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

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:

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 

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

How to Build the Client Angular Web Application Using MEAN Stack

This is the second and concluding part of the CRUD application using MEAN stack. Here I walk through from scratch, all the steps required to create the client Angular web application. The Angular application consumes the Employee Web API created in the Part 1 and works as the front end UI.


Part 1: ☞ https://morioh.com/p/8afdb1f100c8?f=60d94da986deb67e97e9ce2b

0:0:00    Start
1:00   Recap from Part 1
2:30  Start Building Angular Front end 
13:00 Create an employee interface on the client-side
15:00 Create an employee service on client 
18:30  Create an employee list component
30:48  Create a page for adding employees
45:35 Running the complete CRUD application

#angular #mean #crud

How to Build the Client Angular Web Application Using MEAN Stack

Get Started With MongoDB Atlas Cloud for Creating a MEAN Stack CRUD

This tutorial s intended to prepare the new learners of MongoDB to host a MongoDB database on their hosted Atlas Cloud. This is a preparatory lecture for a series on MEAN stack tutorials that will end up creating a CRUD application with Employee database. 


MongoDB Atlas provides an easy way to host and manage your data in the cloud. This tutorial guides you through creating an Atlas cluster, connecting to it, and loading sample data.


#MongoDB #Atlas #MongoDBAtlas #CreateMongoDBAtlasCluster #mean 

Get Started With MongoDB Atlas Cloud for Creating a MEAN Stack CRUD
Verdie  Murray

Verdie Murray

1653897591

Get Started with MongoDB Atlas Cloud for Creating A MEAN Stack CRUD

This tutorial s intended to prepare the new learners of MongoDB to host a MongoDB database on their hosted Atlas Cloud. This is a preparatory lecture for a series on MEAN stack tutorials that will end up creating a CRUD application with Employee database. 
MongoDB Atlas provides an easy way to host and manage your data in the cloud. This tutorial guides you through creating an Atlas cluster, connecting to it, and loading sample data.

Github- https://github.com/krchome


#mongodb  #atlas #mean #database 

Get Started with MongoDB Atlas Cloud for Creating A MEAN Stack CRUD
Joshua Yates

Joshua Yates

1651723566

MEAN vs. MERN vs. MEVN - What’s the Difference

MEAN, MERN, MEVN - What they're all about...

You might've heard about the MEAN, MERN or MEVN stacks. Let's see what they're about and how they differ!


MEAN Vs. MERN Vs. MEVN Stack: What's the difference?

Applications and websites are taking the world by storm. Whether a professional or personal need, every person understands the value of dedicated apps and websites for their business or individual requirements. These interfaces to the internet hold an excellent reputation in ensuring the best marketing, attracting customers, and generating profitable revenues. Do you ever wonder how mobile app developers achieve so much from these applications?

Technology and its optimized use are the keys to success for any mobile or web app. As a result, many businesses resort to outsourcing their website development or mobile app development. It seems a good step, especially when the key decision-makers have least or no information about the development.

The issue starts when small businesses have to manage their web or app creation on limited budgets. Further, the basics of technologies are a must to understand the proper funding for web or apps creation. But what are these technologies?

Development requires a set of technologies, just like different departments in an organization. Henceforth, it is crucial to have the essential technologies that will help create but ensure smooth operations of the web and mobile applications. Do you know what the set of these technologies are called?

What is a technology stack?

A technology stack is the list of all necessary tools and technologies required in dedicated mobile application development. These are the frameworks, operating systems, languages, databases, web servers, APIs, scripting languages, etc.

Thus, all these different technologies work together to deliver an ultimate website or mobile application. Furthermore, the powerful combination of the technology stack makes the selection process easy and quick for the developers who can quickly start concentrating on quality development.

While the development or IT world is leading the way for new technologies, three stacks, i.e., MEAN, MERN, and MEVN, are front-runners in web or mobile app development. Let us understand these popular technology stacks and their differences one by one.

What is the MEAN stack?

MEAN stack is often synonyms with the entire technology stack due to its wide popularity. However, the reusable codes, end-to-end Java support, and use of a single language throughout the stack make MERN a preferred technology stack.

Components:

●      Database manager: MongoDB

●      Server-side application framework: ExpressJS

●      Front-end application framework: Angular

●      Cross-platform framework: Node.js

Advantages:

●      MEAN stack is best for the applications that are set to be hosted on the cloud. Thus, it comes with unparalleled flexibility, scalability, and rapid development.

●      It is one of the best-recommended technology stacks for full-stack JavaScript developers.

Disadvantages:

●      The consistent developments and updates in the MEAN stack make it challenging to manage for software developers.

What is the MERN stack?

MERN stack comes as an updated version to the MEAN. As a result, the high-end applications created using the MERN offer highly engaging and interactive user interfaces.

Components:

●      Database manager: MongoDB

●      Server-side application framework: ExpressJS

●      Front-end application framework: React

●      Cross-platform framework: Node.js

Advantages:

●      MERN stack offers multiple unmatchable performance and benefits to the developers like MVC, quick performance, efficiency, etc.

●      It has global support from the developers’ community and is behind the leading user interfaces of Airbnb, Dropbox, Facebook, etc.

Disadvantages:

●      React is based on a library, and hence developers have to look for third-party services.

What is the MEVN stack?

MEVN stack offers one of the quickest developments of different mobile and web-based applications. It uses multiple tools to provide high-end performing apps for numerous clients.

Components:

●      Database manager: MongoDB

●      Server-side application framework: ExpressJS

●      Front-end application framework: Vue.js

●      Cross-platform framework: Node.js

Advantages:

●      MEVN stack offers multiple benefits like MVC, platform independence, and works on a single language- JavaScript.

●      It gives a better understanding of server-side and client-side development.

Disadvantages:

●      Vue.js is a new language and still lacks support from the developers’ community.

MEAN Vs. MERN Vs. MEVN Stack: What’s the difference?

So, MEAN, MERN, and MEVN - MongoDB, Expressjs, and NodeJS remain the same. The difference is only in selecting the front-end frameworks, i.e., Angular in MEAN, React in MERN, and Vue.js in MEVN. Thus, it all comes to the difference between the front-end frameworks selected for mobile app development.

●      Angular (in MEAN): It comes from the Angular team of the technological giant Google. It is one of the traditional front-end frameworks that are typescript-based and is perfect for multiple developments- native desktop, native mobile, mobile web, web, etc.

The reduced customization options make developers search for other better options in front-end frameworks.

●      React (in MERN): The social media giant Facebook creates leading web applications that run effectively in a browser. The vast scope of scalability with popularity in the IT consultants, startups, and freelancers give React a stronghold against the traditional Angular. Not to miss are the multiple customization options offered to all mobile app developers.

●      Vue.js (in MEVN): The front-end framework developed by Evan You, a Chinese IT professional, in 2014. While it is still struggling to create a market like Angular and React, the developers are banking on its speed, quality documentation, approachability, and versatility.

Thus, any developer with knowledge of HTML5, CSS, JavaScript, etc., can select the technology stack based on the website or mobile app requirements. However, it is hard to declare a single winner as all three - MEAN, MERN, and MEVN have their separate set of benefits.

After knowing the difference between all three available, a careful selection of the front-end framework ensures the selection of the optimized technology for web or mobile application development.


MEAN vs. MERN vs. MEVN Stacks: What’s the Difference?

Within the JavaScript ecosystem, a particular app will consist of a combination of technologies, called a “stack.” The MEAN, MERN, and MEVN (pronounced “Mevin”) stacks are among the most popular technology stacks that developers use to create websites and mobile applications. But, what are the major differences between these stacks? And, do developers prefer one over the other? 

Today, we’ll explore the basics of MEAN, MERN, and MEVN and look at the benefits of using each technology stack in a development project. 

Here’s what we’ll cover in this blog: 

  • What is a technology stack? 
  • What are the MEAN, MERN, and MEVN stacks?
  • Which is better: MEAN, MERN, or MEVN? 
  • Why does Kenzie Academy teach the MERN stack?

What is a technology stack? 

First things first, tech stacks are comprised of the many components which work together to produce an application which serves the needs of users and businesses: 

  • A front-end framework does most of the “heavy lifting” of building a complex website or mobile app, so the developers and designers can focus on providing the specific features and fixes needed by the users and the business.
  • Often a CSS framework helps translate the design decisions of designers to easily-achievable and reproducible steps for developers.
  • A back-end framework does most of the “heavy lifting” of communication between the website or mobile app and the database and business logic.
  • Miscellaneous back-end technologies used for a variety of tasks, such as organizing the gradual release of experimental features, tracking and reporting errors in the system, or accommodating sudden increases in usage (such as when something goes viral).
  • Database technologies store and organize data, as well as provide features for searching, filtering, and reporting data.

What are the MEAN, MERN, and MEVN stacks? 

MEAN, MERN, and MEVN are technology stacks that help people create websites and mobile applications. All three include MongoDB, Express.js (hence why each begins with “ME”), and NodeJS. The primary difference between the MEAN, MERN, and MEVN stacks can be found in the third letter of each acronym. MEAN is a JavaScript stack that relies on the use of Angular for its front-end JavaScript framework, meanwhile, the MERN stack relies on React and its ecosystem. Finally, MEVN relies on Vue.js and its ecosystem. Here’s a more in-depth picture of the elements that make up these stacks: 

  • MongoDB – A “NoSQL” database used for storing data for a back-end JavaScript application. If your application has users, MongoDB is an example of a database where your user information will be kept.
  • Express.js – A light backend framework for serving content to the web. If your backend application needs to send information across the Internet, Express provides tools for accomplishing that.
  • Angular/React/Vue.js – Front-end JavaScript framework
  • NodeJS –  A JavaScript “runtime environment” used to run JavaScript outside of a browser, such as on a backend server in the cloud.

A big similarity between MERN, MEVN, and MEAN is that they’re all composed of open-source technologies. They also offer protection against Cross-Site Scripting (XSS) attacks, active support and helpful documentation, and user-interface components for building reusable bits of a web app. MEAN, MEVN, and MERN stacks are not complete without a testing suite, for ensuring everything works as intended, as well as support for MVC architecture (or similar)—which is a great way to produce a well-organized codebase. 

Ok. So, what’s the difference between Angular, React, and Vue? 

Since the primary difference between these stacks lies in their use of Angular, React, and Vue.js, let’s explore what those frameworks do. 

  • Angular is a front-end framework led by the Angular team at Google. It is Typescript-based by default and used for web, mobile web, native mobile, and native desktop development. Angular is popular in the business world and is often used by traditional organizations, like hospitals and banks. Angular is a more opinionated framework in that it isn’t as customizable as frameworks like React or Vue. 
  • React is a front-end framework for creating web applications to be run inside the browser. It was originally designed by Facebook to be able to support many millions of users. React is the most popular among the startup, freelance, and software consultancy crowds. It’s highly customizable and really good at scaling for massive numbers of users, which makes it easy for teams to be confident about future growth. 
  • Vue.js is an easy-to-learn JavaScript framework. It’s known for its approachability, versatility, quality documentation, and speed. Vue was released in 2014 and is considered an up-and-comer as it doesn’t have the same market reach as frameworks like Angular and React just yet—but it is a fan favorite!

Which is better MEAN, MERN, or MEVN? 

MEAN stack vs. MERN stack vs. MEVN stack… which one is best? These stacks are all very similar, which is why we only teach one to Kenzie Software Engineering students. Once a developer knows one of these 3 stacks, it won’t be too difficult to learn the others. All of these stacks are useful – it just depends on the type of project you’re taking on. 

In short, one isn’t necessarily better than another. Choosing the right stack for your software development process depends on the front-end framework or related tools you’d like to use. If a developer or team prefers to work with Angular, they may choose to work with the MEAN stack. Meanwhile, a developer or team who enjoys working with React will gravitate towards MERN. 

Why does Kenzie Academy teach the MERN stack? 

At Kenzie, we have been teaching our students the MERN stack since the beginning. Here’s why. 

More startups report preferring to use the MERN stack for app development. And, based on feedback from some of our Employer Partners, we’ve learned it’s an in-demand stack those new to the industry will benefit from learning. Here’s a visualization of the MERN stack: 

mern stack

As one of the most widely used stacks in web development today, Kenzie teaches the MERN stack to prepare graduates for a wide variety of Junior Web Developer roles. The MERN stack is also an excellent starting place for development beyond the web because it marries skills, concepts, and software design paradigms from across the broader world of software development.

Our immersive curriculum reaches beyond the MERN stack by also focusing on engineering skills, problem-solving and metacognitive strategies, as well as common programming language patterns, equipping students to adapt quickly to new tech. The ability to pick up new languages and tooling allows our graduates to grow in their careers beyond their first role.


MEAN vs. MERN vs. MEVN Stacks ? What's the difference ?

Full stack development

First, we have to know that those stacks are Full stacks so you do the Frontend and the Backend.
They are all JavaScript!
The Frontend and the Backend will be done through JavaScript Frameworks.


Technologies you should know before the comparison

JavaScript:
It's a programming (scripting) language used both on the client-side and server-side that allows you to make web pages interactive.

Node.js:
It's a runtime environment that executes JavaScript outside the browser as writing Backend code.

Express.js: It is a back end web application framework for Node. It is designed for building web applications and APIs.

MongoDB: It's noSQL (Json-like) database .

Angular: It is a client side JavaScript framework was developed by Google.

React: It is a client side JavaScript framework was developed by Facebook.

Vue: It is a client side JavaScript framework was developed by a single person Evan You who was working in Google.


What is the difference between them ?

In those stacks there are common letters which are M - E - N .

  • M: MongoDB
  • E: Express.js
  • N: Node.js

Those are the Backend technologies be while the Frontend technologies are A - R - V for MEAN - MERN - MEVN .

Then Obviously,

  • A: Angular
  • R: React
  • V: Vue

So, choose the stack that you love but before you have to learn the basics if web development which are:

  • HTML5
  • CSS3
  • JavaScript
  • JS Dom and Bom
  • ES6 Syntax
  • SASS (optional but preferred)
  • CSS Frameworks ( Bootstrap, Tailwind CSS, etc...)

#mean #mern #mevn #mongodb #express #angular #react #vue

MEAN vs. MERN vs. MEVN - What’s the Difference

Which Web Development Stacks To Use in 2022 - Guide for Business Owners

https://www.blog.duomly.com/which-web-development-stack-to-use/

Web development stacks are the combinations of software used for developing websites. They can be used for front-end, back-end, or full-stack web development. Different stacks are used for various purposes, and each has its own advantages and disadvantages.

In 2022, the most popular web development stacks will be ReactJS and NodeJS. These stacks are extremely versatile and can be used for various purposes. They are both very popular among developers, and they are both backed by large companies (Facebook and Google, respectively).

If you’re interested in learning more about web development stacks and which ones will be popular in 2022, be sure to read the whole article.

#web-development #web #python #javascript #JavaScript #react #angular #vue #php #mean #mern #lamp 

Which Web Development Stacks To Use in 2022 - Guide for Business Owners

How to Host A MEAN Stack App on The AWS EC2

It’s overt from the title only, we are showing below how to host a MEAN stack app on the AWS EC2 instance serve for free. Before heading towards this tutorial, we will first introduce you all to the technology stacks that we have used here. In the following tutorial, we have set up a production-ready web server from scratch on the Amazon EC2 (Elastic Compute Cloud) service. Then, later we have shown how to deploy the custom Mean Stack hosting application to it which mainly supports the user registration as well as the authentication.

#aws #mean 

How to Host A MEAN Stack App on The AWS EC2
Lina  Biyinzika

Lina Biyinzika

1643223600

How to Implement A Simple K-means Algorithm

Simple-Kmeans-Clustering-Algorithm

Abstract

K-means is a centroid-based algorithm, or a distance-based algorithm, where we calculate the distances to assign a point to a cluster. In K-means, each cluster is associated with a centroid. The main objective of the K-Means algorithm is to minimize the sum of distances between the points and their respective cluster centroid. Here is an example of how this algorithm works:

K-means algorithm result

To use this work on your researches or projects you need:

  • Python 3.7.0
  • Python packages:
    • matplotlib
    • numpy
    • scikit_learn

To install Python:

First, check if you already have it installed or not.

python3 --version

If you don't have python 3 in your computer you can use the code below:

sudo apt-get update
sudo apt-get install python3

To install packages via pip install:

sudo pip3 install matplotlib numpy scikit_learn

If you haven't installed pip, you can use the codes below in your terminal:

sudo apt-get update
sudo apt install python3-pip

You should check and update your pip:

pip3 install --upgrade pip

Author: SamanKhamesian
Source Code: https://github.com/SamanKhamesian/Simple-Kmeans-Clustering-Algorithm
License: Apache-2.0 License

#algorithm #mean 

How to Implement A Simple K-means Algorithm

How to Predict Housing Prices with Linear Regression?

How-to-Predict-Housing-Prices-with-Linear-Regression

The final objective is to estimate the cost of a certain house in a Boston suburb. In 1970, the Boston Standard Metropolitan Statistical Area provided the information. To examine and modify the data, we will use several techniques such as data pre-processing and feature engineering. After that, we'll apply a statistical model like regression model to anticipate and monitor the real estate market.

Project Outline:

  • EDA
  • Feature Engineering
  • Pick and Train a Model
  • Interpret
  • Conclusion

EDA

Before using a statistical model, the EDA is a good step to go through in order to:

  • Recognize the data set
  • Check to see if any information is missing.
  • Find some outliers.
  • To get more out of the data, add, alter, or eliminate some features.

Importing the Libraries

  • Recognize the data set
  • Check to see if any information is missing.
  • Find some outliers.
  • To get more out of the data, add, alter, or eliminate some features.

# Import the libraries #Dataframe/Numerical libraries import pandas as pd import numpy as np #Data visualization import plotly.express as px import matplotlib import matplotlib.pyplot as plt import seaborn as sns #Machine learning model from sklearn.linear_model import LinearRegression

Reading the Dataset with Pandas

#Reading the data path='./housing.csv' housing_df=pd.read_csv(path,header=None,delim_whitespace=True)

 CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
00.0063218.02.3100.5386.57565.24.09001296.015.3396.904.9824.0
10.027310.07.0700.4696.42178.94.96712242.017.8396.909.1421.6
20.027290.07.0700.4697.18561.14.96712242.017.8392.834.0334.7
30.032370.02.1800.4586.99845.86.06223222.018.7394.632.9433.4
40.069050.02.1800.4587.14754.26.06223222.018.7396.905.3336.2
.............................................
5010.062630.011.9300.5736.59369.12.47861273.021.0391.999.6722.4
5020.045270.011.9300.5736.12076.72.28751273.021.0396.909.0820.6
5030.060760.011.9300.5736.97691.02.16751273.021.0396.905.6423.9
5040.109590.011.9300.5736.79489.32.38891273.021.0393.456.4822.0
5050.047410.011.9300.5736.03080.82.50501273.021.0396.907.8811.9

Have a Look at the Columns

Crime: It refers to a town's per capita crime rate.

ZN: It is the percentage of residential land allocated for 25,000 square feet.

Indus: The amount of non-retail business lands per town is referred to as the indus.

CHAS: CHAS denotes whether or not the land is surrounded by a river.

NOX: The NOX stands for nitric oxide content (part per 10m)

RM: The average number of rooms per home is referred to as RM.

AGE: The percentage of owner-occupied housing built before 1940 is referred to as AGE.

DIS: Weighted distance to five Boston employment centers are referred to as dis.

RAD: Accessibility to radial highways index

TAX: The TAX columns denote the rate of full-value property taxes per $10,000 dollars.

B: B=1000(Bk — 0.63)2 is the outcome of the equation, where Bk is the proportion of blacks in each town.

PTRATIO: It refers to the student-to-teacher ratio in each community.

LSTAT: It refers to the population's lower socioeconomic status.

MEDV: It refers to the 1000-dollar median value of owner-occupied residences.

Data Preprocessing

# Check if there is any missing values. housing_df.isna().sum() CRIM       0 ZN         0 INDUS      0 CHAS       0 NOX        0 RM         0 AGE        0 DIS        0 RAD        0 TAX        0 PTRATIO    0 B          0 LSTAT      0 MEDV       0 dtype: int64

No missing values are found

We examine our data's mean, standard deviation, and percentiles.

housing_df.describe()

Graph Data

 CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATMEDV
count506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000
mean3.61352411.36363611.1367790.0691700.5546956.28463468.5749013.7950439.549407408.23715418.455534356.67403212.65306322.532806
std8.60154523.3224536.8603530.2539940.1158780.70261728.1488612.1057108.707259168.5371162.16494691.2948647.1410629.197104
min0.0063200.0000000.4600000.0000000.3850003.5610002.9000001.1296001.000000187.00000012.6000000.3200001.7300005.000000
25%0.0820450.0000005.1900000.0000000.4490005.88550045.0250002.1001754.000000279.00000017.400000375.3775006.95000017.025000
50%0.2565100.0000009.6900000.0000000.5380006.20850077.5000003.2074505.000000330.00000019.050000391.44000011.36000021.200000
75%3.67708312.50000018.1000000.0000000.6240006.62350094.0750005.18842524.000000666.00000020.200000396.22500016.95500025.000000
max88.976200100.00000027.7400001.0000000.8710008.780000100.00000012.12650024.000000711.00000022.000000396.90000037.97000050.000000

The crime, area, sector, nitric oxides, 'B' appear to have multiple outliers at first look because the minimum and maximum values are so far apart. In the Age columns, the mean and the Q2(50 percentile) do not match.

We might double-check it by examining the distribution of each column.

Inferences

  1. The rate of crime is rather low. The majority of values are in the range of 0 to 25. With a huge value and a value of zero.
  2. The majority of residential land is zoned for less than 25,000 square feet. Land zones larger than 25,000 square feet represent a small portion of the dataset.
  3. The percentage of non-retial commercial acres is mostly split between two ranges: 0-13 and 13-23.
  4. The majority of the properties are bordered by the river, although a tiny portion of the data is not.
  5. The content of nitrite dioxide has been trending lower from.3 to.7, with a little bump towards.8. It is permissible to leave a value in the range of 0.1–1.
  6. The number of rooms tends to cluster around the average.
  7. With time, the proportion of owner-occupied units rises.
  8. As the number of weights grows, the weight distance between 5 employment centers reduces. It could indicate that individuals choose to live in new high-employment areas.
  9. People choose to live in places with limited access to roadways (0-10). We have a 30th percentile outlier.
  10. The majority of dwelling taxes are in the range of $200-450, with large outliers around $700,000.
  11. The percentage of people with lower status tends to cluster around the median. The majority of persons are of lower social standing.

Because the model is overly generic, removing all outliers will underfit it. Keeping all outliers causes the model to overfit and become excessively accurate. The data's noise will be learned.

The approach is to establish a happy medium that prevents the model from becoming overly precise. When faced with a new set of data, however, they generalise well.

We'll keep numbers below 600 because there's a huge anomaly in the TAX column around 600.

new_df=housing_df[housing_df['TAX']<600]

Looking at the Distribution

Looking-at-the-Distribution

The overall distribution, particularly the TAX, PTRATIO, and RAD, has improved slightly.

Correlation

Correlation

Perfect correlation is denoted by the clear values. The medium correlation between the columns is represented by the reds, while the negative correlation is represented by the black.

With a value of 0.89, we can see that 'MEDV', which is the medium price we wish to anticipate, is substantially connected with the number of rooms 'RM'. The proportion of black people in area 'B' with a value of 0.19 is followed by the residential land 'ZN' with a value of 0.32 and the percentage of black people in area 'ZN' with a value of 0.32.

The metrics that are most connected with price will be plotted.

The-metrics-that-are-most-connected

Feature Engineering

Feature Scaling

Gradient descent is aided by feature scaling, which ensures that all features are on the same scale. It makes locating the local optimum much easier.

Mean standardization is one strategy to employ. It substitutes (target-mean) for the target to ensure that the feature has a mean of nearly zero.

def standard(X):    '''Standard makes the feature 'X' have a zero mean'''    mu=np.mean(X) #mean    std=np.std(X) #standard deviation    sta=(X-mu)/std # mean normalization    return mu,std,sta     mu,std,sta=standard(X) X=sta X

 CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTAT
0-0.6091290.092792-1.019125-0.2809760.2586700.2791350.162095-0.167660-2.105767-0.235130-1.1368630.401318-0.933659
1-0.575698-0.598153-0.225291-0.280976-0.4237950.0492520.6482660.250975-1.496334-1.032339-0.0041750.401318-0.219350
2-0.575730-0.598153-0.225291-0.280976-0.4237951.1897080.0165990.250975-1.496334-1.032339-0.0041750.298315-1.096782
3-0.567639-0.598153-1.040806-0.280976-0.5325940.910565-0.5263500.773661-0.886900-1.3276010.4035930.343869-1.283945
4-0.509220-0.598153-1.040806-0.280976-0.5325941.132984-0.2282610.773661-0.886900-1.3276010.4035930.401318-0.873561
..........................................
501-0.519445-0.5981530.585220-0.2809760.6048480.3060040.300494-0.936773-2.105767-0.5746821.4456660.277056-0.128344
502-0.547094-0.5981530.585220-0.2809760.604848-0.4000630.570195-1.027984-2.105767-0.5746821.4456660.401318-0.229652
503-0.522423-0.5981530.585220-0.2809760.6048480.8777251.077657-1.085260-2.105767-0.5746821.4456660.401318-0.820331
504-0.444652-0.5981530.585220-0.2809760.6048480.6060461.017329-0.979587-2.105767-0.5746821.4456660.314006-0.676095
505-0.543685-0.5981530.585220-0.2809760.604848-0.5344100.715691-0.924173-2.105767-0.5746821.4456660.401318-0.435703

Choose and Train the Model

For the sake of the project, we'll apply linear regression.

Typically, we run numerous models and select the best one based on a particular criterion.

Linear regression is a sort of supervised learning model in which the response is continuous, as it relates to machine learning.

Form of Linear Regression

y= θX+θ1 or y= θ1+X1θ2 +X2θ3 + X3θ4

y is the target you will be predicting

0 is the coefficient

x is the input

We will Sklearn to develop and train the model

#Import the libraries to train the model from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression

Allow us to utilise the train/test method to learn a part of the data on one set and predict using another set using the train/test approach.

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4) #Create and Train the model model=LinearRegression().fit(X_train,y_train) #Generate prediction predictions_test=model.predict(X_test) #Compute loss to evaluate the model coefficient= model.coef_ intercept=model.intercept_ print(coefficient,intercept) [7.22218258] 24.66379606613584

In this example, you will learn the model using below hypothesis:

Price= 24.85 + 7.18* Room

It is interpreted as:

For a decided price of a house:

A 7.18-unit increase in the price is connected with a growth in the number of rooms.

As a side note, this is an association, not a cause!

Interpretation

You will need a metric to determine whether our hypothesis was right. The RMSE approach will be used.

Root Means Square Error (RMSE) is defined as the square root of the mean of square error. The difference between the true and anticipated numbers called the error. It's popular because it can be expressed in y-units, which is the median price of a home in our scenario.

def rmse(predict,actual):    return np.sqrt(np.mean(np.square(predict - actual))) # Split the Data into train and test set X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4) #Create and Train the model model=LinearRegression().fit(X_train,y_train) #Generate prediction predictions_test=model.predict(X_test) #Compute loss to evaluate the model coefficient= model.coef_ intercept=model.intercept_ print(coefficient,intercept) loss=rmse(predictions_test,y_test) print('loss: ',loss) print(model.score(X_test,y_test)) #accuracy [7.43327725] 24.912055881970886 loss: 3.9673165450580714 0.7552661033654667 Loss will be 3.96

This means that y-units refer to the median value of occupied homes with 1000 dollars.

This will be less by 3960 dollars.

While learning the model you will have a high variance when you divide the data. Coefficient and intercept will vary. It's because when we utilized the train/test approach, we choose a set of data at random to place in either the train or test set. As a result, our theory will change each time the dataset is divided.

This problem can be solved using a technique called cross-validation.

Improvisation in the Model

With 'Forward Selection,' we'll iterate through each parameter to assist us choose the numbers characteristics to include in our model.

Forward Selection

  1. Choose the most appropriate variable (in our case based on high correlation)
  2. Add the next best variable to the model
  3. Some predetermined conditions must meet.

We'll use a random state of 1 so that each iteration yields the same outcome.

cols=[] los=[] los_train=[] scor=[] i=0 while i < len(high_corr_var):    cols.append(high_corr_var[i])        # Select inputs variables    X=new_df[cols]        #mean normalization    mu,std,sta=standard(X)    X=sta        # Split the data into training and testing    X_train,X_test,y_train,y_test= train_test_split(X,y,random_state=1)        #fit the model to the training    lnreg=LinearRegression().fit(X_train,y_train)        #make prediction on the training test    prediction_train=lnreg.predict(X_train)        #make prediction on the testing test    prediction=lnreg.predict(X_test)        #compute the loss on train test    loss=rmse(prediction,y_test)    loss_train=rmse(prediction_train,y_train)    los_train.append(loss_train)    los.append(loss)        #compute the score    score=lnreg.score(X_test,y_test)    scor.append(score)        i+=1

We have a big 'loss' with a smaller collection of variables, yet our system will overgeneralize in this scenario. Although we have a reduced 'loss,' we have a large number of variables. However, if the model grows too precise, it may not generalize well to new data.

In order for our model to generalize well with another set of data, we might use 6 or 7 features. The characteristic chosen is descending based on how strong the price correlation is.

high_corr_var ['RM', 'ZN', 'B', 'CHAS', 'RAD', 'DIS', 'CRIM', 'NOX', 'AGE', 'TAX', 'INDUS', 'PTRATIO', 'LSTAT']

With 'RM' having a high price correlation and LSTAT having a negative price correlation.

# Create a list of features names feature_cols=['RM','ZN','B','CHAS','RAD','CRIM','DIS','NOX'] #Select inputs variables X=new_df[feature_cols] # Split the data into training and testing sets X_train,X_test,y_train,y_test= train_test_split(X,y, random_state=1) # feature engineering mu,std,sta=standard(X) X=sta # fit the model to the trainning data lnreg=LinearRegression().fit(X_train,y_train) # make prediction on the testing test prediction=lnreg.predict(X_test) # compute the loss loss=rmse(prediction,y_test) print('loss: ',loss) lnreg.score(X_test,y_test) loss: 3.212659865936143 0.8582338376696363

The test set yielded a loss of 3.21 and an accuracy of 85%.

Other factors, such as alpha, the learning rate at which our model learns, could still be tweaked to improve our model. Alternatively, return to the preprocessing section and working to increase the parameter distribution.

For more details regarding scraping real estate data you can contact Scraping Intelligence today

https://www.websitescraper.com/how-to-predict-housing-prices-with-linear-regression.php

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