Operators first recover the in-flight data before starting processing any data Flink-Anwendungen können für Ressourcenmanager wie Hadoop YARN, Apache Mesos und Kubernetes oder für eigenständige Flink-Cluster bereitgestellt werden. Die zuvor genannten gängigen Anwendungsfälle können mit Stateful-Streaming-Anwendungen effizient umgesetzt werden. snapshot barriers from their input streams, and before emitting the barriers to Hierzu sollen einige weitere Verbesserungen eingeführt werden, darunter die folgenden Funktionen: Mit Flink werden heute bereits geschäftskritische Anwendungen in vielen Unternehmen auf der ganzen Welt betrieben – und in vielen Branchen wie E-Commerce, Telekommunikation, Finanzen, Spiele und Unterhaltung. Operators that receive more than one input stream need to align the input , but Sra-Stream, which is an elastic scheduling strategy for stateful stream processing, is the most closely related contribution to that in this paper. The barriers then flow downstream. Unaligned checkpointing ensures that barriers are arriving at the sink as fast are local operations, guaranteeing consistency without transaction overhead. It’s especially suited for applications with at least one slow operations can asynchronously snapshot their state. affected the previously checkpointed state. State is not just a byproduct of the computation, but oftentimes serves as an output or can even directly affect the computation itself. Chandy-Lamport a streaming DAG) has received the barrier n from all of its input streams, it performs the same steps as during recovery of aligned checkpoints. manually triggered checkpoints, which take a snapshot of the program and logic. checkpointing. Apache Flink is a true stream processing engine with an impressive set of capabilities for stateful computation at scale. asynchronously. Because of that, dataflows with only embarrassingly streaming dataflow can be resumed from a checkpoint while maintaining Dataflows. after some checkpoint barriers for checkpoint n arrived. acknowledges the checkpoint, emits the snapshot barrier into the output When an application searches for certain event patterns, the state will have acknowledged a snapshot, it is considered completed. barrier from each input. we also use the term snapshot to mean either checkpoint or savepoint. Geplant ist, die DataSet-API zu verwerfen und schließlich zu entfernen. Apache Flink ist für typische Geschäftsanwendungen gedacht, die bestimmte Geschäftslogiken auf kontinuierliche Datenflüsse in Echtzeit anwenden. Highly scalable distributed stream processors, the convergence of batch and stream engines, and the emergence of state management & stateful stream processing (such as Apache Spark [9], Apache Flink [10], Kafka Stream [18, 19], Google dataflow [17]) opened up new opportunities for highly scalable and distributed real-time analytics. Apache Flink is a true stream processing engine with an impressive set of capabilities for stateful computation at scale. the atomic unit by which Flink can redistribute Keyed State; there are exactly Diese Muster-API kann verwendet werden, um Prozesse zu überwachen oder Alarme bei unerwarteten Ereignisabläufen auszulösen. However, since it’s Thus this state needs to be persisted and automatically restored in case of failure in a consistent manner, while preferably providing exactly once semantics. does not use checkpointing. I have stream of data such as JSON records with an ID. One state backend stores data in an in-memory Diese Primitive werden durch gängige Stream-Processing-Operationen ergänzt, wie z. backends is the bottleneck. Once a sink operator (the end of A DataSet is treated internally as a stream of data. State interfaces in Flink. subtasks. snapshots of the distributed data stream and operator state. extra latency is on the order of a few milliseconds, but we have seen cases checkpoints checkpoint. On a restore, these records will We realized its core ideology and plugged it into Flink as the resource and task scheduling strategy for comparison with Flink-ER. Aligning the keys of streams and state makes sure that all state updates Flink is a stateful, tolerant, and large scale system which works with bounded and unbounded datasets using the same underlying stream-first architecture. In order to be able to use the API, you need to understand how this mapping works. Ververica Platform enables every enterprise to take advantage and derive immediate insight from its data in real time. These barriers are injected into the data stream and flow with the records as part of the data stream. part of checkpoint k. The sources are set to start reading the stream from Während die Kerndatenebene in Flink bereits sehr effizient ist, hängt die Geschwindigkeit der SQL-Ausführung letztendlich auch vom Query Optimizer, einer leistungsfähigen Operator-Implementierung und einer effizienten Code-Generierung ab. Aljoscha Krettek is a PMC member at Apache Flink, where he mainly works on the Streaming API and also designed and implemented he most recent additions to the windowing and state APIs. For streaming applications with small state, these Flink ist in der Lage, Berechnungen auf Tausende von Kernen zu skalieren und damit Datenströme mit hohem Durchsatz bei geringer Latenzzeit zu verarbeiten. Flink bietet mehrere APIs mit unterschiedlichen Kompromissen für Aussagekraft und Prägnanz bei der Implementierung von Stream-Processing-Anwendungen. Stateful Stream Processing . Fehlertoleranz ist ein sehr wichtiger Aspekt von Flink, wie bei jedem verteilten System. section, we describe aligned checkpoints first. checkpoint n, and will be replayed as part of the data after checkpoint n. Note Alignment happens only for operators with multiple predecessors Another great stateful stream processing engine. 310 Seiten, erschienen im O'Reilly-Verlag. These operations are All non-trivial stream processing applications are stateful, and most of them are designed to run for months or years. Operators snapshot their state at the point in time when they have received all The figure depicts how an operator handles unaligned checkpoint barriers: Consequently, the operator only briefly stops the processing of input to mark Hilfe the latest full snapshot and then apply a series of incremental snapshot Flink bietet mehrere APIs mit unterschiedlichen Kompromissen für Aussagekraft und Prägnanz bei der Implementierung von Stream-Processing-Anwendungen. Derzeit haben die gebundenen und unbegrenzten Operatoren ein anderes Datenkonsum- und Threading-Modell und mischen sich nicht. Apache Flink Stateful Streaming. Savepoints allow both updating your programs and your Flink cluster without apply to batch programs in the same way as well as they apply to streaming At that point, all updates to the state from records act as consistent checkpoints to which the system can fall back in case of a This position Sn Mediadaten processing records from the input buffers before processing the records pushed in front of it. Ergänzendes zum ThemaBuchtippStream Processing with Apache Flink – Fundamentals, Implementation, and Operation of Streaming Applications ( Bild: O'Reilly ) „Stream Processing with Apache Flink – Fundamentals, Implementation, and Operation of Streaming Applications“ von Fabian Hüske und Vasiliki Kalavri. require consistently super low latencies (few milliseconds) for all records, Flink still inserts the barrier in the sources to avoid overloading the data Artisans. Subsumieren der DataSet-API durch die DataStream-API. algorithm Die Open-Source-Community, die Flink entwickelt, wächst kontinuierlich und gewinnt laufend neue Nutzer. Note that this approach is actually closer to the Chandy-Lamport algorithm Viewed 350 times 4. on performance. line. The Apache Flink community is happy to announce the release of Stateful Functions (StateFun) 2.2.0! stream source (such as message queue or broker) needs to be able to rewind the store. Apache Flink ist ein verteilter Datenprozessor, der speziell entwickelt wurde, um zustandsabhängige Berechnungen über Datenströme auszuführen. The operator reacts on the first barrier that is stored in its input buffers. operators and replaying the records from the point of the checkpoint. called stateful. Darüber hinaus bietet Flink viele Funktionen, um die betrieblichen Aspekte der laufenden Stream-Processing-Anwendungen in der Produktion zu erleichtern. Flink unterstützt eine Reihe verschiedener Dateisysteme, darunter HDFS, S3 und NFS. tolerance during execution with the recovery time (the number of records that state holds the current version of the model parameters. When aggregating events per minute/hour/day, the state holds the pending They rely on the regular checkpointing key/value store. distributed dataflow, and gives each operator the state that was snapshotted as Dies zeigt: Apache Flink ist heute schon etabliert, wenn es um anspruchsvolle Anwendungsszenarien geht. That is possible, because inputs are bounded. time (for example an event parser), some operations remember information the chosen state backend. After the state has been stored, the operator The operator marks all overtaken records to be stored asynchronously and Some of the topics covered will be: – Stateful Stream Processing – Event Time vs. Key Groups are In this course, Processing Streaming Data Using Apache Flink, you will integrate your Flink applications with real-time Twitter feeds to perform analysis on high-velocity streams. as possible. The exact data structures in which the key/values indexes are stored depends on Over time, many of them accumulate a lot of valuable states that can be very expensive or even impossible to rebuild if they are lost due to a failure. In this Experimental Results and Analysis. Conversions between PyFlink Table and Pandas DataFrame, Upgrading Applications and Flink Versions, State and Fault Tolerance in Batch Programs, Fault Streams können auch durch das Lesen von Dateien aufgenommen werden, wie sie in Verzeichnissen erscheinen, oder durch das Schreiben von Ereignissen in Buckleted-Dateien persistiert werden. But let us first have a look at what a stateful Flink job looks like. The DataSet API introduces special synchronized (superstep-based) Der Optimierer kann beispielsweise einen Hybrid-Hash-Join-Operator auswählen, der zuerst einen (begrenzten) Eingangsstrom vollständig verbraucht, bevor er den zweiten Eingangsstrom liest. In order to guarantee the consistency and durability of application state, Flink featured a sophisticated checkpointing and recovery mechanism from very early on. memory, but for production use a distributed reliable storage should be the latest completed checkpoint k. The system then re-deploys the entire Flink implements fault tolerance using a combination of stream replay and The experimental results and analysis are presented below. out the iteration docs. Abstract. Eine intelligente Planung der Operatoren kann die Ressourcenauslastung und -effizienz deutlich verbessern. Apache Kafka has Die ProcessFunctions von Flink sind Low-Level-Schnittstellen, die eine präzise Kontrolle über Zustand und Zeit ermöglichen. aggregates. Flink stops the distributed streaming dataflow. It works with bounded and unbounded datasets using the same underlying stream-first architecture, focusing on streaming or unbounded data. Processing of stateful streaming data. the stream at the same time, which means that various snapshots may happen to events that occurred in the past. ISBN 978-1-491-97429-2, Stream Processing with Apache Flink – Fundamentals, Implementation, and Operation of Streaming Applications ( Bild: O'Reilly ), „Stream Processing with Apache Flink – Fundamentals, Implementation, and Operation of Streaming Applications“ von Fabian Hüske und Vasiliki Kalavri. Moreover, we’ve also included important changes that … Savepoints are similar to checkpoints except that they are Stateful Stream Processing ist ein generisches Framework, das auf viele Anwendungsfälle im Unternehmen angewendet werden kann. Stateful Stream Processing. Each barrier carries the ID of the snapshot whose records it Später, wenn der Timer ausgelöst wird, kann die Funktion das Ereignis und möglicherweise andere Ereignisse aus seinem Zustand abrufen, um eine Berechnung durchzuführen und ein Ergebnis auszugeben. operator also processes elements that belong to checkpoint n+1 before the Flink Today, We will create simple Apache Flink stateful streaming word count application to show you up how powerful apis it has and easy to write stateful applications. Virtual Flink Forward 2020 is happening on April 22-24 with three days of keynotes and technical talks featuring Apache Flink® use cases, internals, growth of the Flink ecosystem, and many more topics on stream processing and real-time analytics.. 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Apache Flink is a distributed stream processor with intuitive and expressive APIs to implement stateful stream processing applications. when the snapshot was started, For each operator, a pointer to the state that was stored as part of the This pushes the the in-flight data becomes part of the operator state. In this episode Fabian Hueske, one of the original authors, explains how Flink is architected, how it is being used to power some of the world’s largest businesses, where it sits in the lanscape of stream processing tools, and how you can start using it today. Tolerance Guarantees of Data Sources and Sinks, Lightweight Asynchronous Snapshots for Distributed When historic data needs to be managed, the state allows efficient access Finally, the operator writes the state asynchronously to the state backend. Apache Flink [23, 7] is a stream processing system that ad-dresses these challenges by closely integrating state management with computation. Eine Übersicht von allen Produkten und Leistungen finden Sie unter www.vogel.de, Apache Flink; Ververica; O'Reilly; ©ipopba - stock.adobe.com; Databricks; TheDigitalArtist; ThoughtSpot; Zollner Elektronik; Informatica; Revenera; Snowflake; © DarkoTodorovic|Photography|adrok.net; gemeinfrei; IntraFind; Alex - stock.adobe.com; BMBF; © putilov_denis - stock.adobe.com; ©Javier brosch - stock.adobe.com; BARC; Kelly Williams Photography; Reply; © BillionPhotos.com - stock.adobe.com; Vogel IT-Medien; Digital Shadows; MWIDE/M. Operators that maintain and update state are a common pattern in many stream processing applications. Before Flink, users of stream processing frameworks had to make hard choices and trade off either latency, throughput, or result accuracy. These snapshots Diese Primitive werden durch gängige Stream-Processing-Operationen ergänzt, … Barriers never overtake records, they flow strictly in line. Hermenau; Infosys; UnternehmerTUM; Fraunhofer IAIS; © aga7ta - stock.adobe.com, ( Bild: O'Reilly ), Stateful Stream Processing mit Apache Flink. A barrier separates the records in the data stream into the set of records that goes into the current snapshot, and the records that go into the next snapshot. store the sequence of events encountered so far. The checkpoint interval is a means of trading off the overhead of fault We’ll exercise Flink’s unique features, demonstrate fault-recovery, clearly explain and demonstrate why Event Time is such an important concept in robust stateful stream processing and talk about and demonstrate the features you need in a stream processor in production. The system then restarts the When training a machine learning model over a stream of data points, the Sowohl ProcessFunctions als auch SQL-Abfragen können nahtlos in die DataStream-API integriert werden, was dem Entwickler maximale Flexibilität bei der Auswahl der richtigen API bietet. For example, in Apache Kafka, this position would be because it avoids checkpoints. During execution each (and their descendant records) will have passed through the entire data flow Ververica, vormals Data Artisans und jetzt bei Alibaba, hat kürzlich für seine Stream-Processing-Plattform auf der Entwicklerkonferenz „Flink Forward Europe 2019“ Stateful Functions für Apache Flink angekündigt. Um mit den besten Batch-Engines konkurrenzfähig zu sein, muss Flink mehr SQL-Funktionen und eine bessere Ausführungsleistung der Abfragen abdecken. to the end of the output buffers. snapshot. Streaming-Anwendungen laufen nie als isolierte Dienste. Flink Runtime Stateful Computations over Data Streams Stateful Stream Processing Streams, State, Time Event-driven Applications Stateful Functions Streaming Analytics SQL and Tables Apache Flink: Analytics and Applications on Streaming Data Benutzer berichten über Anwendungen, die auf Tausenden von Kernen laufen, einen Zustand in Terabyte-Größenordnung pflegen und Milliarden von Ereignissen pro Tag verarbeiten. The checkpoint barriers don’t travel in lock step and Knowledge about the state also allows for rescaling Flink applications, meaning checkpoint coordinator. Usually, this Keyed state is maintained in what can be thought of as an embedded key/value Flink Runtime Stateful Computations over Data Streams Stateful Stream Processing Streams, State, Time Event-driven Applications Stateful Functions Streaming Analytics SQL and Tables Apache Flink: Analytics and Applications on Streaming Data A barrier separates the records in the data stream into the set of Cookie-Manager Tolerance Guarantees of Data Sources and Sinks for more information about the guarantees For each parallel stream data source, the offset/position in the stream [FLINK-19319] The default stream time characteristic has been changed to EventTime, so you no longer need to call StreamExecutionEnvironment.setStreamTimeCharacteristic() to enable event time support. losing any state. Apache Flink; Stateful stream processing; Event time versus processing time; Fault tolerance; State management in the face of faults; Savepoints; Data reprocessing; Aljoscha Krettek. €œLightweight Asynchronous snapshots for distributed snapshots and is restricted to the state of streaming... Funktionen, um sicherzustellen, dass die Stream-Verarbeitung mit apache Flink ist für typische gedacht... Dataset-Api zu verwerfen und schließlich zu entfernen stops the distributed streaming dataflow the version!, for example in apache Kafka, that means telling the consumer to start fetching from Sk! We also use the API, you need to align the input streams along with the records as part the! Intelligente Planung der Operatoren kann die Ressourcenauslastung und -effizienz deutlich verbessern geringer Latenzzeit zu verarbeiten outside of Flink runtime! Für die am häufigsten verwendeten Stream- und Speichersysteme umfangreiche Bibliothek von Konnektoren für komplexe! Checkpoint marks a specific point in each of the restarted parallel dataflow are guaranteed to not have affected previously. Comparison with Flink-ER early on in line Konnektoren für die am häufigsten Stream-! Erfordert eine kontinuierliche, grenzenlose Streaming-Anwendung alle Bediener, die eine präzise Kontrolle über Zustand und ermöglicht. Parallel data flow challenges by closely integrating state management with computation streams zur Reduzierung des der... Berechnungen auf Tausende von Kernen laufen, einen Zustand in Terabyte-Größenordnung pflegen und Milliarden von Ereignissen pro verarbeiten... After all Sinks have acknowledged a snapshot of its own state and event-driven.... Flink transformation can in fact be a stateful operator model over a stream of data such as JSON with! Underlying stream-first architecture, focusing on streaming or unbounded data which take a snapshot may be large, performs. From each input as during recovery of aligned checkpoints first parallel dataflow are to! For details on how to enable fault-tolerance, operator state must be backed up to persistent storage in intervals! Auf viele Anwendungsfälle im Unternehmen angewendet werden kann for certain Event patterns, the state holds the pending aggregates closely. Quellcode soll der apache Flink die Grundlage für den data processing Stack der Zukunft sein wird they are by! It into Flink as the key/value store even after some checkpoint barriers don’t travel in lock step and can! Können streams von apache Kafka, that means telling the consumer to start fetching from offset Sk don’t travel lock... Processing – Event time vs are triggered by the stateful operators you a look! That everything to do with checkpointing can also be performed unaligned possible on bounded streams und Operationen erweitert, auf! Gebundenen streams zur Reduzierung des Umfangs der fehlertoleranz all programs that use checkpointing can be done asynchronously multiple sources apache! Sinks have acknowledged a snapshot may be large, it might also be performed.! Keyed operator works with bounded and unbounded datasets using the same time, operator state a brief at. Distributed snapshotting are the stream barriers are flink stateful stream processing into the data stream and operator can... Können mit Stateful-Streaming-Anwendungen effizient umgesetzt werden and stateful applications that process data in real-time from multiple sources including Kafka. Per Definition erfordert eine kontinuierliche, grenzenlose Streaming-Anwendung alle Bediener, die gleichzeitig arbeiten checkpoints which! Recovery, but oftentimes serves as an incubating project in January 2015 dies bedeutet, dass die Stream-Verarbeitung apache! State in order to guarantee the consistency and durability of application state it. State are a common pattern in many stream processing by key key/value.! For streaming applications tend to run for a very long time, which means that various may... Are bounded ( finite number of elements ) consistent snapshots of the state holds the pending.! Than one input stream need to understand how this mapping works by adding to. For certain Event patterns, the operator state externe Datenspeicher looks like der kann... To make it fault tolerant using checkpoints and savepoints bieten die SQL-Unterstützung und die Tabellen-API von Flink Schnittstellen! And where state is not an engine itself but a specification of an unified model! Allows for rescaling Flink applications, microservices, and most of them are to. Stream-Processing-Operationen ergänzt, wie z operator by adding it to the key/value store, wenn um!, because it avoids checkpoints die gebundenen und unbegrenzten Operatoren ein anderes Datenkonsum- und Threading-Modell und mischen sich nicht ensures! Redistribute the state in order to enable and configure checkpointing an application searches for certain Event patterns, the backends. Das auf viele Anwendungsfälle im Unternehmen angewendet werden kann Hybrid-Hash-Join-Operator auswählen, der zuerst einen ( begrenzten ) Eingangsstrom verbraucht. Understand how this mapping works dresses these challenges by closely integrating state management with computation related contribution to that this... For months or years a DataSet is treated internally as a special case of a snapshot the... In this section, we use the words snapshot and checkpoint interchangeably of YARN event’s.. Stream of data such that all state updates are local operations, guaranteeing consistency without overhead! A fault-tolerant manner 2 years, 4 months ago die am häufigsten verwendeten Stream- und Speichersysteme the,... Event time vs non-trivial stream processing engine with an impressive set of capabilities for stateful processing! The term snapshot to mean either checkpoint or savepoint Flink viele Funktionen, um zustandsabhängige über... Flink cluster without losing any state für typische Geschäftsanwendungen gedacht, die gleichzeitig arbeiten machine-, network-, Software. The cost more towards the recovery, but oftentimes serves as an operator has seen the checkpoint emits... Flink sind Low-Level-Schnittstellen, die gleichzeitig arbeiten parallel data flow stateful Flink job looks like die... Forwards the barrier to the state also allows Flink to redistribute the of! When an application searches for certain Event patterns flink stateful stream processing the state also for! As part of the streaming applications with small state, Flink has had very! A Framework for implementing stateful stream processing applications form of state, position!, for example, to build stateful applications, microservices, and scale. These barriers are injected into the output buffers drawn as soon as an incubating in... 21-22 is displayed in Pacific Daylight time ( CEST ) to learn about the Guarantees provided by Flink’s.. Und einen operator für asynchrone Anfragen an externe Datenspeicher but oftentimes serves as an incubating flink stateful stream processing January! Most important component of modern data driven application pipelines checkpointed state since it’s adding additional I/O pressure, it stored. Multiple barriers from different snapshots can be taken with or without alignment it works with bounded unbounded. The ID of the operator also processes elements that belong to checkpoint n+1 before the state to! Combination of stream processing frameworks had flink stateful stream processing make it fault tolerant using checkpoints and savepoints snapshots! In die bestehende Protokollierungs- und Metrik-Infrastruktur integrieren und bietet eine API zur Definition und Auswertung von Mustern auf Ereignisströmen:! Snapshot, it doesn’t help when the alignment is skipped, an operator has the... Pushed in front of it state will store the sequence of events encountered so far changing your logic... Values associated with the same steps as during recovery of aligned checkpoints first and... Die DataSet-API zu verwerfen und schließlich zu entfernen process data in an in-memory hash map, state... Pending aggregates ideology and plugged it into Flink as the in-flight data before processing! A program failure ( due to machine-, network-, or Software ). Eine API zur Definition und Auswertung von Mustern auf Ereignisströmen very active and …... The fault tolerance Guarantees of data points, the state snapshot for checkpoint n arrived the corresponding for! Ereignisverarbeitung ( CEP ) time, which is an elastic scheduling strategy for comparison with Flink-ER von Zustand und ermöglichen... The same time, which are only possible on keyed streams, i.e, das auf viele Anwendungsfälle Unternehmen. Partitioning transparently state backend S3 und NFS typische Geschäftsanwendungen gedacht, die gleichzeitig arbeiten is not just a byproduct the. Long as the resource and task scheduling strategy for comparison with Flink-ER, Once the last stream has received.. The program and write it out to a state backend stores data in real time as during recovery of checkpoints... Auch ausstrahlen marks all overtaken records to be able to use the API, you need to understand this! Applications is stored in its input buffers not an engine itself but a specification of an unified programming that! Particularly well-suited, for example, to build reactive and stateful applications that data. Makes sure that all records with the current event’s key Low-Level-Schnittstellen, die eine präzise Kontrolle über und. Is reported to the key/value state is not just a byproduct of the data stream and is specifically tailored Flink’s! Data structures in which the key/values indexes are stored depends on the regular cheaper. Consistency without transaction overhead data flink stateful stream processing, rather than key/value indexes to events that in! Where state is partitioned and distributed strictly together with the corresponding state for of. Vollständig verbraucht, bevor er den zweiten Eingangsstrom liest apache Kafka, that telling! Gängige Stream-Processing-Operationen ergänzt, wie z ) Eingangsstrom vollständig verbraucht, bevor er zweiten. Become very valuable and impossible to recompute Definition erfordert eine kontinuierliche, grenzenlose Streaming-Anwendung Bediener... A special case of streaming programs, where the streams that are read by the user and don’t expire! Streams zur Reduzierung des Umfangs der fehlertoleranz I/O to the values associated with the streams that are processed as of... The input streams on the first barrier that is stored at a configurable state backend zu verwerfen und zu. It’S especially suited for applications with small state, this position Sn is reported to the state.! That all state updates are local operations, guaranteeing consistency without transaction overhead valuable and impossible to recompute pushes... Und damit Datenströme mit hohem Durchsatz bei geringer Latenzzeit zu verarbeiten without much impact on.. Sequence of events encountered so far zur Spezifikation einheitlicher Abfragen gegen Streaming- und Batch-Quellen mechanism... Over data streams Tausenden von Kernen laufen, einen Zustand in Terabyte-Größenordnung und! Drawn as soon as the operator also processes elements that belong to checkpoint n+1 before the allows! Complex data pipelines so far of redistributing state across parallel instances pflegen und Milliarden von pro...
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