GR8Conf 2011: Tuning Grails Applications by Peter LedbrookGR8Conf
This document discusses various techniques for tuning Grails applications for performance. It covers profiling tools for the server, database, and UI. Specific strategies are presented for improving database queries, caching, asynchronous processing, and reducing network traffic. The document emphasizes profiling first before optimizing and notes diminishing returns from performance work.
An adaptive and eventually self healing framework for geo-distributed real-ti...Angad Singh
This document discusses an adaptive and self-healing framework for real-time data ingestion across geographically distributed data centers. It describes the problem domain of ingesting 15 billion events per day across multiple schemas and data types from various sources. The proposed architecture includes an ingestion layer using technologies like Storm, Kafka and HDFS to ingest, transform and replicate streaming and batch data. It also includes a serving layer using Aerospike to provide low-latency aggregated user views. Issues encountered with technologies like Storm and Kafka are discussed, as well as features still under development.
The document discusses three proposed cluster computing frameworks: CloudMirror, Mesos, and Omega.
CloudMirror addresses challenges of providing bandwidth guarantees for interactive workloads in the cloud. It proposes a new network abstraction model based on application communication structure and a workload placement algorithm for efficient resource allocation.
Mesos targets sharing cluster resources across frameworks. It introduces a two-level resource allocation and isolation model to allow sharing while preventing interference. Mesos was implemented in C++ and evaluated using various macrobenchmarks showing improved resource utilization and scalability.
Omega is a proposed scheduler architecture that avoids centralized control. It allows schedulers parallel access to the entire cluster and uses optimistic concurrency control. Simulations showed Omega improved scheduling performance
This document discusses coordination challenges with microservices architectures and distributed systems. It presents two approaches for coordinating business transactions across microservices: 1) using events and handling all possible failure scenarios, which becomes complex, and 2) employing a Saga Execution Coordinator (SEC) that sequentially executes the steps of a business transaction and logs each step, allowing for compensation of incomplete transactions. The SEC approach provides a simpler way to coordinate multi-step transactions across multiple services and databases. In summary, the document covers coordination challenges in distributed systems, two approaches for coordinating business transactions across microservices (using events or a Saga Execution Coordinator), and how the SEC approach provides a simpler solution.
This document summarizes a presentation about near real-time analytics platforms at Uber and LinkedIn. It discusses use cases for streaming analytics, challenges with scalability and operations, and new platforms developed using Apache Samza and SQL. Key points include how Samza is used to build streaming applications with SQL queries, operators, and support for multi-stage workflows. The platforms aim to simplify deployment and management of streaming jobs through interfaces like AthenaX.
Distributed Task Scheduling with Akka, Kafka and CassandraDavid van Geest
https://www.youtube.com/watch?v=s3GfXTnzG_Y
Dynamically scheduled tasks are at the heart of PagerDuty's microservices. They deliver incident alerts, on-call notifications, and manage myriad administrative chores. Historically, these tasks were scheduled and run using an in-house library built on Cassandra, but that solution had begun to show its age.
Early in 2016, the Core team at PagerDuty built a new Task Scheduler using Akka, Kafka, and Cassandra. After six weeks in development, the Scheduler is now running in production. This talk discusses how the strengths of the three technologies were leveraged to solve the challenges of resilient, distributed task scheduling.
This talk will present a number of distributed system concepts in the real-world context of the Scheduler project. How can you dynamically adjust for increased task load with zero downtime? Can you guarantee task ordering across many servers? Do your tasks still run when an entire datacenter goes down? What happens if your tasks are scheduled twice? Attendees can expect to see how all of these challenges were addressed.
Some familiarity with distributed queueing and actor systems will be helpful for attendees of this talk.
This document discusses the future of serverless computing moving beyond stateless functions to include state management. It argues that serverless currently focuses on ease of development but lacks support for state, limiting its usefulness. The future of serverless lies in abstracting state management using techniques like CRDTs, event sourcing and actor models to provide stateful functions and enable new programming models like Azure Durable Functions and Lightbend Cloud State. This will allow serverless to fulfill its promise of revolutionizing cloud computing.
Architecting for the cloud elasticity securityLen Bass
Concurrency and state management are important considerations for achieving elasticity in cloud systems. There are three types of state: session state kept by clients, server-side state kept in processes, and persistent state stored externally. Server-side state makes scaling difficult, while stateless servers allow elasticity. Memcached provides a way to synchronize small amounts of in-memory state across servers to support stateless services running elastically in the cloud.
GR8Conf 2011: Tuning Grails Applications by Peter LedbrookGR8Conf
This document discusses various techniques for tuning Grails applications for performance. It covers profiling tools for the server, database, and UI. Specific strategies are presented for improving database queries, caching, asynchronous processing, and reducing network traffic. The document emphasizes profiling first before optimizing and notes diminishing returns from performance work.
An adaptive and eventually self healing framework for geo-distributed real-ti...Angad Singh
This document discusses an adaptive and self-healing framework for real-time data ingestion across geographically distributed data centers. It describes the problem domain of ingesting 15 billion events per day across multiple schemas and data types from various sources. The proposed architecture includes an ingestion layer using technologies like Storm, Kafka and HDFS to ingest, transform and replicate streaming and batch data. It also includes a serving layer using Aerospike to provide low-latency aggregated user views. Issues encountered with technologies like Storm and Kafka are discussed, as well as features still under development.
The document discusses three proposed cluster computing frameworks: CloudMirror, Mesos, and Omega.
CloudMirror addresses challenges of providing bandwidth guarantees for interactive workloads in the cloud. It proposes a new network abstraction model based on application communication structure and a workload placement algorithm for efficient resource allocation.
Mesos targets sharing cluster resources across frameworks. It introduces a two-level resource allocation and isolation model to allow sharing while preventing interference. Mesos was implemented in C++ and evaluated using various macrobenchmarks showing improved resource utilization and scalability.
Omega is a proposed scheduler architecture that avoids centralized control. It allows schedulers parallel access to the entire cluster and uses optimistic concurrency control. Simulations showed Omega improved scheduling performance
This document discusses coordination challenges with microservices architectures and distributed systems. It presents two approaches for coordinating business transactions across microservices: 1) using events and handling all possible failure scenarios, which becomes complex, and 2) employing a Saga Execution Coordinator (SEC) that sequentially executes the steps of a business transaction and logs each step, allowing for compensation of incomplete transactions. The SEC approach provides a simpler way to coordinate multi-step transactions across multiple services and databases. In summary, the document covers coordination challenges in distributed systems, two approaches for coordinating business transactions across microservices (using events or a Saga Execution Coordinator), and how the SEC approach provides a simpler solution.
This document summarizes a presentation about near real-time analytics platforms at Uber and LinkedIn. It discusses use cases for streaming analytics, challenges with scalability and operations, and new platforms developed using Apache Samza and SQL. Key points include how Samza is used to build streaming applications with SQL queries, operators, and support for multi-stage workflows. The platforms aim to simplify deployment and management of streaming jobs through interfaces like AthenaX.
Distributed Task Scheduling with Akka, Kafka and CassandraDavid van Geest
https://www.youtube.com/watch?v=s3GfXTnzG_Y
Dynamically scheduled tasks are at the heart of PagerDuty's microservices. They deliver incident alerts, on-call notifications, and manage myriad administrative chores. Historically, these tasks were scheduled and run using an in-house library built on Cassandra, but that solution had begun to show its age.
Early in 2016, the Core team at PagerDuty built a new Task Scheduler using Akka, Kafka, and Cassandra. After six weeks in development, the Scheduler is now running in production. This talk discusses how the strengths of the three technologies were leveraged to solve the challenges of resilient, distributed task scheduling.
This talk will present a number of distributed system concepts in the real-world context of the Scheduler project. How can you dynamically adjust for increased task load with zero downtime? Can you guarantee task ordering across many servers? Do your tasks still run when an entire datacenter goes down? What happens if your tasks are scheduled twice? Attendees can expect to see how all of these challenges were addressed.
Some familiarity with distributed queueing and actor systems will be helpful for attendees of this talk.
This document discusses the future of serverless computing moving beyond stateless functions to include state management. It argues that serverless currently focuses on ease of development but lacks support for state, limiting its usefulness. The future of serverless lies in abstracting state management using techniques like CRDTs, event sourcing and actor models to provide stateful functions and enable new programming models like Azure Durable Functions and Lightbend Cloud State. This will allow serverless to fulfill its promise of revolutionizing cloud computing.
Architecting for the cloud elasticity securityLen Bass
Concurrency and state management are important considerations for achieving elasticity in cloud systems. There are three types of state: session state kept by clients, server-side state kept in processes, and persistent state stored externally. Server-side state makes scaling difficult, while stateless servers allow elasticity. Memcached provides a way to synchronize small amounts of in-memory state across servers to support stateless services running elastically in the cloud.
Kafka summit SF 2019 - the art of the event-streaming appNeil Avery
Have you ever imagined what it would be like to build a massively scalable streaming application on Kafka, the challenges, the patterns and the thought process involved? How much of the application can be reused? What patterns will you discover? How does it all fit together? Depending upon your use case and business, this can mean many things. Starting out with a data pipeline is one thing, but evolving into a company-wide real-time application that is business critical and entirely dependent upon a streaming platform is a giant leap. Large-scale streaming applications are also called event streaming applications. They are classically different from other data systems; event streaming applications are viewed as a series of interconnected streams that are topologically defined using stream processors; they hold state that models your use case as events. Almost like a deconstructed realtime database.
In this talk, I step through the origins of event streaming systems, understanding how they are developed from raw events to evolve into something that can be adopted at an organizational scale. I start with event-first thinking, Domain Driven Design to build data models that work with the fundamentals of Streams, Kafka Streams, KSQL and Serverless (FaaS). Building upon this, I explain how to build common business functionality by stepping through patterns for Scalable payment processing Run it on rails: Instrumentation and monitoring Control flow patterns (start, stop, pause) Finally, all of these concepts are combined in a solution architecture that can be used at enterprise scale. I will introduce enterprise patterns such as events-as-a-backbone, events as APIs and methods for governance and self-service. You will leave talk with an understanding of how to model events with event-first thinking, how to work towards reusable streaming patterns and most importantly, how it all fits together at scale.
Philip Thompson is a software engineer at DataStax and contributor to Apache Cassandra. The document discusses Apache Cassandra, an open source, distributed database built for scalability and high availability. It describes Cassandra's architecture including data distribution across nodes, replication, consistency levels, and mechanisms for repair and anti-entropy.
This document discusses client-side load balancing in a cloud computing environment. It describes how a client-side load balancer can distribute requests across backend web servers in a scalable way without requiring control of the infrastructure. The proposed architecture uses static anchor pages hosted on Amazon S3 that contain JavaScript code to select a web server based on its reported load. The JavaScript then proxies the request to that server and updates the page content. This approach achieves high scalability and adaptiveness without hardware load balancers or layer 2 optimizations.
Enabling Presto to handle massive scale at lightning speedShubham Tagra
Presto User Group Singapore Meetup - March 2019.
These slides talk through the current state of Presto and features that help Presto work better in cloud and a glimpse into the roadmap
Architecting for the cloud cloud providersLen Bass
The document discusses cloud providers and services available on Amazon Web Services. It provides an overview of compute, storage, database, and other services and how they can provide redundancy across availability zones and regions. Examples are given of different outage scenarios that can occur at the zone, region, or provider level and strategies for architecting applications to mitigate risks from these outages.
An Efficient Decentralized Load Balancing Algorithm in Cloud ComputingAisha Kalsoom
This document proposes a new efficient decentralized load balancing algorithm for cloud computing. It consists of two phases: 1) a request sequencing phase where incoming user requests are sequenced to minimize wait times, and 2) a load transferring phase where a load balancer calculates resource utilization of each VM and transfers tasks to less utilized VMs. This algorithm aims to improve load balancing performance and achieve more efficient resource utilization in cloud computing environments.
Enhancing minimal virtual machine migration in cloud environmenteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A load balancing model based on cloud partitioning for the public cloud. ppt Lavanya Vigrahala
Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
This paper discusses challenges in diagnosing errors when deploying Hadoop ecosystems. It provides 15 examples of specific errors that can occur with Hbase/Hadoop deployment on Amazon EC2, along with potential root causes. The paper also classifies errors as operational, configuration, software, or resource-related. It identifies inconsistencies across component logs, high signal-to-noise ratios, and uncertainty in correlating events as difficulties for error diagnosis. The paper contributes examples to a repository for mapping deployment symptoms to fault trees to determine root causes.
The document describes several Oracle database background processes. ABMR performs block media recovery by fetching corrupted blocks from standby databases. DBRM sets and enforces resource plans. DIAG processes handle deadlocks and diagnostic tasks. EMNC coordinates event notifications by spawning slave processes. FBDA archives old row versions for flashback and maintains metadata. GEN0 offloads tasks to improve performance. I/O slaves simulate asynchronous I/O. Connection brokers and pooled servers manage database resident connection pooling. RCBG handles cache invalidations in Oracle RAC. RP processes replay workload captures in parallel. SMCO coordinates space management with slave processes. VKRM and VKTM manage resource scheduling and publish time references.
The document presents methods for modeling and analyzing workflow overhead in distributed systems, introducing approaches for calculating cumulative overhead, and reports on experiments applying optimization techniques like job clustering and resource provisioning to evaluate their effects on reducing overhead and improves workflow performance on different computational environments and applications.
Data-Oriented Programming with Clojure and Jackdaw (Charles Reese, Funding Ci...confluent
When Funding Circle needed to scale its lending platform, we chose Kafka and Clojure. More than a programming language, Clojure is an interactive development environment with which you can build up an application function by function in a continuous unbroken flow. Since 2016 we have been developing our lending platform using Clojure and Kafka Streams, and today we process millions of transaction dollars daily. In 2018 we released "Jackdaw", our open-source Clojure library for working with Kafka Streams. In this talk, attendees will learn a radical new approach to building stream processing applications in a highly productive environment--one they can use immediately via Jackdaw or apply to their favorite programming system.
Base paper ppt-. A load balancing model based on cloud partitioning for the ...Lavanya Vigrahala
A load balancing model based on cloud partitioning for the public cloud. -Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
Lambda-less Stream Processing @Scale in LinkedIn
The document discusses challenges with stream processing including data accuracy and reprocessing. It proposes a "lambda-less" approach using windowed computations and handling late and out-of-order events to produce eventually correct results. Samza is used to implement stream processing jobs with local state stored durably in Kafka. This avoids duplicating code for real-time and batch processing while supporting reprocessing through resetting offsets. The approach scales to large datasets by using Hadoop for offline experimentation before pushing logic online.
Proactive performance monitoring with adaptive thresholdsJohn Beresniewicz
Presentation given at UKOUG 2008 conference on the Adaptive Thresholds technology in Oracle database 10.2+ and Enterprise Manager 11. Adaptive Thresholds allows users to do consistent and effective performance monitoring across systems and architectures by using statistical characterization of metric streams to automatically set and adapt monitoring thresholds independent of application workload.
Unified Stream Processing at Scale with Apache Samza - BDS2017Jacob Maes
The shift to stream processing at LinkedIn has accelerated over the past few years. We now have over 200 Samza applications in production processing more than 260B events per day. Many of these are new applications, but there have also been more migrations from existing online and offline applications. To support the influx of new use cases, we have improved the flexibility, efficiency and reliability of Apache Samza.
In this talk, we will take a brief look at the broader streaming ecosystem at LinkedIn, then we will zoom in on a few representative use cases and explain how they are powered by recent advancements to Apache Samza including a unified high level API, flexible deployment model, batch processing, and more.
Will it Scale? The Secrets behind Scaling Stream Processing ApplicationsNavina Ramesh
This talk was presented at the Apache Big Data 2016, North America conference that was held in Vancouver, CA (http://events.linuxfoundation.org/events/archive/2016/apache-big-data-north-america/program/schedule)
This document discusses load balancing in cloud computing. It begins by defining cloud computing and some of its key characteristics like broad network access, rapid elasticity, and pay-as-you-go pricing. It then discusses how load balancing can improve performance in distributed cloud environments by redistributing load, improving response times, and better utilizing resources. The document outlines different load balancing techniques like virtual machine migration and throttled load balancing using a load balancer, virtual machines, and a data center controller. It also proposes a trust and reliability based algorithm that prioritizes data centers for load balancing based on calculated trust values that consider factors like initialization time, machine performance, and fault rates.
Reactive applications are becoming a de-facto industry standard and, if employed correctly, toolkits like Lightbend Reactive Platform make the implementation easier than ever. But design of these systems might be challenging as it requires particular mindset shift to tackle problems we might not be used to.
In this talk, we’re going to discuss the most common things I’ve seen in the field that prevented applications working as expected. I’d like to talk about typical pitfalls that might cause problems, about trade-offs that might not be fully understood and important choices that might be overlooked. These include persistent actors pitfalls, tackling of network partitions, proper implementations of graceful shutdown or distributed transactions, trade-offs of micro-services or actors and more.
This talk should be interesting for anyone who is thinking about, implementing, or has already deployed a reactive application. My goal is to provide a comprehensive explanation of common problems to be sure they won’t be repeated by fellow developers. The talk is a little bit more focused on the Lightbend platform but understanding of the concepts we are going to talk about should be beneficial for everyone interested in this field.
Scaling a Core Banking Engine Using Apache Kafka | Peter Dudbridge, Thought M...HostedbyConfluent
Core banking is one of the last bastions for the mainframe. As many other industries have moved to the cloud, why are most of the world’s banks yet to follow?
The answer lies in a bank's conflicting needs: correctness and scale - historically achievable using a monolithic application running on a large mainframe. The clock is ticking for the banks as we approach an inflection point where the mainframes become too expensive, and aren’t flexible enough to meet the modern banking consumers needs
A simple lift and shift onto the cloud does not work. As we distribute our core processing we spend an increasing amount of time on the network, and race conditions lurk that threaten ‘correctness’
This session explores how Thought Machine’s core banking system ‘Vault’ was built in a cloud first manner, leveraging Kafka to enable asynchronous and parallel processing at scale, specifically focusing on the architectural patterns we have used to ensure ‘correctness’ in such an environment
The document discusses distributed resource scheduling frameworks and compares several open source schedulers. It covers the architectural evolution of resource scheduling including monolithic, two-level, shared state, distributed, and hybrid models. Prominent frameworks discussed include Kubernetes, Swarm, Mesos, YARN, and others. The document outlines key aspects of distributed scheduling like resource types, container orchestration, application support, networking, storage, scalability, and security.
Scylla Summit 2022: Scylla 5.0 New Features, Part 1ScyllaDB
Discover the new features and capabilities of Scylla Open Source 5.0 directly from the engineers who developed it. This second block of lightning talks will cover the following topics:
- New IO Scheduler and Disk Parallelism
- Per-Service-Level Timeouts
- Better Workload Estimation for Backpressure and Out-of-Memory Conditions
- Large Partition Handling Improvements
- Optimizing Reverse Queries
To watch all of the recordings hosted during Scylla Summit 2022 visit our website here: https://www.scylladb.com/summit.
Kafka summit SF 2019 - the art of the event-streaming appNeil Avery
Have you ever imagined what it would be like to build a massively scalable streaming application on Kafka, the challenges, the patterns and the thought process involved? How much of the application can be reused? What patterns will you discover? How does it all fit together? Depending upon your use case and business, this can mean many things. Starting out with a data pipeline is one thing, but evolving into a company-wide real-time application that is business critical and entirely dependent upon a streaming platform is a giant leap. Large-scale streaming applications are also called event streaming applications. They are classically different from other data systems; event streaming applications are viewed as a series of interconnected streams that are topologically defined using stream processors; they hold state that models your use case as events. Almost like a deconstructed realtime database.
In this talk, I step through the origins of event streaming systems, understanding how they are developed from raw events to evolve into something that can be adopted at an organizational scale. I start with event-first thinking, Domain Driven Design to build data models that work with the fundamentals of Streams, Kafka Streams, KSQL and Serverless (FaaS). Building upon this, I explain how to build common business functionality by stepping through patterns for Scalable payment processing Run it on rails: Instrumentation and monitoring Control flow patterns (start, stop, pause) Finally, all of these concepts are combined in a solution architecture that can be used at enterprise scale. I will introduce enterprise patterns such as events-as-a-backbone, events as APIs and methods for governance and self-service. You will leave talk with an understanding of how to model events with event-first thinking, how to work towards reusable streaming patterns and most importantly, how it all fits together at scale.
Philip Thompson is a software engineer at DataStax and contributor to Apache Cassandra. The document discusses Apache Cassandra, an open source, distributed database built for scalability and high availability. It describes Cassandra's architecture including data distribution across nodes, replication, consistency levels, and mechanisms for repair and anti-entropy.
This document discusses client-side load balancing in a cloud computing environment. It describes how a client-side load balancer can distribute requests across backend web servers in a scalable way without requiring control of the infrastructure. The proposed architecture uses static anchor pages hosted on Amazon S3 that contain JavaScript code to select a web server based on its reported load. The JavaScript then proxies the request to that server and updates the page content. This approach achieves high scalability and adaptiveness without hardware load balancers or layer 2 optimizations.
Enabling Presto to handle massive scale at lightning speedShubham Tagra
Presto User Group Singapore Meetup - March 2019.
These slides talk through the current state of Presto and features that help Presto work better in cloud and a glimpse into the roadmap
Architecting for the cloud cloud providersLen Bass
The document discusses cloud providers and services available on Amazon Web Services. It provides an overview of compute, storage, database, and other services and how they can provide redundancy across availability zones and regions. Examples are given of different outage scenarios that can occur at the zone, region, or provider level and strategies for architecting applications to mitigate risks from these outages.
An Efficient Decentralized Load Balancing Algorithm in Cloud ComputingAisha Kalsoom
This document proposes a new efficient decentralized load balancing algorithm for cloud computing. It consists of two phases: 1) a request sequencing phase where incoming user requests are sequenced to minimize wait times, and 2) a load transferring phase where a load balancer calculates resource utilization of each VM and transfers tasks to less utilized VMs. This algorithm aims to improve load balancing performance and achieve more efficient resource utilization in cloud computing environments.
Enhancing minimal virtual machine migration in cloud environmenteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A load balancing model based on cloud partitioning for the public cloud. ppt Lavanya Vigrahala
Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
This paper discusses challenges in diagnosing errors when deploying Hadoop ecosystems. It provides 15 examples of specific errors that can occur with Hbase/Hadoop deployment on Amazon EC2, along with potential root causes. The paper also classifies errors as operational, configuration, software, or resource-related. It identifies inconsistencies across component logs, high signal-to-noise ratios, and uncertainty in correlating events as difficulties for error diagnosis. The paper contributes examples to a repository for mapping deployment symptoms to fault trees to determine root causes.
The document describes several Oracle database background processes. ABMR performs block media recovery by fetching corrupted blocks from standby databases. DBRM sets and enforces resource plans. DIAG processes handle deadlocks and diagnostic tasks. EMNC coordinates event notifications by spawning slave processes. FBDA archives old row versions for flashback and maintains metadata. GEN0 offloads tasks to improve performance. I/O slaves simulate asynchronous I/O. Connection brokers and pooled servers manage database resident connection pooling. RCBG handles cache invalidations in Oracle RAC. RP processes replay workload captures in parallel. SMCO coordinates space management with slave processes. VKRM and VKTM manage resource scheduling and publish time references.
The document presents methods for modeling and analyzing workflow overhead in distributed systems, introducing approaches for calculating cumulative overhead, and reports on experiments applying optimization techniques like job clustering and resource provisioning to evaluate their effects on reducing overhead and improves workflow performance on different computational environments and applications.
Data-Oriented Programming with Clojure and Jackdaw (Charles Reese, Funding Ci...confluent
When Funding Circle needed to scale its lending platform, we chose Kafka and Clojure. More than a programming language, Clojure is an interactive development environment with which you can build up an application function by function in a continuous unbroken flow. Since 2016 we have been developing our lending platform using Clojure and Kafka Streams, and today we process millions of transaction dollars daily. In 2018 we released "Jackdaw", our open-source Clojure library for working with Kafka Streams. In this talk, attendees will learn a radical new approach to building stream processing applications in a highly productive environment--one they can use immediately via Jackdaw or apply to their favorite programming system.
Base paper ppt-. A load balancing model based on cloud partitioning for the ...Lavanya Vigrahala
A load balancing model based on cloud partitioning for the public cloud. -Load balancing in the cloud computing environment has an important impact on the performance. Good load balancing makes cloud computing more efficient and improves user satisfaction. This article introduces a better load balance model for the public cloud based on the cloud partitioning concept with a switch mechanism to choose different strategies for different situations. The algorithm applies the game theory to the load balancing strategy to improve the efficiency in the public cloud environment.
Lambda-less Stream Processing @Scale in LinkedIn
The document discusses challenges with stream processing including data accuracy and reprocessing. It proposes a "lambda-less" approach using windowed computations and handling late and out-of-order events to produce eventually correct results. Samza is used to implement stream processing jobs with local state stored durably in Kafka. This avoids duplicating code for real-time and batch processing while supporting reprocessing through resetting offsets. The approach scales to large datasets by using Hadoop for offline experimentation before pushing logic online.
Proactive performance monitoring with adaptive thresholdsJohn Beresniewicz
Presentation given at UKOUG 2008 conference on the Adaptive Thresholds technology in Oracle database 10.2+ and Enterprise Manager 11. Adaptive Thresholds allows users to do consistent and effective performance monitoring across systems and architectures by using statistical characterization of metric streams to automatically set and adapt monitoring thresholds independent of application workload.
Unified Stream Processing at Scale with Apache Samza - BDS2017Jacob Maes
The shift to stream processing at LinkedIn has accelerated over the past few years. We now have over 200 Samza applications in production processing more than 260B events per day. Many of these are new applications, but there have also been more migrations from existing online and offline applications. To support the influx of new use cases, we have improved the flexibility, efficiency and reliability of Apache Samza.
In this talk, we will take a brief look at the broader streaming ecosystem at LinkedIn, then we will zoom in on a few representative use cases and explain how they are powered by recent advancements to Apache Samza including a unified high level API, flexible deployment model, batch processing, and more.
Will it Scale? The Secrets behind Scaling Stream Processing ApplicationsNavina Ramesh
This talk was presented at the Apache Big Data 2016, North America conference that was held in Vancouver, CA (http://events.linuxfoundation.org/events/archive/2016/apache-big-data-north-america/program/schedule)
This document discusses load balancing in cloud computing. It begins by defining cloud computing and some of its key characteristics like broad network access, rapid elasticity, and pay-as-you-go pricing. It then discusses how load balancing can improve performance in distributed cloud environments by redistributing load, improving response times, and better utilizing resources. The document outlines different load balancing techniques like virtual machine migration and throttled load balancing using a load balancer, virtual machines, and a data center controller. It also proposes a trust and reliability based algorithm that prioritizes data centers for load balancing based on calculated trust values that consider factors like initialization time, machine performance, and fault rates.
Reactive applications are becoming a de-facto industry standard and, if employed correctly, toolkits like Lightbend Reactive Platform make the implementation easier than ever. But design of these systems might be challenging as it requires particular mindset shift to tackle problems we might not be used to.
In this talk, we’re going to discuss the most common things I’ve seen in the field that prevented applications working as expected. I’d like to talk about typical pitfalls that might cause problems, about trade-offs that might not be fully understood and important choices that might be overlooked. These include persistent actors pitfalls, tackling of network partitions, proper implementations of graceful shutdown or distributed transactions, trade-offs of micro-services or actors and more.
This talk should be interesting for anyone who is thinking about, implementing, or has already deployed a reactive application. My goal is to provide a comprehensive explanation of common problems to be sure they won’t be repeated by fellow developers. The talk is a little bit more focused on the Lightbend platform but understanding of the concepts we are going to talk about should be beneficial for everyone interested in this field.
Scaling a Core Banking Engine Using Apache Kafka | Peter Dudbridge, Thought M...HostedbyConfluent
Core banking is one of the last bastions for the mainframe. As many other industries have moved to the cloud, why are most of the world’s banks yet to follow?
The answer lies in a bank's conflicting needs: correctness and scale - historically achievable using a monolithic application running on a large mainframe. The clock is ticking for the banks as we approach an inflection point where the mainframes become too expensive, and aren’t flexible enough to meet the modern banking consumers needs
A simple lift and shift onto the cloud does not work. As we distribute our core processing we spend an increasing amount of time on the network, and race conditions lurk that threaten ‘correctness’
This session explores how Thought Machine’s core banking system ‘Vault’ was built in a cloud first manner, leveraging Kafka to enable asynchronous and parallel processing at scale, specifically focusing on the architectural patterns we have used to ensure ‘correctness’ in such an environment
The document discusses distributed resource scheduling frameworks and compares several open source schedulers. It covers the architectural evolution of resource scheduling including monolithic, two-level, shared state, distributed, and hybrid models. Prominent frameworks discussed include Kubernetes, Swarm, Mesos, YARN, and others. The document outlines key aspects of distributed scheduling like resource types, container orchestration, application support, networking, storage, scalability, and security.
Scylla Summit 2022: Scylla 5.0 New Features, Part 1ScyllaDB
Discover the new features and capabilities of Scylla Open Source 5.0 directly from the engineers who developed it. This second block of lightning talks will cover the following topics:
- New IO Scheduler and Disk Parallelism
- Per-Service-Level Timeouts
- Better Workload Estimation for Backpressure and Out-of-Memory Conditions
- Large Partition Handling Improvements
- Optimizing Reverse Queries
To watch all of the recordings hosted during Scylla Summit 2022 visit our website here: https://www.scylladb.com/summit.
This document proposes a new scheduler architecture called Omega for large compute clusters. It discusses limitations of current monolithic and two-level scheduler designs in scaling to meet increasing workload demands. The key aspects of Omega are that it uses a shared state across parallel schedulers and lock-free optimistic concurrency control to provide both flexibility in implementing new policies and high performance at scale. It evaluates Omega's performance compared to other designs using simulations and traces of real Google production workloads, finding that interference is low and Omega can match or exceed other approaches. It also demonstrates Omega's flexibility by implementing a scheduler that adjusts MapReduce job resources based on overall cluster utilization.
Distributed Resource Scheduling Frameworks, Is there a clear Winner ?Naganarasimha Garla
Distributed resource scheduling frameworks like Kubernetes, Mesos, YARN, and Swarm each take different architectural approaches to scheduling resources across a cluster. The document provides an overview of each framework's architecture, key features related to scheduling like priority, isolation, and support for multiple container types. It also compares the frameworks based on functional attributes such as resource granularity, scheduler support, oversubscription, and support for isolation and applications.
Apache Mesos is a cluster manager that provides efficient resource sharing for distributed applications across a shared pool of nodes. It allows organizations to run applications like Hadoop, Spark, and Storm on large clusters with high utilization. Mesos addresses issues with prior solutions that constrained everything as "jobs" or required static partitioning. It has been adopted by companies like Twitter, Airbnb, and Hubspot to improve efficiency and allow applications to dynamically scale resources.
This document provides an overview of building cloud-ready applications in .NET. It defines what makes an application cloud-ready, discusses common issues with legacy applications, and recommends design patterns and practices to address these issues, including loose coupling, high cohesion, messaging, service discovery, API gateways, and resiliency policies. It includes code examples and links to additional resources.
Megastore combines the scalability of NoSQL with the ACID properties of relational databases. It uses Paxos replication across data centers to provide high availability with low latency. The data is partitioned into entity groups which are replicated independently to allow for scale. Transactions within a group use multi-version concurrency control and across groups use two-phase commit. Coordinators track write ordering to prevent conflicts during reads and writes. Metrics from Google showed Megastore provided low latency access even with widespread data distribution.
Mesos is a cluster manager that provides efficient resource isolation and sharing across distributed applications running on a shared pool of nodes. It provides APIs for resource management and scheduling and can run applications like Hadoop, Spark, and Kafka across large compute clusters. Mesos was created at Berkeley and adopted by companies like Twitter, Airbnb, and Hubspot to improve resource utilization, allow dynamic scaling of services, and reduce the operational complexity of managing large fleets of servers. Mesos also integrates with Docker and Kubernetes to enable containerized workloads and services to be deployed and orchestrated on Mesos clusters.
This document proposes the Zeta Architecture, an enterprise architecture that enables simplified business processes and scalable data integration. It aims to leverage all existing hardware, maintain some isolation, improve data backup and disaster recovery, and allow dynamic allocation of resources. The architecture consists of seven pluggable components: distributed file system, real-time data storage, pluggable compute model, deployment/container management, solution architecture, enterprise applications, and dynamic global resource management. It is designed to simplify applications and accommodate business needs.
Webinar Slides: MySQL HA/DR/Geo-Scale - High Noon #2: Galera ClusterContinuent
Galera Cluster vs. Continuent Tungsten Clusters
Building a Geo-Scale, Multi-Region and Highly Available MySQL Cloud Back-End
This second installment of our High Noon series of on-demand webinars is focused on Galera Cluster (including MariaDB Cluster & Percona XtraDB Cluster). It looks at some of the key characteristics of Galera Cluster and how it fares as a MySQL HA / DR / Geo-Scale solution, especially when compared to Continuent Tungsten Clustering.
Watch this webinar to learn how to do better MySQL HA / DR / Geo-Scale.
AGENDA
- Goals for the High Noon Webinar Series
- High Noon Series: Tungsten Clustering vs Others
- Galera Cluster (aka MariaDB Cluster & Percona XtraDB Cluster)
- Key Characteristics
- Certification-based Replication
- Galera Multi-Site Requirements
- Limitations Using Galera Cluster
- How to do better MySQL HA / DR / Geo-Scale?
- Galera Cluster vs Tungsten Clustering
- About Continuent & Its Solutions
PRESENTER
Matthew Lang - Customer Success Director – Americas, Continuent - has over 25 years of experience in database administration, database programming, and system architecture, including the creation of a database replication product that is still in use today. He has designed highly available, scaleable systems that have allowed startups to quickly become enterprise organizations, utilizing a variety of technologies including open source projects, virtualization and cloud.
Apolicy achieving least privilege access in kubernetes - https://apolicy.io/joanwlevin
This document provides an overview of role-based access control (RBAC) in Kubernetes, including common pitfalls and best practices for implementing the principle of least privilege. It discusses RBAC concepts like roles, clusterroles, rules, resources, and bindings. It also covers aggregating clusterroles, using non-resource URLs, and tools for analyzing access like kubectl can-i. The document emphasizes isolating risky access, using audit trails to monitor usage, and finding a balance between security and operational needs.
We talk a lot about Galera Cluster being great for High Availability, but what about Disaster Recovery (DR)? Database outages can occur when you lose a data centre due to data center power outages or natural disaster, so why not plan appropriately in advance?
In this webinar, we will discuss the business considerations including achieving the highest possible uptime, analysis business impact as well as risk, focus on disaster recovery itself, as well as discussing various scenarios, from having no offsite data to having synchronous replication to another data centre.
This webinar will cover MySQL with Galera Cluster, as well as branches MariaDB Galera Cluster as well as Percona XtraDB Cluster (PXC). We will focus on architecture solutions, DR scenarios and have you on your way to success at the end of it.
Your data is in Prometheus, now what? (CurrencyFair Engineering Meetup, 2016)Brian Brazil
Prometheus is a next-generation monitoring system with a time series database at it's core. Once you have a time series database, what do you do with it though? This talk will look at getting data in, and more importantly how to use the data you collect productively.
Contact us at prometheus@robustperception.io
This document provides an introduction to real-time operating systems (RTOSes) and FreeRTOS. It discusses the need for timely and reliable task scheduling in applications like robotics. It defines common scheduling terminology and describes Rate Monotonic Scheduling. FreeRTOS is introduced as a free and lightweight RTOS that provides task scheduling and peripheral access APIs. The document gives instructions for setting up FreeRTOS on Arduino and Raspberry Pi, and provides example code demonstrating task creation and scheduling. Questions are provided to help understand FreeRTOS concepts and functionality.
The document provides an overview of NoSQL databases and their advantages over relational databases for handling large, distributed datasets in cloud computing environments. Some key points:
- NoSQL databases can scale horizontally to support distributed and heterogeneous data better than relational databases. They do not require rigid schemas and support flexible data models.
- NoSQL is well-suited for cloud computing where data is distributed globally and data volumes are large and growing rapidly. It reduces the need to maintain relationships between distributed records.
- Common NoSQL data models include key-value, document, columnar, and graph databases. These models provide more flexibility than relational databases for semi-structured and unstructured data.
Monitoring Hadoop with Prometheus (Hadoop User Group Ireland, December 2015)Brian Brazil
Brian Brazil is an engineer passionate about reliable systems. He worked at Google SRE for 7 years and is now the founder of Robust Perception. Prometheus is an open source monitoring system inspired by Borgmon. It is mainly written in Go and used by over 100 companies. Prometheus regularly polls metrics from instrumented jobs and services. This allows it to provide alerts when things go wrong and insights into performance over time.
- Clickhouse is being used by GoEuro to replace their Graphite backend for monitoring, as their previous Graphite setup required too much maintenance and tuning over time to handle their scale of 20 million visitors per month, 150 engineers, and 600+ releases per week.
- Key reasons for choosing Clickhouse include its built-in replication, sharding, linear scalability, and a GraphiteMergeTree table engine that provides 100% compatibility with the Graphite query language.
- Downsides of Clickhouse include initial dependency on Zookeeper for sharding/replication and slower read queries against sharded data, but it currently uses only 2 CPU cores and 2GB RAM to handle GoEuro's monitoring needs.
Kai Sasaki from Treasure Data discusses their efforts to implement auto scaling for their distributed Presto and Hive query engines. They decoupled the storage layer from the processing engines to allow dynamic scaling. They migrated infrastructure to AWS CodeDeploy and Auto Scaling Groups to automate deployments and scaling. They implemented target tracking auto scaling based on CPU usage but found it did not work well due to conservative scaling in behavior and long running queries blocking instance termination. Future work includes real auto scaling without target tracking and auto query migration during outages.
Discover the benefits of outsourcing SEO to Indiadavidjhones387
"Discover the benefits of outsourcing SEO to India! From cost-effective services and expert professionals to round-the-clock work advantages, learn how your business can achieve digital success with Indian SEO solutions.
Ready to Unlock the Power of Blockchain!Toptal Tech
Imagine a world where data flows freely, yet remains secure. A world where trust is built into the fabric of every transaction. This is the promise of blockchain, a revolutionary technology poised to reshape our digital landscape.
Toptal Tech is at the forefront of this innovation, connecting you with the brightest minds in blockchain development. Together, we can unlock the potential of this transformative technology, building a future of transparency, security, and endless possibilities.
Instagram has become one of the most popular social media platforms, allowing people to share photos, videos, and stories with their followers. Sometimes, though, you might want to view someone's story without them knowing.
Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdfFlorence Consulting
Quattordicesimo Meetup di Milano, tenutosi a Milano il 23 Maggio 2024 dalle ore 17:00 alle ore 18:30 in presenza e da remoto.
Abbiamo parlato di come Axpo Italia S.p.A. ha ridotto il technical debt migrando le proprie APIs da Mule 3.9 a Mule 4.4 passando anche da on-premises a CloudHub 1.0.
Understanding User Behavior with Google Analytics.pdfSEO Article Boost
Unlocking the full potential of Google Analytics is crucial for understanding and optimizing your website’s performance. This guide dives deep into the essential aspects of Google Analytics, from analyzing traffic sources to understanding user demographics and tracking user engagement.
Traffic Sources Analysis:
Discover where your website traffic originates. By examining the Acquisition section, you can identify whether visitors come from organic search, paid campaigns, direct visits, social media, or referral links. This knowledge helps in refining marketing strategies and optimizing resource allocation.
User Demographics Insights:
Gain a comprehensive view of your audience by exploring demographic data in the Audience section. Understand age, gender, and interests to tailor your marketing strategies effectively. Leverage this information to create personalized content and improve user engagement and conversion rates.
Tracking User Engagement:
Learn how to measure user interaction with your site through key metrics like bounce rate, average session duration, and pages per session. Enhance user experience by analyzing engagement metrics and implementing strategies to keep visitors engaged.
Conversion Rate Optimization:
Understand the importance of conversion rates and how to track them using Google Analytics. Set up Goals, analyze conversion funnels, segment your audience, and employ A/B testing to optimize your website for higher conversions. Utilize ecommerce tracking and multi-channel funnels for a detailed view of your sales performance and marketing channel contributions.
Custom Reports and Dashboards:
Create custom reports and dashboards to visualize and interpret data relevant to your business goals. Use advanced filters, segments, and visualization options to gain deeper insights. Incorporate custom dimensions and metrics for tailored data analysis. Integrate external data sources to enrich your analytics and make well-informed decisions.
This guide is designed to help you harness the power of Google Analytics for making data-driven decisions that enhance website performance and achieve your digital marketing objectives. Whether you are looking to improve SEO, refine your social media strategy, or boost conversion rates, understanding and utilizing Google Analytics is essential for your success.
Gen Z and the marketplaces - let's translate their needsLaura Szabó
The product workshop focused on exploring the requirements of Generation Z in relation to marketplace dynamics. We delved into their specific needs, examined the specifics in their shopping preferences, and analyzed their preferred methods for accessing information and making purchases within a marketplace. Through the study of real-life cases , we tried to gain valuable insights into enhancing the marketplace experience for Generation Z.
The workshop was held on the DMA Conference in Vienna June 2024.
5. Static partitioning
Web cluster、DB cluster、Hadoop ClusterなどのClusterは独
自のサーバー群を持っていてsharingしない
● hard to utilize machines
● hard to scale elastically
● hard to deal with failures
絵でわかる(p30~p40):
https://speakerdeck.com/benh/apache-mesos-nyc-meetup
8. Dynamic sharing
Running multiple frameworks in a single cluster can
● maximize utilization
● sharing data between frameworks
● simplify the infrastructure
9. Dynamic sharingの課題
Dynamic sharingのメリットは大きい一方で、Cluster
schedulingは複雑化になります:
● a wide range of requirements and policies have to be
taken into account
● clusters and their workloads keep growing and since the
scheduler's workload is roughly proportional to the
cluster size, the scheduler is at risk of becoming a
scalability bottleneck.
13. Monolithic scheduler
use a single, centralized scheduling algorithm for all jobs.
Google's current(2013) cluster scheduler is effectively
monolithic, acquired many optimizations over the years:
provide internal parallelism and multi-threading to address
head-of-line blocking and scalability.
17. Two-level scheduler(Mesos)
An obvious fix to the issues of static partition is to adjust the
allocation of resource to each scheduler dynamically, using
a central coordinator to decide how many resources each
sub-cluster can have.
Mesos works best when
1) tasks are short-lived
2) relinquish resources frequently
3) job sizes are small compared to the size of the cluster
19. Clusterのworkloads
simple two-way split:
● batch jobs: perform a computation and then finish. For
simplicity we put all low priority jobs and those marked
as "best effort" or "batch" into the batch category
● service jobs: long-running service jobs that provide end
user operations(e.g., web services) and internal
infrastructure services(e.g. storage service, naming
service, locking service)
20. Cluster traces from Google
● most(>80%) jobs are batch jobs
● the majority of resources (55-80%) are
allocated to service jobs
● service jobs typically run for much longer(20-
40% of them run for over a month) and have
fewer tasks than batch jobs
※ YahooとFacebookのworkloadsも似ている
21. Googleのニーズ
● Many batch jobs are short, and fast turnaround is important, so a lightweight, low-quality
approach to placement works just fine.
● Long-running, high-priority service jobs must meet stringent availability and performance targets,
so careful placement of their tasks is needed to maximize resistance to failures and provide
good performance.
● "head of line blocking" problem: while it is very reasonable to spend a few seconds making a
decision whose effects last for several weeks, it can be problematic if an interactive batch job
has to wait for such a calculation. This problem can be avoided by introducing parallelism.
つまりGoogleのニーズ:require a scheduler architecture that
● can accommodate both types of jobs
● flexibly support job-specific policies
● and also scale to an ever-growing amount of scheduling work.
22. なぜgoogleは不採用?
Monolithic schedulerとtwo-level schedulerはgoogleのニーズに満たせない:
1) Monolithic scheduler:
● It complicates an already difficult job: the scheduler has to minimize the
time a job spends waiting before it starts running.
● It is surprisingly difficult to support a wide range of policies in a sustainable
manner using a single-algorithm implementation.
This kind of software engineering consideration, rather than performance
scalability implementation, was our primary motivation to move to an
architecture that supported concurrent, independent scheduling components.
performance scalabilityよりsoftware engineeringの考えですね!
23. なぜgoogleは不採用?
Monolithic schedulerとtwo-level schedulerはgoogleのニーズに満たせない:
2) Two-level scheduler:
● No global view of the overall cluster state
● Lock issue: pessimistic concurrency control
● Assumptions that resource become available frequently and scheduler
decisions are quick, so works best when short tasks/relinquish resource
frequently/small job size compared to the size of the cluster: but google's
cluster workloads do not have these properties, especially in the case of
service jobs
24. Share-state scheduler(Omega)
● each scheduler can full access to the entire cluster
● use optimistic concurrency control
This immediately eliminate two of the issues of the two-
level scheduler approach:
➔ limited parallelism due to pessimistic concurrency
control
➔ restricted visibility of resources in a scheduler
framework
25. Share-state scheduler(Omega)
● No central resource allocator in Omega(be simplified to a persistent data store)
● All of the resource-allocation take place in the schedulers.
● "cell state": a resilient master copy of the resource allocation maintained in the cluster. Each
scheduler is given a private, local, frequently-updated copy of cell state for making scheduling
decisions. The scheduler can see the entire state of the cell.
● Omega schedulers operate completely in parallel and do not have to wait for jobs in other
schedulers and there is no inter-scheduler head of line blocking.
The performance viability of the share-state approach is ultimately determined
by the frequency at which transactions fail and the costs of such failures.
The batch scheduler is the main scalability bottleneck, the Omega model can
scale to a high workload while still providing good behavior for service jobs.
26. cluster schedulersの比較
Approach Resource
Choice
Interference Alloc.
granularity
Cluster-wide
policies
Monolithic all available none(serialized) global policy strict priority
(preemption)
Statically partitioned fixed subnet none
(partitioned)
per-partition
policy
scheduler-
dependent
Two-level(Mesos) dynamic subnet pessimistic hoarding strict fairness
Shared-state(Omega) all available optimistic per-scheduler
policy
free-for-all,
priority
preemption
27. MesosとPaaSの話
PaaS検証の背景(p3):multiple workloads, multiple tenantsのPaaS上マルチClustersのresource sharing
問題
(Dynamic sharingのcluster scheduler)
PaaS上のworkloads:long running processes/one-off tasks/scheduled jobs
service jobsの割合はより高く、service jobsのschedulingはもっと重要
Mesos frameworks for Long running services:
Aurora/Marathon/SingularityなどありますがOmegaのpaper(2013)が指摘したMesosの問題(特にService
jobsの問題)
Mesosの最新状況や各frameworksの対応はどうになっているか
28. MesosとPaaSの話
Kubernetesについて
Run Kubernetes on Mesos:
https://github.com/mesosphere/kubernetes-mesos
Run Kubernetes on Hadoop YARN:
http://hortonworks.com/blog/docker-kubernetes-apache-hadoop-yarn/