Delayed indexing with background_job and searchkick

Delayed indexing with background_job and searchkick

- 5 mins

Photo by Icons8 team via Unsplash

The problem

A client had problems with their search indexes being out of date compared to the latest updates to their content. Due to the distributed nature of the app a blocking solution provided a less-than optimal user experience so a more appropriate solution was required.

The situation

The software comprises of three applications:

  1. A Ruby on Rails JSON-speaking API
  2. A React front-end for consuming the data as a user
  3. A second React app for backoffice tasks.

The application is search-first in design so it’s important that data is findable and accurate in the search results. With the existing implementation, reindexing may not occur until a server restart which could be some time.

The search functionality is backed by Elasticsearch and interfaced using the searchkick gem.

The solution

I chose delayed_job as my queue manager, owing to the fact that this is a commercial project and delayed_job does not place any restrictions on use with the MIT license.

Following their readme, it was easy to fit to the specifics of the application. Since the application is using mongoid I needed specific support for this, which what provided with the delayed_job_mongoid gem.

# Gemfile
gem 'delayed_job_mongoid'

Because the application is using Rails 4, it is necessary to specify the queue adapter in application.rb

# config/application.rb
config.active_job.queue_adapter = :delayed_job

delayed_job needs a redis instance to manage its queues so I provided one on the default ports.

Queueing jobs

Now it is easy to delay any existing method by calling it through the delay method, like so:

# old

# new

The real data models in question have some embedded models which needed to also be reindexed, as their data is provided to searchkick to represent one entity in the elasticsearch index.

In order to accommodate this and to automatically queue a reindex on save for the model, add an after_save hook with the reindex action.

# models/article.rb
def after_save
    # and any other relations that are embedded!
    # If you're using active record then ymmv

Now whenever our model is saved or updated, it’ll automatically generate a job to reindex the model. Neat!

Consuming the queue

To recap so far there is:

  1. A method to queue our long-running jobs
  2. A datastore to back our job queue
  3. Integration with mongoid and the after_save callback

The next thing is for us to actually execute the queued jobs.

In local development this is pretty straightforward. Run our rails app again but use the provided rake task to wait and listen for our jobs and take the necessary action.

In the simplest form this can be done with

bundle exec rake jobs:work

The deployment environment understands Procfile so add a queue process to that and ensure it’s running as part of the deployment monitoring process.

# Procfile
web: bundle exec puma -C config/puma.rb
queue: bundle exec rake jobs:work

Monitoring queue health

The rake jobs:work task is fine, but not very chatty so it can be hard during development to ensure that jobs aren’t being missed or some other problem is occurring now that we’re relying on a separate worker process to do some of our work.

To ensure our queue is being consumed correctly I took a couple of steps.

Firstly in the API application I provided an endpoint to poll for the queue status. This simply returns the count of jobs in the delayed_job queue.

def queue_status
    data = {
        in_queue: Delayed::Job.all.length

    render json: data, status: :ok

The admin application can then poll this endpoint and report on the state of the queue to the administrator. It should be obvious if the number is high, really anything above zero for more than a few seconds, or rising that something is not right with the queue.

Screenshot of indexing status

Secondly for local and staging purposes including the delayed_job_web gem mounts a small sinatra application which provides stats on queue length, pending, processing and failed jobs.

It doesn’t go into huge detail but it’s a massive boost to confidence and being able to test out tasks and actions through the rails console with visual feedback.

If you’re using mongoid like I am then there’s an open pull request with mongoid support and you can use the proposed fork in your gemfile by targeting

# Gemfile
gem "delayed_job_web", github: 'brunoporto/delayed_job_web'

Delayed job web quickstart


All in all this has increased confidence in the search indexing with visual feeback to the administrator and ensured that updates do not tie up the web server unnecessarily.

If you have a Ruby on Rails application with tricky bugs or unexpected requirements, why not get in touch?

Peter Mellett

Peter Mellett

I put the punctuation in the computer

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