We present Burst, an analytic query system with a scalable and flexible approach to performing lowlatency ad hoc analysis over large complex datasets. The architecture consists of hardwareefficient scan techniques and a language facility to transform an extensible set of ad hoc declarative queries into imperative physical scan plans. These plans are multicast across all nodes/cores of a two level sharded/distributed ingestion, storage, and execution topology and executed. The first release of this system is the query engine behind the Flurry Explorer product. Here we explore the design details of that system as well as the incremental ingestion pipeline enhancement currently being implemented for the next major release.
A Query Model for Ad Hoc Queries using a Scanning ArchitectureFlurry, Inc.
Systems like Hadoop, Hbase and Hive allowed the world to take huge strides in managing and analyzing large amounts of data. Products like Flurry analytics make efficient use of large amounts of hardware using these tools to build statistics for hundreds of thousands of applications. However, these tools require the end user to first set up relevant analytics queries and then wait days for the results. If the results prompt new questions or the original query is not quite right, the user must rerun and wait again for the results.
We present the Burst system developed at Flurry to support low-latency single pass queries over very large and complex mobile application streams. We have created a data schema and query model that can answer very complex ad-hoc queries over data, and is highly parallelizable while maintaining low-latency. We implement these scans so that they are time and space efficient using the advanced disk scanning techniques provided by the underlying operating system.
This report describes how the Aucfanlab team used Azure’s Data Factory service to
implement the orchestration and monitoring of all data pipelines for our “Aucfan
Datalake” project.
Data analysis using hive ql & tableaupkale1708
The purpose of this study is to develop a system which will assist a user to determine if a location can be entitled as a “Safe” residence or not. The output will be based on an analysis carried out on the local crime history of the city. This involves examining a huge geolocation data and zeroing down to a single area. The area with majority crime incidents will be highlighted as Unsafe. Clicking/hovering on a single record will display name, associated crime and its rank depending on number of crimes occurred. Big Data Hadoop and Hive systems are implemented in Azure for the analysis.
An overview of crime report and analysis shows a significant amount of information related to crime. Multiple factors need to be considered while studying the different aspects of crime. These multiple measures are found in Uniform Crime Reports data and the National Crime Victimization Survey, a survey that interrogates the victim about their experience. Our paper depicts the nature and characteristics of crime using Hadoop Big Data systems, especially Hive in Azure. Besides, the map of the Geo-location presents which area is safe or unsafe. The results of different Hive queries are visualized using Tableau.
Cache mechanism to avoid dulpication of same thing in hadoop system to speed ...eSAT Journals
Abstract Cloud computing provides a proper platform for hosting large-scale data-intensive applications. MapReduce is a programming model as well as a framework that supports the model. The main idea of the MapReduce model is to hide details of parallel execution and allow users to focus only on data processing strategies. Hadoop is an open-source implementation for MapReduce. For storage and analysis of online or streaming data which is big in size. Most organization are moving toward Apaches Hadoop HDFS. Applications like log processors, search engines etc. ueses hadoop Map reduce for computing and HDFS for storage. Hadoop is popular for analysis, storage and processing of very large data but require to make changes in hadoop system. There is no mechanism to identify duplicate computations which increase processing time and unnecessary data transmission .To co-locate related files by considering content and using locality sensitive hashing algorithm. By storing related files in same cluster using cache mechanism which improve data locality mechanism and avoids repeated execution of task, both helps to speed up execution of hadoop. Keywords-Distributed file system, Datanode, Locality Sensitive Hashing
A Query Model for Ad Hoc Queries using a Scanning ArchitectureFlurry, Inc.
Systems like Hadoop, Hbase and Hive allowed the world to take huge strides in managing and analyzing large amounts of data. Products like Flurry analytics make efficient use of large amounts of hardware using these tools to build statistics for hundreds of thousands of applications. However, these tools require the end user to first set up relevant analytics queries and then wait days for the results. If the results prompt new questions or the original query is not quite right, the user must rerun and wait again for the results.
We present the Burst system developed at Flurry to support low-latency single pass queries over very large and complex mobile application streams. We have created a data schema and query model that can answer very complex ad-hoc queries over data, and is highly parallelizable while maintaining low-latency. We implement these scans so that they are time and space efficient using the advanced disk scanning techniques provided by the underlying operating system.
This report describes how the Aucfanlab team used Azure’s Data Factory service to
implement the orchestration and monitoring of all data pipelines for our “Aucfan
Datalake” project.
Data analysis using hive ql & tableaupkale1708
The purpose of this study is to develop a system which will assist a user to determine if a location can be entitled as a “Safe” residence or not. The output will be based on an analysis carried out on the local crime history of the city. This involves examining a huge geolocation data and zeroing down to a single area. The area with majority crime incidents will be highlighted as Unsafe. Clicking/hovering on a single record will display name, associated crime and its rank depending on number of crimes occurred. Big Data Hadoop and Hive systems are implemented in Azure for the analysis.
An overview of crime report and analysis shows a significant amount of information related to crime. Multiple factors need to be considered while studying the different aspects of crime. These multiple measures are found in Uniform Crime Reports data and the National Crime Victimization Survey, a survey that interrogates the victim about their experience. Our paper depicts the nature and characteristics of crime using Hadoop Big Data systems, especially Hive in Azure. Besides, the map of the Geo-location presents which area is safe or unsafe. The results of different Hive queries are visualized using Tableau.
Cache mechanism to avoid dulpication of same thing in hadoop system to speed ...eSAT Journals
Abstract Cloud computing provides a proper platform for hosting large-scale data-intensive applications. MapReduce is a programming model as well as a framework that supports the model. The main idea of the MapReduce model is to hide details of parallel execution and allow users to focus only on data processing strategies. Hadoop is an open-source implementation for MapReduce. For storage and analysis of online or streaming data which is big in size. Most organization are moving toward Apaches Hadoop HDFS. Applications like log processors, search engines etc. ueses hadoop Map reduce for computing and HDFS for storage. Hadoop is popular for analysis, storage and processing of very large data but require to make changes in hadoop system. There is no mechanism to identify duplicate computations which increase processing time and unnecessary data transmission .To co-locate related files by considering content and using locality sensitive hashing algorithm. By storing related files in same cluster using cache mechanism which improve data locality mechanism and avoids repeated execution of task, both helps to speed up execution of hadoop. Keywords-Distributed file system, Datanode, Locality Sensitive Hashing
Big Data is an evolution of Business Intelligence (BI).
Whereas traditional BI relies on data warehouses limited in size
(some terabytes) and it hardly manages unstructured data and
real-time analysis, the era of Big Data opens up a new technological
period offering advanced architectures and infrastructures
allowing sophisticated analyzes taking into account these new
data integrated into the ecosystem of the business . In this article,
we will present the results of an experimental study on the performance
of the best framework of Big Analytics (Spark) with the
most popular databases of NoSQL MongoDB and Hadoop. The
objective of this study is to determine the software combination
that allows sophisticated analysis in real time.
A Review of Elastic Search: Performance Metrics and challengesrahulmonikasharma
The most important aspect of a search engine is the search. Elastic search is a highly scalable search engine that stores data in a structure, optimized for language based searches. When it comes to using Elastic search, there are lots of metrics engendered. By using Elastic search to index millions of code repositories as well as indexing critical event data, you can satisfy the search needs of millions of users while instantaneously providing strategic operational visions that help you iteratively improve customer service. In this paper we are going to study about Elastic searchperformance metrics to watch, important Elastic search challenges, and how to deal with them. This should be helpful to anyone new to Elastic search, and also to experienced users who want a quick start into performance monitoring of Elastic search.
PERFORMANCE EVALUATION OF SOCIAL NETWORK ANALYSIS ALGORITHMS USING DISTRIBUTE...Journal For Research
Analysis on Social Networking sites such as Facebook, Flickr and Twitter has long been a trending topic of fascination for data analysts, researchers and enthusiasts in the recent years to maximize the value of knowledge acquired from processing and analysis of the data. Apache Spark is an Open-source data-parallel computation engine that offers faster solutions compared to traditional Map-Reduce engines such as Apache Hadoop. This paper discusses the performance evaluation of Apache Spark for analyzing social network data. The performance of analysis varies significantly based on the algorithms being implemented. This is the reason to what makes this analysis worthwhile of evaluation with respect to their versatility and diverse nature in the dynamic field of Social Network Analysis. We compare performance of Apache Spark by evaluating the performance using various algorithms (PageRank, Connected Components, Counting Triangle, K-Means and Cosine Similarity) making efficient use of the Spark cluster.
Dynamic and repeatable transformation of existing Thesauri and Authority list...DESTIN-Informatique.com
Integrating applications & projects
= Dynamic & repeatable transformation of existing Thesauri and Authority lists into SKOS
+ Cross-tabulation of Concepts Linked Data
Presentation to the Linked Data Meeting
University College of London, September 14th 2010
by Christophe Dupriez, Destin SSEB, working for Belgium Poison Centre
Organizations adopt different databases for big data which is huge in volume and have different data models. Querying big data is challenging yet crucial for any business. The data warehouses traditionally built with On-line Transaction Processing (OLTP) centric technologies must be modernized to scale to the ever-growing demand of data. With rapid change in requirements it is important to have near real time response from the big data gathered so that business decisions needed to address new challenges can be made in a timely manner. The main focus of our research is to improve the performance of query execution for big data.
We discuss revise scheduling with streaming files warehouses, which blend the features of traditional files warehouses and also data supply systems. In our setting, external sources push append-only files streams into your warehouse with many inter introduction times. While classic data warehouses are normally refreshed during downtimes, streaming warehouses usually are updated while new files arrive. We design the streaming warehouse revise problem as a scheduling trouble, where jobs correspond to processes which load brand-new data in to tables, and whoever objective is usually to minimize files staleness with time. We next propose the scheduling framework that grips the troubles encountered with a stream manufacturing facility: view hierarchies and also priorities, files consistency, lack of ability to pre-empt changes, heterogeneity connected with update jobs brought on by different inter introduction times and also data quantities among various sources, and also transient clog. A story feature in our framework will be that arranging decisions tend not to depend with properties connected with update jobs such as deadlines, but instead on the effects of revise jobs with data staleness.
Getting Started With Mobile Analytics: iOS Connect Santa Clara Meetup | Flurr...Flurry, Inc.
How to get started with Mobile Analytics:
- How to determine KPIs
- Who are my users
- Which campaigns are working?
- User behavior & lifecycle tracking
- Getting Started with Flurry Analytics
Head of Flurry Simon Khalaf breaks down 2014 app usage in his annual State of AppNation presentation. Learn why messaging will become the operating platform on mobile, why teens' behavior signals the beginning of the end of PC, and how retail in our pocket is changing everything!
Best Strategy for Developing App Architecture and High Quality AppFlurry, Inc.
Yahoo has been developing several success mobile apps in Taiwan. We’re going to share our best strategy for developing mobile apps. Learning how to use YDevelopKit to save your development resource and using DevOps to retain high quality result simultaneously.
Big Data is an evolution of Business Intelligence (BI).
Whereas traditional BI relies on data warehouses limited in size
(some terabytes) and it hardly manages unstructured data and
real-time analysis, the era of Big Data opens up a new technological
period offering advanced architectures and infrastructures
allowing sophisticated analyzes taking into account these new
data integrated into the ecosystem of the business . In this article,
we will present the results of an experimental study on the performance
of the best framework of Big Analytics (Spark) with the
most popular databases of NoSQL MongoDB and Hadoop. The
objective of this study is to determine the software combination
that allows sophisticated analysis in real time.
A Review of Elastic Search: Performance Metrics and challengesrahulmonikasharma
The most important aspect of a search engine is the search. Elastic search is a highly scalable search engine that stores data in a structure, optimized for language based searches. When it comes to using Elastic search, there are lots of metrics engendered. By using Elastic search to index millions of code repositories as well as indexing critical event data, you can satisfy the search needs of millions of users while instantaneously providing strategic operational visions that help you iteratively improve customer service. In this paper we are going to study about Elastic searchperformance metrics to watch, important Elastic search challenges, and how to deal with them. This should be helpful to anyone new to Elastic search, and also to experienced users who want a quick start into performance monitoring of Elastic search.
PERFORMANCE EVALUATION OF SOCIAL NETWORK ANALYSIS ALGORITHMS USING DISTRIBUTE...Journal For Research
Analysis on Social Networking sites such as Facebook, Flickr and Twitter has long been a trending topic of fascination for data analysts, researchers and enthusiasts in the recent years to maximize the value of knowledge acquired from processing and analysis of the data. Apache Spark is an Open-source data-parallel computation engine that offers faster solutions compared to traditional Map-Reduce engines such as Apache Hadoop. This paper discusses the performance evaluation of Apache Spark for analyzing social network data. The performance of analysis varies significantly based on the algorithms being implemented. This is the reason to what makes this analysis worthwhile of evaluation with respect to their versatility and diverse nature in the dynamic field of Social Network Analysis. We compare performance of Apache Spark by evaluating the performance using various algorithms (PageRank, Connected Components, Counting Triangle, K-Means and Cosine Similarity) making efficient use of the Spark cluster.
Dynamic and repeatable transformation of existing Thesauri and Authority list...DESTIN-Informatique.com
Integrating applications & projects
= Dynamic & repeatable transformation of existing Thesauri and Authority lists into SKOS
+ Cross-tabulation of Concepts Linked Data
Presentation to the Linked Data Meeting
University College of London, September 14th 2010
by Christophe Dupriez, Destin SSEB, working for Belgium Poison Centre
Organizations adopt different databases for big data which is huge in volume and have different data models. Querying big data is challenging yet crucial for any business. The data warehouses traditionally built with On-line Transaction Processing (OLTP) centric technologies must be modernized to scale to the ever-growing demand of data. With rapid change in requirements it is important to have near real time response from the big data gathered so that business decisions needed to address new challenges can be made in a timely manner. The main focus of our research is to improve the performance of query execution for big data.
We discuss revise scheduling with streaming files warehouses, which blend the features of traditional files warehouses and also data supply systems. In our setting, external sources push append-only files streams into your warehouse with many inter introduction times. While classic data warehouses are normally refreshed during downtimes, streaming warehouses usually are updated while new files arrive. We design the streaming warehouse revise problem as a scheduling trouble, where jobs correspond to processes which load brand-new data in to tables, and whoever objective is usually to minimize files staleness with time. We next propose the scheduling framework that grips the troubles encountered with a stream manufacturing facility: view hierarchies and also priorities, files consistency, lack of ability to pre-empt changes, heterogeneity connected with update jobs brought on by different inter introduction times and also data quantities among various sources, and also transient clog. A story feature in our framework will be that arranging decisions tend not to depend with properties connected with update jobs such as deadlines, but instead on the effects of revise jobs with data staleness.
Getting Started With Mobile Analytics: iOS Connect Santa Clara Meetup | Flurr...Flurry, Inc.
How to get started with Mobile Analytics:
- How to determine KPIs
- Who are my users
- Which campaigns are working?
- User behavior & lifecycle tracking
- Getting Started with Flurry Analytics
Head of Flurry Simon Khalaf breaks down 2014 app usage in his annual State of AppNation presentation. Learn why messaging will become the operating platform on mobile, why teens' behavior signals the beginning of the end of PC, and how retail in our pocket is changing everything!
Best Strategy for Developing App Architecture and High Quality AppFlurry, Inc.
Yahoo has been developing several success mobile apps in Taiwan. We’re going to share our best strategy for developing mobile apps. Learning how to use YDevelopKit to save your development resource and using DevOps to retain high quality result simultaneously.
Yahoo Mobile Developer Conference NYC - Mobile Revolution: Seven Years OnFlurry, Inc.
Yahoo SVP of Publishing Products Simon Khalaf's keynote presentation from the NYC Yahoo Mobile Developer Conference on Aug 26, 2015. Mobile app industry insights and trends delivered from Flurry Analytics and the 720,000 apps we track.
Yahoo Mobile Meetup: Bangalore & Hyderabad December 2015Flurry, Inc.
Following on from the success of our first Mobile Meetup in June, we are excited to host our second mobile developer event in Bangalore, India.
During this event, we will discuss the latest around the yahoo Mobile Developer Suit, with a deep dive into Flurry Analytics, and the Yahoo App Publishing Platform. We have some our best Flurry and YAP product and engineering people coming in from the US to speak to you.
Yahoo Mobile Developer Conference: State of MobileFlurry, Inc.
Flurry CEO Simon Khalaf's 2015 State of Mobile presentation from the Yahoo Mobile Developer Conference. The latest stats from Flurry Analytics, including the growth of the $3.3 TRILLION mobile economy!
2016 Yahoo Taiwan Mobile Developer Conference Flurry, Inc.
We have hosted the 1st Yahoo Mobile Developer Conference (YMDC) in Taiwan. Please refer to the presentation to learn more about the latest Yahoo's technologies provided for mobile developers.
Please go to developer.yahoo.com to learn more!
Learn about the latest enhancements to the Yahoo Mobile Developer Suite, including Flurry Analytics and Yahoo App Publishing. We have invited our partner, PicCollage(拼貼趣) to share how they leverage Flurry Analytics and Explorer to optimize their App performance. Also, we have Cheetah Mobile(獵豹移動), one of the fastest growing app publisher in the world, will share how they leverage Native Ads to build a sustainable business model.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
A whitepaper is about How big data engines are used for exploring and preparing data, building pipelines, and delivering data sets to ML applications.
https://www.qubole.com/resources/white-papers/big-data-engineering-for-machine-learning
Deep Dive on ElasticSearch Meetup event on 23rd May '15 at www.meetup.com/abctalks
Agenda:
1) Introduction to NOSQL
2) What is ElasticSearch and why is it required
3) ElasticSearch architecture
4) Installation of ElasticSearch
5) Hands on session on ElasticSearch
Streaming Data Analytics with ksqlDB and Superset | Robert Stolz, PresetHostedbyConfluent
Streaming data systems have been growing rapidly in importance to the modern data stack. Kafka’s kSQL provides an interface for analytic tools that speak SQL. Apache Superset, the most popular modern open-source visualization and analytics solution, plugs into nearly any data source that speaks SQL, including Kafka. Here, we review and compare methods for connecting Kafka to Superset to enable streaming analytics use cases including anomaly detection, operational monitoring, and online data integration.
Presentation detailed about capabilities of In memory Analytic using Apache Spark. Apache Spark overview with programming mode, cluster mode with Mosos, supported operations and comparison with Hadoop Map Reduce. Elaborating Apache Spark Stack expansion like Shark, Streaming, MLib, GraphX
Near Real time Indexing Kafka Messages to Apache Blur using Spark StreamingDibyendu Bhattacharya
My presentation at recently concluded Apache Big Data Conference Europe about the Reliable Low Level Kafka Spark Consumer I developed and an use case of real time indexing to Apache Blur using this consumer
AI&BigData Lab 2016. Сарапин Виктор: Размер имеет значение: анализ по требова...GeeksLab Odessa
4.6.16 AI&BigData Lab
Upcoming events: goo.gl/I2gJ4H
Как устроить анализ данных 40 млн. человек за 5 лет так, чтобы это выглядело почти в реальном времени.
A whitepaper from qubole about the Tips on how to choose the best SQL Engine for your use case and data workloads
https://www.qubole.com/resources/white-papers/enabling-sql-access-to-data-lakes
Scaling JPA applications or deploying them to flexible resources can be a challenge. How do I scale, what is the impact on caching and how can I reuse resources? In this talk we will work through these challenges with real examples using JPA and EclipseLink. Exploring where and when to apply best practices and the many features available for caching, scalability, resource sharing and elastic deployments.
HPC and cloud distributed computing, as a journeyPeter Clapham
Introducing an internal cloud brings new paradigms, tools and infrastructure management. When placed alongside traditional HPC the new opportunities are significant But getting to the new world with micro-services, autoscaling and autodialing is a journey that cannot be achieved in a single step.
Railsplitter is a framework which significantly reduces development cost to expose a hierarchical data model as a production quality Create, Read, Update, and Delete (CRUD) web service. Railsplitter adopts JSON API [10] as the standard for the service definition given its focus on consumption by front-end developers. Inherent in the design of JSON API are capabilities that reduce the number of round trips from client to server to fetch or update data. Updates on disparate models can happen in a single request allowing the server to build atomicity guarantees. Rather than starting from scratch with a domain-specific language (DSL) to describe a data model, Railsplitter adopts Java Persistence API (JPA) [6] - a modeling definition that is rich and has a long tenure of proven provider implementations. Unlike other approaches, Railsplitter addresses the fundamental needs of flexible, model driven authorization, interoperability with client side applications, and test automation.
The Global Village: How Mobile Games Cross Borders, or Fail toFlurry, Inc.
Some entertainers, like Michael Jackson, become worldwide stars. But others stop at the border. With games, the same is true. We’ll delve into why. What appeals to a broader audience? What games work mainly in just one country, or spread to adjacent countries? Flurry captures analytics on more than 170,000 app makers and 150 billion play sessions a month.
At #Source14 (www.flurrysource14.com) on April 22, 2014, Flurry CEO and President Simon Khalaf presented "The Age of Living Mobile". This data-rich presentation for 500+ attendees covers mobile disruption industry-by-industry, the rise of mobile addicts and the massive business opportunities ahead. Video of his 20 minute talk is also available on YouTube: https://www.youtube.com/watch?v=N_gwwAay_vs&list=UU3CqvKG-iPJQr7isTLkvirQ
Simon Khalaf throws down the gauntlet at #Source13 with a data-packed presentation. "Ignore the Series A crunch. It's time to innovate. Disrupt an industry."
Flurry CEO Simon Khalaf presents growth trends in the mobile first economy on iOS and Android smartphones and tablets. Trends include time spent across: TV, Web and apps; time spent per app category and revenue growth. Simon also debunks investor skepticism about continued growth potential.
Flurry Presents at Digital Analytics Association Symposium - San Francisco, C...Flurry, Inc.
This presentation covers key insights across the mobile app economy including market size, addressable market, consumer trends, what non-mobile industries and categories are being disrupted by mobile apps, comparison of consumer behavior on media platforms vs. ad spending and an outlook for what's next as the industry matures.
Games on Smartphones & Tablets: Demand, Revenue, Cost, Business Model, Usage,...Flurry, Inc.
iOS and Android game insights presented at Smartphone and Tablet Gaming Summit in SF, garnered from Flurry and Activision mobile game publishing partnership. Insights and data include: market size; expected DAUs; revenue potential; demographic differences, etc. per game type / genre; usage behavior; spending behavior; demographic (age, gender) differences across tablet vs. smartphone form factor and game genres.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
A General Purpose Extensible Scanning Query Architecture for Ad Hoc Analytics
1.
A General Purpose Extensible Scanning Query Architecture for Ad Hoc Analytics
Erik Freed Brian Anderson
Flurry/Yahoo Flurry/Yahoo
erikfreed@yahooinc.com briananderson@yahooinc.com
Abstract
We present Burst, an analytic query system with a scalable and flexible approach to performing lowlatency ad hoc
analysis over large complex datasets. The architecture consists of hardwareefficient scan techniques and a
language facility to transform an extensible set of ad hoc declarative queries into imperative physical scan plans.
These plans are multicast across all nodes/cores of a two level sharded/distributed ingestion, storage, and execution
topology and executed. The first release of this system is the query engine behind the Flurry Explorer product. Here
we explore the design details of that system as well as the incremental ingestion pipeline enhancement currently
being implemented for the next major release.
NOTE: This copy has had performance numbers updated and is not the same as the one submitted to Tech Pulse.
1. Introduction
The promise of the Flurry Explorer Product is to invite the user into an unstructured interactive discovery session
where they can easily pose arbitrary offthecuff and potentially complex questions about end user behavior. If they
get back answers quickly enough then their next question starts a virtuous cycle of more targeted questions
continuously leading to more specific and valuable results. The first major release of the back end query engine
engineered to fully support this type of exploration was developed in the Flurry Analytics group in Q1 2015 and
delivered as part of a limited beta of the Explorer feature within Flurry Analytics. We successfully utilized a unique
hyper distributed/parallel/concurrent object tree scanning model with a simple daily batched ingestion system for
this limited audience. The next major release of this scanning architecture replaces the batched ingestion system
with a more scalable incremental data ingestion pipeline to expand the reach of Explorer to all Flurry customers.
Here we present the architectural basis and specifics of the previous and upcoming release.
2. Background
For those of us who have spent any time with production scale SQL databases, seeing large table scans being sorted
and joined in a query plan is cause for panic. We can only relax once we find a way to constrain that query and/or
implement heavyweight indices so the query transforms into pure index lookups and partial joins. However for
analytics the use cases are inherently unbounded, personalized, and constantly evolving while the corpora are
typically enormous. This makes adding indices intractable in most cases. These limitations forced us to reevaluate
our previous nemesis, the full table scan. We determined that if we could make the scans efficient enough, distribute
the scans across enough nodes and CPU cores, and develop a query language that could take an arbitrary ad hoc
analytic question and transform it into an instance of this hyper paralleldistributedconcurrent scan model, then we
would have an attractively simple general purpose model. We reasoned that this model would scale well not only in
terms of input size and general query complexity, but in terms of feature development time, risk, and effort.
page 1 of 7
2.
Explorer Burst: A General Purpose Extensible Scanning Query Architecture for Ad Hoc Analytics
3. Top Level View
The basic components of the Burst ecosystem are:
1. External Datasource(s)
2. Ingestion Subsystem
3. Data Model
4. Sample Store
5. Dataset Store
6. Query Subsystem
The previous release of Burst had a simplified batched ingestion model where the exporting Mapreduce jobs wrote
the entire history of a given mobile application’s event stream into new HDFS sequence files on a daily basis. These
datasets were then read into memory on demand as users posed queries. This initial beta pipeline design is being
replaced by the incremental version described in subsequent sections. The rest of the architecture described here is
as currently deployed.
Each of these components (other than the external data sources) are deployed on one or more clusters called a Cell
where each Cell is comprised of a master node, a failover master node and a set of worker nodes. Each Cell has its
own Apache Kafka [KAFKA], Apache HBase[HBASE], and Apache Spark[SPARK] clusters deployed. The Master
(and failover Master) node contains the master process for each of these systems as well as a Docker [DOCKER]
container populated with all of the Burst specific JVM service processes. The Worker nodes are populated only by
the associated Spark, HBase, and Kafka worker specific deployments. Burst does not itself deploy anything directly
onto Worker nodes.
4. Data Sources
Burst is inherently schema independent as well as agnostic to the specific technology of the external datasource.
However the data source must have the following basic characteristics:
1. it must be in a schema that can be expressed in the relationships and datatypes of the Burst Data Model
2. The external data model can be partitioned into two levels of well defined shards:
a. The first level is composed of a set of Domain instances that each represent a subset of data that is
the input to a single query e.g. for Flurry Explorer, this is a event stream associated with a single
‘Mobile Application’ or constructed ‘Mobile Application Group’. A query can only be executed
against a single Domain at a time.
b. The second level is a strict partitioning across a Domain creating order independent subsets of
Item instances that each has a well defined rooted acyclic object model (tree) that can be scanned
in a depth first, preferably time ordered, traversal. For Flurry Explorer, this is a single ‘Mobile
Device’, each of which has a set of time ordered ‘sessions’, each of which has a set of time
ordered ‘events’, each of which has a set of unordered keyvalue map ‘parameters’
page 2 of 7
3.
Explorer Burst: A General Purpose Extensible Scanning Query Architecture for Ad Hoc Analytics
3. The external data source physical form can be exported as both a periodic historical batch and a continuous
incremental update and fed to the Burst Kafka based Ingestion API. e.g. for Flurry this is our 2,000 node,
six petabyte, ~50 trillion mobile device events, evergrowing HBase cluster with custom MapReduce jobs
performing both initializing batch and daily incremental update feeds.
5. Ingestion
The new Burst Ingestion Subsystem design starts with a Kafka queues that provide a controlplane
(control/administration), and the dataplane (data feeds). The data source is responsible for sending and responding
to the controlplane, as well as feeding the dataplane in response to controlplane messages. An Apache Spark
based process model manages controlplane and dataplane operations. It is responsible for transforming the
schema of the external system into an appropriate Burst schema, updating the Sample Store as it arrives.
6. Data Model
The Burst Data Model has the following requirements/features/implementation details:
1. It is schema independent, but schema defined.
2. It is schema versioned, and supports heterogeneous versioned collections.
3. The data model/schema supports type structures, singular and plural structure reference relationships, value
collections, value maps, and atomic data types (boolean, byte, short, int, long, double, string)
4. The data model/schema inherently defines a tree with a well defined root as part of well defined traversal
5. Data is encoded in a single byte array where the disk storage encoding is identical to the inmemory format.
6. This encoding is an unrolled depth first traversal of the object tree as a linear sequence of bytes. The
reading from disk into memory and traversal scans are in the same exact byte order and thus can take direct
advantage of the OS disk mmap semantics with the associated high performance kernel buffer management
and aggressive prefetching. The data can be cached in memory or not depending on your preferences with
respect to repeated queries on identical datasets . 1
7. All interpretation of atomic data fields are done insitu within the byte array ondemand iff any given field
is accessed in a query. The data model structures are not ever deserialized and no ephemeral objects are
created. This is similar to columnar storage, as it eliminates much of the costs of accessing unused columns
in standard bulk serializing models, but along with a higher degree of inherent simplicity and attendant
efficiency. A truly adhoc system, where it is not known what fields will be accessed at what frequency, if
at all, is not an ideal columnar storage candidate.
8. Fetching, in memory storage, and scans of the data model generate zero JVM objects. They bypass the
JVM memory models as well. The byte sequence traversal is scanned using efficient stack based protocols
with data accesses performed via ‘unsafe off heap’ libraries. The problems associated with large JVM 2
heaps are minimized as none of this memory is actually ‘seen’ by the JVM. The JVM processes have quite
small heap sizes.
9. There are various optimizations for immutable encodings e.g. for value maps we store the keys and the
values as twin sorted arrays using a binary search to lookup key values. We also use dictionaries to reduce
string storage requirements.
1
Burst may support streaming query processing in a future release
2
‘Unsafe’ refers to a design pattern where Java code is written using the same techniques the Java libraries use to access non
JVM heap memory (e.g. Network & Disc IO). It is called unsafe because JVM manufacturers do not offer support for these lower
level libraries, even though they are extensively used and quite reliable.
page 3 of 7
4.
Explorer Burst: A General Purpose Extensible Scanning Query Architecture for Ad Hoc Analytics
7. Sample Store
The Burst architecture uses a Apache HBase keyvalue store, to reliably and efficiently store the continuous largely
unordered incremental feed of assorted Item updates from assorted Domains coming from one or more external data
sources. This data is stored in one of a plurality of tables each called a Province . Each arriving update is a new 3
cell, encoded in the Burst Data Model, in a row keyed by the specific Item, Domain and Channel in a single 4
Province table where the given Domain is hosted.
8. Dataset Store
For a query to be executed over a Domain, appropriate rows in the Sample Store and appropriate update cells for
each Item must scanned and transformed into a Dataset in Brio Data Model encoding. This transformation is called
melding and happens locally on each worker node. Each node creates and stores a single partition of the Dataset. 5
These partitions are the most recent ‘view’ of the data as a single byte array cached in local disc (magnetic or solid
state). When a query is executed, if the local Worker node has cached the partition, and if it is not considered ‘stale’
then it is read directly from disc and no meld is required. The melding can also customize the dataset by down
sampling items along with other forms of object tree filtering if it is desired to reduce the datasets size for
performance/resource utilization reasons. It is also possible to have more than one defined and reified custom
Dataset ‘view’ per Domain.
Caching
It is vital that the Dataset partitions be loaded into memory quickly and released aggressively in order to manage
expensive/limited DRAM resources efficiently. The load of a Dataset partition is a simple mmap()call of a single
file as a single byte array into offheap memory managed directly by the OS. The scan can proceed before the file
has been fully read due to the natural OS semantics of paged disc reads with linear order prefetching. Since there
3
Provinces are used to subdivide the overall dataset into separate tables so that efficient table operations can be used to manage,
move, and cleanup data as needed in manageable chunks.
4
An Ingestion API/Sample Store management artifact
5
i.e. without replication or fault tolerance. In the case of worker node failure, these dataset partitions are recreated on whatever
replica location is targeted by HBase/Spark for the next query.
page 4 of 7
5.
Explorer Burst: A General Purpose Extensible Scanning Query Architecture for Ad Hoc Analytics
are essentially zero onheap artifacts associated with this load, the release of the byte array has minimal GC
implications. In this way, the local disc, especially if it is SSD, acts as a cost effective second level DRAM cache. 6
9. Query Engine
The Query Subsystem has an API that consists of a programmerfriendly declarative query language called SILQ
which is translated into a machinefriendly imperative query language called GIST. Both of these are textual
languages with a well defined grammar and syntax . The details of this are described in [SILQ]. Here we will say 7
that these languages provide a rich and extensible set of aggregation, dimensioning, filtering and causal/temporal
reasoning features. Burst clients form their queries as SILQ, which the SILQ pipeline transforms into GIST. The
GIST pipeline transforms those into well defined execution plans that are multicast to worker nodes. The
multidimensional result model is gathered and delivered back to the client.
Execution Models
These execution plans contain:
1. Traversal Model a simple numeric array based state machine holding the semantics of what to do where
in the object tree traversal
2. Result Schema the semantics of all aggregations, dimensions, and merges and joins.
3. Closures filters and traversal data model updates in generated and JIT optimized JVM byte code
4. Routes Log structured record of graph automata paths
Zap Data Structures
Because of the extreme number of objects visited and the prolific object churn associated with standard data
structures, Burst requires specialized data structures called Zap structures for inner loops. These are designed to 8
use nothing but simple off heap blocks of memory, preallocated in perthread chunks, reused over and over again,
and with all needed functions coded using unsafe access patterns. There are just a two of these currently : 9
● Zap Maps: The object tree scan requires a nested overlay of lightweight hash maps with the ability to join 10
with child/peer maps on the fly as the traversal unfolds from parent to child. The ways these nested self
joins can be expressed is an important part of how GIST creates complex adhoc multidimensional result
models. The performance of Zap Maps is a key factor in the overall performance of the system.
6
If desired, a future version of Burst may support ‘streaming’ semantics where the scan is executed as the data is read from disc
and never cached in memory.
7
very convenient for unit and system testing!
8
‘Zero Allocation Protocol’
9
We are working on another structure, a Zap Lexicon that eliminates the use of standard JVM strings which are quite noisy from
the perspective of JVM object creation
10
something like a cross join
page 5 of 7
6.
Explorer Burst: A General Purpose Extensible Scanning Query Architecture for Ad Hoc Analytics
● Zap Routes: For causal/temporal reasoning we implemented an off heap logstructured recording structure
with a graph automata to discover and capture ‘paths’ through sequences of events. This how ‘Funnels’ are
implemented in the Explorer product..
Concurrency
Because each of the Item instances in a Dataset partition is part of a sequence of individual order independent object
trees, we refine our concurrency model to a single core/thread dedicated to each traversal. Each of these can be
executed in parallel on available cores using a fixed pool model. This makes hardware happy as the linear byte array
being scanned is read solely by a single Core.
Spark
Like the Ingestion Subsystem, the Query Subsystem is built on top of Apache Spark with a Spark Executor on each 11
worker node initialized with a Query Kernel that can execute scan plans. The scan traversals are carefully designed
to use a minimum of JVM memory and create a minimum of JVM objects. There is essentially no JVM memory
overhead to the storage and execution models other than created by the ipc protocols.
10. Performance
Because of the efficiency of the scanning techniques involved, one can think of Burst as an objects scanned per
second machine and so the performance of queries is almost exclusively about how many objects the query needs to
visit. As an example, in the Flurry mobile analytics world, queries that only look at the top level object in the tree
(the User or Mobile Device) run much faster than queries that need to visit the sessions associated with that User. At
the next level, queries that need to visit the events in each session run slower than ones that only look at sessions.
Generally the complexity of the query in terms of what data is accessed and what results are generated at each object
is not nearly as impactful.
In our 250 node, 6 SATA spindle, 48 haswell hthread cluster, we see a sustained 50 QPS with >1,000 applications in
memory. Datasets cold load in <10s, cache load in <1s. Generally we scan about 200K objects/sec/hthread.
11. Future Work
The Burst architecture was designed to be extensible and the GIST language is implemented on top of a ‘plugin’
abstraction. We have a working first version plugin of a next generation of SILQ/GIST called HYDRA, that
combines both into a single language that is more performant in a few key areas. One is that you can combine any
number of queries into a single concurrent scan . We are also well into developing more efficient filtering using 12
code generated predicates that can be used by both HYDRA and for melding.
12. Conclusions
By rigorously constraining the data to be queried in terms of a two level partition model, where the first level
partition (Domains) subdivides the entire dataset into individually queryable subsets, and a second level partition
(Items) defines unordered parallel/distributed partitions of sequences of scannable object graphs, and by
implementing hyper paralleldistributedconcurrent scans we can provide a linearly scaling, cost effective,
completely general purpose, ad hoc low latency query engine. The first version is deployed in beta behind the
11
Burst does not use Spark features extensively in fact for the most part it uses Spark as a distributed process manager. The actual
Spark execution model is a very simple single stage scatter/gather model. The implementation abstracts this facility so as to make
it easy to move to a different distributed process manager or to roll our own multicast execution model such as with JGroups.
12
This is an important optimization for multiple use case including 1) ‘dashboards’ where a mobile application displays an
initial UI view with a fixed set of personalized queries 2) when a dataset is melded, it is critical to provide metadata about that
dataset to the query clients in terms of a fixed set of queries e.g. for the Flurry product the UI needs to display user, session,
event, and parameter counts as well as parameter keys and value frequencies to help inform users about formed query relevance
during interactive query sessions.
page 6 of 7
7.
Explorer Burst: A General Purpose Extensible Scanning Query Architecture for Ad Hoc Analytics
recently released Explorer Product. The next release introduces an incremental ingestion pipeline allowing this
query system to scale to serve all Flurry Explorer customers.
13. References
● [DREMEL] Sergey Melnik and Andrey Gubarev and Jing Jing Long and Geoffrey Romer and Shiva
Shivakumar and Matt Tolton and Theo Vassilakis, “Dremel: Interactive Analysis of WebScale Datasets”,
Proc. of the 36th Int'l Conf on Very Large Data Bases: http://research.google.com/pubs/pub36632.html
● [DRUID] Druid, “Open Source Data Store for Interactive Analytics at Scale”: http://druid.io/
● [BLINK] AmpLab, “Queries with Bounded Errors and Bounded Response Times on Very Large Data”:
http://blinkdb.org/
● [DRILL] MAPR, “Industry's First SchemaFree SQL Engine for Big Data”:
https://www.mapr.com/products/apachedrill
● [TEZ] https://tez.apache.org/
● [PRESTO] https://prestodb.io/
● [SPARK] http://spark.apache.org/
● [DOCKER] https://www.docker.com/
● [HBASE] http://hbase.apache.org/
● [KAFKA] http://kafka.apache.org/
● [SILQ]
https://docs.google.com/a/yahooinc.com/document/d/1of2GDtLJuItLdNQxDO7E24D6T8hOGspdKnm8l
FnDkM/edit?usp=sharing
page 7 of 7