Tachyon: Reliable, Memory Speed Storage for Cluster Computing Frameworks
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Is 'Distributed' Worth It? Benchmarking Apache Spark with Mesos
Is `Distributed' worth it? Benchmarking Apache Spark with Mesos N. Satra (ns532) January 13, 2015 Abstract A lot of research focus lately has been on building bigger dis- tributed systems to handle `Big Data' problems. This paper exam- ines whether typical problems for web-scale companies really benefits from the parallelism offered by these systems, or can be handled by a single machine without synchronisation overheads. Repeated runs of a movie review sentiment analysis task in Apache Spark were carried out using Apache Mesos and Mesosphere, on Google Compute Engine clusters of various sizes. Only a marginal improvement in run-time was observed on a distributed system as opposed to a single node. 1 Introduction Research in high performance computing and `big data' has recently focussed on distributed computing. The reason is three-pronged: • The rapidly decreasing cost of commodity machines, compared to the slower decline in prices of high performance or supercomputers. • The availability of cloud environments like Amazon EC2 or Google Compute Engine that let users access large numbers of computers on- demand, without upfront investment. • The development of frameworks and tools that provide easy-to-use id- ioms for distributed computing and managing such large clusters of machines. Large corporations like Google or Yahoo are working on petabyte-scale problems which simply can't be handled by single computers [9]. However, 1 smaller companies and research teams with much more manageable sizes of data have jumped on the bandwagon, using the tools built by the larger com- panies, without always analysing the performance tradeoffs. It has reached the stage where researchers are suggesting using the same MapReduce idiom hammer on all problems, whether they are nails or not [7]. -
Comparison of Spark Resource Managers and Distributed File Systems
2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) Comparison of Spark Resource Managers and Distributed File Systems Solmaz Salehian Yonghong Yan Department of Computer Science & Engineering, Department of Computer Science & Engineering, Oakland University, Oakland University, Rochester, MI 48309-4401, United States Rochester, MI 48309-4401, United States [email protected] [email protected] Abstract— The rapid growth in volume, velocity, and variety Section 4 provides information of different resource of data produced by applications of scientific computing, management subsystems of Spark. In Section 5, Four commercial workloads and cloud has led to Big Data. Traditional Distributed File Systems (DFSs) are reviewed. Then, solutions of data storage, management and processing cannot conclusion is included in Section 6. meet demands of this distributed data, so new execution models, data models and software systems have been developed to address II. CHALLENGES IN BIG DATA the challenges of storing data in heterogeneous form, e.g. HDFS, NoSQL database, and for processing data in parallel and Although Big Data provides users with lot of opportunities, distributed fashion, e.g. MapReduce, Hadoop and Spark creating an efficient software framework for Big Data has frameworks. This work comparatively studies Apache Spark many challenges in terms of networking, storage, management, distributed data processing framework. Our study first discusses analytics, and ethics [5, 6]. the resource management subsystems of Spark, and then reviews Previous studies [6-11] have been carried out to review open several of the distributed data storage options available to Spark. Keywords—Big Data, Distributed File System, Resource challenges in Big Data. -
Alluxio: a Virtual Distributed File System by Haoyuan Li A
Alluxio: A Virtual Distributed File System by Haoyuan Li A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Ion Stoica, Co-chair Professor Scott Shenker, Co-chair Professor John Chuang Spring 2018 Alluxio: A Virtual Distributed File System Copyright 2018 by Haoyuan Li 1 Abstract Alluxio: A Virtual Distributed File System by Haoyuan Li Doctor of Philosophy in Computer Science University of California, Berkeley Professor Ion Stoica, Co-chair Professor Scott Shenker, Co-chair The world is entering the data revolution era. Along with the latest advancements of the Inter- net, Artificial Intelligence (AI), mobile devices, autonomous driving, and Internet of Things (IoT), the amount of data we are generating, collecting, storing, managing, and analyzing is growing ex- ponentially. To store and process these data has exposed tremendous challenges and opportunities. Over the past two decades, we have seen significant innovation in the data stack. For exam- ple, in the computation layer, the ecosystem started from the MapReduce framework, and grew to many different general and specialized systems such as Apache Spark for general data processing, Apache Storm, Apache Samza for stream processing, Apache Mahout for machine learning, Ten- sorflow, Caffe for deep learning, Presto, Apache Drill for SQL workloads. There are more than a hundred popular frameworks for various workloads and the number is growing. Similarly, the storage layer of the ecosystem grew from the Apache Hadoop Distributed File System (HDFS) to a variety of choices as well, such as file systems, object stores, blob stores, key-value systems, and NoSQL databases to realize different tradeoffs in cost, speed and semantics. -
Crystal: a Unified Cache Storage System for Analytical Databases
Crystal: A Unified Cache Storage System for Analytical Databases Dominik Durner∗ Badrish Chandramouli Yinan Li Technische Universität München Microsoft Research Microsoft Research [email protected] [email protected] [email protected] ABSTRACT 1.1 Challenges Cloud analytical databases employ a disaggregated storage model, These caching solutions usually operate as a black-box at the file or where the elastic compute layer accesses data persisted on remote block level for simplicity, employing standard cache replacement cloud storage in block-oriented columnar formats. Given the high policies such as LRU to manage the cache. In spite of their sim- latency and low bandwidth to remote storage and the limited size of plicity, these solutions have not solved several architectural and fast local storage, caching data at the compute node is important and performance challenges for cloud databases: has resulted in a renewed interest in caching for analytics. Today, • Every DBMS today implements its own caching layer tailored to each DBMS builds its own caching solution, usually based on file- its specific requirements, resulting in a lot of work duplication or block-level LRU. In this paper, we advocate a new architecture of across systems, reinventing choices such as what to cache, where a smart cache storage system called Crystal, that is co-located with to cache, when to cache, and how to cache. compute. Crystal’s clients are DBMS-specific “data sources” with • Databases increasingly support analytics over raw data formats push-down predicates. Similar in spirit to a DBMS, Crystal incorpo- such as CSV and JSON, and row-oriented binary formats such as rates query processing and optimization components focusing on Apache Avro [6] – all very popular in the data lake [16]. -
Scalable Collaborative Caching and Storage Platform for Data Analytics
Scalable Collaborative Caching and Storage Platform for Data Analytics by Timur Malgazhdarov A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto c Copyright 2018 by Timur Malgazhdarov Abstract Scalable Collaborative Caching and Storage Platform for Data Analytics Timur Malgazhdarov Master of Applied Science Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto 2018 The emerging Big Data ecosystem has brought about dramatic proliferation of paradigms for analytics. In the race for the best performance, each new engine enforces tight cou- pling of analytics execution with caching and storage functionalities. This one-for-all ap- proach has led to either oversimplifications where traditional functionality was dropped or more configuration options that created more confusion about optimal settings. We avoid user confusion by following an integrated multi-service approach where we assign responsibilities to decoupled services. In our solution, called Gluon, we build a collab- orative cache tier that connects state-of-art analytics engines with a variety of storage systems. We use both open-source and proprietary technologies to implement our archi- tecture. We show that Gluon caching can achieve 2.5x-3x speedup when compared to uncustomized Spark caching while displaying higher resource utilization efficiency. Fi- nally, we show how Gluon can integrate traditional storage back-ends without significant performance loss when compared to vanilla analytics setups. ii Acknowledgements I would like to thank my supervisor, Professor Cristiana Amza, for her knowledge, guidance and support. It was my privilege and honor to work under Professor Amza's supervision. -
An Architecture for Fast and General Data Processing on Large Clusters
An Architecture for Fast and General Data Processing on Large Clusters Matei Zaharia Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2014-12 http://www.eecs.berkeley.edu/Pubs/TechRpts/2014/EECS-2014-12.html February 3, 2014 Copyright © 2014, by the author(s). All rights reserved. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. An Architecture for Fast and General Data Processing on Large Clusters by Matei Alexandru Zaharia A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the GRADUATE DIVISION of the UNIVERSITY OF CALIFORNIA, BERKELEY Committee in charge: Professor Scott Shenker, Chair Professor Ion Stoica Professor Alexandre Bayen Professor Joshua Bloom Fall 2013 An Architecture for Fast and General Data Processing on Large Clusters Copyright c 2013 by Matei Alexandru Zaharia Abstract An Architecture for Fast and General Data Processing on Large Clusters by Matei Alexandru Zaharia Doctor of Philosophy in Computer Science University of California, Berkeley Professor Scott Shenker, Chair The past few years have seen a major change in computing systems, as growing data volumes and stalling processor speeds require more and more applications to scale out to distributed systems. -
Thesis Enabling Autoscaling for In-Memory Storage In
THESIS ENABLING AUTOSCALING FOR IN-MEMORY STORAGE IN CLUSTER COMPUTING FRAMEWORK Submitted by Bibek Raj Shrestha Department of Computer Science In partial fulfillment of the requirements For the Degree of Master of Science Colorado State University Fort Collins, Colorado Spring 2019 Master’s Committee: Advisor: Sangmi Lee Pallickara Shrideep Pallickara Stephen C. Hayne Copyright by Bibek Raj Shrestha 2019 All Rights Reserved ABSTRACT ENABLING AUTOSCALING FOR IN-MEMORY STORAGE IN CLUSTER COMPUTING FRAMEWORK IoT enabled devices and observational instruments continuously generate voluminous data. A large portion of these datasets are delivered with the associated geospatial locations. The increased volumes of geospatial data, alongside the emerging geospatial services, pose computational chal- lenges for large-scale geospatial analytics. We have designed and implemented STRETCH, an in-memory distributed geospatial storage that preserves spatial proximity and enables proactive autoscaling for frequently accessed data. STRETCH stores data with a delayed data dispersion scheme that incrementally adds data nodes to the storage system. We have devised an autoscaling feature that proactively repartitions data to alleviate computational hotspots before they occur. We compared the performance of STRETCH with Apache Ignite and the results show that STRETCH provides up to 3 times the throughput when the system encounters hotspots. STRETCH is built on Apache Spark and Ignite and interacts with them at runtime. ii ACKNOWLEDGEMENTS I would like to thank my advisor, Dr. Sangmi Lee Pallickara, for her enthusiastic support and sincere guidance that have resulted in driving this research project to completion. I would also like to thank my thesis committee members, Dr. Shrideep Pallickara and Dr. -
Alluxio: a Virtual Distributed File System
Alluxio: A Virtual Distributed File System Haoyuan Li Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2018-29 http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-29.html May 7, 2018 Copyright © 2018, by the author(s). All rights reserved. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. Acknowledgement I would like to thank my advisors, Ion Stoica and Scott Shenker, for their support and guidance throughout this journey, and for providing me with great opportunities. They are both inspiring mentors, and continue to lead by example. They always challenge me to push ideas and work further, and share their advice and experience on life and research. Being able to learn from both of them has been my great fortune. Alluxio: A Virtual Distributed File System by Haoyuan Li A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Ion Stoica, Co-chair Professor Scott Shenker, Co-chair Professor John Chuang Spring 2018 Alluxio: A Virtual Distributed File System Copyright 2018 by Haoyuan Li 1 Abstract Alluxio: A Virtual Distributed File System by Haoyuan Li Doctor of Philosophy in Computer Science University of California, Berkeley Professor Ion Stoica, Co-chair Professor Scott Shenker, Co-chair The world is entering the data revolution era. -
Thesis, We Describe Two Main Areas That Lack Support for Long-Running Applications in Traditional System Designs
Supporting Long-Running Applications in Shared Compute Clusters Panagiotis Garefalakis Department of Computing Imperial College London This dissertation is submitted for the degree of Doctor of Philosophy June 2020 Abstract Clusters that handle data-intensive workloads at a data-centre scale have become commonplace. In this setting, clusters are typically shared across several users and applications, and consolidate workloads that range from traditional analytics applications to critical services, stream processing, machine learning, and complex data processing applications. This constitutes a growing class of applications called long-running, consisting of containers that are used for durations ranging from hours to months. Even though long-running applications occupy a significant amount of resources in shared compute clusters today, there is currently rudimentary systems support that not only hinders application performance and resilience but also decreases cluster resource efficiency i.e., the effective utility extracted from cluster resources. In this thesis, we describe two main areas that lack support for long-running applications in traditional system designs. First, the way modern data processing frameworks execute complex computation tasks as part of shared long-running containers is broken. Even though these frameworks enable users to combine different types of computation as part of the same application using high-level programming interfaces, they ignore their diverse latency and throughput requirements during execution. Second, existing systems that allocate resources for long-running applications in the form of containers lack an expressive interface that can capture their placement requirements in shared compute clusters. Such placements can be expressed by means of complex constraints and are critical for both the performance and resilience of long-running applications. -
Performance Tuning and Evaluation of Iterative Algorithms in Spark
Performance Tuning and Evaluation of Iterative Algorithms in Spark Janani Gururam Department of Computer Science University of Maryland College Park, MD 20742 [email protected] Abstract. Spark is a widely used distributed, open-source framework for machine learning, relational queries, graph analytics and stream process- ing. This paper discusses key features of Spark that differentiate it from other MapReduce frameworks, focusing specifically on how iterative algo- rithms are implemented and optimized. We then analyze the impact of Resilient Distributed Dataset (RDD) caching, Serialization, Disk I/O, and other parameter considerations to improve performance in Spark. Finally, we discuss benchmarking tools currently available to Spark users. While there are several general purpose tools available, we do note that only a few Spark-specific solutions exist, and do not fully bridge the gap between workload characterization and performance implications. 1 Introduction Spark is an open-source, in-memory distributed framework for big data processing. The core engine of Spark underlays several modules for specialized analytical computation, including MLlib for machine learning applications, GraphX for graph analytics, SparkSQL for queries on structured, relational data, and Spark Streaming [25]. Spark provides several APIs for languages like Java, Scala, R, and Python. The base data structure in Spark is a Resilient Distributed Dataset(RDD), an immutable, dis- tributed collection of records. There are two types of operations in Spark: transformations, which perform operations on an input RDD to produce one or more new RDDs, and actions, which launch the series of transformations to return the final result RDD. Operations in Spark are lazily evaluated. -
A Machine Learning Data Processing Framework
tf.data: A Machine Learning Data Processing Framework Derek G. Murray Jiří Šimša Lacework* Google [email protected] [email protected] Ana Klimovic Ihor Indyk ETH Zurich* Google [email protected] [email protected] ABSTRACT Training machine learning models requires feeding input data for models to ingest. Input pipelines for machine learning jobs are often challenging to implement efficiently as they require reading large volumes of data, applying complex transformations, and transfer- ring data to hardware accelerators while overlapping computation and communication to achieve optimal performance. We present tf.data, a framework for building and executing efficient input pipelines for machine learning jobs. The tf.data API provides op- erators that can be parameterized with user-defined computation, composed, and reused across different machine learning domains. Figure 1: CDF showing the fraction of compute time that These abstractions enable users to focus on the application logic millions of ML training jobs executed in our fleet over one of data processing, while tf.data’s runtime ensures that pipelines month spend in the input pipeline. 20% of jobs spend more run efficiently. than a third of their compute time ingesting data. We demonstrate that input pipeline performance is critical to the end-to-end training time of state-of-the-art machine learning models. tf.data delivers the high performance required, while 1 INTRODUCTION avoiding the need for manual tuning of performance knobs. We Data is the lifeblood of machine learning (ML). Training ML models show that tf.data features, such as parallelism, caching, static op- requires steadily pumping examples for models to ingest and learn timizations, and optional non-deterministic execution are essential from. -
Scaling Data Mining in Massively Parallel Dataflow Systems
Scaling Data Mining in Massively Parallel Dataflow Systems vorgelegt von Dipl.-Inf. Sebastian Schelter aus Selb der Fakult¨atIV - Elektrotechnik und Informatik der Technischen Universit¨atBerlin zur Erlangung des akademischen Grades Doktor der Ingenieurwissenschaften - Dr. Ing. - genehmigte Dissertation Promotionsausschuss: Gutachter: Prof. Dr. Volker Markl Database Systems and Information Management Group, TU Berlin Prof. Dr. Klaus Robert M¨uller Machine Learning and Intelligent Data Analysis Group, TU Berlin Prof. Reza Zadeh, PhD Institute for Computational & Mathematical Engineering, Stanford University Berlin 2015 D 83 Acknowledgements I would like to express my gratitude to the people who accompanied me in the course of my studies and helped me shape this thesis. I'd like to first and foremost thank my advisor Volker Markl. He introduced me to the academic world, gave me freedom in choosing my research topics, and motivated me many times to leave my comfort zone and take on the challenges involved in conducting a PhD. Furthermore, I want to express my appreciation to Klaus-Robert M¨ullerand Reza Zadeh for agreeing to review this thesis. Very importantly, I'd like to thank my colleagues Christoph Boden, Max Heimel, Alan Akbik, Stephan Ewen, Fabian H¨uske, Kostas Tzoumas, Asterios Katsifodimos and Isabel Drost at the Database and Information Systems Group of TU Berlin, who helped me advance my research through collaboration, advice and discussions. I also enjoyed working with the DIMA students Christoph Nagel, Janani Chakkaradhari, and Till Rohrmann who conducted excellent master thesis in the context of my line of research. Furthermore, I'd like to thank my colleagues Mikio Braun, J´er^omeKunegis, J¨orgFliege and Mathias Peters for ideas and discussions in the research projects in which we worked together.