Cross Datacenter Replication aka CDCR has been a long requested feature in Apache Solr. In this talk, we will discuss CDCR as released in Apache Solr 6.0 and beyond to understand its use-cases, limitations, setup and performance. We will also take a quick look at the future enhancements that can further simplify and scale this feature.
An overview of building and serving Lucene indexes on a Hadoop cluster with Solr for text and parametric searching, as presented at Cleveland Hadoop User Group on 13 January 2014.
Deploying and managing SolrCloud in the cloud using the Solr Scale Toolkitthelabdude
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 demonstrate how to provision, configure, and manage a SolrCloud cluster in Amazon EC2, using a Fabric/boto based solution for automating SolrCloud operations. Attendees will come away with a solid understanding of how to operate a large-scale Solr cluster, as well as tools to help them do it. Tim will also demonstrate these tools live during his presentation. Covered technologies, include: Apache Solr, Apache ZooKeeper, Linux, Python, Fabric, boto, Apache Kafka, Apache JMeter.
An overview of building and serving Lucene indexes on a Hadoop cluster with Solr for text and parametric searching, as presented at Cleveland Hadoop User Group on 13 January 2014.
Deploying and managing SolrCloud in the cloud using the Solr Scale Toolkitthelabdude
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 demonstrate how to provision, configure, and manage a SolrCloud cluster in Amazon EC2, using a Fabric/boto based solution for automating SolrCloud operations. Attendees will come away with a solid understanding of how to operate a large-scale Solr cluster, as well as tools to help them do it. Tim will also demonstrate these tools live during his presentation. Covered technologies, include: Apache Solr, Apache ZooKeeper, Linux, Python, Fabric, boto, Apache Kafka, Apache JMeter.
Scaling Through Partitioning and Shard Splitting in Solr 4thelabdude
Over the past several months, Solr has reached a critical milestone of being able to elastically scale-out to handle indexes reaching into the hundreds of millions of documents. At Dachis Group, we've scaled our largest Solr 4 index to nearly 900M documents and growing. As our index grows, so does our need to manage this growth.
In practice, it's common for indexes to continue to grow as organizations acquire new data. Over time, even the best designed Solr cluster will reach a point where individual shards are too large to maintain query performance. In this Webinar, you'll learn about new features in Solr to help manage large-scale clusters. Specifically, we'll cover data partitioning and shard splitting.
Partitioning helps you organize subsets of data based on data contained in your documents, such as a date or customer ID. We'll see how to use custom hashing to route documents to specific shards during indexing. Shard splitting allows you to split a large shard into 2 smaller shards to increase parallelism during query execution.
Attendees will come away from this presentation with a real-world use case that proves Solr 4 is elastically scalable, stable, and is production ready.
These slides were presented at the Great Indian Developer Summit 2014 at Bangalore. See http://www.developermarch.com/developersummit/session.html?insert=ShalinMangar2
"SolrCloud" is the name given to Apache Solr's feature set for fault tolerant, highly available, and massively scalable capabilities. SolrCloud has enabled organizations to scale, impressively, into the billions of documents with sub-second search!
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.
In the big data world, our data stores communicate over an asynchronous, unreliable network to provide a facade of consistency. However, to really understand the guarantees of these systems, we must understand the realities of networks and test our data stores against them.
Jepsen is a tool which simulates network partitions in data stores and helps us understand the guarantees of our systems and its failure modes. In this talk, I will help you understand why you should care about network partitions and how can we test datastores against partitions using Jepsen. I will explain what Jepsen is and how it works and the kind of tests it lets you create. We will try to understand the subtleties of distributed consensus, the CAP theorem and demonstrate how different data stores such as MongoDB, Cassandra, Elastic and Solr behave under network partitions. Finally, I will describe the results of the tests I wrote using Jepsen for Apache Solr and discuss the kinds of rare failures which were found by this excellent tool.
Solr cluster with SolrCloud at lucenerevolution (tutorial)searchbox-com
In this presentation we aim to show how to make a high availability Solr cloud with 4.1 using only Solr and a few bash scripts. The goal is to present an infrastructure which is self healing using only cheap instances based on ephemeral storage. We will start by providing a comprehensive overview of the relation between collections, Solr cores, shards and cluster nodes. We continue by an introduction to Solr 4.x clustering using zookeeper with a particular emphasis on cluster state status/monitoring and solr collection configuration. The core of our presentation will be demonstrated using a live cluster. We will show how to use cron and bash to monitor the state of the cluster and the state of its nodes. We will then show how we can extend our monitoring to auto generate new nodes, attach them to the cluster, and assign them shardes (selecting between missing shardes or replication for HA). We will show that using a high replication factor it is possible to use ephemeral storage for shards without the risk of data loss, greatly reducing the cost and management of the architecture. Future work discussions, which might be engaged using an open source effort, include monitoring activity of individual nodes as to scale the cluster according to traffic and usage.
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.
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.
Lucene Revolution 2013 - Scaling Solr Cloud for Large-scale Social Media Anal...thelabdude
My presentation focuses on how we implemented Solr 4 to be the cornerstone of our social marketing analytics platform. Our platform analyzes relationships, behaviors, and conversations between 30,000 brands and 100M social accounts every 15 minutes. Combined with our Hadoop cluster, we have achieved throughput rates greater than 8,000 documents per second. Our index currently contains more than 620M documents and is growing by 3 to 4 million documents per day. My presentation will include details about: 1) Designing a Solr Cloud cluster for scalability and high-availability using sharding and replication with Zookeeper, 2) Operations concerns like how to handle a failed node and monitoring, 3) How we deal with indexing big data from Pig/Hadoop as an example of using the CloudSolrServer in SolrJ and managing searchers for high indexing throughput, 4) Example uses of key features like real-time gets, atomic updates, custom hashing, and distributed facets. Attendees will come away from this presentation with a real-world use case that proves Solr 4 is scalable, stable, and is production ready.
Solr 4: Run Solr in SolrCloud Mode on your local file system.gutierrezga00
Running Solr in SolrCloud Mode on your local file system using Solr version 4.10.3. It demonstrate how configure the Apache Solr binaries so that you can create any number of SolrCloud instances without having the need to modified the binaries.
YouTube: http://youtu.be/70AKyQYoLqM
Download sample SolrCloud scripts: https://github.com/gutierrezga00/SolrCloud_LocalFileSystem
Presentation Slides: http://www.slideshare.net/gutierrezga00/solr-cloud-local-file-system
Download Solr version 4.10.3: http://lucene.apache.org/solr/
Download Zookeeper version 3.4.6: http://zookeeper.apache.org/
Presented by Mark Miller, Software Developer, Cloudera
Apache Lucene/Solr committer Mark Miller talks about how Solr has been integrated into the Hadoop ecosystem to provide full text search at "Big Data" scale. This talk will give an overview of how Cloudera has tackled integrating Solr into the Hadoop ecosystem and highlights some of the design decisions and future plans. Learn how Solr is getting 'cozy' with Hadoop, which contributions are going to what project, and how you can take advantage of these integrations to use Solr efficiently at "Big Data" scale. Learn how you can run Solr directly on HDFS, build indexes with Map/Reduce, load Solr via Flume in 'Near Realtime' and much more.
How SolrCloud Changes the User Experience In a Sharded Environmentlucenerevolution
Presented by Erick Erickson, Lucid Imagination - See conference video - http://www.lucidimagination.com/devzone/events/conferences/lucene-revolution-2012
The next major release of Solr (4.0) will include "SolrCloud", which provides new distributed capabilities for both in-house and externally-hosted Solr installations. Among the new capabilities are: Automatic Distributed Indexing, High Availability and Failover, Near Real Time searching and Fault Tolerance. This talk will focus, at a high level, on how these new capabilities impact the design of Solr-based search applications primarily from infrastructure and operational perspectives.
High availability of data across geographic regions for search and analytical applications is a challenging task. Mission critical applications need effective failover strategies across data centers. Apache Solr offers Cross Data Center Replication (CDCR) as a feature from 6.0 and has added more features in subsequent releases.
The first part of session will center on an active-passive design model with one data-center as the primary and other data-centers as secondary clusters. The second design model centers on designing an active-active bidirectional setup such that both querying and indexing traffic can gracefully be redirected to the failover cluster.
The third part of session will center on an actual use case: An analytics application with high availability. We will discuss the improvements observed in terms of maintenance, performance, and throughput.
The session concludes with challenges and/or limitations in the current design and what improvements are forthcoming for Cross Data Center Replication in Apache Solr.
Scaling Through Partitioning and Shard Splitting in Solr 4thelabdude
Over the past several months, Solr has reached a critical milestone of being able to elastically scale-out to handle indexes reaching into the hundreds of millions of documents. At Dachis Group, we've scaled our largest Solr 4 index to nearly 900M documents and growing. As our index grows, so does our need to manage this growth.
In practice, it's common for indexes to continue to grow as organizations acquire new data. Over time, even the best designed Solr cluster will reach a point where individual shards are too large to maintain query performance. In this Webinar, you'll learn about new features in Solr to help manage large-scale clusters. Specifically, we'll cover data partitioning and shard splitting.
Partitioning helps you organize subsets of data based on data contained in your documents, such as a date or customer ID. We'll see how to use custom hashing to route documents to specific shards during indexing. Shard splitting allows you to split a large shard into 2 smaller shards to increase parallelism during query execution.
Attendees will come away from this presentation with a real-world use case that proves Solr 4 is elastically scalable, stable, and is production ready.
These slides were presented at the Great Indian Developer Summit 2014 at Bangalore. See http://www.developermarch.com/developersummit/session.html?insert=ShalinMangar2
"SolrCloud" is the name given to Apache Solr's feature set for fault tolerant, highly available, and massively scalable capabilities. SolrCloud has enabled organizations to scale, impressively, into the billions of documents with sub-second search!
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.
In the big data world, our data stores communicate over an asynchronous, unreliable network to provide a facade of consistency. However, to really understand the guarantees of these systems, we must understand the realities of networks and test our data stores against them.
Jepsen is a tool which simulates network partitions in data stores and helps us understand the guarantees of our systems and its failure modes. In this talk, I will help you understand why you should care about network partitions and how can we test datastores against partitions using Jepsen. I will explain what Jepsen is and how it works and the kind of tests it lets you create. We will try to understand the subtleties of distributed consensus, the CAP theorem and demonstrate how different data stores such as MongoDB, Cassandra, Elastic and Solr behave under network partitions. Finally, I will describe the results of the tests I wrote using Jepsen for Apache Solr and discuss the kinds of rare failures which were found by this excellent tool.
Solr cluster with SolrCloud at lucenerevolution (tutorial)searchbox-com
In this presentation we aim to show how to make a high availability Solr cloud with 4.1 using only Solr and a few bash scripts. The goal is to present an infrastructure which is self healing using only cheap instances based on ephemeral storage. We will start by providing a comprehensive overview of the relation between collections, Solr cores, shards and cluster nodes. We continue by an introduction to Solr 4.x clustering using zookeeper with a particular emphasis on cluster state status/monitoring and solr collection configuration. The core of our presentation will be demonstrated using a live cluster. We will show how to use cron and bash to monitor the state of the cluster and the state of its nodes. We will then show how we can extend our monitoring to auto generate new nodes, attach them to the cluster, and assign them shardes (selecting between missing shardes or replication for HA). We will show that using a high replication factor it is possible to use ephemeral storage for shards without the risk of data loss, greatly reducing the cost and management of the architecture. Future work discussions, which might be engaged using an open source effort, include monitoring activity of individual nodes as to scale the cluster according to traffic and usage.
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.
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.
Lucene Revolution 2013 - Scaling Solr Cloud for Large-scale Social Media Anal...thelabdude
My presentation focuses on how we implemented Solr 4 to be the cornerstone of our social marketing analytics platform. Our platform analyzes relationships, behaviors, and conversations between 30,000 brands and 100M social accounts every 15 minutes. Combined with our Hadoop cluster, we have achieved throughput rates greater than 8,000 documents per second. Our index currently contains more than 620M documents and is growing by 3 to 4 million documents per day. My presentation will include details about: 1) Designing a Solr Cloud cluster for scalability and high-availability using sharding and replication with Zookeeper, 2) Operations concerns like how to handle a failed node and monitoring, 3) How we deal with indexing big data from Pig/Hadoop as an example of using the CloudSolrServer in SolrJ and managing searchers for high indexing throughput, 4) Example uses of key features like real-time gets, atomic updates, custom hashing, and distributed facets. Attendees will come away from this presentation with a real-world use case that proves Solr 4 is scalable, stable, and is production ready.
Solr 4: Run Solr in SolrCloud Mode on your local file system.gutierrezga00
Running Solr in SolrCloud Mode on your local file system using Solr version 4.10.3. It demonstrate how configure the Apache Solr binaries so that you can create any number of SolrCloud instances without having the need to modified the binaries.
YouTube: http://youtu.be/70AKyQYoLqM
Download sample SolrCloud scripts: https://github.com/gutierrezga00/SolrCloud_LocalFileSystem
Presentation Slides: http://www.slideshare.net/gutierrezga00/solr-cloud-local-file-system
Download Solr version 4.10.3: http://lucene.apache.org/solr/
Download Zookeeper version 3.4.6: http://zookeeper.apache.org/
Presented by Mark Miller, Software Developer, Cloudera
Apache Lucene/Solr committer Mark Miller talks about how Solr has been integrated into the Hadoop ecosystem to provide full text search at "Big Data" scale. This talk will give an overview of how Cloudera has tackled integrating Solr into the Hadoop ecosystem and highlights some of the design decisions and future plans. Learn how Solr is getting 'cozy' with Hadoop, which contributions are going to what project, and how you can take advantage of these integrations to use Solr efficiently at "Big Data" scale. Learn how you can run Solr directly on HDFS, build indexes with Map/Reduce, load Solr via Flume in 'Near Realtime' and much more.
How SolrCloud Changes the User Experience In a Sharded Environmentlucenerevolution
Presented by Erick Erickson, Lucid Imagination - See conference video - http://www.lucidimagination.com/devzone/events/conferences/lucene-revolution-2012
The next major release of Solr (4.0) will include "SolrCloud", which provides new distributed capabilities for both in-house and externally-hosted Solr installations. Among the new capabilities are: Automatic Distributed Indexing, High Availability and Failover, Near Real Time searching and Fault Tolerance. This talk will focus, at a high level, on how these new capabilities impact the design of Solr-based search applications primarily from infrastructure and operational perspectives.
High availability of data across geographic regions for search and analytical applications is a challenging task. Mission critical applications need effective failover strategies across data centers. Apache Solr offers Cross Data Center Replication (CDCR) as a feature from 6.0 and has added more features in subsequent releases.
The first part of session will center on an active-passive design model with one data-center as the primary and other data-centers as secondary clusters. The second design model centers on designing an active-active bidirectional setup such that both querying and indexing traffic can gracefully be redirected to the failover cluster.
The third part of session will center on an actual use case: An analytics application with high availability. We will discuss the improvements observed in terms of maintenance, performance, and throughput.
The session concludes with challenges and/or limitations in the current design and what improvements are forthcoming for Cross Data Center Replication in Apache Solr.
Real-time Big Data Analytics Engine using ImpalaJason Shih
Cloudera Impala is an open-source under Apache Licence enable real-time, interactive analytical SQL queries of the data stored in HBase or HDFS. The work was inspired by Google Dremel paper which is also the basis for Google BigQuery. It provide access same unified storage platform base on it's own distributed query engine but does not use mapreduce. In addition, it use also the same metadata, SQL syntax (HiveQL-like) ODBC driver and user interface (Hue Beeswax) as Hive. Besides the traditional Hadoop approach, aim to provide low-cost solution for resiliency and batch-oriented distributed data processing, we found more and more effort in the Big Data world pursuing the right solution for ad-hoc, fast queries and realtime data processing for large datasets. In this presentation, we'll explore how to run interactive queries inside Impala, advantages of the approach, architecture and understand how it optimizes data systems including also practical performance analysis.
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.
As a service provider, Rackspace is constantly bringing new OpenStack capacity online. In this session, we will detail a myriad of challenges around adding new compute capacity. These include: planning, automation, organizational, quality assurance, monitoring, security, networking, integration, and more.
Similar to Cross Datacenter Replication in Apache Solr 6 (20)
Apache Solr is a powerful search and analytics engine with features such as full-text search, faceting, joins, sorting and capable of handling large amounts of data across a large number of servers. However, with all that power and scalability comes complexity. Solr 6 supports a Parallel SQL feature which provides a simplified, well-known interface to your data in Solr, performs key operations such as sorts and shuffling inside Solr for massive speedups, provides best-practices based query optimization and by leveraging the scalability of SolrCloud and a clever implementation, allows you to throw massive amounts of computation power behind analytical queries.
In this talk, we will explore the why, what and how of Parallel SQL and its building block Streaming Expressions in Solr 6 with a hint of the exciting new developments around this feature.
Presented at Indian Institute of Information Technology (IIIT) Allahabad on 21 Oct 2009 to students about the Apache Software Foundation, Lucene, Solr, Hadoop and on the benefits of contributing to open source projects. The target audience was sophomore, junior and senior B.Tech students.
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
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Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Designing for Privacy in Amazon Web ServicesKrzysztofKkol1
Data privacy is one of the most critical issues that businesses face. This presentation shares insights on the principles and best practices for ensuring the resilience and security of your workload.
Drawing on a real-life project from the HR industry, the various challenges will be demonstrated: data protection, self-healing, business continuity, security, and transparency of data processing. This systematized approach allowed to create a secure AWS cloud infrastructure that not only met strict compliance rules but also exceeded the client's expectations.
Multiple Your Crypto Portfolio with the Innovative Features of Advanced Crypt...Hivelance Technology
Cryptocurrency trading bots are computer programs designed to automate buying, selling, and managing cryptocurrency transactions. These bots utilize advanced algorithms and machine learning techniques to analyze market data, identify trading opportunities, and execute trades on behalf of their users. By automating the decision-making process, crypto trading bots can react to market changes faster than human traders
Hivelance, a leading provider of cryptocurrency trading bot development services, stands out as the premier choice for crypto traders and developers. Hivelance boasts a team of seasoned cryptocurrency experts and software engineers who deeply understand the crypto market and the latest trends in automated trading, Hivelance leverages the latest technologies and tools in the industry, including advanced AI and machine learning algorithms, to create highly efficient and adaptable crypto trading bots
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
Advanced Flow Concepts Every Developer Should KnowPeter Caitens
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Quarkus Hidden and Forbidden ExtensionsMax Andersen
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Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Why React Native as a Strategic Advantage for Startup Innovation.pdfayushiqss
Do you know that React Native is being increasingly adopted by startups as well as big companies in the mobile app development industry? Big names like Facebook, Instagram, and Pinterest have already integrated this robust open-source framework.
In fact, according to a report by Statista, the number of React Native developers has been steadily increasing over the years, reaching an estimated 1.9 million by the end of 2024. This means that the demand for this framework in the job market has been growing making it a valuable skill.
But what makes React Native so popular for mobile application development? It offers excellent cross-platform capabilities among other benefits. This way, with React Native, developers can write code once and run it on both iOS and Android devices thus saving time and resources leading to shorter development cycles hence faster time-to-market for your app.
Let’s take the example of a startup, which wanted to release their app on both iOS and Android at once. Through the use of React Native they managed to create an app and bring it into the market within a very short period. This helped them gain an advantage over their competitors because they had access to a large user base who were able to generate revenue quickly for them.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
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Unleash Unlimited Potential with One-Time Purchase
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Large Language Models and the End of ProgrammingMatt Welsh
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Globus Connect Server Deep Dive - GlobusWorld 2024Globus
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Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
4. Agenda
• Review a typical Solr deployment architecture
• Challenges of running a Solr deployment across data centers
• Cross Data Centre Replication (CDCR) in Solr
• Setup and configuration
• Limitations
• Alternative strategies
• Future work
8. CDCR Anti-patterns - Remote ZK and Solr
C
Solr
Zookeeper
DC 1
C C
DC 2
C C C
DC 3
9. Why not a single Solr Cloud?
• Same update is transferred to each replica
• Synchronous indexing means burst-indexing is constrained by cross
DC bandwidth
• Increased latency for indexing operations
• Need a ZooKeeper node in a 3rd DC to break ties
• Search requests are not DC-aware, may choose a remote replica
10. Cross Datacenter Replication in Solr
• Let’s call it CDCR for short
• Accommodate two or more data centres
• Active/passive setup for disaster recovery
• Support limited bandwidth links
• Eventually consistent passive cluster
12. CDCR in Solr 6
• Scalable: no SPoF and/or bottleneck
• Peer cluster can have a different replication factor
• Asynchronous updates; no penalty for indexing
• Push operations for low latency replication
• Low overhead — uses existing transaction logs and indexes
• Leader-to-leader communication ensures update is sent only once
to peer cluster
16. How to failover?
• Change configuration on target to make it the source
• Point indexers to the new target
• Change configuration on source to make it the new target
• May require stopping indexing during the conversion process —
especially if you want to revert the change
17. CDCR support in Solr 6+
• Active/passive setup either for disaster recovery or for low latency
querying
• Solr clusters with existing data can be converted to a source cluster
from Solr 6.2 onwards
• Low to medium indexing traffic
18. CDCR Limitations and gotchas
• By default CDCR is disabled — invoke START to enable on both
source and target
• Soft commits are not replicated to target — must schedule
autoSoftCommit explicitly on target
• Different set of configurations required on source and target
• Daisy-chaining is possible but not well tested — add all targets to
the same source cluster
19. CDCR Limitations and gotchas
• Not suitable for applications requiring high throughput indexing —
some knobs exist for tuning replication speeds
• Update log buffers can grow indefinitely when target clusters are
down — can work around by disabling buffering for the time being
if there is only one target
• No automatic failover between source and target — explicit actions
required to modify configurations and point indexing pipelines to
the new source
• No Active/active setup
20. Alternative strategy
• Use a proper queue such as Apache Kafka to feed source and target DCs
simultaneously
• Use external versions in conjunction with versions generated by Solr —
DocBasedVersionConstraintsProcessorFactory
• Watch the video for “Solr Cross-Datacenter Replication and Consistency at Scale”
by Oliver Bates, Apple Inc. — http://sched.co/8ArU
• Pros: Supports high indexing throughputs and active/active replication
• Cons: Additional systems required, managing consistency is difficult and requires in
depth Solr expertise, all atomic updates must go to a single DC, cannot support
delete-by-query
21. Problems we solved
• Synchronous indexing to replicas — build separate asynchronous
indexing pipeline
• Limited size of the update log — use update log as the queue
• How to track replication progress to preserve consistency on target
clusters in case the source leader dies — checkpoints
• Bootstrapping target cluster with indexes when update logs are
incomplete
• New replicas on source have no logs to replicate — replicate
update logs during recovery
22. Future work
• Move configuration out of solrconfig.xml and into API calls
• Dynamically add/remove/change target cluster information
• Cap update log to a max size and fall back to index replication if
necessary
• Refactor and combine CdcrUpdateLog
• Better monitoring: capture transfer rate and latency info
• Add support for rate limiting replication between source and target
• Active/active?