traffictriada.blogg.se - Movie suggester

#Movie suggester movie
#Movie suggester code

Rate them to give Max a clearer idea of what you want, or pick one that looks promising. If the first round ended without a pick, Max would launch “The Ratings Game.” Rapid-fire suggestions of movies that one algorithm or another has decided you’ll love. Dinosaurs or Interpretive Dance? LGBT Docudramas or ’80s Cartoons? The best answer (both!) was unfortunately never an option. Or perhaps “One Simple Question.” Max sifts through Netflix’s most ridiculous genre tags and offers you a choice of two. Jackson bellows the entire Racial Slur Dictionary and scrubs brain chunks out of car upholstery. Jackson can’t find his supersuit, or the kind where Samuel L.

#Movie suggester movie

Jackson… or Dakota Fanning? Click a face and Max would cue up a movie starring the winner. Would you rather watch something starring Samuel L. For example: “Celebrity Mood Ring.” Two actors’ faces pop up onscreen. The average Max experience started with some kind of Pick Two. Max sucked at his job if you wanted something light and breezy, he’d probably end up tossing you Hotel Rwanda. Click that button (the one that says you smell), and Max’s quippy disembodied voice would guide you through three quiz games, engineered to sleuth out exactly what movie you felt like watching at that exact moment. But hey, it’s not like I have any say in the matter. And at some undisclosed point this June, Netflix will give its current site a complete design overhaul, and when that happens, the last coded remnants of Max will probably blink out of existence forever. Netflix quietly rolled out Max in June 2013, exclusively for the people who watch Netflix on Playstation 3 (and later, Playstation 4). Max is (or was) a kooky talking game show on Netflix that would pick out movies for you. See that box above? The one insinuating that you smell bad? That’s Max. forBoundedOutOfOrderness(Duration.ofMillis(OUT_OF_ORDER_NESS)) Val text = env.socketTextStream("localhost", 9999) Here you want to connect to the local 9999 port. Obtain the input data by connecting to the socket. Val tableEnv = StreamTableEnvironment.create(env, bSettings) Val bSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode().build() Val env = StreamExecutionEnvironment.getExecutionEnvironment set up the streaming execution environment You can learn more about it in the documentation.

Kafka Streams is built on top of the Kafka producer/consumer API, and abstracts away some of the low-level complexities. In the context of the above example it looks like this: You use it in your Java applications to do stream processing. Kafka Streams a stream processing library, provided as part of Apache Kafka.

enrichment (deriving values within a stream of a events, or joining out to another stream)Īs you mentioned, there are a large number of articles about this without wanting to give you yet another link to follow, I would recommend this one.

aggregate (for example, the sum of a field over a period of time, or a count of events in a given window).

Stream processing is used to do things like: This, in a rather crude nutshell, is stream processing. Maybe that stream we'll use for reporting, or driving another application that needs to respond to only red widgets events: We want to filter that stream based on a characteristic of the 'widget', and if it's red route it to another stream. Let's imagine we want to take this unbounded stream of events, perhaps its manufacturing events from a factory about 'widgets' being manufactured. An unbounded stream of events could be temperature readings from a sensor, network data from a router, order from an e-commerce system, and so on. Taking that unbounded stream of events, we often want to do something with it. Stream Processing is based on the fundamental concept of unbounded streams of events (in contrast to static sets of bounded data as we typically find in relational databases). What we want to achieve is to add artificial delay between window and sink operators to postpone sink emition.

It is located in one region (with a read replica in a different region)īecause we are using event time characteristics with 1 minute tumbling window all regions' sink emit their records nearly at the same time.

It is hosted in AWS via RDS (currently it is a PostgreSQL).

#Movie suggester code

The exact same code is running in each region.

It is hosted in AWS via Kinesis Data Analytics (KDA).

The application emits the data into a Postgres sink.

So, for each session we will have 1 computed record.

The windowing is specified by a reduce and a process functions.

The application has windowing with 1 minute tumbling window.

The application shards ( keyBy) events based on the sessionId field.

The application uses event time characteristics.

We have an Apache Flink application which processes events