Summary The way to build maintainable software and systems is through composition of individual pieces. By making those pieces high quality and flexible they can be used in surprising ways that the original creators couldn’t have imagined. One such component that has gone above and beyond its originally envisioned use case is BookKeeper, a distributed storage system that is optimized for durability and speed. In this episode Matteo Merli shares the story behind the creation of BookKeeper, the various ways that it is being used today, and the architectural aspects that make it such a strong building block for projects such as Pulsar. He also shares some of the other interesting systems that have been built on top of it and an amusing war story of running it at scale in its early years.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial. Your host is Tobias Macey and today I’m interviewing Matteo Merli about Apache BookKeeper, a scalable, fault-tolerant, and low-latency storage service optimized for real-time workloads

Interview

Introduction How did you get involved in the area of data management? Can you describe what BookKeeper is and the story behind it? What are the most notable features/capabilities of BookKeeper? What are some of the ways that BookKeeper is being used? How has your work on Pulsar influenced the features and product direction of BookKeeper? Can you describe the architecture of a BookKeeper cluster?

How have the design and goals of BookKeeper changed or evolved over time?

What is the impact of record-oriented storage on data distribution/allocation within the cluster when working with variable record sizes? What are some of the operational considerations that users should be aware of? What are some of the most interesting/compelling features from your perspective? What are some of the most often overlooked or misunderstood capabilities of BookKeeper? What are the most interesting, innovative, or unexpected ways that you have seen BookKeeper used? What

Summary

One of the critical components for modern data infrastructure is a scalable and reliable messaging system. Publish-subscribe systems have been popular for many years, and recently stream oriented systems such as Kafka have been rising in prominence. This week Rajan Dhabalia and Matteo Merli discuss the work they have done on Pulsar, which supports both options, in addition to being globally scalable and fast. They explain how Pulsar is architected, how to scale it, and how it fits into your existing infrastructure.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers A few announcements:

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Your host is Tobias Macey and today I’m interviewing Rajan Dhabalia and Matteo Merli about Pulsar, a distributed open source pub-sub messaging system

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Pulsar is and what the original inspiration for the project was? What have been some of the most challenging aspects of building and promoting Pulsar? For someone who wants to run Pulsar, what are the infrastructure and network requirements that they should be considering and what is involved in deploying the various components? What are the scaling factors for Pulsar and what aspects of deployment and administration should users pay special attention to? What projects or services do you consider to be competitors to Pulsar and what makes it stand out in comparison? The documentation mentions that there is an API layer that provides drop-in compatibility with Kafka. Does that extend to also supporting some of the plugins that have developed on top of Kafka? One of the popular aspects of Kafka is the persistence of the message log, so I’m curious how Pulsar manages long-term storage and reprocessing of messages that have already been acknowledged? When is Pulsar the wrong tool to use? What are some of the improvements or new features that you have planned for the future of Pulsar?

Contact Info

Matteo

merlimat on GitHub @merlimat on Twitter

Rajan

@dhabaliaraj on Twitter rhabalia on GitHub

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Pulsar Publish-Subscribe Yahoo Streamlio ActiveMQ Kafka Bookkeeper SLA (Service Level Agreement) Write-Ahead Log Ansible Zookeeper Pulsar Deployme