I designed a component that collects application runtime data, which is sent to the analysis server via Kafka. In most cases, apps will integrate Kafka. In order to avoid connect to same Kafka twice, I need to determine whether the app uses Kafka. If the app uses Kafka, I directly reuse the connections.
So, how can I predict app uses kafka ?
And if app integrate spring-kafka， what should I do ?
avoid connect to same Kafka twice,
There should be no issue with this. Clients are lightweight enough to create more than one of them in one app, from one machine, etc
determine whether the app uses Kafka.
Beyond decompilation, if it's a fat jar, you can jar -tf app.jar | grep -i Kafka, however this would only tell you there's files with the word "Kafka" in the package, not necessarily that any Apache Kafka clients are in use
Related
I'm trying to find examples of kafka connect with springboot. It looks like there is no spring boot integration for kafka connect. Can some one point me in the right direction to be able to listen to changes on mysql db?
Kafka Connect doesn't really need Spring Boot because there is nothing for you to code for it, and it really works best when ran in distributed mode, as a cluster, not embedded within other (single-instance) applications. I suppose if you did want to do it, then you could copy relevent portions of the source code, but that of course isn't using Spring Boot, and you'd have to wire it all yourself
The framework itself consists of a few core Java dependencies that have already been written (Debezium or Confluent JDBC Connector, for your mysql example), and two config files. One for Kafka Connect to know the bootstrap servers, serializers, etc. and another for the actual MySQL connector. So, if you want to use Kafka Connect, run it by itself, then just write the consumer in the Spring app.
The alternatives to Kafka Connect itself would be to use Apache Camel within a Spring application (Spring Integration) or Spring Cloud Dataflow and interfacing with those Kafka "components" (which aren't using the Connect API, AFAIK)
Another option, specific for listening to MySQL, is to use Debezium Engine within your code.
I have messages coming in from Kafka. So I am planning to write a listener and "onMessage". I want to process it and push it in to solr.
So my question is more architectural, like I have worked on web apps all my career, so in big data how to deploy the spring kafka listener, so I can process thousands of messages a second.
How do I make my spring code use multiple nodes to distribute the
load?
I am planning to write a SpringBoot application to run in
a tomcat container.
If you use the same group id for all instances, different partitions will be assigned to different consumers (instances of your application).
So, be sure that you specified enough partitions in the topic you are going to consume.
I am trying to work through a solution where the workflow is like this:
User hits a microservice to upload images
That microservice de-duplicates the image and if it really is new, queues it up for processing
The processing chain lives in Spring Cloud Dataflow
The microservice already exists, and we are trying to extend it to do the fancy processing. My initial cut was to use the Http Source from the sample starter pack since that would be something I didn't have to create. The problem is that the source doesn't register itself with Spring Discovery server, so there is no way to get an end point without making gross assumptions (like it lives on the dataflow server at port XYZ).
We can create a Queue endpoint and send the data directly a Queue source that receives the outside event and forwards it to an SCDF queue.
What would be awesome is if DataFlow could connect the start of the queue for me, without repackaging the microservice as a Source.
The major issue with Spring Data Flow is that it does not automatically start up deployed streams when the server starts up, and we need to be reasonably sure that microservice is always up.
The lifecycle of the server is decoupled from the apps it deploys, that was intentional.
I'm not following your thoughts on how dataflow could connect the start of the queue, but from your description there's a few things you could do:
You would need to modify the app in order to have it registered with eureka, but this is a very simple operation, no more than a few lines of code:
You can either start from a stream app perspective: https://start-scs.cfapps.io/ , select http source, your binder, and then add the spring-cloud-netflix library as well as #EnableDiscoveryClient at the Main boot class
Start with http://start.spring.io Select Stream Rabbit or Stream Kafka, add Web and netflix libraries, then add the #EnableDiscoveryClient and #EnableBinding annotations and create a simple HTTP endpoint for your use case.
In any case should be a small addition.
You can also open an issue at :https://github.com/spring-cloud-stream-app-starters/http/issues suggesting that we add #EnableDiscoveryClient to the http source app, we can take that in consideration on our next iteration as well.
I'll try to clarify few bits.
upload images -> if it really is new -> queues it up for processing
Upon a new upload event, you'd want to process the image. Here's a similar use-case, but more of a real-time streaming style solution. This is not what you're looking to do, but I thought it might be useful.
Porting the image processing code to a Spring Cloud Stream application is as simple as adding #EnableBinding(Processor.class). It is the same business logic - whether you're running it separately or orchestrating it via SCDF, it is still a standalone microservice. However, SCDF expects it to be either a Source, Processor, Sink, or Task application types. We will be opening this up to support any arbitrary "functions" (lambdas) in the future release.
We can create a Queue endpoint and send the data directly a Queue source that receives the outside event and forwards it to an SCDF queue.
This is one of the standard solutions. You can directly consume new events (images) from a queue/topic and process it in the image-processor that we created in previous step. The named-channel support in DSL facilitates just that.
What would be awesome is if DataFlow could connect the start of the queue for me, without repackaging the microservice as a Source.
I'm not sure I understand this. If I were to assume, you're looking for "named-channel" as source and that is supported.
The major issue with Spring Data Flow is that it does not automatically start up deployed streams when the server starts up, and we need to be reasonably sure that microservice is always up.
The moment you deploy a Stream in SCDF, all the individual steps included in the DSL (i.e., stream definition) are resolved and deployed as standalone apps in the target runtime (cloud foundry, kubernetes, etc.,). Once deployed, it is left to the platform where the apps run for lifecycle management. SCDF does not retain or track the app states.
I want to setup a network of brokers because I need to serve a number of users at the same time. I discovered that I may use either embedded brokers and start every broker in a Java Code or download full apache activemq distribution and run multiple instances.
For the moment, I don't have any specific reason to use embedded brokers. But on the other hand I don't have any reasons against using embedded brokers. Could you please give a hint what may be real disadvantages of using embedded brokers?
Thanks, Cheers
You probably want stand-alone broker(s).
Embedded brokers (in an application context) are usually used inside an application server to provide for fast response/low latency to application code in the running application server. The embedded broker would then store-and-forward the messages to another broker or interested clients. Other use cases including using embedded broker in unit tests, or within an embedded IOT-style computer.
I am novice to Spring Cloud Data flow and Stream Cloud Streaming Applications.
Currently my project diagram looks like following :
I route a POST request from outside client using zuul API gateway to a microservice called Composite. Composite creates a stream using REST POST and deployes onto Spring Cloud Data Flow Server. As far as I know the microservices mongodb and file run as co-existing JVM processes. If My client has to know the status of stream, status of the processed data, How should Composite Microservice interact with Spring Cloud Data Flow Server? Currently when I make POST call to deploy the stream I dont even get the status from SCDF Server. Does SCDF expose any hooks to look at the individual apps? Also how can I change the flow #runtime to create a dynamic mesh?
Currently I am using Local Spring Cloud Data Flow Server for development.
Runtime platform is local
Local runtime is recommended only for development purpose and if you're preparing for production, please make sure to choose a platform variant (eg: cf, k8s, yarn, ..) that comes with non-functional requirements to support reliable and durable execution of all the applications running in streaming pipeline.
As far as I know the microservices mongodb and file run as co-existing JVM processes.
If your stream definition is file | mongodb, you'd have 2 different JVM's even when using Local runtime. They're independent Boot applications.
How should Composite Microservice interact with Spring Cloud Data Flow Server?
Not clear what you mean by "composite" here. All the microservice applications in SCDF communicate via messaging middleware such as Kafka or Rabbit. SCDF provides the orchestration capability to run such applications into various runtime platforms.
Currently when I make POST call to deploy the stream I dont even get the status from SCDF Server
You can use SCDF's REST-APIs to query for current status of the apps and it is platform agnostic. You can view the list of supported APIs by hitting the root URL (see image below) - there's a gap in docs - we will fix it. Following APIs could be useful for status checks.
Does SCDF expose any hooks to look at the individual apps?
Once the apps are deployed in a runtime platform, you can take advantage of Boot's actuator endpoints to explore more details such as trace, metrics, health, env among others at each application level. See Boot's actuator endpoints for more details. For instance, if your mongodb app is running locally and on port 23000, then you can check granular metrics for this application at: http://localhost:23000/metrics.
[As an FYI: future SCDF releases would include integrating Spring Boot + Spring Cloud Sleuth metrics and visual representation of the same.]
Also how can I change the flow #runtime to create a dynamic mesh?
If you're referring to editing a running streaming pipeline with addition/deletes, we are currently exploring design approach to support this functionality.