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Retry Design Pattern for Microservices Tutorial with Examples for API Developers
Full Video Link https://youtu.be/sli5D29nCw4 Hello friends, new #video on #retrypattern #designpattern for #microservices #tutorial for #api #developer #programmers is published on #codeonedigest #youtube channel. @java #java #aws #awsclo
In this video we will learn about Retry design pattern for microservices. In a microservices architecture, the retry pattern is a common pattern for recovering from transient errors. An application lost connectivity for a short period of time. A component is unavailable for a short period of time. This usually happens during maintenance or automatic recovery from a crash. A component is…
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Mercado Livre está recrutando 380 novos profissionais para preencher vagas de emprego ao redor do Brasil
O Mercado Livre, empresa de tecnologia que oferece soluções de comércio eletrônico, está em busca de profissionais em várias regiões do Brasil. As vagas de emprego estão abertas para engenheiro pleno de software, desenvolvedor sênior de backend, analista pleno de planejamento, supervisor de operações, auxiliar de logísticas, entre outras funções. São mais de 380 vagas abertas para candidatos de nível médio e superior, existem ainda oportunidades disponíveis para pessoas com deficiência (PcD).
Vagas ofertadas pelo Mercado Livre
Veja abaixo algumas vagas de emprego disponíveis:
Engenheiro de software de backend – Sudeste
Necessário ter trabalhado com ambientes de microsserviços e sistemas distribuídos, o profissional deve possuir experiência com linguagens Java ou Golang, sendo um diferencial dominar tecnologias como NodeJs, New Relic, Datadog e afins, conhecer as metodologias ágeis, como Scrum e Kanban, os sistemas assíncronos e as mensageiras.
Desenvolvedor sênior de backend para tecnologia – Sul
Necessário ter trabalhado com qualidade, segurança e tecnologias, incluindo Clean Code, projeto de segurança de aplicativo Web aberto, testes unitários e funcionais e padrões de design de microsserviços, como Retry Pattern e Circuit Breaker, contar com experiência no desenvolvimento de Backend em ambientes de alto desempenho e escalabilidade vertical e horizontal, com foco em métricas de latência, rendimento e cache.
Supervisor de operações – Sudeste
Necessário possuir formação superior completa em administração de empresas, engenharia ou logística, ter experiência na gestão de centros de distribuição, desejável estar disponível para atuar presencialmente no segundo ou terceiro turno em Cajamar, no estado de São Paulo.
Analista pleno de planejamento da Last Mile – Nordeste
Necessário possuir formação superior completa, ter experiência no desenvolvimento e na gestão de projetos, sendo desejável ter trabalhado em planejamento financeiro e redução de custos operacionais, também é exigido conhecimentos sobre as ferramentas do Office e Google, incluindo Google Sheets, Excel, Tableau, Data Studio e a construção de Dashboards. O profissional deve ter disponibilidade para atuar na regional norte, nordeste ou centro-oeste, no horário das 09:00 às 18:48 horas, de segunda a sexta.
Líder de equipe – Sudeste
Necessário possuir formação superior completa em engenharia, logística, administração de empresas, tecnologia, gestão de processos e de produção ou similares, desejável experiência na liderança de equipes operacionais próprias e terceirizadas e na gestão de pessoas, assim como em logística, gemba, indústria, varejo, e-commerce, transportes, distribuição, inbound, triagem e ou expedição. O profissional deve ter disponibilidade para atuar em Avaré, no interior do estado de São Paulo.
Auxiliar de logística – Sudeste
Necessário ter ensino médio completo e ter competido em equipe, com comprovada experiência para fazer seu trabalho da melhor maneira possível. O profissional deve contar com conhecimentos sobre a qualidade do produto a ser entregue e como se conectar com o usuário, e estar disponível para trabalhar presencialmente em Perus, no estado de São Paulo.
Executivo de vendas de campo – Norte
Necessário possuir formação superior completa ou estar cursando, além de contar com conhecimentos de nível intermediário sobre o Pacote Office, sistemas, chats, bots, ferramentas de busca e aplicativos. O candidato deve ter experiência e disponibilidade para atuar de porta em porta é imprescindível, sendo necessário possuir veículo próprio e morar na região de Uberlândia, Uberaba e Araguari, no estado de Minas Gerais.
Participe do processo seletivo do Mercado Livre
Os candidatos interessados em concorrer às vagas de emprego mencionadas deverão acessar a página de carreiras do Mercado Livre, no site é possível encontrar a lista completa de cargos disponíveis. Para confirmar a participação no processo seletivo é necessário preencher os solicitado no site da empresa.
Benefícios e vantagens de atuar no Mercado Livre
Os candidatos selecionados para trabalhar no Mercado Livre terão direito a diversos benefícios, dentre eles:
Plano de saúde;
Vale-alimentação;
Vale-transporte;
Auxílio-creche;
Previdência privada;
Plano odontológico;
Seguro de vida;
Telefone da empresa;
Vantagens e descontos.
Conheça o Mercado Livre
Em março de 1999, Marcos Galperin, co-fundador e CEO, enquanto terminava seu MBA na escola de negócios da universidade Stanford, escreveu o plano de negócios do MercadoLivre e começou a formar uma equipe de profissionais para colocá-lo em execução, as operações do Mercado Livre na Argentina começaram em agosto de 1999, alcançando posteriormente outros países da América Latina. A versão brasileira do site surgiu em 1999, com Stelleo Tolda como presidente do Mercado Livre no Brasil.
O post Mercado Livre está recrutando 380 novos profissionais para preencher vagas de emprego ao redor do Brasil apareceu primeiro em Petrosolgas.
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RxJava support 2.0 releases support for Android apps
The RxJava team has released version 2.0 of their reactive Java framework, after an 18 month development cycle. RxJava is part of the ReactiveX family of libraries and frameworks, which is in their words, "a combination of the best ideas from the Observer pattern, the Iterator pattern, and functional programming". The project's "What's different in 2.0" is a good guide for developers already familiar with RxJava 1.x.
RxJava 2.0 is a brand new implementation of RxJava. This release is based on the Reactive Streams specification, an initiative for providing a standard for asynchronous stream processing with non-blocking back pressure, targeting runtime environments (JVM and JavaScript) as well as network protocols.
Reactive implementations have concepts of publishers and subscribers, as well as ways to subscribe to data streams, get the next stream of data, handle errors and close the connection.
The Reactive Streams spec will be included in JDK 9 as java.util.concurrent.Flow. The following interfaces correspond to the Reactive Streams spec. As you can see, the spec is small, consisting of just four interfaces:
· Flow.Processor<T,R>: A component that acts as both a Subscriber and Publisher.
· Flow.Publisher<T>: A producer of items (and related control messages) received by Subscribers.
· Flow.Subscriber<T>: A receiver of messages.
· Flow.Subscription: Message control linking a Flow.Publisher and Flow.Subscriber.
Spring Framework 5 is also going reactive. To see how this looks, refer to Josh Long's Functional Reactive Endpoints with Spring Framework 5.0.
To learn more about RxJava 2.0, InfoQ interviewed main RxJava 2.0 contributor, David Karnok.
InfoQ: First of all, congrats on RxJava 2.0! 18 months in the making, that's quite a feat. What are you most proud of in this release?
David Karnok: Thanks! In some way, I wish it didn't take so long. There was a 10 month pause when Ben Christensen, the original author who started RxJava, left and there was no one at Netflix to push this forward. I'm sure many will contest that things got way better when I took over the lead role this June. I'm proud my research into more advanced and more performant Reactive Streams paid off and RxJava 2 is the proof all of it works.
InfoQ: What’s different in RxJava 2.0 and how does it help developers?
Karnok: There are a lot of differences between version 1 and 2 and it's impossible to list them all here ,but you can visit the dedicated wiki page for a comprehensible explanation. In headlines, we now support the de-facto standard Reactive Streams specification, have significant overhead reduction in many places, no longer allow nulls, have split types into two groups based on support of or lack of backpressure and have explicit interop between the base reactive types.
InfoQ: Where do you see RxJava used the most (e.g. IoT, real-time data processing, etc.)?
Karnok: RxJava is more dominantly used by the Android community, based on the feedback I saw. I believe the server side is dominated by Project Reactor and Akka at the moment. I haven't specifically seen IoT mentioned or use RxJava (it requires Java), but maybe they use some other reactive library available on their platform. For real-time data processing people still tend to use other solutions, most of them not really reactive, and I'm not aware of any providers (maybe Pivotal) who are pushing for reactive in this area.
InfoQ: What benefits does RxJava provide Android more than other environments that would explain the increased traction?
Karnok: As far as I see, Android wanted to "help" their users solving async and concurrent problems with Android-only tools such as AsyncTask, Looper/Handler etc.
Unfortunately, their design and use is quite inconvenient, often hard to understand or predict and generally brings frustration to Android developers. These can largely contribute to callback hell and the difficulty of keeping async operations off the main thread.
RxJava's design (inherited from the ReactiveX design of Microsoft) is dataflow-oriented and orthogonalized where actions execute from when data appears for processing. In addition, error handling and reporting is a key part of the flows. With AsyncTask, you had to manually work out the error delivery pattern and cancel pending tasks, whereas RxJava does that as part of its contract.
In practical terms, having a flow that queries several services in the background and then presents the results in the main thread can be expressed in a few lines with RxJava (+Retrofit) and a simple screen rotation will cancel the service calls promptly.
This is a huge productivity win for Android developers, and the simplicity helps them climb the steep learning curve the whole reactive programming's paradigm shift requires. Maybe at the end, RxJava is so attractive to Android because it reduces the "time-to-market" for individual developers, startups and small companies in the mobile app business.
There was nothing of a comparable issue on the desktop/server side Java, in my opinion, at that time. People learned to fire up ExecutorService's and wait on Future.get(), knew about SwingUtilities.invokeLater to send data back to the GUI thread and otherwise the Servlet API, which is one thread per request only (pre 3.0) naturally favored blocking APIs (database, service calls).
Desktop/server folks are more interested in the performance benefits a non-blocking design of their services offers (rather than how easy one can write a service). However, unlike Android development, having just RxJava is not enough and many expect/need complete frameworks to assist their business logic as there is no "proper" standard for non-blocking web services to replace the Servlet API. (Yes, there is Spring (~Boot) and Play but they feel a bit bandwagon-y to me at the moment).
InfoQ: HTTP is a synchronous protocol and can cause a lot of back pressure when using microservices. Streaming platforms like Akka and Apache Kafka help to solve this. Does RxJava 2.0 do anything to allow automatic back pressure?
Karnok: RxJava 2's Flowable type implements the Reactive Streams interface/specification and does support backpressure. However, the Java level backpressure is quite different from the network level backpressure. For us, backpressure means how many objects to deliver through the pipeline between different stages where these objects can be non uniform in type and size. On the network level one deals with usually fixed size packets, and backpressure manifests via the lack of acknowledgement of previously sent packets. In classical setup, the network backpressure manifests on the Java level as blocking calls that don't return until all pieces of data have been written. There are non-blocking setups, such as Netty, where the blocking is replaced by implicit buffering, and as far as I know there are only individual, non-uniform and non Reactive Streams compatible ways of handling those (i.e., a check for canWrite which one has to spin over/retry periodically). There exist libraries that try to bridge the two worlds (RxNetty, some Spring) with varying degrees of success as I see it.
InfoQ: Do you think reactive frameworks are necessary to handle large amounts of traffic and real-time data?
Karnok: It depends on the problem complexity. For example, if your task is to count the number of characters in a big-data source, there are faster and more dedicated ways of doing that. If your task is to compose results from services on top of a stream of incoming data in order to return something detailed, reactive-based solutions are quite adequate. As far as I know, most libraries and frameworks around Reactive Streams were designed for throughput and not really for latency. For example, in high-frequency trading, the predictable latency is very important and can be easily met by Aeron but not the main focus for RxJava due to the unpredictable latency behavior.
InfoQ: Does HTTP/2 help solve the scalability issues that HTTP/1.1 has?
Karnok: This is not related to RxJava and I personally haven't played with HTTP/2 but only read the spec. Multiplexing over the same channel is certainly a win in addition to the support for explicit backpressure (i.e., even if the network can deliver, the client may still be unable to process the data in that volume) per stream. I don't know all the technical details but I believe Spring Reactive Web does support HTTP/2 transport if available but they hide all the complexity behind reactive abstractions so you can express your processing pipeline in RxJava 2 and Reactor 3 terms if you wish.
InfoQ: Java 9 is projected to be featuring some reactive functionality. Is that a complete spec?
Karnok: No. Java 9 will only feature 4 Java interfaces with 7 methods total. No stream-like or Rx-like API on top of that nor any JDK features built on that.
InfoQ: Will that obviate the need for RxJava if it is built right into the JDK?
Karnok: No and I believe there's going to be more need for a true and proven library such as RxJava. Once the toolchains grow up to Java 9, we will certainly provide adapters and we may convert (or rewrite) RxJava 3 on top of Java 9's features (VarHandles).
One of my fears is that once Java 9 is out, many will try to write their own libraries (individuals, companies) and the "market" gets diluted with big branded-low quality solutions, not to mention the increased amount of "how does RxJava differ from X" questions.
My personal opinion is that this is already happening today around Reactive Streams where certain libraries and frameworks advertise themselves as RS but fail to deliver based on it. My (likely biased) conjecture is that RxJava 2 is the closest library/technology to an optimal Reactive Streams-based solution that can be.
InfoQ: What's next for RxJava?
Karnok: We had fantastic reviewers, such as Jake Wharton, during the development of RxJava 2. Unfortunately, the developer previews and release candidates didn't generate enough attention and despite our efforts, small problems and oversights slipped into the final release. I don't expect major issues in the coming months but we will keep fixing both version 1 and 2 as well as occasionally adding new operators to support our user base. A few companion libraries, such as RxAndroid, now provide RxJava 2 compatible versions, but the majority of the other libraries don't yet or haven't yet decided how to go forward. In terms of RxJava, I plan to retire RxJava 1 within six months (i.e., only bugfixes then on), partly due to the increasing maintenance burden on my "one man army" for some time now; partly to "encourage" the others to switch to the RxJava 2 ecosystem. As for RxJava 3, I don't have any concrete plans yet. There are ongoing discussions about splitting the library along types or along backpressure support as well as making the so-called operator-fusion elements (which give a significant boost to our performance) a standard extension of the Reactive Streams specification.
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Cloud scale applications typically require high concurrency to achieve desired performance when accessing remote data. The new Storage Java SDK simplifies building such applications by offering asynchronous operations, eliminating the need to create and manage a large thread-pool. This new SDK uses the RxJava reactive programming model for asynchronous operations, also relying on Netty HTTP client for REST requests. Get started with the Azure Storage SDK for Java now.
Azure Storage SDK v10 for Java adopts the next-generation Storage SDK design providing thread-safe types that were introduced earlier with the Storage Go SDK release. This new SDK is built to effectively move data without any buffering on the client, and provides interfaces close to the ones in the Storage REST APIs. Some of the improvements in the new SDK are:
Asynchronous programming model with RxJava
Low-level APIs consistent with Storage REST APIs
New high-level APIs built for convenience
Thread-safe interfaces
Consistent versioning across all Storage SDKs
Asynchronous programming model with RxJava
Now that the Storage SDK supports RxJava it is easier to build event driven applications. This is because it allows you to compose sequences together with the observer pattern. The following sample, that uploads a directory of xml files as they are found, depicts this pattern:
// Walk the directory and filter for .xml files Stream walk = Files.walk(filePath).filter(p -> p.toString().endsWith(".xml")); // Upload files found asynchronously into Blob storage in 20 concurrent operations Observable.fromIterable(() -> walk.iterator()).flatMap(path -> { BlockBlobURL blobURL = containerURL.createBlockBlobURL(path.getFileName().toString()); FileChannel fc = FileChannel.open(path); return TransferManager.uploadFileToBlockBlob( fc, blobURL, BlockBlobURL.MAX_PUT_BLOCK_BYTES, null) .toObservable() .doOnError(throwable -> { if (throwable instanceof RestException) { System.out.println("Failed to upload " + path + " with error:" + ((RestException) throwable).response().statusCode()); } else { System.out.println(throwable.getMessage()); } }) .doAfterTerminate(() -> { System.out.println("Upload of " + path + " completed"); fc.close(); }); }, 20) // Max concurrency of 20 - this is usually determined based on the number of cores you have in your environment .subscribe();
The full sample is located at the Azure Storage Java SDK samples repository.
The sample above calls TransferManager.uploadFileToBlockBlob, a high-level API, as the Observable emits signals, in this case java.nio.file.Path type. By using flatMap, we can configure the maximum concurrent connections, which is set to 20 in this example. If we were to upload these files using the Azure Storage SDK v7, we would have to create threads (up to 20) and manage them, whereas in the example above RxJava manages the threadpool uploading the same data set concurrently in a lot fewer threads, which is more resource efficient.
For more information, read about RxJava and Reactive programming model.
Low-level APIs consistent with storage REST APIs
Low-level APIs exist on the URL types, e.g. BlockBlobURL, and are designed to be simple wrappers around the REST APIs providing convenience but no hidden behavior. Each call to these low-level APIs guarantees exactly one REST request sent (excluding retries). Further, the names on these types have been updated to make their behavior more clear. For example, PutBlob is now Upload, PutBlock is now StageBlock, and PutBlockList is now CommitBlockList, and more.
BlockBlobURL blobURL = containerURL.createBlockBlobURL("mysampledata"); String data = "Hello world!"; blobURL.upload(Flowable.just(ByteBuffer.wrap(data.getBytes())), data.length(), null, null, null) .subscribe(blockBlobsUploadResponse -> { System.out.println("Status code: " + blockBlobsUploadResponse.statusCode()); }, throwable -> { System.out.println("Throwable: " + throwable.getMessage()); });
New high-level APIs, built for convenience
The TransferManager class is where we provide convenient high-level APIs that internally call the other lower-level APIs. For example, the uploadFileToBlockBlob method can upload a 1GB file by internally making 10 x StageBlock calls (with each block configured as 100MB in size) followed by 1 call to CommitBlockList to atomically commit the uploaded blocks in the Blob service.
Single response = TransferManager.uploadFileToBlockBlob( FileChannel.open(filePath), blobURL, BlockBlobURL.MAX_PUT_BLOCK_BYTES, null) .doOnError(throwable -> { if (throwable instanceof RestException) { System.out.println("Failed to upload " + filePath + " with error:" + ((RestException) throwable).response().statusCode()); } else { System.out.println(throwable.getMessage()); } }) .doAfterTerminate(() -> System.out.println()); response.subscribe(commonRestResponse -> {System.out.println(commonRestResponse.statusCode());});
Thread-safe interfaces
Earlier Storage SDKs (version 9 or earlier) offered objects such as CloudBlockBlob and CloudBlobContainer which weren’t thread safe and were mutable in such a way that could result in issues during runtime. The new Storage SDKs (v10 or above) provide interfaces closer to the Storage REST APIs and most of the associated objects are immutable allowing them to be shared.
For instance, when you want to perform an operation on a blob (e.g., https://ift.tt/2JWvDpm), you construct a BlockBlobURL object with the Blob URI and all the associated REST API operations are methods of that type. You can call Upload, StageBlock and CommitBlockList on that URI object. These methods all return a Single (io.reactivex.Single) of RestResponse<THeaders, TBody> that wraps the REST API response, which is immutable. It does not modify the created instance of the BlockBlobURL type.
New Storage SDK versions
The new SDKs follow a new versioning strategy that is tied to the Storage Service REST API version. Version 10, the current release, will be tied to the Storage REST API version 2018-03-28. All the new Storage SDKs across all programming languages will use V10 for REST API 2018-03-28 release so it is easy for you to navigate through different versions. When a new SDK is released supporting the next REST API release, its major version will be bumped (v10 to V11 for instance) regardless of any breaking changes in the client. This is mainly due to possible behavior changes in the Service when moving from one REST API version to another.
Versions earlier than 10 will be reserved for the older Storage SDK design. Any Storage SDK with the version 10 or later will adopt the new SDK design.
Get started now
To get started with the Azure Storage SDK v10 for Java, use the following Blob maven package (File and Queue coming soon).
<dependency> <groupid>com.microsoft.azure</groupid> <artifactid>azure-storage-blob</artifactid> <version>10.0.1-Preview</version> </dependency>
Here are a few helpful links to help you get started:
Roadmap
Azure Storage SDK v10 for Java is currently in Preview and supports Blob storage only. We’ll be releasing a few updates soon adding more functionality based on user feedback. So please check it out and let us know your feedback on GitHub. Here are a few of the major changes that are scheduled for a release soon:
Support for 2018-03-28 coming very soon
Support for Queue, File services
GA release
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The post Build richer applications with the new asynchronous Azure Storage SDK for Java | Blog appeared first on MusicCosmoS.
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Retry Design Pattern for Microservices Tutorial with Examples for API De... Full Video Link https://youtu.be/sli5D29nCw4Hello friends, new #video on #retrypattern #designpattern for #microservices #tutorial for #api #developer #programmers is published on #codeonedigest #youtube channel. @java #java #aws #awscloud @awscloud @AWSCloudIndia #salesforce #Cloud #CloudComputing @YouTube #youtube #azure #msazure #microservices #circuitbreaker #microservicesarchitecture #microservicestutorial #whataremicroservices #retrydesignpattern #retrydesignpatternjava #retrydesignpatternc# #retrypattern #retrypatternvscircuitbreaker #retrypatternjava #retrypatternc# #retrypatternjavascript #retrypatternspringboot #retrypatternmicroservices #microservicepatterns #microservicedesignpatterns #microservicedesignpatternsspringboot #circuitbreakerpattern #retry
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A successful design pattern for consuming RESTful web services in Java
One of the most common tasks is to consume RESTful web services in Java, in this post I will explain a design pattern using Jersey client.
Step 1 : Configuration
The project will include the dependencies described in the following pom.xml, as you can see there we have the following support:
Jackson : For serialization and deserialization
slf4j : For logging
failsafe: Api for retry logic
And at…
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Full Stack Java Developer (API) (QR5101) - Qualogy B.V.
#vacature #ict Profile: - A Bachelor and/or Master degree in Information Technology - At least basic knowledge of Continuous Delivery - At least 6 years of Java development experience (knowledge of Scala is an advantage) - A firm grasp of modern API design - Experience in developing front-end frameworks such as AngularJS or Polymer - Basic knowledge of resilience patterns such as failover, retry, and load balancing - You lead others, or others look to you for guidance - Excellent communication and problem-solving skills - http://dlvr.it/Q0tW82
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RX JAVA 2.0 releases support for Android
The RxJava team has released version 2.0 of their reactive Java framework, after an 18 month development cycle. RxJava is part of the ReactiveX family of libraries and frameworks, which is in their words, "a combination of the best ideas from the Observer pattern, the Iterator pattern, and functional programming". The project's "What's different in 2.0" is a good guide for developers already familiar with RxJava 1.x.
RxJava 2.0 is a brand new implementation of RxJava. This release is based on the Reactive Streams specification, an initiative for providing a standard for asynchronous stream processing with non-blocking back pressure, targeting runtime environments (JVM and JavaScript) as well as network protocols.
Reactive implementations have concepts of publishers and subscribers, as well as ways to subscribe to data streams, get the next stream of data, handle errors and close the connection.
The Reactive Streams spec will be included in JDK 9 as java.util.concurrent.Flow. The following interfaces correspond to the Reactive Streams spec. As you can see, the spec is small, consisting of just four interfaces:
· Flow.Processor<T,R>: A component that acts as both a Subscriber and Publisher.
· Flow.Publisher<T>: A producer of items (and related control messages) received by Subscribers.
· Flow.Subscriber<T>: A receiver of messages.
· Flow.Subscription: Message control linking a Flow.Publisher and Flow.Subscriber.
Spring Framework 5 is also going reactive. To see how this looks, refer to Josh Long's Functional Reactive Endpoints with Spring Framework 5.0.
To learn more about RxJava 2.0, InfoQ interviewed main RxJava 2.0 contributor, David Karnok.
InfoQ: First of all, congrats on RxJava 2.0! 18 months in the making, that's quite a feat. What are you most proud of in this release?
David Karnok: Thanks! In some way, I wish it didn't take so long. There was a 10 month pause when Ben Christensen, the original author who started RxJava, left and there was no one at Netflix to push this forward. I'm sure many will contest that things got way better when I took over the lead role this June. I'm proud my research into more advanced and more performant Reactive Streams paid off and RxJava 2 is the proof all of it works.
InfoQ: What’s different in RxJava 2.0 and how does it help developers?
Karnok: There are a lot of differences between version 1 and 2 and it's impossible to list them all here ,but you can visit the dedicated wiki page for a comprehensible explanation. In headlines, we now support the de-facto standard Reactive Streams specification, have significant overhead reduction in many places, no longer allow nulls, have split types into two groups based on support of or lack of backpressure and have explicit interop between the base reactive types.
InfoQ: Where do you see RxJava used the most (e.g. IoT, real-time data processing, etc.)?
Karnok: RxJava is more dominantly used by the Android community, based on the feedback I saw. I believe the server side is dominated by Project Reactor and Akka at the moment. I haven't specifically seen IoT mentioned or use RxJava (it requires Java), but maybe they use some other reactive library available on their platform. For real-time data processing people still tend to use other solutions, most of them not really reactive, and I'm not aware of any providers (maybe Pivotal) who are pushing for reactive in this area.
InfoQ: What benefits does RxJava provide Android more than other environments that would explain the increased traction?
Karnok: As far as I see, Android wanted to "help" their users solving async and concurrent problems with Android-only tools such as AsyncTask, Looper/Handler etc.
Unfortunately, their design and use is quite inconvenient, often hard to understand or predict and generally brings frustration to Android developers. These can largely contribute to callback hell and the difficulty of keeping async operations off the main thread.
RxJava's design (inherited from the ReactiveX design of Microsoft) is dataflow-oriented and orthogonalized where actions execute from when data appears for processing. In addition, error handling and reporting is a key part of the flows. With AsyncTask, you had to manually work out the error delivery pattern and cancel pending tasks, whereas RxJava does that as part of its contract.
In practical terms, having a flow that queries several services in the background and then presents the results in the main thread can be expressed in a few lines with RxJava (+Retrofit) and a simple screen rotation will cancel the service calls promptly.
This is a huge productivity win for Android developers, and the simplicity helps them climb the steep learning curve the whole reactive programming's paradigm shift requires. Maybe at the end, RxJava is so attractive to Android because it reduces the "time-to-market" for individual developers, startups and small companies in the mobile app business.
There was nothing of a comparable issue on the desktop/server side Java, in my opinion, at that time. People learned to fire up ExecutorService's and wait on Future.get(), knew about SwingUtilities.invokeLater to send data back to the GUI thread and otherwise the Servlet API, which is one thread per request only (pre 3.0) naturally favored blocking APIs (database, service calls).
Desktop/server folks are more interested in the performance benefits a non-blocking design of their services offers (rather than how easy one can write a service). However, unlike Android development, having just RxJava is not enough and many expect/need complete frameworks to assist their business logic as there is no "proper" standard for non-blocking web services to replace the Servlet API. (Yes, there is Spring (~Boot) and Play but they feel a bit bandwagon-y to me at the moment).
InfoQ: HTTP is a synchronous protocol and can cause a lot of back pressure when using microservices. Streaming platforms like Akka and Apache Kafka help to solve this. Does RxJava 2.0 do anything to allow automatic back pressure?
Karnok: RxJava 2's Flowable type implements the Reactive Streams interface/specification and does support backpressure. However, the Java level backpressure is quite different from the network level backpressure. For us, backpressure means how many objects to deliver through the pipeline between different stages where these objects can be non uniform in type and size. On the network level one deals with usually fixed size packets, and backpressure manifests via the lack of acknowledgement of previously sent packets. In classical setup, the network backpressure manifests on the Java level as blocking calls that don't return until all pieces of data have been written. There are non-blocking setups, such as Netty, where the blocking is replaced by implicit buffering, and as far as I know there are only individual, non-uniform and non Reactive Streams compatible ways of handling those (i.e., a check for canWrite which one has to spin over/retry periodically). There exist libraries that try to bridge the two worlds (RxNetty, some Spring) with varying degrees of success as I see it.
InfoQ: Do you think reactive frameworks are necessary to handle large amounts of traffic and real-time data?
Karnok: It depends on the problem complexity. For example, if your task is to count the number of characters in a big-data source, there are faster and more dedicated ways of doing that. If your task is to compose results from services on top of a stream of incoming data in order to return something detailed, reactive-based solutions are quite adequate. As far as I know, most libraries and frameworks around Reactive Streams were designed for throughput and not really for latency. For example, in high-frequency trading, the predictable latency is very important and can be easily met by Aeron but not the main focus for RxJava due to the unpredictable latency behavior.
InfoQ: Does HTTP/2 help solve the scalability issues that HTTP/1.1 has?
Karnok: This is not related to RxJava and I personally haven't played with HTTP/2 but only read the spec. Multiplexing over the same channel is certainly a win in addition to the support for explicit backpressure (i.e., even if the network can deliver, the client may still be unable to process the data in that volume) per stream. I don't know all the technical details but I believe Spring Reactive Web does support HTTP/2 transport if available but they hide all the complexity behind reactive abstractions so you can express your processing pipeline in RxJava 2 and Reactor 3 terms if you wish.
InfoQ: Java 9 is projected to be featuring some reactive functionality. Is that a complete spec?
Karnok: No. Java 9 will only feature 4 Java interfaces with 7 methods total. No stream-like or Rx-like API on top of that nor any JDK features built on that.
InfoQ: Will that obviate the need for RxJava if it is built right into the JDK?
Karnok: No and I believe there's going to be more need for a true and proven library such as RxJava. Once the toolchains grow up to Java 9, we will certainly provide adapters and we may convert (or rewrite) RxJava 3 on top of Java 9's features (VarHandles).
One of my fears is that once Java 9 is out, many will try to write their own libraries (individuals, companies) and the "market" gets diluted with big branded-low quality solutions, not to mention the increased amount of "how does RxJava differ from X" questions.
My personal opinion is that this is already happening today around Reactive Streams where certain libraries and frameworks advertise themselves as RS but fail to deliver based on it. My (likely biased) conjecture is that RxJava 2 is the closest library/technology to an optimal Reactive Streams-based solution that can be.
InfoQ: What's next for RxJava?
Karnok: We had fantastic reviewers, such as Jake Wharton, during the development of RxJava 2. Unfortunately, the developer previews and release candidates didn't generate enough attention and despite our efforts, small problems and oversights slipped into the final release. I don't expect major issues in the coming months but we will keep fixing both version 1 and 2 as well as occasionally adding new operators to support our user base. A few companion libraries, such as RxAndroid, now provide RxJava 2 compatible versions, but the majority of the other libraries don't yet or haven't yet decided how to go forward. In terms of RxJava, I plan to retire RxJava 1 within six months (i.e., only bugfixes then on), partly due to the increasing maintenance burden on my "one man army" for some time now; partly to "encourage" the others to switch to the RxJava 2 ecosystem. As for RxJava 3, I don't have any concrete plans yet. There are ongoing discussions about splitting the library along types or along backpressure support as well as making the so-called operator-fusion elements (which give a significant boost to our performance) a standard extension of the Reactive Streams specification.
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Text
The RxJava team has released version 2.0 of their reactive Java framework
The RxJava team has released version 2.0 of their reactive Java framework, after an 18 month development cycle. RxJava is part of the ReactiveX family of libraries and frameworks, which is in their words, "a combination of the best ideas from the Observer pattern, the Iterator pattern, and functional programming". The project's "What's different in 2.0" is a good guide for developers already familiar with RxJava 1.x.
RxJava 2.0 is a brand new implementation of RxJava. This release is based on the Reactive Streams specification, an initiative for providing a standard for asynchronous stream processing with non-blocking back pressure, targeting runtime environments (JVM and JavaScript) as well as network protocols.
Reactive implementations have concepts of publishers and subscribers, as well as ways to subscribe to data streams, get the next stream of data, handle errors and close the connection.
The Reactive Streams spec will be included in JDK 9 as java.util.concurrent.Flow. The following interfaces correspond to the Reactive Streams spec. As you can see, the spec is small, consisting of just four interfaces:
· Flow.Processor<T,R>: A component that acts as both a Subscriber and Publisher.
· Flow.Publisher<T>: A producer of items (and related control messages) received by Subscribers.
· Flow.Subscriber<T>: A receiver of messages.
· Flow.Subscription: Message control linking a Flow.Publisher and Flow.Subscriber.
Spring Framework 5 is also going reactive. To see how this looks, refer to Josh Long's Functional Reactive Endpoints with Spring Framework 5.0.
To learn more about RxJava 2.0, InfoQ interviewed main RxJava 2.0 contributor, David Karnok.
InfoQ: First of all, congrats on RxJava 2.0! 18 months in the making, that's quite a feat. What are you most proud of in this release?
David Karnok: Thanks! In some way, I wish it didn't take so long. There was a 10 month pause when Ben Christensen, the original author who started RxJava, left and there was no one at Netflix to push this forward. I'm sure many will contest that things got way better when I took over the lead role this June. I'm proud my research into more advanced and more performant Reactive Streams paid off and RxJava 2 is the proof all of it works.
InfoQ: What’s different in RxJava 2.0 and how does it help developers?
Karnok: There are a lot of differences between version 1 and 2 and it's impossible to list them all here ,but you can visit the dedicated wiki page for a comprehensible explanation. In headlines, we now support the de-facto standard Reactive Streams specification, have significant overhead reduction in many places, no longer allow nulls, have split types into two groups based on support of or lack of backpressure and have explicit interop between the base reactive types.
InfoQ: Where do you see RxJava used the most (e.g. IoT, real-time data processing, etc.)?
Karnok: RxJava is more dominantly used by the Android community, based on the feedback I saw. I believe the server side is dominated by Project Reactor and Akka at the moment. I haven't specifically seen IoT mentioned or use RxJava (it requires Java), but maybe they use some other reactive library available on their platform. For real-time data processing people still tend to use other solutions, most of them not really reactive, and I'm not aware of any providers (maybe Pivotal) who are pushing for reactive in this area.
InfoQ: What benefits does RxJava provide Android more than other environments that would explain the increased traction?
Karnok: As far as I see, Android wanted to "help" their users solving async and concurrent problems with Android-only tools such as AsyncTask, Looper/Handler etc.
Unfortunately, their design and use is quite inconvenient, often hard to understand or predict and generally brings frustration to Android developers. These can largely contribute to callback hell and the difficulty of keeping async operations off the main thread.
RxJava's design (inherited from the ReactiveX design of Microsoft) is dataflow-oriented and orthogonalized where actions execute from when data appears for processing. In addition, error handling and reporting is a key part of the flows. With AsyncTask, you had to manually work out the error delivery pattern and cancel pending tasks, whereas RxJava does that as part of its contract.
In practical terms, having a flow that queries several services in the background and then presents the results in the main thread can be expressed in a few lines with RxJava (+Retrofit) and a simple screen rotation will cancel the service calls promptly.
This is a huge productivity win for Android developers, and the simplicity helps them climb the steep learning curve the whole reactive programming's paradigm shift requires. Maybe at the end, RxJava is so attractive to Android because it reduces the "time-to-market" for individual developers, startups and small companies in the mobile app business.
There was nothing of a comparable issue on the desktop/server side Java, in my opinion, at that time. People learned to fire up ExecutorService's and wait on Future.get(), knew about SwingUtilities.invokeLater to send data back to the GUI thread and otherwise the Servlet API, which is one thread per request only (pre 3.0) naturally favored blocking APIs (database, service calls).
Desktop/server folks are more interested in the performance benefits a non-blocking design of their services offers (rather than how easy one can write a service). However, unlike Android development, having just RxJava is not enough and many expect/need complete frameworks to assist their business logic as there is no "proper" standard for non-blocking web services to replace the Servlet API. (Yes, there is Spring (~Boot) and Play but they feel a bit bandwagon-y to me at the moment).
InfoQ: HTTP is a synchronous protocol and can cause a lot of back pressure when using microservices. Streaming platforms like Akka and Apache Kafka help to solve this. Does RxJava 2.0 do anything to allow automatic back pressure?
Karnok: RxJava 2's Flowable type implements the Reactive Streams interface/specification and does support backpressure. However, the Java level backpressure is quite different from the network level backpressure. For us, backpressure means how many objects to deliver through the pipeline between different stages where these objects can be non uniform in type and size. On the network level one deals with usually fixed size packets, and backpressure manifests via the lack of acknowledgement of previously sent packets. In classical setup, the network backpressure manifests on the Java level as blocking calls that don't return until all pieces of data have been written. There are non-blocking setups, such as Netty, where the blocking is replaced by implicit buffering, and as far as I know there are only individual, non-uniform and non Reactive Streams compatible ways of handling those (i.e., a check for canWrite which one has to spin over/retry periodically). There exist libraries that try to bridge the two worlds (RxNetty, some Spring) with varying degrees of success as I see it.
InfoQ: Do you think reactive frameworks are necessary to handle large amounts of traffic and real-time data?
Karnok: It depends on the problem complexity. For example, if your task is to count the number of characters in a big-data source, there are faster and more dedicated ways of doing that. If your task is to compose results from services on top of a stream of incoming data in order to return something detailed, reactive-based solutions are quite adequate. As far as I know, most libraries and frameworks around Reactive Streams were designed for throughput and not really for latency. For example, in high-frequency trading, the predictable latency is very important and can be easily met by Aeron but not the main focus for RxJava due to the unpredictable latency behavior.
InfoQ: Does HTTP/2 help solve the scalability issues that HTTP/1.1 has?
Karnok: This is not related to RxJava and I personally haven't played with HTTP/2 but only read the spec. Multiplexing over the same channel is certainly a win in addition to the support for explicit backpressure (i.e., even if the network can deliver, the client may still be unable to process the data in that volume) per stream. I don't know all the technical details but I believe Spring Reactive Web does support HTTP/2 transport if available but they hide all the complexity behind reactive abstractions so you can express your processing pipeline in RxJava 2 and Reactor 3 terms if you wish.
InfoQ: Java 9 is projected to be featuring some reactive functionality. Is that a complete spec?
Karnok: No. Java 9 will only feature 4 Java interfaces with 7 methods total. No stream-like or Rx-like API on top of that nor any JDK features built on that.
InfoQ: Will that obviate the need for RxJava if it is built right into the JDK?
Karnok: No and I believe there's going to be more need for a true and proven library such as RxJava. Once the toolchains grow up to Java 9, we will certainly provide adapters and we may convert (or rewrite) RxJava 3 on top of Java 9's features (VarHandles).
One of my fears is that once Java 9 is out, many will try to write their own libraries (individuals, companies) and the "market" gets diluted with big branded-low quality solutions, not to mention the increased amount of "how does RxJava differ from X" questions.
My personal opinion is that this is already happening today around Reactive Streams where certain libraries and frameworks advertise themselves as RS but fail to deliver based on it. My (likely biased) conjecture is that RxJava 2 is the closest library/technology to an optimal Reactive Streams-based solution that can be.
InfoQ: What's next for RxJava?
Karnok: We had fantastic reviewers, such as Jake Wharton, during the development of RxJava 2. Unfortunately, the developer previews and release candidates didn't generate enough attention and despite our efforts, small problems and oversights slipped into the final release. I don't expect major issues in the coming months but we will keep fixing both version 1 and 2 as well as occasionally adding new operators to support our user base. A few companion libraries, such as RxAndroid, now provide RxJava 2 compatible versions, but the majority of the other libraries don't yet or haven't yet decided how to go forward. In terms of RxJava, I plan to retire RxJava 1 within six months (i.e., only bugfixes then on), partly due to the increasing maintenance burden on my "one man army" for some time now; partly to "encourage" the others to switch to the RxJava 2 ecosystem. As for RxJava 3, I don't have any concrete plans yet. There are ongoing discussions about splitting the library along types or along backpressure support as well as making the so-called operator-fusion elements (which give a significant boost to our performance) a standard extension of the Reactive Streams specification.
0 notes