Java Event Framework

[Janne Desir]

Thebiggest problem with this nave approach is that it breaks proper encapsullation by exposing raise as public. I can't see a way around it, and maybe my whole pattern is wrong. I would like some ideas.

There's also a related problem: I would like the events to be raised immediately after the method raising them returns. Is there a way to synchronize this using threads or some other construct? The caller code, of course, can't be involved in the task of synchronization. It has to be completely transparent to it.

The best thing to do here is to avoid implementing your own event framework in the first place, and instead rely on some existing library. Out of the box Java provides EventListener, and at a minimum you can follow the patterns documented there. Even for non-GUI applications most of this advice applies.

allows you to register callbacks to be executed once [a task submitted to an Executor] is complete, or if the computation is already complete, immediately. This simple addition makes it possible to efficiently support many operations that the basic Future interface cannot support.

represents an object with an operational state, with methods to start and stop. For example, webservers, RPC servers, and timers can implement the Service interface. Managing the state of services like these, which require proper startup and shutdown management, can be nontrivial, especially if multiple threads or scheduling is involved.

For event handling across multiple threads in a single JVM, I found some good candidates like Jetlang. But in my search for a distributed equivalent , I couldn't find anything that was lightweight enough to offer good performance.

Edit:Putting numbers to indicate performance is a bit difficult. But for example, if you implement a heartbeating mechanism using events and the heartbeat interval is 5 seconds, the heartbeat receiver should receive a sent heartbeat within say a second or two.

Generally, a lightweight implementation gives good performance. A event handling mechanism involving a web server or any kind of centralized hub requiring powerful hardware (definitely not lightweight) to give good performance is not what I'm looking for.

For distributed Event processing you could use Esper.It could process up to 500 000 event/s on a dual CPU 2GHz Intel based hardware.It's very stable because many banks use this solution. It supports JMS input and output adapter based on Spring JMS templates. So you could use any JMS implementation for event processing, i.e. ActiveMQ.

Whichever tool you use I'd recommend hiding the middleware APIs from your application logic. For example if you used the Apache Camel approach to hiding middleware you could then easily switch from AMQP to SEDA to JMS to ActiveMQ to JavaSpaces to your own custom MINA transport based on your exact requirements.

If you want to use a message broker I'd recommend using Apache ActiveMQ which is the most popular and powerful open source message broker with the largest most active community behind it both inside Apache and outside it.

You can make an architecture in which your triggers a publish a document in capped collection and your observer thread waits for it using a tailable cursor. If you did not understand what I have said above you need to brush up your MongoDB and java skills

Avis is also being used for chat, virtual presence, and smart room automation where typically 10-20 computers are communicating over an Avis-based messaging bus. Its commercial cousin (Mantara Elvin) is used for high-volume commercial trade event processing.

This is where Distributed SQL comes in. CockroachDB provides us with a fully distributed database that includes support for transactions. It allows us to scale our database to match the needs of our system, but also to publish events from our microservices in a way that will be consistent and resilient to failures.

You'll be implementing these ideas using the Spring Data JPA framework, along with Spring Boot. You'll modify a series of microservices so that events emitted from one service can be consumed in another. And you'll do all of this using CockroachDB so that your system can scale from the database all the way through to the application.

The Lambda function handler is the method in your function code that processes events. When your function is invoked, Lambda runs the handler method. Your function runs until the handler returns a response, exits, or times out.

When Lambda invokes your handler, the Lambda runtime receives an event as a JSON-formatted string and converts it into an object. For the previous example, a sample event might look like the following:

Lambda supports records in Java 17 and later runtimes. In all Java runtimes, you can use a class to represent event data. The following example takes a list of integers and a context object as input, and returns the sum of all integers in the list.

Lambda runs your static code and the class constructor during the initialization phase before invoking your function for the first time. Resources created during initialization stay in memory between invocations and can be reused by the handler thousands of times. Thus, you can add initialization code outside of your main handler method to save compute time and reuse resources across multiple invocations.

In the following example, the client initialization code is outside the main handler method. The runtime initializes the client before the function serves its first event. Subsequent events are much faster because Lambda doesn't need to initialize the client again.

You specify the type of object that the event maps to in the handler method's signature. In the preceding example, the Java runtime deserializes the event into a type that implements the Map interface. String-to-string maps work for flat events like the following:

If you define your own input type, it should be a deserializable, mutable plain old Java object (POJO), with a default constructor and properties for each field in the event. Keys in the event that don't map to a property as well as properties that aren't included in the event are dropped without error.

The output type can be an object or void. The runtime serializes return values into text. If the output is an object with fields, the runtime serializes it into a JSON document. If it's a type that wraps a primitive value, the runtime returns a text representation of that value.

The RequestHandler interface is a generic type that takes two parameters: the input type and the output type. Both types must be objects. When you use this interface, the Java runtime deserializes the event into an object with the input type, and serializes the output into text. Use this interface when the built-in serialization works with your input and output types.

To use your own serialization, implement the RequestStreamHandler interface. With this interface, Lambda passes your handler an input stream and output stream. The handler reads bytes from the input stream, writes to the output stream, and returns void.

Control the dependencies in your function's deployment package. The AWS Lambda execution environment contains a number of libraries. To enable the latest set of features and security updates, Lambda will periodically update these libraries. These updates may introduce subtle changes to the behavior of your Lambda function. To have full control of the dependencies your function uses, package all of your dependencies with your deployment package.

Minimize the complexity of your dependencies. Prefer simpler frameworks that load quickly on execution environment startup. For example, prefer simpler Java dependency injection (IoC) frameworks like Dagger or Guice, over more complex ones like Spring Framework.

Minimize your deployment package size to its runtime necessities. This will reduce the amount of time that it takes for your deployment package to be downloaded and unpacked ahead of invocation. For functions authored in Java, avoid uploading the entire AWS SDK library as part of your deployment package. Instead, selectively depend on the modules which pick up components of the SDK you need (e.g. DynamoDB, Amazon S3 SDK modules and Lambda core libraries).

Take advantage of execution environment reuse to improve the performance of your function. Initialize SDK clients and database connections outside of the function handler, and cache static assets locally in the /tmp directory. Subsequent invocations processed by the same instance of your function can reuse these resources. This saves cost by reducing function run time.

Use a keep-alive directive to maintain persistent connections. Lambda purges idle connections over time. Attempting to reuse an idle connection when invoking a function will result in a connection error. To maintain your persistent connection, use the keep-alive directive associated with your runtime. For an example, see Reusing Connections with Keep-Alive in Node.js.

Use environment variables to pass operational parameters to your function. For example, if you are writing to an Amazon S3 bucket, instead of hard-coding the bucket name you are writing to, configure the bucket name as an environment variable.

Avoid using recursive code in your Lambda function, wherein the function automatically calls itself until some arbitrary criteria is met. This could lead to unintended volume of function invocations and escalated costs. If you do accidentally do so, set the function reserved concurrency to 0 immediately to throttle all invocations to the function, while you update the code.

Do not use non-documented, non-public APIs in your Lambda function code. For AWS Lambda managed runtimes, Lambda periodically applies security and functional updates to Lambda's internal APIs. These internal API updates may be backwards-incompatible, leading to unintended consequences such as invocation failures if your function has a dependency on these non-public APIs. See the API reference for a list of publicly available APIs.

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