Signals and Threads

"Alternative data" is Wall Street's name for information that comes from non-traditional sources: satellite photos of parking lots, credit card panels, weather forecasts. Eric Mannes has spent over a decade at Jane Street, first as a commodities trader and now helping lead the firm's alternative data team. In this episode, Eric and Ron talk about what it takes to turn messy external data into datasets a trading strategy can rely on. Along the way, they cover the day oil futures settled at a negative price and the systems that broke as a result; the years when the commodities desk's risk system was one very large Excel spreadsheet; the hard question of what a company even is; and why better ML models raise the value of careful data engineering.You can find the transcript for this episode on our website.Some links to topics that came up in the discussion:FiggieAdverse selection2020 Russia–Saudi Arabia oil price warDual-listed company"LAMBDA: The ultimate Excel worksheet function"TrinoThe Bitter LessonLearn more about Jane Street’s internship program.Apply for Data Engineering roles at Jane Street

In university Jacob Baskin studied at the intersection of computer science and economics, thinking about systems that incentivize people to express their true preferences. He put those ideas into practice at Google, where he worked on ad serving, before joining Jane Street’s database infrastructure team. In this episode, Ron and Jacob discuss Superstore, a distributed columnar database now central to Jane Street’s tech stack that Jacob began building practically the day he started. How do you support wide-ranging analytical queries while transactional writes stream in at the speed of trading systems? And what’s it like when your first design doc leads to an eight-figure hardware purchase? After building Superstore Jacob has since gone back to his roots, thinking about schemes for bidding on compute time as he works to optimize usage of the Hive, Jane Street’s massive compute cluster for research. You can find the transcript for this episode on our website. Some links to topics that came up in the discussion: Mechanism design, second-price auction MapReduce, BigTable, Google File System Vertica Apache Parquet CockroachDB Paxos BitTorrent

Nate Foster is a professor at EPFL in Switzerland in the Networked Systems Abstractions Lab, and a visiting researcher at Jane Street on the Networking team. In this episode, he and Ron consider what happens when you bring a software mindset to network engineering. Can you use programming language theory and formal methods to realize the dream of software-defined networks? Along the way, they discuss how hyperscalers have shaped networking hardware; the return (or not) of multicast; the ways ML workloads are reshaping the networking layer; and the success Jane Street has had using an early Internet protocol, BGP, together with a more declarative high-level specification language. You can find the transcript for this episode on our website. Some links to topics that came up in the discussion: P4 (Programming language Lenses (bidirectional transformation) OpenFlow Kleene algebra with tests NetKAT End-to-end principle Border Gateway Protocol “Stable Internet routing without Global Coordination,” aka the Gao-Rexford conditions Unison file synchronizer Barefoot Networks

Will Wilson is the founder and CEO of Antithesis, which is trying to change how people test software. The idea is that you run your application inside a special hypervisor environment that intelligently (and deterministically) explores the program’s state space, allowing you to pinpoint and replay the events leading to crashes, bugs, and violations of invariants. In this episode, he and Ron take a broad view of testing, considering not just “the unreasonable effectiveness of example-based tests” but also property-based testing, fuzzing, chaos testing, type systems, and formal methods. How do you blend these techniques to find the subtle, show-stopper bugs that will otherwise wake you up at 3am? As Will has discovered, making testing less painful is actually a tour of some of computer science’s most vexing and interesting problems. You can find the transcript for this episode on our website. Some links to topics that came up in the discussion: Antithesis, Will’s company FoundationDB’s deterministic simulation framework QuickCheck — the original Haskell property-based testing library, by Koen Claessen and John Hughes Hypothesis — property-based testing for Python, created by David MacIver QuviQ — John Hughes’ company commercializing QuickCheck, including automotive testing work Netflix Chaos Monkey Goodhart’s law — “When a measure becomes a target, it ceases to be a good measure” CAP theorem — the impossibility result for distributed systems that FoundationDB claims to have in some sense violated. Paxos — the consensus algorithm FoundationDB reimplemented from scratch Large cardinals, an area Will studied before abandoning mathematics Lyapunov exponent — measure of chaotic divergence Chesterton’s fence The Story of the Flash Fill Feature in Excel Building a C compiler with a team of parallel Claudes Barak Richman, “How Community Institutions Create Economic Advantage: Jewish Diamond Merchants in New York”

Chris Lattner is the creator of LLVM and led the development of the Swift language at Apple. With Mojo, he’s taking another big swing: How do you make the process of getting the full power out of modern GPUs productive and fun? In this episode, Ron and Chris discuss how to design a language that’s easy to use while still providing the level of control required to write state of the art kernels. A key idea is to ask programmers to fully reckon with the details of the hardware, but making that work manageable and shareable via a form of type-safe metaprogramming. The aim is to support both specialization to the computation in question as well as to the hardware platform. “Somebody has to do this work,” Chris says, “if we ever want to get to an ecosystem where one vendor doesn’t control everything.”You can find the transcript for this episode on our website.Some links to topics that came up in the discussion:Democratizing AI compute (an 11-part series)Modular AIMojoMLIRSwift