Structure a Vue.js App from Containers and Components

Recently we’ve begun using Vue.js as a frontend framework for one of our infrastructure projects. We’ve contracted Dr. Yoram Kornatzky to join our Delivery team and dive together, headlong, into this brave new world.

In this blog post by Yoram, and others to come, we’ll share snippets from this journey. We hope you find these beneficial, and invite you to share your own experiences and feedback.

Vue.js using Vuex for state management does not have a clear distinction between containers and components. This is in clear contrast to React using Redux for state management.

We argue that such a distinction between containers components in beneficial for Vue.js as well.


Dyploma is a system for managing containerized applications and services on top of Kubernetes in Outbrain. Dyploma includes the concepts of:

  • artifacts
  • builds
  • deployments
  • services

Dyploma is made out of a Java Spring backend and a Python command-line tool (CLI). The command-line tool operates through API calls to the backend.

The Dyploma Web Application

To facilitate broader adoption of containers within Outbrain, we set up to develop a web application that will have the capabilities of the Dyploma CLI.

The web application will operate by fetching data from the backend and sending operations for execution in the backend. This will be done through the same REST API used by the CLI.

A Vue.js Web Application

We chose Vue.js for constructing the web application. The app was constructed using vue-cli with the webpack template.

The application has three generic screens:

  • list
  • detail
  • form

All concepts have screens from each of these types with similar structure and look and feel, but with different actions and different data.


Vuex is the standard state management approach for Vue.js.

Containers vs Components in React

Let us first recap what are containers and components in React.

A container interacts with the Redux and contains a component. The container supplies data to the component through selectors on the store and provides the actions on the store to the component.

Components are given data and render HTML. They use the actions provided from their container to interact with the state. Such actions modify the state, resulting in the selectors fetching new data, and causing the component to be rendered again.

Vue.js with Vuex

Vue.js standard practice does not have the containers vs components distinction. While constructing the Dyploma web application we found it useful to make such a distinction for the benefits of better code structure and reusability.

Let us first describe how the structure of the Dyploma web application.

Generic Components

We constructed three generic components:

  1. list
  2. detail
  3. form

Which can be composed of a component tree that can have more than 3 levels.

Each of these generic screens was used with some variations by multiple types of data. But the look and feel could be configured through a common JSON describing for each type of data, the different fields.

Type Specific Actions and Getters

The getters and actions to be used for each type of data were different. We constructed our Vuex store with modules and needed to use a separate module for each type.

Distinguish Components and Containers

So we had to think how to resolve two opposite requirements. For the benefits of reusability, we need unified generic components. But for the type specific actions and data, we need to use separate modules. We decided up front that the whole app will be constructed as a set of single file components (SFC).

To resolve these two opposite directions, we found it useful to think of our app as consisting of two things:

  • containers – type-specific that interact with store
  • components – generic


We defined each component to a data props for the data it should render, and a description of the structure of data. For any changes and actions required, it will emit an event.

Data is passed from a component to its constituents with v-bind, like v-bind:list=”deployments”.

Events are hooked up with v-on like v-on:search=”search”.

Components are composed of smaller components. Events are propagated up the tree of components. This bottom-up propagation may be disturbing to some, but it is the best approach for Vue.js. In this respect, Vue.js is definitely different from React.

The component is a single file component (SFC).

Such a component is not necessarily functional.

A Container for Each Type of Data

A container knows which module of the store it deals with, and knows its actions and getters. It fetches data from the store using getters. Such data is passed to the components as props.

It listens to events coming from the components using v-on like v-on:search=”search”. In response to such events, it dispatches actions.

The container does not render anything itself, this is done by the component it contains.

The container is a single file component (SFC)s.

A Clean Separation Facilitates Reusability

This clean separation of components and containers make it simpler to see opportunities for reusability. Come to think of it, in most web apps, the real effort in reusability is reusability of the component. The mixing of components and containers causes many components to be coupled with the store. This makes it harder to identify reusability. By distinguishing components and containers, we isolate the components from the store and see more clearly opportunities for reusability.

Easier Testing

Writing unit tests becomes easier with this separation. One can write three classes of tests:

  1. components
  2. containers
  3. store

Each becoming simpler.

We will discuss this further in a separate article.


Split your Vue.js web app into containers and components.

Live Tail in Kubernetes / Docker Based environment

At Outbrain we are big believers in Observability.

What is Observability, and what is the difference between Observability and Monitoring? I will leave the explanation to Baron Schwartz @xaprb:

“Monitoring tells you whether the system works.  Observability lets you ask why it’s not working.”


@ Outbrain we are currently in the midst of migrating to a Kubernetes / Docker based environment.

This presented many new challenges around understanding why things don’t work.

In this post I will be sharing with you our logging implementation which is the first tool used to understand the why.

But first thing first, a short review of our current standard logging architecture:

We use a standard ELK stack for the majority of our logging needs. By standard I mean Logstash on bare metal nodes, Elasticsearch for storage and Kibana for visualizing and analytics.  Apache Kafka is transport layer for all of the above.

A very simplified sketch of the system:

Of course the setup is a bit more complex in real life since Outbrain’s infrastructure is spread across thousands of servers, in multiple physical data centers and cloud providers; and there are multiple Elasticsearch clusters for different use cases.

Add to the equation that these systems are used in a self-serve model, meaning the engineers are creating and updating configurations by themselves – and you end up with a complex system which must be robust and resilient, or the users will lose trust in the system.

The move to Kubernetes presented new challenges and requirements, specifically related to the logging tools:

  • Support multiple Kubernetes clusters and data centers.
  • We don’t want to us “kubectl”, because managing keys is a pain especially in a multi cluster environment.
  • Provide a way to tail logs and even edit log file. This should be available on a single pod or across a service deployed in multiple pods.
  • Leverage existing technologies: Kafka, ELK stack and Log4j on the client side
  • Support all existing logging sources like multiline and Json.
  • Don’t forget services which don’t run in Kubernetes, yes we still need to support those.


So how did we meet all those requirements? Time to talk about our new Logging design.

The new architecture is based on a standard Kubernetes logging setup – Fluentd daemonset running on each Kubelet node, and all services are configured to send logs to stdout / err  instead of a file.

The Fluentd agent is collecting the pod’s logs and adding the Kubernetes level labels to every message.

The Fluentd plugin we’re using is the kubernetes_metadata_filter.

After the messages are enriched they are stored in a Kafka topic.

A pool of Logstash agents (Running as pods in Kubernetes) are consuming and parsing messages from Kafka as needed.

Once parsed messages can be indexed into Elasticsearch or routed to another topic.

A sketch of the setup described:

And now it is time to introduce CTail.

Ctail, stands for Containers Tail, it is an Outbrain homegrown tool written in Go, and based on a server and client side components.

A CTail server side component runs per datacenter or per Kubernetes cluster, consuming messages from a Kafka topic named “CTail” and based on the Kubernetes app label creates a stream which can be consumed via the CTail client component.

Since order is important for log messages, and since Kafka only guarantees order for messages in the same partition, we had to make sure messages are partitioned by the pod_id.

With this new setup and tooling, when Outbrain engineers want to live tail their logs, all they need to do is launch the CTail client.

Once the Ctail client starts, it will query Consul, which is what we use for service discovery, to locate all of the CTail servers; register to their streams and will perform aggregations in memory – resulting in a live stream of log entries.

Here is a sketch demonstrating the environment and an example of the CTail client output:


To view logs from all pods of a service called “ob1ktemplate” all you need is to run is:

# ctail-client -service ob1ktemplate -msg-only

2017-06-13T19:16:25.525Z ob1ktemplate-test-ssages-2751568960-n1kwd: Running 5 self tests now...
2017-06-13T19:16:25.527Z ob1ktemplate-test-ssages-2751568960-n1kwd: Getting uri http://localhost:8181/Ob1kTemplate/
2017-06-13T19:16:25.529Z ob1ktemplate-test-ssages-2751532409-n1kxv: uri http://localhost:8181/Ob1kTemplate/ returned status code 200
2017-06-13T19:16:25.529Z ob1ktemplate-test-ssages-2751532409-n1kxv: Getting uri http://localhost:8181/Ob1kTemplate/api/echo?name='Ob1kTemplate'
2017-06-13T19:16:25.531Z ob1ktemplate-test-ssages-2751568954-n1rte: uri http://localhost:8181/Ob1kTemplate/api/echo?name='Ob1kTemplate' returned status code 200

Or logs of a specific pod:

# ctail-client -service ob1ktemplate -msg-only -pod ob1ktemplate-test-ssages-2751568960-n1kwd

2017-06-13T19:16:25.525Z ob1ktemplate-test-ssages-2751568960-n1kwd: Running 5 self tests now...
2017-06-13T19:16:25.527Z ob1ktemplate-test-ssages-2751568960-n1kwd: Getting uri 
2017-06-13T19:16:25.529Z ob1ktemplate-test-ssages-2751568960-n1kwd: uri http://localhost:8181/Ob1kTemplate/ returned status code 200


This is how we solve this challenge.

Interested in reading more about other challenges we encountered during the migration? Either wait for our next blog, or reach out to

visibility at

Keep bugs out of production

Production bugs are painful and can severely impact a dev team’s velocity. My team at Outbrain has succeeded in implementing a work process that enables us to send new features to production free of bugs, a process that incorporates automated functions with team discipline.

Why should I even care?

Bugs happen all the time – and they will be found locally or in production. But the main difference between preventing and finding the bug in a pre-production environment is the cost: according to IBM’s research, fixing a bug in production can cost X5 times more than discovering it in pre-production environments (during the design, local development, or test phase).

Let’s describe one of the scenarios happen once a bug reaches production:

  • A customer finds the bug and alerts customer service.
  • The bug is logged by the production team.
  • The developer gets the description of the bug, opens the spec, and spends time reading it over.
  • The developer then will spend time recreating the bug.
  • The developer must then reacquaint him/herself with the code to debug it.
  • Next, the fix must undergo tests.
  • The fix is then built and deployed in other environments.
  • Finally, the fix goes through QA testing (requiring QA resources).

How to stop bugs from reaching production

To catch and fix bugs at the most time-and-cost efficient stage, we follow these steps, adhering to the several core principles:

Stage 1 – Local Environment and CI

Step 1: Design well. Keep it simple.

Create the design before coding: try to divide difficult problems into smaller parts/steps/modules that you can tackle one by one, thinking of objects with well-defined responsibilities. Share the plans with your teammates at design-review meetings. Good design is a key to reducing bugs and improving code quality.

Step 2: Start Coding

Code should be readable and simple. Design and development principles are your best friends. Use SOLID, DRY, YAGNI, KISS and Polymorphism to implement your code.
Unit tests are part of the development process. We use them to test individual code units and ensure that the unit is logically correct.
Unit tests are written and executed by developers. Most of the time we use
JUnit  as our testing framework.

Step 3: Use code analysis tools

To help ensure and maintain the quality of our code, we use several automated code-analysis tools:
FindBugs – A static code analysis tool that detects possible bugs in Java programs, helping us to improve the correctness of our code.
Checkstyle –  Checkstyle is a development tool to help programmers write Java code that adheres to a coding standard. It automates the process of checking Java code.

Step 4: Perform code reviews

We all know that code reviews are important. There are many best practices online (see 7 Ways to Up Level Your Code Review Skills, Best Practices for Peer Code Review, and Effective Code Reviews), so let’s focus on the tools we use. All of our code commits are populated to ReviewBoard, and developers can review the committed code, see at any point in time the latest developments, and share input.
For the more crucial teams, we have a build that makes sure every commit has passed a code review – in the case that a review has not be done, the build will alert the team that there was an unreviewed change.
Regardless of whether you are performing a post commit, a pull request, or a pre-commit review, you should always aim to check and review what’s being inserted into your codebase.

Step 5: CI

This is where all code is being integrated. We use TeamCity to enforce our code standards and correctness by running unit tests, FindBugs validations Checkstyle rules and other type of policies.

Stage 2 – Testing Environment

Step 1: Run integration tests

Check if the system as a whole works. Integration testing is also done by developers, but rather than testing individual components, it aims to test across components. A system consists of many separate components like code, database, web servers, etc.
Integration tests are able to spot issues like wiring of components, network access, database issues, etc. We use Jenkins and TeamCity to run CI tests.

Step 2: Run functional tests

Check that each feature is implemented correctly by comparing the results for a given input with the specification. Typically, this is not done at the development level.
Test cases are written based on the specification, and the actual results are compared with the expected results. We run functional tests using Selenium and Protractor for UI testing and Junit for API testing.

Stage 3 – Staging Environment

This environment is often referred to as a preproduction sandbox, a system testing area, or simply a staging area. Its purpose is to provide an environment that simulates your actual production environment as closely as possible so you can test your application in conjunction with other applications.
Move a small percentage of real production requests to the staging environment where QA tests the features.

Stage 4 – Production Environment

Step 1: Deploy gradually

Deployment is a process that delivers our code into production machines. If some errors occurred during deployment, our Continuous Delivery system will pause the deployment, preventing the problematic version to reach all the machines, and allow us to roll back quickly.

Step 2: Incorporate feature flags

All our new components are released with feature flags, which basically serve to control the full lifecycle of our features.  Feature flags allow us to manage components and compartmentalize risk.

Step 3: Release gradually

There are two ways to make our release gradual:

  1. We test new features on a small set of users before releasing to everyone.
  2. Open the feature initially to, say, 10% of our customers, then 30%, then 50%, and then 100%.

Both methods allow us to monitor and track problematic scenarios in our systems.

Step 4: Monitor and Alerts

We use the ELK stack consisting of Elasticsearch, Logstash, and Kibana to manage our logs and events data.
For Time Series Data we use Prometheus as the metric storage and alerting engine.
Each developer can set up his own metrics and build grafana dashboards.
Setting the alerts is also part of the developer’s work and it is his responsibility to tune the threshold for triggering the PagerDuty alert.
PagerDuty is an automated call, texting, and email service, which escalates notifications between responsible parties to ensure the issues are addressed by the right people at the right time.

stop bugs
All in All,
Don’t let the bugs fly out of control.


Migrating Elephants – How To Migrate Petabyte Scale Hadoop Clusters With Zero Downtime


Outbrain has been an early adopter of Hadoop and we, the team operating it, have acquired a lot of experience running it in production in terms of data ingestion, processing, monitoring, upgrading etc. This also means that we have a significant ecosystem around each cluster, with both open source and in-house systems.


A while back we decided to upgrade both the hardware and software versions of our Hadoop clusters.

“Why is that a big problem?” you might ask, so let me explain a bit about our current Hadoop architecture. We have two clusters of 300 machines in two different data centers, production and DR. Each cluster has a total dataset size of 1.5 PB with 5TB of compressed data loaded into it each day. There are ~10,000 job executions daily of about 1200 job definitions that were written by dozens of developers, data scientists and various other stakeholders within the company, spread across multiple teams around the globe. These jobs do everything from moving data into Hadoop (for ex. Sqoop or Mysql to Hive data loads), processing in Hadoop (for ex. running Hive, Scalding or Pig jobs), and pushing the results into external data stores (for ex. Vertica, Cassandra, Mysql etc.). An additional dimension of complexity originates from the dynamic nature of the system since developers, data scientists and researchers are pushing dozens of changes to how flows behave in production on a daily basis.

This system needed be migrated to run on new hardware, using new versions of multiple components of the Hadoop ecosystem, without impacting production processes and active users. A partial list of the components and technologies that are currently being used and should be taken into consideration is HDFS, Map-Reduce, Hive, Pig, Scalding and Sqoop. On top of that, of course, we have several more in-house services for data delivery, monitoring and retention that we have developed.

I’m sure you’ll agree that this is quite an elephant.

Storming Our Brains

We sat down with our users, and started thinking about a process to achieve this goal and quickly arrived at several guidelines that our selected process should abide by:

  1. Both Hadoop clusters (production and DR) should always be kept fully operational
  2. The migration process must be reversible
  3. Both value and risk should be incremental

After scratching our heads for quite a while, we came up with these options:


  1. In place: In place migration of the existing cluster to new version and then rolling the hardware upgrade by gradually pushing new machines into the cluster and removing the old machines. This is the simplest approach and you should probably have a very good reason to choose a different path if you can afford the risk. However since upgrading the system in place would expose clients to a huge change in an uncontrolled manner and is not by any means an easily reversible process we had to forego this option.
  2. Flipping the switch: The second option is to create a new cluster on new hardware, sync the required data, stop processing on the old cluster and move it to the new one. The problem here is that we still couldn’t manage the risk, because we would be stopping all processing and moving it to the new cluster. We wouldn’t know if the new cluster can handle the load or if each flow’s code is compatible with the new component’s version. As a matter of fact, there are a lot of unknowns that made it clear we had to split the problem into smaller pieces. The difficulty with splitting in this approach is that once you move a subset of the processing from the old cluster to the new, these results will no longer be accessible on the old cluster. This means that we would have had to migrate all dependencies of that initial subset. Since we have 1200 flow definitions with marvelous and beautiful interconnections between them, the task of splitting them would not have been practical and very quickly we found that we would have to migrate all flows together.
  3. Side by side execution: The 3rd option is to start processing on the new cluster without stopping the old cluster. This is a sort of an active-active approach, because both Hadoop clusters, new and old, will contain the processing results. This would allow us to migrate parts of the workload without risking interfering with any working pipeline in the old cluster. Sounds good, right?


First Steps

To better understand the chosen solution let’s take a look at our current architecture:

We have a framework that allows applications to push raw event data into multiple Hadoop clusters. For the sake of simplicity the diagram describes only one cluster. 

Once the data reaches Hadoop, processing begins to take place using a framework for orchestrating data flows we’ve developed in house that we like to call the Workflow Engine.

Each Workflow Engine belongs to a different business group. That Workflow Engine is responsible for triggering and orchestrating the execution of all flows developed and owned by that group. Each job execution can trigger more jobs on its current Workflow Engine or trigger jobs in other business groups’ Workflow Engines. We use this partitioning mainly for management and scale reasons but during the planning of the migration it provided us with a natural way to partition the workload, since there are very few dependencies between groups vs within each group.


Now that you have a better understanding of the existing layout you can see that the first step is to install a new Hadoop cluster with all required components of its ecosystem and begin pushing data into it.

To achieve this, we configured our dynamic data delivery pipeline system to send all events to the new cluster as well as the old, so now we have a new cluster with a fully operational data delivery pipeline:


Side by Side

Let’s think a bit about what options we had for running a side by side processing architecture.

We could use the same set of Workflow Engines to execute their jobs on both clusters, active and new. While this method would have the upside of saving machines and lower operational costs it would potentially double the load on each machine since jobs are assigned to machines in a static manner. This is due to the fact that each Workflow Engine is assigned a business group and all jobs that belong to this group are executed from it. To isolate the current production jobs execution from the ones for the new cluster we decided to allocate independent machines for the new cluster.


Let the Processing Commence!

Now that we have a fully operational Hadoop cluster running alongside our production cluster, and we now have raw data delivered into it, you might be tempted to say: “Great! Bring up a set of Workflow Engines and let’s start side by side processing!”.


Well… not really.


Since there are so many jobs and they doing varied types of operations we can’t really assume that letting them run side by side is a good idea. For instance, if a job calculates some results and then pushes them to MySql, these results will be pushed twice. Aside from doubling the load on the data bases for no good reason it may cause in some cases corruption or inconsistencies of the data due to race conditions. In essence, every job that writes to an external datasource should be allowed to run only once.


So we’ve described two types of execution modes a WorkflowEngine can have:

Leader: Run all the jobs!

Secondary: Run all jobs except those that might have a side effect external to that Hadoop cluster (e.g. write to external database or trigger an applicative service). This will be done automatically by the framework thus preventing any effort from the development teams.


When a Workflow Engine is in secondary mode, jobs executed from it can read from any source, but write only to a specific Hadoop cluster. That way they are essentially filling it up  and syncing (to a degree) with the other cluster.


Lets Do This…

Phase 1 of the migration should look something like this:


Notice that I’ve only included a Workflow Engine for one group in the diagram for simplicity but it will look similar for all other groups.

So the idea is to bring up a new Workflow Engine and give it the role of a migration secondary. This way it will run all jobs except for those writing to external data stores, thus eliminating all side effects external to the new Hadoop cluster.

By doing so, we were able to achieve multiple goals:

  1. Test basic software integration with the new Hadoop cluster version and all services of the ecosystem (hive, pig, scalding, etc.)
  2. Test new cluster’s hardware and performance compared to the currently active cluster
  3. Safely upgrade each business group’s Workflow Engine separately without impacting other groups.


Since the new cluster is running on new hardware and with a new version of Hadoop ecosystem, this is a huge milestone towards validating our new architecture. The fact the we managed to do so without risking any downtime that could have resulted from failing processing flows, wrong cluster configurations or any other potential issue was key in achieving our migration goals. 


Once we were confident that all phase 1 jobs were operating properly on the new cluster we could continue to phase 2 in which a migration leader becomes secondary and the secondary becomes a leader. Like this:


In this phase all jobs will begin running from the new Workflow Engine impacting all production systems, while the old Workflow Engine will only run jobs that create data to the old cluster. This method actually offers a fairly easy way to rollback to the old cluster in case of any serious failure (even after a few days or weeks) since all intermediate data will continue to be available on the old cluster.


The Overall Plan

The overall process is to push all Workflow Engines to phase 1 and then test and stabilize the system. We were able to run 70% (!) of our jobs in this phase. That’s 70% of our code, 70% of our integrations and APIs and at least 70% of the problems you would experience in a real live move. We were able to fix issues, analyze system performance and validate results. Only once everything seems to be working properly we can start pushing the groups to phase 2 one by one into a tested, stable new cluster.

Once again we benefit from the incremental nature of the process. Each business group can be pushed into phase 2 independently of other groups thus reducing risk and increasing our ability to debug and analyze issues. Additionally, each business group can start leveraging the new cluster’s capabilities (e.g. features from newer version, or improved performance) immediately after they have moved to phase 2 and not after we have migrated every one of the ~1200 jobs to run on the new cluster. One pain point that can’t be ignored is that inter-group dependencies can make this a significantly more complicated feat as you need to bring into consideration the state of multiple groups when migrating.


What Did We Achieve?

  1. Incremental Migration – Due to the fact that we had an active – active migration that we could apply on each business group, we benefited in terms of mitigating risk and gaining value from the new system gradually.
  2. Reversible process- since we kept all old workflowEngines (that executed their jobs on the old Hadoop cluster) in a state of secondary execution mode, all intermediate data was still being processed and was available in case we needed to revert groups independently from each other.
  3. Minimal impact on users – Since we defined an automated transition of jobs between secondary and leader modes users, didn’t need to duplicate any of their jobs.


What Now?

We have completed the upgrade and migration of our main cluster and have already started the migration of our DR cluster.

There are a lot more details and concerns to bring into account when migrating a production system at this scale. However, the basic abstractions we’ve introduced here, and the capabilities we’ve infused our systems with have equipped us with the tools to migrate elephants.

For more information about this project you can check out the video from Strata 2017 London where I discussed it in more detail.

Failure Testing for your private cloud – Introducing GomJabbar

Fork me on GitHub

TL;DR Chaos Drills can contribute a lot to your services resilience, and it’s actually quite a fun activity. We’ve built a tool called GomJabbar to help you run those drills.

Here at Outbrain we manage quite a large scale deployment of hundreds of services / modules, and thousands of hosts. We practice CI/CD, and implemented quite a sound infrastructure, which we believe is scalable, performant, and resilient. We do however experience many production issues on a daily basis, just like any other large scale organization. You simply can’t ensure a 100% fault free system. Servers will crash, run out of disk space, and lose connectivity to the network. Software will experience bugs, and erroneous conditions. Our job as software engineers is to anticipate these conditions, and design our code to handle them gracefully.

For quite a long time we were looking into ways of improving our resilience, and validate our assumptions, using a tool like Netflix’s Chaos Monkey. We also wanted to make sure our alerting system actually triggers when things go wrong. The main problem we were facing is that Chaos Monkey is a tool that was designed to work with cloud infrastructure, while we maintain our own private cloud.

The main motivation for developing such a tool, is that failures have the tendency of occurring when you’re least prepared, and in the least desirable time, e.g. Friday nights, when you’re out having a pint with your buddies. Now, to be honest with ourselves, when things fail during inconvenient times, we don’t always roll our sleeves and dive in to look for the root cause. Many times the incident will end after a service restart, and once the alerts clear we forget about it.

Wouldn’t it be great if we could have “chaos drills”, where we could practice handling failures, test and validate our assumptions, and learn how to improve our infrastructure?

Chaos Drills at Outbrain

We built GomJabbar exactly for the reasons specified above. Once a week, at a well known time, mid day, we randomly select a few targets where we trigger failures. At this point, the system should either auto-detect the failures, and auto-heal, or bypass them. In some cases alerts should be triggered to let teams know that a manual intervention is required.

After each chaos drill we conduct a quick take-in session for each of the triggered failures, and ask ourselves the following questions:

  1. Did the system handle the failure case correctly?
  2. Was our alerting strategy effective?
  3. Did the team have the knowledge to handle, and troubleshoot the failure?
  4. Was the issue investigated thoroughly?

These take-ins lead to super valuable inputs, which we probably wouldn’t collect any other way.

How did we kick this off?

Before we started running the chaos drills, there were a lot of concerns about the value of such drills, and the time it will require. Well, since eliminating our fear from production is one of the key goals of this activity, we had to take care of that first.

"I must not fear.
 Fear is the mind-killer.
 Fear is the little-death that brings total obliteration.
 I will face my fear.
 I will permit it to pass over me and through me.
 And when it has gone past I will turn the inner eye to see its path.
 Where the fear has gone there will be nothing. Only I will remain."

(Litany Against Fear - Frank Herbert - Dune)

So we started a series of chats with the teams, in order to understand what was bothering them, and found ways to mitigate it. So here goes:

  • There’s an obvious need to avoid unnecessary damage.
    • We’ve created filters to ensure only approved targets get to participate in the drills.
      This has a side effect of pre-marking areas in the code we need to take care of.
    • We currently schedule drills via, so teams know when to be ready, and if the time is inappropriate,
      we reschedule.
    • When we introduce a new kind of fault, we let everybody know, and explain what should they prepare for in advance.
    • We started out from minor faults like graceful shutdowns, continued to graceless shutdowns,
      and moved on to more interesting testing like faulty network emulation.
  • We’ve measured the time teams spent on these drills, and it turned out to be negligible.
    Most of the time was spent on preparations. For example ensuring we have proper alerting,
    and correct resilience features in the clients.
    This is actually something you need to do anyway. At the end of the day, we’ve heard no complaints about interruptions, nor time waste.
  • We’ve made sure teams, and engineers on call were not left on their own. We wanted everybody to learn
    from this drill, and when they were’nt sure how to proceed, we jumped in to help. It’s important
    to make everyone feel safe about this drill, and remind everybody that we only want to learn and improve.

All that said, it’s important to remember that we basically simulate failures that occur on a daily basis. It’s only that when we do that in a controlled manner, it’s easier to observe where are our blind spots, what knowledge are we lacking, and what we need to improve.

Our roadmap – What next?

  • Up until now, this drill was executed in a semi-automatic procedure. The next level is to let the teams run this drill on a fixed interval, at a well known time.
  • Add new kinds of failures, like disk space issues, power failures, etc.
  • So far, we were only brave enough to run this on applicative nodes, and there’s no reason to stop there. Data-stores, load-balancers, network switches, and the like are also on our radar in the near future.
  • Multi-target failure injection. For example, inject a failure to a percentage of the instances of some module in a random cluster. Yes, even a full cluster outage should be tested at some point, in case you were asking yourself.

The GomJabbar Internals

GomJabbar is basically an integration between a discovery system, a (fault) command execution scheduler, and your desired configuration. The configuration contains mostly the target filtering rules, and fault commands.

The fault commands are completely up to you. Out of the box we provide the following example commands, (but you can really write your own script to do what suits your platform, needs, and architecture):

  • Graceful shutdowns of service instances.
  • Graceless shutdowns of service instances.
  • Faulty Network Emulation (high latency, and packet-loss).

Upon startup, GomJabbar drills down via the discovery system, fetches the clusters, modules, and their instances, and passes each via the filters provided in the configuration files. This process is also performed periodically. We currently support discovery via consul, but adding other methods of discovery is quite trivial.

When a users wishes to trigger faults, GomJabbar selects a random target, and returns it to the user, along with a token that identifies this target. The user can then trigger one of the configured fault commands, or scripts, on the random target. At this point GomJabbar uses the configured CommandExecutor in order to execute the remote commands on the target hosts.

GomJabbar also maintains a audit log of all executions, which allows you to revert quickly in the face of a real production issue, or an unexpected catastrophe cause by this tool.

What have we learned so far?

If you’ve read so far, you may be asking yourself what’s in it for me? What kind of lessons can I learn from these drills?

We’ve actually found and fixed many issues by running these drills, and here’s what we can share:

  1. We had broken monitoring and alerting around the detection of the integrity of our production environment. We wanted to make sure that everything that runs in our data-centers is managed, and at a well known (version, health, etc). We’ve found that we didn’t compute the difference between the desired state, and the actual state properly, due to reliance on bogus data-sources. This sort of bug attacked us from two sides: once when we triggered graceful shutdowns, and once for graceless shutdowns.
  2. We’ve found services that had no owner, became obsolete, and were basically running unattended in production. The horror.
  3. During the faulty network emulations, we’ve found that we had clients that didn’t implement proper resilience features, and caused cascading failures in the consumers several layers up our service stack. We’ve also noticed that in some cases, the high latency also cascaded. This was fixed by adding proper timeouts, double-dispatch, and circuit-breakers.
  4. We’ve also found that these drills motivated developers to improve their knowledge about the metrics we expose, logs, and the troubleshooting tools we provide.


We’ve found the chaos drills to be an incredibly useful technique, which helps us improve our resilience and integrity, while helping everybody learn about how things work. We’re by no means anywhere near perfection. We’re actually pretty sure we’ll find many many more issues we need to take care of. We’re hoping this exciting new tool will help us move to the next level, and we hope you find it useful too 😉

I WANT IT ALL – Go Hybrid

When I was a kid, my parents used to tell me that I can’t have my cake and eat too.  Actually that’s a lie, they never said that. Still it is something I hear parents say quite often. And not just parents. I meet the same phrase everywhere I go. People constantly taking a firm, almost religious stance about choosing one thing over another: Mac vs PC, Android vs iOS, Chocolate vs Vanilla (obviously Chocolate!).

So I’d like to take a moment to take a different, more inclusive approach.

Forget Mac vs PC. Forget Chocolate vs Vanilla.

I don’t want to choose. I Want it all!

At Outbrain, the core of our compute infrastructure is based on bare metal servers. With a fleet of over 6000 physical nodes, spread across 3 datacenters, we’ve learned over the years how to manage an efficient, tailored environment that caters to our unique needs. One of which being the processing and serving of over 250 Billion personalized recommendations a month, to over 550 Million unique users.

Still, we cannot deny that the Cloud brings forth advantages that are hard to achieve in bare metal environments. And in the spirit of inclusiveness (and maximising value), we want to leverage these advantages to complement and extend what we’ve already built. Whether focusing on workloads that require a high level of elasticity, such as ad-hoc research projects involving large amount of data, or simply external services that can increase our productivity. We’ve come to view Cloud Solutions as supplemental to our tailored infrastructure rather than a replacement.


Over recent months, we’ve been experimenting with 3 different vectors involving the Cloud:



Our world revolves around publications, especially news. As such, whenever a major news event occurs, we feel immediate, potentially high impact. Users rush to publisher sites, where we are installed. They want their news, they want their recommendations, and they want them all now.

For example, when Carrie Fisher, AKA Princess Leia, passed away last December, we saw a 30% traffic increase on top of our usual peak traffic. That’s quite a spike.

Since usually we do not know when the breaking news event will be, it means that we are required to keep enough extra capacity to support such surges.

By leveraging the cloud, we can keep that additional extra capacity to bare minimum, relying instead on the inherent elasticity of the cloud, provisioning only what we need when we need it.

Doing this can improve the efficiency of our environment and cost model.

Ad-hoc Projects

A couple of months back one of researchers came up with an interesting behavioral hypothesis. For the discussion at hand, lets say that it was “people who like chocolate are more likely to raise pet gerbils.” (drop a comment with the word “gerbils” to let me know that you’ve read thus far). That sounded interesting, but raised a challenge. To validate or disprove this, we needed to analyze over 600 Terabytes of data.

We could have run it on our internal Hadoop environment, but that came with a not-so-trivial price tag. Not only did we have to provision additional capacity in our Hadoop cluster to support the workload, we anticipated the analysis to also carry impact on existing workloads running in the cluster. And all this before getting into operational aspects such as labor and lead time.

Instead, we chose to upload the data into Google’s BigQuery. This gave us both shorter lead times for the setup and very nice performance. In addition, 3 months into the project, when the analysis was completed, we simply shut down the environment and were done with it. As simple as that!


We use Fastly for dynamic content acceleration. Given the scale we mentioned, this has the side-effect of generating about 15 Terabytes of Fastly access logs each month. For us, there’s a lot of interesting information in those logs. And so, we had 3 alternatives when deciding how to analyse them:

  •      SaaS based log analysis vendors
  •      An internal solution, based on the ELK stack
  •      A cloud based solution, based on BigQuery and DataStudio

After performing a PoC and running the numbers, we found that the BigQuery option – if done right – was the most effective for us. Both in terms of cost, and amount of required effort.

There are challenges when designing and running a hybrid environment. For example, you have to make sure you have consolidated tools to manage both on-prem and Cloud resources. The predictability of your monthly cost isn’t as trivial as before (no one likes surprises there!), controls around data can demand substantial investments… but that doesn’t make the fallback to “all Vanilla” or “all Chocolate” a good one. It just means that you need to be mindful and prepared to invest in tooling, education and processes.


In summary, I’d like to revisit my parents advice, and try to improve on it a bit (which I’m sure they won’t mind!):

Be curious. Check out what is out there. If you like what you see – try it out. At worst, you’ll learn something new. At best, you’ll have your cake… and eat it too.


X tips [x>5] for building a bulletproof deployment pipeline with Jenkins

Continuous delivery is a methodology where each commit can potentially get into production in a timely manner.

Jenkins Pipeline is one of the tools out there that automates the delivery process to make it short, robust, and without human intervention as much as possible.

We have recently done such an integration on our team at Outbrain, so here are some tips and advises from our humble experience.

X. You should have done this ages ago (so do it today)

Don’t wait till you have all the building blocks in place. Start with a partial pipeline and add all the automated steps you already have in place. It will give you the motivation to add more automation and improve the visibility of the process.

The pipeline set of plugins in Jenkins are ~1 year old in its current form, So it is mature and well documented. Definitely ready to use.

X. Validate artifacts and source code consistency across pipeline

That tip I read in the Teamcity pipeline post but is relevant for Jenkins as well. Make sure that the same version of sources and artifacts is used across all stages. Otherwise, commit might be pushed while the pipeline is executing, and you might end up deploying untested version.

X. Use commit hook with message regexp

Well, if I will try to generalise this tip I would say: try to ask for as little human intervention as possible (when it is not required). A good place to start is a commit hook. It works in a way that when a developer push code with a specific commit message — in our case #d2p (deploy to production), the pipeline is automatically triggered.

Here is a code sample from Jenkinsfile (the pipeline configuration file):

gitCommitMessage = sh(returnStdout: true, script: 'git log -1 --pretty=%B').trim()
deployToProd = (gitCommitMessage =~ /#d2p/ || params.DEPLOY_TAG == "#d2p") //we also allow '#d2p' when triggering manually

X. Try the Blue Ocean view

Blue Ocean set of plugins are in release-candidate stage as of the time of writing (now it is GA). Stable enough and a very good UI— especially for parallel stages. So I would recommend using. In addition, it is working side-by-side with the old UI.

All is green
When something goes wrong

X. Ask for user authorization on sensitive operations

If you still not sure that your monitoring system is robust enough, start by automating the pipeline, and ask for developer authorization before the actual deploy to production.

Here is a code sample from Jenkinsfile:

timeout(time:5, unit:'HOURS') {
  input message: 'Deploy to production?', ok: 'Deploy!'

X. Integrate slack or other notifications

Slack is awesome and has a very documented API as well. Sending notifications on pipeline triggering and progress helps to communicate the work between team members. We use it to send start, completed, and failure notifications right now. We plan to integrate the above approval input with a slack bot so we can approve it directly from slack.

A slack notification

X. Make the pipeline fast (parallelize it)

Making pipeline turnaround time short helps to keep work efficient and fun. Set a target for the total turnaround time. Our target is less than 10 minutes. One of the easiest ways to keep it fast is by running stages that are independent in parallel. For example, we run a deployment to a test machine in parallel to the integration tests and deployment to a canary machine in parallel to our black-box tests.

Here is a code sample from Jenkinsfile:

stage("Testing: phase a") {
    parallel 'JUnit': {
        stage("junit") {
            sh '...'
    }, 'Deploy to simulator': {
        stage("Deploy to simulator") {
            sh '...'
stage("Testing: phase b") {
    parallel 'Simulator tests': {
        stage("Simulator tests") {
            sh '...'
    }, 'Canary server': {
        stage("Deploy to canary") {
            sh '...'
        stage("Tests on canary") {
            sh '...'

X more tips in the great post below:

Enjoy Piping!


P.S. – The original post was published on my personal blog:

I am going to have a talk at Jenkins Use Conference that is based on this blog post:

You are welcome to there for more details!

Effective Testing with Loan Pattern in Scala

Tests are crucial in systems that rely on CI/CD as part of their release cycle. One of the challenges is to write stable tests that work for you without spending a lot of time on maintaining bad tests.

Tests are Hard

They’re hard to write, hard to maintain and it’s even harder to stabilize a flaky test. At Outbrain, we take special pride in our ability (for the most part) to deliver new features to production and doing so with the confidence that only reliable tests can give you. These tests play a crucial role in our ability to deliver fast, good and stable code making sure no regression bugs were introduced in the process. It is crucial then, to not only maintain good test suites (unit tests, integration, and e2e) but also to fix any test that misbehaves (flaky tests).

We have a special environment to facilitate integration and e2e tests called simulation environment (it is only one of the set of tools we have for that purpose). This is a dedicated set of servers which we use to simulate our production environment. We deploy every new version of our services to that environment before we deploy to production, and run tests that check new flows of code, regression, and interoperability to other services.

In order to write an effective test for a new feature, we sometimes need to setup the environment with entities that are required to the feature we’re testing. If, for example, our new feature is to register a car to an owner (a Person entity). Before running the tests we need the required entities, a Car and a Person in our database. We’re not trying to test a flow for creating a new car, or a new person in this scenario. Therefore there is no need in creating the car and/or the person entities explicitly in the test before the actual test scenario happens. And in order to make our tests as clear and succinct as possible — we don’t want to be creating this data explicitly in each and every test.


Bad Practices

So, it was a common practice (albeit a bad one) to have pre-existing data on which we would rely on to run tests (for the whole simulation environment!). This led to two big (interconnected) problems:

  1. No test isolation – a test mistakenly deleting some or all of the pre-existing data, for example, would do so for all the tests that run in that environment
  2. Flaky tests – tests running concurrently are creating, deleting and generally changing data that affects others, which in turn would fail tests for no good reason — which makes it really hard to analyze and fix a failing test


We’ve tackled this problem by creating the needed data before the tests in a test class and deleting it after the test run. Which mitigated the problem somewhat — not only the tests in the same class were interconnected but also added boilerplate to the test class. Now, a test class looked something like (assuming these are entities autogenerated by Scalike for the relevant tables):

[javascript][gist id=a3f4c2721f3cedfc97d0b4650f41c48e file=MyTestClassScalaTestVanilla.scala][/javascript]


[javascript][gist id=1d0ae89d8838d77a74f6262ffd8c21e1 file=MyTestClassSpecs2Vanilla.scala][/javascript]

Looking at this, we were presented with a challenge. First, the data is created for all the tests that run in a class, which must be deleted only after all tests have finished running — this means that the tests are not isolated one from another and potentially may become flaky. Second, we wanted an elegant way of creating and deleting the needed entities seamlessly in order to minimize the boilerplate for each test class.

Note: It is possible however, in Specs2, to make a better solution by using the ‘Scope’ trait like so:

[javascript][gist id=e47bfe8119f8b426f24e5267badb009d file=ContextAfter.scala][/javascript]

And using it in a test like so:

[javascript][gist id=54eb6ec0bbc1f034e29b8a77b0fc09f8 file=MyTestClassWithContextAfter.scala][/javascript]

It’s a good solution, for a simpler problem than we faced. We needed the tests running in a single transaction, with a supplied session and a configurable db name (indicating a set of Scalike connection parameters).

Enter Loan Pattern

We first encountered this pattern when using ScalaTest and quickly moved to using it also in Specs2 (as most of our tests are written in Specs2). From ScalaTest documentation for Sharing fixtures:

“A test fixture is composed of the objects and other artifacts (files, sockets, database connections, etc.) tests use to do their work. When multiple tests need to work with the same fixtures, it is important to try and avoid duplicating the fixture code across those tests.”
“If you need to both pass a fixture object into a test and perform cleanup at the end of the test, you’ll need to use the loan pattern”

Which means, we can use fixtures to set up ‘artifacts’ for the tests to use, promoting the DRY principle by minimizing code duplication. It is also a good way to reduce boilerplate when writing tests. So, we wrote this one simple trait:


[javascript][gist id=0c629f47395900eb86c32142d95abe91 file=TestDataSupportScalaTest.scala][/javascript]

Let’s go over what’s happening in this trait. We’re mixing in a custom trait called ‘DefaultGenerator’ which gives us the ‘DefaultObjects’ which are the entities we need to be pre-created for our tests to run. We have two private methods. One that calls ‘create’ on ‘DefaultObjects’ with a custom name to generate the needed entities. The other calls ‘cleanup’ on the test data to clean the environment after the test has finished running. And the star of this trait, the method (or fixture if you will) ‘withTestData’ which gets the test function as a parameter, calls the private method ‘createTestData’, calls the test and passing it the data we just generated and finally cleans up the generated data after the test finishes.

When mixing this trait in our test class, we get the following code:

[javascript][gist id=3cac004b5e5626de5a67293c233232e1 file=MyTestClassWithTestDataSupportScalaTest.scala][/javascript]

‘testData’ is the data generated in our ‘withTestData’ method (a car and a person in our case).

The Specs2 version of the Loan Pattern is a bit more complex, as we’ve added some more bells and whistles to make it easier for us to create those entities in our domain. We’re using Scalike to create the entities in MySQL database, and we need a somewhat more refined control over the session we’re using, DB name etc’.


[javascript][gist id=4bfbbb0c62a8f89e3a0b87d6dcd6d2b5 file=DataContextName.scala][/javascript]

[javascript][gist id=c1aab66a12dbf5206b0c41edde62b63c file=DataContextDbName.scala][/javascript]

[javascript][gist id=38a6c3de8f78fb0cb1d2a2275e2ff42a file=PackageObjectTestData.scala][/javascript]

[javascript][gist id=5230ad4a1b92a5270713a70c0bb75d17 file=DefaultDataContextSpecs2.scala][/javascript]

It’s very similar to the ScalaTest flavor, but with several changes we needed to make to better facilitate our needs in the Specs2 tests. We have a mechanism to initialize a named DB connection, with a named connection pool and an explicit session. Besides these additions, it’s pretty similar to ScalaTest — generate the test data, run the test and clean the generated data.

The test class now looks like this:

[javascript][gist id=64701c4df08ecb2d29da4fc82c70c0bc file=TestClassDataContext.scala][/javascript]
[javascript][gist id=d85b13035f768656f97b36f0df81c0e4 file=MyTestClassWithTestDataSupportSpecs2.scala][/javascript]


We tackled several issues our team faced on a day to day basis, which made our simulation environment unstable, hard to maintain and generally very frustrating to work on. By extracting data generation and cleanup to an external trait and using a clever mechanism to reduce boilerplate, we managed to clean and simplify the test class, reduce code duplication and generally made our lives easier. Tests are still hard, but a bit easier to write and nicer to read. What do you think?

Code Retreat @ Outbrain

c0de-retr3at logo-06

Some people say writing code is kind of an art.
I don’t think so.
Well, maybe it is true if you are writing an ASCII-Art script or you are a Brainfuck programmer. I think that in most cases writing code is an engineering. Writing a program that will do something is like a car taking you from A to B that someone engineered. When a programmer writes code it should do something: data crunching, tasks automation or driving a car. Something. For me, an art is a non-productive effort of some manner. Writing a program is not such case. Or maybe it is more like a martial-art (or marshaling-art :-)) where you fight your code to do something.

So — what’s the problem?

Most of the programs I know that evolve over time, needs to have a quality which is not an art, but an ability to be maintainable. That’s usually reflect in a high level of readability.

Readability is difficult.

First, when you write some piece code, there is a mental mode you are in, and a mental model you have about the code and how it should be. When you or someone else read the code, they don’t have that model in mind, and usually, they read only a fragment of the entire code base.
In addition, there are various languages and styles of coding. When you read something that was written by someone else, with a different style or in a different language it is like reading a novel someone wrote in a different dialect or language.
That is why, when you write code you should be thoughtful to the “future you” reading the code, by making the code more readable. I am not going to talk here about how to do it, design patterns or best practices to writing your code, but I would just say that practicing and experience are important aspects in relate to code readability and maintainability.
As a programmer, when I retrospect what I did last week at work I can estimate about 50% of the time or less was coding. Among other things were writing this blog post, meetings (which I try to eliminate as much as possible) and all sort of work and personal stuff that happens during the day. When writing code, among the main goals are the quality, answering the requirements and do both in a timely manner.
From a personal perspective, one of my main goals is improving my skill-set as a programmer. Many times, I find that goal in a conflict with the goals above that were dictated by business needs.

Practice and more practice

There are few techniques that come to solve that by practicing on classroom tasks. Among them are TDD Kata’s and code-retreat days. Mainly their agenda says: “let’s take a ‘classroom’ problem, and try to solve it over and over again, in various techniques, constraints, languages and methodologies in order to improve our skill-set and increase our set of tools, rather than answering business needs”.

Code Retreat @ Outbrain — What do we do there?

So, in Outbrain we are doing code-retreat sessions. Well, we call it code-retreat because we write code and it is a classroom tasks (and a buzzy name), but it is not exactly the religious Corey-Haines-full-Saturday Code-Retreat. It’s an hour and a half sessions, every two weeks, that we practice writing code. Anyone who wants to code is invited — not only the experts — and the goals are: improve your skills, have fun, meet developers from other teams in Outbrain that usually you don’t work with (mixing with others) and learn new stuff.
We are doing it for a couple of months now. Up until now, all sessions were about fifteen minutes of presentation/introduction to the topic, and the rest was coding.
In all sessions, the task was Conway’s game of life. The topics that we covered were:

  • Cowboy programming — this was the first session we did. The game of life was presented and each coder could choose how to implement it upon her own wish. The main goal was an introduction to the game of life so in the next sessions we can concentrate on the style itself. I believe an essential part of improving the skills is the fact that we solve the same problem repeatedly.
  • The next session was about Test-Driven-Development. We watched uncle-bob short example of TDD, and had a fertile discussion while coding about some of the principles, such as: don’t write code if you don’t have a failing test.

After that, we did a couple of pair programming sessions. In those sessions, one of the challenges was matching pairs. Sometimes we do it in a lottery and sometimes people could group together by their own selection, but it was dictated by the popular programming languages that the developers choose to use: Java, Kotlin, Python or JavaScript. We plan to do language introduction sessions in the future.
In addition, we talked about other styles we might practice in the future: mob-programming and pairs switches.
These days we are having functional programming sessions, the first one was with the constraint of “all-immutable” (no loops, no mutable variables, etc’) and it will be followed by more advanced constructs of functional programming.
All-in-all I think we are having a lot of fun, a lot of retreat and little coding. Among the main challenges are keeping the people on board as they have a lot of other tasks to do, keeping it interesting and setting the right format (pairs/single/language). The sessions are internal for Outbrain employees right now, but tweet me (@ohadshai) in case you are around and would like to join.
And we also have cool stickers:
c0de-retr3at logo-07c0de-retr3at logo-05c0de-retr3at logo-04
P.S. — all the materials and examples are here:

The original post was published in my personal blog:

From complex monolith to scalable workflow





One of the core functionalities in Outbrain’s solution is our crawling system.

The crawler fetches web pages (e.g. articles), and index them in our database.

The crawlers can be divided into several high-level steps:

  1. Fetch – download the page
  2. Resolve – identify the page context (e.g. what is the domain?)
  3. Extract – try to extract features out of the HTML – like title, image, description, content etc.
  4. NLP – run NLP algorithms using the extracted features, in order to classify and categorize the page.
  5. Save – store to the DB.



The old implementation

The crawler module was one of the first modules that the first Outbrainers had to implement, back in Outbrain’s small-start-up days 6-7 years ago.


In January 2015 we have decided that it was time to sunset the old crawlers, and rewrite everything from scratch. The main motivations for this decision were:

  1. Crawlers were implemented as a long monolith, without clear steps (OOP as opposed to FP).
  2. Crawlers were implemented as a library that was used in many different services, according to the use-case. Each change forced us to build and check all services.
  3. Technologies are not up-to-date anymore (Jetty vs. Netty, sync vs. async development etc.).



The new implementation

When designing the new architecture of our crawlers, we tried to follow the following ideas:

  1. Simple step-by-step (workflow) architecture, simplifying the flow as much as possible.
  2. Split complex logic to micro-services for easier scale and debug.
  3. Use async flows with queues when possible, to control bursts better.
  4. Use newer technologies like Netty and Kafka.


Our first decision was to split the main flow into 3 services. Since the crawler flow, like many others, is basically an ETL (Extract, Transform & Load) – we have decided to split the main flow into 3 different services:

  1. Extract – fetch the HTML and resolve the domain.
  2. Transform – take features out of the HTML (title, images, content…) + run NLP algorithms.
  3. Load – save to the DB.


The implementation of those services is based on “workflow” ideas. We created interface to implement a step, and each service contains several steps, each step doing a single and simple calculation. For example, some of the steps in the “Transform” service are:







In addition we have implemented a class called Router – that is injected with all the steps it needs to run, and is in charge of running them one after the other, reporting errors and skipping unnecessary steps (for example, no need to run categorization when content extraction failed).


Furthermore, every logic that was a bit complex was extracted out of those services to a dedicated micro-service. For example, the fetch part (download the page from the web) was extracted to a different micro-service. This helped us encapsulate fallback logic (between different http clients) and some other related logics we had outside of the main flow. This is also very helpful when we want to debug – we just make an API call to that service to get the same result the main flow gets.


We modeled each piece of data we extracted out of the page into features, so each page would eventually translated into a list of features:






Publish Date



The data flow in those services was very simple. Each step got all the features that were created up to its run, and added (if needed) one or more features to its output. That way the features list (starting with only URL) got “inflated” going over all the steps, reaching the “Load” part with all the features we need to save.

Screen Shot 2017-01-19 at 12.38.45 PM



The migration


One of the most painful parts of such rewrites is the migration. Since this is a very important core-functionality in Outbrain, we could not just change it and cross fingers that everything is OK. In addition, it took several months to build this new flow, and we wanted to test as we go – in production – and not wait until we are done.


The main concept for the migration was to create this new flow side by side with the old flow, having them both run in the same time in production, allowing us to test the new flow without harming production.


The main steps of the migration were

  1. Create the new services and start implementing some of the functionality. Do not save anything in the end.
  2. Start calls from the old-flow to the new one, a-synchronically, passing all features that the old flow calculated.
  3. Each step in the new flow that we implement can compare its results to the old-flow results, and report when the results are different.
  4. Implement the “save” part – but do it only for a small part of the pages – control it by a setting.
  5. Evaluate the new-flow using the comparison done between the old and new flows results.
  6. Gradually enable the new-flow for more and more pages – monitoring the effect in production.
  7. Once feeling comfortable enough, remove the old-flow and run everything only in the new-flow.


The approach can be described as “TDD” in production. We have created a skeleton for the new-flow and started streaming the crawls into it, while actually it does almost nothing. We have started writing the functionality – each one tested, in production, compared to the old-flow. Once all steps were done and tested, we have replaced the old-flow by the new one.



Where we are now

As of December 20th 2016 we are running only the new flow for 100% of the traffic.

The changes we already see:

  1. Throughput/Scale: we have increased the throughput (#crawls per minute) from 2K to ~15K.
  2. Simplicity: time to add new features or solve bugs decreased dramatically. There is no good KPI here but most bugs are solved in 1-2 hours, including production integration.
  3. Less production issues: it is easier for the QA to understand and even debug the flow (using calls to the micro services) – so some of the issues are eliminated even be getting to developers.
  4. Bursts handling: due to the queues architecture we endure bursts much better. It also allows simple recovery after one of the services is down (maintenance for example).

5. Better auditing: thanks to the workflow architecture, it was very easy to add audit message using our ELK infrastructure (Elastic search, Log-stash, Kibana). The crawling flow today reports the outcome (and issues) of every step it does, allowing us, the QA, and even the field guys to understand the crawl in details without the need of a developer.