dapr-store | Sample application showcasing the use of Dapr to build | Microservice library

If you refer to the Provided Sinks list and examine the source code for some of them, you'll notice that the pattern is usually:

1. Construct the sink configuration (usually taking values from IConfiguration, inline or a combination of both)
2. Pass the configuration to the sink registration.

Then the sink implementation instantiates the required services to push logs to.

An alternate approach I could suggest is registering Serilog without any arguments (UseSerilog()) and then configure the static Serilog.Log class using the built IServiceProvider:

You can use a new gcloud feature and impersonate your local credential like that:

You have to send the query and body as variables in post request like shown here

I have been struggling with a similar issue In my gradle spring boot project I am using the output of the following for adding modules in jlink in my dockerfile with (openjdk:17-alpine):

Since you have everything containerized, it's way simpler.

Just set up a small healthcheck endpoint with Spring Web which serves to see if the server is still running, something like:

You are better off avoiding serializable isolation level. The way the serializable guarantee is provided is often deadlock prone.

If you can't alter your stored procs to use more targeted locking hints that guarantee the results you require at a lesser isolation level then you can prevent this particular deadlock scenario shown by ensuring that all locks are taken out on ServiceChange first before any are taken out on ServiceChangeParameter.

One way of doing this would be to introduce a table variable in spGetManageServicesRequest and materialize the results of

There are two solutions which can be used depending on necessity of using istio features.

If no istio features are planned to use, it can be solved using native kubernetes. In turn, if some istio feature are intended to use, it can be solved using istio virtual service. Below are two options:

1. Native kubernetes

Service-x should be pointed to the backend of service-b deployment. Below is selector which points to deployment: service-b:

It's an interesting problem. I have to confess I don't really know how I would do it - it depends a lot on exactly how fast the processing needs to occur, and a lot of other factors not mentioned - such as what constraints to do you have in terms of the technology stack you have, is it on-premise or in the cloud, must the solution be coded by you/your team or can you buy some $$ tool. For future reference, for architecture questions especially, any context you can provide is really helpful - e.g. constraints.

I did think of Pub-Sub, which may offer patterns you can use, but you really just need a simple implementation that will work within your code base, AND very importantly you only have one consuming client, the RabbitMQ queue - it's not like you have X number of random clients wanting the data. So an off-the-shelf Pub-Sub solution might not be a good fit.

Assuming you want a "home-grown" solution, this is what has come to mind so far:

("flow" connectors show data flow, which could be interpreted as a 'push'; where as the other lines are UML "dependency" lines; e.g. the match engine depends on data held in the batch, but it's agnostic as to how that happens).

• The external data source is where the data is coming from. I had not made any assumptions about how that works or what control you have over it.
• Interface, all this does is take the raw data and put it into batches that can be processed later by the Match Engine. How the interface works depends on how you want to balance (a) the data coming in, and (b) what you know the match engine expects.
• Batches are thrown into a batch queue. It's job is to ensure that no data is lost before its processed, that processing can be managed (order of batch processing, resilience, etc).
• Match engine, works fast on the assumption that the size of each batch is a manageable number of records/changes. It's job is to take changes and ask who's interested in them, and return the results to the RabbitMQ. So its inputs are just the batches and the user & user matching rules (more on that later). How this actually works I'm not sure, worst case it iterates through each rule seeing who has a match - what you're doing now, but...

Key point: the queue would also allow you to scale-out the number of match engine instances - but, I don't know what affect that has downstream on the RabbitMQ and it's downstream consumers (the order in which the updates would arrive, etc).

What's not shown: caching. The match engine needs to know what the matching rules are, and which users those rules relate to. The fastest way to do that look-up is probably in memory, not a database read (unless you can be smart about how that happens), which brings me to this addition:

• Data Source is wherever the user data, and user matching rules, are kept. I have assumed they are external to "Your Solution" but it doesn't matter.
• Cache is something that holds the user matches (rules) & user data. It's sole job is to hold these in a way that is optimized for the Match Engine to work fast. You could logically say it was part of the match engine, or separate. How you approach this might be determined by whether or not you intend to scale-out the match engine.
• Data Provider is simply the component whose job it is to fetch user & rule data and make it available for caching.

So, the Rule engine, cache and data provider could all be separate components, or logically parts of the one component / microservice.

I've managed to find the answer.

1. I'm not that smart if I've decided that Traefik would take /proxy and simply redicrect all request to /api/*. The official docs (https://doc.traefik.io/traefik/routing/routers/) says that (I'm quoting):

Use Path if your service listens on the exact path only. For instance, Path: /products would match /products but not /products/shoes.

Use a Prefix matcher if your service listens on a particular base path but also serves requests on sub-paths. For instance, PathPrefix: /products would match /products but also /products/shoes and /products/shirts. Since the path is forwarded as-is, your service is expected to listen on /products.

1. I did not use any middleware for replacing substring of path

Now answer as example.

First at all: code for microservice in main.go file