Empowering Pinterest Data Scientists and Machine Learning Engineers with PySpark

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Pinterest
Pinterest is a social bookmarking site where users collect and share photos of their favorite events, interests and hobbies. One of the fastest growing social networks online, Pinterest is the third-largest such network behind only Facebook and Twitter.

Data scientists and machine learning engineers at Pinterest found themselves hitting major challenges with existing tools. Hive and Presto were readily accessible tools for large scale data transformations, but complex logic is difficult to write in SQL. Some engineers wrote complex logics in Cascading or Scala Spark jobs, but these have a steep learning curve and take significantly more time to learn and build jobs. Furthermore, data scientists and machine learning engineers often trained models in a small-scale notebook environment, but they lacked the tools to perform large-scale inference.

To combat these challenges, we, (machine learning and data processing platform engineers), built and productionized PySpark infrastructure. The PySpark infrastructure gives our users the following capabilities:

  • Writing logic using the familiar Python language and libraries, in isolated environments that allow experimenting with new packages.
  • Rapid prototyping from our JupyterHub deployment, enabling users to interactively try out feature transformations, model ideas, and data processing jobs.
  • Integration with our internal workflow system, so that users can easily productionize their PySpark applications as scheduled workflows.

PySpark on Kubernetes as a minimum viable product (MVP)

We first built an MVP PySpark infrastructure on Pinterest Kubernetes infrastructure with Spark Standalone Mode and tested with users for feedback.

Figure 1. An overview of the MVP architecture

The infrastructure consists of Kubernetes pods carrying out different tasks:

  • Spark Master managing cluster resources
  • Workers — where Spark executors are spawned
  • Jupyter servers assigned to each user

When users launch PySpark applications from those Jupyter servers, Spark drivers are created in the same pod as Jupyter and the requested executors in worker pods.

This architecture enabled our users to experience the power of PySpark for the first time. Data scientists were able to quickly grasp Python UDFs, transform features, and perform batch inference of TensorFlow models with terabytes of data.

This architecture, however, had some limitations:

  • Jupyter notebook and PySpark driver share resources since they are in the same pod.
  • Driver’s port and address are hard-coded in the config.
  • Users can launch only one PySpark application per assigned Jupyter server.
  • Python dependency per user/team is difficult.
  • Resource management is limited to FIFO approach across all the users (no queue defined).

As the demand for PySpark grew, we worked on a production-grade PySpark infrastructure based on Yarn, Livy, and Sparkmagic.

Production-grade PySpark infrastructure

Figure 2: An overview of the production architecture

In this architecture, each Spark application runs on the YARN cluster. We use Apache Livy to proxy between our internal JupyterHub, the Spark application and the YARN cluster. On Jupyter, Sparkmagic provides a PySpark kernel that forwards the PySpark code to a running Spark application. Conda provides isolated Python environments for each application.

With this architecture, we offer two development approaches.

Interactive development:

  1. A user creates a conda environment zip containing Python packages they need, if any.
  2. From JupyterHub, they create a notebook with PySpark kernel from Sparkmagic.
  3. In the notebook, they declare resources required, conda environment, and other configuration. Livy launches a Spark application on the YARN cluster.
  4. Sparkmagic ships the user’s Jupyter cells (via Livy) to the PySpark application. Livy proxies results back to the Jupyter notebook.

See the attached picture (see Appendix) for a full annotated example of a Jupyter notebook.

Non-interactive development (ad-hoc and production workflow runs):

  1. A Pinterest-internal Job Submission Service acts as the gateway to the YARN cluster.
  2. In development, the user’s local Python code base is packaged into an archive and submitted to launch a PySpark application in YARN.
  3. In scheduled production runs, the production build’s archive is submitted instead.

Benefits

This infrastructure offers us the following benefits:

  1. No resources sharing between Jupyter notebook and PySpark drivers
  2. No hard-coded drivers’ ports and addresses
  3. Users can launch many PySpark applications
  4. Efficient resource allocation and isolation with aggressive dynamic allocation for high resource utilization
  5. Python dependency per user is supported
  6. Resource accountable
  7. Dr. Elephant for PySpark Job analyses

Technical details

Pinterest JupyterHub Integration: (benefits #1,2,3)

We made the Sparkmagic kernel available in Jupyter. When the kernel is selected, a config managed by ZooKeeper is loaded with all necessary dependencies.

We set up Apache Livy, which provides a REST API proxy from Jupyter to the YARN cluster and PySpark applications.

A YARN cluster: (benefit #4)

  • Efficient resource allocation and isolation. We define a queue structure with Fair Scheduler to ensure dedicated resources and preemptable under certain conditions (e.g. after waiting for at least 10 minutes) but a portion of non-preemptable resources will be held for queues with minResource being set. Scheduler and resource manager logs are to manage cluster resources.
  • Aggressive Dynamic allocation policy for high resource utilization. We set the policy where a PySpark application holds at most a certain amount of executors and automatically releases resources once they don’t need. This policy makes sure resources are recycled faster, leading to a better resource utilization.

Python Dependency Management: (benefit #5)

Users can try various Python libraries (e.g. different ML frameworks) without asking platform engineers to install them. To that end, we created a Jenkins job to package a conda environment based on a requirement file, and archive it as a zip file on S3. PySpark applications launched with “ — archives” to broadcast zip file to driver along with all executors, and reset both “PYSPARKPYTHON” (for driver) as well as “spark.yarn.appMasterEnv.PYSPARKPYTHON” (for executors). That way, each application runs under in an isolated Python environment with all libraries needed.

Integrating with Pinterest-internal Job Submission Service (JSS): (benefit #6)

To productionize PySpark applications, users leverage the internal workflow system to schedule. We provided a workflow template to integrate with job submission interfaces to specify code location, parameters, and a Python environment artifact to use.

Self-service job performance analysis: (benefit #7)

We forked the open-sourced Dr. Elephant, and added new heuristics to analyze application’s configuration with various kinds of runtime metrics (executor, job, stage, …). This service provides tuning suggestions and offers guidelines on how to write a spark job properly. The service alleviates users’ debugging-and-troubleshooting pain, boosting the velocity. Moreover, it avoids resource waste and improves cluster stability. Below is an example of the performance analysis.

Figure 3: An overview of Dr. Elephant

Impacts

PySpark is now being used throughout our Product Analytics and Data Science, and Ads teams for a wide range of use cases.

  • Training: users can train models with mllib or any Python machine learning frameworks (e.g. TensorFlow) iteratively with any size of data.
  • Inference: users can test and productionize their Python codes for inferences without depending on platform engineers.
  • Ad-hoc analyses: users can perform various ad-hoc analyses as needed.

Moreover, our users now have the freedom to explore various Python dependencies and use Python UDF for large scale data.

Acknowledgement

We thank David Liu (EM, Machine Learning Platform team), Ang Zhang (EM, Data Processing Platform team), Tais (our TPM), Pinterest Product Analytics and Data Science organization (Sarthak Shah, Grace Huang, Minli Zhang, Dan Lee, Ladi Ositelu), Compute-Platform team (Harry Zhang, June Liu), Data Processing Platform team (Zaheen Aziz), Jupyter team (Prasun Ghosh — Tech Lead) for their support and the collaborations.

Appendix — An example of our use-case (Appendix):

Below is an example of how our users train a model, and run inference logic at scale from their Jupyter notebook with PySpark. We leave explanations in each cell.

Pinterest
Pinterest is a social bookmarking site where users collect and share photos of their favorite events, interests and hobbies. One of the fastest growing social networks online, Pinterest is the third-largest such network behind only Facebook and Twitter.
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Android Engineer, Client Excellence
Mexico City, MEX

About Pinterest:  

Millions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love. In your role, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping Pinners make their lives better in the positive corner of the internet.

On the Client Excellence team you ensure Pinners have a high quality experience on Pinterest. You do this by improving our critical client metrics like crash-free users and by upgrading our supported libraries and operating systems. You also partner with other engineering teams to improve the developer experience and champion operational excellence.

What you’ll do:

  • Improve the quality of our apps by monitoring and improving core client metrics e.g. crash-free user rate, app size, memory management and cpu usage
  • Drive library and OS upgrades with minimal disruption across Pinterest
  • Partner with other engineering teams to improve client developer experience
  • Champion operational excellence across all client engineering teams

What we’re looking for:

  • Deep understanding of Android development and best practices in Java or Kotlin
  • Knowledge on multi-threading, logging, memory management, caching and builds on Android
  • Expertise in developing and debugging across a diverse service stack including storage and data solutions
  • Demonstrated track record of improving software quality with stable releases
  • Experience on platform teams/initiatives, driving technology adoption across feature teams
  • Keeps up to date with new technologies to understand what should be incorporated 
  • Strong collaboration and communication skills
Backend Engineer, Discovery Measurements
Mexico City, MEX

About Pinterest:  

Millions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love. In your role, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping Pinners make their lives better in the positive corner of the internet.

Pinterest personalizes millions of experiences by using machine learning algorithms to sift through our catalog of one hundred billion Pins to find the best content for each Pinner. It is critical to measure the users experience across Pinterest and identify opportunities for improvement. The Discovery Measurements team’s charter is to establish human-powered ground truth for major Pinterest products, e.g. Search and Ads, and develop company critical measurements about relevance, domain quality, session experience, retention, etc. As we look to scale these platforms both vertically and horizontally, we’re looking for strong software engineers to join the team to drive technical excellence and curiosity. We need someone who has experience as a backend developer as well as drive to dive into challenging data processing and data mining problems.

What you’ll do:

  • Build a platform that enables teams to evaluate and train their ML models
  • Design and scale company-wide online & offline measurement platforms for organic and ad content
  • Design and develop company critical measurements, including relevance, domain quality, session experience, retention, user satisfaction
  • Establish technical foundation to generate insightful signals about Pin and Pinners that could power other ML models in the Pinterest ecosystem
  • Partner with cross-functional stakeholders to align engineering efforts for high impact technical initiatives

What we’re looking for:

  • Fluent in any of the following languages: C/C++, Java, JavaScript, Python
  • Exposure to architectural patterns of a large, high-scale web application (e.g., well-designed APIs, high volume data pipelines, efficient algorithms)
  • Model of software engineering best practices, including agile development, unit testing, code reviews, design documentation, debugging, and problem solving
  • Familiar with large data processing and measurement
  • Curiosity for leveraging data and metrics to identify challenging opportunities and build impactful solutions
Engineering Manager, Client Excellence
Mexico City, MEX

About Pinterest:  

Millions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love. In your role, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping Pinners make their lives better in the positive corner of the internet.

We’re looking for an Engineering Manager to build out the Client Excellence team. This team of Android, iOS, Web and API engineers is responsible for ensuring Pinners have a high quality experience on Pinterest. They do this by creating tools to monitor and improve our critical client metrics like crash-free sessions, keeping our critical libraries up to date and partnering with other engineering teams to champion operational excellence.

What you’ll do:

  • Build out an experienced team of Android/iOS/Web/API engineers and help them develop new skills and advance in their careers
  • Provide a vision to the team, drive technical excellence and partner with key stakeholders to prioritize and deliver on the team's roadmap
  • Improve the quality of our apps by monitoring and improving core client metrics e.g. crash-free user rate, app size, memory management and cpu usage
  • Create an operational strategy to drive library and OS upgrades with minimal disruption across Pinterest
  • Partner with other engineering teams to discover future opportunities to improve client developer experience
  • Champion operational excellence across all client engineering teams

What we’re looking for:

  • Strong communication, people development and software project management skills
  • Ability to deliver on immediate goals and form long-term strategies around technology, processes, and people
  • Demonstrated track record of improving software quality with stable releases
  • Ability to dive deeply into platform metrics (e.g. crash rates, logging) to identify opportunities for focus
  • Experience leading platform teams/initiatives, driving technology adoption across feature teams
Fullstack Engineer, Discovery Measure...
Mexico City, MEX

About Pinterest:  

Millions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love. In your role, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping Pinners make their lives better in the positive corner of the internet.

Pinterest personalizes millions of experiences by using machine learning algorithms to sift through our catalog of one hundred billion Pins to find the best content for each Pinner. It is critical to measure the users experience across Pinterest and identify opportunities for improvement. The Discovery Measurements team’s charter is to establish human-powered ground truth for major Pinterest products, e.g. Search and Ads, and develop company critical measurements about relevance, domain quality, session experience, retention, and more. As we look to scale these platforms both vertically and horizontally, we’re looking for strong software engineers to join the team to drive technical excellence and curiosity. We need someone who has experience as a full-stack engineer to dive into challenging human-in-the-loop AI problems.

What you’ll do:

  • You will start by building human-in-the-loop AI platforms to power ML models on production
  • Design and implement the UI layer by closely working with Data Scientist, Product Managers, and Machine Learning engineers
  • Contribute to the new unified human computation backend service
  • Build the scalable backend API infrastructure which can be used to measure and evaluate all various deep learning and machine learning models on production

What we’re looking for:

  • Mastery in frontend stack (Javascript/HTML/CSS), familiarity with modern frontend frameworks (e.g. React/Redux)
  • Knowledge of backend stack (Java, Python, Go) and how they interact with MySQL, Redis, Kafka, etc.
  • Good judgment about shipping improvement quickly while ensuring the sustainability of platforms
  • Ability to measure and improve large scale platforms
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