Apache kafka part 1
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This document provides an overview of Kafka concepts including producers, consumers, brokers, topics, partitions, and consumer groups. It discusses how producers publish data to topics which are divided into partitions that can be distributed across brokers for scalability. Consumers subscribe to topics in a consumer group to receive and process messages. The document outlines Kafka's core APIs and explores key configurations for optimization including compression, log compaction, and throughput. It provides examples of how partitioning impacts load balancing, ordering, and consumer assignment.
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Kafka connect
Andrew Stevenson from DataMountaineer presented on Kafka Connect. Kafka Connect is a common framework that facilitates data streams between Kafka and other systems. It handles delivery semantics, offset management, serialization/deserialization and other complex tasks, allowing users to focus on domain logic. Connectors can load and unload data from various systems like Cassandra, Elasticsearch, and MongoDB. Configuration files are used to deploy connectors with no code required.
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Many companies are adopting Apache Kafka to power their data pipelines, including LinkedIn, Netflix, and Airbnb. Kafka’s ability to handle high throughput real-time data makes it a perfect fit for solving the data integration problem, acting as the common buffer for all your data and bridging the gap between streaming and batch systems.
However, building a data pipeline around Kafka today can be challenging because it requires combining a wide variety of tools to collect data from disparate data systems. One tool streams updates from your database to Kafka, another imports logs, and yet another exports to HDFS. As a result, building a data pipeline can take significant engineering effort and has high operational overhead because all these different tools require ongoing monitoring and maintenance. Additionally, some of the tools are simply a poor fit for the job: the fragmented nature of the data integration tools ecosystem lead to creative but misguided solutions such as misusing stream processing frameworks for data integration purposes.
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eventbrite_kafka_summit_event_logo_v3-035858-edited.png
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The document discusses Kafka, an open-source distributed event streaming platform. It provides an overview of Kafka concepts including producers that write data, consumers that read data, topics to which data is published, brokers that manage the cluster, and Zookeeper which manages the cluster metadata. It also discusses partitions for parallelism and consumer groups for distributing consumption across consumers.
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Apache Kafka is a distributed streaming platform and distributed publish-subscribe messaging system. It uses a log abstraction to order events and replicate data across clusters. Kafka allows developers to publish and subscribe to streams of records known as topics. Producers publish data to topics and consumers subscribe to topics to process streams of records. The Kafka ecosystem includes tools like KStreams for stream processing and KSQL for querying streams of data.
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Apache Kafka is an open source distributed streaming platform used for building real-time data pipelines and applications. It allows for publishing and subscribing to streams of records, storing streams of records in a fault-tolerant way, and processing streams of records as they occur. Kafka has a producer-broker-consumer architecture and four core APIs. It provides advantages such as fault tolerance, scalability, and integration with stream processing systems. However, it also has limitations such as requiring coding and expertise to customize. Major companies like Apple, Netflix, and Walmart use Kafka.
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Copy of Kafka-Camus
Apache Kafka is a distributed streaming platform that allows for publishing and subscribing to streams of records. It uses a broker system and partitions topics to allow for scaling and parallelism. LinkedIn's Camus is a MapReduce job that moves data from Kafka to HDFS in distributed fashion. It consists of three stages: setup, the MapReduce job, and cleanup.
Notes leo kafka
Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming apps. It allows applications to publish and subscribe to streams of records, and processes large amounts of continuous data easily and reliably. Producers write data to topics which are divided into partitions. Consumers can join a consumer group to read from topics and process the data in parallel. Records are stored on disk for a configurable period to allow consumption from past records.
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Apache Kafka is an open source distributed streaming platform used for building real-time data pipelines and applications. It allows for publishing and subscribing to streams of records, storing streams of records in a fault-tolerant way, and processing streams of records as they occur. Kafka has a producer-broker-consumer architecture and four core APIs. It provides advantages such as fault tolerance, scalability, and integration with stream processing systems. However, it also has limitations such as requiring coding and expertise to customize. Major companies like Apple, Netflix, and Walmart use Kafka.
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Fast data arrives in real time and potentially high volume. Rapid processing, filtering and aggregation is required to ensure timely reaction and actual information in user interfaces. Doing so is a challenge, make this happen in a scalable and reliable fashion is even more interesting. This session introduces Apache Kafka as the scalable event bus that takes care of the events as they flow in and Kafka Streams for the streaming analytics. Both Java and Node applications are demonstrated that interact with Kafka and leverage Server Sent Events and WebSocket channels to update the Web UI in real time. User activity performed by the audience in the Web UI is processed by the Kafka powered back end and results in live updates on all clients. Kafka Streams and KSQL are used to analyze the real time events in real time and publish events with the live findings.
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Whether you are deploying a new application in Microservices or transitioning from a monolithic database application to a cloud-ready architecture, you will inevitably face the decision of either creating a service mesh of API’s – or – using an event bus for better durability, reliability and extensibility of your application. If you choose to go the event bus route, Kafka is an excellent choice for several reasons. One key technology not to overlook is Avro Schemas. They provide a definition for your event payload, just like an API, to ensure all of the event consumers can reliably consume the events. They also handle schema evolution as requirements change and much, much more.
In this talk we will discuss all the nuances and considerations around using Avro Schemas for your JSON event payloads. From developer tools, to DevOps approaches, versioning, governance and some “gotchas” we found when working with Avro Schemas and the Confluent Schema Registry.
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Title: Riding The Streaming Wave DIY Style
Speaker: Konstantine Karantasis (https://linkedin.com/in/karantasis//)
Date: Tuesday, September 19, 2017
Event: https://meetup.com/Athens-Big-Data/events/242856317/
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Reactive programming has been touted as one of the most important new programming paradigms in recent history. The idea of designing software as asynchronous streams of data is compelling, as evidenced by the growing popularity of reactive programming and the frameworks based on it. In this session we’ll explore what it is, where it came from, and look at its implementation in the Reactive Extensions API.
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Abstract:
Kafka 0.11 added a new feature called "exactly-once guarantees". In this talk, we will explain what "exactly-once" means in the context of Kafka and data stream processing and how it effects application development. The talk will go into some details about exactly-once namely the new idempotent producer and transactions and how both can be exploited to simplify application code: for example, you don't need to have complex deduplication code in your input path, as you can rely on Kafka to deduplicate messages when data is produces by an upstream application. Transactions can be used to write multiple messages into different topics and/or partitions and commit all writes in an atomic manner (or abort all writes so none will be read by a downstream consumer in read-committed mode). Thus, transactions allow for applications with strong consistency guarantees, like in the financial sector (e.g., either send a withdrawal and deposit message to transfer money or none of them). Finally, we talk about Kafka's Streams API that makes exactly-once stream processing as simple as it can get.
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1) Apache Beam is an open source unified model for defining both batch and streaming data processing pipelines. It allows writing pipelines once that can run on multiple distributed processing backends.
2) The Beam model separates the data processing logic from runtime requirements. It defines concepts like processing time vs event time to allow portability across batch and streaming runners.
3) Beam supports extensible IO connectors and aims to allow pipelines written in one language to run on different runtimes through language-specific SDKs. Currently, Java and Python SDKs can run on backends like Apache Spark, Flink, and Google Cloud Dataflow.
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Apache kafka part 1
- 1. Shrawan Nirala Images are from Apache and other Site
- 2. A number of application servers talking to each other.
- 5. Agenda We will discuss about these topics which are core of the kafka. ● Producer ● Consumer ● Broker ● Topic ● Partition ● Segment ● Consumer Group ● Group Coordinator
- 6. Next Session ● Optimization ● Producer Configuration ● Consumer Configuration ● Role of Zookeeper ● Compression ● Log compaction ● Persistence ● Throughput ● Election
- 8. Kafka has five core APIs ● Producer API ● Consumer API ● Streams API ● Connector API ● Admin API
- 9. Let's dive in and explore Kafka
- 10. Producer | Consumer ● The Producer API allows an application to publish a stream of records to one or more Kafka topics. ● The Consumer API allows an application to subscribe to one or more topics and process the stream of records produced to them.
- 11. Topic | Partition | Replica ● A Topic is a category or feed name to which records are published. ● There is only one leader partition and one or more in-sync replicas (ISRs) for a topic with multiple partitions. While the leader partition performs read and write, the ISR ensures backup.
- 12. Each partition is an ordered, immutable sequence of records that is continually appended to a structured commit log.
- 13. Load balancing across Partition ● The producer sends data directly to the broker that is the leader for the partition. ● But which Partition if more than one partition ? ● The client controls which partition it publishes messages, But How ? ● If the key chosen was a Tip Id then all data for a given Trip would be sent to the same partition. ● Without a key, two messages on the same key could go to different partitions and be processed by different consumers in the group out of order.
- 14. Partition | Segments Log.Segment.bytes :controls the segment file size for the log. Log.retention.ms, Log.retention.bytes
- 15. Topic : location-data partition
- 16. Complexity of finding a message in a topic given its partition and offset.
- 17. Consumer Group
- 18. Number of Partition == Number of Consumer
- 19. Number of Partition > Number of Consumer
- 20. Number of Partition < Number of Consumer ● Kafka is able to failover to such idle consumers in cases where an active consumer dies, or when a new partition is added.
- 21. Thank You