Why stop the world when you can change it? Design and implementation of Incre...confluent
Since its initial release, the Kafka group membership protocol has offered Connect, Streams and Consumer applications an ingenious and robust way to balance resources among distributed processes. The process of rebalancing, as it's widely known, allows Kafka APIs to define an embedded protocol for load balancing within the group membership protocol itself. Until now, rebalancing has been working under the simple assumption that every time a new group generation is created, the members join after first releasing all of their resources, getting a whole new load assignment by the time the new group is formed. This allows Kafka APIs to provide task fault-tolerance and elasticity on top of the group membership protocol. However, due to its side-effects on multi-tenancy and scalability this simple approach in rebalancing, also known as stop-the-world effect, is limiting larger scale deployments. Because of stop-the-world, application tasks get interrupted only for most of them to receive the same resources after rebalancing. In this technical deep dive, I'll discuss the proposition of Incremental Cooperative Rebalancing as a way to alleviate stop-the-world and optimize rebalancing in Kafka APIs. We'll cover: * The internals of Incremental Cooperative Rebalancing * Uses cases that benefit from Incremental Cooperative Rebalancing * Implementation in Kafka Connect * Performance results in Kafka Connect clusters
Deploy an Elastic, Resilient, Load-Balanced Cluster in 5 Minutes with SenlinQiming Teng
This is a talk from the Austin OpenStack summit. It demonstrates how a resilient, elastic and load-balanced cluster can be deployed using senlin, heat, ceilometer, lbaas v2, nova.
Introduction to the Incremental Cooperative Protocol of KafkaGuozhang Wang
Anyone who has used Kafka consumer groups or operated a Kafka Streams application is likely familiar with the rebalancing protocol, which is used to (re)distribute partitions among the consumers of a group whenever there is a change in membership or in the topics subscribed to. The current protocol takes the safest possible approach of pausing all work and revoking ownership of all partitions so that a new assignment can be made. This “stop-the-world” approach can be frustrating especially when the mapping of partitions to the consumer that owns them barely changes. In KIP-429 we introduce incremental cooperative rebalancing for the consumer client, a new rebalancing protocol that allows consumers to retain ownership and continue fetching for their owned partitions while a rebalance is in progress. This proposal trades extra rebalances for the ability to revoke only those partitions which are to be migrated to another consumer for overall workload balance.
Why stop the world when you can change it? Design and implementation of Incre...confluent
Since its initial release, the Kafka group membership protocol has offered Connect, Streams and Consumer applications an ingenious and robust way to balance resources among distributed processes. The process of rebalancing, as it's widely known, allows Kafka APIs to define an embedded protocol for load balancing within the group membership protocol itself. Until now, rebalancing has been working under the simple assumption that every time a new group generation is created, the members join after first releasing all of their resources, getting a whole new load assignment by the time the new group is formed. This allows Kafka APIs to provide task fault-tolerance and elasticity on top of the group membership protocol. However, due to its side-effects on multi-tenancy and scalability this simple approach in rebalancing, also known as stop-the-world effect, is limiting larger scale deployments. Because of stop-the-world, application tasks get interrupted only for most of them to receive the same resources after rebalancing. In this technical deep dive, I'll discuss the proposition of Incremental Cooperative Rebalancing as a way to alleviate stop-the-world and optimize rebalancing in Kafka APIs. We'll cover: * The internals of Incremental Cooperative Rebalancing * Uses cases that benefit from Incremental Cooperative Rebalancing * Implementation in Kafka Connect * Performance results in Kafka Connect clusters
Deploy an Elastic, Resilient, Load-Balanced Cluster in 5 Minutes with SenlinQiming Teng
This is a talk from the Austin OpenStack summit. It demonstrates how a resilient, elastic and load-balanced cluster can be deployed using senlin, heat, ceilometer, lbaas v2, nova.
Introduction to the Incremental Cooperative Protocol of KafkaGuozhang Wang
Anyone who has used Kafka consumer groups or operated a Kafka Streams application is likely familiar with the rebalancing protocol, which is used to (re)distribute partitions among the consumers of a group whenever there is a change in membership or in the topics subscribed to. The current protocol takes the safest possible approach of pausing all work and revoking ownership of all partitions so that a new assignment can be made. This “stop-the-world” approach can be frustrating especially when the mapping of partitions to the consumer that owns them barely changes. In KIP-429 we introduce incremental cooperative rebalancing for the consumer client, a new rebalancing protocol that allows consumers to retain ownership and continue fetching for their owned partitions while a rebalance is in progress. This proposal trades extra rebalances for the ability to revoke only those partitions which are to be migrated to another consumer for overall workload balance.
Designing HPC & Deep Learning Middleware for Exascale Systemsinside-BigData.com
DK Panda from Ohio State University presented this deck at the 2017 HPC Advisory Council Stanford Conference.
"This talk will focus on challenges in designing runtime environments for exascale systems with millions of processors and accelerators to support various programming models. We will focus on MPI, PGAS (OpenSHMEM, CAF, UPC and UPC++) and Hybrid MPI+PGAS programming models by taking into account support for multi-core, high-performance networks, accelerators (GPGPUs and Intel MIC), virtualization technologies (KVM, Docker, and Singularity), and energy-awareness. Features and sample performance numbers from the MVAPICH2 libraries will be presented."
Watch the video: http://wp.me/p3RLHQ-glW
Learn more: http://hpcadvisorycouncil.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
RAC-Installing your First Cluster and DatabaseNikhil Kumar
RAC - Installing your First RAC
Abstract : Oracle Real Application Clusters have been one of the hottest technologies in the market since 2001 prior this is know OPS in 8i. Oracle has brought revolution in the field of database by enhancing RAC technologies in it each version. This presentation will give introduction of RAC and features introduced in each version of RAC. This presentation contains the demo of building Oracle clusterware from the scratch. Also we will discuss the new components and its features during installation. This presentation and demo will be done on version 11GR2. Which will be used as a base for our next presentation Viz. Upgradation of RAC 11GR2 to 12C RAC.
This presentation will give brief insight information of RAC infrastructure setup. Sometimes DBA doesn’t fully aware of prerequisite and verification steps that needs to perform before installing clusterware, So this session will cover thing to consider before installing clusterware and best practices followed during the whole process.
Agenda
Introduction of RAC
Installation of Clusterware.
Creating diskgroup / Adding disk to Diskgroup using ASMCA.
Creation of ACFS Volume.
Installation of RAC Database using DBCA.
In this video from the 2017 HPC Advisory Council Stanford Conference, Christian Kniep from Gaikai presents: Best Practices: State of Linux Containers.
"Linux Containers gain more and more momentum in all IT ecosystems. This talk provides an overview about what happened in the container landscape (in particular Docker) during the course of the last year and how it impacts datacenter operations, HPC and High-Performance Big Data. Furthermore Christian will give an update/extend on the ‘things to explore’ list he presented in the last Lugano workshop, applying what he learned and came across during the year 2016."
Watch the video: http://wp.me/p3RLHQ-glP
Learn more: http://qnib.org
and
http://www.hpcadvisorycouncil.com/events/2017/stanford-workshop/
Sign up for our insideHPC Newsletter: http:/insidehpc.com/newsletter
1. Double Orchestration of Redis Enterprise cluster on Kubernetes
**************
In this session we'll display how we deploy a highly available database on Kubernetes. The considerations we took when deploying a stateful application, and the challenges of answering different clients' demands for different k8s environments.
2. Operators to the rescue: stateful applications made easy with operators
**************
Kubernetes 1.7 introduced an import feature called custom controllers. This allows you to customise your Kubernetes installation and add your own resources to be managed in the native Kubernetes manner.
The session will display the operator concept and cover our journey with developing the Redis Labs operator - why we chose it and how we use it.
Static Membership: Rebalance Strategy Designed for the Cloud (Boyang Chen,Con...confluent
In this presentation, we introduce static membership (KIP-345) and share the story of adopting it at Pinterest. The static membership aims to improve the availability of stream applications, consumer groups and other applications built on top of it. The original rebalance protocol relies on the group coordinator to allocate entity ids to group members. These generated ids are ephemeral and will change when members restart and rejoin. For consumer based apps, this "dynamic membership" can cause a large percentage of tasks re-assigned to different instances during administrative operations such as code deploys, configuration updates and periodic restarts. For large state applications, shuffled tasks need a long time to recover their local states before processing and cause applications to be partially or entirely unavailable. At Pinterest, the group membership is stable between administrative operations. Motivated by this observation, we modified the Kafka's group management protocol allowing group members to provide persistent entity ids. Group membership remains unchanged based on those ids, thus no rebalance will be triggered. We can conveniently leverage Kubernetes or other cloud management frameworks to provide entity ids. By adopting static membership to the realtime infrastructure at Pinterest, applications resume processing only a few seconds after administrative operations finish. Previously with dynamic membership, it can take more than 30 minutes before applications resume. The talk is organized as follows: we first review Kafka's group management protocol and demonstrate high availability use cases that dynamic membership is unable to support. Then we share the design and adoption story of static membership. At the end, we do a live demo to show the impact of static membership.
Kubernetes Overview - Deploy your app with confidenceOmer Barel
In this presentation I explain the basics of the Kubernetes platform, alongside a dive into the core primitives (building blocks) you use, as well as yaml examples for each primitive
Galera Cluster for MySQL vs MySQL (NDB) Cluster: A High Level Comparison Severalnines
Galera Cluster for MySQL, Percona XtraDB Cluster and MariaDB Cluster (the three “flavours” of Galera Cluster) make use of the Galera WSREP libraries to handle synchronous replication.MySQL Cluster is the official clustering solution from Oracle, while Galera Cluster for MySQL is slowly but surely establishing itself as the de-facto clustering solution in the wider MySQL eco-system.
In this webinar, we will look at all these alternatives and present an unbiased view on their strengths/weaknesses and the use cases that fit each alternative.
This webinar will cover the following:
MySQL Cluster architecture: strengths and limitations
Galera Architecture: strengths and limitations
Deployment scenarios
Data migration
Read and write workloads (Optimistic/pessimistic locking)
WAN/Geographical replication
Schema changes
Management and monitoring
Designing HPC & Deep Learning Middleware for Exascale Systemsinside-BigData.com
DK Panda from Ohio State University presented this deck at the 2017 HPC Advisory Council Stanford Conference.
"This talk will focus on challenges in designing runtime environments for exascale systems with millions of processors and accelerators to support various programming models. We will focus on MPI, PGAS (OpenSHMEM, CAF, UPC and UPC++) and Hybrid MPI+PGAS programming models by taking into account support for multi-core, high-performance networks, accelerators (GPGPUs and Intel MIC), virtualization technologies (KVM, Docker, and Singularity), and energy-awareness. Features and sample performance numbers from the MVAPICH2 libraries will be presented."
Watch the video: http://wp.me/p3RLHQ-glW
Learn more: http://hpcadvisorycouncil.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
RAC-Installing your First Cluster and DatabaseNikhil Kumar
RAC - Installing your First RAC
Abstract : Oracle Real Application Clusters have been one of the hottest technologies in the market since 2001 prior this is know OPS in 8i. Oracle has brought revolution in the field of database by enhancing RAC technologies in it each version. This presentation will give introduction of RAC and features introduced in each version of RAC. This presentation contains the demo of building Oracle clusterware from the scratch. Also we will discuss the new components and its features during installation. This presentation and demo will be done on version 11GR2. Which will be used as a base for our next presentation Viz. Upgradation of RAC 11GR2 to 12C RAC.
This presentation will give brief insight information of RAC infrastructure setup. Sometimes DBA doesn’t fully aware of prerequisite and verification steps that needs to perform before installing clusterware, So this session will cover thing to consider before installing clusterware and best practices followed during the whole process.
Agenda
Introduction of RAC
Installation of Clusterware.
Creating diskgroup / Adding disk to Diskgroup using ASMCA.
Creation of ACFS Volume.
Installation of RAC Database using DBCA.
In this video from the 2017 HPC Advisory Council Stanford Conference, Christian Kniep from Gaikai presents: Best Practices: State of Linux Containers.
"Linux Containers gain more and more momentum in all IT ecosystems. This talk provides an overview about what happened in the container landscape (in particular Docker) during the course of the last year and how it impacts datacenter operations, HPC and High-Performance Big Data. Furthermore Christian will give an update/extend on the ‘things to explore’ list he presented in the last Lugano workshop, applying what he learned and came across during the year 2016."
Watch the video: http://wp.me/p3RLHQ-glP
Learn more: http://qnib.org
and
http://www.hpcadvisorycouncil.com/events/2017/stanford-workshop/
Sign up for our insideHPC Newsletter: http:/insidehpc.com/newsletter
1. Double Orchestration of Redis Enterprise cluster on Kubernetes
**************
In this session we'll display how we deploy a highly available database on Kubernetes. The considerations we took when deploying a stateful application, and the challenges of answering different clients' demands for different k8s environments.
2. Operators to the rescue: stateful applications made easy with operators
**************
Kubernetes 1.7 introduced an import feature called custom controllers. This allows you to customise your Kubernetes installation and add your own resources to be managed in the native Kubernetes manner.
The session will display the operator concept and cover our journey with developing the Redis Labs operator - why we chose it and how we use it.
Static Membership: Rebalance Strategy Designed for the Cloud (Boyang Chen,Con...confluent
In this presentation, we introduce static membership (KIP-345) and share the story of adopting it at Pinterest. The static membership aims to improve the availability of stream applications, consumer groups and other applications built on top of it. The original rebalance protocol relies on the group coordinator to allocate entity ids to group members. These generated ids are ephemeral and will change when members restart and rejoin. For consumer based apps, this "dynamic membership" can cause a large percentage of tasks re-assigned to different instances during administrative operations such as code deploys, configuration updates and periodic restarts. For large state applications, shuffled tasks need a long time to recover their local states before processing and cause applications to be partially or entirely unavailable. At Pinterest, the group membership is stable between administrative operations. Motivated by this observation, we modified the Kafka's group management protocol allowing group members to provide persistent entity ids. Group membership remains unchanged based on those ids, thus no rebalance will be triggered. We can conveniently leverage Kubernetes or other cloud management frameworks to provide entity ids. By adopting static membership to the realtime infrastructure at Pinterest, applications resume processing only a few seconds after administrative operations finish. Previously with dynamic membership, it can take more than 30 minutes before applications resume. The talk is organized as follows: we first review Kafka's group management protocol and demonstrate high availability use cases that dynamic membership is unable to support. Then we share the design and adoption story of static membership. At the end, we do a live demo to show the impact of static membership.
Kubernetes Overview - Deploy your app with confidenceOmer Barel
In this presentation I explain the basics of the Kubernetes platform, alongside a dive into the core primitives (building blocks) you use, as well as yaml examples for each primitive
Galera Cluster for MySQL vs MySQL (NDB) Cluster: A High Level Comparison Severalnines
Galera Cluster for MySQL, Percona XtraDB Cluster and MariaDB Cluster (the three “flavours” of Galera Cluster) make use of the Galera WSREP libraries to handle synchronous replication.MySQL Cluster is the official clustering solution from Oracle, while Galera Cluster for MySQL is slowly but surely establishing itself as the de-facto clustering solution in the wider MySQL eco-system.
In this webinar, we will look at all these alternatives and present an unbiased view on their strengths/weaknesses and the use cases that fit each alternative.
This webinar will cover the following:
MySQL Cluster architecture: strengths and limitations
Galera Architecture: strengths and limitations
Deployment scenarios
Data migration
Read and write workloads (Optimistic/pessimistic locking)
WAN/Geographical replication
Schema changes
Management and monitoring
-- Created using PowToon -- Free sign up at http://www.powtoon.com/ -- Create animated videos and animated presentations for free. PowToon is a free tool that allows you to develop cool animated clips and animated presentations for your website, office meeting, sales pitch, nonprofit fundraiser, product launch, video resume, or anything else you could use an animated explainer video. PowToon's animation templates help you create animated presentations and animated explainer videos from scratch. Anyone can produce awesome animations quickly with PowToon, without the cost or hassle other professional animation services require.
Optimizing Your Author Website for Google and Social MediaKatherine Cowley
To really optimize your author website, you need to do more than just SEO: you need to make your website content stimulating, searchable, sharable, and savable. This presentation was given at the ANWA Time Out For Writer's Conference on September 16, 2016.
Solr Compute Cloud - An Elastic SolrCloud Infrastructure Nitin S
Scaling search platforms for serving hundreds of millions of documents with low latency and high throughput workloads at an optimized cost is an extremely hard problem. BloomReach has implemented Sc2, which is an elastic Solr infrastructure for Big Data applications, supporting heterogeneous workloads and hosted in the cloud. It dynamically grows/shrinks search servers to provide application and pipeline level isolation, NRT search and indexing, latency guarantees, and application-specific performance tuning. In addition, it provides various high availability features such as differential real-time streaming, disaster recovery, context aware replication, and automatic shard and replica rebalancing, all with a zero downtime guarantee for all consumers. This infrastructure currently serves hundreds of millions of documents in millisecond response times with a load ranging in the order of 200-300K QPS.
This presentation will describe an innovate implementation of scaling Solr in an elastic fashion. It will review the architecture and take a deep dive into how each of these components interact to make the infrastructure truly elastic, real time, and robust while serving latency needs.
Solr Exchange: Introduction to SolrCloudthelabdude
SolrCloud is a set of features in Apache Solr that enable elastic scaling of search indexes using sharding and replication. In this presentation, Tim Potter will provide an architectural overview of SolrCloud and highlight its most important features. Specifically, Tim covers topics such as: sharding, replication, ZooKeeper fundamentals, leaders/replicas, and failure/recovery scenarios. Any discussion of a complex distributed system would not be complete without a discussion of the CAP theorem. Mr. Potter will describe why Solr is considered a CP system and how that impacts the design of a search application.
Organizations continue to adopt Solr because of its ability to scale to meet even the most demanding workflows. Recently, LucidWorks has been leading the effort to identify, measure, and expand the limits of Solr. As part of this effort, we've learned a few things along the way that should prove useful for any organization wanting to scale Solr. Attendees will come away with a better understanding of how sharding and replication impact performance. Also, no benchmark is useful without being repeatable; Tim will also cover how to perform similar tests using the Solr-Scale-Toolkit in Amazon EC2.
Ingestion and Dimensions Compute and Enrich using Apache ApexApache Apex
Presenter: Devendra Tagare - DataTorrent Engineer, Contributor to Apex, Data Architect experienced in building high scalability big data platforms.
This talk will be a deep dive into ingesting unbounded file data and streaming data from Kafka into Hadoop. We will also cover data enrichment and dimensional compute. Customer use-case and reference architecture.
Scaling SolrCloud to a Large Number of Collections - Fifth Elephant 2014Shalin Shekhar Mangar
The traditional and typical search use case is the one large search collection distributed among many nodes and shared by all users. However, there is a class of applications which need a large number of small or medium collections which can be used, managed and scaled separately. This talk will cover our effort in helping a client set up a large scale SolrCloud setup with thousands of collections running on hundreds of nodes. I will describe the bottlenecks that we found in SolrCloud when running a large number of collections. I will also take you through the multiple features and optimizations that we contributed to Apache Solr to reduce or remove the choke points in the system. Finally, I will talk about the benchmarking process and the lessons learned from supporting such an installation in production.
[RightScale Webinar] Architecting Databases in the cloud: How RightScale Doe...RightScale
Your database is the foundation of your application. With cloud comes new advantages and considerations for architecting and deployment. Find out how RightScale uses SQL and NoSQL databases such as MySQL, MongoDB, and Cassandra to provide a scalable, distributed, and highly available service around the globe.
Uber has one of the largest Kafka deployment in the industry. To improve the scalability and availability, we developed and deployed a novel federated Kafka cluster setup which hides the cluster details from producers/consumers. Users do not need to know which cluster a topic resides and the clients view a "logical cluster". The federation layer will map the clients to the actual physical clusters, and keep the location of the physical cluster transparent from the user. Cluster federation brings us several benefits to support our business growth and ease our daily operation. In particular, Client control. Inside Uber there are a large of applications and clients on Kafka, and it's challenging to migrate a topic with live consumers between clusters. Coordinations with the users are usually needed to shift their traffic to the migrated cluster. Cluster federation enables much control of the clients from the server side by enabling consumer traffic redirection to another physical cluster without restarting the application. Scalability: With federation, the Kafka service can horizontally scale by adding more clusters when a cluster is full. The topics can freely migrate to a new cluster without notifying the users or restarting the clients. Moreover, no matter how many physical clusters we manage per topic type, from the user perspective, they view only one logical cluster. Availability: With a topic replicated to at least two clusters we can tolerate a single cluster failure by redirecting the clients to the secondary cluster without performing a region-failover. This also provides much freedom and alleviates the risks for us to carry out important maintenance on a critical cluster. Before the maintenance, we mark the cluster as a secondary and migrate off the live traffic and consumers. We will present the details of the architecture and several interesting technical challenges we overcame.
Building a near real time search engine & analytics for logs using solrlucenerevolution
Presented by Rahul Jain, System Analyst (Software Engineer), IVY Comptech Pvt Ltd
Consolidation and Indexing of logs to search them in real time poses an array of challenges when you have hundreds of servers producing terabytes of logs every day. Since the log events mostly have a small size of around 200 bytes to few KBs, makes it more difficult to handle because lesser the size of a log event, more the number of documents to index. In this session, we will discuss the challenges faced by us and solutions developed to overcome them. The list of items that will be covered in the talk are as follows.
Methods to collect logs in real time.
How Lucene was tuned to achieve an indexing rate of 1 GB in 46 seconds
Tips and techniques incorporated/used to manage distributed index generation and search on multiple shards
How choosing a layer based partition strategy helped us to bring down the search response times.
Log analysis and generation of analytics using Solr.
Design and architecture used to build the search platform.
Streaming in Practice - Putting Apache Kafka in Productionconfluent
This presentation focuses on how to integrate all these components into an enterprise environment and what things you need to consider as you move into production.
We will touch on the following topics:
- Patterns for integrating with existing data systems and applications
- Metadata management at enterprise scale
- Tradeoffs in performance, cost, availability and fault tolerance
- Choosing which cross-datacenter replication patterns fit with your application
- Considerations for operating Kafka-based data pipelines in production
Going from single instance to RAC, would the total CPU usages of the RAC cluster equal that of the single instance? In this presentation we go through the difference and discuss the overhead of RAC, explain vertical and horizontal scaling and explain which is best and why.
Similar to Solr Lucene Revolution 2014 - Solr Compute Cloud - Nitin (20)
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
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Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
2. Solr Compute Cloud – An Elastic
Solr Infrastructure
Nitin Sharma
- Member of technical staff, BloomReach
- nitin.sharma@bloomreach.com
3. Abstract
Scaling search platforms is an extremely hard problem
• Serving hundreds of millions of documents
• Low latency
• High throughput workloads
• Optimized cost.
At BloomReach, we have implemented SC2, an elastic Solr infrastructure for big data applications
that:
• Supports heterogeneous workloads while hosted in the cloud.
• Dynamically grows/shrinks search servers
• Application and Pipeline level isolation, NRT search and indexing.
• Offers latency guarantees and application-specific performance tuning.
• Provides high-availability features like cluster replacement, cross-data center support, disaster
recovery etc.
4. About Us
BloomReach
BloomReach has developed a personalized discovery platform that features applications that analyze
big data to makes our customers’ digital content more discoverable, relevant and profitable.
Myself
I work on search platform scaling for BloomReach’s big data. My relevant experience and background
includes scaling real-time services for latency sensitive applications and building performance and search-
quality metrics infrastructure for personalization platforms.
6. BloomReach’s Applications
Organic
Search
Contentunderstanding
What it does
Content optimization,
management and measurement
Benefit
Enhanced discoverability and
customer acquisition in organic search
What it does
Personalized onsite search and
navigation across devices
Benefit
Relevant and consistent onsite
experiences for new and known users
What it does
Merchandising tool that understa
nds products and identifies oppo
rtunities
Benefit
Prioritize and optimize
online merchandising
SNAP
Compass
7. Agenda
• BloomReach search use cases and architecture
• Old architecture and issues
• Scaling challenges
• Elastic SolrCloud architecture and benefits
• Lessons learned
8. BloomReach Search Use Cases
1. Front-end (serving) queries – Uptime and Latency sensitive
2. Batch search pipelines – Throughput sensitive
3. Time bound indexing requirements – Customer Specific
4. Time bound Solr config updates
9. BloomReach Search Architecture
Solr
Cluster
Zookeeper Ensemble Map Reduce
Pipelines (Reads)
Indexing Pipelines
Pipeline 1
Pipeline 2
Pipeline n
Indexing 1
Indexing 2
Indexing n
Heavy Load
Moderate Load
Light Load
Legend
Public API
Search Traffic
Search Traffic
10. Throughput Issues…
Solr
Cluster
Zookeeper Ensemble
Pipeline 1
Pipeline 2
Pipeline n
Indexing 1
Indexing 2
Indexing n
Public API
Search Traffic
● Heterogeneous read
workload
● Same collection - different
pipelines, different query
patterns, different schedule
● Cache tuning is virtually
impossible
● Larger pipeline starving the
small ones
● Machine utilization
determines throughput and
stability of a pipeline at any
point
● No isolation among jobs
11. Stability and Uptime Issues…
Solr
Cluster
Zookeeper Ensemble
Pipeline 1
Pipeline 2
Pipeline n
Indexing 1
Indexing 2
Indexing n
Public API
Search Traffic
● Bad clients – bring down
the cluster/degrade
performance
● Bad queries (with heavy
load) – render nodes
unresponsive
● Garbage collection issues
● ZK stability issues (as we
scale collections)
● CPU /Load Issues
● Higher number of
concurrent pipelines,
higher number of issues
12. Indexing Issues…
Solr
Cluster
Zookeeper Ensemble
Pipeline 1
Pipeline 2
Pipeline n
Indexing 1
Indexing 2
Indexing n
Public API
Search Traffic
● Commit frequencies vary
with indexer types
● Indexer run during another
pipeline – performance
● Indexer client leaks
● Too many stored fields
● Non-batch updates
13. Rethinking…
• Shared cluster for pipelines does not scale.
• Guaranteeing an uptime of 99.99+ - non trivial
• Every job runs great in isolation. When you put them together, they fail.
• Running index-heavy load and read-heavy load - cluster performance issues.
• Any direct access to production cluster – cluster stability (client leaks, bad queries etc.).
What if every pipeline had its own cluster?
14. Solr Compute Cloud (SC2)
• Elastic Infrastructure – Provision Solr Clusters on demand, on-the-fly.
• Create, Use, Terminate Model - Create a temporary cluster with necessary data, use it and throw it away.
• Technologies behind SC2 (built in House)
Cluster Management API - Dynamic cluster provisioning and resource allocation.
Solr HAFT – High availability and data management library for SolrCloud.
• Isolation - Pipelines get their own cluster. One cannot disrupt another.
• Dynamic Scaling – Every pipeline can state its own replication requirements.
• Production Safeguard - No direct access. Safeguards from bad clients/access patterns.
• Cost Saving – Provision for the average; withstand peak with elastic growth.
15. Solr Compute Cloud
Solr
Cluster
Zookeeper Ensemble
Pipeline 1
Solr
Compute
Cloud
API
Solr Cluster
Collection A
Replicas: 6
1. Read pipeline requests
collection and desired
replicas from SC2 API.
2. SC2 API provisions
cluster dynamically with
needed setup (and
streams Solr data).
3. SC2 calls HAFT service to
replicate data from
production to provisioned
cluster.
4. Pipeline uses this cluster
to run job.
1
4
Request: {Collection: A, Replica: 6}
2
Solr
HAFT
Service
3
3
Read
Replicate
16. Solr Compute Cloud…
Solr
Cluster
Zookeeper Ensemble
Pipeline 1
Solr
Compute
Cloud
API
Solr Cluster
Collection A
Replicas: 6
1. Pipeline finishes running
the job.
2. Pipeline calls SC2 API to
terminate the cluster.
3. SC2 terminates the
cluster.
2
Terminate: {Cluster}
3
Solr
HAFT
Service
1
17. Solr Compute Cloud – Read Pipeline View
Zookeeper Ensemble
Pipeline 1
Solr
Compute
Cloud
API
Solr Cluster
Collection A
Replicas: 6
Request: {Collection: A, Replica: 6}
Pipeline 2
Solr Cluster
Collection B
Replicas: 2
Request: {Collection: B, Replica: 2}
Pipeline n
Solr Cluster
Collection C
Replicas: 1
Request: {Collection: C, Replica: 1}
Solr
HAFT
Service
Production
Solr Cluster
18. Solr Compute Cloud – Indexing
Production
Solr Cluster
Zookeeper Ensemble
Indexing
Solr
Compute
Cloud
API
Solr Cluster
Collection A
Replicas: 6
1. Read pipeline requests
collection and desired
replicas from SC2 API.
2. SC2 API provisions
cluster dynamically with
needed setup (and
streams Solr data).
1. Indexer uses this cluster
to index the data.
2. Indexer calls HAFT
service to replicate the
index from dynamic
cluster to production.
3. HAFT service reads data
from dynamic cluster and
replicates to production
Solr.
1
3
Request: {Collection: A, Replica: 2}
2
Replicate
Solr HAFT Service
4
5
Read
19. Solr Compute Cloud – Global View
Zookeeper Ensemble
Solr
Compute
Cloud
API
Solr HAFT Service
Production
Solr Cluster
Indexing Pipelines 1
Elastic Clusters
Read Pipelines 1
Read Pipelines n
Indexing Pipelines n
Provision: {Cluster}
Terminate: {Cluster}
Replicate Index
Replicate Index
Run Job
20. Solr Compute Cloud API
1. API to provision clusters on demand.
2. Dynamic cluster and resource allocation (includes cost optimization)
3. Track request state, cluster performance and cost.
4. Terminate long-running, runaway clusters.
21. Solr HAFT Service
1. High availability and fault tolerance
2. Home-grown technology
3. Open Source - (Work in progress)
4. Features
• One push disaster recovery
• High availability operations
• Replace node
• Add replicas
• Repair collection
• Collection versioning
• Cluster backup operations
• Dynamic replica creation
• Cluster clone
• Cluster swap
• Cluster state reconstruction
22. Solr HAFT Service
Clone Alias
Clone Collections
Custom Commit Node Replacement
Node Repair
Clone Cluster
Collection Versioning
Black Box Recording
Lucene Segment
Optimize
Index Management Actions
High Availability Actions
Cluster Backup Operations
Solr Metadata
Zookeeper
Metadata
Verification Monitoring
Solr HAFT Service – Functional View
Dynamic Replica
Creation
Cluster Clone
Cluster Swap
Cluster State
Reconstruction
23. Disaster Recovery in New Architecture
Old
Production
Solr
Cluster
Zookeeper Ensemble
New
Solr
Cluster
Zookeeper Ensemble
Solr HAFT Service
Push
Button
Recovery
Brave Soul on Pager Duty
1
2
DNS
3
1. Guy on Pager clicks the
recovery button
2. Solr HAFT Service
triggers
Cluster Setup
State Reconstruction
Cluster Clone
Cluster Swap
3. Production DNS – New
Cluster
24. SC2 vs Non-SC2 (Stability Features)
Property Non-SC2 SC2
Linear Scalability for Heterogeneous
Workload
Pipeline Level Isolation
Dynamic Collection Scaling
Prevention from Bad Clients
Pipeline Specific Performance
No Direct Access to Production Cluster
Can Sleep at night?
25. SC2 vs Non-SC2 (Availability Features)
Property Non-SC2 SC2
Cross Data-Center Support
Cluster Cloning
Collection Versioning
One-Push Disaster Recovery
Repair API for Nodes/Collections
Node Replacement
26. Lessons Learned
1. Solr is a search platform. Do not use it as a database (for scans and lookups).
Evaluate your stored fields.
2. Understand access patterns, QPS and queries in detail. Be careful when tuning
caches.
3. Have access control for large-scale jobs that directly talk to your cluster. (Internal
DDOS attacks are hard to track.)
4. Instrument every piece of infrastructure and collect metrics.
5. Build automated disaster recovery (You will need it. )
GM. Thanks for making it to the session
I am Nitin…
The Talk is about SC2 which was built inside bloomreach to scale to our search use cases.
If things go as planned, we should have a few mins for questions. If not I will be more than happy to talk offline. Please post your questions in the activity feed
Typical Search Platforms have low latency requirements as we scale # of collections and # of documents
Performance, Availability, Scalability and Stability
Job Level Isolation with latency and SLA guarantees
Diaster Recovery Platform
This presentation will describe an innovative implementation of scaling Solr in an elastic fashion..
Bloomreach is a big data based marketing platform
We offer products that make our customer’s digital content more relevant
What kind of search use cases does bloomreach have?
A year worth of work and home grown technologies. Staying at a high level
Glad to go over offline.
Latency Sensitive Frontend Applications (Incoming customer queries through api)
Huge Batch (Map reduce based jobs) jobs constant analyzing and figuring out the relevant content
Time Sensitive index reads and writes
Picture is worth a 1000 words
Search Traffic – Different set of queries (utilizing all sorts of features faceting, sorting , custom ranking)
ETL pipelines, Product based pipelines
Pipelines Running at different schedules, different QPS and different throughput requirements.
Indexer (Partial and Full indexing)
Lets go over what kind of issues we encountered with this setup
Red Circle indicates the issues.
Multiple Products, Mulitple read pipleines. They run at different schedules. One can starve the other. One can screw up the latencies of the other.
The setup is very static
Un even load. Mem/CPU Usage varies and so does the machine utilization
OOM
PermGen issues (#Collections)
Old Gen Issues
High CPU
Bad Queries
Even Sharding
Unused features
Highlighting
Random jars
Bad clients – bring down the cluster/degrade performance (Any misbehaving job shoud Fail instead of bringing cluster down?)
Bad queries (with heavy load) – render nodes unresponsive
Perm Gen , Old Gen , Young Gen (Parallel GC vs CMS)
ZK txn log and data dirs in different drives.
We have developed multiple custom components. They might cause exceptions during querytime
Auto commits did help to a certain extent but not too much
No job level isolation
Client Leaks
No Rate limiting
Summary of issues
Ok how do we achieve that?
Introducing SC2
Spend More time…
Linearly Scalable with Dynamic Scaling
No direct access to production. Any misbehaving job “Fails” instead of bringing cluster down?
Cost Saving:
Instead of from , you replicate to production
Access to production is only through replication and not through any other means