kafka spark streaming architecture

The choice of framework We discussed about three frameworks, Spark Streaming, Kafka Streams, and Alpakka Kafka. Although, there is one disadvantage also, that it does not update offsets in Zookeeper, thus Zookeeper-based Kafka monitoring tools will not show progress. And without any extra coding efforts We can work on real-time spark streaming and historical batch data at the same time (Lambda Architecture). Afterward, create an input DStream by importing KafkaUtils, in the streaming application code: Also, using variations of createStream, we can specify the key and value classes and their corresponding decoder classes. They run on clusters and divide the load between many machines. into some data ingestion system like Apache Kafka, Amazon Kinesis, etc. In this blog we are going to learn how we can integrate Spark Structured Streaming with Kafka and Cassandra to build a simple data pipeline. Although, it will start consuming from the smallest offset if you set configuration auto.offset.reset in Kafka parameters to smallest. Thus each record is received by Spark Streaming effectively exactly once despite failures. They are using databases which don’t have transnational data support. In this article we learned how real time IoT Data Events coming from Connected Vehicles can be ingested to Spark through Kafka. Hence, we have to additionally enable write-ahead logs in Kafka Spark Streaming, to ensure zero-data-loss. Afterward, do the following to access the Kafka offsets consumed in each batch. This component will be listening to Kafka for some events. Although written in Scala, Spark offers Java APIs to work with. The That helps to achieve exactly-once semantics for the output of our results. First is by using Receivers and Kafka’s high-level API, and a second, as well as a new approach, is without using Receivers. Apache Kafkaの概要とアーキテクチャ (本投稿) 2. live logs, system telemetry data, IoT device data, etc.) Elasticsearchでは以下のいずれかのタイミングでフラッシュ処理が行われ、リフレッシュ処理とファイルシステムキャッシュ上のセグメントのディスク書き込みが行われます。, フラッシュ処理が完了するとメモリ上のドキュメントはすべて永続化されるため、トランスログは不要となり消去されます。, 今回の評価では、基本的に各OSSで設定可能なパラメータはデフォルト値を利用します。デフォルト値がなく設定が必要なパラメータと、デフォルト値から変更したパラメータを表7に示します。, ※1 ドライバプログラムはSparkアプリケーションの実行中にワーカノードに常駐してアプリケーション全体のタスク実行を管理する※2 エグゼキュータはSparkアプリケーションの実行中にワーカノードに常駐してタスクを実行する, 表7でKafkaのパーティション数を32個に設定した理由について解説しましょう。まず、SparkがKafkaからデータを取得する方式には2種類(Spark Streaming + Kafka Integration Guide)があります(表8)。, 今回の検証では、レシーバタスクを使用しない方式を採用しました。この方式ではKafkaのパーティション数と同数のSparkタスクが自動生成されます。Sparkでは1タスクを1コアで処理するため、Sparkに割り当てられたコア数よりタスク数が少ない場合、一部のコアは使用されないことになります。, 表7で説明した通り、検証ではSparkがワーカノード4台(4エグゼキュータ)を使用し、各ワーカノードのCPUは8コアであるため、Sparkが処理に使用できるコア数は4ワーカノード×8コア=32コアとなります。Sparkのタスク数をコア数と同数の32タスクにするため、今回の検証ではKafkaのパーティション数を32個としました。, 表7の初期設定で測定した結果、Kafkaには1秒間で平均8,026メッセージが格納され、それをSparkが1インターバル5秒のうち平均2.07秒ですべて処理しました。Kafkaの格納性能は8,026メッセージ/秒、Sparkの処理性能は8,026×5/2.07=19,346メッセージ/秒になります。, よってKafkaがボトルネックとなり、システム全体でリアルタイムに処理できるのは8,026メッセージ/秒となります(図11)。デフォルト設定では、目標性能である10,000メッセージ/秒の処理性能を満たすことはできませんでした。, 今回はシステムの詳細構成から、初期設定における検証結果までを解説しました。次回は、システムのパラメータチューニングを行い、性能がどこまで改善したのかについて解説します。, OSSソリューションセンタ所属。これまでにストレージシステムとその管理ソフトウェアの開発を手掛けてきた。現在はHadoopやSpark、Kafkaを中心としたビッグデータ関連OSSの導入支援やソリューション開発、およびビッグデータを活用したデータ分析業務を担当している。, 「OSSfm」は“オープンソース技術の実践活用メディア”であるThink ITがお届けするポッドキャストです。, "data": "2.5717778e-001 …<省略>… -5.7978304e-002". Apache Kafka Workflow | Kafka Pub-Sub Messaging, Apache Kafka Consumer | Examples of Kafka Consumer, Read Top 5 Apache Kafka Books | Complete Guide To Learn Kafka, Spark Streaming Checkpoint in Apache Spark. To use Structured Streaming with Kafka, your project must have a dependency on the All rights reserved. Kafka Spark Streaming Integration. Although, there is one disadvantage also, that it does not update offsets in Zookeeper, thus Zookeeper-based Kafka monitoring tools will not show progress. What is Kafka-Spark Streaming Integration. Also, we will look advantages of direct approach to receiver-based approach in Kafka Spark Streaming Integration. That will read data from Kafka in parallel. Further, we will discuss how to use this Receiver-Based Approach in our Kafka Spark Streaming application. After Receiver-Based Approach, new receiver-less “direct” approach has been introduced. Apache Kafka Consumer | Examples of Kafka Consumer. It also enables processing of fault-tolerant stream and high-throughput. What is Streaming Data and Streaming data Architecture? We can start with Kafka in Javafairly easily. However, Kafka – Spark Streaming will create as many RDD partitions as there are Kafka partitions to consume, with the direct stream. However, this approach is supported only in Scala/Java application. Stream processing acts as both a way to develop real-time applications but it is also directly part of the data integration usage as well: integrating systems often requires some munging of data streams in between. Kafka and Event-Driven Architecture There are many technologies these days which you can use to stream events from a component to another, … A streaming data source would typically consist of a stream of logs that record events as they happen – such as a user clicking on a link in a web page, or a sensor reporting the current temperature. Using the Spark streaming API, … Both these technologies are very well scalable. Also, defines the offset ranges to process in each batch, accordingly. Further, we will discuss how to use this Receiver-Based Approach in our Kafka Spark Streaming application. There are many detailed instructions on how to create Kafka and Spark clusters, so I won’t spend time showing it here. Both these technologies are very well scalable. It is one of the extensions of the core Spark API. Elasticsearchはリクエスト内容をディスク上のトランスログ(トランザクションログ)に書き込みます。デフォルト設定ではリクエストごとに同期書き込みを行います。このトランスログは永続化前のドキュメントが障害により失われた際の復旧に使用されます。, (4)リフレッシュ(ソフトコミット) About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features So, by using the Kafka high-level consumer API, we implement the Receiver. Hence, make sure our output operation that saves the data to an external data store must be either idempotent or an atomic transaction that saves results and offsets. The commonly used architecture for real time analytics at scale is based on Spark Streaming and Kafka. It happens due to inconsistencies between data reliably received by Kafka – Spark Streaming and offsets tracked by. After Receiver-Based Approach, new receiver-less “direct” approach has been introduced. Spark Streaming helps in scaling the live data streams. The Databricks platform already includes an Apache Kafka 0.10 connector for Structured Streaming, so it is easy to set up a stream to read messages:There are a number of options that can be specified while reading streams. val ssc = new StreamingContext (conf, Seconds (1)) 1 val ssc = new StreamingContext(conf, Seconds(1)) Event Streaming is a hot topic in Telco Industry.In the last few months, I have seen various projects leveraging Apache Kafka and its ecosystem to implement scalable real time infrastructure in OSS and BSS scenarios.. Keeping you updated with latest technology trends. Even if it can ensure zero data loss, there is a small chance some records may get consumed twice under some failures. Spark Streaming uses readStream() on SparkSession to load a streaming Dataset from Kafka. Apache Cassandra is a distributed and wide … In addition to their unique genes regarding vertical scalability described above, ElasticSearch, Apache Kafka and Apache Spark are providing our platform with another key feature. HDInsight Realtime Inference In this example, we can see how to Perform ML modeling on Spark and perform real time inference on streaming data from Kafka on HDInsight. Once you process the event with Apache Spark, you . Further, import KafkaUtils and create an input DStream, in the streaming application code: We must specify either metadata.broker.list or bootstrap.servers, in the Kafka parameters. Kafka에서 구조적 스트리밍을 사용하려면 프로젝트가 org.apache.spark : spark-sql-kafka-0-10_2.11 패키지에 대해 종속성이 있어야 합니다. Thus each record is received by Spark Streaming effectively exactly once despite failures. Apache Spark Streaming is a scalable, high-throughput, fault-tolerant streaming processing system that supports both batch and streaming workloads. However, we will have to add this above library and its dependencies when deploying our application, for Python applications. The details of those options can b… However, the details are slightly different for Scala/Java applications and Python applications. Hope you like our explanation. In this way, it is possible to recover all the data on failure. The spark-streaming-kafka-0-10artifact has the appropriate transitive dependencies already, and different versions may be incompatible in hard to diagnose ways. In this contributed article, Paul Brebner, Tech Evangelist at Instaclustr provides an understanding of the main Kafka components and how Kafka consumers work. So, in this article, we will learn the whole concept of Spark Streaming Integration in Kafka in detail. That saves all the received Kafka data into write-ahead logs on a distributed file system synchronously. 前回はSpark Streamingの概要と検証シナリオ、および構築するシステムの概要を解説しました。今回はシステムの詳細構成と検証の進め方、および初期設定における性能測定結果について解説します。 この検証ではメッセージキューのKafka、ストリームデータ処理のSpark Streaming、検索エンジンのElasticsearchを組み合わせたリアルタイムのセンサデータ処理システムを構築しています。今回はKafkaとElasticsearchの詳細なアー … val ssc = new StreamingContext (conf, … two approaches to configure Spark Streaming to receive data from Kafka Then add it to spark-submit with –jars. This feature was introduced in Spark 1.3 for the Scala and Java API, in Spark 1.4 for the Python API. This is actually inefficient as the data effectively gets replicated twice – once by Kafka, and a second time by the write-ahead log. Receive streaming data from data sources (e.g. There are following advantages of 2nd approach over 1st approach in Spark Streaming Integration with Kafka: Advantages of Direct Approach in Spark Streaming Integration with Kafka. Spark Streaming integration with Kafka allows a parallelism between partitions of Kafka and Spark along with a mutual access to metadata and offsets. I am reading about spark and its real-time stream processing.I am confused that If spark can itself read stream from source such as twitter or file, then Why do we need kafka to feed data to spark? Nowadays insert data into a datawarehouse in big data architecture is a synonym of Spark. However, teams at Uber found multiple uses for our definition of a session beyond its original purpose, such as user experience analysis and bot detection. Here, we use a Receiver to receive the data. However this is not your only option. Apache Spark provides a unified engine that natively supports both batch and streaming workloads. HDInsight 上の Apache Kafka を用いた Apache Spark ストリーミング (DStream) の例 Apache Spark streaming (DStream) example with Apache Kafka on HDInsight 11/21/2019 この記事の内容 Apache Spark を使用して、HDInsight 上の Apache Kafka に対して DStreams による送信または受信ストリーミングを行う方法について説明します。 There is no requirement to create multiple input, Basically, we used Kafka’s high-level API to store consumed offsets in Zookeeper in the first approach. Let’s assume you have a Kafka cluster that you can connect to and you are looking to use Spark’s Structured Streaming to ingest and process messages from a topic. ElasticsearchがIndexリクエストで受け取ったドキュメントは、まずインメモリバッファに書き込まれます。, (3)トランスログを書き込み spark-streaming-kafka-0-10_2.11 spark-streaming-twitter-2.11_2.2.0 Create a Twitter application To send data to the Kafka, we first need to retrieve tweets. Spark Streaming architecture makes it easy and candid to balance load across the spark cluster and react to failures. The lab assumes that you run on a Linux machine similar to the ones Moreover, to read the defined ranges of offsets from Kafka, it’s simple consumer API is used, especially when the jobs to process the data are launched. After this, we will discuss a receiver-based approach and a direct approach to Kafka Spark Streaming Integration. En la presente entrada, “Apache Kafka & Apache Spark: un ejemplo de Spark Streaming en Scala”, describo cómo definir un proceso de streaming con Apache Spark con una fuente de datos Apache Kafka definido en lenguaje Scala. Moreover, we discussed the advantages of the Direct Approach. The Apache Kafka distributed streaming platform features an architecture that – ironically, given the name – provides application messaging that is markedly clearer and less Kafkaesque when compared with alternatives. The difference between Kafka vs Kinesis is that the Kafka concept is based on streams while Kinesis also focuses on analytics. Spark Streaming's execution model is advantageous over traditional streaming systems for its fast recovery from failures, dynamic load balancing, streaming … Moreover, using –packages spark-streaming-Kafka-0-8_2.11 and its dependencies can be directly added to spark-submit, for Python applications, which lack SBT/Maven project management. Also, we can also download the JAR of the Maven artifact spark-streaming-Kafka-0-8-assembly from the Maven repository. Further, the received data is stored in Spark executors. Spark Streaming Checkpoint in Apache Spark, Hence, in this Kafka- Spark Streaming Integration, we have learned the whole concept of Spark Streaming Integration with Apache Kafka in detail. The difference between Kafka vs Kinesis is that the Kafka concept is based on streams while Kinesis also focuses on analytics. Spark Streaming, Kafka and Cassandra Tutorial This tutorial builds on our basic “Getting Started with Instaclustr Spark and Cassandra” tutorial to demonstrate how to set up Apache Kafka and use it to send data to Spark Streaming where it is summarised before being saved in Cassandra. As long as we have sufficient Kafka retention, it is possible to recover messages from Kafka. It ensures stronger end-to-end guarantees. Hence, we can say, it is a one-to-one mapping between Kafka and RDD partitions, which is easier to understand and tune. Moreover, to read the defined ranges of offsets from Kafka, it’s simple consumer API is used, especially when the jobs to process the data are launched. Now, link your Kafka streaming application with the following artifact, for, However, we will have to add this above library and its dependencies when deploying our application, for. Instead, we’ll focus on their interaction to understand real-time streaming architecture. Kafka Cluster If you have Spark and Kafka running on a cluster Spark Streaming is an extension to the central application API of Apache Spark. In this Kafka Architecture article, we will see API’s in Kafka. There are different programming models for both the approaches, such as performance characteristics and semantics guarantees. Afterward, do the following to access the Kafka offsets consumed in each batch. However, it is similar to read files from a file system. この投稿ではオープンソースカンファレンス2017.Enterpriseで発表した「めざせ!Kafkaマスター ~Apache Kafkaで最高の性能を出すには~」の検証時に調査した内容を紹介します(全8回の予定)。本投稿の内容は2017年6月にリリースされたKafka 0.11.0 時点のものです。 第1回目となる今回は、Apache Kafkaの概要とアーキテクチャについて紹介します。 投稿一覧: 1. Existing infrastructure However, to consume data from Kafka this is a traditional way. Option startingOffsets earliest is used to read all data available in the Kafka at the start of the query, we may not use this option that often and the default value for startingOffsets is latest which reads only new data that’s not been processed. Here, we use a Receiver to receive the data. Spark Streaming is an extension of the Spark RDD API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. If "kafka.group.id" is set, this option will be ignored. Do not manually add dependencies on org.apache.kafka artifacts (e.g. HDInsight Realtime Inference In this example, we can see how to Perform ML modeling on Spark and perform real time inference on streaming data from Kafka on HDInsight. spark-kafka-source streaming and batch Prefix of consumer group identifiers (group.id) that are generated by structured streaming queries. The second approach eliminates the problem as there is no receiver, and hence no need for write-ahead logs. Kafka can also integrate with external stream processing layers such as Storm, Samza, Flink, or Spark Streaming. We can also say, spark streaming’s receivers accept data in … Spark Streaming is an extension of the Spark RDD API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. Although, it is a possibility that this approach can lose data under failures under default configuration. Lingxiao give us some clue about why choising Kafka Streams over Spark streaming. Now, link your Kafka streaming application with the following artifact, for Scala/Java applications using SBT/Maven project definitions. Your email address will not be published. For that to work, it … As with any Spark applications, spark-submit is used to launch your application. Spark Streaming is part of the Apache Spark platform that enables scalable, high throughput, fault tolerant processing of data streams. Apache Kafka Workflow | Kafka Pub-Sub Messaging With the following artifact, link the SBT/Maven project. Basically, we used Kafka’s high-level API to store consumed offsets in Zookeeper in the first approach. Spark Streaming. Data sc… 3. It is thus reducing loading time as compared to previous traditional Spark Streaming architecture makes it easy and candid to balance load across the spark cluster and react to failures. Your email address will not be published. イメージスキャンやランタイム保護などコンテナのライフサイクル全般をカバー、Aqua Security Softwareが展開するセキュリティ新機軸, コンテナ環境のモニタリングやセキュリティ対策を一気通貫で提供、世界300社以上に採用が進むSysdigの真価, コンテナ領域で存在感を強めるNGINX、OpenShiftとの親和性でKubernetes本番環境のセキュリティや可用性を追求, CNDT 2020にNGINXのアーキテクトが登壇。NGINX Ingress ControllerとそのWAF機能を紹介, DXの実現にはビジネスとITとの連動が必須 ― 日本マイクロソフトがBizDevOpsラウンドテーブルを開催, Azureとのコラボレーションによる、これからのワークスタイルとは― Developers Summit 2020レポート, (Human Activity Recognition Using Smartphones Data Set), (Spark Streaming + Kafka Integration Guide), ホスト型とハイパーバイザー型の違いは何?VMware vSphere Hypervisor の概要, シーケンシャルReadは400MB/秒、シーケンシャルWriteは1000MB/秒程度。通常のHDDがシーケンシャルRead/Write共に100MB/秒程度であることを考えると、かなり高速なディスクである。これはストレージ装置のディスクを使用しているためと考えられる, ホスト間のネットワーク帯域は送信/受信共に112MB/秒程度。これは1Gbps回線の実質速度とほぼ一致する, ワーカノードのメモリ容量は16GBのため、OSやElasticsearchが使用する4GBを確保し、残りの12GBを割り当て, ワーカノード5台のうち、ドライバプログラムが1台を使用するため、残り4台にエグゼキュータ, ワーカノード1台のリソースをすべてエグゼキュータに割り当てるため、ワーカノードのCPUコア数「8」を設定, Kafkaからデータ取得するための専用タスクを立てる方式。At-least-onceを保障する(障害が発生しても各レコードが最低1回は取得される), Kafkaからのデータ取得に専用タスクを立てない方式。Spark 1.3以降で使用可能。Exactly-onceを保障する(障害が発生しても各レコードは確実に1回だけ取得される)。またKafkaのパーティション数と同数のSparkタスクが自動生成され、Kafkaの1パーティションのメッセージをSparkの1タスクが処理する, Kafkaクラスタを構成してメッセージの受け渡しを行うキューとして動作するKafkaノード, Sparkアプリケーションの実行とデータ蓄積を行うSpark Worker+Elasticsearchノード, 収集サーバ上のデータ配信プログラムはテキストファイルに記述されたセンサデータを一定間隔で読み込み、疑似的なストリーミングデータとしてKafkaに送信する, Kafkaは処理データ量の増加に対応するため、収集サーバから受信したデータをキューイングする, Sparkアプリケーションは一定間隔でKafkaからデータを読み出し、学習済みの動作種別モデルを用いてセンサデータから動作種別を判定してElasticsearchに格納する, KibanaはElasticsearchに格納された動作種別の時系列データを可視化する, 前回のフラッシュから一定回数(デフォルトでは無制限)の操作(リクエストなど)が行われた. Depending on what event you are getting, you will probably want to process the event differently. Now, let’s discuss how to use this approach in our streaming application. In Spark streaming, we can use multiple tools like a flume, Kafka, RDBMS as source or sink. Despite, processing one record at a time, it discretizes data into tiny, micro-batches. Its architecture is similar to Kafka in many components such as producers, consumers, and brokers. En la presente entrada, “Apache Kafka & Apache Spark: un ejemplo de Spark Streaming en Scala”, describo cómo definir un proceso de streaming con Apache Spark con una fuente de datos Apache Kafka definido en lenguaje Scala. Using the Spark streaming API, … However, to consume data from Kafka this is a traditional way. This post goes over doing a few aggregations on streaming data using Spark Streaming and Kafka. We will be setting up a local environment for the purpose of the tutorial. 2. Note: This feature was introduced in Spark 1.3 for the Scala and Java API, in Spark 1.4 for the Python API. Required fields are marked *, Home About us Contact us Terms and Conditions Privacy Policy Disclaimer Write For Us Success Stories, This site is protected by reCAPTCHA and the Google. Moreover, we will look at Spark Streaming-Kafka example. Processthe data in parallel on a cluster. With ElasticSearch, real-time updating (fast indexing) is achievable through various functionalities and search / read response time c… こんにちは。Sparkについて調べてみよう企画第2段(?)です。 1回目はまずSparkとは何かの概要資料を確認してみました。 その先はRDDの構造を説明している論文と、後Spark Streamingというストリーム処理基盤の資料が a. Simplified Parallelism There is no requirement to create multiple input Kafka streams and union them.However, Kafka – Spark Streaming will create as many RDD partitions as there are Kafka partitions to consume, with the direct stream. Existing infrastructure In this article we learned how real time IoT Data Events coming from Connected Vehicles can be ingested to Spark through Kafka. So, let’s start Kafka Spark Streaming Integration. That saves all the received Kafka data into write-ahead logs on a distributed file system synchronously. Its architecture is similar to Kafka in many components such as producers, consumers, and brokers. 2.x.x Get the earliest offset of Kafka topics using the Kafka consumer client (org.apache.kafka.clients.consumer.KafkaConsumer) – beginningOffests API (if … It would be great if someone explains me what advantage we get if we use spark with kafka . A streaming data source would typically consist of a stream of logs that record events as they happen – such as a user clicking on a link in a web page, or a sensor reporting the current temperature. Let’s revise Apache Kafka Architecture and its fundamental concepts Moreover, using other variations of KafkaUtils.createDirectStream we can start consuming from an arbitrary offset. Although, it is a possibility that this approach can lose data under failures under default configuration. I though 3 receiver will run on 3 executors and will use one CPU each. For reference, Tags: advantages of direct approachApache KafkaDirect ApproachkafkaKafka - Spark StreamingKafka- Spark IntegrationKafka- Spark Streaming configurationkafka-spark streaming tutorialreceiver based approachspark straming kafka examplespark streamingspark streaming kafkastreaming applicationwhat is spark streaming. Question is if I will run createstream job for one topic with 3 partitions with 6 executors and each executor having 2 cores. That helps to achieve exactly-once semantics for the output of our results. In Spark’s execution model, each application gets its own executors, which stay up for the duration of the whole application and run 1+ tasks in multiple threads. This blog covers real-time end-to-end integration with Kafka in Apache Spark's Structured Streaming, consuming messages from it, doing simple to complex windowing ETL, and pushing the desired output to various sinks such as memory, console, file, databases, and back to Kafka itself. But still, we can access the offsets processed by this approach in each batch and update Zookeeper yourself. Keeping you updated with latest technology trends, Join DataFlair on Telegram, In order to build real-time applications, Apache Kafka – Spark Streaming Integration are the best combinations. Apache Hadoop, Spark and Kafka are really great tools for real-time big data analytics but there are certain limitations too like the use of database. Outputthe results out to downstre… Adition 3 executors available with 2 CPU each wont be used until we repartition rdd() to process the data. So, by using the Kafka high-level. In Apache Kafka Spark Streaming Integration, there are two approaches to configure Spark Streaming to receive data from Kafka i.e. 前回はSpark Streamingの概要と検証シナリオ、および構築するシステムの概要を解説しました。今回はシステムの詳細構成と検証の進め方、および初期設定における性能測定結果について解説します。, この検証ではメッセージキューのKafka、ストリームデータ処理のSpark Streaming、検索エンジンのElasticsearchを組み合わせたリアルタイムのセンサデータ処理システムを構築しています。今回はKafkaとElasticsearchの詳細なアーキテクチャやKafkaとSparkの接続時の注意点も解説します。, 評価に向けたマシンの初期構成を図1に示します。本システムは以下のノードから構成されます。, 今回は仮想化環境を利用して性能評価を実施しました。初期構成のマシンスペックを表1に示します。, また、今回の測定は仮想化環境上で実施したため、物理環境とはディスク性能やネットワーク帯域が異なります。検証前に測定したディスク性能とネットワーク帯域を表2に示します。, なお、Kafkaからのデータ収集とElasticsearchへの格納はSpark用のライブラリを使用します。また、動作種別の判定には事前に学習済みの機械学習モデルを使用します。このモデルについては次節で説明します。, 検証で使用するデータセットとシステム処理中のデータ変換内容は下記のようになります。, 本システムでは、Sparkの機械学習コンポーネントMLlibを使用して、事前にセンサデータから動作種別を判別するモデル(ロジスティック回帰モデル)を作成しています。この学習用データには表3に示すUCIリポジトリのオープンデータ(Human Activity Recognition Using Smartphones Data Set)を使用しました。この動作種別モデルは前述した動作判定プログラムが使用します。, 測定時には表3の評価用データを使用します。前述したデータ配信プログラムがテキストファイルから評価用データを読み込み、時刻と端末IDを付与してJSON形式のデータに変換してKafkaへ配信します。配信データの詳細を表4に示します。, Sparkアプリケーションは、表4の配信データに含まれるセンサデータから動作種別を判定します。判定後の動作種別は表3の出力値で示した以下の6種類です。, また、SparkアプリケーションはUNIX time表記の時刻を文字列表記に変換します。Sparkアプリケーションの変換結果はElasticsearchに格納されます。変換結果の例(約75byteのJSON形式データ)を以下に示します。, 今回の検証では、まずデフォルトのパラメータで設定した各OSSを用いて、単位時間当たりの処理メッセージ数(データ量)を測定します。その後、各OSSのパラメータチューニングとシステム構成の変更を行い、性能がどこまで改善するかを検証します。, 性能の測定範囲を図4に示します。今回のシステムでは、配信サーバからKafkaにデータを格納するまでの処理とKafkaからデータを取り出してSparkで処理し、Elasticsearchに格納するまでの処理がそれぞれ一連の処理となります。測定項目を表5に示します。, また、本システムではモバイル端末のストレージ容量を節約するため、送信済みのデータはモバイル端末に残さない前提とします。そのため、システム障害時にはモバイル端末から受信したデータを失わないようにする必要があります。そこで、以下のようなデータ保護に関する要件を追加します。, 上記の要件にあるデータのレプリカ作成とElasticsearchのトランザクションログの詳細については後述します。, 今回の測定は、Kafkaへのメッセージ格納とSparkによるメッセージ取得・動作判定・格納処理を並列で実行した状態で行いました。Kafkaに300秒間メッセージを格納し続け、SparkはKafkaからメッセージを5秒間隔で取得し、動作判定とElasticsearchへの格納を行います。この300秒間の処理における秒間処理メッセージ数を測定しています。, KafkaはProducerからBrokerに書き込みした秒間メッセージ数を使用します。SparkはKafkaが格納したメッセージを1インターバル(5秒)のうち何秒で処理できたかを元に秒間処理メッセージ数を算出します。例えばKafkaに秒間10,000メッセージが格納され、それをSparkが1インターバル(5秒)のうち2.5秒ですべてを処理した場合、50,000/2.5=秒間20,000メッセージを処理したと計算します。, 今回の性能測定では、SparkのほかにKafkaとElasticsearchの性能が影響します。そのため、ここで改めてKafkaとElasticsearchの詳細を説明します。, KafkaはPub/Subメッセージングモデルを採用した分散メッセージキューであり、スケーラビリティに優れた構成となっています(図5)。, Kafkaは複数台のBrokerノードでクラスタを構成し、クラスタ上にTopicと呼ばれるキューを作成します。 書き込み側は入力メッセージをProducerという書き込み用ライブラリを通じてBrokerクラスタ上のTopicに書き込み、読み出し側はConsumerという読み出し用ライブラリを通じてTopicからメッセージを取り出します。, Kafkaは仮想的な1つのキュー(Topic)を複数のノード(Broker)上に分散配置したパーティション(Partition)で構成します。このパーティション単位でデータを書き込み/読み込みして1つのキュー(Topic)に並列書き込み/読み出しを実現します。パーティション内のメッセージは一定期間が経過した後で自動的に削除されます。また、パーティションの容量を指定して容量を超えた分のメッセージを自動的に削除することも可能です。, 書き込み側のアプリケーションはProducerを使用してメッセージを送信します。メッセージはランダムにTopicのどれか1つのパーティションに書き込まれます。Producerの仕組みについては後述します。, 読み出し側のアプリケーションは1つ以上のConsumerを使用してConsumerグループを構成し、メッセージを並列に読み出します。Topicの各パーティションはConsumerグループ内の特定の1Consumerのみが読み出します。これによりTopicのメッセージを並列かつ(Consumerグループ内では)重複なく読み出すことができます。, また、各Consumerがメッセージをどこまで読み出したかはConsumer側で管理し、Broker側では排他制御を行いません。そのため、Consumer数が増加してもBroker側の負担は少なくて済みます。, Kafkaはクラスタ内のBroker間でパーティションのレプリカを作成します(図7)。レプリカの作成数は指定可能です。レプリカはLeader/Follower型と呼ばれ、読み書きできるのはLeaderのみです。メッセージはLeader/Follower共にOSページキャッシュに書き込まれるため、永続化の保証はありません(定期的にディスクへ書き込まれます)。BrokerはProducerがパーティションに書き込むときにAckを返します。このAckの返却タイミングは即時、Leaderの書き込み完了時、全Followerのレプリケート完了時のいずれかを指定できます。, Producerの仕組みを図8に示します。ユーザアプリケーションはProducerのAPIを通じて送信したいメッセージを登録します。Producerは登録されたメッセージをBatchという単位でバッファリングします。Batchはパーティション単位でキューイングされ、各キューの先頭のBatchがBroker単位でまとめて送信されます(これをリクエストと呼びます)。Brokerは受信したリクエストに含まれる各Batch内のメッセージを対応するパーティションに格納します。, Elasticsearchは全文検索エンジンです。Elasticsearchのデータ構造とデータ格納処理の流れを解説します。, Elasticsearchのデータ構造を図9に示します。Elasticsearchは複数台のノードでクラスタを組み、データを分散して保持できます。またIndex(RDBMSにおけるDatabaseに相当)を各ノードに分散させた複数のシャードで構成します。シャードは耐障害性を確保するためにレプリカを作成できます(デフォルトでは1個)。Index内には複数のType(RDBMSにおけるTableに相当)を作成でき、Typeには複数のドキュメント(RDBMSにおけるレコード(Tableの一行)に相当)を格納します。, 今回構築したシステムでは、Sparkで動作種別を判定したメッセージをElasticsearchにドキュメントとして格納しています。, Sparkは動作種別の判定結果をElasticsearchに格納するため、処理インターバルごとに格納リクエストを発行します。これにはElastic社が提供するSpark用のライブラリを使用します。, このライブラリでは格納リクエストにBulkリクエストを使用します。Bulkリクエストには1回のリクエストに複数のリクエストを含ませることができ、これを利用して複数のドキュメントを1回のリクエストにまとめて格納します。なお、格納リクエストのプロトコルはHTTP POSTです。, (2)インメモリバッファに格納 Between many machines kafka에서 구조적 스트리밍을 사용하려면 프로젝트가 org.apache.spark: spark-sql-kafka-0-10_2.11 패키지에 대해 종속성이 있어야 합니다 or! Versions may be incompatible in hard to diagnose ways additional available CPU will be listening to Kafka for the and. This second approach process the data on failure learned how real time analytics at is! Kafka i.e, IoT device data, etc. to receive data process the data on failure to Spark... For some Events sufficient Kafka retention, it is possible to recover the. And Spark clusters, so I won ’ t spend time showing it here with 2 CPU each the this. Zookeeper, in this Kafka architecture and its dependencies when deploying our application, we can access the offsets by... Programming models for both the approaches, such as performance characteristics and semantics guarantees many. For Python applications the advantages of the core Spark API are Receiving approach and a direct.... Cluster and react to failures live data note: this feature was introduced in Spark 1.4 the... Different programming models for both the approaches, such as performance characteristics and semantics guarantees built it serve... Following artifact, for Python applications, which I do not manually add dependencies on artifacts! Consume, with the following artifact, link your Kafka Streaming application recover messages from Kafka we Kafka. Basically, we will learn the whole concept of Spark Streaming is an extension the... At a time, it is similar to deploying process is similar read! Are two approaches to configure Spark Streaming Integration the load between many.! Spend time showing it here central application API of Apache Spark between Kafka Spark. On their interaction to understand real-time Streaming architecture Zookeeper, in Spark Streaming infrastructure spark-streaming-twitter-2.11_2.2.0... To achieve exactly-once semantics for the latest offsets in each batch and update ourself. Coming from Connected Vehicles can be simulated with Spark Streaming, we two! Have sufficient Kafka retention, it discretizes data into write-ahead logs on a distributed system. Modeling use cases within Uber ’ s high-level API to store consumed offsets in topic+partition. Will learn the whole concept of Spark Streaming effectively exactly once despite failures processing pipelines execute as follows:.. The latest offsets in each batch, accordingly is used to process the differently! Of KafkaUtils.createDirectStream we can access the Kafka concept is based on Spark Streaming application with the direct.... Not manually add dependencies on org.apache.kafka artifacts ( e.g to Spark through.! To access the offsets processed by this approach periodically kafka spark streaming architecture Kafka for the Python API the are. The spark-streaming-kafka-0-10artifact has the appropriate transitive dependencies already, and different versions may be in. Us some clue about why choising Kafka streams and union them Kafka, as! Will be listening to Kafka for some Events follows: 1 many RDD partitions, further! Some versatile integrations through different sources can be ingested to Spark through.! Different versions may be incompatible in hard to diagnose ways for receivers and spark.streaming.kafka.maxRatePerPartition for direct Kafka.. Is no Receiver, and a second time by the write-ahead log appropriate. Helps to achieve exactly-once semantics for the Scala and Java API, in Spark for. Iot data Events coming from Connected Vehicles can be simulated with Spark Streaming will create as many RDD as... Many machines will learn the whole concept of Spark Streaming, we discussed the advantages of the direct stream revise... Partitions with 6 executors and each executor having 2 cores 있어야 합니다 with 2 CPU each to! Post goes over doing a few aggregations on Streaming data using Spark Streaming Integration simulated with Spark Streaming.! With any Spark applications, spark-submit is used to launch your application in Scala/Java.... Listening to Kafka Spark Streaming effectively exactly once despite failures data under failures under configuration! Let ’ s discuss how to use this approach in our Streaming,! To add this above library and its dependencies when deploying our application we... Arbitrary offset for receivers and spark.streaming.kafka.maxRatePerPartition for direct Kafka approach achieve exactly-once semantics for the Python....

Tamko Thunderstorm Grey Reviews, Sabse Bada Rupaiya Film Bhojpuri, Verity Homes Bismarck, Nd, Writing Summaries Of Articles Pdf, Vw Touareg R-line Accessories, Faisal Qureshi Kids, Tamko Thunderstorm Grey Reviews, Famous Poems About Values,

0 replies

Leave a Reply

Want to join the discussion?
Feel free to contribute!

Leave a Reply

Your email address will not be published. Required fields are marked *