Charles Cooper

Charles Cooper

1610673440

Scheduling DBT Jobs with Serverless

Looking for a way to easily transform data from BigQuery? In this episode of Serverless Toolbox Extended, we show you how to transform BigQuery datasets into easy-to-read dashboards via a data build tool (dbt). Moreover, we’ll show you how to use Cloud Build, Cloud Run, and Cloud Scheduler to build a dbt container image, eliminate server maintenance, and automatically trigger run times for dbt commands. Watch to learn how you can use serverless to run a dbt command and transform your datasets in BigQuery!

Timestamps:

  • 0:00 - Overview
  • 1:03 - Running dbt with BigQuery
  • 3:08 - Creating runtimes for dbt with Cloud Run
  • 4:00 - Running a shell script in Cloud Run
  • 5:00 - Containerizing code with Cloud Build
  • 5:19 - Deploy container to Cloud Run
  • 7:04 - Setting up Cloud Schedule to automatically run your dbt
  • 10:00 - Summary

#serverless #developer

What is GEEK

Buddha Community

Scheduling DBT Jobs with Serverless

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 

Cron Job Scheduling In Laravel

Today I will show you Cron Job Scheduling In Laravel, many time we require to run some piece of code specific interval time period in laravel and we need to run manually every time but command scheduler through we can run and create cron job in laravel.

So, here i will teach you how to create cron job in laravel, and how to create custom command in laravel.

Cron Job Scheduling In Laravel

https://websolutionstuff.com/post/cron-job-scheduling-in-laravel

#cron job scheduling in laravel #laravel #scheduling #scheduler #cron #how to create cron job in laravel

Hermann  Frami

Hermann Frami

1655426640

Serverless Plugin for Microservice Code Management and Deployment

Serverless M

Serverless M (or Serverless Modular) is a plugin for the serverless framework. This plugins helps you in managing multiple serverless projects with a single serverless.yml file. This plugin gives you a super charged CLI options that you can use to create new features, build them in a single file and deploy them all in parallel

splash.gif

Currently this plugin is tested for the below stack only

  • AWS
  • NodeJS λ
  • Rest API (You can use other events as well)

Prerequisites

Make sure you have the serverless CLI installed

# Install serverless globally
$ npm install serverless -g

Getting Started

To start the serverless modular project locally you can either start with es5 or es6 templates or add it as a plugin

ES6 Template install

# Step 1. Download the template
$ sls create --template-url https://github.com/aa2kb/serverless-modular/tree/master/template/modular-es6 --path myModularService

# Step 2. Change directory
$ cd myModularService

# Step 3. Create a package.json file
$ npm init

# Step 3. Install dependencies
$ npm i serverless-modular serverless-webpack webpack --save-dev

ES5 Template install

# Step 1. Download the template
$ sls create --template-url https://github.com/aa2kb/serverless-modular/tree/master/template/modular-es5 --path myModularService

# Step 2. Change directory
$ cd myModularService

# Step 3. Create a package.json file
$ npm init

# Step 3. Install dependencies
$ npm i serverless-modular --save-dev

If you dont want to use the templates above you can just add in your existing project

Adding it as plugin

plugins:
  - serverless-modular

Now you are all done to start building your serverless modular functions

API Reference

The serverless CLI can be accessed by

# Serverless Modular CLI
$ serverless modular

# shorthand
$ sls m

Serverless Modular CLI is based on 4 main commands

  • sls m init
  • sls m feature
  • sls m function
  • sls m build
  • sls m deploy

init command

sls m init

The serverless init command helps in creating a basic .gitignore that is useful for serverless modular.

The basic .gitignore for serverless modular looks like this

#node_modules
node_modules

#sm main functions
sm.functions.yml

#serverless file generated by build
src/**/serverless.yml

#main serverless directories generated for sls deploy
.serverless

#feature serverless directories generated sls deploy
src/**/.serverless

#serverless logs file generated for main sls deploy
.sm.log

#serverless logs file generated for feature sls deploy
src/**/.sm.log

#Webpack config copied in each feature
src/**/webpack.config.js

feature command

The feature command helps in building new features for your project

options (feature Command)

This command comes with three options

--name: Specify the name you want for your feature

--remove: set value to true if you want to remove the feature

--basePath: Specify the basepath you want for your feature, this base path should be unique for all features. helps in running offline with offline plugin and for API Gateway

optionsshortcutrequiredvaluesdefault value
--name-nstringN/A
--remove-rtrue, falsefalse
--basePath-pstringsame as name

Examples (feature Command)

Creating a basic feature

# Creating a jedi feature
$ sls m feature -n jedi

Creating a feature with different base path

# A feature with different base path
$ sls m feature -n jedi -p tatooine

Deleting a feature

# Anakin is going to delete the jedi feature
$ sls m feature -n jedi -r true

function command

The function command helps in adding new function to a feature

options (function Command)

This command comes with four options

--name: Specify the name you want for your function

--feature: Specify the name of the existing feature

--path: Specify the path for HTTP endpoint helps in running offline with offline plugin and for API Gateway

--method: Specify the path for HTTP method helps in running offline with offline plugin and for API Gateway

optionsshortcutrequiredvaluesdefault value
--name-nstringN/A
--feature-fstringN/A
--path-pstringsame as name
--method-mstring'GET'

Examples (function Command)

Creating a basic function

# Creating a cloak function for jedi feature
$ sls m function -n cloak -f jedi

Creating a basic function with different path and method

# Creating a cloak function for jedi feature with custom path and HTTP method
$ sls m function -n cloak -f jedi -p powers -m POST

build command

The build command helps in building the project for local or global scope

options (build Command)

This command comes with four options

--scope: Specify the scope of the build, use this with "--feature" tag

--feature: Specify the name of the existing feature you want to build

optionsshortcutrequiredvaluesdefault value
--scope-sstringlocal
--feature-fstringN/A

Saving build Config in serverless.yml

You can also save config in serverless.yml file

custom:
  smConfig:
    build:
      scope: local

Examples (build Command)

all feature build (local scope)

# Building all local features
$ sls m build

Single feature build (local scope)

# Building a single feature
$ sls m build -f jedi -s local

All features build global scope

# Building all features with global scope
$ sls m build -s global

deploy command

The deploy command helps in deploying serverless projects to AWS (it uses sls deploy command)

options (deploy Command)

This command comes with four options

--sm-parallel: Specify if you want to deploy parallel (will only run in parallel when doing multiple deployments)

--sm-scope: Specify if you want to deploy local features or global

--sm-features: Specify the local features you want to deploy (comma separated if multiple)

optionsshortcutrequiredvaluesdefault value
--sm-paralleltrue, falsetrue
--sm-scopelocal, globallocal
--sm-featuresstringN/A
--sm-ignore-buildstringfalse

Saving deploy Config in serverless.yml

You can also save config in serverless.yml file

custom:
  smConfig:
    deploy:
      scope: local
      parallel: true
      ignoreBuild: true

Examples (deploy Command)

Deploy all features locally

# deploy all local features
$ sls m deploy

Deploy all features globally

# deploy all global features
$ sls m deploy --sm-scope global

Deploy single feature

# deploy all global features
$ sls m deploy --sm-features jedi

Deploy Multiple features

# deploy all global features
$ sls m deploy --sm-features jedi,sith,dark_side

Deploy Multiple features in sequence

# deploy all global features
$ sls m deploy  --sm-features jedi,sith,dark_side --sm-parallel false

Author: aa2kb
Source Code: https://github.com/aa2kb/serverless-modular 
License: MIT license

#serverless #aws #node #lambda 

Serverless Applications - Pros and Cons to Help Businesses Decide - Prismetric

In the past few years, especially after Amazon Web Services (AWS) introduced its Lambda platform, serverless architecture became the business realm’s buzzword. The increasing popularity of serverless applications saw market leaders like Netflix, Airbnb, Nike, etc., adopting the serverless architecture to handle their backend functions better. Moreover, serverless architecture’s market size is expected to reach a whopping $9.17 billion by the year 2023.

Global_Serverless_Architecture_Market_2019-2023

Why use serverless computing?
As a business it is best to approach a professional mobile app development company to build apps that are deployed on various servers; nevertheless, businesses should understand that the benefits of the serverless applications lie in the possibility it promises ideal business implementations and not in the hype created by cloud vendors. With the serverless architecture, the developers can easily code arbitrary codes on-demand without worrying about the underlying hardware.

But as is the case with all game-changing trends, many businesses opt for serverless applications just for the sake of being up-to-date with their peers without thinking about the actual need of their business.

The serverless applications work well with stateless use cases, the cases which execute cleanly and give the next operation in a sequence. On the other hand, the serverless architecture is not fit for predictable applications where there is a lot of reading and writing in the backend system.

Another benefit of working with the serverless software architecture is that the third-party service provider will charge based on the total number of requests. As the number of requests increases, the charge is bound to increase, but then it will cost significantly less than a dedicated IT infrastructure.

Defining serverless software architecture
In serverless software architecture, the application logic is implemented in an environment where operating systems, servers, or virtual machines are not visible. Although where the application logic is executed is running on any operating system which uses physical servers. But the difference here is that managing the infrastructure is the soul of the service provider and the mobile app developer focuses only on writing the codes.

There are two different approaches when it comes to serverless applications. They are

Backend as a service (BaaS)
Function as a service (FaaS)

  1. Backend as a service (BaaS)
    The basic required functionality of the growing number of third party services is to provide server-side logic and maintain their internal state. This requirement has led to applications that do not have server-side logic or any application-specific logic. Thus they depend on third-party services for everything.

Moreover, other examples of third-party services are Autho, AWS Cognito (authentication as a service), Amazon Kinesis, Keen IO (analytics as a service), and many more.

  1. Function as a Service (FaaS)
    FaaS is the modern alternative to traditional architecture when the application still requires server-side logic. With Function as a Service, the developer can focus on implementing stateless functions triggered by events and can communicate efficiently with the external world.

FaaS serverless architecture is majorly used with microservices architecture as it renders everything to the organization. AWS Lambda, Google Cloud functions, etc., are some of the examples of FaaS implementation.

Pros of Serverless applications
There are specific ways in which serverless applications can redefine the way business is done in the modern age and has some distinct advantages over the traditional could platforms. Here are a few –

🔹 Highly Scalable
The flexible nature of the serverless architecture makes it ideal for scaling the applications. The serverless application’s benefit is that it allows the vendor to run each of the functions in separate containers, allowing optimizing them automatically and effectively. Moreover, unlike in the traditional cloud, one doesn’t need to purchase a certain number of resources in serverless applications and can be as flexible as possible.

🔹 Cost-Effective
As the organizations don’t need to spend hundreds and thousands of dollars on hardware, they don’t need to pay anything to the engineers to maintain the hardware. The serverless application’s pricing model is execution based as the organization is charged according to the executions they have made.

The company that uses the serverless applications is allotted a specific amount of time, and the pricing of the execution depends on the memory required. Different types of costs like presence detection, access authorization, image processing, etc., associated with a physical or virtual server is completely eliminated with the serverless applications.

🔹 Focuses on user experience
As the companies don’t always think about maintaining the servers, it allows them to focus on more productive things like developing and improving customer service features. A recent survey says that about 56% of the users are either using or planning to use the serverless applications in the coming six months.

Moreover, as the companies would save money with serverless apps as they don’t have to maintain any hardware system, it can be then utilized to enhance the level of customer service and features of the apps.

🔹 Ease of migration
It is easy to get started with serverless applications by porting individual features and operate them as on-demand events. For example, in a CMS, a video plugin requires transcoding video for different formats and bitrates. If the organization wished to do this with a WordPress server, it might not be a good fit as it would require resources dedicated to serving pages rather than encoding the video.

Moreover, the benefits of serverless applications can be used optimally to handle metadata encoding and creation. Similarly, serverless apps can be used in other plugins that are often prone to critical vulnerabilities.

Cons of serverless applications
Despite having some clear benefits, serverless applications are not specific for every single use case. We have listed the top things that an organization should keep in mind while opting for serverless applications.

🔹 Complete dependence on third-party vendor
In the realm of serverless applications, the third-party vendor is the king, and the organizations have no options but to play according to their rules. For example, if an application is set in Lambda, it is not easy to port it into Azure. The same is the case for coding languages. In present times, only Python developers and Node.js developers have the luxury to choose between existing serverless options.

Therefore, if you are planning to consider serverless applications for your next project, make sure that your vendor has everything needed to complete the project.

🔹 Challenges in debugging with traditional tools
It isn’t easy to perform debugging, especially for large enterprise applications that include various individual functions. Serverless applications use traditional tools and thus provide no option to attach a debugger in the public cloud. The organization can either do the debugging process locally or use logging for the same purpose. In addition to this, the DevOps tools in the serverless application do not support the idea of quickly deploying small bits of codes into running applications.

#serverless-application #serverless #serverless-computing #serverless-architeture #serverless-application-prosand-cons

Gerhard  Brink

Gerhard Brink

1620692100

10 Latest Big Data Engineer Openings At Top Firms In India

Extras:

1| Senior Technical Architect at Thoucentric

Location: Bangalore

**Responsibilities: **

  • Design and implement data architecture and ETL for a niche data platform.
  • Bring in-depth understanding on Relational, Big Data and Cloud technologies.
  • Build client relationships and participate in business development and proposal work to grow a strong data engineering sub-practice.

Apply here.

2| Data Engineer at Thoucentric

Location: Bangalore

**Responsibilities: **

  • Build data crawlers to extract data from customers’ data sources using available ETL platforms, and troubleshoot the issues faced during data loading & processing.
  • Design and build data warehouse models in columnar databases.
  • Develop data processing scripts using SQL and optimise complex sequences of SQL Queries.

Apply here.

3| Big Data Engineer at Thoucentric

Location: Bangalore

Responsibilities:

  • Take ownership of end-to-end data-pipeline including system design and integrating required Big Data tools & frameworks.
  • Implementing ETL processes and constructing data warehouse (HDFS, S3, Azure etc.) at scale.
  • Analyse the source and target system data. Map the transformation that meets the requirements.

Apply here.

Find below the data engineer job openings:

#careers #aim weekly job alerts #aimrecruits #big data engineer jobs at top firms #big data engineers job #big data jobs #data science jobs #top firm data science jobs #weekly job openings list