Software rollout is the process of replacing the version of an application that is currently running in production with a new version. Many subtle and catastrophic bugs occur during software rollout. There are many existing techniques to improve the odds of a rollout completing successfully, but these techniques don't work well when the application has shared, persistent, mutable state. In this paper, we present a practical framework to test the rollout of stateful applications. Our framework uses diff testing to verify that the new version of an application behaves identically to the currently running version that will be replaced. The framework has three main components to safely and efficiently compare the behavior of the two versions. First, we implement database branching on top of Postgres. Second, we implement an efficient algorithm to diff two database branches. Third, we describe how to replay client requests to improve test coverage. Finally, we identify three common categories of rollout bugs and demonstrate how our framework can find these bugs with minimal performance overhead.

When writing a distributed application, conventional wisdom says to split your application into separate services that can be rolled out independently. This approach is well-intentioned, but a microservices-based architecture like this often backfires, introducing challenges that counteract the benefits the architecture tries to achieve. Fundamentally, this is because microservices conflate logical boundaries (how code is written) with physical boundaries (how code is deployed). In this paper, we propose a different programming methodology that decouples the two in order to solve these challenges. With our approach, developers write their applications as logical monoliths, offload the decisions of how to distribute and run applications to an automated runtime, and deploy applications atomically. Our prototype implementation reduces application latency by up to 15× and reduces cost by up to 9× compared to the status quo.

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model (PaLM). We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies.

Many recent machine learning models rely on fine-grained dynamic control flow for training and inference. In particular, models based on recurrent neural networks and on reinforcement learning depend on recurrence relations, data-dependent conditional execution, and other features that call for dynamic control flow. These applications benefit from the ability to make rapid control-flow decisions across a set of computing devices in a distributed system. For performance, scalability, and expressiveness, a machine learning system must support dynamic control flow in distributed and heterogeneous environments.

Spanner is Google’s scalable, multiversion, globally distributed, and synchronously replicated database. It is the first system to distribute data at global scale and support externally-consistent distributed transactions. This article describes how Spanner is structured, its feature set, the rationale underlying various design decisions, and a novel time API that exposes clock uncertainty. This API and its implementation are critical to supporting external consistency and a variety of powerful features: nonblocking reads in the past, lock-free snapshot transactions, and atomic schema changes, across all of Spanner.

Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products. In this article, we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable.

article Share on The Google file system Authors: Sanjay Ghemawat Google GoogleView Profile , Howard Gobioff Google GoogleView Profile , Shun-Tak Leung Google GoogleView Profile Authors Info & Claims ACM SIGOPS Operating Systems ReviewVolume 37Issue 5December 2003 pp 29–43https://doi.org/10.1145/1165389.945450Online:19 October 2003Publication History 2,957citation60,150DownloadsMetricsTotal Citations2,957Total Downloads60,150Last 12 Months3,038Last 6 weeks280 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We present a new limited form of interprocedural analysis called field analysis that can be used by a compiler to reduce the costs of modern language features such as object-oriented programming, automatic memory management, and run-time checks required for type safety. Unlike many previous interprocedural analyses, our analysis is cheap, and does not require access to the entire program. Field analysis exploits the declared access restrictions placed on fields in a modular language (e.g. field access modifiers in Java) in order to determine useful properties of fields of an object. We describe our implementation of field analysis in the Swift optimizing compiler for Java, as well a set of optimizations that exploit the results of field analysis. These optimizations include removal of run-time tests, compile-time resolution of method calls, object inlining, removal of unnecessary synchronization, and stack allocation. Our results demonstrate that field analysis is efficient and effective. Speedups average 7% on a wide range of applications, with some times reduced by up to 27%. Compile time overhead of field analysis is about 10%.

Article Continuous profiling: where have all the cycles gone? Share on Authors: Jennifer M. Anderson View Profile , Lance M. Berc View Profile , Jeffrey Dean View Profile , Sanjay Ghemawat View Profile , Monika R. Henzinger View Profile , Shun-Tak A. Leung View Profile , Richard L. Sites View Profile , Mark T. Vandevoorde View Profile , Carl A. Waldspurger View Profile , William E. Weihl View Profile Authors Info & Claims SOSP '97: Proceedings of the sixteenth ACM symposium on Operating systems principlesOctober 1997 Pages 1–14https://doi.org/10.1145/268998.266637Published:01 October 1997 175citation1,209DownloadsMetricsTotal Citations175Total Downloads1,209Last 12 Months17Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

This article describes the Digital Continuous Profiling Infrastructure, a sampling-based profiling system designed to run continuously on production systems. The system supports multiprocessors, works on unmodified executables, and collects profiles for entire systems, including user programs, shared libraries, and the operating system kernel. Samples are collected at a high rate (over 5200 samples/sec. per 333MHz processor), yet with low overhead (1–3% slowdown for most workloads). Analysis tools supplied with the profiling system use the sample data to produce a precise and accurate accounting, down to the level of pipeline stalls incurred by individual instructions, of where time is bring spent. When instructions incur stalls, the tools identify possible reasons, such as cache misses, branch mispredictions, and functional unit contention. The fine-grained instruction-level analysis guides users and automated optimizers to the causes of performance problems and provides important insights for fixing them.

Thor is an object-oriented database system designed for use in a heterogeneous distributed environment. It provides highly-reliable and highly-available persistent storage for objects, and supports safe sharing of these objects by applications written in different programming languages.Safe heterogeneous sharing of long-lived objects requires encapsulation: the system must guarantee that applications interact with objects only by invoking methods. Although safety concerns are important, most object-oriented databases forgo safety to avoid paying the associated performance costs.This paper gives an overview of Thor's design and implementation. We focus on two areas that set Thor apart from other object-oriented databases. First, we discuss safe sharing and techniques for ensuring it; we also discuss ways of improving application performance without sacrificing safety. Second, we describe our approach to cache management at client machines, including a novel adaptive prefetching strategy.The paper presents performance results for Thor, on several OO7 benchmark traversals. The results show that adaptive prefetching is very effective, improving both the elapsed time of traversals and the amount of space used in the client cache. The results also show that the cost of safe sharing can be negligible; thus it is possible to have both safety and high performance.

No abstract available.

To provide high availability for services such as mail or bulletin boards, data must be replicated. One way to guarantee consistency of replicated data is to force service operations to occur in the same order at all sites, but this approach is expensive. For some applications a weaker causal operation order can preserve consistency while providing better performance. This paper describes a new way of implementing causal operations. Our technique also supports two other kinds of operations: operations that are totally ordered with respect to one another and operations that are totally ordered with respect to all other operations. The method performs well in terms of response time, operation-processing capacity, amount of stored state, and number and size of messages; it does better than replication methods based on reliable multicast techniques.

This paper describes the design and implementation of the Harp file system. Harp is a replicated Unix file system accessible via the VFS interface. It provides highly available and reliable storage for files and guarantees that file operations are executed atomically in spite of concurrency and failures. It uses a novel variation of the primary copy replication technique that provides good performance because it allows us to trade disk accesses for network communication. Harp is intended to be used within a file service in a distributed network; in our current implementation, it is accessed via NFS. Preliminary performance results indicate that Harp provides equal or better response time and system capacity than an unreplicated implementation of NFS that uses Unix files directly.