In this paper I speak about BigWorld technology, WoT server, Apache Kafka and how we started to use it together. What difficulties we had and how we had solved them.
Speaker: Damien Gasparina, Engineer, Confluent
Here's how to fail at Apache Kafka brilliantly!
https://www.meetup.com/Paris-Data-Engineers/events/260694777/
This document discusses the evolution of Kafka clusters at AppsFlyer over time. The initial cluster had 4 brokers and handled hundreds of millions of messages with low partitioning and replication. A new cluster was designed with more brokers, replication across availability zones, and higher partitioning to support billions of messages. However, this led to issues like uneven leader distribution and failures. Various solutions were implemented like increasing brokers, splitting topics, and hardware upgrades. Ongoing testing and monitoring helped identify more problems and improvements around replication, partitioning, and automation. Key lessons learned included balancing replication and leaders, supporting dynamic changes, and thorough testing of failure scenarios.
Espresso Database Replication with Kafka, Tom Quiggleconfluent
This document discusses using Apache Kafka for database replication in LinkedIn's ESPRESSO database system. It provides an overview of ESPRESSO's architecture and transition from per-instance to per-partition replication using Kafka. Key aspects covered include Kafka configuration, the message protocol for ensuring in-order delivery, and checkpointing by the Kafka producer to allow resuming replication from the last committed transaction after failures.
Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
Apache Kafka is a distributed publish-subscribe messaging system that was originally created by LinkedIn and contributed to the Apache Software Foundation. It is written in Scala and provides a multi-language API to publish and consume streams of records. Kafka is useful for both log aggregation and real-time messaging due to its high performance, scalability, and ability to serve as both a distributed messaging system and log storage system with a single unified architecture. To use Kafka, one runs Zookeeper for coordination, Kafka brokers to form a cluster, and then publishes and consumes messages with a producer API and consumer API.
Apache Kafka becoming the message bus to transfer huge volumes of data from various sources into Hadoop.
It's also enabling many real-time system frameworks and use cases.
Managing and building clients around Apache Kafka can be challenging. In this talk, we will go through the best practices in deploying Apache Kafka
in production. How to Secure a Kafka Cluster, How to pick topic-partitions and upgrading to newer versions. Migrating to new Kafka Producer and Consumer API.
Also talk about the best practices involved in running a producer/consumer.
In Kafka 0.9 release, we’ve added SSL wire encryption, SASL/Kerberos for user authentication, and pluggable authorization. Now Kafka allows authentication of users, access control on who can read and write to a Kafka topic. Apache Ranger also uses pluggable authorization mechanism to centralize security for Kafka and other Hadoop ecosystem projects.
We will showcase open sourced Kafka REST API and an Admin UI that will help users in creating topics, re-assign partitions, Issuing
Kafka ACLs and monitoring Consumer offsets.
Real-time Data Ingestion from Kafka to ClickHouse with Deterministic Re-tries...HostedbyConfluent
In a real-time data ingestion pipeline for analytical processing, efficient and fast data loading to a columnar database such as ClickHouse favors large blocks over individual rows. Therefore, applications often rely on some buffering mechanism such as Kafka to store data temporarily, and having a message processing engine to aggregate Kafka messages into large blocks which then get loaded to the backend database. Due to various failures in this pipeline, a naive block aggregator that forms blocks without additional measures, would cause data duplication or data loss. We have developed a solution to avoid these issues, thereby achieving exactly-once delivery from Kafka to ClickHouse. Our solution utilizes Kafka’s metadata to keep track of blocks that we intend to send to ClickHouse, and later uses this metadata information to deterministically re-produce ClickHouse blocks for re-tries in case of failures. The identical blocks are guaranteed to be deduplicated by ClickHouse. We have also developed a run-time verification tool that monitors Kafka’s internal metadata topic, and raises alerts when the required invariants for exactly-once delivery are violated. Our solution has been developed and deployed to the production clusters that span multiple datacenters at eBay.
Apache Kafka is a distributed streaming platform used for building real-time data pipelines and streaming apps. It provides a unified, scalable, and durable platform for handling real-time data feeds. Kafka works by accepting streams of records from one or more producers and organizing them into topics. It allows both storing and forwarding of these streams to consumers. Producers write data to topics which are replicated across clusters for fault tolerance. Consumers can then read the data from the topics in the order it was produced. Major companies like LinkedIn, Yahoo, Twitter, and Netflix use Kafka for applications like metrics, logging, stream processing and more.
Apache Kafka is a distributed messaging system that allows for publishing and subscribing to streams of records, known as topics, in a fault-tolerant and scalable way. It is used for building real-time data pipelines and streaming apps. Producers write data to topics which are committed to disks across partitions and replicated for fault tolerance. Consumers read data from topics in a decoupled manner based on offsets. Kafka can process streaming data in real-time and at large volumes with low latency and high throughput.
Stream Me Up, Scotty: Transitioning to the Cloud Using a Streaming Data Platformconfluent
Many enterprises have a large technical debt in legacy applications hosted in on-premises data centers. There is a strong desire to modernize and move to a cloud-based infrastructure, but the world won’t stop for you to transition. Existing applications need to be supported and enhanced; data from legacy platforms is required to make decisions that drive the business. On the other hand, data from cloud-based applications does not exist in a vacuum. Legacy applications need access to these cloud data sources and vice versa.
Can an enterprise have it both ways? Can new applications be built in the cloud while existing applications are maintained in a private data center?
Monsanto has adopted a cloud-first mentality—today most new development is focused on the cloud. However, this transition did not happen overnight.
Chrix Finne and Bob Lehmann share their experience building and implementing a Kafka-based cross-data-center streaming platform to facilitate the move to the cloud—in the process, kick-starting Monsanto’s transition from batch to stream processing. Details include an overview of the challenges involved in transitioning to the cloud and a deep dive into the cross-data-center stream platform architecture, including best practices for running this architecture in production and a summary of the benefits seen after deploying this architecture.
Healthcare data comes in many shapes and sizes making ingestion difficult for a variety of batch and near real time use cases. By Cerner evolving its architecture to adopt Apache Kafka, Cerner was able to build a modular architecture for current and future use cases. Reviewing the evolution of Cerner’s uses, developers can help to avoid mistakes and set themselves up for success.
Streaming ETL - from RDBMS to Dashboard with KSQLBjoern Rost
Apache Kafka is a massively scalable message queue that is being used at more and more places connecting more and more data sources. This presentation will introduce Kafka from the perspective of a mere mortal DBA and share the experience of (and challenges with) getting events from the database to Kafka using Kafka connect including poor-man’s CDC using flashback query and traditional logical replication tools. To demonstrate how and why this is a good idea, we will build an end-to-end data processing pipeline. We will discuss how to turn changes in database state into events and stream them into Apache Kafka. We will explore the basic concepts of streaming transformations using windows and KSQL before ingesting the transformed stream in a dashboard application.
Apache Kafka is a distributed streaming platform. It provides a high-throughput distributed messaging system that can handle trillions of events daily. Many large companies use Kafka for application logging, metrics collection, and powering real-time analytics. The current version is 0.8.2 and upcoming versions will include a new consumer, security features, and support for transactions.
This document provides an overview of Kafka, a distributed streaming platform. It can publish and subscribe to streams of records, store streams durably across clusters, and process streams as they occur. The Kafka cluster stores streams of records in topics. It has four main APIs: Producer API to publish data, Consumer API to subscribe to topics, Streams API to transform streams, and Connector API to connect Kafka and other systems. Records in Kafka topics are partitioned and ordered with offsets for scalability and fault tolerance. Consumers subscribe to topics in consumer groups to process partitions in parallel.
This document provides an overview of Kafka including basic concepts like topics, brokers, and partitions. It discusses how to install and run Kafka, configure topics and clusters, and monitor and troubleshoot Kafka. It also demonstrates producing, consuming, and loading test scenarios. Key lessons learned are around balancing replication, partitions, and leaders across brokers while ensuring adequate disk space, IOPS, and retention periods. Automating cluster changes and backing up messages is also recommended.
This is an overview of interesting features from Apache Pulsar. Keep in mind that by the time I did this presentation I did not have used Pulsar yet. It's just my first impressions from the list of features.
Strata+Hadoop 2017 San Jose: Lessons from a year of supporting Apache Kafkaconfluent
The number of deployments of Apache Kafka at enterprise scale has greatly increased in the years since Kafka’s original development in 2010. Along with this rapid growth has come a wide variety of use cases and deployment strategies that transcend what Kafka’s creators imagined when they originally developed the technology. As the scope and reach of streaming data platforms based on Apache Kafka has grown, the need to understand monitoring and troubleshooting strategies has as well.
Dustin Cote and Ryan Pridgeon share their experience supporting Apache Kafka at enterprise-scale and explore monitoring and troubleshooting techniques to help you avoid pitfalls when scaling large-scale Kafka deployments.
Topics include:
- Effective use of JMX for Kafka
- Tools for preventing small problems from becoming big ones
- Efficient architectures proven in the wild
- Finding and storing the right information when it all goes wrong
Visit www.confluent.io for more information.
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. This talk gives an introduction to Apache Kafka, a distributed messaging system. It will cover both how Kafka works, as well as how it is used at LinkedIn for log aggregation, messaging, ETL, and real-time stream processing.
Troubleshooting Kafka's socket server: from incident to resolutionJoel Koshy
LinkedIn’s Kafka deployment is nearing 1300 brokers that move close to 1.3 trillion messages a day. While operating Kafka smoothly even at this scale is testament to both Kafka’s scalability and the operational expertise of LinkedIn SREs we occasionally run into some very interesting bugs at this scale. In this talk I will dive into a production issue that we recently encountered as an example of how even a subtle bug can suddenly manifest at scale and cause a near meltdown of the cluster. We will go over how we detected and responded to the situation, investigated it after the fact and summarize some lessons learned and best-practices from this incident.
Apache Kafka is a high-throughput distributed messaging system that can be used for building real-time data pipelines and streaming apps. It provides a publish-subscribe messaging model and is designed as a distributed commit log. Kafka allows for both push and pull models where producers push data and consumers pull data from topics which are divided into partitions to allow for parallelism.
Kafka is a distributed messaging system that allows for publishing and subscribing to streams of records, known as topics. Producers write data to topics and consumers read from topics. The data is partitioned and replicated across clusters of machines called brokers for reliability and scalability. A common data format like Avro can be used to serialize the data.
Tales from the four-comma club: Managing Kafka as a service at Salesforce | L...HostedbyConfluent
Apache Kafka is a key part of the Big Data infrastructure at Salesforce, enabling publish/subscribe and data transport in near real-time at enterprise scale handling trillions of messages per day. In this session, hear from the teams at Salesforce that manage Kafka as a service, running over a hundred clusters across on-premise and public cloud environments with over 99.9% availability. Hear about best practices and innovations, including:
* How to manage multi-tenant clusters in a hybrid environment
* High volume data pipelines with Mirus replicating data to Kafka and blob storage
* Kafka Fault Injection Framework built on Trogdor and Kibosh
* Automated recovery without data loss
* Using Envoy as an SNI-routing Kafka gateway
We hope the audience will have practical takeaways for building, deploying, operating, and managing Kafka at scale in the enterprise.
Kafka meetup JP #3 - Engineering Apache Kafka at LINEkawamuray
This document summarizes a presentation about engineering Apache Kafka at LINE. Some key points:
- LINE uses Apache Kafka as a central data hub to pass data between services, handling over 140 billion messages per day.
- Data stored in Kafka includes application logs, data mutations, and task requests. This data is used for tasks like data replication, analytics, and asynchronous processing.
- Performance optimizations have led to target latencies below 1ms for 50% of produces and below 10ms for 99% of produces.
- SystemTap, a Linux tracing tool, helped identify slow disk reads causing delayed Kafka responses, improving performance.
- Having a single Kafka cluster as a data hub makes inter-service
Running Apache Kafka in production is only the first step in the Kafka operations journey. Professional Kafka users are ready to handle all possible disasters - because for most businesses having a disaster recovery plan is not optional.
In this session, we’ll discuss disaster scenarios that can take down entire Kafka clusters and share advice on how to plan, prepare and handle these events. This is a technical session full of best practices - we want to make sure you are ready to handle the worst mayhem that nature and auditors can cause.
Visit www.confluent.io for more information.
A la rencontre de Kafka, le log distribué par Florian GARCIALa Cuisine du Web
Kafka c’est un peu la nouvelle star sur la scène des files de messages. Pourtant Kafka ne se présente pas en tant que tel, c’est un log distribué !
Alors qu’est ce que c’est ? Comment ça marche ? Et surtout comment et pourquoi je l’utilise ?
Dans cette session, on décortique la bête pour tout vous expliquer ! Au programme : des concepts, des cas d’usage, du streaming et un retour d’expérience !
The Foundations of Multi-DC Kafka (Jakub Korab, Solutions Architect, Confluen...confluent
1. The document discusses various architectures for running Kafka in a multi-datacenter environment including running Kafka natively in multiple datacenters, mirroring data between datacenters, and using hierarchical Zookeeper quorums.
2. Key considerations for multi-DC Kafka include replication settings, consumer reconfiguration needs during outages, and handling consumer offsets and processing state across datacenters.
3. Native multi-DC Kafka is preferred but mirroring can be an alternative approach for inter-region traffic when latency is over 30ms or datacenters cannot be combined into a single cluster. Asynchronous mirroring acts differently than a single Kafka cluster and impacts operations.
Kafka is an open source messaging system that can handle massive streams of data in real-time. It is fast, scalable, durable, and fault-tolerant. Kafka is commonly used for stream processing, website activity tracking, metrics collection, and log aggregation. It supports high throughput, reliable delivery, and horizontal scalability. Some examples of real-time use cases for Kafka include website monitoring, network monitoring, fraud detection, and IoT applications.
Apache Kafka is a fast, scalable, durable and distributed messaging system. It is designed for high throughput systems and can replace traditional message brokers. Kafka has better throughput, partitioning, replication and fault tolerance compared to other messaging systems, making it suitable for large-scale applications. Kafka persists all data to disk for reliability and uses distributed commit logs for durability.
Kafka's basic terminologies, its architecture, its protocol and how it works.
Kafka at scale, its caveats, guarantees and use cases offered by it.
How we use it @ZaprMediaLabs.
Kafka is primarily used to build real-time streaming data pipelines and applications that adapt to the data streams. It combines messaging, storage, and stream processing to allow storage and analysis of both historical and real-time data.
Apache Kafka is a distributed messaging system that allows for publishing and subscribing to streams of records, known as topics, in a fault-tolerant and scalable way. It is used for building real-time data pipelines and streaming apps. Producers write data to topics which are committed to disks across partitions and replicated for fault tolerance. Consumers read data from topics in a decoupled manner based on offsets. Kafka can process streaming data in real-time and at large volumes with low latency and high throughput.
Stream Me Up, Scotty: Transitioning to the Cloud Using a Streaming Data Platformconfluent
Many enterprises have a large technical debt in legacy applications hosted in on-premises data centers. There is a strong desire to modernize and move to a cloud-based infrastructure, but the world won’t stop for you to transition. Existing applications need to be supported and enhanced; data from legacy platforms is required to make decisions that drive the business. On the other hand, data from cloud-based applications does not exist in a vacuum. Legacy applications need access to these cloud data sources and vice versa.
Can an enterprise have it both ways? Can new applications be built in the cloud while existing applications are maintained in a private data center?
Monsanto has adopted a cloud-first mentality—today most new development is focused on the cloud. However, this transition did not happen overnight.
Chrix Finne and Bob Lehmann share their experience building and implementing a Kafka-based cross-data-center streaming platform to facilitate the move to the cloud—in the process, kick-starting Monsanto’s transition from batch to stream processing. Details include an overview of the challenges involved in transitioning to the cloud and a deep dive into the cross-data-center stream platform architecture, including best practices for running this architecture in production and a summary of the benefits seen after deploying this architecture.
Healthcare data comes in many shapes and sizes making ingestion difficult for a variety of batch and near real time use cases. By Cerner evolving its architecture to adopt Apache Kafka, Cerner was able to build a modular architecture for current and future use cases. Reviewing the evolution of Cerner’s uses, developers can help to avoid mistakes and set themselves up for success.
Streaming ETL - from RDBMS to Dashboard with KSQLBjoern Rost
Apache Kafka is a massively scalable message queue that is being used at more and more places connecting more and more data sources. This presentation will introduce Kafka from the perspective of a mere mortal DBA and share the experience of (and challenges with) getting events from the database to Kafka using Kafka connect including poor-man’s CDC using flashback query and traditional logical replication tools. To demonstrate how and why this is a good idea, we will build an end-to-end data processing pipeline. We will discuss how to turn changes in database state into events and stream them into Apache Kafka. We will explore the basic concepts of streaming transformations using windows and KSQL before ingesting the transformed stream in a dashboard application.
Apache Kafka is a distributed streaming platform. It provides a high-throughput distributed messaging system that can handle trillions of events daily. Many large companies use Kafka for application logging, metrics collection, and powering real-time analytics. The current version is 0.8.2 and upcoming versions will include a new consumer, security features, and support for transactions.
This document provides an overview of Kafka, a distributed streaming platform. It can publish and subscribe to streams of records, store streams durably across clusters, and process streams as they occur. The Kafka cluster stores streams of records in topics. It has four main APIs: Producer API to publish data, Consumer API to subscribe to topics, Streams API to transform streams, and Connector API to connect Kafka and other systems. Records in Kafka topics are partitioned and ordered with offsets for scalability and fault tolerance. Consumers subscribe to topics in consumer groups to process partitions in parallel.
This document provides an overview of Kafka including basic concepts like topics, brokers, and partitions. It discusses how to install and run Kafka, configure topics and clusters, and monitor and troubleshoot Kafka. It also demonstrates producing, consuming, and loading test scenarios. Key lessons learned are around balancing replication, partitions, and leaders across brokers while ensuring adequate disk space, IOPS, and retention periods. Automating cluster changes and backing up messages is also recommended.
This is an overview of interesting features from Apache Pulsar. Keep in mind that by the time I did this presentation I did not have used Pulsar yet. It's just my first impressions from the list of features.
Strata+Hadoop 2017 San Jose: Lessons from a year of supporting Apache Kafkaconfluent
The number of deployments of Apache Kafka at enterprise scale has greatly increased in the years since Kafka’s original development in 2010. Along with this rapid growth has come a wide variety of use cases and deployment strategies that transcend what Kafka’s creators imagined when they originally developed the technology. As the scope and reach of streaming data platforms based on Apache Kafka has grown, the need to understand monitoring and troubleshooting strategies has as well.
Dustin Cote and Ryan Pridgeon share their experience supporting Apache Kafka at enterprise-scale and explore monitoring and troubleshooting techniques to help you avoid pitfalls when scaling large-scale Kafka deployments.
Topics include:
- Effective use of JMX for Kafka
- Tools for preventing small problems from becoming big ones
- Efficient architectures proven in the wild
- Finding and storing the right information when it all goes wrong
Visit www.confluent.io for more information.
Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza. This talk gives an introduction to Apache Kafka, a distributed messaging system. It will cover both how Kafka works, as well as how it is used at LinkedIn for log aggregation, messaging, ETL, and real-time stream processing.
Troubleshooting Kafka's socket server: from incident to resolutionJoel Koshy
LinkedIn’s Kafka deployment is nearing 1300 brokers that move close to 1.3 trillion messages a day. While operating Kafka smoothly even at this scale is testament to both Kafka’s scalability and the operational expertise of LinkedIn SREs we occasionally run into some very interesting bugs at this scale. In this talk I will dive into a production issue that we recently encountered as an example of how even a subtle bug can suddenly manifest at scale and cause a near meltdown of the cluster. We will go over how we detected and responded to the situation, investigated it after the fact and summarize some lessons learned and best-practices from this incident.
Apache Kafka is a high-throughput distributed messaging system that can be used for building real-time data pipelines and streaming apps. It provides a publish-subscribe messaging model and is designed as a distributed commit log. Kafka allows for both push and pull models where producers push data and consumers pull data from topics which are divided into partitions to allow for parallelism.
Kafka is a distributed messaging system that allows for publishing and subscribing to streams of records, known as topics. Producers write data to topics and consumers read from topics. The data is partitioned and replicated across clusters of machines called brokers for reliability and scalability. A common data format like Avro can be used to serialize the data.
Tales from the four-comma club: Managing Kafka as a service at Salesforce | L...HostedbyConfluent
Apache Kafka is a key part of the Big Data infrastructure at Salesforce, enabling publish/subscribe and data transport in near real-time at enterprise scale handling trillions of messages per day. In this session, hear from the teams at Salesforce that manage Kafka as a service, running over a hundred clusters across on-premise and public cloud environments with over 99.9% availability. Hear about best practices and innovations, including:
* How to manage multi-tenant clusters in a hybrid environment
* High volume data pipelines with Mirus replicating data to Kafka and blob storage
* Kafka Fault Injection Framework built on Trogdor and Kibosh
* Automated recovery without data loss
* Using Envoy as an SNI-routing Kafka gateway
We hope the audience will have practical takeaways for building, deploying, operating, and managing Kafka at scale in the enterprise.
Kafka meetup JP #3 - Engineering Apache Kafka at LINEkawamuray
This document summarizes a presentation about engineering Apache Kafka at LINE. Some key points:
- LINE uses Apache Kafka as a central data hub to pass data between services, handling over 140 billion messages per day.
- Data stored in Kafka includes application logs, data mutations, and task requests. This data is used for tasks like data replication, analytics, and asynchronous processing.
- Performance optimizations have led to target latencies below 1ms for 50% of produces and below 10ms for 99% of produces.
- SystemTap, a Linux tracing tool, helped identify slow disk reads causing delayed Kafka responses, improving performance.
- Having a single Kafka cluster as a data hub makes inter-service
Running Apache Kafka in production is only the first step in the Kafka operations journey. Professional Kafka users are ready to handle all possible disasters - because for most businesses having a disaster recovery plan is not optional.
In this session, we’ll discuss disaster scenarios that can take down entire Kafka clusters and share advice on how to plan, prepare and handle these events. This is a technical session full of best practices - we want to make sure you are ready to handle the worst mayhem that nature and auditors can cause.
Visit www.confluent.io for more information.
A la rencontre de Kafka, le log distribué par Florian GARCIALa Cuisine du Web
Kafka c’est un peu la nouvelle star sur la scène des files de messages. Pourtant Kafka ne se présente pas en tant que tel, c’est un log distribué !
Alors qu’est ce que c’est ? Comment ça marche ? Et surtout comment et pourquoi je l’utilise ?
Dans cette session, on décortique la bête pour tout vous expliquer ! Au programme : des concepts, des cas d’usage, du streaming et un retour d’expérience !
The Foundations of Multi-DC Kafka (Jakub Korab, Solutions Architect, Confluen...confluent
1. The document discusses various architectures for running Kafka in a multi-datacenter environment including running Kafka natively in multiple datacenters, mirroring data between datacenters, and using hierarchical Zookeeper quorums.
2. Key considerations for multi-DC Kafka include replication settings, consumer reconfiguration needs during outages, and handling consumer offsets and processing state across datacenters.
3. Native multi-DC Kafka is preferred but mirroring can be an alternative approach for inter-region traffic when latency is over 30ms or datacenters cannot be combined into a single cluster. Asynchronous mirroring acts differently than a single Kafka cluster and impacts operations.
Kafka is an open source messaging system that can handle massive streams of data in real-time. It is fast, scalable, durable, and fault-tolerant. Kafka is commonly used for stream processing, website activity tracking, metrics collection, and log aggregation. It supports high throughput, reliable delivery, and horizontal scalability. Some examples of real-time use cases for Kafka include website monitoring, network monitoring, fraud detection, and IoT applications.
Apache Kafka is a fast, scalable, durable and distributed messaging system. It is designed for high throughput systems and can replace traditional message brokers. Kafka has better throughput, partitioning, replication and fault tolerance compared to other messaging systems, making it suitable for large-scale applications. Kafka persists all data to disk for reliability and uses distributed commit logs for durability.
Kafka's basic terminologies, its architecture, its protocol and how it works.
Kafka at scale, its caveats, guarantees and use cases offered by it.
How we use it @ZaprMediaLabs.
Kafka is primarily used to build real-time streaming data pipelines and applications that adapt to the data streams. It combines messaging, storage, and stream processing to allow storage and analysis of both historical and real-time data.
Building streaming data applications using Kafka*[Connect + Core + Streams] b...Data Con LA
Abstract:- Apache Kafka evolved from an enterprise messaging system to a fully distributed streaming data platform for building real-time streaming data pipelines and streaming data applications without the need for other tools/clusters for data ingestion, storage and stream processing. In this talk you will learn more about: A quick introduction to Kafka Core, Kafka Connect and Kafka Streams through code examples, key concepts and key features. A reference architecture for building such Kafka-based streaming data applications. A demo of an end-to-end Kafka-based streaming data application.
Building Streaming Data Applications Using Apache KafkaSlim Baltagi
Apache Kafka evolved from an enterprise messaging system to a fully distributed streaming data platform for building real-time streaming data pipelines and streaming data applications without the need for other tools/clusters for data ingestion, storage and stream processing.
In this talk you will learn more about:
1. A quick introduction to Kafka Core, Kafka Connect and Kafka Streams: What is and why?
2. Code and step-by-step instructions to build an end-to-end streaming data application using Apache Kafka
Apache Kafka - Scalable Message-Processing and more !Guido Schmutz
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. How can me make sure that all these event are accepted and forwarded in an efficient and reliable way? This is where Apache Kafaka comes into play, a distirbuted, highly-scalable messaging broker, build for exchanging huge amount of messages between a source and a target.
This session will start with an introduction into Apache and presents the role of Apache Kafka in a modern data / information architecture and the advantages it brings to the table. Additionally the Kafka ecosystem will be covered as well as the integration of Kafka in the Oracle Stack, with products such as Golden Gate, Service Bus and Oracle Stream Analytics all being able to act as a Kafka consumer or producer.
Fundamentals and Architecture of Apache KafkaAngelo Cesaro
Fundamentals and Architecture of Apache Kafka.
This presentation explains Apache Kafka's architecture and internal design giving an overview of Kafka internal functions, including:
Brokers, Replication, Partitions, Producers, Consumers, Commit log, comparison over traditional message queues.
Full recorded presentation at https://www.youtube.com/watch?v=2UfAgCSKPZo for Tetrate Tech Talks on 2022/05/13.
Envoy's support for Kafka protocol, in form of broker-filter and mesh-filter.
Contents:
- overview of Kafka (usecases, partitioning, producer/consumer, protocol);
- proxying Kafka (non-Envoy specific);
- proxying Kafka with Envoy;
- handling Kafka protocol in Envoy;
- Kafka-broker-filter for per-connection proxying;
- Kafka-mesh-filter to provide front proxy for multiple Kafka clusters.
References:
- https://adam-kotwasinski.medium.com/deploying-envoy-and-kafka-8aa7513ec0a0
- https://adam-kotwasinski.medium.com/kafka-mesh-filter-in-envoy-a70b3aefcdef
This document provides an introduction to Apache Kafka, an open-source distributed event streaming platform. It discusses Kafka's history as a project originally developed by LinkedIn, its use cases like messaging, activity tracking and stream processing. It describes key Kafka concepts like topics, partitions, offsets, replicas, brokers and producers/consumers. It also gives examples of how companies like Netflix, Uber and LinkedIn use Kafka in their applications and provides a comparison to Apache Spark.
Kafka is a distributed streaming platform used for building real-time data pipelines and streaming applications. It allows for publishing and subscribing to streams of records known as topics in a fault-tolerant, scalable, and fast manner. Producers publish data to topics while consumers subscribe to topics and process the data streams. The Kafka cluster stores these topic partitions across servers and replicates the data for fault tolerance. It provides ordering and processing guarantees through offsets as it retains data for a configurable period of time.
Kafka is a distributed, replicated, and partitioned platform for handling real-time data feeds. It allows both publishing and subscribing to streams of records, and is commonly used for applications such as log aggregation, metrics, and streaming analytics. Kafka runs as a cluster of one or more servers that can reliably handle trillions of events daily.
Unleashing Real-time Power with Kafka.pptxKnoldus Inc.
Unlock the potential of real-time data streaming with Kafka in this session. Learn the fundamentals, architecture, and seamless integration with Scala, empowering you to elevate your data processing capabilities. Perfect for developers at all levels, this hands-on experience will equip you to harness the power of real-time data streams effectively.
From a kafkaesque story to The Promised LandRan Silberman
LivePerson moved from an ETL based data platform to a new data platform based on emerging technologies from the Open Source community: Hadoop, Kafka, Storm, Avro and more.
This presentation tells the story and focuses on Kafka.
Capital One Delivers Risk Insights in Real Time with Stream Processingconfluent
Speakers: Ravi Dubey, Senior Manager, Software Engineering, Capital One + Jeff Sharpe, Software Engineer, Capital One
Capital One supports interactions with real-time streaming transactional data using Apache Kafka®. Kafka helps deliver information to internal operation teams and bank tellers to assist with assessing risk and protect customers in a myriad of ways.
Inside the bank, Kafka allows Capital One to build a real-time system that takes advantage of modern data and cloud technologies without exposing customers to unnecessary data breaches, or violating privacy regulations. These examples demonstrate how a streaming platform enables Capital One to act on their visions faster and in a more scalable way through the Kafka solution, helping establish Capital One as an innovator in the banking space.
Join us for this online talk on lessons learned, best practices and technical patterns of Capital One’s deployment of Apache Kafka.
-Find out how Kafka delivers on a 5-second service-level agreement (SLA) for inside branch tellers.
-Learn how to combine and host data in-memory and prevent personally identifiable information (PII) violations of in-flight transactions.
-Understand how Capital One manages Kafka Docker containers using Kubernetes.
Watch the recording: https://videos.confluent.io/watch/6e6ukQNnmASwkf9Gkdhh69?.
Introduction to Kafka Streams PresentationKnoldus Inc.
Kafka Streams is a client library providing organizations with a particularly efficient framework for processing streaming data. It offers a streamlined method for creating applications and microservices that must process data in real-time to be effective. Using the Streams API within Apache Kafka, the solution fundamentally transforms input Kafka topics into output Kafka topics. The benefits are important: Kafka Streams pairs the ease of utilizing standard Java and Scala application code on the client end with the strength of Kafka’s robust server-side cluster architecture.
Azure Event Hubs - Behind the Scenes With Kasun Indrasiri | Current 2022HostedbyConfluent
Azure Event Hubs - Behind the Scenes With Kasun Indrasiri | Current 2022
Azure Event Hubs is a hyperscale PaaS event stream broker with protocol support for HTTP, AMQP, and Apache Kafka RPC that accepts and forwards several trillion (!) events per day and is available in all global Azure regions. This session is a look behind the curtain where we dive deep into the architecture of Event Hubs and look at the Event Hubs cluster model, resource isolation, and storage strategies and also review some performance figures.
Making Apache Kafka Even Faster And More ScalablePaulBrebner2
Introduction to the 6th Community over Code Performance Engineering track and my talk on Apache Kafka Performance changes resulting from architectural changes including KRaft and the introduction of Kafka Tiered Storage.
Consensus in Apache Kafka: From Theory to Production.pdfGuozhang Wang
In this talk I'd like to cover an everlasting story in distributed systems: consensus. More specifically, the consensus challenges in Apache Kafka, and how we addressed it starting from theory in papers to production in the cloud.
Data Models and Consumer Idioms Using Apache Kafka for Continuous Data Stream...Erik Onnen
The document discusses Urban Airship's use of Apache Kafka for processing continuous data streams. It describes how Urban Airship uses Kafka for analytics, operational data, and presence data. Producers write device data to Kafka topics, and consumers create indexes from the data in databases like HBase and write to operational data warehouses. The document also covers Kafka concepts, best use cases, limitations, and examples of data structures for storing device metadata in Kafka streams.
В докладе я расскажу, как выглядит World of Tanks Server (кластер кластеров) со всеми веб-сервисами, которые существуют вокруг. Какие узкие места с точки зрения отказоустойчивости есть внутри кластера, между кластерами, во взаимодействии с внешними веб-сервисами. Как мы решаем возникающие проблемы технически, процессно, проектно.
Программирование как способ выражения мыслей. Levon Avakyan
Я расскажу на простейших примерах как функционирует современный компьютер, какие языки программирования бывают, для чего они используются, какие парадигмы лежат в их основе. По сути, язык программирования это инструмент, с помощью которого можно рассказать машине, чего же мы от неё хотим, тем самым воплотив свои мысли.
In this presentation I will speak how are the SRE and DevOps, what is a reliability. Also about the reliability approach in Competitive Gaming in Wargaming and show a few cases.
Архитектура мета игры Wargaming. Глобальная карта 2.0.Levon Avakyan
В своем докладе я расскажу что такое Глобальная карта, как она устроена, какие технологии, архитектурыне решения, принципы и подходы используются. Как мы боремся с высокими нагрузками, с какими проблемами сталкиваемся, и как их решаем.
Осознанный выбор. Python 3 для реализации сервисного шлюза клиента World of T...Levon Avakyan
Доклад о том, зачем нам понадобился сервисный шлюз для клиента WoT, как выбирались и проверялись технологические решения, плюсы и минусы использования Python 3 + asyncio в этом конкретном случае. +Бонус: выбор, отслеживание и визуализация метрик приложения
Кланы в Wargaming. От странички на танковом портале до мультиплатфермнного с...Levon Avakyan
Кланы являются неотъемлемой частью любой MMO. И игры Wargaming не стали исключением, а вместе с бурным развитием трилогии ,и World of Tanks в частности, быстро изменялись требования к кланам как у пользователей так и у бизнеса. Доклад расскажет о том пути, который мы прошли ,создавая поддержку кланов в Wargaming, какие трудности преодолевали и какие уроки выучили, создавая игровой сервис, который радует миллионы наших игроков.
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Оперирование - это важный компонент в жизненном цикле любого продукта или сервиса. Для высоконагруженных проектов с огромным количеством связей, простейшие запросы на оперирование, представляют собой достаточно нетривиальную задачу. А бизнес требует вносить изменения ASAP. Кроме того, несмотря на высокую нагрузку, мы должны обеспечивать высокое качество сервиса для пользователей несмотря ни на что. Каждый раз мы решаем уникальные инженерные задачи, чтобы можно было играть в «Кланах» и на «Глобальной карте» по всему миру. Доклад расскажет о тех проблемах с которыми мы сталкиваемся и о best practices по управлению приложениями, инфраструктурой и сторонними компонентами для их решения
HCL Nomad Web – Best Practices and Managing Multiuser Environmentspanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-nomad-web-best-practices-and-managing-multiuser-environments/
HCL Nomad Web is heralded as the next generation of the HCL Notes client, offering numerous advantages such as eliminating the need for packaging, distribution, and installation. Nomad Web client upgrades will be installed “automatically” in the background. This significantly reduces the administrative footprint compared to traditional HCL Notes clients. However, troubleshooting issues in Nomad Web present unique challenges compared to the Notes client.
Join Christoph and Marc as they demonstrate how to simplify the troubleshooting process in HCL Nomad Web, ensuring a smoother and more efficient user experience.
In this webinar, we will explore effective strategies for diagnosing and resolving common problems in HCL Nomad Web, including
- Accessing the console
- Locating and interpreting log files
- Accessing the data folder within the browser’s cache (using OPFS)
- Understand the difference between single- and multi-user scenarios
- Utilizing Client Clocking
Andrew Marnell: Transforming Business Strategy Through Data-Driven InsightsAndrew Marnell
With expertise in data architecture, performance tracking, and revenue forecasting, Andrew Marnell plays a vital role in aligning business strategies with data insights. Andrew Marnell’s ability to lead cross-functional teams ensures businesses achieve sustainable growth and operational excellence.
Quantum Computing Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
Book industry standards are evolving rapidly. In the first part of this session, we’ll share an overview of key developments from 2024 and the early months of 2025. Then, BookNet’s resident standards expert, Tom Richardson, and CEO, Lauren Stewart, have a forward-looking conversation about what’s next.
Link to recording, presentation slides, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.
DevOpsDays Atlanta 2025 - Building 10x Development Organizations.pptxJustin Reock
Building 10x Organizations with Modern Productivity Metrics
10x developers may be a myth, but 10x organizations are very real, as proven by the influential study performed in the 1980s, ‘The Coding War Games.’
Right now, here in early 2025, we seem to be experiencing YAPP (Yet Another Productivity Philosophy), and that philosophy is converging on developer experience. It seems that with every new method we invent for the delivery of products, whether physical or virtual, we reinvent productivity philosophies to go alongside them.
But which of these approaches actually work? DORA? SPACE? DevEx? What should we invest in and create urgency behind today, so that we don’t find ourselves having the same discussion again in a decade?
Generative Artificial Intelligence (GenAI) in BusinessDr. Tathagat Varma
My talk for the Indian School of Business (ISB) Emerging Leaders Program Cohort 9. In this talk, I discussed key issues around adoption of GenAI in business - benefits, opportunities and limitations. I also discussed how my research on Theory of Cognitive Chasms helps address some of these issues
Linux Support for SMARC: How Toradex Empowers Embedded DevelopersToradex
Toradex brings robust Linux support to SMARC (Smart Mobility Architecture), ensuring high performance and long-term reliability for embedded applications. Here’s how:
• Optimized Torizon OS & Yocto Support – Toradex provides Torizon OS, a Debian-based easy-to-use platform, and Yocto BSPs for customized Linux images on SMARC modules.
• Seamless Integration with i.MX 8M Plus and i.MX 95 – Toradex SMARC solutions leverage NXP’s i.MX 8 M Plus and i.MX 95 SoCs, delivering power efficiency and AI-ready performance.
• Secure and Reliable – With Secure Boot, over-the-air (OTA) updates, and LTS kernel support, Toradex ensures industrial-grade security and longevity.
• Containerized Workflows for AI & IoT – Support for Docker, ROS, and real-time Linux enables scalable AI, ML, and IoT applications.
• Strong Ecosystem & Developer Support – Toradex offers comprehensive documentation, developer tools, and dedicated support, accelerating time-to-market.
With Toradex’s Linux support for SMARC, developers get a scalable, secure, and high-performance solution for industrial, medical, and AI-driven applications.
Do you have a specific project or application in mind where you're considering SMARC? We can help with Free Compatibility Check and help you with quick time-to-market
For more information: https://www.toradex.com/computer-on-modules/smarc-arm-family
TrsLabs - Fintech Product & Business ConsultingTrs Labs
Hybrid Growth Mandate Model with TrsLabs
Strategic Investments, Inorganic Growth, Business Model Pivoting are critical activities that business don't do/change everyday. In cases like this, it may benefit your business to choose a temporary external consultant.
An unbiased plan driven by clearcut deliverables, market dynamics and without the influence of your internal office equations empower business leaders to make right choices.
Getting things done within a budget within a timeframe is key to Growing Business - No matter whether you are a start-up or a big company
Talk to us & Unlock the competitive advantage
Technology Trends in 2025: AI and Big Data AnalyticsInData Labs
At InData Labs, we have been keeping an ear to the ground, looking out for AI-enabled digital transformation trends coming our way in 2025. Our report will provide a look into the technology landscape of the future, including:
-Artificial Intelligence Market Overview
-Strategies for AI Adoption in 2025
-Anticipated drivers of AI adoption and transformative technologies
-Benefits of AI and Big data for your business
-Tips on how to prepare your business for innovation
-AI and data privacy: Strategies for securing data privacy in AI models, etc.
Download your free copy nowand implement the key findings to improve your business.
HCL Nomad Web – Best Practices und Verwaltung von Multiuser-Umgebungenpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-nomad-web-best-practices-und-verwaltung-von-multiuser-umgebungen/
HCL Nomad Web wird als die nächste Generation des HCL Notes-Clients gefeiert und bietet zahlreiche Vorteile, wie die Beseitigung des Bedarfs an Paketierung, Verteilung und Installation. Nomad Web-Client-Updates werden “automatisch” im Hintergrund installiert, was den administrativen Aufwand im Vergleich zu traditionellen HCL Notes-Clients erheblich reduziert. Allerdings stellt die Fehlerbehebung in Nomad Web im Vergleich zum Notes-Client einzigartige Herausforderungen dar.
Begleiten Sie Christoph und Marc, während sie demonstrieren, wie der Fehlerbehebungsprozess in HCL Nomad Web vereinfacht werden kann, um eine reibungslose und effiziente Benutzererfahrung zu gewährleisten.
In diesem Webinar werden wir effektive Strategien zur Diagnose und Lösung häufiger Probleme in HCL Nomad Web untersuchen, einschließlich
- Zugriff auf die Konsole
- Auffinden und Interpretieren von Protokolldateien
- Zugriff auf den Datenordner im Cache des Browsers (unter Verwendung von OPFS)
- Verständnis der Unterschiede zwischen Einzel- und Mehrbenutzerszenarien
- Nutzung der Client Clocking-Funktion
The Evolution of Meme Coins A New Era for Digital Currency ppt.pdfAbi john
Analyze the growth of meme coins from mere online jokes to potential assets in the digital economy. Explore the community, culture, and utility as they elevate themselves to a new era in cryptocurrency.
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...SOFTTECHHUB
I started my online journey with several hosting services before stumbling upon Ai EngineHost. At first, the idea of paying one fee and getting lifetime access seemed too good to pass up. The platform is built on reliable US-based servers, ensuring your projects run at high speeds and remain safe. Let me take you step by step through its benefits and features as I explain why this hosting solution is a perfect fit for digital entrepreneurs.
How Can I use the AI Hype in my Business Context?Daniel Lehner
𝙄𝙨 𝘼𝙄 𝙟𝙪𝙨𝙩 𝙝𝙮𝙥𝙚? 𝙊𝙧 𝙞𝙨 𝙞𝙩 𝙩𝙝𝙚 𝙜𝙖𝙢𝙚 𝙘𝙝𝙖𝙣𝙜𝙚𝙧 𝙮𝙤𝙪𝙧 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙣𝙚𝙚𝙙𝙨?
Everyone’s talking about AI but is anyone really using it to create real value?
Most companies want to leverage AI. Few know 𝗵𝗼𝘄.
✅ What exactly should you ask to find real AI opportunities?
✅ Which AI techniques actually fit your business?
✅ Is your data even ready for AI?
If you’re not sure, you’re not alone. This is a condensed version of the slides I presented at a Linkedin webinar for Tecnovy on 28.04.2025.
Enhancing ICU Intelligence: How Our Functional Testing Enabled a Healthcare I...Impelsys Inc.
Impelsys provided a robust testing solution, leveraging a risk-based and requirement-mapped approach to validate ICU Connect and CritiXpert. A well-defined test suite was developed to assess data communication, clinical data collection, transformation, and visualization across integrated devices.
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep DiveScyllaDB
Want to learn practical tips for designing systems that can scale efficiently without compromising speed?
Join us for a workshop where we’ll address these challenges head-on and explore how to architect low-latency systems using Rust. During this free interactive workshop oriented for developers, engineers, and architects, we’ll cover how Rust’s unique language features and the Tokio async runtime enable high-performance application development.
As you explore key principles of designing low-latency systems with Rust, you will learn how to:
- Create and compile a real-world app with Rust
- Connect the application to ScyllaDB (NoSQL data store)
- Negotiate tradeoffs related to data modeling and querying
- Manage and monitor the database for consistently low latencies
What is Model Context Protocol(MCP) - The new technology for communication bw...Vishnu Singh Chundawat
The MCP (Model Context Protocol) is a framework designed to manage context and interaction within complex systems. This SlideShare presentation will provide a detailed overview of the MCP Model, its applications, and how it plays a crucial role in improving communication and decision-making in distributed systems. We will explore the key concepts behind the protocol, including the importance of context, data management, and how this model enhances system adaptability and responsiveness. Ideal for software developers, system architects, and IT professionals, this presentation will offer valuable insights into how the MCP Model can streamline workflows, improve efficiency, and create more intuitive systems for a wide range of use cases.
2. Table Of Contents 2
• World Of Tanks Server
• BigWorld Technology
• Cluster of clusters
• World of Tanks and Big Data
• Apache Kafka
• How it works?
• Best practices
• World of Tanks Server and Kafka
• Implementation
• Difficulties
4. BigWorld Technology 4
BigWorld Technology is BigWorld's middleware for implementing Massively Multiplayer Online Games.
• Scalability, reliability, efficiency
• Occam's Razor
• Improve the worst case
• Client/server bandwidth is valuable
• Keep information together; Avoid two-way calls
• Avoid bottlenecks; Make the system distributed
• Where possible, do communication in batches
7. 7
• Heat maps (player positions, damage, detection, etc)
• Battle results ( by arena, by player, by vehicle)
• Matchmaker data
Approximately. 20K RPS
World of Tanks and Big Data
8. 8
• Data Warehouse
• Player Relationship Managment Platfrom
• Wargaming Rating Managment System
• Ranked Battles Leaderboads
• Strongholds Service
World of Tanks Data Consumers
10. Apache Kafka 10
Concept:
• Kafka is run as a cluster on one or more servers.
• The Kafka cluster stores streams of records in categories
called topics.
• Each record consists of a key, a value, and a timestamp.
Capabilities:
• It lets you publish and subscribe to streams of records.
• It lets you store streams of records in a fault-tolerant way.
• It lets you process streams of records as they occur.
Guarantees
• Messages sent by a producer to a particular topic partition will be
appended in the order they are sent
• A consumer instance sees records in the order they are stored in
the log.
• For a topic with replication factor N, we will tolerate up to N-1
server failures without losing any records committed to the log.
Apache Kafka® is a distributed streaming platform.
12. Apache Kafka 12
A topic is a category or feed name to which records are published
Topics and Logs
13. Apache Kafka 13
Consumers label themselves with a consumer group name, and each record published to a topic is
delivered to one consumer instance within each subscribing consumer group.
Consumer Groups
18. Producer Implementation 18
• python-kafka (https://github.com/dpkp/kafka-python)
• librdkafka (https://github.com/edenhill/librdkafka)
Technology
python-kafka librdkafka
python C++
a lot of bugs more stable
memory leaks with disabled GC thread safe
21. Message Schema 21
Schema Registry provides a serving layer for your metadata. It provides a RESTful interface for storing
and retrieving Avro schemas. It stores a versioned history of all schemas, provides multiple
compatibility settings and allows evolution of schemas according to the configured compatibility
setting. It provides serializers that plug into Kafka clients that handle schema storage and retrieval for
Kafka messages that are sent in the Avro format.
Schema Registry
25. Infrastructure improvements 25
• Local Kafka cluster in every Data Center
• One Schema Registry
• Replication to the central Kafka cluster using Mirror
Maker
• Configure topics in according with best practice
• Improve monitoring
Apache Kafka
27. Plans 27
• Backup data in message queues
• Migrate to librdkafka
• Schema versioning
28. Conclusion 28
• Apache Kafka is powerful tool for transferring, storing and processing
data
• No solution is working out of the box
• Analyze, do experiments, improve your solution continuously
#5: Scalability, reliability, efficiency
Главная цель - сделать расширяемую, надежную, эффективную систему
•Occam's Razor
The simplest design that satisfies all requirements should be considered the best, or in the words of Ein-
stein, "Make things as simple as possible, but no simpler".
•Improve the worst case
In general (mainly when it comes to the client experience), the worst case should be improved over the
average case.
•Client/server bandwidth is valuable
The most important resource is the bandwidth between the client and server. After this, it is probably CPU,
and then intra-server bandwidth.
•Keep information together; Avoid two-way calls
Information (or data) that is often used together should be easily accessed together. •
Avoid bottlenecks; Make the system distributed
•Where possible, do communication in batches
There is a fairly high overhead in sending a single packet. Лучше 1 большой, чем 10 маленьких
#21: Scalability, reliability, efficiency
The general goal is to produce a scalable, reliable, and efficient system. This should be done while keeping
as much simplicity and flexibility as possible.
•Occam's Razor
The simplest design that satisfies all requirements should be considered the best, or in the words of Ein-
stein, "Make things as simple as possible, but no simpler".
•Improve the worst case
In general (mainly when it comes to the client experience), the worst case should be improved over the
average case. For example, it is not beneficial to have a blinding fast and accurate situation when a client
is not near a cell boundary if the experience is poor when he is near one.
•Client/server bandwidth is valuable
The most important resource is the bandwidth between the client and server. After this, it is probably CPU,
and then intra-server bandwidth.
•Keep information together; Avoid two-way calls
Information (or data) that is often used together should be easily accessed together. For example, a large
amount of the data processed together in the game is related to objects that are geometrically close. It
makes sense then to use data partitioning based on locality.
It is also expensive to have to request information from a separate server machine when it is necessary.
This is true for a number of reasons including the extra hops and coordination required and (maybe even
more importantly) the reduced likelihood of being able to batch requests together.
•Avoid bottlenecks; Make the system distributed
The design should try to avoid single central points where things occur. This approach can cause perfor-
mance bottlenecks and make the design non-scalable. It can also introduce a single point of failure, there-
fore raising fault tolerance issues.
•Where possible, do communication in batches
There is a fairly high overhead in sending a single packet. That is, it is a lot more expensive to send ten
individual packets than it is to send one packet that is ten times bigger.
#23: Scalability, reliability, efficiency
The general goal is to produce a scalable, reliable, and efficient system. This should be done while keeping
as much simplicity and flexibility as possible.
•Occam's Razor
The simplest design that satisfies all requirements should be considered the best, or in the words of Ein-
stein, "Make things as simple as possible, but no simpler".
•Improve the worst case
In general (mainly when it comes to the client experience), the worst case should be improved over the
average case. For example, it is not beneficial to have a blinding fast and accurate situation when a client
is not near a cell boundary if the experience is poor when he is near one.
•Client/server bandwidth is valuable
The most important resource is the bandwidth between the client and server. After this, it is probably CPU,
and then intra-server bandwidth.
•Keep information together; Avoid two-way calls
Information (or data) that is often used together should be easily accessed together. For example, a large
amount of the data processed together in the game is related to objects that are geometrically close. It
makes sense then to use data partitioning based on locality.
It is also expensive to have to request information from a separate server machine when it is necessary.
This is true for a number of reasons including the extra hops and coordination required and (maybe even
more importantly) the reduced likelihood of being able to batch requests together.
•Avoid bottlenecks; Make the system distributed
The design should try to avoid single central points where things occur. This approach can cause perfor-
mance bottlenecks and make the design non-scalable. It can also introduce a single point of failure, there-
fore raising fault tolerance issues.
•Where possible, do communication in batches
There is a fairly high overhead in sending a single packet. That is, it is a lot more expensive to send ten
individual packets than it is to send one packet that is ten times bigger.
#24: Scalability, reliability, efficiency
The general goal is to produce a scalable, reliable, and efficient system. This should be done while keeping
as much simplicity and flexibility as possible.
•Occam's Razor
The simplest design that satisfies all requirements should be considered the best, or in the words of Ein-
stein, "Make things as simple as possible, but no simpler".
•Improve the worst case
In general (mainly when it comes to the client experience), the worst case should be improved over the
average case. For example, it is not beneficial to have a blinding fast and accurate situation when a client
is not near a cell boundary if the experience is poor when he is near one.
•Client/server bandwidth is valuable
The most important resource is the bandwidth between the client and server. After this, it is probably CPU,
and then intra-server bandwidth.
•Keep information together; Avoid two-way calls
Information (or data) that is often used together should be easily accessed together. For example, a large
amount of the data processed together in the game is related to objects that are geometrically close. It
makes sense then to use data partitioning based on locality.
It is also expensive to have to request information from a separate server machine when it is necessary.
This is true for a number of reasons including the extra hops and coordination required and (maybe even
more importantly) the reduced likelihood of being able to batch requests together.
•Avoid bottlenecks; Make the system distributed
The design should try to avoid single central points where things occur. This approach can cause perfor-
mance bottlenecks and make the design non-scalable. It can also introduce a single point of failure, there-
fore raising fault tolerance issues.
•Where possible, do communication in batches
There is a fairly high overhead in sending a single packet. That is, it is a lot more expensive to send ten
individual packets than it is to send one packet that is ten times bigger.
#26: Scalability, reliability, efficiency
The general goal is to produce a scalable, reliable, and efficient system. This should be done while keeping
as much simplicity and flexibility as possible.
•Occam's Razor
The simplest design that satisfies all requirements should be considered the best, or in the words of Ein-
stein, "Make things as simple as possible, but no simpler".
•Improve the worst case
In general (mainly when it comes to the client experience), the worst case should be improved over the
average case. For example, it is not beneficial to have a blinding fast and accurate situation when a client
is not near a cell boundary if the experience is poor when he is near one.
•Client/server bandwidth is valuable
The most important resource is the bandwidth between the client and server. After this, it is probably CPU,
and then intra-server bandwidth.
•Keep information together; Avoid two-way calls
Information (or data) that is often used together should be easily accessed together. For example, a large
amount of the data processed together in the game is related to objects that are geometrically close. It
makes sense then to use data partitioning based on locality.
It is also expensive to have to request information from a separate server machine when it is necessary.
This is true for a number of reasons including the extra hops and coordination required and (maybe even
more importantly) the reduced likelihood of being able to batch requests together.
•Avoid bottlenecks; Make the system distributed
The design should try to avoid single central points where things occur. This approach can cause perfor-
mance bottlenecks and make the design non-scalable. It can also introduce a single point of failure, there-
fore raising fault tolerance issues.
•Where possible, do communication in batches
There is a fairly high overhead in sending a single packet. That is, it is a lot more expensive to send ten
individual packets than it is to send one packet that is ten times bigger.
#28: Scalability, reliability, efficiency
The general goal is to produce a scalable, reliable, and efficient system. This should be done while keeping
as much simplicity and flexibility as possible.
•Occam's Razor
The simplest design that satisfies all requirements should be considered the best, or in the words of Ein-
stein, "Make things as simple as possible, but no simpler".
•Improve the worst case
In general (mainly when it comes to the client experience), the worst case should be improved over the
average case. For example, it is not beneficial to have a blinding fast and accurate situation when a client
is not near a cell boundary if the experience is poor when he is near one.
•Client/server bandwidth is valuable
The most important resource is the bandwidth between the client and server. After this, it is probably CPU,
and then intra-server bandwidth.
•Keep information together; Avoid two-way calls
Information (or data) that is often used together should be easily accessed together. For example, a large
amount of the data processed together in the game is related to objects that are geometrically close. It
makes sense then to use data partitioning based on locality.
It is also expensive to have to request information from a separate server machine when it is necessary.
This is true for a number of reasons including the extra hops and coordination required and (maybe even
more importantly) the reduced likelihood of being able to batch requests together.
•Avoid bottlenecks; Make the system distributed
The design should try to avoid single central points where things occur. This approach can cause perfor-
mance bottlenecks and make the design non-scalable. It can also introduce a single point of failure, there-
fore raising fault tolerance issues.
•Where possible, do communication in batches
There is a fairly high overhead in sending a single packet. That is, it is a lot more expensive to send ten
individual packets than it is to send one packet that is ten times bigger.
#29: Scalability, reliability, efficiency
The general goal is to produce a scalable, reliable, and efficient system. This should be done while keeping
as much simplicity and flexibility as possible.
•Occam's Razor
The simplest design that satisfies all requirements should be considered the best, or in the words of Ein-
stein, "Make things as simple as possible, but no simpler".
•Improve the worst case
In general (mainly when it comes to the client experience), the worst case should be improved over the
average case. For example, it is not beneficial to have a blinding fast and accurate situation when a client
is not near a cell boundary if the experience is poor when he is near one.
•Client/server bandwidth is valuable
The most important resource is the bandwidth between the client and server. After this, it is probably CPU,
and then intra-server bandwidth.
•Keep information together; Avoid two-way calls
Information (or data) that is often used together should be easily accessed together. For example, a large
amount of the data processed together in the game is related to objects that are geometrically close. It
makes sense then to use data partitioning based on locality.
It is also expensive to have to request information from a separate server machine when it is necessary.
This is true for a number of reasons including the extra hops and coordination required and (maybe even
more importantly) the reduced likelihood of being able to batch requests together.
•Avoid bottlenecks; Make the system distributed
The design should try to avoid single central points where things occur. This approach can cause perfor-
mance bottlenecks and make the design non-scalable. It can also introduce a single point of failure, there-
fore raising fault tolerance issues.
•Where possible, do communication in batches
There is a fairly high overhead in sending a single packet. That is, it is a lot more expensive to send ten
individual packets than it is to send one packet that is ten times bigger.