Flo: a Semantic Foundation for Progressive Stream Processing

Shadaj Laddad, Alvin Cheung, Joseph M. Hellerstein, Mae Milano

Existing streaming languages have a variety of semantic models and guarantees that are often incompatible. In this paper, we identify two general yet precise semantic properties: streaming progress and eager execution. We formally define these properties in the context of Flo, a parameterized streaming language that abstracts over dataflow operators and the underlying structure of streams. To demonstrate the generality of our properties, we show how key ideas from representative streaming and incremental computation systems—Flink, LVars, and DBSP—have semantics that can be modeled in Flo and guarantees that map to our properties.

ACM / PDF

Suki: Choreographed Distributed Dataflow in Rust

Shadaj Laddad, Alvin Cheung, Joseph M. Hellerstein

Programming models for distributed dataflow have long focused on analytical workloads that allow the runtime to dynamically place and schedule compute logic. Meanwhile, models that enable fine-grained control over placement, such as actors, make global optimization difficult. In this extended abstract, we present Suki, an embedded Rust DSL that lets developers implement streaming dataflow with explicit placement of computation. Key to this choreographic programming approach is our use of staged programming, which lets us expose a high-level Rust API while compiling local compute units into individual binaries with zero-overhead.

arXiv / PDF

Towards Relational Contextual Equality Saturation

Tyler Hou, Shadaj Laddad, Joseph M. Hellerstein

PDF

Optimizing Distributed Protocols with Query Rewrites

David Chu, Rithvik Panchapakesan, Shadaj Laddad, Lucky E. Katanahas, Chris Liu, Kaushik Shivakumar, Natacha Crooks, Joseph M. Hellerstein, Heidi Howard

Distributed protocols such as 2PC and Paxos lie at the core of many systems in the cloud, but standard implementations do not scale. This paper presents an approach for scaling any distributed protocol by applying rule-driven rewrites, borrowing from query optimization. Manual rule-driven applications of decoupling and partitioning improve the throughput of 2PC by 5x and Paxos by 3x, and match state-of-the-art throughput in recent work.

ACM / PDF

Keep CALM and CRDT On

Shadaj Laddad^*, Conor Power^*, Mae Milano, Alvin Cheung, Natacha Crooks, Joseph M. Hellerstein

CRDT guarantees extend only to data updates; observations of CRDT state are unconstrained and unsafe. We propose an agenda that embraces the simplicity of CRDTs, but provides richer, more uniform guarantees. We extend CRDTs with a query model that reasons about which queries are safe without coordination by applying monotonicity results from the CALM Theorem.

VLDB / PDF

Towards Auto-Generated Data Systems

Alvin Cheung, Maaz Bin Safeer Ahmad, Brandon Haynes, Chanwut Kittivorawong, Shadaj Laddad, Xiaoxuan Liu, Chenglong Wang, Cong Yan

VLDB / PDF

Optimizing Stateful Dataflow with Local Rewrites

Shadaj Laddad, Conor Power, Tyler Hou, Alvin Cheung, Joseph M. Hellerstein

Optimizing a stateful dataflow language is a challenging task. There are strict correctness constraints for preserving properties expected by downstream consumers, a large space of possible optimizations, and complex analyses that must reason about the behavior of the program over time. But with e-graphs, we can dramatically simplify the process of building a correct optimizer while yielding more consistent results! In this short paper, we discuss our early work using e-graphs to develop an optimizer for the Hydroflow dataflow language.

arXiv / PDF

Initial Steps Toward a Compiler for Distributed Programs

Joseph M. Hellerstein, Shadaj Laddad, Mae Milano, Conor Power, Mingwei Samuel

ACM / PDF

Code Transpilation for Hardware Accelerators

Yuto Nishida, Sahil Bhatia, Shadaj Laddad, Hasan Genc, Yakun Sophia Shao, Alvin Cheung

arXiv / PDF

Katara: Synthesizing CRDTs with Verified Lifting

Shadaj Laddad, Conor Power, Mae Milano, Alvin Cheung, Joseph M. Hellerstein

In this paper, we present Katara, a program synthesis-based system that takes sequential data type implementations and automatically synthesizes verified CRDT designs from them. Katara is able to automatically synthesize CRDTs for a wide variety of scenarios, from reproducing classic CRDTs to synthesizing novel designs based on specifications in existing literature. Crucially, our synthesized CRDTs are fully, automatically verified, eliminating entire classes of common errors and reducing the process of producing a new CRDT from a painstaking paper proof of correctness to a lightweight specification.

ACM / PDF / GitHub

VizSmith: Automated Visualization Synthesis by Mining Data-Science Notebooks

Rohan Bavishi, Shadaj Laddad, Hiroaki Yoshida, Mukul R. Prasad, Koushik Sen

We built a tool called VizSmith which enables code reuse for visualizations by mining visualization code from Kaggle notebooks and creating a database of 7176 reusable Python functions. Given a dataset, columns to visualize and a text query from the user, VizSmith searches this database for appropriate functions, runs them and displays the generated visualizations to the user.

IEEE / PDF

Fluid Quotes: Metaprogramming across Abstraction Boundaries with Dependent Types

Shadaj Laddad and Koushik Sen

We introduce Fluid Quotes, a new technique that uses dependent types to let users pass quotes through abstraction boundaries in runtime code while splicing them ahead-of-time. This technique enables new metaprogramming capabilities by eliminating the traditional requirement of co-locating parameter expressions with call-sites. Fluid Quotes capture not only source code but also associated runtime context to ensure correctness.

ACM / PDF

ScalaPy: Seamless Python Interoperability for Cross-Platform Scala Programs

Shadaj Laddad and Koushik Sen

ACM / PDF