Kafka Streams Interactive Queries and gRPC
#kafka #grpc #developer
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In this kafka spark streaming tutorial you will learn what is apache kafka, architecture of apache kafka & how to setup a kafka cluster, what is spark & it’s features, components of spark and hands on demo on integrating spark streaming with apache kafka and integrating spark flume with apache kafka.
# Kafka Spark Streaming #Kafka Tutorial #Kafka Training #Kafka Course #Intellipaat
Kafka Streams Interactive Queries and gRPC
#kafka #grpc #developer
Welcome to my blog, hey everyone in this article we are going to be working with queries in Django so for any web app that you build your going to want to write a query so you can retrieve information from your database so in this article I’ll be showing you all the different ways that you can write queries and it should cover about 90% of the cases that you’ll have when you’re writing your code the other 10% depend on your specific use case you may have to get more complicated but for the most part what I cover in this article should be able to help you so let’s start with the model that I have I’ve already created it.
**Read More : **How to make Chatbot in Python.
Read More : Django Admin Full Customization step by step
let’s just get into this diagram that I made so in here:
Describe each parameter in Django querset
we’re making a simple query for the myModel table so we want to pull out all the information in the database so we have this variable which is gonna hold a return value and we have our myModel models so this is simply the myModel model name so whatever you named your model just make sure you specify that and we’re gonna access the objects attribute once we get that object’s attribute we can simply use the all method and this will return all the information in the database so we’re gonna start with all and then we will go into getting single items filtering that data and go to our command prompt.
Here and we’ll actually start making our queries from here to do this let’s just go ahead and run** Python manage.py shell** and I am in my project file so make sure you’re in there when you start and what this does is it gives us an interactive shell to actually start working with our data so this is a lot like the Python shell but because we did manage.py it allows us to do things a Django way and actually query our database now open up the command prompt and let’s go ahead and start making our first queries.
#django #django model queries #django orm #django queries #django query #model django query #model query #query with django
My team, Expedia Group™ Commerce Data, needed to join events coming in on two (and more in the future) Kafka topics to provide a realtime stream view of our bookings. This is a pretty standard requirement, but our team was not very experienced with Kafka Streams, and we had a few wrinkles that made going with an “out of the box” Kafka Streams join less attractive than dropping down to the Processor API.
What we needed, in a nutshell, was to:
There are two approaches to writing a Kafka Streams application:
Developers prefer the DSL for most situations because it simplifies some common use cases and lets you accomplish a lot with very little code. But you sacrifice some control when using the DSL. There’s a certain amount of magic going on under the covers that’s hidden by the
KTable abstractions. And the out-of-the-box joins available between these abstractions may not fit all use cases.
The most common way I see the DSL characterized is as “expressive,” which just means “hides lots of stuff from you.” Sometimes explicit is better. And for some (like me), the “raw” Processor API just seems to fit my brain better than the DSL abstractions.
Most documentation I found around Kafka Streams leans towards using the DSL (Confluent docs state “it is recommended for most users”), but the Processor API has a much simpler interface than the DSL in many respects. You still build a stream topology, but you only use Source nodes (to read from Kafka topics), Sink nodes (to write to Kafka topics), and Processor nodes (to do stuff to Kafka events flowing through your topology). Plus the DSL is built on top of the Processor API, so if it’s good enough for the DSL, it should be good enough for our humble project (in fact, as a Confluent engineer says, “the DSL compiles down to the Processor API”).
Processor nodes have to implementProcessor
, which has a process
method you override which takes the key and the value of the event that is traversing your Kafka Streams topology. Processors also have access to aProcessorContext object which contains useful information on the current event being processed (like what topic & partition it was consumed from) and a
forward method that is used to send the event to a downstream node in your topology.
To illustrate the difference, here’s a comparison of doing a repartition on a stream in the DSL and the Processor API.
#kafka-streams #kafka #streaming #data-science