Open Sourcing Querybook, Pinterest’s Collaborative Big Data Hub

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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.

An efficient big data solution for an increasingly remote-working world.

By Charlie Gu | Tech Lead, Analytics Platform, Lena Ryoo | Software Engineer, Analytics Platform, and Justin Mejorada-Pier | Engineering Manager, Analytics Platform

With more than 300 billion Pins, Pinterest is powering an ever-growing and unique dataset that maps interests, ideas, and intent. As a data-driven company, Pinterest uses data insights and analysis to make product decisions and evaluations to improve the Pinner experience for more than 450 million monthly users. To continuously make these improvements, especially in an increasingly remote environment, it’s more important than ever for teams to be able to compose queries, create analyses, and collaborate with one another. Today we’re taking Querybook, our solution for more efficient and collaborative big data access, and open sourcing it for the community.

The common starting point for any analysis at Pinterest is an ad-hoc query that gets executed on a SparkSQL, Hive, Presto cluster, or any Sqlalchemy compatible engine. We built Querybook to provide a responsive and simple web UI for such analysis so data scientists, product managers, and engineers can discover the right data, compose their queries, and share their findings. In this post, we’ll discuss the motivation to build Querybook, its features, architecture, and our work to open source the project.

The Journey

The proposal to build Querybook started in 2017 as an intern project. During that time, we used a vendor-supplied web application as the query UI. There were often user complaints about that tool regarding its UI, speed & stability, lack of visualizations, as well as difficulty in sharing. Before long, we realized there was a great opportunity to build a better querying interface.

We started to interview data scientists and engineers about their workflows while scoping out technical details. Shortly, we realized most were organizing their queries outside of the official tool, and many used apps like Evernote. Although Jupyter had a notebook user experience, its requirement to use Python/R and the lack of table metadata integration deterred many users. Based on this finding, our team decided Querybook’s query interface would be a document where users can compose queries and write analyses all in one place with the power of collocated metadata and the simplicity of a note-taking app.

Released internally in March 2018, Querybook became the official solution to query big data at Pinterest. Nowadays, Querybook on average has 500 DAUs and 7k daily query runs. With an internal user rating of 8.1/10, it’s one of the highest-rated internal tools at Pinterest.

Feature Highlights

Figure 1. Querybook’s Doc UI

When a user first visits, they‘ll quickly notice its distinctive DataDoc interface. This is the primary place for users to query and analyze. Each DataDoc is composed of a list of cells which can be one of three types: text, query, or chart.

  • The text cell comes with built-in rich-text support for users to jot down their ideas or insights.
  • The query cell is used to compose and execute queries.
  • The chart cell is used to create visualizations based on execution results. Similar to Google Docs, when users are granted access to a DataDoc, they can collaborate with each other in real-time.

With the intuitive charting UI, users can easily turn a DataDoc into an illustrative dashboard. You can choose from different visualization options, such as time-series, pie-charts, scatter plots, and more. You can then connect your visualization to the results of any query on your DataDoc and post-process them with sorting and aggregation as needed. To automatically update these charts, you can use the scheduling options and select your desired cadence. The scheduler can notify users of success or failure. Combined with the templating option powered by Jinja, creating a live updating DataDoc is very quick.

Scheduling and visualization features aren’t intended to replace tools such as Airflow or Superset. Rather, these features provide users a simple and fast way to experiment with their queries and iterate on them. Often, Pinterest engineers use Querybook as the first step to compose queries before creating production-level workflows and dashboards.

Last but not least, Querybook comes with an automated query analytics system. Every query executed gets analyzed to extract metadata such as referenced tables and query runners. Querybook uses this information to automatically update its data schema and search ranking, as well as to show a table’s frequent users and query examples. The more queries, the more documented the tables become.

Architecture

Figure 2. Overview of Querybook’s architecture

To understand how Querybook works, we’ll walk through the process of composing and executing a query.

  1. The first step is to create a DataDoc and write the query in a cell. While the user types, the user’s query gets streamed to the server via Socket.IO.
  2. The server then pushes the delta to all users reading that DataDoc via Redis. At the same time, the server would save the updated DataDoc in the database and create an async job for the worker to update the DataDoc content in ElasticSearch. This allows the DataDoc to be searched later.
  3. Once the query is written, the user can execute the query by clicking the run button. The server would then create a record in the database and insert a query job into the Redis task queue. The worker receives the task and sends the query to the query engine (Presto, Hive, SparkSQL, or any Sqlalchemy compatible engine). While the query is running, the worker pushes live updates to the UI via Socket.IO.
  4. When the execution is completed, the worker loads the query result and uploads it in batches to a configurable storage service (e.g. S3). Finally, the browser gets notified of the query completion and makes a request to the server to load the query result and display it to the user.

For brevity, this section only focused on one user flow of Querybook. However, all the infrastructure used is covered. Querybook allows some of it to be customized. For example, you can choose to upload execution results to either S3, Google Cloud Storage, or a local file. In addition, MySQL can also be swapped with any Sqlalchemy-compatible database such as Postgres.

The Path To Open Source

After noticing the success that Querybook had internally, we decided to open source it. One challenge we bumped into was how to make it generic while preserving some of the Pinterest-specific integrations. For this, we decided to have a two-layer organization through a plugin system and to add an Admin UI.

The Admin UI lets companies configure Querybook’s query engines, table metadata ingestion, and access permissions from a single friendly interface. Previously, these configurations were done inside configuration files and required a code change as well as a deployment to be reflected. With this new UI, admins can make live Querybook changes without going through code or config files.

Figure 3. The Admin UI

The plugin system integrates Querybook with the internal systems at Pinterest by utilizing Python’s importlib. With the plugin system, developers can configure auth, customize query engines, and implement exporters to internal sites. Customized behaviors provided by the plugin system allow Querybook to be optimized for the user’s workflow at Pinterest while ensuring the open-source is generic for the public.

You can check out more of Querybook’s features and its documentation on Querybook.org, and you can reach us at querybook@pinterest.com.

Acknowledgments: We want to thank the following engineers that have made contributions to Querybook: Lauren Mitchell, Langston Dziko, Mohak Nahta, and Franklin Shiao. And to Chunyan Wang, Dave Burgess, and David Chaiken for their critical advice and support.

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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Open jobs at Pinterest
Machine Learning Engineer
San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US

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.

Our new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our PinFlex landing page to learn more. 

With more than 400 million users around the world and 300 billion ideas saved, Pinterest Machine Learning engineers build personalized experiences to help Pinners create a life they love. With just over 3,000 global employees, our teams are small, mighty, and still growing. At Pinterest, you’ll experience hands-on access to an incredible vault of data and contribute large-scale recommendation systems in ways you won’t find anywhere else.

What you’ll do:

  • Build cutting edge technology using the latest advances in deep learning and machine learning to personalize Pinterest
  • Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search), while gaining knowledge of how ML works in different areas
  • Use data driven methods and leverage the unique properties of our data to improve candidates retrieval
  • Work in a high-impact environment with quick experimentation and product launches
  • Keeping up with industry trends in recommendation systems 

 

What we’re looking for:

  • 2+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning)
  • End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)
  • Nice to have:
    • M.S. or PhD in Machine Learning or related areas
    • Publications at top ML conferences
    • Expertise in scalable realtime systems that process stream data
    • Passion for applied ML and the Pinterest product

 

#LI-HYBRID
#LI-LA1

Our Commitment to Diversity:

At Pinterest, our mission is to bring everyone the inspiration to create a life they love—and that includes our employees. We’re taking on the most exciting challenges of our working lives, and we succeed with a team that represents an inclusive and diverse set of identities and backgrounds.

iOS Engineer, Product
San Francisco, CA, US; New York City, NY, US; Portland, OR, US; Seattle, WA, US

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.

Our new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our PinFlex landing page to learn more. 

We are looking for inquisitive, well-rounded iOS engineers to join our Product engineering teams. Working closely with product managers, designers, and backend engineers, you’ll play an important role in enabling the newest technologies and experiences. You will build robust frameworks & features. You will empower both developers and Pinners alike. You’ll have the opportunity to find creative solutions to thought-provoking problems. Even better, because we covet the kind of courageous thinking that’s required in order for big bets and smart risks to pay off, you’ll be invited to create and drive new initiatives, seeing them from inception through to technical design, implementation, and release.

What you’ll do:

  • Build out Pinner-facing frontend features in iOS to power the future of inspiration on Pinterest
  • Contribute to and lead each step of the product development process, from ideation to implementation to release; from rapidly prototyping, running A/B tests, to architecting and building solutions that can scale to support millions of users
  • Partner with design, product, and backend teams to build end to end functionality
  • Put on your Pinner hat to suggest new product ideas and features
  • Employ automated testing to build features with a high degree of technical quality, taking responsibility for the components and features you develop
  • Grow as an engineer by working with world-class peers on varied and high impact projects

What we’re looking for:

  • Deep understanding of iOS development and best practices in Objective C and/or Swift, e.g. xCode, app states, memory management, etc
  • 2+ years of industry iOS application development experience, building consumer or business facing products
  • Experience in following best practices in writing reliable and maintainable code that may be used by many other engineers
  • Ability to keep up-to-date with new technologies to understand what should be incorporated
  • Strong collaboration and communication skills

Product iOS Engineering teams: 

Creator Incentives 

Home Product

Native Publishing

Search Product

Social Growth

Our Commitment to Diversity:

At Pinterest, our mission is to bring everyone the inspiration to create a life they love—and that includes our employees. We’re taking on the most exciting challenges of our working lives, and we succeed with a team that represents an inclusive and diverse set of identities and backgrounds.

Machine Learning Engineer, Core Engi...
San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US

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.

Our new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our PinFlex landing page to learn more. 

With more than 400 million users around the world and 300 billion ideas saved, Pinterest Machine Learning engineers build personalized experiences to help Pinners create a life they love. With just over 3,000 global employees, our teams are small, mighty, and still growing. At Pinterest, you’ll experience hands-on access to an incredible vault of data and contribute large-scale recommendation systems in ways you won’t find anywhere else.

What you’ll do:

  • Build cutting edge technology using the latest advances in deep learning and machine learning to personalize Pinterest
  • Partner closely with teams across Pinterest to experiment and improve ML models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search), while gaining knowledge of how ML works in different areas
  • Use data driven methods and leverage the unique properties of our data to improve candidates retrieval
  • Work in a high-impact environment with quick experimentation and product launches
  • Keeping up with industry trends in recommendation systems 

 

What we’re looking for:

  • 2+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning)
  • End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark)
  • Nice to have:
    • M.S. or PhD in Machine Learning or related areas
    • Publications at top ML conferences
    • Expertise in scalable realtime systems that process stream data
    • Passion for applied ML and the Pinterest product

 

#LI-HYBRID
#LI-LA1

Our Commitment to Diversity:

At Pinterest, our mission is to bring everyone the inspiration to create a life they love—and that includes our employees. We’re taking on the most exciting challenges of our working lives, and we succeed with a team that represents an inclusive and diverse set of identities and backgrounds.

Software Engineer, Infrastructure
San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US

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.

Our new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our PinFlex landing page to learn more. 

The Pinterest Infrastructure Engineering organization builds, scales, and evolves the systems which the rest of Pinterest Engineering uses to deliver inspiration to the world.  This includes source code management, continuous integration, artifact packaging, continuous deployment, service traffic management, service registration and discovery, as well as holistic observability and the underlying compute runtime and container orchestration.  A collection of platforms and capabilities which accelerate development velocity while protecting Pinterest’s production availability for one of the world’s largest public cloud workloads. 

What you’ll do:

  • Design, develop, and operate large scale, distributed systems and networks
  • Work with Engineering customers to understand new requirements and address them in a scalable and efficient manner
  • Actively work to improve the developer process and experience in all phases from coding to operation

What we’re looking for:

  • 2+ years of industry software engineering experience
  • Experience building & operating large scale distributed systems and/or networks
  • Experience in Python, Java, C++, or Go or another language and a willingness to learn
  • Bonus: Experience deploying and operating large scale workloads on a public cloud footprint

Available Hiring Teams: Cloud Delivery Platform (Infra Eng), Code & Language Runtime (Infra Eng), Traffic (Infra Eng), Cloud Systems (Infra Eng), Online Systems (Data Eng), Key Value Systems (Data Eng), Real Time Analytics (Data Eng), Storage & Caching (Data Eng), ML Serving Platform (Data Eng)

 

#LI-SG1

Our Commitment to Diversity:

At Pinterest, our mission is to bring everyone the inspiration to create a life they love—and that includes our employees. We’re taking on the most exciting challenges of our working lives, and we succeed with a team that represents an inclusive and diverse set of identities and backgrounds.

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