The document discusses the software stacks used by companies like Google and Facebook to run services at massive scale. It describes key components of their infrastructure like MapReduce for distributed processing, BigTable for storage, and Borg for cluster management. The stacks are optimized for data-intensive workloads across thousands of machines through replication, load balancing, and fault tolerance. Current research aims to improve utilization, stream processing, and distributed algorithms to enable real-time insights from huge datasets.
Using HBase Co-Processors to Build a Distributed, Transactional RDBMS - Splic...Chicago Hadoop Users Group
John Leach Co-Founder and CTO of Splice Machine with 15+ years software development and machine learning experience will discuss how to use HBase co-processors to build an ANSI-99 SQL database with 1) parallelization of SQL execution plans, 2) ACID transactions with snapshot isolation and 3) consistent secondary indexing.
Transactions are critical in traditional RDBMSs because they ensure reliable updates across multiple rows and tables. Most operational applications require transactions, but even analytics systems use transactions to reliably update secondary indexes after a record insert or update.
In the Hadoop ecosystem, HBase is a key-value store with real-time updates, but it does not have multi-row, multi-table transactions, secondary indexes or a robust query language like SQL. Combining SQL with a full transactional model over HBase opens a whole new set of OLTP and OLAP use cases for Hadoop that was traditionally reserved for RDBMSs like MySQL or Oracle. However, a transactional HBase system has the advantage of scaling out with commodity servers, leading to a 5x-10x cost savings over traditional databases like MySQL or Oracle.
HBase co-processors, introduced in release 0.92, provide a flexible and high-performance framework to extend HBase. In this talk, we show how we used HBase co-processors to support a full ANSI SQL RDBMS without modifying the core HBase source. We will discuss how endpoint transactions are used to serialize SQL execution plans over to regions so that computation is local to where the data is stored. Additionally, we will show how observer co-processors simultaneously support both transactions and secondary indexing.
The talk will also discuss how Splice Machine extended the work of Google Percolator, Yahoo Labs’ OMID, and the University of Waterloo on distributed snapshot isolation for transactions. Lastly, performance benchmarks will be provided, including full TPC-C and TPC-H results that show how Hadoop/HBase can be a replacement of traditional RDBMS solutions.
To view the accompanying slide deck: http://www.slideshare.net/ChicagoHUG/
(Berkeley CS186 guest lecture) Big Data Analytics Systems: What Goes Around C...Reynold Xin
(Berkeley CS186 guest lecture)
Big Data Analytics Systems: What Goes Around Comes Around
Introduction to MapReduce, GFS, HDFS, Spark, and differences between "Big Data" and database systems.
Apache Giraph: start analyzing graph relationships in your bigdata in 45 minu...rhatr
The genesis of Hadoop was in analyzing massive amounts of data with a mapreduce framework. SQL-onHadoop has followed shortly after that, paving a way to the whole schema-on-read notion. Discovering graph relationship in your data is the next logical step. Apache Giraph (modeled on Google’s Pregel) lets you apply the power of BSP approach to the unstructured data. In this talk we will focus on practical advice of how to get up and running with Apache Giraph, start analyzing simple data sets with built-in algorithms and finally how to implement your own graph processing applications using the APIs provided by the project. We will then dive into how Giraph integrates with the Hadoop ecosystem (Hive, HBase, Accumulo, etc.) and will also provide a whirlwind tour of Giraph architecture.
Using HBase Co-Processors to Build a Distributed, Transactional RDBMS - Splic...Chicago Hadoop Users Group
John Leach Co-Founder and CTO of Splice Machine with 15+ years software development and machine learning experience will discuss how to use HBase co-processors to build an ANSI-99 SQL database with 1) parallelization of SQL execution plans, 2) ACID transactions with snapshot isolation and 3) consistent secondary indexing.
Transactions are critical in traditional RDBMSs because they ensure reliable updates across multiple rows and tables. Most operational applications require transactions, but even analytics systems use transactions to reliably update secondary indexes after a record insert or update.
In the Hadoop ecosystem, HBase is a key-value store with real-time updates, but it does not have multi-row, multi-table transactions, secondary indexes or a robust query language like SQL. Combining SQL with a full transactional model over HBase opens a whole new set of OLTP and OLAP use cases for Hadoop that was traditionally reserved for RDBMSs like MySQL or Oracle. However, a transactional HBase system has the advantage of scaling out with commodity servers, leading to a 5x-10x cost savings over traditional databases like MySQL or Oracle.
HBase co-processors, introduced in release 0.92, provide a flexible and high-performance framework to extend HBase. In this talk, we show how we used HBase co-processors to support a full ANSI SQL RDBMS without modifying the core HBase source. We will discuss how endpoint transactions are used to serialize SQL execution plans over to regions so that computation is local to where the data is stored. Additionally, we will show how observer co-processors simultaneously support both transactions and secondary indexing.
The talk will also discuss how Splice Machine extended the work of Google Percolator, Yahoo Labs’ OMID, and the University of Waterloo on distributed snapshot isolation for transactions. Lastly, performance benchmarks will be provided, including full TPC-C and TPC-H results that show how Hadoop/HBase can be a replacement of traditional RDBMS solutions.
To view the accompanying slide deck: http://www.slideshare.net/ChicagoHUG/
(Berkeley CS186 guest lecture) Big Data Analytics Systems: What Goes Around C...Reynold Xin
(Berkeley CS186 guest lecture)
Big Data Analytics Systems: What Goes Around Comes Around
Introduction to MapReduce, GFS, HDFS, Spark, and differences between "Big Data" and database systems.
Apache Giraph: start analyzing graph relationships in your bigdata in 45 minu...rhatr
The genesis of Hadoop was in analyzing massive amounts of data with a mapreduce framework. SQL-onHadoop has followed shortly after that, paving a way to the whole schema-on-read notion. Discovering graph relationship in your data is the next logical step. Apache Giraph (modeled on Google’s Pregel) lets you apply the power of BSP approach to the unstructured data. In this talk we will focus on practical advice of how to get up and running with Apache Giraph, start analyzing simple data sets with built-in algorithms and finally how to implement your own graph processing applications using the APIs provided by the project. We will then dive into how Giraph integrates with the Hadoop ecosystem (Hive, HBase, Accumulo, etc.) and will also provide a whirlwind tour of Giraph architecture.
Steve Watt, Chief Architect, Hadoop and Big Data, Red Hat - 21st BDL meetupbigdatalondon
"Hadoop Analytics on your data in place"
Steve Watt leads engineering for the Hadoop and Big Data program at Red Hat. Most recently Steve has been focusing on Hadoop Interoperability and better enabling Hadoop support for alternative filesystems. Prior to Red Hat, Steve spent 2 years at Hewlett-Packard, first co-founding the Hadoop business and then leading engineering as the Hadoop CTO. Prior to HP, Steve was at IBM for 10 years where he created IBMs first Hadoop Distribution and was part of the team that built BigSheets, the first spreadsheet interface for Hadoop.
Building a Scalable Web Crawler with Hadoop by Ahad Rana from CommonCrawl
Ahad Rana, engineer at CommonCrawl, will go over CommonCrawl’s extensive use of Hadoop to fulfill their mission of building an open, and accessible Web-Scale crawl. He will discuss their Hadoop data processing pipeline, including their PageRank implementation, describe techniques they use to optimize Hadoop, discuss the design of their URL Metadata service, and conclude with details on how you can leverage the crawl (using Hadoop) today.
Transformation Processing Smackdown; Spark vs Hive vs PigLester Martin
Compare and contrast using Spark, Hive and Pig for transformation processing requirements. Video of my "talk" at https://www.youtube.com/watch?v=36_MayK5eU4.
Conference page for the talk is at https://devnexus.com/s/devnexus2017/presentations/17533.
HBaseCon 2015: HBase Operations in a FlurryHBaseCon
With multiple clusters of 1,000+ nodes replicated across multiple data centers, Flurry has learned many operational lessons over the years. In this talk, you'll explore the challenges of maintaining and scaling Flurry's cluster, how we monitor, and how we diagnose and address potential problems.
How Adobe Does 2 Million Records Per Second Using Apache Spark!Databricks
Adobe’s Unified Profile System is the heart of its Experience Platform. It ingests TBs of data a day and is PBs large. As part of this massive growth we have faced multiple challenges in our Apache Spark deployment which is used from Ingestion to Processing.
Adaptive Query Execution: Speeding Up Spark SQL at RuntimeDatabricks
Over the years, there has been extensive and continuous effort on improving Spark SQL’s query optimizer and planner, in order to generate high quality query execution plans. One of the biggest improvements is the cost-based optimization framework that collects and leverages a variety of data statistics (e.g., row count, number of distinct values, NULL values, max/min values, etc.) to help Spark make better decisions in picking the most optimal query plan.
Prediction as a service with ensemble model in SparkML and Python ScikitLearnJosef A. Habdank
Watch the recording of the speech done at Spark Summit Brussles 2016 here:
https://www.youtube.com/watch?v=wyfTjd9z1sY
Data Science with SparkML on DataBricks is a perfect platform for application of Ensemble Learning on massive a scale. This presentation describes Prediction-as-a-Service platform which can predict trends on 1 billion observed prices daily. In order to train ensemble model on a multivariate time series in thousands/millions dimensional space, one has to fragment the whole space into subspaces which exhibit a significant similarity. In order to achieve this, the vastly sparse space has to undergo dimensionality reduction into a parameters space which then is used to cluster the observations. The data in the resulting clusters is modeled in parallel using machine learning tools capable of coefficient estimation at the massive scale (SparkML and Scikit Learn). The estimated model coefficients are stored in a database to be used when executing predictions on demand via a web service. This approach enables training models fast enough to complete the task within a couple of hours, allowing daily or even real time updates of the coefficients. The above machine learning framework is used to predict the airfares used as support tool for the airline Revenue Management systems.
What do you talk about to a hall full of database gurus? Instead of science - my talk focused on the art. What made Hadoop successful? What can we learn from it? What principles work well in building software for large scale services? What are some interesting unsolved problems in a world overrun by open-source (and VC investments :-))
HBaseCon2017 Community-Driven Graphs with JanusGraphHBaseCon
Graphs are well-suited for many use cases to express and process complex relationships among entities in enterprise and social contexts. Fueled by the growing interest in graphs, there are various graph databases and processing systems that dot the graph landscape. JanusGraph is a community-driven project that continues the legacy of Titan, a pioneer of open source graph databases. JanusGraph is a scalable graph database optimized for large scale transactional and analytical graph processing. In the session, we will introduce JanusGraph, which features full integration with the Apache TinkerPop graph stack. We will discuss JanusGraph's optimized storage model that relies on HBase for fast graph transversal and processing.
by Jason Plurad and Jing Chen He of IBM
Typesafe & William Hill: Cassandra, Spark, and Kafka - The New Streaming Data...DataStax Academy
Typesafe did a survey of Spark usage last year and found that a large percentage of Spark users combine it with Cassandra and Kafka. This talk focuses on streaming data scenarios that demonstrate how these three tools complement each other for building robust, scalable, and flexible data applications. Cassandra provides resilient and scalable storage, with flexible data format and query options. Kafka provides durable, scalable collection of streaming data with message-queue semantics. Spark provides very flexible analytics, everything from classic SQL queries to machine learning and graph algorithms, running in a streaming model based on "mini-batches", offline batch jobs, or interactive queries. We'll consider best practices and areas where improvements are needed.
Steve Watt, Chief Architect, Hadoop and Big Data, Red Hat - 21st BDL meetupbigdatalondon
"Hadoop Analytics on your data in place"
Steve Watt leads engineering for the Hadoop and Big Data program at Red Hat. Most recently Steve has been focusing on Hadoop Interoperability and better enabling Hadoop support for alternative filesystems. Prior to Red Hat, Steve spent 2 years at Hewlett-Packard, first co-founding the Hadoop business and then leading engineering as the Hadoop CTO. Prior to HP, Steve was at IBM for 10 years where he created IBMs first Hadoop Distribution and was part of the team that built BigSheets, the first spreadsheet interface for Hadoop.
Building a Scalable Web Crawler with Hadoop by Ahad Rana from CommonCrawl
Ahad Rana, engineer at CommonCrawl, will go over CommonCrawl’s extensive use of Hadoop to fulfill their mission of building an open, and accessible Web-Scale crawl. He will discuss their Hadoop data processing pipeline, including their PageRank implementation, describe techniques they use to optimize Hadoop, discuss the design of their URL Metadata service, and conclude with details on how you can leverage the crawl (using Hadoop) today.
Transformation Processing Smackdown; Spark vs Hive vs PigLester Martin
Compare and contrast using Spark, Hive and Pig for transformation processing requirements. Video of my "talk" at https://www.youtube.com/watch?v=36_MayK5eU4.
Conference page for the talk is at https://devnexus.com/s/devnexus2017/presentations/17533.
HBaseCon 2015: HBase Operations in a FlurryHBaseCon
With multiple clusters of 1,000+ nodes replicated across multiple data centers, Flurry has learned many operational lessons over the years. In this talk, you'll explore the challenges of maintaining and scaling Flurry's cluster, how we monitor, and how we diagnose and address potential problems.
How Adobe Does 2 Million Records Per Second Using Apache Spark!Databricks
Adobe’s Unified Profile System is the heart of its Experience Platform. It ingests TBs of data a day and is PBs large. As part of this massive growth we have faced multiple challenges in our Apache Spark deployment which is used from Ingestion to Processing.
Adaptive Query Execution: Speeding Up Spark SQL at RuntimeDatabricks
Over the years, there has been extensive and continuous effort on improving Spark SQL’s query optimizer and planner, in order to generate high quality query execution plans. One of the biggest improvements is the cost-based optimization framework that collects and leverages a variety of data statistics (e.g., row count, number of distinct values, NULL values, max/min values, etc.) to help Spark make better decisions in picking the most optimal query plan.
Prediction as a service with ensemble model in SparkML and Python ScikitLearnJosef A. Habdank
Watch the recording of the speech done at Spark Summit Brussles 2016 here:
https://www.youtube.com/watch?v=wyfTjd9z1sY
Data Science with SparkML on DataBricks is a perfect platform for application of Ensemble Learning on massive a scale. This presentation describes Prediction-as-a-Service platform which can predict trends on 1 billion observed prices daily. In order to train ensemble model on a multivariate time series in thousands/millions dimensional space, one has to fragment the whole space into subspaces which exhibit a significant similarity. In order to achieve this, the vastly sparse space has to undergo dimensionality reduction into a parameters space which then is used to cluster the observations. The data in the resulting clusters is modeled in parallel using machine learning tools capable of coefficient estimation at the massive scale (SparkML and Scikit Learn). The estimated model coefficients are stored in a database to be used when executing predictions on demand via a web service. This approach enables training models fast enough to complete the task within a couple of hours, allowing daily or even real time updates of the coefficients. The above machine learning framework is used to predict the airfares used as support tool for the airline Revenue Management systems.
What do you talk about to a hall full of database gurus? Instead of science - my talk focused on the art. What made Hadoop successful? What can we learn from it? What principles work well in building software for large scale services? What are some interesting unsolved problems in a world overrun by open-source (and VC investments :-))
HBaseCon2017 Community-Driven Graphs with JanusGraphHBaseCon
Graphs are well-suited for many use cases to express and process complex relationships among entities in enterprise and social contexts. Fueled by the growing interest in graphs, there are various graph databases and processing systems that dot the graph landscape. JanusGraph is a community-driven project that continues the legacy of Titan, a pioneer of open source graph databases. JanusGraph is a scalable graph database optimized for large scale transactional and analytical graph processing. In the session, we will introduce JanusGraph, which features full integration with the Apache TinkerPop graph stack. We will discuss JanusGraph's optimized storage model that relies on HBase for fast graph transversal and processing.
by Jason Plurad and Jing Chen He of IBM
Typesafe & William Hill: Cassandra, Spark, and Kafka - The New Streaming Data...DataStax Academy
Typesafe did a survey of Spark usage last year and found that a large percentage of Spark users combine it with Cassandra and Kafka. This talk focuses on streaming data scenarios that demonstrate how these three tools complement each other for building robust, scalable, and flexible data applications. Cassandra provides resilient and scalable storage, with flexible data format and query options. Kafka provides durable, scalable collection of streaming data with message-queue semantics. Spark provides very flexible analytics, everything from classic SQL queries to machine learning and graph algorithms, running in a streaming model based on "mini-batches", offline batch jobs, or interactive queries. We'll consider best practices and areas where improvements are needed.
SMACK Stack 1.0 has been Spark, Mesos, Akka, Cassandra and Kafka working into different cohesive systems delivering different solutions for different use cases. Haven't heard about it before? Oh man! Where have you been? https://www.google.com/search?q=smack+stack+1.0
SMACK Stack 1.1 we go a step further Streaming, Mesos, Analytics, Cassandra and Kafka and Joe Stein will walk through in detail some of the different viable options for Streaming and Analytics with Mesos, Kafka and Cassandra.
Big Data Streams Architectures. Why? What? How?Anton Nazaruk
With a current zoo of technologies and different ways of their interaction it's a big challenge to architect a system (or adopt existed one) that will conform to low-latency BigData analysis requirements. Apache Kafka and Kappa Architecture in particular take more and more attention over classic Hadoop-centric technologies stack. New Consumer API put significant boost in this direction. Microservices-based streaming processing and new Kafka Streams tend to be a synergy in BigData world.
This is an exam cheat sheet hopes to cover all keys points for GCP Data Engineer Certification Exam
Let me know if there is any mistake and I will try to update it
How do you get data from your sources into your Redshift data warehouse? We'll show how to use AWS Glue and Amazon Kinesis Firehose to make it easy to automate the work to get data loaded.
Level: Intermediate
Speakers:
Jay Formosa - Solutions Architect, AWS
Aser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
Organizations often need to quickly analyze large amounts of data, such as logs generated from a wide variety of sources and formats. However, traditional approaches require a lot of time and effort designing complex data transformation and loading processes; and configuring data warehouses. Using AWS, you can start querying your datasets within minutes. In this session you will learn how you can deploy a managed Presto environment in minutes to interactively query log data using standard ANSI SQL. Presto is a popular open source SQL engine for running interactive analytic queries against data sources of all sizes. We will talk about common use cases and best practices for running Presto on Amazon EMR.
Loading Data into Redshift: Data Analytics Week at the SF LoftAmazon Web Services
Loading Data into Redshift: Data Analytics Week at the San Francisco Loft
How do you get data from your sources into your Redshift data warehouse? We'll show how to use AWS Glue and Amazon Kinesis Firehose to make it easy to automate the work to get data loaded.
Level: Intermediate
Speakers:
Aser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
Vikram Gangulavoipalyam - Enterprise Solutions Architect, AWS
The slides for the first ever SnappyData webinar. Covers SnappyData core concepts, programming models, benchmarks and more.
SnappyData is open sourced here: https://github.com/SnappyDataInc/snappydata
We also have a deep technical paper here: http://www.snappydata.io/snappy-industrial
We can be easily contacted on Slack, Gitter and more: http://www.snappydata.io/about#contactus
by Peter Dalton, Principal Consultant AWS and Taz Sayed, Sr Technical Account Manager AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
Introduciton to Apache Cassandra for Java Developers (JavaOne)zznate
The database industry has been abuzz over the past year about NoSQL databases. Apache Cassandra, which has quickly emerged as a best-of-breed solution in this space, is used at many companies to achieve unprecedented scale while maintaining streamlined operations.
This presentation goes beyond the hype, buzzwords, and rehashed slides and actually presents the attendees with a hands-on, step-by-step tutorial on how to write a Java application on top of Apache Cassandra. It focuses on concepts such as idempotence, tunable consistency, and shared-nothing clusters to help attendees get started with Apache Cassandra quickly while avoiding common pitfalls.
Similar to What does it take to make google work at scale (20)
PostgreSQL is very differently architected and presents none
of these problems. All PostgreSQL operations are multi-versioned using
Multi-Version Concurrency Control (MVCC). As a result, common
operations such as re-indexing, adding or dropping columns, and
recreating views can be performed online and without excessive locking,
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
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
6. CamSa
S
6http://camsas.org @CamSysAtScale
What we’ll chat about
1.Datacenter hardware
2.Scalable software stacks
a.Google
b.Facebook
3.Differences compared to HPC
4.Current research directions
Please ask questions - any
time! :)
12. CamSa
S
12http://camsas.org @CamSysAtScale
How’s this different to HPC?
Emphasis on commodity hardware
No expensive interconnect
Mid-range machines
Energy/performance/cost trade-off essential
Massive automation
Very small number of on-site staff
Automated software bootstrap
Fault tolerant design
Each component can fail
Software must be aware and compensate
13. CamSa
S
13http://camsas.org @CamSysAtScale
The software side
Machine MachineMachine
Linux kernel
(customized)
Linux kernel
(customized)
Linux kernel
(customized)
Transparent distributed systems
Containers
Containers
We’ll look at what goes here!
16. CamSa
S
16http://camsas.org @CamSysAtScale
GFS/Colossus
Bulk data block storage system
Optimized for large files (GB-size)
Supports small files, but not common case
Read, write, append modes
Colossus = GFSv2, adds some improvements
e.g., Reed-Solomon-based erasure coding
better support for latency-sensitive applications
sharded meta-data layer, rather than single master
19. CamSa
S
19http://camsas.org @CamSysAtScale
The Google Stack
Figure from M. Schwarzkopf, “Operating system support for warehouse-scale
computing”, PhD thesis, University of Cambridge, 2015 (to appear).
Details & Bibliography: http://malteschwarzkopf.de/research/assets/google-stack.pdf
21. CamSa
S
21http://camsas.org @CamSysAtScale
BigTable (2006)
• Distributed tablets (~1 GB) hold subsets of map
• On top of Colossus, which handles replication and
fault tolerance: only one (active) server per tablet!
• Reads & writes within a row are transactional
– Independently of the number of columns touched
– But: no cross-row transactions possible
– Turns out users find this hard to deal with
22. CamSa
S
22http://camsas.org @CamSysAtScale
BigTable (2006)
• META0 tablet is “root“ for name resolution
– Like a coordinator/leader
– Nested META1 tablets improve meta-data lookup
• Elect master (META0 tablet server) using
Chubby, and to maintain list of tablet servers &
schemas
– 5-way replicated Paxos consensus on data in Chubby
• Built atop GFS; building block for MegaStore
– “Next gen” with transactions: Spanner
24. CamSa
S
24http://camsas.org @CamSysAtScale
Spanner (2012)
• BigTable insufficient for some consistency needs
• Often have transactions across >1 data centres
– May buy app on Play Store while travelling in the U.S.
– Hit U.S. server, but customer billing data is in U.K.
– Or may need to update several replicas for fault tolerance
• Wide-area consistency is hard
– due to long delays and clock skew
– no global, universal notion of time
– NTP not accurate enough, PTP doesn’t work (jittery links)
25. CamSa
S
25http://camsas.org @CamSysAtScale
Spanner (2012)
• Spanner offers transactional consistency: full RDBMS
power, ACID properties, at global scale!
• Secret sauce: hardware-assisted clock sync
– Using GPS and atomic clocks in data centres
• Use global timestamps and Paxos to reach consensus
– Still have a period of uncertainty for write TX: wait it out!
– Each timestamp is an interval:
Definitely in
the future
tt.latest
Definitely in
the past
tt.earliest
tabs
26. CamSa
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26http://camsas.org @CamSysAtScale
The Google Stack
Figure from M. Schwarzkopf, “Operating system support for warehouse-scale
computing”, PhD thesis, University of Cambridge, 2015 (to appear).
Details & Bibliography: http://malteschwarzkopf.de/research/assets/google-stack.pdf
27. CamSa
S
27http://camsas.org @CamSysAtScale
MapReduce (2004)
• Parallel programming framework for scale
– Run a program on 100’s to 10,000’s machines
• Framework takes care of:
– Parallelization, distribution, load-balancing, scaling up
(or down) & fault-tolerance
• Accessible: programmer provides two methods ;-)
– map(key, value) → list of <key’, value’> pairs
– reduce(key’, value’) → result
– Inspired by functional programming
28. CamSa
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28http://camsas.org @CamSysAtScale
MapReduce
Input
Map
Reduce
Output
Shuffle
X: 5 X: 3 Y: 1 Y: 7
Y: 8X: 8
Results: X: 8, Y: 8
Perform Map() query against local data
matching input specification
Aggregate gathered results for each
intermediate key using Reduce()
End user can query results via
distributed key/value store
Slide originally due to S. Hand’s distributed systems lecture course at Cambridge:
http://www.cl.cam.ac.uk/teaching/1112/ConcDisSys/DistributedSystems-1B-H4.pdf
29. CamSa
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29http://camsas.org @CamSysAtScale
MapReduce: Pros & Cons
• Extremely simple, and:
– Can auto-parallelize (since operations on every
element in input are independent)
– Can auto-distribute (since rely on underlying
Colossus/BigTable distributed storage)
– Gets fault-tolerance (since tasks are idempotent, i.e.
can just re-execute if a machine crashes)
• Doesn’t really use any sophisticated distributed
systems algorithms (except storage replication)
• However, not a panacea:
– Limited to batch jobs, and computations which are
expressible as a map() followed by a reduce()
30. CamSa
S
30http://camsas.org @CamSysAtScale
The Google Stack
Figure from M. Schwarzkopf, “Operating system support for warehouse-scale
computing”, PhD thesis, University of Cambridge, 2015 (to appear).
Details & Bibliography: http://malteschwarzkopf.de/research/assets/google-stack.pdf
32. CamSa
S
32http://camsas.org @CamSysAtScale
Dremel (2010)
Stores protocol buffers
Google’s universal serialization format
Nested messages → nested columns
Repeated fields → repeated records
Efficient encoding
Many sparse records: don’t store NULL fields
Record re-assembly
Need to put results back together into records
Use a Finite State Machine (FSM) defined by
protocol buffer structure
33. CamSa
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33http://camsas.org @CamSysAtScale
The Google Stack
Figure from M. Schwarzkopf, “Operating system support for warehouse-scale
computing”, PhD thesis, University of Cambridge, 2015 (to appear).
Details & Bibliography: http://malteschwarzkopf.de/research/assets/google-stack.pdf
36. CamSa
S
36http://camsas.org @CamSysAtScale
Borg/Omega: workloads
Jobs/tasks: counts
CPU/RAM: resource seconds [i.e. resource * job runtime in sec.]
Cluster A
Medium size
Medium utilization
Cluster B
Large size
Medium utilization
Cluster C
Medium (12k mach.)
High utilization
Public trace
Figure from M. Schwarzkopf et al., “Omega: flexible, scalable schedulers for large
compute clusters”, Proceedings of EuroSys 2013.
39. CamSa
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39http://camsas.org @CamSysAtScale
Borg/Omega: workloads
Batch jobs have a longer-tailed CPI distribution:
lower scheduling priority in kernel scheduler.
Figures from M. Schwarzkopf, “Operating system support for warehouse-scale
computing”, PhD thesis, University of Cambridge, 2015 (to appear).
41. CamSa
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41http://camsas.org @CamSysAtScale
How’s this different to HPC?
Generally avoid tight synchronization
No barrier-syncs!
Asynchronous replication, eventual consistency
Much more data-intensive
Lower compute-to-data ratio
Aggressive resource sharing for utilization
42. CamSa
S
42http://camsas.org @CamSysAtScale
The facebook Stack
Figure from M. Schwarzkopf, “Operating system support for warehouse-scale
computing”, PhD thesis, University of Cambridge, 2015 (to appear).
Details & Bibliography: http://malteschwarzkopf.de/research/assets/facebook-stack.pdf
43. CamSa
S
43http://camsas.org @CamSysAtScale
The facebook Stack
Figure from M. Schwarzkopf, “Operating system support for warehouse-scale
computing”, PhD thesis, University of Cambridge, 2015 (to appear).
Details & Bibliography: http://malteschwarzkopf.de/research/assets/facebook-stack.pdf
44. CamSa
S
44http://camsas.org @CamSysAtScale
The facebook Stack
Figure from M. Schwarzkopf, “Operating system support for warehouse-scale
computing”, PhD thesis, University of Cambridge, 2015 (to appear).
Details & Bibliography: http://malteschwarzkopf.de/research/assets/facebook-stack.pdf
45. CamSa
S
45http://camsas.org @CamSysAtScale
Haystack & f4
Blob stores, hold photos, videos
not: status updates, messages, like counts
Items have a level of hotness
How many users are currently accessing this?
Baseline “cold” storage: MySQL
Want to cache close to users
Reduces network traffic
Reduces latency
But cache capacity is limited!
Replicate for performance, not resilience
47. CamSa
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47http://camsas.org @CamSysAtScale
How about other companies?
Very similar stacks.
Microsoft, Yahoo, Twitter all similar in principle.
Typical set-up:
Front-end serving systems and fast back-ends.
Batch data processing systems.
Multi-tier structured/unstructured storage hierarchy.
Coordination system and cluster scheduler.
Minor differences owed to business focus
e.g., Amazon focused on inventory/shopping cart.
48. CamSa
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48http://camsas.org @CamSysAtScale
Open source software
Lots of open reimplementations!
MapReduce → Hadoop, Spark, Metis
GFS → HDFS
BigTable → HBase, Cassandra
Borg/Omega → Mesos, Firmament
But also some releases from companies…
Presto (Facebook)
Kubernetes (Google)
49. CamSa
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49http://camsas.org @CamSysAtScale
Current research
Many hot topics!
This distributed infrastructure is still new.
Make many-core machines go further
Increase utilization, schedule better.
Deal with co-location interference.
Rapid insights on huge datasets
Fast, incremental stream processing, e.g. Naiad.
Approximate analytics, e.g. BlinkDB.
Distributed algorithms
New consensus systems, e.g. RAFT.
50. CamSa
S
50http://camsas.org @CamSysAtScale
Conclusions
1.Running at huge (10k+ machines) scale
requires different software stacks.
2.Pretty interesting systems
a.Do read the papers!
b.(Partial) Bibliography:
http://malteschwarzkopf.de/research/assets/google-stack.pdf
http://malteschwarzkopf.de/research/assets/facebook-stack.pdf
1.Lots of activity in this area!
a.Open source software
b.Research initiatives
![What does it take
to make Google
work at scale?
Malte Schwarzkopf [@ms705]
Cambridge Systems at Scale
http://camsas.org – @CamSysAtScale
CamSa
S](https://image.slidesharecdn.com/whatdoesittaketomakegoogleworkatscale-150825083956-lva1-app6892/85/What-does-it-take-to-make-google-work-at-scale-1-320.jpg)
