One of the advantages of being disorderly is that one is constantly making exciting discoveries. - A.A. Milne


Distributed systems are ubiquitious, but they remain notoriously difficult to reason about and program. Our research at Disorderly Labs operates at the intersection of distributed systems, data management, programming languages and formal verification. We build languages, models and tools to help tame the fundamental complexity of distributed programming.


  • Lineage-driven fault injection exploits data provenance to identify and explain bugs in fault tolerant distributed systems. Our recent collaboration with Netflix demonstrates the efficacy of the approach on real-world systems.
  • Disorderly programming explores the use of declarative, data-centric languages (such as Bloom and Dedalus) for programming distributed systems.
  • Programmable storage applies the disorderly programming philosophy to the orchestration of component-based object storage systems.


Growing a Protocol (To appear in HotCloud 2017)

Large-scale distributed systems must be built to anticipate and mitigate a variety of hardware and software failures. In order to build confidence that fault-tolerant systems are correctly implemented, Netflix (and similar enterprises) regularly run failure drills in which faults are deliberately injected in their production system. The combinatorial space of failure scenarios is too large to explore exhaustively. Existing failure testing approaches either randomly explore the space of potential failures randomly or exploit the “hunches” of domain experts to guide the search. Random strategies waste resources testing “uninteresting” faults, while programmer-guided approaches are only as good as human intuition and only scale with human effort. In this paper, we describe how we adapted and implemented a research prototype called lineage-driven fault injection (LDFI) to automate failure testing at Netflix. Along the way, we describe the challenges that arose adapting the LDFI model to the complex and dynamic realities of the Netflix architecture. We show how we implemented the adapted algorithm as a service atop the existing tracing and fault injection infrastructure, and present early results.
Failure is always an option; in large-scale data management systems, it is practically a certainty. Fault-tolerant protocols and components are notoriously difficult to implement and debug. Worse still, choosing existing fault-tolerance mechanisms and integrating them correctly into complex systems remains an art form, and programmers have few tools to assist them. We propose a novel approach for discovering bugs in fault-tolerant data management systems: lineage-driven fault injection. A lineage-driven fault injector reasons backwards from correct system outcomes to determine whether failures in the execution could have prevented the outcome. We present MOLLY, a prototype of lineage-driven fault injection that exploits a novel combination of data lineage techniques from the database literature and state-of-the-art satisfiability testing. If fault-tolerance bugs exist for a particular configuration, MOLLY finds them rapidly, in many cases using an order of magnitude fewer executions than random fault injection. Otherwise, MOLLY certifies that the code is bug-free for that configuration.
Recent research has explored using Datalog-based languages to express a distributed system as a set of logical invariants. Two properties of distributed systems proved difficult to model in Datalog. First, the state of any such system evolves with its execution. Second, deductions in these systems may be arbitrarily delayed, dropped, or reordered by the unreliable network links they must traverse. Previous efforts addressed the former by extending Datalog to include updates, key constraints, persistence and events, and the latter by assuming ordered and reliable delivery while ignoring delay. These details have a semantics outside Datalog, which increases the complexity of the language or its interpretation, and forces programmers to think operationally. We argue that the missing component from these previous languages is a notion of time. In this paper we present Dedalus, a foundation language for programming and reasoning about distributed systems. Dedalus reduces to a subset of Datalog with negation, aggregate functions, successor and choice, and admits an explicit representation of time into the logic language. We show that Dedalus provides a declarative foundation for the two signature features of distributed systems: mutable state, and asynchronous processing and communication. Given these two features, we address three important properties of programs in a domain-specific manner: a notion of safety appropriate to non-terminating computations, stratified monotonic reasoning with negation over time, and efficient evaluation over time via a simple execution strategy. We also provide conservative syntactic checks for our temporal notions of safety and stratification. Our experience implementing full-featured systems in variants of Datalog suggests that Dedalus is well-suited to the specification of rich distributed services and protocols, and provides both cleaner semantics and richer tests of correctness.

Orchestrated Chaos: Applying Failure Testing Research at Scale

I See What You Mean: what a tiny language can teach us about gigantic systems

Outwards from the Middle of the Maze

Monkeys in labcoats

Monkeys in Lab Coats: Applying Failure Testing Research @Netflix

Monkeys in labcoats

Peter Alvaro Principal Investigator

Peter Alvaro is an Assistant Professor of Computer Science at UC Santa Cruz. He earned his PhD at UC Berkeley, where he was advised by Joe Hellerstein.

Kamala Aspiring Ml Wizard

Kamala started her PhD at UCSC in Fall, 2015. Her interests include reasoning about large scale distributed systems and applied machine learning, specifically how and when we might be able to apply machine learning effectively to understand complex systems better.

Kathryn Dahlgren Resident Koder Kat

Kathryn is a PhD student in Computer Science at UCSC. Her interests orbit research and developments in databases and distributed systems.

Tuan Kohd Wizard In Training

Tuan is a CS Ph.D student at UCSC, whose interests are in distributed systems and machine learning

Nikhil Kini Fault Injector

Operator on the intersection of logic, statistical relational learning, and distributed systems.

Ashutosh Code Wrangler

Ashutosh is a M.S. student at U.C. Santa Cruz, interested in databases, distributed computing and machine learning.

Read more about Ashutosh.

Sarah Borland Chaos Coordinator

Sarah is a CS undergraduate student at UC Santa Cruz interested in distributed systems and databases.

Asha Karim Chaotic Good

Asha is an undergraduate CS student at UC Santa Cruz pursuing her interests in large-scale distributed systems and machine learning.

Kolton Andrus Gremlin, Inc

Joseph M. Hellerstein UC Berkeley

Boaz Leskes

Casey Rosenthal Netflix

The Register
Netflix Tech Blog