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kappa architecture kafka

By persisting these events in an ordered immutable log data structure that can be replayed at high-throughput, it can also serve batch needs. While redesigning this system, we also realized that we didn’t need to query Hive every ten seconds for ten seconds worth of data, since that would have been inefficient. This is one of the most common requirement today across businesses. You may be wondering: what is a kappa architecture? Dies kann durch Während ein solches Vorhaben fortschreitet, kristallisieren sich einige Schwierigkeiten heraus. Downstream applications and dedicated Elastic or Hive publishers then consume data from these sinks. Dies hat eine Duplizierung der Berechnungslogik sowie eine komplexe Verwaltung der Architektur für beide Pfade zur … We updated the backfill system for this job by combining both approaches using the principles outlined above, resulting in the creation of our Hive connector as a streaming source using Spark’s Source API. Usually in Lambda architecture, we need to keep hot and cold pipelines in sync as we need to run same computation in cold path later as we run in hot path. Streaming, Flink, o.ä. We’ve modeled these results in Figure 2, below: When we swap out the Kafka connectors with Hive to create a backfill, we preserve the original streaming job’s state persistence, windowing, and triggering semantics keeping in line with our principles. Es wird geschätzt, dass der Aufwand in vielen BigData-Projekten bis zu 90% aus Datenbereinigung besteht. ↩, So genannt in Putting Apache Kafka to Use: A Practical Guide to Building a Stream Data Platform ↩, RSS-Feed abonnieren NTT is a global telecommunications company headquartered in Tokyo, Japan. Additionally, many of Uber’s production pipelines currently process data from Kafka and disperse it back to Kafka sinks. Kreps’ key idea was to replay data into a Kafka stream from a structured data source such as an Apache Hive table. werden nötig, um die Daten zu bereinigen1. In the Streaming Data Warehouse, tables are represented by topics. Viele Datenbanken erlauben es zudem, über Änderungen an Tabellenzeilen zu Die Grundüberlegung zur Kappa-Architektur ist einfach erklärt. When you want to do the reprocessing, start a second instance of your stream processing job that starts processing from the beginning of the retained data, but direct this output data to a new output table. beschreibt das Problem der doppelten Komplexität in seinem Artikel The Apache Hive to Apache Kafka replay method (Approach 1) can run the same exact streaming pipeline with no code changes, making it very easy to use. Leveraging a Lambda architecture allows engineers to reliably backfill a streaming pipeline, but it also requires maintaining two disparate codebases, one for batch and one for streaming. Sharding1),la… It is based on a streaming architecture in which an incoming series of data is first stored in a messaging engine like Apache Kafka. geschrieben. technische Anforderungen, so dass der Code früher oder später auseinander läuft und noch mehr Wartungsaufwand This setup then simply reruns the streaming job on these replayed Kafka topics, achieving a unified codebase between both batch and streaming pipelines and production and backfill use cases. Kafka Streams (oder Streams API) ist eine Java-Bibliothek z… Daten vorzunehmen - das heißt klar zu definieren, in welchem Format die Daten eintreffen. Mehr zum Thema Streams und Modellieren von Events findet sich in diesem vorherhigen Blogpost. NoETL plädiert dafür, genau wie in Programmiersprachen eine “strenge Typisierung” der Wer Daten Das gilt auch für alle Questioning the Lambda Architecture. Da stellte sich für Kreps die berechtigte Frage: Brauchen wir überhaupt einen Batch-Layer? Dashboard lädt seine Daten aus der neuen Tabelle. And is a seperate batch layer faster than recomputing with a stream processing engine for batch analytics? Instead, we relaxed our watermarking from ten seconds to two hours, so that at every trigger event, we read two hours’ worth of data from Hive. At Uber, we use robust data processing systems such as Apache Flink and Apache Spark to power the streaming applications that helps us calculate up-to-date pricing, enhance driver dispatching, and fight fraud on our platform. Well, it is an architecture for real time processing systems that tries to resolve the disadvantages of the Lambda Architecture. Auf der anderen Seite werden die Systeme des Unternehmens voneinander entkoppelt. rider experiences remains one of the largest stateful streaming use cases within Uber’s core business. produziert. The Kappa Architecture is a brain child of Linkedin’s engineering team, they came up with this solution to avoid code sharing between two different paths (hot and cold). Es wird empfohlen, für die strenge Typisierung ein unternehmensweit einheitliches Datenformat zu wählen, mit dem die Beyond switching to the Hive connector, tuning the event-time windows, and watermarketing parameters for an efficient backfill, the backfilling solution sh… Here we have a canonical datastore that is an append-only immutable log store present as a part of Kappa architecture. Die Streaming-Jobs schreiben die von ihnen produzierten Daten entweder zurück in das Apps ausgelesen werden. Für die Langzeitdatenhaltung können die Daten weiterhin aus dem Streaming-System nach am Ursprungsort in diesem Format erzeugt werden, entfällt die Datenbereinigung. This novel solution not only allows us to more seamlessly join our data sources for streaming analytics, but has also improved developer productivity. In Spark’s batch mode, Structured Streaming queries ignore event-time windows and watermarking when they run a batch query against a Hive table. The following diagram shows the logical components that fit into a big data architecture. From the log, data is streamed through a computational system and fed into auxiliary stores for serving. If you are interested in building systems designed to handle data at scale, visit Uber’s, Artificial Intelligence / Machine Learning, Engineering SQL Support on Apache Pinot at Uber, Women in Data Science at Uber: Moving the World With Data in 2020—and Beyond, Building a Large-scale Transactional Data Lake at Uber Using Apache Hudi, Monitoring Data Quality at Scale with Statistical Modeling, Uber’s Data Platform in 2019: Transforming Information to Intelligence, Productionizing Distributed XGBoost to Train Deep Tree Models with Large Data Sets at Uber, Evolving Michelangelo Model Representation for Flexibility at Scale, Meet Michelangelo: Uber’s Machine Learning Platform, Uber’s Big Data Platform: 100+ Petabytes with Minute Latency, Introducing Domain-Oriented Microservice Architecture, Why Uber Engineering Switched from Postgres to MySQL, H3: Uber’s Hexagonal Hierarchical Spatial Index, Introducing Ludwig, a Code-Free Deep Learning Toolbox, The Uber Engineering Tech Stack, Part I: The Foundation, Introducing AresDB: Uber’s GPU-Powered Open Source, Real-time Analytics Engine. For instance, a window w0 triggered at t0 is always computed before. If you are interested in building systems designed to handle data at scale, visit Uber’s careers page. fließen dann weiter in use-case-bezogene Datenbanken und Systeme. Kappa architecture. ermöglichen es, Daten zu persistieren und erneut durchzuspielen (sogenanntes replay). Switching between streaming and batch jobs should be as simple as switching out a Kafka data source with Hive in the pipeline. Die Kappa-Archi… Um bei einem Progra… In keeping with principle three, this feature of our system ensures that no changes are imposed on downstream pipelines except for switching to the Hive connector, tuning the event time window size, and watermarking duration for efficiency during a backfill. Writing an idempotent replayer would have been tricky, since we would have had to ensure that replayed events were replicated in the new Kafka topic in roughly the same order as they appeared in the original Kafka topic. Wenn die Daten Diese Architektur wurde in vielen Unternehmen umgesetzt, oft zusammen mit der Einführung eines Datenmengen im Batchlayer vorverarbeitet, das fehlende Delta im Speedlayer in Nahechtzeit nachholt, und das Ganze im A Kappa Architecture system is like a Lambda Architecture system with the batch processing system removed. While the streaming pipeline runs in real time, the batch pipeline is scheduled at a delayed interval to reprocess data for the most accurate results. Kappa architecture is a streaming-first architecture deployment pattern – where data coming from streaming, IoT, batch or near-real time (such as change data capture), is ingested into a messaging system like Apache Kafka. Consumer liest aus einem Topic. ist aber keine wesentliche Eigenschaft dieser Systeme und damit entfällt auch das L. Als Ablösung für ETL wird CTP vorgeschlagen - consume, transform, produce. Datenmengen entwickelt. This solution offers the benefits of Approach 1 while skipping the logistical hassle of having to replay data into a temporary Kafka topic first. Kappa architecture at NTT Com: Building a streaming analytics stack with Druid and Kafka This is a guest post from Paolo Lucente, Big Data Architect @ NTT GIN. Entworfen wurde diese von Jay Kreps, dem Initiator bekannter Big-Data-Technologien wie Kafka und Samza. Much like the. Kreps’ key idea was to replay data into a Kafka stream from a structured data source such as an Apache Hive table. In this instance, while the event is missed by the streaming pipeline, a backfill pipeline with a few days worth of lag can easily attribute this event to its correct session. Such solutions can process data at a massive scale in real time with. It can be deployed with fixed memory. Warum brauche ich - Die Verarbeitung unbeschränkter Mengen und die Kappa-Architektur. Even if we could use extra resources to enable a one-shot backfill for multiple days worth of data, we would need to implement a rate-limiting mechanism for generated data to keep from overwhelming our downstream sinks and consumers who may need to align their backfills with that of our upstream pipeline. This feature allows us to use the same production cluster configuration as the production stateful streaming job instead of throwing extra resources at the backfill job. nutzen und alle Ergebnisse sofort verarbeiten? Mit der Lambda-Architektur wurde ein neuer skalierbarer Umgang mit großen We backfill the dataset efficiently by specifying backfill specific trigger intervals and event-time windows. Since we chose Spark Streaming, an extension of Spark’s API for stream processing that we leverage for our stateful streaming applications, we also had  the option of leveraging the Structured Streaming unified declarative API and reusing the streaming code for a backfill. Ranked in the Fortune Global 500 list, NTT is the fourth largest telecommunications company in the world. 2. z.B. However, this approach requires setting up one-off infrastructure resources (such as dedicated topics for each backfilled Kafka topic) and replaying weeks worth of data into our Kafka cluster. Er lädt die gleichen Daten aus dem Streaming-System nochmal von Anfang an. A backfill pipeline typically re-computes the data after a reasonable window of time has elapsed to account for late-arriving and out-of-order events, such as when a rider waits to rate a driver until their next Uber app session. Stream Data Platform2, aussehen und einen Datensee ersetzen könnte. At ASPGems we choose Apache Spark as our Analytics Engine and not only for Spark Streaming. Essentially, we wanted to replace Kafka reads with performing a Hive query within the event windows in between the triggers. einfaches Polling erreicht werden. Apache Kafka, was auch schon Zu guter Letzt müssen auch schlicht zwei verschiedene Systeme betrieben werden, beide mit völlig Kappa Architecture is a simplification of Lambda Architecture. 4). Die Kernarchitektur bildet ein verteiltes Transaktions-Log. Die Input-Topics werden üblicherweise in Langzeitspeicher im Speed-Layer der Lambda-Architektur genutzt wird) und werden dann mit einem Stream Processing Framework wie Spark Im Gegensatz zu einem “Netz” von Systemen, das kreuz und Wird eine Datenquelle in das feste Data scientists, analysts, and operations managers at Uber began to use our session definition as a canonical session definition when running backwards-looking analyses over large periods of time. Data Lakes. Kappa architecture is a software architecture that mainly focuses on stream processing data. At Uber, we use robust data processing systems such as Apache Flink and Apache Spark to power the streaming applications that helps us calculate up-to-date pricing, enhance driver dispatching, and, . Rather than using a relational DB like SQL or a key-value store like Cassandra, the canonical data store in a Kappa Architecture system is an append-only immutable log. Another challenge with this strategy was that, in practice, it would limit how many days’ worth of data we could effectively replay into a Kafka topic. Beides kann über Kafka Connect geschehen, ein Tool zum Laden von Daten nach und von Kafka, das Frontends, Services, oder Sensoren schreiben ihre Events in Kafka-Topics, die Input-Topics genannt werden. Approach 2: Leverage a unified Dataset API in Spark, , an extension of Spark’s API for stream processing that we leverage for our stateful streaming applications, we also had  the option of leveraging the Structured Streaming. While designing a scalable, seamless system to backfill Uber’s streaming pipeline, we found that implementing Kappa architecture in production is easier said than done. We discovered that a stateful streaming pipeline without a robust backfilling strategy is ill-suited for covering such disparate use cases. For our first iteration of the backfill solution, we considered two approaches: In this strategy, we replayed old events from a structured data source such as a Hive table back into a Kafka topic and re-ran the streaming job over the replayed topic in order to regenerate the data set. verwenden. }, Putting Apache Kafka to Use: A Practical Guide to Building a Stream Data Platform, « Spark Performance-Optimierung - RDDs, Shuffles, Partitionen, Daten modellieren und austauschen mit Apache Avro ». (die Extrahierung) und wir genügen auch den Anforderungen des Prinzips der In a 2014 blog post, Jay Kreps accurately coined the term Kappa architectureby pointing out the pitfalls of the Lambda architecture and proposing a potential software evolution. Jeder Datenstrom wird dabei zum Zeitpunkt des Auftretens und als Event modelliert erfasst. The solution shouldn’t necessitate any additional steps or dedicated code paths. Kafka, he argued, checks all of the boxes required for the Lambda Architecture. Kafka ist dazu entwickelt, Datenströme zu speichern und zu verarbeiten, und stellt eine Schnittstelle zum Laden und Exportieren von Datenströmen zu Drittsystemen bereit. You implement your transformation logic twice, once in the batch system and once in the stream processing system. Thus building a Kappa architecture on cloud may exhibit certain limitations. To counteract these limitations, Apache Kafka’s co-creator, Jay Kreps suggested using a Kappa architecture. Er schreibt die korrekten oder Golden Gate in Oracle. To replace ba… Jeder Strom kann auch jederzeit in ein weiteres System überführt werden. Backfilling more than a handful of days’ worth of data (a frequent occurrence) could easily lead to replaying days’ worth of client logs and trip-level data into Uber’s Kafka self-serve infrastructure all at once, overwhelming the system’s infrastructure and causing lags. HDFS oder S3 geschrieben werden. Following diagram shows one way of implementing Kappa architecture using Kafka and Databricks: [Note] Unfortunately, as of this writing neither Azure nor AWS offers a streaming system (e.g. From years’ research and development experience on data visualization and data analysis, I am very interested on the request/response performance of ad hoc big data query. (sie werden also in andere Daten transformiert), und das Ergebnis wird zurück in die Plattform oder ein Drittsystem auf jeden ihnen erlaubten Strom zugreifen. ein kontinuierlicher Strom von Events. entsteht. In der Lambda-Architektur müssen alle Prozesse zwei mal BigData-Projekten alle verfügbaren unstrukturierten Daten in Rohform in einen Datensee gekippt, in der Hoffnung, ein 4 min read. In order to synthesize both approaches into a solution that suited our needs, we chose to model our new streaming system as a Kappa architecture by modeling a Hive table as a streaming source in Spark, and thereby turning the table into an unbounded stream. Since we’re in backfill mode, we can control the amount of data consumed by one window, allowing us to backfill at a much faster rate than a simply re-running the job with production settings. Kappa Architecture. Lamda Architecture. Moving from Lambda and Kappa Architectures to Kappa+ at Uber Kappa+ is a new approach developed at Uber to overcome the limitations of the Lambda and Kappa architectures. The sheer effort and impracticality of these tasks made the Hive to Kafka replay method difficult to justify implementing at scale in our stack. To support systems that require both the low latency of a streaming pipeline and the correctness of a batch pipeline, many organizations utilize Lambda architectures, a concept first, Leveraging a Lambda architecture allows engineers to reliably backfill a streaming pipeline. Streaming-System, oder wenn sie beispielsweise auf einem Dashboard angezeigt werden sollen, in eine Datenbank. Eine Examples include: 1. Ein persistentes Streaming-System hält die Daten üblicherweise nicht ewig vorrätig. We initially built it to serve low latency features for many advanced modeling use cases powering Uber’s. Umgekehrt interessieren sich auch die By providing low-latency, distributed, event topics it can allow rapid access to events as they occur for real-time processing in a pub/sub pattern. Am Beispiel von Apache Kafka lässt sich eine solche Plattform gut umsetzen. Kurzzeitig wird der Schreibbedarf in die Datenbank dadurch höher, aber Hier wählt man einen Zeitraum, There is a need to process data that arrives at high rates with low latency to get insights fast, and that needs an architecture which allows that. We implemented this solution in Spark Streaming, but other organizations can apply the principles we discovered while designing this system to other streaming processing systems, such as Apache Flink. We implemented these changes to put the stateful streaming job in Figure 1 into backfill mode with a Hive connector. organisatorischer Ebene - Daten aus verschiedensten Quellen können miteinander kombiniert oder korreliert werden. The main premise behind the Kappa Architecture is that you can perform both real-time and batch processing, especially for analytics, with a single technology stack. Konsumenten nur für Datenströme, nicht für andere Systeme, und alle Streams funktionieren nach dem gleichen Prinzip - Das Pendant zur Lambdaarchitektur ist die Kappa-Architektur (Abb. So what is Kappa Architecture The proposal of Jay Kreps is so simple: Use kafka (or other system) that will let you retain the full log of the data you need to reprocess. Die Plattform selbst ist ebenfalls wie ein Strom aufgebaut. In simple terms, the “real time data analytics” means that gather the data, then ingest it and process (analyze) it in nearreal-time. Apache Kafka, was auch schon im Speed-Layer der Lambda-Architektur genutzt wird) und werden dann mit einem Stream Processing Framework wie Spark Streaming, Flink, o.ä. Phobos plugin which saves your kafka events to the database ruby kafka event-sourcing kappa phobos kafka-events kappa-architecture Updated Jan 10, 2019 Wie eingangs erwähnt, werden in vielen Similarly, running a Spark Streaming job in a batch mode (Approach 2) instead of using the unified API presented us with resource constraint issues when backfilling data over multiple days as this strategy was likely to overwhelm downstream sinks and other systems consuming this data. The data which the streaming pipeline produced serves use cases that span dramatically different needs in terms of correctness and latency. Our backfiller computes the windowed aggregations in the order in which they occur. benachrichtigen, so dass diese direkt in die Stream Data Plattform geschrieben werden können, z.B. Our backfiller computes the windowed aggregations in the order in which they occur. Verteilt man die Datenbank auf ein Cluster (durch z.B. Silobildung gibt es nicht. Introducing Base Web, Uber’s New Design System for Building Websites in... Streamific, the Ingestion Service for Hadoop Big Data at Uber Engineering, Uber Engineering’s Micro Deploy: Deploying Daily with Confidence, The Uber Engineering Tech Stack, Part II: The Edge and Beyond. Having established the need for a scalable backfilling strategy for Uber’s stateful streaming pipelines, we reviewed the current state-of-the-art techniques for building a backfilling solution. Die Daten implementiert werden, einmal für Batch und ein mal Realtime. This job has event-time windows of ten seconds, which means that every time the watermark for the job advances by ten seconds, it triggers a window, and the output of each window is persisted to the internal state store. Dafür hat sich Apache Avro in meinen Projekten bewährt. Jedem Stream wird dann ein festes Schema zugeordnet, damit jeder Konsument genau There are a lot of variat… Es bietet eine einfache Um bei einem Programmierfehler die Daten erneut zu verarbeiten, wird der korrigierte Streaming-Job parallel zum Ein Producer schreibt in ein Topic, ein Kafka Streams oder Spark Streaming, Data sources. How we use Kappa Architecture At the end, Kappa Architecture is design pattern for us. For example, it should work equally well with stateful or stateless applications, as well as event-time windows, processing-time windows, and session windows. das Oplog in MongoDB Die Position eines Consumers im Topic, der Offset, wird gespeichert, so dass bei einem Ein Nachteil der Lambda-Architektur ist ihre Komplexität.A drawback to the lambda architecture is its complexity. Die abgeleiteten Topics werden in Datenbanken geschrieben, die wiederum von Dashboards und sonstigen Landen diese klar definierten Daten nun direkt in einer zentralen Streaming Plattform, können unterschiedliche Dienste In der Kappa-Architektur landen sämtliche Daten in einem zentralen Streaming-System (z.B. document.getElementById('rss-feed-btn').onclick = function() { All big data solutions start with one or more data sources. Modellierungssprache, ein Serialisierungssystem, und unterstützt Schema-Evolution. In this strategy, we replayed old events from a structured data source such as a Hive table back into a Kafka topic and re-ran the streaming job over the replayed topic in order to regenerate the data set. Event-time windowing operations and watermarking should work the same way in the backfill and the production job. The solution shouldn’t necessitate any additional steps or dedicated code paths. jeweiligen Streams modelliert werden. quer Daten miteinander austauscht, fließen die Daten in eine Stream Data Plattform, werden dort verarbeitet, und This setup then simply reruns the streaming job on these replayed Kafka topics, achieving a unified codebase between both batch and streaming pipelines and production and backfill use cases. stammt von 2014 und empfiehlt noch, je nach Anforderung an Latenz entweder ein Batch- oder ein Realtime-System zu Plattform, die alle Daten sammelt und als Ströme zur Verfügung stellt. verarbeitet. In der Kappa-Architektur landen sämtliche Daten in einem zentralen Streaming-System (z.B. instead of using the unified API presented us with resource constraint issues when backfilling data over multiple days as this strategy was likely to overwhelm downstream sinks and other systems consuming this data. , and the emergence of these systems over the past several years has unlocked an industry-wide ability to write streaming data processing applications at low latencies, a functionality previously impossible to achieve at scale. Much like the Kafka source in Spark, our streaming Hive source fetches data at every trigger event from a Hive table instead of a Kafka topic. Tweets are ingested from Kafka; Trident (STORM) saves data to HDFS Trident (STORM) computes counts and stores them in memory; Hadoop MapReduce procesess files on HDFS and generates others with counts of hashtags by date After testing our approaches, and deciding on a combination of these two methods, we settled on the following principles for building our solution: Preserving the windowing and watermarking semantics of the original streaming job while running in backfill mode (the principle we outlined in the third point, above) allows us to ensure correctness by running events in the order they occur. However, this approach requires setting up one-off infrastructure resources (such as dedicated topics for each backfilled Kafka topic) and replaying weeks worth of data into our Kafka cluster. For example, we can  take one day to backfill a few day’s worth of data. To support systems that require both the low latency of a streaming pipeline and the correctness of a batch pipeline, many organizations utilize Lambda architectures, a concept first proposed by Nathan Marz. Similarly, running a Spark Streaming job in a batch mode (Approach 2). While a lot of literature exists describing how to build a Kappa architecture, there are few use cases that describe how to successfully pull it off in production. After testing our approaches, and deciding on a combination of these two methods, we settled on the following principles for building our solution: 1. In this blog post we have presented two example applications for Lambda and Kappa architectures, respectively. Many guides on the topic omit discussion around performance-cost calculations that engineers need to consider when making an architectural decision, especially since Kafka and YARN clusters have limited resources. How we use Kappa Architecture We use Kafka as Stream Data Platform Instead of Samza we feel more comfortable with Spark Streaming. Kappa-Architekturen sind der nächste Evolutionsschritt im Fast-Data-Umfeld. Both of the two most common methodologies, replaying data to Kafka from Hive and backfilling as a batch job didn’t scale to our data velocity or require too many cluster resources. Static files produced by applications, such as we… The sheer effort and impracticality of these tasks made the Hive to Kafka replay method difficult to justify implementing at scale in our stack. While this strategy achieves maximal code reuse, it falters when trying to backfill data over long periods of time. produziert, muss nichts über die Systeme wissen, die die Daten konsumieren. We reviewed and tested these two approaches, but found neither scalable for our needs; instead, we decided to combine them by finding a way to leverage the best features of these solutions for our backfiller while mitigating their downsides. However, since streaming systems are inherently unable to guarantee event order, they must make trade-offs in how they handle late data. Gather data – In this stage, a system should connect to source of the raw data; which is commonly referred as source feeds. We use/clone this pattern in almost our projects. Such solutions can process data at a massive scale in real time with exactly-once semantics, and the emergence of these systems over the past several years has unlocked an industry-wide ability to write streaming data processing applications at low latencies, a functionality previously impossible to achieve at scale. Der Artikel Writing an idempotent replayer would have been tricky, since we would have had to ensure that replayed events were replicated in the new Kafka topic in roughly the same order as they appeared in the original Kafka topic. Typically, streaming systems mitigate this out-of-order problem by using event-time windows and watermarking. it is possible to have real-time analysis for domain-agonistic big data. but it also requires maintaining two disparate codebases, one for batch and one for streaming. Apache Kafka ist ein Open-Source-Software-Projekt der Apache Software Foundation, das insbesondere der Verarbeitung von Datenströmen dient. A backfill pipeline is thus not only useful to counter delays, but also to fill minor inconsistencies and holes in data caused by the streaming pipeline. Die Daten As a result, we found that the best approach was modeling our Hive connector as a streaming source. You stitch together the results from both systems at query time to produce a complete answer.

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