Истинная красота 17 серия * Русская озвучка YouTube HD смотреть онлайн 16.01.2021

Publish Date : 2021-01-15

Истинная красота   17 серия * Русская озвучка YouTube HD смотреть онлайн 16.01.2021

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Истинная красота 17 серия * Русская озвучка YouTube HD смотреть онлайн 16.01.2021


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This release brings support for Cassandra 2.1, while remaining compatible with 1.2 and 2.0. It also introduces a new object mapping API, which simplifies the conversion of query results to custom Java classes. Finally, it includes several improvements (for a full list, refer to the ).

Most Java applications use custom Java classes to represent their data (for example UserProfile and Address in our first example). Converting back and forth between those classes and the driver's own types (Row and TupleValue) involves some boilerplate code and can be automated.

The object mapper is deliberately simple: its primary goal is to replace boilerplate code, not to hide Cassandra from the developer. Therefore it avoids complex features like lazy-loading or entity proxies.

This new version of the driver is available from the  (note that the object mapper is published as a separate artifact), and as a . Refer to the  if you are upgrading from a previous version.

java.lang.IllegalArgumentException: Expected class com.datastax.driver.core.PlainTextAuthProvider (specified by advanced.auth-provider.class) to be a subtype of com.datastax.oss.driver.api.core.auth.AuthProvider

The comes with an object mapper that removes boilerplate of writing queries and lets you focus on your application objects. This example shows how to use mapper to build Data Access Objects ( DAOs ) to access Apache Cassandra™ in a Java application.

The DataStax Distribution of is a production-ready distributed database, compatible with open-source Cassandra. It adds a few features that aren't available in the open-source distribution, including monitoring, improved batch, and streaming data processing.

DataStax also provides a Java client for its distribution of Apache Cassandra. This driver is highly tunable and can take advantage of all the extra features in the DataStax distribution, yet it's fully compatible with the open-source version, too.

In this tutorial, we covered the basic concepts of the DataStax Java Driver for Apache Cassandra. We connected to the database and created a keyspace and table. Also, we inserted data into the table and ran a query to retrieve it.

This alpha release is an early preview for evaluation by bleeding edge adopters. It has not been fully tested and is not production-ready. The basic request execution logic is functional, but many features are still missing, notably: schema and token metadata, metrics, the query builder, non-default policy implementations, compression.

In the meantime, you can try the new API and provide feedback on the , or even pick a ticket and create a pull request (please discuss it with us beforehand, as some tickets may require additional information).

The DataStax Java driver for Cassandra uses an asynchronous architecture. This allows client code to get query results in a non-blocking way, via Future instances. In this post, we take a closer look at this concept, and use it to implement a client-side equivalent to the SELECT...IN query.

On my laptop, the future takes about 4 milliseconds to complete, which gives the main thread time for a few iterations in the loop. Of course, that loop is for demonstration purposes only; you don't need it since the call to get is blocking. get also has a variant that waits for a given amount of time. If you decide to give up on the future after the timeout has elapsed, it's good practice to cancel it.

Future is a nice abstraction, but it's a bit limited in its use: you can either check periodically if it's done, or wait for its result in a blocking manner. That's why the ResultSetFutures returned by the Java driver extend ListenableFuture. This interface is part of Google's library; it's a specialized Future that allows the execution of callbacks upon completion.

Note the last argument to addCallback: it is an executor responsible for providing the thread which will execute the callback. With sameThreadExecutor, this will be the client thread if the future has already completed by the time we register the callback, or the Netty I/O thread otherwise. This is fine if the callback is lightweight; for more compute-intensive tasks, consider providing your own executor to avoid blocking I/O threads for too long.

This is not necessarily optimal: this query will be sent to a coordinator node, which will then have to query replicas for each partition key. Considering that we have a smart driver, it would be more efficient to send an individual query for each partition key (SELECT * FROM users WHERE id = ?), which would reach the right replica directly. Then all that's left is to collate the results client-side.

Now we use Guava's successfulAsList to transform the List<Future<ResultSet>> into a Future<List<ResultSet>> (slightly pedantic side note: in functional programming terms, this is similar to the sequence operation on a traversable functor).

public static Future queryAllAsList(Session session, String query, Object... partitionKeys) { List futures = sendQueries(session, query, partitionKeys); return Futures.successfulAsList(futures); } [/code] The client gets a single future containing the list of results: [code gutter="true" language="java"] Future future = ResultSets.queryAllAsList(session, "SELECT * FROM users WHERE id = ?", UUID.fromString("e6af74a8-4711-4609-a94f-2cbfab9695e5"), UUID.fromString("281336f4-2a52-4535-847c-11a4d3682ec1") //... ); for (ResultSet rs : future.get()) { ... // process the result set } [/code] There is one drawback: the compound future only completes after the slowest response has arrived. The client won't have access to any of the results before that. It would be valuable to get the results as they become available, to start processing them right away, or stream them to a consumer. Let's see another approach to fix that.

Note: inCompletionOrder is available since Guava 17.0; the Java driver currently uses an older version, so we need to override the dependency in our POM. The client now gets a list of futures, but they are guaranteed to be in completion order. So it can retrieve the results sequentially, with the guarantee that it won't wait unnecessarily while other results were available:

(The magic behind inCompletionOrder is that the futures it returns are in fact delegates, that get resolved sequentially each time one of the original futures completes — see for more details).

describes itself as "a library for composing asynchronous and event-based programs". One of its core abstractions is Observable, which can be seen as a concurrent iterator. An observable emits a sequence of values over time. Observers can register to an observable, to be notified as the values become available.

This translates really well to our case: once again, we start with the list of futures from our asynchronous queries; then we transform each future into an observable, and finally merge all the observables into a single one.

public static Observable queryAllAsObservable(Session session, String query, Object... partitionKeys) { List futures = sendQueries(session, query, partitionKeys); Scheduler scheduler = Schedulers.io(); List observables = Lists.transform(futures, (ResultSetFuture future) -> Observable.from(future, scheduler)); return Observable.merge(observables); } [/code] Note that we need to provide a Scheduler instance to Observable.from, otherwise each individual observator blocks on the corresponding future.

The Java driver allows you to take advantage of its non-blocking nature through its executeAsync method. Guava and RxJava provide powerful combinators to transform and compose these asynchronous results. We hope this article gave you the motivation to further explore the APIs and use reactive patterns in your code. The examples in this article were kept simple for brevity, but they could be expanded in various ways.

Today we released into DataStax Labs the DataStax Java Driver Spring Boot Starter. This starter streamlines the process of building standalone that use or databases. This is preview software so we have not yet pushed this to Maven Central and instead provide a tarball artifact that contains the starter jar, the starter test jar, README files, the demo described later in this blog post, and an install_maven.sh bash script.

To install the starter and the test module, run the install_maven.sh script from the extracted tarball directory. If you hit any issues along the way, post a question on and we’ll help you out. We also have the documentation and the demo on the .

To build Spring applications with Cassandra or DataStax, you’ll need the included and configured in your project, and the DataStax Java Driver Spring Boot Starter makes that easy by providing a familiar way of configuring the driver through Spring properties and profiles. The following is an example of configuring the driver in a Spring application.yml file (you can use application.properties as well if you prefer).

The Starter even comes with two flavors of embedded integration test options: and . These allow you to write integration tests that will spin up a Cassandra instance for you, run your tests against it, and shut it down as part of your regular application build/CI process. For , the project is leveraged to start an instance of Cassandra. For , we leverage test infrastructure modules that are published alongside the .

Before running the demo, make sure that you have an instance of a Cassandra or DataStax database started and that you are pointing to it in the application.yml. Also, double-check that your local-datacenter name is defined correctly in that yml file.

This repo contains a collection of graph examples. The intent is to provide more complete and extensive examples than what is reasonable to include in DataStax documentation or blogposts. This will include DataStax Graph Loader mapping scripts, schemas, example traversals, things to try in DataStax Studio, and application code examples. Feel free to use and modify any of these for your own purposes. There is no warranty or implied official support, but hopefully the examples will be useful as a starting point to show various ways of loading and experimenting with graph data. And if you see anything that could be improved or added, issue reports and pull requests are always welcome!

DataStax Enterprise is powered by the best distribution of Apache Cassandra™.©2020 DataStax, All rights reserved. DataStax, Titan, and TitanDB are registered trademark of DataStax, Inc. and its subsidiaries in the United States and/or other countries.Apache, Apache Cassandra, Cassandra, Apache Tomcat, Tomcat, Apache Lucene, Lucene, Apache Solr, Apache Hadoop, Hadoop, Apache Spark, Spark, Apache TinkerPop, TinkerPop, Apache Kafka and Kafka are either registered trademarks or trademarks of the Apache Software Foundation or its subsidiaries in Canada, the United States and/or other countries.

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