If there are mutexes involved, now = mutex_wait_time + time...

scheduling handler classes

One can pass a handler class to rufus-scheduler when scheduling. Rufus will instantiate it and that instance will be available via job#handler.

class MyHandler
  attr_reader :count
  def initialize
    @count = 0
  end
  def call(job)
    @count += 1
    puts ". #{self.class} called at #{Time.now} (#{@count})"
  end
end

job = scheduler.schedule_every '35m', MyHandler

job.handler
  # => #<MyHandler:0x000000021034f0>
job.handler.count
  # => 0

If you want to keep that "block feeling":

job_id =
  scheduler.every '10m', Class.new do
    def call(job)
      puts ". hello #{self.inspect} at #{Time.now}"
    end
  end

pause and resume the scheduler

The scheduler can be paused via the #pause and #resume methods. One can determine if the scheduler is currently paused by calling #paused?.

While paused, the scheduler still accepts schedules, but no schedule will get triggered as long as #resume isn't called.

job options

name: string

Sets the name of the job.

scheduler.cron '*/15 8 * * *', name: 'Robert' do |job|
  puts "A, it's #{Time.now} and my name is #{job.name}"
end

job1 =
  scheduler.schedule_cron '*/30 9 * * *', n: 'temporary' do |job|
    puts "B, it's #{Time.now} and my name is #{job.name}"
  end
# ...
job1.name = 'Beowulf'

blocking: true

By default, jobs are triggered in their own, new threads. When blocking: true, the job is triggered in the scheduler thread (a new thread is not created). Yes, while a blocking job is running, the scheduler is not scheduling.

overlap: false

Since, by default, jobs are triggered in their own new threads, job instances might overlap. For example, a job that takes 10 minutes and is scheduled every 7 minutes will have overlaps.

To prevent overlap, one can set overlap: false. Such a job will not trigger if one of its instances is already running.

The :overlap option is considered before the :mutex option when the scheduler is reviewing jobs for triggering.

mutex: mutex_instance / mutex_name / array of mutexes

When a job with a mutex triggers, the job's block is executed with the mutex around it, preventing other jobs with the same mutex from entering (it makes the other jobs wait until it exits the mutex).

This is different from overlap: false, which is, first, limited to instances of the same job, and, second, doesn't make the incoming job instance block/wait but give up.

:mutex accepts a mutex instance or a mutex name (String). It also accept an array of mutex names / mutex instances. It allows for complex relations between jobs.

Array of mutexes: original idea and implementation by Rainux Luo

Note: creating lots of different mutexes is OK. Rufus-scheduler will place them in its Scheduler#mutexes hash... And they won't get garbage collected.

The :overlap option is considered before the :mutex option when the scheduler is reviewing jobs for triggering.

timeout: duration or point in time

It's OK to specify a timeout when scheduling some work. After the time specified, it gets interrupted via a Rufus::Scheduler::TimeoutError.

scheduler.in '10d', timeout: '1d' do
  begin
    # ... do something
  rescue Rufus::Scheduler::TimeoutError
    # ... that something got interrupted after 1 day
  end
end

The :timeout option accepts either a duration (like "1d" or "2w3d") or a point in time (like "2013/12/12 12:00").

:first_at, :first_in, :first, :first_time

This option is for repeat jobs (cron / every) only.

It's used to specify the first time after which the repeat job should trigger for the first time.

In the case of an "every" job, this will be the first time (modulo the scheduler frequency) the job triggers. For a "cron" job as well, the :first will point to the first time the job has to trigger, the following trigger times are then determined by the cron string.

scheduler.every '2d', first_at: Time.now + 10 * 3600 do
  # ... every two days, but start in 10 hours
end

scheduler.every '2d', first_in: '10h' do
  # ... every two days, but start in 10 hours
end

scheduler.cron '00 14 * * *', first_in: '3d' do
  # ... every day at 14h00, but start after 3 * 24 hours
end

:first, :first_at and :first_in all accept a point in time or a duration (number or time string). Use the symbol you think makes your schedule more readable.

Note: it's OK to change the first_at (a Time instance) directly:

job.first_at = Time.now + 10
job.first_at = Rufus::Scheduler.parse('2029-12-12')

The first argument (in all its flavours) accepts a :now or :immediately value. That schedules the first occurrence for immediate triggering. Consider:

require 'rufus-scheduler'

s = Rufus::Scheduler.new

n = Time.now; p [ :scheduled_at, n, n.to_f ]

s.every '3s', first: :now do
  n = Time.now; p [ :in, n, n.to_f ]
end

s.join

that'll output something like:

[:scheduled_at, 2014-01-22 22:21:21 +0900, 1390396881.344438]
[:in, 2014-01-22 22:21:21 +0900, 1390396881.6453865]
[:in, 2014-01-22 22:21:24 +0900, 1390396884.648807]
[:in, 2014-01-22 22:21:27 +0900, 1390396887.651686]
[:in, 2014-01-22 22:21:30 +0900, 1390396890.6571937]
...

:last_at, :last_in, :last

This option is for repeat jobs (cron / every) only.

It indicates the point in time after which the job should unschedule itself.

scheduler.cron '5 23 * * *', last_in: '10d' do
  # ... do something every evening at 23:05 for 10 days
end

scheduler.every '10m', last_at: Time.now + 10 * 3600 do
  # ... do something every 10 minutes for 10 hours
end

scheduler.every '10m', last_in: 10 * 3600 do
  # ... do something every 10 minutes for 10 hours
end

:last, :last_at and :last_in all accept a point in time or a duration (number or time string). Use the symbol you think makes your schedule more readable.

Note: it's OK to change the last_at (nil or a Time instance) directly:

job.last_at = nil
  # remove the "last" bound

job.last_at = Rufus::Scheduler.parse('2029-12-12')
  # set the last bound

times: nb of times (before auto-unscheduling)

One can tell how many times a repeat job (CronJob or EveryJob) is to execute before unscheduling by itself.

scheduler.every '2d', times: 10 do
  # ... do something every two days, but not more than 10 times
end

scheduler.cron '0 23 * * *', times: 31 do
  # ... do something every day at 23:00 but do it no more than 31 times
end

It's OK to assign nil to :times to make sure the repeat job is not limited. It's useful when the :times is determined at scheduling time.

scheduler.cron '0 23 * * *', times: (nolimit ? nil : 10) do
  # ...
end

The value set by :times is accessible in the job. It can be modified anytime.

job =
  scheduler.cron '0 23 * * *' do
    # ...
  end

# later on...

job.times = 10
  # 10 days and it will be over

Job methods

When calling a schedule method, the id (String) of the job is returned. Longer schedule methods return Job instances directly. Calling the shorter schedule methods with the job: true also returns Job instances instead of Job ids (Strings).

  require 'rufus-scheduler'

  scheduler = Rufus::Scheduler.new

  job_id =
    scheduler.in '10d' do
      # ...
    end

  job =
    scheduler.schedule_in '1w' do
      # ...
    end

  job =
    scheduler.in '1w', job: true do
      # ...
    end

Those Job instances have a few interesting methods / properties:

id, job_id

Returns the job id.

job = scheduler.schedule_in('10d') do; end
job.id
  # => "in_1374072446.8923042_0.0_0"

scheduler

Returns the scheduler instance itself.

opts

Returns the options passed at the Job creation.

job = scheduler.schedule_in('10d', tag: 'hello') do; end
job.opts
  # => { :tag => 'hello' }

original

Returns the original schedule.

job = scheduler.schedule_in('10d', tag: 'hello') do; end
job.original
  # => '10d'

callable, handler

callable() returns the scheduled block (or the call method of the callable object passed in lieu of a block)

handler() returns nil if a block was scheduled and the instance scheduled otherwise.

# when passing a block

job =
  scheduler.schedule_in('10d') do
    # ...
  end

job.handler
  # => nil
job.callable
  # => #<Proc:0x00000001dc6f58@/home/jmettraux/whatever.rb:115>

and

# when passing something else than a block

class MyHandler
  attr_reader :counter
  def initialize
    @counter = 0
  end
  def call(job, time)
    @counter = @counter + 1
  end
end

job = scheduler.schedule_in('10d', MyHandler.new)

job.handler
  # => #<Method: MyHandler#call>
job.callable
  # => #<MyHandler:0x0000000163ae88 @counter=0>

source_location

Added to rufus-scheduler 3.8.0.

Returns the array [ 'path/to/file.rb', 123 ] like Proc#source_location does.

require 'rufus-scheduler'

scheduler = Rufus::Scheduler.new

job = scheduler.schedule_every('2h') { p Time.now }

p job.source_location
  # ==> [ '/home/jmettraux/rufus-scheduler/test.rb', 6 ]

scheduled_at

Returns the Time instance when the job got created.

job = scheduler.schedule_in('10d', tag: 'hello') do; end
job.scheduled_at
  # => 2013-07-17 23:48:54 +0900

last_time

Returns the last time the job triggered (is usually nil for AtJob and InJob).

job = scheduler.schedule_every('10s') do; end

job.scheduled_at
  # => 2013-07-17 23:48:54 +0900
job.last_time
  # => nil (since we've just scheduled it)

# after 10 seconds

job.scheduled_at
  # => 2013-07-17 23:48:54 +0900 (same as above)
job.last_time
  # => 2013-07-17 23:49:04 +0900

previous_time

Returns the previous #next_time

scheduler.every('10s') do |job|
  puts "job scheduled for #{job.previous_time} triggered at #{Time.now}"
  puts "next time will be around #{job.next_time}"
  puts "."
end

last_work_time, mean_work_time

The job keeps track of how long its work was in the last_work_time attribute. For a one time job (in, at) it's probably not very useful.

The attribute mean_work_time contains a computed mean work time. It's recomputed after every run (if it's a repeat job).

next_times(n)

Returns an array of EtOrbi::EoTime instances (Time instances with a designated time zone), listing the n next occurrences for this job.

Please note that for "interval" jobs, a mean work time is computed each time and it's used by this #next_times(n) method to approximate the next times beyond the immediate next time.

unschedule

Unschedule the job, preventing it from firing again and removing it from the schedule. This doesn't prevent a running thread for this job to run until its end.

threads

Returns the list of threads currently "hosting" runs of this Job instance.

kill

Interrupts all the work threads currently running for this job instance. They discard their work and are free for their next run (of whatever job).

Note: this doesn't unschedule the Job instance.

Note: if the job is pooled for another run, a free work thread will probably pick up that next run and the job will appear as running again. You'd have to unschedule and kill to make sure the job doesn't run again.

running?

Returns true if there is at least one running Thread hosting a run of this Job instance.

scheduled?

Returns true if the job is scheduled (is due to trigger). For repeat jobs it should return true until the job gets unscheduled. "at" and "in" jobs will respond with false as soon as they start running (execution triggered).

pause, resume, paused?, paused_at

These four methods are only available to CronJob, EveryJob and IntervalJob instances. One can pause or resume such jobs thanks to these methods.

job =
  scheduler.schedule_every('10s') do
    # ...
  end

job.pause
  # => 2013-07-20 01:22:22 +0900
job.paused?
  # => true
job.paused_at
  # => 2013-07-20 01:22:22 +0900

job.resume
  # => nil

tags

Returns the list of tags attached to this Job instance.

By default, returns an empty array.

job = scheduler.schedule_in('10d') do; end
job.tags
  # => []

job = scheduler.schedule_in('10d', tag: 'hello') do; end
job.tags
  # => [ 'hello' ]

[]=, [], key?, has_key?, keys, values, and entries

Threads have thread-local variables, similarly Rufus-scheduler jobs have job-local variables. Those are more like a dict with thread-safe access.

job =
  @scheduler.schedule_every '1s' do |job|
    job[:timestamp] = Time.now.to_f
    job[:counter] ||= 0
    job[:counter] += 1
  end

sleep 3.6

job[:counter]
  # => 3

job.key?(:timestamp) # => true
job.has_key?(:timestamp) # => true
job.keys # => [ :timestamp, :counter ]

Locals can be set at schedule time:

job0 =
  @scheduler.schedule_cron '*/15 12 * * *', locals: { a: 0 } do
    # ...
  end
job1 =
  @scheduler.schedule_cron '*/15 13 * * *', l: { a: 1 } do
    # ...
  end

One can fetch the Hash directly with Job#locals. Of course, direct manipulation is not thread-safe.

job.locals.entries do |k, v|
  p "#{k}: #{v}"
end

call

Job instances have a #call method. It simply calls the scheduled block or callable immediately.

job =
  @scheduler.schedule_every '10m' do |job|
    # ...
  end

job.call

Warning: the Scheduler#on_error handler is not involved. Error handling is the responsibility of the caller.

If the call has to be rescued by the error handler of the scheduler, call(true) might help:

require 'rufus-scheduler'

s = Rufus::Scheduler.new

def s.on_error(job, err)
  if job
    p [ 'error in scheduled job', job.class, job.original, err.message ]
  else
    p [ 'error while scheduling', err.message ]
  end
rescue
  p $!
end

job =
  s.schedule_in('1d') do
    fail 'again'
  end

job.call(true)
  #
  # true lets the error_handler deal with error in the job call

AtJob and InJob methods

time

Returns when the job will trigger (hopefully).

next_time

An alias for time.

EveryJob, IntervalJob and CronJob methods

next_time

Returns the next time the job will trigger (hopefully).

count

Returns how many times the job fired.

EveryJob methods

frequency

It returns the scheduling frequency. For a job scheduled "every 20s", it's 20.

It's used to determine if the job frequency is higher than the scheduler frequency (it raises an ArgumentError if that is the case).

IntervalJob methods

interval

Returns the interval scheduled between each execution of the job.

Every jobs use a time duration between each start of their execution, while interval jobs use a time duration between the end of an execution and the start of the next.

CronJob methods

brute_frequency

An expensive method to run, it's brute. It caches its results. By default it runs for 2017 (a non leap-year).

  require 'rufus-scheduler'

  Rufus::Scheduler.parse('* * * * *').brute_frequency
    #
    # => #<Fugit::Cron::Frequency:0x00007fdf4520c5e8
    #      @span=31536000.0, @delta_min=60, @delta_max=60,
    #      @occurrences=525600, @span_years=1.0, @yearly_occurrences=525600.0>
      #
      # Occurs 525600 times in a span of 1 year (2017) and 1 day.
      # There are least 60 seconds between "triggers" and at most 60 seconds.

  Rufus::Scheduler.parse('0 12 * * *').brute_frequency
    # => #<Fugit::Cron::Frequency:0x00007fdf451ec6d0
    #      @span=31536000.0, @delta_min=86400, @delta_max=86400,
    #      @occurrences=365, @span_years=1.0, @yearly_occurrences=365.0>
  Rufus::Scheduler.parse('0 12 * * *').brute_frequency.to_debug_s
    # => "dmin: 1D, dmax: 1D, ocs: 365, spn: 52W1D, spnys: 1, yocs: 365"
      #
      # 365 occurrences, at most 1 day between each, at least 1 day.

The CronJob#frequency method found in rufus-scheduler < 3.5 has been retired.

looking up jobs

Scheduler#job(job_id)

The scheduler #job(job_id) method can be used to look up Job instances.

  require 'rufus-scheduler'

  scheduler = Rufus::Scheduler.new

  job_id =
    scheduler.in '10d' do
      # ...
    end

  # later on...

  job = scheduler.job(job_id)

Scheduler #jobs #at_jobs #in_jobs #every_jobs #interval_jobs and #cron_jobs

Are methods for looking up lists of scheduled Job instances.

Here is an example:

  #
  # let's unschedule all the at jobs

  scheduler.at_jobs.each(&:unschedule)

Scheduler#jobs(tag: / tags: x)

When scheduling a job, one can specify one or more tags attached to the job. These can be used to look up the job later on.

  scheduler.in '10d', tag: 'main_process' do
    # ...
  end
  scheduler.in '10d', tags: [ 'main_process', 'side_dish' ] do
    # ...
  end

  # ...

  jobs = scheduler.jobs(tag: 'main_process')
    # find all the jobs with the 'main_process' tag

  jobs = scheduler.jobs(tags: [ 'main_process', 'side_dish' ]
    # find all the jobs with the 'main_process' AND 'side_dish' tags

Scheduler#running_jobs

Returns the list of Job instance that have currently running instances.

Whereas other "_jobs" method scan the scheduled job list, this method scans the thread list to find the job. It thus comprises jobs that are running but are not scheduled anymore (that happens for at and in jobs).

misc Scheduler methods

Scheduler#unschedule(job_or_job_id)

Unschedule a job given directly or by its id.

Scheduler#shutdown

Shuts down the scheduler, ceases any scheduler/triggering activity.

Scheduler#shutdown(:wait)

Shuts down the scheduler, waits (blocks) until all the jobs cease running.

Scheduler#shutdown(wait: n)

Shuts down the scheduler, waits (blocks) at most n seconds until all the jobs cease running. (Jobs are killed after n seconds have elapsed).

Scheduler#shutdown(:kill)

Kills all the job (threads) and then shuts the scheduler down. Radical.

Scheduler#down?

Returns true if the scheduler has been shut down.

Scheduler#started_at

Returns the Time instance at which the scheduler got started.

Scheduler #uptime / #uptime_s

Returns since the count of seconds for which the scheduler has been running.

#uptime_s returns this count in a String easier to grasp for humans, like "3d12m45s123".

Scheduler#join

Lets the current thread join the scheduling thread in rufus-scheduler. The thread comes back when the scheduler gets shut down.

#join is mostly used in standalone scheduling script (or tiny one file examples). Calling #join from a web application initializer will probably hijack the main thread and prevent the web application from being served. Do not put a #join in such a web application initializer file.

Scheduler#threads

Returns all the threads associated with the scheduler, including the scheduler thread itself.

Scheduler#work_threads(query=:all/:active/:vacant)

Lists the work threads associated with the scheduler. The query option defaults to :all.

• :all : all the work threads
• :active : all the work threads currently running a Job
• :vacant : all the work threads currently not running a Job

Note that the main schedule thread will be returned if it is currently running a Job (ie one of those blocking: true jobs).

Scheduler#scheduled?(job_or_job_id)

Returns true if the arg is a currently scheduled job (see Job#scheduled?).

Scheduler#occurrences(time0, time1)

Returns a hash { job => [ t0, t1, ... ] } mapping jobs to their potential trigger time within the [ time0, time1 ] span.

Please note that, for interval jobs, the #mean_work_time is used, so the result is only a prediction.

Scheduler#timeline(time0, time1)

Like #occurrences but returns a list [ [ t0, job0 ], [ t1, job1 ], ... ] of time + job pairs.

dealing with job errors

The easy, job-granular way of dealing with errors is to rescue and deal with them immediately. The two next sections show examples. Skip them for explanations on how to deal with errors at the scheduler level.

block jobs

As said, jobs could take care of their errors themselves.

scheduler.every '10m' do
  begin
    # do something that might fail...
  rescue => e
    $stderr.puts '-' * 80
    $stderr.puts e.message
    $stderr.puts e.stacktrace
    $stderr.puts '-' * 80
  end
end

callable jobs

Jobs are not only shrunk to blocks, here is how the above would look like with a dedicated class.

scheduler.every '10m', Class.new do
  def call(job)
    # do something that might fail...
  rescue => e
    $stderr.puts '-' * 80
    $stderr.puts e.message
    $stderr.puts e.stacktrace
    $stderr.puts '-' * 80
  end
end

TODO: talk about callable#on_error (if implemented)

(see scheduling handler instances and scheduling handler classes for more about those "callable jobs")

Rufus::Scheduler#stderr=

By default, rufus-scheduler intercepts all errors (that inherit from StandardError) and dumps abundant details to $stderr.

If, for example, you'd like to divert that flow to another file (descriptor), you can reassign $stderr for the current Ruby process

$stderr = File.open('/var/log/myapplication.log', 'ab')

or, you can limit that reassignement to the scheduler itself

scheduler.stderr = File.open('/var/log/myapplication.log', 'ab')

Rufus::Scheduler#on_error(job, error)

We've just seen that, by default, rufus-scheduler dumps error information to $stderr. If one needs to completely change what happens in case of error, it's OK to overwrite #on_error

def scheduler.on_error(job, error)

  Logger.warn("intercepted error in #{job.id}: #{error.message}")
end

On Rails, the on_error method redefinition might look like:

def scheduler.on_error(job, error)

  Rails.logger.error(
    "err#{error.object_id} rufus-scheduler intercepted #{error.inspect}" +
    " in job #{job.inspect}")
  error.backtrace.each_with_index do |line, i|
    Rails.logger.error(
      "err#{error.object_id} #{i}: #{line}")
  end
end

Callbacks

Rufus::Scheduler #on_pre_trigger and #on_post_trigger callbacks

One can bind callbacks before and after jobs trigger:

s = Rufus::Scheduler.new

def s.on_pre_trigger(job, trigger_time)
  puts "triggering job #{job.id}..."
end

def s.on_post_trigger(job, trigger_time)
  puts "triggered job #{job.id}."
end

s.every '1s' do
  # ...
end

The trigger_time is the time at which the job triggers. It might be a bit before Time.now.

Warning: these two callbacks are executed in the scheduler thread, not in the work threads (the threads where the job execution really happens).

Rufus::Scheduler#around_trigger

One can create an around callback which will wrap a job:

def s.around_trigger(job)
  t = Time.now
  puts "Starting job #{job.id}..."
  yield
  puts "job #{job.id} finished in #{Time.now-t} seconds."
end

The around callback is executed in the thread.

Rufus::Scheduler#on_pre_trigger as a guard

Returning false in on_pre_trigger will prevent the job from triggering. Returning anything else (nil, -1, true, ...) will let the job trigger.

Note: your business logic should go in the scheduled block itself (or the scheduled instance). Don't put business logic in on_pre_trigger. Return false for admin reasons (backend down, etc), not for business reasons that are tied to the job itself.

def s.on_pre_trigger(job, trigger_time)

  return false if Backend.down?

  puts "triggering job #{job.id}..."
end

Rufus::Scheduler.new options

:frequency

By default, rufus-scheduler sleeps 0.300 second between every step. At each step it checks for jobs to trigger and so on.

The :frequency option lets you change that 0.300 second to something else.

scheduler = Rufus::Scheduler.new(frequency: 5)

It's OK to use a time string to specify the frequency.

scheduler = Rufus::Scheduler.new(frequency: '2h10m')
  # this scheduler will sleep 2 hours and 10 minutes between every "step"

Use with care.

lockfile: "mylockfile.txt"

This feature only works on OSes that support the flock (man 2 flock) call.

Starting the scheduler with lockfile: '.rufus-scheduler.lock' will make the scheduler attempt to create and lock the file .rufus-scheduler.lock in the current working directory. If that fails, the scheduler will not start.

The idea is to guarantee only one scheduler (in a group of schedulers sharing the same lockfile) is running.

This is useful in environments where the Ruby process holding the scheduler gets started multiple times.

If the lockfile mechanism here is not sufficient, you can plug your custom mechanism. It's explained in advanced lock schemes below.

:scheduler_lock

(since rufus-scheduler 3.0.9)

The scheduler lock is an object that responds to #lock and #unlock. The scheduler calls #lock when starting up. If the answer is false, the scheduler stops its initialization work and won't schedule anything.

Here is a sample of a scheduler lock that only lets the scheduler on host "coffee.example.com" start:

class HostLock
  def initialize(lock_name)
    @lock_name = lock_name
  end
  def lock
    @lock_name == `hostname -f`.strip
  end
  def unlock
    true
  end
end

scheduler =
  Rufus::Scheduler.new(scheduler_lock: HostLock.new('coffee.example.com'))

By default, the scheduler_lock is an instance of Rufus::Scheduler::NullLock, with a #lock that returns true.

:trigger_lock

(since rufus-scheduler 3.0.9)

The trigger lock in an object that responds to #lock. The scheduler calls that method on the job lock right before triggering any job. If the answer is false, the trigger doesn't happen, the job is not done (at least not in this scheduler).

Here is a (stupid) PingLock example, it'll only trigger if an "other host" is not responding to ping. Do not use that in production, you don't want to fork a ping process for each trigger attempt...

class PingLock
  def initialize(other_host)
    @other_host = other_host
  end
  def lock
    ! system("ping -c 1 #{@other_host}")
  end
end

scheduler =
  Rufus::Scheduler.new(trigger_lock: PingLock.new('main.example.com'))

By default, the trigger_lock is an instance of Rufus::Scheduler::NullLock, with a #lock that always returns true.

As explained in advanced lock schemes, another way to tune that behaviour is by overriding the scheduler's #confirm_lock method. (You could also do that with an #on_pre_trigger callback).

:max_work_threads

In rufus-scheduler 2.x, by default, each job triggering received its own, brand new, thread of execution. In rufus-scheduler 3.x, execution happens in a pooled work thread. The max work thread count (the pool size) defaults to 28.

One can set this maximum value when starting the scheduler.

scheduler = Rufus::Scheduler.new(max_work_threads: 77)

It's OK to increase the :max_work_threads of a running scheduler.

scheduler.max_work_threads += 10

Rufus::Scheduler.singleton

Do not want to store a reference to your rufus-scheduler instance? Then Rufus::Scheduler.singleton can help, it returns a singleton instance of the scheduler, initialized the first time this class method is called.

Rufus::Scheduler.singleton.every '10s' { puts "hello, world!" }

It's OK to pass initialization arguments (like :frequency or :max_work_threads) but they will only be taken into account the first time .singleton is called.

Rufus::Scheduler.singleton(max_work_threads: 77)
Rufus::Scheduler.singleton(max_work_threads: 277) # no effect

The .s is a shortcut for .singleton.

Rufus::Scheduler.s.every '10s' { puts "hello, world!" }

advanced lock schemes

As seen above, rufus-scheduler proposes the :lockfile system out of the box. If in a group of schedulers only one is supposed to run, the lockfile mechanism prevents schedulers that have not set/created the lockfile from running.

There are situations where this is not sufficient.

By overriding #lock and #unlock, one can customize how schedulers lock.

This example was provided by Eric Lindvall:

class ZookeptScheduler < Rufus::Scheduler

  def initialize(zookeeper, opts={})
    @zk = zookeeper
    super(opts)
  end

  def lock
    @zk_locker = @zk.exclusive_locker('scheduler')
    @zk_locker.lock # returns true if the lock was acquired, false else
  end

  def unlock
    @zk_locker.unlock
  end

  def confirm_lock
    return false if down?
    @zk_locker.assert!
  rescue ZK::Exceptions::LockAssertionFailedError => e
    # we've lost the lock, shutdown (and return false to at least prevent
    # this job from triggering
    shutdown
    false
  end
end

This uses a zookeeper to make sure only one scheduler in a group of distributed schedulers runs.

The methods #lock and #unlock are overridden and #confirm_lock is provided, to make sure that the lock is still valid.

The #confirm_lock method is called right before a job triggers (if it is provided). The more generic callback #on_pre_trigger is called right after #confirm_lock.

:scheduler_lock and :trigger_lock

(introduced in rufus-scheduler 3.0.9).

Another way of prodiving #lock, #unlock and #confirm_lock to a rufus-scheduler is by using the :scheduler_lock and :trigger_lock options.

See :trigger_lock and :scheduler_lock.

The scheduler lock may be used to prevent a scheduler from starting, while a trigger lock prevents individual jobs from triggering (the scheduler goes on scheduling).

One has to be careful with what goes in #confirm_lock or in a trigger lock, as it gets called before each trigger.

Warning: you may think you're heading towards "high availability" by using a trigger lock and having lots of schedulers at hand. It may be so if you limit yourself to scheduling the same set of jobs at scheduler startup. But if you add schedules at runtime, they stay local to their scheduler. There is no magic that propagates the jobs to all the schedulers in your pack.

parsing cronlines and time strings

(Please note that fugit does the heavy-lifting parsing work for rufus-scheduler).

Rufus::Scheduler provides a class method .parse to parse time durations and cron strings. It's what it's using when receiving schedules. One can use it directly (no need to instantiate a Scheduler).

require 'rufus-scheduler'

Rufus::Scheduler.parse('1w2d')
  # => 777600.0
Rufus::Scheduler.parse('1.0w1.0d')
  # => 777600.0

Rufus::Scheduler.parse('Sun Nov 18 16:01:00 2012').strftime('%c')
  # => 'Sun Nov 18 16:01:00 2012'

Rufus::Scheduler.parse('Sun Nov 18 16:01:00 2012 Europe/Berlin').strftime('%c %z')
  # => 'Sun Nov 18 15:01:00 2012 +0000'

Rufus::Scheduler.parse(0.1)
  # => 0.1

Rufus::Scheduler.parse('* * * * *')
  # => #<Fugit::Cron:0x00007fb7a3045508
  #      @original="* * * * *", @cron_s=nil,
  #      @seconds=[0], @minutes=nil, @hours=nil, @monthdays=nil, @months=nil,
  #      @weekdays=nil, @zone=nil, @timezone=nil>

It returns a number when the input is a duration and a Fugit::Cron instance when the input is a cron string.

It will raise an ArgumentError if it can't parse the input.

Beyond .parse, there are also .parse_cron and .parse_duration, for finer granularity.

There is an interesting helper method named .to_duration_hash:

require 'rufus-scheduler'

Rufus::Scheduler.to_duration_hash(60)
  # => { :m => 1 }
Rufus::Scheduler.to_duration_hash(62.127)
  # => { :m => 1, :s => 2, :ms => 127 }

Rufus::Scheduler.to_duration_hash(62.127, drop_seconds: true)
  # => { :m => 1 }

cronline notations specific to rufus-scheduler

first Monday, last Sunday et al

To schedule something at noon every first Monday of the month:

scheduler.cron('00 12 * * mon#1') do
  # ...
end

To schedule something at noon the last Sunday of every month:

scheduler.cron('00 12 * * sun#-1') do
  # ...
end
#
# OR
#
scheduler.cron('00 12 * * sun#L') do
  # ...
end

Such cronlines can be tested with scripts like:

require 'rufus-scheduler'

Time.now
  # => 2013-10-26 07:07:08 +0900
Rufus::Scheduler.parse('* * * * mon#1').next_time.to_s
  # => 2013-11-04 00:00:00 +0900

L (last day of month)

L can be used in the "day" slot:

In this example, the cronline is supposed to trigger every last day of the month at noon:

require 'rufus-scheduler'
Time.now
  # => 2013-10-26 07:22:09 +0900
Rufus::Scheduler.parse('00 12 L * *').next_time.to_s
  # => 2013-10-31 12:00:00 +0900

negative day (x days before the end of the month)

It's OK to pass negative values in the "day" slot:

scheduler.cron '0 0 -5 * *' do
  # do it at 00h00 5 days before the end of the month...
end

Negative ranges (-10--5-: 10 days before the end of the month to 5 days before the end of the month) are OK, but mixed positive / negative ranges will raise an ArgumentError.

Negative ranges with increments (-10---2/2) are accepted as well.

Descending day ranges are not accepted (10-8 or -8--10 for example).

a note about timezones

Cron schedules and at schedules support the specification of a timezone.

scheduler.cron '0 22 * * 1-5 America/Chicago' do
  # the job...
end

scheduler.at '2013-12-12 14:00 Pacific/Samoa' do
  puts "it's tea time!"
end

# or even

Rufus::Scheduler.parse("2013-12-12 14:00 Pacific/Saipan")
  # => #<Rufus::Scheduler::ZoTime:0x007fb424abf4e8 @seconds=1386820800.0, @zone=#<TZInfo::DataTimezone: Pacific/Saipan>, @time=nil>

I get "zotime.rb:41:in `initialize': cannot determine timezone from nil"

For when you see an error like:

rufus-scheduler/lib/rufus/scheduler/zotime.rb:41:
  in `initialize':
    cannot determine timezone from nil (etz:nil,tnz:"中国标准时间",tzid:nil)
      (ArgumentError)
    from rufus-scheduler/lib/rufus/scheduler/zotime.rb:198:in `new'
    from rufus-scheduler/lib/rufus/scheduler/zotime.rb:198:in `now'
    from rufus-scheduler/lib/rufus/scheduler.rb:561:in `start'
    ...

It may happen on Windows or on systems that poorly hint to Ruby which timezone to use. It should be solved by setting explicitly the ENV['TZ'] before the scheduler instantiation:

ENV['TZ'] = 'Asia/Shanghai'
scheduler = Rufus::Scheduler.new
scheduler.every '2s' do
  puts "#{Time.now} Hello #{ENV['TZ']}!"
end

On Rails you might want to try with:

ENV['TZ'] = Time.zone.name # Rails only
scheduler = Rufus::Scheduler.new
scheduler.every '2s' do
  puts "#{Time.now} Hello #{ENV['TZ']}!"
end

(Hat tip to Alexander in gh-230)

Rails sets its timezone under config/application.rb.

Rufus-Scheduler 3.3.3 detects the presence of Rails and uses its timezone setting (tested with Rails 4), so setting ENV['TZ'] should not be necessary.

The value can be determined thanks to https://en.wikipedia.org/wiki/List_of_tz_database_time_zones.

Use a "continent/city" identifier (for example "Asia/Shanghai"). Do not use an abbreviation (not "CST") and do not use a local time zone name (not "中国标准时间" nor "Eastern Standard Time" which, for instance, points to a time zone in America and to another one in Australia...).

If the error persists (and especially on Windows), try to add the tzinfo-data to your Gemfile, as in:

gem 'tzinfo-data'

or by manually requiring it before requiring rufus-scheduler (if you don't use Bundler):

require 'tzinfo/data'
require 'rufus-scheduler'

so Rails?

Yes, I know, all of the above is boring and you're only looking for a snippet to paste in your Ruby-on-Rails application to schedule...

Here is an example initializer:

#
# config/initializers/scheduler.rb

require 'rufus-scheduler'

# Let's use the rufus-scheduler singleton
#
s = Rufus::Scheduler.singleton


# Stupid recurrent task...
#
s.every '1m' do

  Rails.logger.info "hello, it's #{Time.now}"
  Rails.logger.flush
end

And now you tell me that this is good, but you want to schedule stuff from your controller.

Maybe:

class ScheController < ApplicationController

  # GET /sche/
  #
  def index

    job_id =
      Rufus::Scheduler.singleton.in '5s' do
        Rails.logger.info "time flies, it's now #{Time.now}"
      end

    render text: "scheduled job #{job_id}"
  end
end

The rufus-scheduler singleton is instantiated in the config/initializers/scheduler.rb file, it's then available throughout the webapp via Rufus::Scheduler.singleton.

Warning: this works well with single-process Ruby servers like Webrick and Thin. Using rufus-scheduler with Passenger or Unicorn requires a bit more knowledge and tuning, gently provided by a bit of googling and reading, see Faq above.

avoid scheduling when running the Ruby on Rails console

(Written in reply to gh-186)

If you don't want rufus-scheduler to trigger anything while running the Ruby on Rails console, running for tests/specs, or running from a Rake task, you can insert a conditional return statement before jobs are added to the scheduler instance:

#
# config/initializers/scheduler.rb

require 'rufus-scheduler'

return if defined?(Rails::Console) || Rails.env.test? || File.split($PROGRAM_NAME).last == 'rake'
  #
  # do not schedule when Rails is run from its console, for a test/spec, or
  # from a Rake task

# return if $PROGRAM_NAME.include?('spring')
  #
  # see https://github.com/jmettraux/rufus-scheduler/issues/186

s = Rufus::Scheduler.singleton

s.every '1m' do
  Rails.logger.info "hello, it's #{Time.now}"
  Rails.logger.flush
end

(Beware later version of Rails where Spring takes care pre-running the initializers. Running spring stop or disabling Spring might be necessary in some cases to see changes to initializers being taken into account.)

rails server -d

(Written in reply to https://github.com/jmettraux/rufus-scheduler/issues/165 )

There is the handy rails server -d that starts a development Rails as a daemon. The annoying thing is that the scheduler as seen above is started in the main process that then gets forked and daemonized. The rufus-scheduler thread (and any other thread) gets lost, no scheduling happens.

I avoid running -d in development mode and bother about daemonizing only for production deployment.

These are two well crafted articles on process daemonization, please read them:

• https://www.mikeperham.com/2014/09/22/dont-daemonize-your-daemons/
• https://www.mikeperham.com/2014/07/07/use-runit/

If, anyway, you need something like rails server -d, why not try bundle exec unicorn -D instead? In my (limited) experience, it worked out of the box (well, had to add gem 'unicorn' to Gemfile first).

executor / reloader

You might benefit from wraping your scheduled code in the executor or reloader. Read more here: https://guides.rubyonrails.org/threading_and_code_execution.html

support

see getting help above.

Author: jmettraux
Source code: https://github.com/jmettraux/rufus-scheduler
License: MIT license

#ruby 

How to Run C/C++ in Sublime Text?

C and C++ are the most powerful programming language in the world. Most of the super fast and complex libraries and algorithms are written in C or C++. Most powerful Kernel programs are also written in C. So, there is no way to skip it.

In programming competitions, most programmers prefer to write code in C or C++. Tourist is considered the worlds top programming contestant of all ages who write code in C++.

During programming competitions, programmers prefer to use a lightweight editor to focus on coding and algorithm designing. Vim, Sublime Text, and Notepad++ are the most common editors for us. Apart from the competition, many software developers and professionals love to use Sublime Text just because of its flexibility.

I have discussed the steps we need to complete in this blog post before running a C/C++ code in Sublime Text. We will take the inputs from an input file and print outputs to an output file without using freopen file related functions in C/C++.

#cpp #c #c-programming #sublimetext #c++ #c/c++

Dicey Issues in C/C++

If you are familiar with C/C++then you must have come across some unusual things and if you haven’t, then you are about to. The below codes are checked twice before adding, so feel free to share this article with your friends. The following displays some of the issues:

1. Using multiple variables in the print function
2. Comparing Signed integer with unsigned integer
3. Putting a semicolon at the end of the loop statement
4. C preprocessor doesn’t need a semicolon
5. Size of the string matters
6. Macros and equations aren’t good friends
7. Never compare Floating data type with double data type
8. Arrays have a boundary
9. Character constants are different from string literals
10. Difference between single(=) and double(==) equal signs.

The below code generates no error since a print function can take any number of inputs but creates a mismatch with the variables. The print function is used to display characters, strings, integers, float, octal, and hexadecimal values onto the output screen. The format specifier is used to display the value of a variable.

1. %d indicates Integer Format Specifier
2. %f indicates Float Format Specifier
3. %c indicates Character Format Specifier
4. %s indicates String Format Specifier
5. %u indicates Unsigned Integer Format Specifier
6. %ld indicates Long Int Format Specifier

A signed integer is a 32-bit datum that encodes an integer in the range [-2147483648 to 2147483647]. An unsigned integer is a 32-bit datum that encodes a non-negative integer in the range [0 to 4294967295]. The signed integer is represented in twos-complement notation. In the below code the signed integer will be converted to the maximum unsigned integer then compared with the unsigned integer.

#problems-with-c #dicey-issues-in-c #c-programming #c++ #c #cplusplus