Bobby from Yahoo presents on running Apache Storm as a service on and off Hadoop. Storm provides low-latency data processing through streaming data flows defined by topologies of spouts and bolts. Yahoo runs Storm as a service and also maintains Spark. Bobby discusses securing standalone Storm, running Storm on YARN for security, reduced overhead and elasticity, and future work including Nimbus high availability and running Storm topologies as unmanaged applications in YARN.
One Grid to rule them all: Building a Multi-tenant Data Cloud with YARNDataWorks Summit
This document discusses using Apache Helix for managing multi-tenant data and applications on YARN. Helix is a generic cluster management framework that handles task and container assignment, failure handling, and workload balancing in a decoupled manner from the core application logic. It provides a high-level overview of key Helix concepts like resources, partitions, and states. The document also outlines how Helix integrates with YARN by using components like the TargetProvider to determine container requirements, Provisioner to acquire/release containers from YARN, and Rebalancer to assign tasks to containers based on constraints. This allows building fault-tolerant applications that can scale efficiently based on workload without having to handle complex cluster management code.
Storm is a distributed and fault-tolerant realtime computation system. It was created at BackType/Twitter to analyze tweets, links, and users on Twitter in realtime. Storm provides scalability, reliability, and ease of programming. It uses components like Zookeeper, ØMQ, and Thrift. A Storm topology defines the flow of data between spouts that read data and bolts that process data. Storm guarantees processing of all data through its reliability APIs and guarantees no data loss even during failures.
PHP Backends for Real-Time User Interaction using Apache Storm.DECK36
Engaging users in real-time is the topic of our times. Whether it’s a game, a shop, or a content-network, the aim remains the same: providing a personalized experience. In this workshop we will look under the hood of Apache Storm and lay a firm foundation on how to use it with PHP. By that, you can leverage your existing codebase and PHP expertise for an entirely new world: real-time analytics and business logic working on message streams. During the course of the workshop, we will introduce Apache Storm and take a look at all of its components. We will then skyrocket the applicability of Storm by showing you how to implement their components with PHP. All exercises will be conducted using an example project, the infamous and most exhilarating lolcat kitten game ever conceived: Plan 9 From Outer Kitten. In order to follow the hands-on excercises, you will need a development VM prepared by us with all relevant system components and our project repositories. To make the workshop experience as smooth as possible for all participants, please bring a prepared computer to the workshop, as there will be no time to deal with installation and setup issues. Please download all prerequisites and install them as described: VM, Plan 9 webapp, Plan 9 storm backend, (Tutorial: https://github.com/DECK36/plan9_workshop_tutorial ).
This document compares the batch and streaming capabilities of Spark and Storm. Spark supports both batch and micro-batch processing while Storm supports micro-batch and real-time stream processing. Spark has been in production mode since 2013 and is implemented in Scala, while Storm has been used since 2011 and is implemented in Clojure and Java. Spark includes libraries for SQL, streaming, and machine learning while Storm uses spouts to read data streams and bolts to filter and join data in topologies. Both integrate with Hadoop and support fault tolerance, though Spark has improved reliability when used with YARN. Performance tests show Spark Streaming can process more records per second than Storm.
Realtime Statistics based on Apache Storm and RocketMQXin Wang
This document discusses using Apache Storm and RocketMQ for real-time statistics. It begins with an overview of the streaming ecosystem and components. It then describes challenges with stateful statistics and introduces Alien, an open-source middleware for handling stateful event counting. The document concludes with best practices for Storm performance and data hot points.
Bobby from Yahoo presents on running Apache Storm as a service on and off Hadoop. Storm provides low-latency data processing through streaming data flows defined by topologies of spouts and bolts. Yahoo runs Storm as a service and also maintains Spark. Bobby discusses securing standalone Storm, running Storm on YARN for security, reduced overhead and elasticity, and future work including Nimbus high availability and running Storm topologies as unmanaged applications in YARN.
One Grid to rule them all: Building a Multi-tenant Data Cloud with YARNDataWorks Summit
This document discusses using Apache Helix for managing multi-tenant data and applications on YARN. Helix is a generic cluster management framework that handles task and container assignment, failure handling, and workload balancing in a decoupled manner from the core application logic. It provides a high-level overview of key Helix concepts like resources, partitions, and states. The document also outlines how Helix integrates with YARN by using components like the TargetProvider to determine container requirements, Provisioner to acquire/release containers from YARN, and Rebalancer to assign tasks to containers based on constraints. This allows building fault-tolerant applications that can scale efficiently based on workload without having to handle complex cluster management code.
Storm is a distributed and fault-tolerant realtime computation system. It was created at BackType/Twitter to analyze tweets, links, and users on Twitter in realtime. Storm provides scalability, reliability, and ease of programming. It uses components like Zookeeper, ØMQ, and Thrift. A Storm topology defines the flow of data between spouts that read data and bolts that process data. Storm guarantees processing of all data through its reliability APIs and guarantees no data loss even during failures.
PHP Backends for Real-Time User Interaction using Apache Storm.DECK36
Engaging users in real-time is the topic of our times. Whether it’s a game, a shop, or a content-network, the aim remains the same: providing a personalized experience. In this workshop we will look under the hood of Apache Storm and lay a firm foundation on how to use it with PHP. By that, you can leverage your existing codebase and PHP expertise for an entirely new world: real-time analytics and business logic working on message streams. During the course of the workshop, we will introduce Apache Storm and take a look at all of its components. We will then skyrocket the applicability of Storm by showing you how to implement their components with PHP. All exercises will be conducted using an example project, the infamous and most exhilarating lolcat kitten game ever conceived: Plan 9 From Outer Kitten. In order to follow the hands-on excercises, you will need a development VM prepared by us with all relevant system components and our project repositories. To make the workshop experience as smooth as possible for all participants, please bring a prepared computer to the workshop, as there will be no time to deal with installation and setup issues. Please download all prerequisites and install them as described: VM, Plan 9 webapp, Plan 9 storm backend, (Tutorial: https://github.com/DECK36/plan9_workshop_tutorial ).
This document compares the batch and streaming capabilities of Spark and Storm. Spark supports both batch and micro-batch processing while Storm supports micro-batch and real-time stream processing. Spark has been in production mode since 2013 and is implemented in Scala, while Storm has been used since 2011 and is implemented in Clojure and Java. Spark includes libraries for SQL, streaming, and machine learning while Storm uses spouts to read data streams and bolts to filter and join data in topologies. Both integrate with Hadoop and support fault tolerance, though Spark has improved reliability when used with YARN. Performance tests show Spark Streaming can process more records per second than Storm.
Realtime Statistics based on Apache Storm and RocketMQXin Wang
This document discusses using Apache Storm and RocketMQ for real-time statistics. It begins with an overview of the streaming ecosystem and components. It then describes challenges with stateful statistics and introduces Alien, an open-source middleware for handling stateful event counting. The document concludes with best practices for Storm performance and data hot points.
Storm: distributed and fault-tolerant realtime computationnathanmarz
Storm is a distributed real-time computation system that provides guaranteed message processing, horizontal scalability, and fault tolerance. It allows users to define data processing topologies and submit them to a Storm cluster for distributed execution. Spouts emit streams of tuples that are processed by bolts. Storm tracks processing to ensure reliability and replays failed tasks. It provides tools for deployment, monitoring, and optimization of real-time data processing.
Some of the biggest issues at the center of analyzing large amounts of data are query flexibility, latency, and fault tolerance. Modern technologies that build upon the success of “big data” platforms, such as Apache Hadoop, have made it possible to spread the load of data analysis to commodity machines, but these analyses can still take hours to run and do not respond well to rapidly-changing data sets.
A new generation of data processing platforms -- which we call “stream architectures” -- have converted data sources into streams of data that can be processed and analyzed in real-time. This has led to the development of various distributed real-time computation frameworks (e.g. Apache Storm) and multi-consumer data integration technologies (e.g. Apache Kafka). Together, they offer a way to do predictable computation on real-time data streams.
In this talk, we will give an overview of these technologies and how they fit into the Python ecosystem. As part of this presentation, we also released streamparse, a new Python that makes it easy to debug and run large Storm clusters.
Links:
* http://parse.ly/code
* https://github.com/Parsely/streamparse
* https://github.com/getsamsa/samsa
Storm-on-YARN: Convergence of Low-Latency and Big-DataDataWorks Summit
adoop plays a central role for Yahoo! to provide personalized experiences for our users and create value for our advertisers. In this talk, we will discuss the convergence of low-latency processing and Hadoop platform. To enable the convergence, we have developed Storm-on-YARN to enable Storm streaming/microbatch applications and Hadoop batch applications hosted in a single cluster. Storm applications could leverage YARN for resource management, and apply Hadoop style security to Hadoop datasets on HDFS and HBase. In Storm-on-YARN, YARN is used to launch Storm application master (Nimbus), and enable Nimbus to request resources for Storm workers (Supervisors). YARN resource manager and Storm scheduler work together to support multi-tenancy and high availability. HDFS enables Storm to achieve higher availability of Nimbus itself. We are introducing Hadoop style security into Storm through JAAS authentication (Kerberos and Digest). Storm servers (Nimbus and DRPC) will be configured with authorization plugins for access control and audit. The security context enables Storm applications to access authorized datasets only (including those created by Hadoop applications). Yahoo! is making our contribution on Storm and YARN available as open source. We will work with industry partners to foster the convergence of low-latency processing and big-data.
Slides from talk given at the NYC Cassandra Meetup. Discussing how Storm works and how it integrates well with Apache Cassandra.
There is also a segway into a example project that uses Storm and Cassandra to implement a scalable reactive web crawler.
http://github.com/tjake/stormscraper
This document provides an introduction to Storm, an open source distributed real-time processing system. It discusses the types of data processing in Storm as either batch or real-time. The key components of a Storm cluster are the Nimbus master node, supervisor worker nodes, and ZooKeeper coordination service. A Storm topology defines the computation as a directed acyclic graph of spouts emitting streams and bolts processing the streams.
Real-Time Big Data at In-Memory Speed, Using StormNati Shalom
Storm, a popular framework from Twitter, is used for real-time event processing. The challenge presented is how to manage the state of your real-time data processing at all times. In addition, you need Storm to integrate with your batch processing system (such as Hadoop) in a consistent manner.
This session will demonstrate how to integrate Storm with an in-memory database/grid, and explore various strategies for integrating the data grid with Hadoop and Cassandra, seamlessly. By achieving smooth integration with consistent management, you will be able to easily manage all the tiers of you Big Data stack in a consistent and effective way.
- See more at: http://nosql2013.dataversity.net/sessionPop.cfm?confid=74&proposalid=5526#sthash.FWIdqRHh.dpuf
The document discusses different technologies for real-time data collection and analysis, including Kafka for collecting and distributing streaming data, Storm for distributed real-time computation, and using PHP and FastCGI to parse real-time logs from Kafka in a Storm topology. It provides an overview of these technologies and their features, and proposes an architecture to collect logs with Kafka, process them with Storm and PHP, and output results through FastCGI.
This document discusses AcuityAds' use of Apache Kafka and Storm for processing over 10 billion daily ad impressions. It describes their architecture with Kafka used to ingest bid request data from multiple sources into partitions. Storm topologies read from Kafka and processed the data to calculate metrics like daily impressions by site. Initial issues included unbalanced Kafka partitions and low Storm uptime due to exceptions. Future improvements involved upgrading versions and adding monitoring capabilities.
Storm is a scalable distributed real-time computation system. It provides a simple programming model through topologies containing spouts that emit streams and bolts that process streams. Storm guarantees processing of all messages through anchoring and tracking tuples in distributed worker processes. It offers fault tolerance through mechanisms like acking tuples and replaying failed tasks. Exactly-once processing can be achieved through techniques like transaction IDs.
Storm: Distributed and fault tolerant realtime computationFerran Galí Reniu
Storm is a distributed realtime computation system that provides primitives for doing realtime computation. It uses a master-worker architecture with Zookeeper for coordination. Topologies in Storm contain spouts that emit streams of tuples and bolts that consume streams to process the tuples. Storm provides guarantees of processing every tuple and fault tolerance through mechanisms like supervisor restarts and Nimbus task reassignment. It is used by many companies for realtime analytics on data streams.
Apache Storm is an open-source distributed real-time processing system. It allows for processing large amounts of streaming data reliably. Storm consists of spouts that intake data streams and bolts that perform processing. Spouts and bolts are connected in topologies to represent processing workflows. Storm distributes the workload of topologies across computer clusters for fault tolerance and high throughput. It uses ZooKeeper for coordination between Storm components like the master Nimbus node and worker Supervisor nodes.
Storm is a distributed real-time computation framework created by Nathan Marz at BackType/Twitter to analyze tweets, links, and users on Twitter in real-time. It provides scalability, fault tolerance, and guarantees of data processing. Storm addresses problems with Hadoop like lack of real-time processing, long latency, and tedious coding through its stream processing capabilities and by being stateless. It has features like scalability, fault tolerance through Zookeeper, and guarantees of at least once processing.
This document provides an overview of resource aware scheduling in Apache Storm. It discusses the challenges of scheduling Storm topologies at Yahoo scale, including increasing heterogeneous clusters, low cluster utilization, and unbalanced resource usage. It then introduces the Resource Aware Scheduler (RAS) built for Storm, which allows fine-grained resource control and isolation for topologies through APIs and cgroups. Key features of RAS include pluggable scheduling strategies, per user resource guarantees, and topology priorities. Experimental results from Yahoo Storm clusters show significant improvements to throughput and resource utilization with RAS. Future work may include improved scheduling strategies and real-time resource monitoring.
Storm is an open-source distributed real-time computation system. It provides a framework for processing unbounded streams of data reliably and fault-tolerantly. Storm allows data to be analyzed in real-time using spouts, bolts, and topologies. It is scalable, fault-tolerant, guarantees processing, and is easy to code. Storm powers many real-time systems at Twitter and is useful for applications like analytics, personalization, and ETL.
Apache Storm is a distributed real-time computation system for processing large amounts of data in real-time. It is fault-tolerant and guarantees message processing. Storm topologies consist of spouts that emit streams of data and bolts that consume and process the streams. Storm provides a simple programming model and is scalable, fault-tolerant, and guarantees processing.
Learning Stream Processing with Apache StormEugene Dvorkin
Over the last couple years, Apache Storm became a de-facto standard for developing real-time analytics and complex event processing applications. Storm enables to tackle real-time data processing challenges the same way Hadoop enables batch processing of Big Data. Storm enables companies to have "Fast Data" alongside with "Big Data". Some use cases where Storm can be used are Fraud Detection, Operation Intelligence, Machine Learning, ETL, Analytics, etc.
In this meetup, Eugene Dvorkin, Architect @WebMD and NYC Storm User Group organizer will teach Apache Storm and Stream Processing fundamentals. While this meeting is geared toward new Storm users, experienced users may find something interesting as well.
Following topics will be covered:
• Why use Apache Storm?
• Common use cases
• Storm Architecture - components, concepts, topology
• Building simple Storm topology with Java and Groovy
• Trident and micro-batch processing
• Fault tolerance and guaranteed message delivery
• Running and monitoring Storm in production
• Kafka
• Storm at WebMD
• Resources
Bobby Evans presented on scaling Apache Storm to support larger topologies and clusters. Currently, the largest Storm cluster at Yahoo contains 2300 nodes and supports topologies with 1500 workers and 4000 executors. The main scalability limitations are the use of Zookeeper for state storage, which is disk-bound, and the processing required in Nimbus for scheduling and collecting metrics. Future work focuses on using an in-memory store like Pacemaker to replace Zookeeper heartbeats, distributing Nimbus processing and data, and implementing topology-aware and load-aware routing to improve scheduling efficiency and network utilization at large scale. The goal is to scale Storm to support 4000 node clusters.
Murakumo is an open-source IaaS cloud controller and API orchestrator developed in 2012 to manage virtual machines, storage, and networks. It uses a thin controller and rich node agent architecture with asynchronous job queue processing. It supports Linux KVM and uses a simple design intended for easy operation and maintenance.
Observability: Beyond the Three Pillars with SpringVMware Tanzu
In this presentation, we’ll explore the basics of the three pillars and what Spring has to offer to implement them for logging (SLF4J), metrics (Micrometer), and distributed tracing (Spring Cloud Sleuth, Zipkin/Brave, OpenTelemetry).
I’ll also talk about how to take your system to the next level, and what else you can find in Spring and related technologies to look under the hood of your running system (Spring Boot Actuator, Logbook, Eureka, Spring Boot Admin, Swagger, Spring HATEOAS) and what our future plans are.
Storm: distributed and fault-tolerant realtime computationnathanmarz
Storm is a distributed real-time computation system that provides guaranteed message processing, horizontal scalability, and fault tolerance. It allows users to define data processing topologies and submit them to a Storm cluster for distributed execution. Spouts emit streams of tuples that are processed by bolts. Storm tracks processing to ensure reliability and replays failed tasks. It provides tools for deployment, monitoring, and optimization of real-time data processing.
Some of the biggest issues at the center of analyzing large amounts of data are query flexibility, latency, and fault tolerance. Modern technologies that build upon the success of “big data” platforms, such as Apache Hadoop, have made it possible to spread the load of data analysis to commodity machines, but these analyses can still take hours to run and do not respond well to rapidly-changing data sets.
A new generation of data processing platforms -- which we call “stream architectures” -- have converted data sources into streams of data that can be processed and analyzed in real-time. This has led to the development of various distributed real-time computation frameworks (e.g. Apache Storm) and multi-consumer data integration technologies (e.g. Apache Kafka). Together, they offer a way to do predictable computation on real-time data streams.
In this talk, we will give an overview of these technologies and how they fit into the Python ecosystem. As part of this presentation, we also released streamparse, a new Python that makes it easy to debug and run large Storm clusters.
Links:
* http://parse.ly/code
* https://github.com/Parsely/streamparse
* https://github.com/getsamsa/samsa
Storm-on-YARN: Convergence of Low-Latency and Big-DataDataWorks Summit
adoop plays a central role for Yahoo! to provide personalized experiences for our users and create value for our advertisers. In this talk, we will discuss the convergence of low-latency processing and Hadoop platform. To enable the convergence, we have developed Storm-on-YARN to enable Storm streaming/microbatch applications and Hadoop batch applications hosted in a single cluster. Storm applications could leverage YARN for resource management, and apply Hadoop style security to Hadoop datasets on HDFS and HBase. In Storm-on-YARN, YARN is used to launch Storm application master (Nimbus), and enable Nimbus to request resources for Storm workers (Supervisors). YARN resource manager and Storm scheduler work together to support multi-tenancy and high availability. HDFS enables Storm to achieve higher availability of Nimbus itself. We are introducing Hadoop style security into Storm through JAAS authentication (Kerberos and Digest). Storm servers (Nimbus and DRPC) will be configured with authorization plugins for access control and audit. The security context enables Storm applications to access authorized datasets only (including those created by Hadoop applications). Yahoo! is making our contribution on Storm and YARN available as open source. We will work with industry partners to foster the convergence of low-latency processing and big-data.
Slides from talk given at the NYC Cassandra Meetup. Discussing how Storm works and how it integrates well with Apache Cassandra.
There is also a segway into a example project that uses Storm and Cassandra to implement a scalable reactive web crawler.
http://github.com/tjake/stormscraper
This document provides an introduction to Storm, an open source distributed real-time processing system. It discusses the types of data processing in Storm as either batch or real-time. The key components of a Storm cluster are the Nimbus master node, supervisor worker nodes, and ZooKeeper coordination service. A Storm topology defines the computation as a directed acyclic graph of spouts emitting streams and bolts processing the streams.
Real-Time Big Data at In-Memory Speed, Using StormNati Shalom
Storm, a popular framework from Twitter, is used for real-time event processing. The challenge presented is how to manage the state of your real-time data processing at all times. In addition, you need Storm to integrate with your batch processing system (such as Hadoop) in a consistent manner.
This session will demonstrate how to integrate Storm with an in-memory database/grid, and explore various strategies for integrating the data grid with Hadoop and Cassandra, seamlessly. By achieving smooth integration with consistent management, you will be able to easily manage all the tiers of you Big Data stack in a consistent and effective way.
- See more at: http://nosql2013.dataversity.net/sessionPop.cfm?confid=74&proposalid=5526#sthash.FWIdqRHh.dpuf
The document discusses different technologies for real-time data collection and analysis, including Kafka for collecting and distributing streaming data, Storm for distributed real-time computation, and using PHP and FastCGI to parse real-time logs from Kafka in a Storm topology. It provides an overview of these technologies and their features, and proposes an architecture to collect logs with Kafka, process them with Storm and PHP, and output results through FastCGI.
This document discusses AcuityAds' use of Apache Kafka and Storm for processing over 10 billion daily ad impressions. It describes their architecture with Kafka used to ingest bid request data from multiple sources into partitions. Storm topologies read from Kafka and processed the data to calculate metrics like daily impressions by site. Initial issues included unbalanced Kafka partitions and low Storm uptime due to exceptions. Future improvements involved upgrading versions and adding monitoring capabilities.
Storm is a scalable distributed real-time computation system. It provides a simple programming model through topologies containing spouts that emit streams and bolts that process streams. Storm guarantees processing of all messages through anchoring and tracking tuples in distributed worker processes. It offers fault tolerance through mechanisms like acking tuples and replaying failed tasks. Exactly-once processing can be achieved through techniques like transaction IDs.
Storm: Distributed and fault tolerant realtime computationFerran Galí Reniu
Storm is a distributed realtime computation system that provides primitives for doing realtime computation. It uses a master-worker architecture with Zookeeper for coordination. Topologies in Storm contain spouts that emit streams of tuples and bolts that consume streams to process the tuples. Storm provides guarantees of processing every tuple and fault tolerance through mechanisms like supervisor restarts and Nimbus task reassignment. It is used by many companies for realtime analytics on data streams.
Apache Storm is an open-source distributed real-time processing system. It allows for processing large amounts of streaming data reliably. Storm consists of spouts that intake data streams and bolts that perform processing. Spouts and bolts are connected in topologies to represent processing workflows. Storm distributes the workload of topologies across computer clusters for fault tolerance and high throughput. It uses ZooKeeper for coordination between Storm components like the master Nimbus node and worker Supervisor nodes.
Storm is a distributed real-time computation framework created by Nathan Marz at BackType/Twitter to analyze tweets, links, and users on Twitter in real-time. It provides scalability, fault tolerance, and guarantees of data processing. Storm addresses problems with Hadoop like lack of real-time processing, long latency, and tedious coding through its stream processing capabilities and by being stateless. It has features like scalability, fault tolerance through Zookeeper, and guarantees of at least once processing.
This document provides an overview of resource aware scheduling in Apache Storm. It discusses the challenges of scheduling Storm topologies at Yahoo scale, including increasing heterogeneous clusters, low cluster utilization, and unbalanced resource usage. It then introduces the Resource Aware Scheduler (RAS) built for Storm, which allows fine-grained resource control and isolation for topologies through APIs and cgroups. Key features of RAS include pluggable scheduling strategies, per user resource guarantees, and topology priorities. Experimental results from Yahoo Storm clusters show significant improvements to throughput and resource utilization with RAS. Future work may include improved scheduling strategies and real-time resource monitoring.
Storm is an open-source distributed real-time computation system. It provides a framework for processing unbounded streams of data reliably and fault-tolerantly. Storm allows data to be analyzed in real-time using spouts, bolts, and topologies. It is scalable, fault-tolerant, guarantees processing, and is easy to code. Storm powers many real-time systems at Twitter and is useful for applications like analytics, personalization, and ETL.
Apache Storm is a distributed real-time computation system for processing large amounts of data in real-time. It is fault-tolerant and guarantees message processing. Storm topologies consist of spouts that emit streams of data and bolts that consume and process the streams. Storm provides a simple programming model and is scalable, fault-tolerant, and guarantees processing.
Learning Stream Processing with Apache StormEugene Dvorkin
Over the last couple years, Apache Storm became a de-facto standard for developing real-time analytics and complex event processing applications. Storm enables to tackle real-time data processing challenges the same way Hadoop enables batch processing of Big Data. Storm enables companies to have "Fast Data" alongside with "Big Data". Some use cases where Storm can be used are Fraud Detection, Operation Intelligence, Machine Learning, ETL, Analytics, etc.
In this meetup, Eugene Dvorkin, Architect @WebMD and NYC Storm User Group organizer will teach Apache Storm and Stream Processing fundamentals. While this meeting is geared toward new Storm users, experienced users may find something interesting as well.
Following topics will be covered:
• Why use Apache Storm?
• Common use cases
• Storm Architecture - components, concepts, topology
• Building simple Storm topology with Java and Groovy
• Trident and micro-batch processing
• Fault tolerance and guaranteed message delivery
• Running and monitoring Storm in production
• Kafka
• Storm at WebMD
• Resources
Bobby Evans presented on scaling Apache Storm to support larger topologies and clusters. Currently, the largest Storm cluster at Yahoo contains 2300 nodes and supports topologies with 1500 workers and 4000 executors. The main scalability limitations are the use of Zookeeper for state storage, which is disk-bound, and the processing required in Nimbus for scheduling and collecting metrics. Future work focuses on using an in-memory store like Pacemaker to replace Zookeeper heartbeats, distributing Nimbus processing and data, and implementing topology-aware and load-aware routing to improve scheduling efficiency and network utilization at large scale. The goal is to scale Storm to support 4000 node clusters.
Murakumo is an open-source IaaS cloud controller and API orchestrator developed in 2012 to manage virtual machines, storage, and networks. It uses a thin controller and rich node agent architecture with asynchronous job queue processing. It supports Linux KVM and uses a simple design intended for easy operation and maintenance.
Observability: Beyond the Three Pillars with SpringVMware Tanzu
In this presentation, we’ll explore the basics of the three pillars and what Spring has to offer to implement them for logging (SLF4J), metrics (Micrometer), and distributed tracing (Spring Cloud Sleuth, Zipkin/Brave, OpenTelemetry).
I’ll also talk about how to take your system to the next level, and what else you can find in Spring and related technologies to look under the hood of your running system (Spring Boot Actuator, Logbook, Eureka, Spring Boot Admin, Swagger, Spring HATEOAS) and what our future plans are.
Terracotta is Java infrastructure software that allows applications to scale across multiple computers without custom coding. It provides transparent clustering at the JVM level through a shared memory space called Network Attached Memory (NAM). Applications using Terracotta are unaware that it is installed and function the same with or without it. This allows state to be shared across instances.
This document summarizes Steve Loughran's research into deploying applications across distributed cloud resources like Amazon EC2 and S3. It discusses moving from single server installations to server farms and cloud computing. Key benefits include scaling easily without large capital costs, but challenges include lack of persistent storage, dynamic IP addresses, and single points of failure. The document provides examples of using EC2 and S3 programmatically through the SmartFrog framework.
The document introduces JStorm, an open source distributed real-time computation framework. It was created by Alibaba to address issues with Apache Storm and improve performance for real-time applications. JStorm has been used by Alibaba to process over 3 trillion messages per day across 3000+ servers. Key features discussed include high throughput, fault tolerance, horizontal scalability, and more powerful scheduling capabilities compared to Storm.
This document summarizes Packet's bare metal cloud platform. It highlights that Packet provides fully dedicated servers without co-tenancy or virtualization. It then describes the challenges of automating provisioning without a hypervisor and how Packet addressed this by building core infrastructure components as microservices. Several of these services like Kant, Tinkerbell, Narwhal and Soren are then summarized in more detail explaining their purpose and benefits. The document concludes by reviewing Packet's current server configurations and integration capabilities.
Matt Tucker discusses how XMPP (Jabber) can be used for cloud services and architectures. Some key benefits of XMPP over traditional web services include its support for real-time bidirectional communication, presence, and easier firewall traversal. Open source XMPP servers like Openfire and client libraries provide tools to build scalable cloud components and services. Examples like Twitter's use of XMPP for its firehose API demonstrate how XMPP can enable new types of cloud applications.
This document provides an overview of stream processing technologies. It defines stream processing as processing events in the order they occur. Common patterns like lambda architecture and kappa architecture are described. Specific stream processing technologies are then outlined, including Apache Storm, Apache Kafka, Apache Samza, Apache Spark Streaming, LinkedIn Databus, AgilData and Hailstorm. The document promotes stream processing by noting how databases already process transactions in order through replication logs.
Apache Traffic Server is a high performance caching proxy that can improve performance and uptime. It is open source software originally created by Yahoo and used widely at Yahoo. It can be used as a content delivery network, reverse proxy, forward proxy, and general proxy. Configuration primarily involves files like remap.config, records.config, and storage.config. Plugins can also be created to extend its functionality.
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...Amazon Web Services
"This is a technical architect's case study of how Loggly has employed the latest social-media-scale technologies as the backbone ingestion processing for our multi-tenant, geo-distributed, and real-time log management system. This presentation describes design details of how we built a second-generation system fully leveraging AWS services including Amazon Route 53 DNS with heartbeat and latency-based routing, multi-region VPCs, Elastic Load Balancing, Amazon Relational Database Service, and a number of pro-active and re-active approaches to scaling computational and indexing capacity.
The talk includes lessons learned in our first generation release, validated by thousands of customers; speed bumps and the mistakes we made along the way; various data models and architectures previously considered; and success at scale: speeds, feeds, and an unmeltable log processing engine."
How to Configure the CA Workload Automation System Agent agentparm.txt FileCA Technologies
Unlock the mystery and power of CA Workload Automation System Agent by understanding how to configure its agentparm.txt file.
For more information on Mainframe solutions from CA Technologies, please visit: http://bit.ly/1wbiPkl
The document proposes a secure and high-performance web server system called Hi-sap. Hi-sap divides web objects into partitions and runs server processes under different user privileges for each partition. This achieves security by preventing scripts in one partition from accessing others. It also improves performance by pooling server processes to fully utilize embedded interpreters, unlike prior systems. The document outlines Hi-sap's design, implementation on Linux with SELinux, and evaluation showing its high performance and scalability compared to alternative approaches.
The document discusses Rohit Yadav and his work with Apache CloudStack. It provides an agenda for understanding CloudStack internals, including getting started as a user or developer, a guided tour of the codebase, common development patterns, and deep dives into key areas like system VMs, networking implementation, and plugins. The document outlines ways to join the CloudStack community and how to contribute code through GitHub pull requests.
Creating pools of Virtual Machines - ApacheCon NA 2013Andrei Savu
My slides on creating pools of virtual machines for ApacheCon NA 2013 in Portland.
Provisionr Source code:
https://github.com/axemblr/axemblr-provisionr
Apache Incubator proposal:
https://github.com/axemblr/axemblr-provisionr/wiki/Provisionr-Proposal
Similar to Multi-tenant Apache Storm as a service (20)
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
2. Hi I’m Bobby (evans@yahoo-inc.com)
2
Low Latency Data Processing Architect
› My team and I provide Apache Storm as a service.
› We also maintain Spark, but that is another talk.
› And we get to play around with deep learning and online machine learning too.
Commiter and PMC/PPMC member for
› Apache Storm incubating
› Apache Hadoop
› Apache Spark
› Apache TEZ
4. Storm Concepts
1. Streams
› Unbounded sequence of tuples
2. Spout
› Source of Stream
› E.g. Read from Twitter streaming API
3. Bolts
› Processes input streams and produces
new streams
› E.g. Functions, Filters, Aggregation,
Joins
4. Topologies
› Network of spouts and bolts
11. Authentication By Type
10/5/201511
HTTP – Using HTTP Authentication or with a Custom Java Servlet
Filter.
Thrift – Kerberos (Possibly through a forwarded TGT)
ZooKeeper
› Kerberos for system processes (Because there is a keytab available)
› a shared secret for worker processes with MD5SUM in ZK.
File System – OS user/group + FS permissions.
Worker to Worker – Can use encryption with shared secret, but we
really need to add in SASL Auth.
External Services (like HBase) – Sorry it is up to you (Sort of …)
13. Credentials Push
(Authenticating with External Services)
10/5/201513
APIs to deliver credentials to a Topology.
ICredentialsListener – informed of credentials updates.
IAutoCredentials – automatically include credentials to push.
ICredentialsRenewer – renew credentials.
Push new Credentials
› storm upload_credentials
› StormSubmitter.pushCredentails
AutoTGT – push forwardable TGT to topology.
› Also logs you into Hadoop/HBase if needed
14. Authorization
10/5/201514
IAuthorizer plugin allows you to decide what is and isn’t allowed
SimpleACLAuthorizer for Nimbus.
Different roles for users
› Administrators can do anything.
› Supervisors
› Users
Topology can configure access to itself as well (rebalance).
DRPCSimpleACLAuthorizer for DRPC.
Can configure client and topology users per function.
Can default open or closed.
Topology can also whitelist users to view info through UI and Logviewer
16. Multi-tenant Scheduler
16
Provides admin resource allotments per user instead of per topology
› Users decide how to divide up their resources per topology
21. Storm on YARN
10/5/201521
Currently
A stand alone storm cluster running on YARN
Has some hacks to avoid port conflicts
No security
No recovery if AM goes down
24. What’s Next?
(If you see anything you like we are hiring…)
24
Nimbus HA/Recovery.
Long lived secure processes in YARN.
Ephemeral ports for storm.
Combine the AM and Nimbus.
Do we need a Supervisor if we have a Node Manager?
Possibly run as Unmanaged AMs and Proxy Users.
Elasticity for storm topologies.
Resource aware scheduling/requests in storm.
Network aware scheduling in YARN and Storm.
Automatic fetching of delegation tokens like Oozie
27. Why Not…
27
No need for a religious war, there are lots of good options out there and
we picked one.
Apache Spark Streaming
We started before Spark Streaming was a possibility.
Storm is currently more advanced in many areas, but not in all.
› Fault Tolerance (I can turn it off in storm)
S4
The community for Storm was more active
Fault Tolerance (I can turn it on in storm)