Monthly Downloads: 721
Programming language: Ruby
License: LGPL, EULA
Latest version: v1.0.4

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Ordered background jobs processing



We've faced some problems using Sidekiq while processing messages from a side system. For instance, the message is a data of an order in particular time. The side system will send a new data of an order on an every change. Orders are frequently updated and a queue containts some closely located messages of the same order.

Sidekiq doesn't guarantee a strict message order, because a queue is processed by multiple threads. For example, we've received 2 messages: M1 and M2. Sidekiq handlers begin to process them parallel, so M2 can be processed before M1.

Parallel processing of such kind of messages can result in:

  • dead locks
  • overwriting new data with old one

Lowkiq has been created to eliminate such problems by avoiding parallel task processing within one entity.


Lowkiq's queues are reliable i.e., Lowkiq saves information about a job being processed and returns incompleted jobs back to the queue on startup.

Jobs in queues are ordered by preassigned execution time, so they are not FIFO queues.

Every job has it's own identifier. Lowkiq guarantees that jobs with equal id are processed by the same thread.

Every queue is divided into a permanent set of shards. A job is placed into particular shard based on an id of the job. So jobs with the same id are always placed into the same shard. All jobs of the shard are always processed with the same thread. This guarantees the sequently processing of jobs with the same ids and excludes the possibility of locks.

Besides the id, every job has a payload. Payloads are accumulated for jobs with the same id. So all accumulated payloads will be processed together. It's useful when you need to process only the last message and drop all previous ones.

A worker corresponds to a queue and contains a job processing logic.

Fixed amount of threads is used to process all job of all queues. Adding or removing queues or it's shards won't affect the amount of threads.

Sidekiq comparison

If Sidekiq is good for your tasks you should use it. But if you use plugins like sidekiq-grouping, sidekiq-unique-jobs, sidekiq-merger or implement your own lock system, you should look at Lowkiq.

For example, sidekiq-grouping accumulates a batch of jobs than enqueues it and accumulates a next batch. With this approach queue can contains two batches with a data of the same order. These batches are parallel processed with different threads, so we come back to the initial problem.

Lowkiq was designed to avoid any types of locking.

Furthermore, Lowkiq's queues are reliable. Only Sidekiq Pro or plugins can add such functionality.

This [benchmark](examples/benchmark) shows overhead on redis usage. This is the results for 5 threads, 100,000 blank jobs:

  • lowkiq: 155 sec or 1.55 ms per job
  • lowkiq +hiredis: 80 sec or 0.80 ms per job
  • sidekiq: 15 sec or 0.15 ms per job

This difference is related to different queues structure. Sidekiq uses one list for all workers and fetches the job entirely for O(1). Lowkiq uses several data structures, including sorted sets for storing ids of jobs. So fetching only an id of a job takes O(log(N)).


Please, look at the presentation.

Every job has following attributes:

  • id is a job identifier with string type.
  • payloads is a sorted set of payloads ordered by it's score. Payload is an object. Score is a real number.
  • perform_in is planned execution time. It's unix timestamp with real number type.
  • retry_count is amount of retries. It's a real number.

For example, id can be an identifier of replicated entity. payloads is a sorted set ordered by score of payload and resulted by grouping a payload of job by it's id. payload can be a ruby object, because it is serialized by Marshal.dump. score can be payload's creation date (unix timestamp) or it's incremental version number. By default score and perform_in are current unix timestamp. retry_count for new unprocessed job equals to -1, for one-time failed is 0, so the planned retries are counted, not the performed ones.

A job execution can be unsuccessful. In this case, its retry_count is incremented, new perform_in is calculated with determined formula and it moves back to a queue.

In case of retry_count is getting >= max_retry_count an element of payloads with less (oldest) score is moved to a morgue, rest elements are moved back to the queue, wherein retry_count and perform_in are reset to -1 and now() respectively.

Calculation algorithm for retry_count and perform_in

  1. a job's been executed and failed
  2. retry_count++
  3. perform_in = now + retry_in (try_count)
  4. if retry_count >= max_retry_count the job will be moved to a morgue.
type retry_count perform_in
new haven't been executed -1 set or now()
new failed 0 now() + retry_in(0)
retry failed 1 now() + retry_in(1)

If max_retry_count = 1, retries stop.

Job merging rules

They are applied when:

  • a job had been in a queue and a new one with the same id was added
  • a job was failed, but a new one with the same id had been added
  • a job from morgue was moved back to queue, but queue had had a job with the same id


  • payloads is merged, minimal score is chosen for equal payloads
  • if a new job and queued job is merged, perform_in and retry_count is taken from the the job from the queue
  • if a failed job and queued job is merged, perform_in and retry_count is taken from the failed one
  • if morgue job and queued job is merged, perform_in = now(), retry_count = -1


# v1 is the first version and v2 is the second
# #{"v1": 1} is a sorted set of a single element, the payload is "v1", the score is 1

# a job is in a queue
{ id: "1", payloads: #{"v1": 1, "v2": 2}, retry_count: 0, perform_in: 1536323288 }
# a job which is being added
{ id: "1", payloads: #{"v2": 3, "v3": 4}, retry_count: -1, perform_in: 1536323290 }

# a resulted job in the queue
{ id: "1", payloads: #{"v1": 1, "v2": 3, "v3": 4}, retry_count: 0, perform_in: 1536323288 }

Morgue is a part of the queue. Jobs in morgue are not processed. A job in morgue has following attributes:

  • id is the job identifier
  • payloads

A job from morgue can be moved back to the queue, retry_count = 0 and perform_in = now() would be set.


# Gemfile

gem 'lowkiq'

Redis >= 3.2


module ATestWorker
  extend Lowkiq::Worker

  self.shards_count = 24
  self.batch_size = 10
  self.max_retry_count = 5

  def self.retry_in(count)
    10 * (count + 1) # (i.e. 10, 20, 30, 40, 50)

  def self.perform(payloads_by_id)
    # payloads_by_id is a hash map
    payloads_by_id.each do |id, payloads|
      # payloads are sorted by score, from old to new (min to max)
      payloads.each do |payload|
        do_some_work(id, payload)

Default values:

self.shards_count = 5
self.batch_size = 1
self.max_retry_count = 25
self.queue_name = self.name

# i.e. 15, 16, 31, 96, 271, ... seconds + a random amount of time
def retry_in(retry_count)
  (retry_count ** 4) + 15 + (rand(30) * (retry_count + 1))
ATestWorker.perform_async [
  { id: 0 },
  { id: 1, payload: { attr: 'v1' } },
  { id: 2, payload: { attr: 'v1' }, score: Time.now.to_i, perform_in: Time.now.to_i },
# payload by default equals to ""
# score and perform_in by default equals to Time.now.to_i

It is possible to redefine perform_async and calculate id, score и perform_in in a worker code:

module ATestWorker
  extend Lowkiq::Worker

  def self.perform_async(jobs)
    jobs.each do |job|
      job.merge! id: job[:payload][:id]

  def self.perform(payloads_by_id)

ATestWorker.perform_async 1000.times.map { |id| { payload: {id: id} } }

Ring app

Lowkiq::Web - a ring app.

  • / - a dashboard
  • /api/v1/stats - queue length, morgue length, lag for each worker and total result


Options and their default values are:

  • Lowkiq.poll_interval = 1 - delay in seconds between queue polling for new jobs. Used only if the queue was empty at previous cycle or error was occured.
  • Lowkiq.threads_per_node = 5 - threads per node.
  • Lowkiq.redis = ->() { Redis.new url: ENV.fetch('REDIS_URL') } - redis connection options
  • Lowkiq.client_pool_size = 5 - redis pool size for queueing jobs
  • Lowkiq.pool_timeout = 5 - client and server redis pool timeout
  • Lowkiq.server_middlewares = [] - a middleware list, used for worker wrapping
  • Lowkiq.on_server_init = ->() {} - a lambda is being executed when server inits
  • Lowkiq.build_scheduler = ->() { Lowkiq.build_lag_scheduler } is a scheduler
  • Lowkiq.build_splitter = ->() { Lowkiq.build_default_splitter } is a splitter
  • Lowkiq.last_words = ->(ex) {} is an exception handler of descendants of StandardError caused the process stop
  • Lowkiq.dump_payload = Marshal.method :dump
  • Lowkiq.load_payload = Marshal.method :load
$logger = Logger.new(STDOUT)

Lowkiq.server_middlewares << -> (worker, batch, &block) do
  $logger.info "Started job for #{worker} #{batch}"
  $logger.info "Finished job for #{worker} #{batch}"

Lowkiq.server_middlewares << -> (worker, batch, &block) do
  rescue => e
    $logger.error "#{e.message} #{worker} #{batch}"
    raise e


Use hiredis for better performance.

# Gemfile

gem "hiredis"
# config

Lowkiq.redis = ->() { Redis.new url: ENV.fetch('REDIS_URL'), driver: :hiredis }


lowkiq -r ./path_to_app

path_to_app.rb must load app. [Example](examples/dummy/lib/app.rb).

Lazy loading of workers modules is unacceptable. For preliminarily loading modules use require or require_dependency for Ruby on Rails.


Send TERM or INT signal to process (Ctrl-C). Process will wait for executed jobs to finish.

Note that if queue is empty, process sleeps poll_interval seconds, therefore, the process will not stop until the poll_interval seconds have passed.


To get trace of all threads of app:

kill -TTIN <pid>
cat /tmp/lowkiq_ttin.txt


docker-compose run --rm --service-port app bash
cd examples/dummy ; bundle exec ../../exe/lowkiq -r ./lib/app.rb


StandardError thrown by worker are handled with middleware. Such exceptions doesn't lead to process stop.

All other exceptions cause the process to stop. Lowkiq will wait for job execution by other threads.

StandardError thrown outside of worker are passed to Lowkiq.last_words. For example, it can happen when Redis connection is lost or when Lowkiq's code has a bug.

Rails integration

# config/routes.rb

Rails.application.routes.draw do
 # ...
 mount Lowkiq::Web => '/lowkiq'
 # ...
# config/initializers/lowkiq.rb

# loading all lowkiq workers
Dir["#{Rails.root}/app/lowkiq_workers/**/*.rb"].each { |file| require_dependency file }

# configuration:
# Lowkiq.redis = -> { Redis.new url: ENV.fetch('LOWKIQ_REDIS_URL') }
# Lowkiq.threads_per_node = ENV.fetch('LOWKIQ_THREADS_PER_NODE').to_i
# Lowkiq.client_pool_size = ENV.fetch('LOWKIQ_CLIENT_POOL_SIZE').to_i
# ...

Lowkiq.server_middlewares << -> (worker, batch, &block) do
  logger = Rails.logger
  tag = "#{worker}-#{Thread.current.object_id}"

  logger.tagged(tag) do
    time_start = Time.now
    logger.info "#{time_start} Started job, batch: #{batch}"
    rescue => e
      logger.error e.message
      raise e
      time_end = Time.now
      logger.info "#{time_end} Finished job, duration: #{time_end - time_start} sec"

# Sentry integration
Lowkiq.server_middlewares << -> (worker, batch, &block) do
  opts = {
    extra: {
      lowkiq: {
        worker: worker.name,
        batch: batch,

  Raven.capture opts do

# NewRelic integration
if defined? NewRelic
  class NewRelicLowkiqMiddleware
    include NewRelic::Agent::Instrumentation::ControllerInstrumentation

    def call(worker, batch, &block)
      opts = {
        category: 'OtherTransaction/LowkiqJob',
        class_name: worker.name,
        name: :perform,

      perform_action_with_newrelic_trace opts do

  Lowkiq.server_middlewares << NewRelicLowkiqMiddleware.new

# Rails reloader, responsible for cleaning of ActiveRecord connections
Lowkiq.server_middlewares << -> (worker, batch, &block) do
  Rails.application.reloader.wrap do

Lowkiq.on_server_init = ->() do
  [[ActiveRecord::Base, ActiveRecord::Base.configurations[Rails.env]]].each do |(klass, init_config)|
    config = init_config.merge 'pool' => Lowkiq.threads_per_node

Execution: bundle exec lowkiq -r ./config/environment.rb


Each worker has several shards:

# worker: shard ids
worker A: 0, 1, 2
worker B: 0, 1, 2, 3
worker C: 0
worker D: 0, 1

Lowkiq uses fixed amount of threads for job processing, therefore it is necessary to distribute shards between threads. Splitter does it.

To define a set of shards, which is being processed by thread, lets move them to one list:

A0, A1, A2, B0, B1, B2, B3, C0, D0, D1

Default splitter evenly distributes shards by threads of a single node.

If threads_per_node is set to 3, the distribution will be:

# thread id: shards
t0: A0, B0, B3, D1
t1: A1, B1, C0
t2: A2, B2, D0

Besides Default Lowkiq has ByNode splitter. It allows to divide the load by several processes (nodes).

Lowkiq.build_splitter = -> () do

So, instead of single process you need to execute multiple ones and to set environment variables up:

# process 0
LOWKIQ_NUMBER_OF_NODES=2 LOWKIQ_NODE_NUMBER=0 bundle exec lowkiq -r ./lib/app.rb

# process 1
LOWKIQ_NUMBER_OF_NODES=2 LOWKIQ_NODE_NUMBER=1 bundle exec lowkiq -r ./lib/app.rb

Summary amount of threads are equal product of ENV.fetch('LOWKIQ_NUMBER_OF_NODES') and Lowkiq.threads_per_node.

You can also write your own splitter if your app needs extra distribution of shards between threads or nodes.


Every thread processes a set of shards. Scheduler select shard for processing. Every thread has it's own instance of scheduler.

Lowkiq has 2 schedulers for your choice. Seq sequentally looks over shards. Lag chooses shard with the oldest job minimizing the lag. It's used by default.

Scheduler can be set up through settings:

Lowkiq.build_scheduler = ->() { Lowkiq.build_seq_scheduler }
# or
Lowkiq.build_scheduler = ->() { Lowkiq.build_lag_scheduler }

Recommendations on configuration


Sum of shards_count of all workers shouldn't be less than Lowkiq.threads_per_node otherwise threads will stay idle.

Sum of shards_count of all workers can be equal to Lowkiq.threads_per_node. In this case thread processes a single shard. This makes sense only with uniform queue load.

Sum of shards_count of all workers can be more than Lowkiq.threads_per_node. In this case shards_count can be counted as a priority. The larger it is, the more often the tasks of this queue will be processed.

There is no reason to set shards_count of one worker more than Lowkiq.threads_per_node, because every thread will handle more than one shard from this queue, so it increases the overhead.


From retry_in and max_retry_count, you can calculate approximate time that payload of job will be in a queue. After max_retry_count is reached the payload with a minimal score will be moved to a morgue.

For default retry_in we receive the following table.

def retry_in(retry_count)
  (retry_count ** 4) + 15 + (rand(30) * (retry_count + 1))
max_retry_count amount of days of job's life
14 1
16 2
18 3
19 5
20 6
21 8
22 10
23 13
24 16
25 20

(0...25).map{ |c| retry_in c }.sum / 60 / 60 / 24

Changing of worker's shards amount

Try to count amount of shards right away and don't change it in future.

If you can disable adding of new jobs, wait for queues to get empty and deploy the new version of code with changed amount of shards.

If you can't do it, follow the next steps:

A worker example:

module ATestWorker
  extend Lowkiq::Worker

  self.shards_count = 5

  def self.perform(payloads_by_id)

Set the number of shards and new queue name:

module ATestWorker
  extend Lowkiq::Worker

  self.shards_count = 10
  self.queue_name = "#{self.name}_V2"

  def self.perform(payloads_by_id)

Add a worker moving jobs from the old queue to a new one:

module ATestMigrationWorker
  extend Lowkiq::Worker

  self.shards_count = 5
  self.queue_name = "ATestWorker"

  def self.perform(payloads_by_id)
    jobs = payloads_by_id.each_with_object([]) do |(id, payloads), acc|
      payloads.each do |payload|
        acc << { id: id, payload: payload }

    ATestWorker.perform_async jobs