Modern enterprise applications are currently undergoing a complete paradigm shift away from traditional transactional processing to combined analytical and transactional processing. This challenge of combining two opposing query types in a single database management system results in additional requirements for transaction management as well. In this paper, we discuss our approach to achieve high throughput for transactional query processing while allowing concurrent analytical queries. We present our approach to distributed snapshot isolation and optimized two-phase commit protocols.

/pdf/apache-flink-stream-and-batch-processing-in-a-single-engine-3u2381y8rg.pdf

Apache flink : Stream and batch processing in a single engine

Naiad is a distributed system for executing data parallel, cyclic dataflow programs. It offers the high throughput of batch processors, the low latency of stream processors, and the ability to perform iterative and incremental computations. Although existing systems offer some of these features, applications that require all three have relied on multiple platforms, at the expense of efficiency, maintainability, and simplicity. Naiad resolves the complexities of combining these features in one framework.A new computational model, timely dataflow, underlies Naiad and captures opportunities for parallelism across a wide class of algorithms. This model enriches dataflow computation with timestamps that represent logical points in the computation and provide the basis for an efficient, lightweight coordination mechanism.We show that many powerful high-level programming models can be built on Naiad's low-level primitives, enabling such diverse tasks as streaming data analysis, iterative machine learning, and interactive graph mining. Naiad outperforms specialized systems in their target application domains, and its unique features enable the development of new high-performance applications.

/pdf/naiad-a-timely-dataflow-system-1z2cah9a5l.pdf

Naiad: a timely dataflow system

MillWheel is a framework for building low-latency data-processing applications that is widely used at Google. Users specify a directed computation graph and application code for individual nodes, and the system manages persistent state and the continuous flow of records, all within the envelope of the framework's fault-tolerance guarantees.This paper describes MillWheel's programming model as well as its implementation. The case study of a continuous anomaly detector in use at Google serves to motivate how many of MillWheel's features are used. MillWheel's programming model provides a notion of logical time, making it simple to write time-based aggregations. MillWheel was designed from the outset with fault tolerance and scalability in mind. In practice, we find that MillWheel's unique combination of scalability, fault tolerance, and a versatile programming model lends itself to a wide variety of problems at Google.

/pdf/millwheel-fault-tolerant-stream-processing-at-internet-scale-3gdfvfn2vm.pdf

MillWheel: fault-tolerant stream processing at internet scale

Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business (e.g. Web logs, mobile usage statistics, and sensor networks). At the same time, consumers of these datasets have evolved sophisticated requirements, such as event-time ordering and windowing by features of the data themselves, in addition to an insatiable hunger for faster answers. Meanwhile, practicality dictates that one can never fully optimize along all dimensions of correctness, latency, and cost for these types of input. As a result, data processing practitioners are left with the quandary of how to reconcile the tensions between these seemingly competing propositions, often resulting in disparate implementations and systems.We propose that a fundamental shift of approach is necessary to deal with these evolved requirements in modern data processing. We as a field must stop trying to groom unbounded datasets into finite pools of information that eventually become complete, and instead live and breathe under the assumption that we will never know if or when we have seen all of our data, only that new data will arrive, old data may be retracted, and the only way to make this problem tractable is via principled abstractions that allow the practitioner the choice of appropriate tradeoffs along the axes of interest: correctness, latency, and cost.In this paper, we present one such approach, the Dataflow Model, along with a detailed examination of the semantics it enables, an overview of the core principles that guided its design, and a validation of the model itself via the real-world experiences that led to its development.

/pdf/the-dataflow-model-a-practical-approach-to-balancing-4mt5p3u6os.pdf

The dataflow model: a practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data processing

This paper introduces Trill -- a new query processor for analytics. Trill fulfills a combination of three requirements for a query processor to serve the diverse big data analytics space: (1) Query Model: Trill is based on a tempo-relational model that enables it to handle streaming and relational queries with early results, across the latency spectrum from real-time to offline; (2) Fabric and Language Integration: Trill is architected as a high-level language library that supports rich data-types and user libraries, and integrates well with existing distribution fabrics and applications; and (3) Performance: Trill's throughput is high across the latency spectrum. For streaming data, Trill's throughput is 2-4 orders of magnitude higher than comparable streaming engines. For offline relational queries, Trill's throughput is comparable to a major modern commercial columnar DBMS.Trill uses a streaming batched-columnar data representation with a new dynamic compilation-based system architecture that addresses all these requirements. In this paper, we describe Trill's new design and architecture, and report experimental results that demonstrate Trill's high performance across diverse analytics scenarios. We also describe how Trill's ability to support diverse analytics has resulted in its adoption across many usage scenarios at Microsoft.

/pdf/trill-a-high-performance-incremental-query-processor-for-1yrh6bkzq9.pdf

Trill: a high-performance incremental query processor for diverse analytics

Windows queries are proving essential to data-stream processing. In this paper, we present an approach for evaluating sliding-window aggregate queries that reduces both space and computation time for query execution. Our approach divides overlapping windows into disjoint panes, computes sub-aggregates over each pane, and "rolls up" the pane-aggregates to computer window-aggregates. Our experimental study shows that using panes has significant performance benefits.

/pdf/no-pane-no-gain-efficient-evaluation-of-sliding-window-3auwmsyror.pdf

No pane, no gain: efficient evaluation of sliding-window aggregates over data streams

Many stream-processing systems enforce an order on data streams during query evaluation to help unblock blocking operators and purge state from stateful operators. Such in-order processing (IOP) systems not only must enforce order on input streams, but also require that query operators preserve order. This order-preserving requirement constrains the implementation of stream systems and incurs significant performance penalties, particularly for memory consumption. Especially for high-performance, potentially distributed stream systems, the cost of enforcing order can be prohibitive. We introduce a new architecture for stream systems, out-of-order processing (OOP), that avoids ordering constraints. The OOP architecture frees stream systems from the burden of order maintenance by using explicit stream progress indicators, such as punctuation or heartbeats, to unblock and purge operators. We describe the implementation of OOP stream systems and discuss the benefits of this architecture in depth. For example, the OOP approach has proven useful for smoothing workload bursts caused by expensive end-of-window operations, which can overwhelm internal communication paths in IOP approaches. We have implemented OOP in two stream systems, Gigascope and NiagaraST. Our experimental study shows that the OOP approach can significantly outperform IOP in a number of aspects, including memory, throughput and latency.

/pdf/out-of-order-processing-a-new-architecture-for-high-5ga241as9x.pdf

Out-of-order processing: a new architecture for high-performance stream systems

To address increasing traffic congestion and its associated consequences, traffic managers are turning to intelligent transportation management. The latte project is extending data stream technology to handle queries that combine live streams with large data archives, motivated by needs in the Intelligent Transportation Systems (ITS) domain. In particular, we focus on queries that combine live data streams with large data archives. We demonstrate such stream-archive queries via the travel-time estimation problem. The demonstration uses the new latte system which has been developed using the NiagaraST stream processing system and the PORTAL transportation data archive.

/pdf/travel-time-estimation-using-niagarast-and-latte-50fjgcnpyl.pdf

Travel time estimation using NiagaraST and latte

Memory efficiency is important for processing high-volume data streams. Previous stream-aggregation methods can exhibit excessive memory overhead in the presence of skewed data distributions. Further, data skew is a common feature of massive data streams. The authors introduce the AdaptWID algorithm, which uses adaptive processing to cope with time-varying data skew. AdaptWID models the memory usage of alternative aggregation algorithms and selects between them at runtime on a group-by-group basis. The authors' experimental study using the NiagaraST stream system verifies that the adaptive algorithm improves memory usage while maintaining execution cost and latency comparable to existing implementations.

AdaptWID: An Adaptive, Memory-Efficient Window Aggregation Implementation

high-speed data streams may overwhelm the capabilities of stream processing systems; techniques such as data prioritization, avoidance of unnecessary processing and on- demand result production may be necessary to reduce processing requirements. However, the dynamic nature of data streams, in terms of both rate and content, makes the application of such techniques challenging. Such techniques have been addressed in the context of static and centralized query optimization; however, they have not been fully addressed for data-stream management systems. In this work, we present a comprehensive framework designed to support prioritization, avoidance of unnecessary work, and on-demand result production over distributed, unreliable, bursty, disordered data sources, typical of many streams. We propose a form of inter-operator feedback, which flows against the stream direction, to communicate the information needed to enable execution of these techniques. This feedback leverages punctuations to describe the subsets of interest. We identify potential sources of feedback information, characterize new types of punctuation to support feedback, and describe the roles of producers, exploiters, and relayers of feedback that query operators may implement. We also present initial experimental observations using the NiagaraST data-stream system.

Inter-Operator Feedback in Data Stream Management Systems via Punctuation

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