Many distributed workloads in today's data centers are written in managed languages such as Java or Ruby. Examples include big data frameworks such as Hadoop, data stores such as Cassandra or applications such as the SOLR search engine. These workloads typically run across many independent language runtime systems on different nodes. This setup represents a source of inefficiency, as these language runtime systems are unaware of each other. For example, they may perform Garbage Collection at times that are locally reasonable but not in a distributed setting. We address these problems by introducing the concept of a Holistic Runtime System that makes runtime-level decisions for the entire distributed application rather than locally. We then present Taurus, a Holistic Runtime System prototype. Taurus is a JVM drop-in replacement, requires almost no configuration and can run unmodified off-the-shelf Java applications. Taurus enforces user-defined coordination policies and provides a DSL for writing these policies. By applying Taurus to Garbage Collection, we demonstrate the potential of such a system and use it to explore coordination strategies for the runtime systems of real-world distributed applications, to improve application performance and address tail-latencies in latency-sensitive workloads.

/pdf/taurus-a-holistic-language-runtime-system-for-coordinating-o0oekgv8kp.pdf

Taurus: A Holistic Language Runtime System for Coordinating Distributed Managed-Language Applications

Apache Hive is an open-source relational database system for analytic big-data workloads. In this paper we describe the key innovations on the journey from batch tool to fully fledged enterprise data warehousing system. We present a hybrid architecture that combines traditional MPP techniques with more recent big data and cloud concepts to achieve the scale and performance required by today's analytic applications. We explore the system by detailing enhancements along four main axis: Transactions, optimizer, runtime, and federation. We then provide experimental results to demonstrate the performance of the system for typical workloads and conclude with a look at the community roadmap.

pdf/apache-hive-from-mapreduce-to-enterprise-grade-big-data-1oj6hftma6.pdf

Apache Hive: From MapReduce to Enterprise-grade Big Data Warehousing

Abstract In the age of the Internet of Things and social media platforms, huge amounts of digital data are generated by and collected from many sources, including sensors, mobile devices, wearable trackers and security cameras. This data, commonly referred to as Big Data, is challenging current storage, processing, and analysis capabilities. New models, languages, systems and algorithms continue to be developed to effectively collect, store, analyze and learn from Big Data. Most of the recent surveys provide a global analysis of the tools that are used in the main phases of Big Data management (generation, acquisition, storage, querying and visualization of data). Differently, this work analyzes and reviews parallel and distributed paradigms, languages and systems used today to analyze and learn from Big Data on scalable computers. In particular, we provide an in-depth analysis of the properties of the main parallel programming paradigms (MapReduce, workflow, BSP, message passing, and SQL-like) and, through programming examples, we describe the most used systems for Big Data analysis (e.g., Hadoop, Spark, and Storm). Furthermore, we discuss and compare the different systems by highlighting the main features of each of them, their diffusion (community of developers and users) and the main advantages and disadvantages of using them to implement Big Data analysis applications. The final goal of this work is to help designers and developers in identifying and selecting the best/appropriate programming solution based on their skills, hardware availability, application domains and purposes, and also considering the support provided by the developer community. 

/pdf/programming-big-data-analysis-principles-and-solutions-1hcgoiot.pdf

Programming big data analysis: principles and solutions

This article aims to implement a prototype screening system to identify flooding-related photos from social media. These photos, associated with their geographic locations, can provide free, timely, and reliable visual information about flood events to the decision-makers. This screening system, designed for application to social media images, includes several key modules: tweet/image downloading, flooding photo detection, and a WebGIS application for human verification. In this study, a training dataset of 4800 flooding photos was built based on an iterative method using a convolutional neural network (CNN) developed and trained to detect flooding photos. The system was designed in a way that the CNN can be re-trained by a larger training dataset when more analyst-verified flooding photos are being added to the training set in an iterative manner. The total accuracy of flooding photo detection was 93% in a balanced test set, and the precision ranges from 46–63% in the highly imbalanced real-time tweets. The system is plug-in enabled, permitting flexible changes to the classification module. Therefore, the system architecture and key components may be utilized in other types of disaster events, such as wildfires, earthquakes for the damage/impact assessment.

Prototyping a Social Media Flooding Photo Screening System Based on Deep Learning

The constant flux of data and queries alike has been pushing the boundaries of data analysis systems. The increasing size of raw data files has made data loading an expensive operation that delays the data-to-insight time. To alleviate the loading cost, in situ query processing systems operate directly over raw data and offer instant access to data. At the same time, analytical workloads have increasing number of queries. Typically, each query focuses on a constantly shifting—yet small—range. As a result, minimizing the workload latency requires the benefits of indexing in in situ query processing. In this paper, we present an online partitioning and indexing scheme, along with a partitioning and indexing tuner tailored for in situ querying engines. The proposed system design improves query execution time by taking into account user query patterns, to (i) partition raw data files logically and (ii) build lightweight partition-specific indexes for each partition. We build an in situ query engine called Slalom to showcase the impact of our design. Slalom employs adaptive partitioning and builds non-obtrusive indexes in different partitions on-the-fly based on lightweight query access pattern monitoring. As a result of its lightweight nature, Slalom achieves efficient query processing over raw data with minimal memory consumption. Our experimentation with both microbenchmarks and real-life workloads shows that Slalom outperforms state-of-the-art in situ engines and achieves comparable query response times with fully indexed DBMS, offering lower cumulative query execution times for query workloads with increasing size and unpredictable access patterns.

Adaptive partitioning and indexing for in situ query processing

Impala von Cloudera ist ein modernes, massiv paralleles Datenbanksystem, welches von Grund auf fur die Bedurfnisse und Anforderungen einer Big Data Umgebung wie Hadoop entworfen wurde. Das Ziel von Impala ist es, klassische SQL-Abfragen mit geringer Latenz und Laufzeit auszufuhren, so wie man es von typischen BI/DW Losungen gewohnt ist. Gleichzeitig sollen dabei sehr grose Quelldaten in Hadoop gelesen werden, ohne dass ein weiterer Extraktionsprozess in zusatzliche Systemlandschaften notwendig ist. Dieses Kapitel soll einen Uberblick uber Impala aus der Benutzerperspektive geben und detaillierter auf die Hauptkomponenten und deren Entwurfsentscheidungen eingehen. Zusatzlich werden wir einen Geschwindigkeitsvergleich mit anderen bekannten SQL-auf-Hadoop Losungen vorstellen, der den besonderen Ansatz von Impala unterstreicht.

Impala: Eine moderne, quellen-offene SQL Engine für Hadoop

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Impala: Eine moderne, quellen-offene SQL Engine für Hadoop