MyHeritage Kakfa use cases - Feb 2014 Meetup
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MyHeritage uses Kafka as a messaging system to handle two main use cases: indexing data to their search system and reporting statistics to their business intelligence system. The document provides an overview of Kafka, describing it as a fast, scalable, durable, distributed messaging system. It then details MyHeritage's implementation, including using Kafka to handle event streaming from producers to consumers that process the data for indexing and reporting. The summary emphasizes that Kafka is very fast, scalable, and extensively used at MyHeritage to handle their high scale systems.
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Exploring the problem of Microservices communication and how both Kafka and Service Mesh solutions address it. We then look at some approaches for combining both.
MQTT and Apache Kafka: The Solution to Poor Internet Connectivity in Africa (...
This is the beautiful story of MQTT and Apache Kafka with the villages of Tanzania. 2G in Africa is not going anywhere any time soon. We serve microfinance banks with limited server resources in remote areas. These banks are beloved in the villages of Africa because they provide cheap loans. In Tanzania and Africa by extension, Agency Banking is the best innovation that ever happened to address financial inclusion. In this model, commercial banks contract third party retail networks as bank agents across the villages. Upon successful application, vetting and approval, these agents are authorized to offer select banking services on behalf of the banks. Bank agents are issued with POS Terminals which have the agency banking application installed. Uchumi Commercial Bank has over 350+ agents all over Tanzania. The bank initially went live with the POS Terminals connecting to the bank servers (bare metal) using REST API. However, many issues emerged. Poor internet connectivity, poor electricity connectivity(agents had to travel far to charge their POS Terminals), limited server capacity that just could not allow scalability, expensive internet bundles. Fadhili Juma will showcase and give a deep analysis on how MQTT and Apache Kafka was the game changer in this resource constrained environment.
user Behavior Analysis with Session Windows and Apache Kafka's Streams API
For many industries the need to group together related events based on a period of activity or inactivity is key. Advertising businesses, content producers are just a few examples of where session windows can be used to better understand user behavior.
While such sessionization has been possible in Apache Kafka up to this point, implementing it has been rather complex and required leveraging low-level APIs. In the most recent release of Kafka, however, new capabilities have been added making session windows much easier to implement.
In this online talk, we’ll introduce the concept of a session window, talk about common use cases, and walk through how Apache Kafka can be used for session-oriented use cases.
Kafka for Real-Time Event Processing in Serverless Environments
(Jeff Sharpe + Alex Srisuwan, Capital One) Kafka Summit SF 2018
Using Kafka as a platform messaging bus is common, but bridging communication between real-time and asynchronous components can become complicated, especially when dealing with serverless environments. This has become increasingly common in modern banking where events need to be processed at near-real-time speed. Serverless environments are well-suited to address these needs, and Kafka remains an excellent solution for providing the reliable, resilient communication layer between serverless components and dedicated stream processing services.
In this talk, we will examine some of the strengths and weaknesses of using Kafka for real-time communication, some tips for efficient interactions with Kafka and AWS Lambda, and a number of useful patterns for maximizing the strengths of Kafka and serverless components.
Monitoring docker container and dockerized applications
This document discusses monitoring Docker containers and applications. It outlines the challenges of monitoring at the container, application, and infrastructure levels. An approach is proposed that uses agents running on each host to collect data from drivers for the host, containers, and applications. The data is sent asynchronously to a central monitoring controller which aggregates, stores, analyzes and derives insights from the data. Predictions, usage patterns, and capacity planning information can be determined to help optimize container resources and performance.
Introduction to Apache Kafka and Confluent... and why they matter!
This is a short introduction to Apache Kafka and Confluent (the company founded by the creator of Kafka). The slides cover Apache Kafka APIs including Kafka Connect and Kafka Streams (part of Apache Kafka). Other open source, ASL licensed, projects are mentioned: #KSQL, Schema Registry, REST Proxy, etc.
Many thanks to Codemotion and Seacom for hosting the event.
Samza tech talk_2015 - strata
This document discusses stream processing at scale using Samza at LinkedIn. It describes how LinkedIn uses Samza for stream processing tasks like tracking analytics from 1.4 trillion events per day and content standardization. Samza provides scalable, fault-tolerant stream processing by partitioning streams and replicating state across clusters of hundreds of nodes. The document outlines LinkedIn's large Samza deployments and upcoming features to improve local state storage, configuration, and development.
Easily Build a Smart Pulsar Stream Processor_Simon Crosby
For organizations with boundless data sources, it is important to analyze, learn, predict and even respond in real time – directly from streaming data. This is important when:
•Data volumes are large, or moving raw data is expensive,
•Data is generated by widely distributed assets (eg: mobile devices),
•Data is of ephemeral value and analysis can’t wait, or
•It is critical to always have the latest insight and extrapolation won’t do.
Use cases include prediction of failures on assembly lines, prediction of traffic flows in cities, predicting demand placed in power grids, detection of hackers, and understanding connection quality in mobile networks. They are characterized by a need to know – now – and require real-time processing of streaming data. Our goal is to enable real-time stream processing for Apache Pulsar in which analysis, learning and prediction are done on-the-fly, with continuous insights streamed back to the broker.
Achieving scale and performance using cloud native environment
ID Platform Product can be used by every Rakuten Group Companies and can easily serve millions of users. Multi-Region product challenges are many, example:
- Ensure 4 9’s availability
- Management across each region
- Alerting and Monitoring across each region
- Auto scaling (Scale up and Scale down) across each region
- Performance (vertical scale up)
- Cost
- DB Consistency Across Multiple Regions
- Resiliency
At Ecosystem Platform Layer for Rakuten, we handle each of these and this presentation is about how we handle these challenging scenarios.
Flink Forward Berlin 2018: Andrew Torson - "Using a sharded Akka distributed ...
A common and reliable way to buffer streaming data in between Flink pipelines is a pair of Flink Kafka Source and Sink. However, in some low-latency streaming firehouse use-cases this option is not the best choice: a) backlog will quickly accumulate in Kafka if Source consumption rate can’t keep up with the Sink production b) Kafka broker implies a double dispatch of all data via broker network IO with unnecessary network hops to and from the Flink cluster. In the Walmart Labs Smart Pricing group, we encountered one such use-case operating Walmart.com Marketplace real-time price regulation service, managing more than 100M of the 3rd party marketplace items in real-time on at least a daily basis. As an alternative to a Kafka-based integration firehose, we decided to introduce a reliable and scalable in-memory-cache-based streaming buffer as an integration hub for our Flink-based Walmart.com Marketplace pipelines. Our main motivation was to avoid firehose backlog accumulation at all cost and maximize total end-to-end throughput. Our implementation is powered by a sharded (using Akka Cluster-Sharding) collection of replicated Akka Distributed Data caches, co-located with Flink Task Managers. Flink pipelines are interacting with this streaming buffer via a pair of custom partitioned Flink Sink and Source components that we wrote specifically to expose this cache to Flink. The resulting latency and throughput performance is better than what a Kafka-broker-based approach offers: a) there is almost never a foreground data exchange over cache cluster network IO as nearly all (determined by the cache miss rate) data is written and read in Flink pipelines through local memory b) cache data size, miss rate and updates volume can be managed via both the shard fill rate (not every write needs to be a new cache record – as opposed to messaging systems like Kafka) and the number of shards to keep alive in memory (if some shards are not actively accessed – they can be automatically killed and spilled to a permanent storage to be recovered later via Akka Persistence). The downside of this cache buffer is a large RAM demand: cache shards are memory-hungry and co-locating it with Flink Task Managers means that this memory will be unavailable to allocate to the Flink Task Manager heap and/or direct buffer.
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How Zillow Unlocked Kafka to 50 Teams in 8 months | Shahar Cizer Kobrinsky, Z...
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Introducing Confluent labs Parallel Consumer client | Anthony Stubbes, Confluent
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A Marriage of Lambda and Kappa: Supporting Iterative Development of an Event ...
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Design and Implementation of Incremental Cooperative Rebalancing
Design and Implementation of Incremental Cooperative Rebalancing
How a Data Mesh is Driving our Platform | Trey Hicks, Gloo
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Relational cloud, A Database-as-a-Service for the Cloud
Relational cloud, A Database-as-a-Service for the Cloud
MQTT and Apache Kafka: The Solution to Poor Internet Connectivity in Africa (...
MQTT and Apache Kafka: The Solution to Poor Internet Connectivity in Africa (...
user Behavior Analysis with Session Windows and Apache Kafka's Streams API
user Behavior Analysis with Session Windows and Apache Kafka's Streams API
Kafka for Real-Time Event Processing in Serverless Environments
Kafka for Real-Time Event Processing in Serverless Environments
Monitoring docker container and dockerized applications
Monitoring docker container and dockerized applications
Introduction to Apache Kafka and Confluent... and why they matter!
Introduction to Apache Kafka and Confluent... and why they matter!
Easily Build a Smart Pulsar Stream Processor_Simon Crosby
Easily Build a Smart Pulsar Stream Processor_Simon Crosby
Achieving scale and performance using cloud native environment
Achieving scale and performance using cloud native environment
Flink Forward Berlin 2018: Andrew Torson - "Using a sharded Akka distributed ...
Flink Forward Berlin 2018: Andrew Torson - "Using a sharded Akka distributed ...
Similar to MyHeritage Kakfa use cases - Feb 2014 Meetup
Strimzi - Where Apache Kafka meets OpenShift - OpenShift Spain MeetUp
Apache Kafka is the most used data streaming broker by companies. It could manage millions of messages easily and it is the base of many architectures based in events, micro-services, orchestration, ... and now cloud environments. OpenShift is the most extended Platform as a Service (PaaS). It is based in Kubernetes and it helps the companies to deploy easily any kind of workload in a cloud environment. Thanks many of its features it is the base for many architectures based in stateless applications to build new Cloud Native Applications. Strimzi is an open source community that implements a set of Kubernetes Operators to help you to manage and deploy Apache Kafka brokers in OpenShift environments.
These slides will introduce you Strimzi as a new component on OpenShift to manage your Apache Kafka clusters.
Slides used at OpenShift Meetup Spain:
- https://www.meetup.com/es-ES/openshift_spain/events/261284764/
Capital One Delivers Risk Insights in Real Time with Stream Processing
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?.
Timesten Architecture
The document discusses Oracle TimesTen In-Memory Database architecture, performance tips, and use cases. It provides an overview of TimesTen Classic and Scaleout architectures, how TimesTen handles persistence through checkpointing and transaction logging, and high-performance concurrency controls. The agenda covers TimesTen functionality, architectures, performance optimization, and when to use TimesTen versus other Oracle in-memory options.
I Heart Log: Real-time Data and Apache Kafka
This presentation discusses how logs and stream-processing can form a backbone for data flow, ETL, and real-time data processing. It will describe the challenges and lessons learned as LinkedIn built out its real-time data subscription and processing infrastructure. It will also discuss the role of real-time processing and its relationship to offline processing frameworks such as MapReduce.
Fundamentals and Architecture of Apache Kafka
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.
Distributed Kafka Architecture Taboola Scale
___________________________________________
Meetup#7 | Session 2 | 21/03/2018 | Taboola
_____________________________________________
In this talk, we will present our multi-DC Kafka architecture, and discuss how we tackle sending and handling 10B+ messages per day, with maximum availability and no tolerance for data loss.
Our architecture includes technologies such as Cassandra, Spark, HDFS, and Vertica - with Kafka as the backbone that feeds them all.
kafka for db as postgres
Introduction to Apache Kafka And Real-Time ETL for DBAs and others who are interested in new ways of working with relational databases
Distributed messaging with Apache Kafka
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.
CouchbasetoHadoop_Matt_Michael_Justin v4
This document discusses LinkedIn's use of Kafka, Hadoop, Storm, and Couchbase in their big data pipeline. It provides an overview of each technology and how LinkedIn uses them together. Specifically, it describes how LinkedIn uses Kafka to stream data to Hadoop for analytics and report generation. It also discusses how LinkedIn uses Hadoop to pre-build and warm Couchbase buckets for improved performance. The presentation includes a use case of streaming member profile and activity data through Kafka to both Hadoop and Couchbase clusters.
Ingestion and Dimensions Compute and Enrich using Apache Apex
Presenter: Devendra Tagare - DataTorrent Engineer, Contributor to Apex, Data Architect experienced in building high scalability big data platforms.
This talk will be a deep dive into ingesting unbounded file data and streaming data from Kafka into Hadoop. We will also cover data enrichment and dimensional compute. Customer use-case and reference architecture.
ApacheCon BigData - What it takes to process a trillion events a day?
This document discusses stream processing at LinkedIn and summarizes two case studies:
1. LinkedIn's communications platform processes over a trillion events per day using Apache Samza for real-time notifications, security monitoring, and other applications.
2. Activity tracking in the News Feed uses stream processing to track metrics like ads clicked and session durations in real-time for hundreds of applications across LinkedIn.
3. The document also outlines some of the hard problems in stream processing like state management, remote I/O, and deployment models that Samza addresses through techniques such as local state caching, asynchronous remote access, and running on YARN or embedded.
MyHeritage backend group - build to scale
MyHeritage BackEnd group was built to scale to support 77 million users, 27 million family trees containing over 1.6 billion individuals, and over 6 billion historical documents.
With big data comes big challenges and this presentation explains the structure, the methodology and the technologies that support scaling up.
The presentation covers:
• How cross R&D continuous deployment and R&D structure supports scalability
• Sharding techniques
• Cassandra usage at MyHeritage
• Our search engine scaling structure
Introduction to Apache Kafka
This document provides an overview of Apache Kafka including its main components, architecture, and ecosystem. It describes how LinkedIn used Kafka to solve their data pipeline problem by decoupling systems and allowing for horizontal scaling. The key elements of Kafka are producers that publish data to topics, the Kafka cluster that stores streams of records in a distributed, replicated commit log, and consumers that subscribe to topics. Kafka Connect and the Schema Registry are also introduced as part of the Kafka ecosystem.
The Evolution of Trillion-level Real-time Messaging System in BIGO - Puslar ...
This document discusses BIGO's evolution from using open-source Kafka to Apache Pulsar for its real-time messaging system. It describes the challenges BIGO faced with Kafka as data scales rapidly grew, including poor scalability and degraded I/O performance. BIGO chose Pulsar for its lightweight horizontal scalability, excellent read-write isolation, and ability to support over a million topics. Typical application scenarios discussed include high throughput event tracking, lightweight traffic balancing, and high performance catch-up reads for machine learning tasks. Future work may involve optimizations for different read/write models and combining SSD and HDD storage.
3450 - Writing and optimising applications for performance in a hybrid messag...
Messaging architectures in any environment, from local standalone deployments through to public clouds, must provide the highest reliability yet maximize their performance. This session gives you an insight into IBM MQ and how applications can be made to perform to their absolute best while maintaining the data integrity that IBM MQ is renowned for. We'll see how this can be achieved through a combination of good application design, system tuning and architectural patterns.
Introduction to Apache Kafka
The document provides an introduction and overview of Apache Kafka presented by Jeff Holoman. It begins with an agenda and background on the presenter. It then covers basic Kafka concepts like topics, partitions, producers, consumers and consumer groups. It discusses efficiency and delivery guarantees. Finally, it presents some use cases for Kafka and positioning around when it may or may not be a good fit compared to other technologies.
kafka simplicity and complexity
Agile Lab is an Italian company that specializes in leveraging innovative technologies like machine learning, big data, and artificial intelligence to satisfy customers' objectives. They have over 50 specialists with deep experience in production environments. The company believes in investing in its team through conferences, R&D projects, and welfare benefits. They also release open source frameworks on GitHub and share knowledge through meetups in Milan and Turin.
Megastore: Providing scalable and highly available storage
This presentation presents the paper from Google about Megastore, their distributed, scalable and high available storage system.
An evening with Jay Kreps; author of Apache Kafka, Samza, Voldemort & Azkaban.
Jay Kreps, Open Source Visionary and Co Founder of Confluent and several open source projects will be visiting LA. I have asked him to come present at our group. He will present his vision and will answer questions regarding Kafka and other projects
Bio:-
Jay is the co-founder and CEO at Confluent a company built around realtime data streams and the open source messaging system Apache Kafka. He is the original author of several of open source projects including Apache Kafka, Apache Samza, Voldemort, and Azkaban.
Stateful streaming and the challenge of state
The different challenges of working with state in a distributed streaming data pipeline and how we solve it with the 3S architecture and Kafka streams stores based on rocksDB
Similar to MyHeritage Kakfa use cases - Feb 2014 Meetup (20)
Strimzi - Where Apache Kafka meets OpenShift - OpenShift Spain MeetUp
Strimzi - Where Apache Kafka meets OpenShift - OpenShift Spain MeetUp
Capital One Delivers Risk Insights in Real Time with Stream Processing
Capital One Delivers Risk Insights in Real Time with Stream Processing
Ingestion and Dimensions Compute and Enrich using Apache Apex
Ingestion and Dimensions Compute and Enrich using Apache Apex
ApacheCon BigData - What it takes to process a trillion events a day?
ApacheCon BigData - What it takes to process a trillion events a day?
The Evolution of Trillion-level Real-time Messaging System in BIGO - Puslar ...
The Evolution of Trillion-level Real-time Messaging System in BIGO - Puslar ...
3450 - Writing and optimising applications for performance in a hybrid messag...
3450 - Writing and optimising applications for performance in a hybrid messag...
Megastore: Providing scalable and highly available storage
Megastore: Providing scalable and highly available storage
An evening with Jay Kreps; author of Apache Kafka, Samza, Voldemort & Azkaban.
An evening with Jay Kreps; author of Apache Kafka, Samza, Voldemort & Azkaban.
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
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National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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MyHeritage Kakfa use cases - Feb 2014 Meetup
- 1. MyHeritage and Kafka Author: Ran Levy Feb 2014
- 2. Agenda • MyHeritage use cases • Possible solutions • Kafka overview • Actual implementation @MyHeritage • Summary
- 3. Use cases • Two major use case: – Indexing to SuperSearch and Record Matching. – Stats reporting to BI.
- 4. Use case 1 • Indexing to SuperSearch and Record Matching
- 5. Use case 1 – con’t • Custom and non-scalable solution that involved changes processing and updating SuperSearch (SOLR over Lucene). • Required solution should support: – Continuous mode. – High throughput. – Scaling up. – Repeating the process from some point. – Guaranteed order of processed items. – Reliable. – Multiple consumers.
- 6. Use case 2 • Statistics reporting to BI system
- 7. Use case 2 – con’t • Required solution should support: • • • • High scale (~500GB of data / day). Scale up – few hundred millions per day. Repeating the process from some point. Multiple consumers.
- 8. Agenda MyHeritage use cases • Possible solutions • Kafka overview • Actual implementation @MyHeritage • Summary
- 9. Possible Solutions • So what we have considered …. – DB • Queues
- 10. Possible Solutions • Key point about queues – Messages are deleted after consumed. – Messages are duplicated to support multiple readers.
- 11. Agenda MyHeritage use cases Possible solutions • Kafka overview • Actual implementation @MyHeritage • Summary
- 12. Kafka Overview • A high throughput distributed messaging system – – – – – Fast Scalable Durable Distributed by design Simplicity (over functionality)
- 13. Kafka Overview • Fast (very fast) – both for producer and consumer Reference: http://research.microsoft.com/en-us/um/people/srikanth/netdb11/netdb11papers/netdb11-final12.pdf
- 14. Kafka Overview • Main entities – Producer – push data. – Consumer – pull data. – Brokers – load balance producers by partition. – Topic – feeds of messages belongs to the same logical category.
- 15. Kafka Overview – some internals • Communication between the clients and the servers is done with a simple, high-performance TCP protocol. • For each topic, the Kafka cluster maintains a partitioned log which is a commit-log (appends only).
- 16. Kafka Overview – some internals • Messages stay on disk when consumed, deleted after defined TTL. • The partitions of the log are distributed over the servers in the Kafka cluster with each server handling data and requests for a share of the partitions. • Each partition is replicated across a configurable number of servers for fault tolerance.
- 17. Agenda MyHeritage use cases Possible solutions Kafka overview • Actual implementation @MyHeritage • Summary
- 18. High Level Overview … Daemons Family Tree changes Topic Family Tree changes Topic part 1 part 1 part 2 part 2 DRBD replica Of Broker 2 part 32 Consumers Activity Topic Indexing part 1 part 1 RecordMatching part 2 part 2 … part 32 … Face recog. Broker 2 … Web Broker 1 … Producers Logstash reader part 32 part 32 Activity Topic DRBD replica Of Broker 1
- 19. Kafka @Myheritage - producers App App Module App Module Module Subscriber Dispatch event Events System Notify Subscriber EventLogger Subscriber Activity Manage r ILogWrite
- 20. Kafka @Myheritage - producers Topic BrokersConfig IStats KafkaWriter ISelector ILogger ISerializer
- 21. Kafka @Myheritage - producers App App Module App Module Module Subscriber Dispatch event Events System Notify Subscriber EventLogger Subscriber KafkaWriter (if failed) Attempt 2nd broker Broker Attempt 1st broker Broker
- 22. Kafka @Myheritage – Consumers (Indexing) 1 Per consumer type, reader per partition KafkaWatermark Get/update watermark Broker 1 EventProcessor EventProcessor EventProcessor Broker 2 Add event to queue IndexingQueue Fetch work IndexingWorkers IndexingWorkers IndexingWorkers Update item SOLR
- 23. Agenda MyHeritage use cases Possible solutions Kafka overview Actual implementation @MyHeritage • Summary
- 24. Summary Kafka is very fast and scalable system, that is extensively used at MyHeritage, and you would want to consider it for high scale systems you are using.
- 25. Thank you and questions ranl@myheritage.com