I am working on an application that uses Amazon Kinesis, and one of the things I was wondering about is how you can roll over an application during an upgrade without data loss on streams. I have heard about things like blue/green deployments and such, but I was wondering what is the best practice for upgrading a data streaming service so you don't loose data from your streams.
For example, my application has an HTTP endpoint that ingests data as a series of POST operations. If I want to replace the service with a newer version, how do I manage existing application streaming to my endpoint?
One common method is having a software load balancer (LB) with a virtual IP; behind this LB there would be at least two HTTP ingestion endpoints during normal operation. During upgrade, each endpoint is announced out and upgraded in turn. The LB ensures that no traffic is forwarded to an announced out endpoint.
(The endpoints themselves can be on separate VMs, Docker containers or physical nodes).
Of course, the stream needs to be finite; the TCP socket/HTTP stream is owned by one of the endpoints. However, as long as the stream can be stopped gracefully, the following flow works, assuming endpoint A owns the current ingestion:
Tell endpoint A not to accept new streams. All new streams will be redirected only to endpoint B by the LB.
Gracefully stop existing streams on endpoint A.
Upgrade A.
Announce A back in.
Rinse and repeat with endpoint B.
As a side point, you would need two endpoints with a load balanced (or master/slave) set-up if you require any reasonable uptime and reliability guarantees.
There are more bespoke methods which allow hot code swap on the same endpoint, but they are more bespoke and rely on specific internal design (e.g. separate process between networking and processing stack connected by IPC).
Related
I have an app that get data from a third-party data source, it will send data to my app automatically and I can't filter it, I can only receive all. When data arrive, my app will transmit this data to a rocketmq topic.
Now I have to make this app a container and deploy it in k8s deployment with 3 replica. But these pods will all get same data and send to the same rocketmq topic.
How do I make this app horizontal scalable without sending duplicate msg to the same rocketmq topic?
Now I have to make this app a container and deploy it in k8s deployment with 3 replica. But these pods will all get same data and send to the same rocketmq topic.
There is no request. My app connect to a server and it will send data to app by TCP. Every Pod will connect to that server.
If you want to do this with more than one instance, they need to coordinate in some way.
Leader Election pattern is a way to run multiple instances, but only one can be active (e.g. when you read from the same queue). This is a pattern to coordinate - only one instance is active at the time. So this pattern only use your replicas for higher availability.
If you want that all your replicas actively work, this can be done with techniques like sharding or partitioning. This is also how e.g. Kafka (e.g. similar to a queue) makes concurrent work from queues.
There are other ways to solve this problem as well, e.g. to implement some form of locks to coordinate - but partitioning or sharding as in Kafka is probably the most "cloud native" solution.
I am dealing with communication between microservices.
For example (fictive example, just for the illustration):
Microservice A - Store Users (getUser, etc.)
Microservice B - Store Orders (createOrder, etc.)
Now if I want to add new Order from the Client app, I need to know user address. So the request would be like this:
Client -> Microservice B (createOrder for userId 5) -> Microservice A (getUser with id 5)
The microservice B will create order with details (address) from the User Microservice.
PROBLEM TO SOLVE: How effectively deal with communication between microservice A and microservice B, as we have to wait until the response come back?
OPTIONS:
Use RestAPI,
Use AMQP, like RabbitMQ and deal with this issue via RPC. (https://www.rabbitmq.com/tutorials/tutorial-six-dotnet.html)
I don't know what will be better for the performance. Is call faster via RabbitMQ, or RestAPI? What is the best solution for microservice architecture?
In your case using direct REST calls should be fine.
Option 1 Use Rest API :
When you need synchronous communication. For example, your case. This option is suitable.
Option 2 Use AMQP :
When you need asynchronous communication. For example when your order service creates order you may want to notify product service to reduce the product quantity. Or you may want to nofity user service that order for user is successfully placed.
I highly recommend having a look at http://microservices.io/patterns/index.html
It all depends on your service's communication behaviour to choose between REST APIs and Event-Based design Or Both.
What you do is based on your requirement you can choose REST APIs where you see synchronous behaviour between services
and go with Event based design where you find services needs asynchronous behaviour, there is no harm combining both also.
Ideally for inter-process communication protocol it is better to go with messaging and for client-service REST APIs are best fitted.
Check the Communication style in microservices.io
REST based Architecture
Advantage
Request/Response is easy and best fitted when you need synchronous environments.
Simpler system since there in no intermediate broker
Promotes orchestration i.e Service can take action based on response of other service.
Drawback
Services needs to discover locations of service instances.
One to one Mapping between services.
Rest used HTTP which is general purpose protocol built on top of TCP/IP which adds enormous amount of overhead when using it to pass messages.
Event Driven Architecture
Advantage
Event-driven architectures are appealing to API developers because they function very well in asynchronous environments.
Loose coupling since it decouples services as on a event of once service multiple services can take action based on application requirement. it is easy to plug-in any new consumer to producer.
Improved availability since the message broker buffers messages until the consumer is able to process them.
Drawback
Additional complexity of message broker, which must be highly available
Debugging an event request is not that easy.
Personally I am not a fan of using a message broker for RPC. It adds unnecessary complexity and overhead.
How do you host your long-lived RabbitMQ consumer in your Users web service? If you make it some static singleton, in your web service how do you deal with scaling and concurrency? Or do you make it a stand-alone daemon process? Now you have two User applications instead of one. What happens if your Users consumer slows down, by the time it consumes the request message the Orders service context might have timed-out and sent another message or given up.
For RPC I would suggest simple HTTP.
There is a pattern involving a message broker that can avoid the need for a synchronous network call. The pattern is for services to consume events from other services and store that data locally in their own database. Then when the time comes when the Orders service needs a user record it can access it from its own database.
In your case, your Users app doesn't need to know anything about orders, but your Orders app needs to know some details about your users. So every time a user is added, modified, removed etc, the Users service emits an event (UserCreated, UserModified, UserRemoved). The Orders service can subscribe to those events and store only the data it needs, such as the user address.
The benefit is that is that at request time, your Orders service has one less synchronous dependency on another service. Testing the service is easier as you have fewer request time dependencies. There are also drawbacks however such as some latency between user record changes occuring and being received by the Orders app. Something to consider.
UPDATE
If you do go with RabbitMQ for RPC then remember to make use of the message TTL feature. If the client will timeout, then set the message expiration to that period. This will help avoid wasted work on the part of the consumer and avoid a queue getting backed up under load. One issue with RPC over a message broker is that once a queue fills up it can add long latencies that take a while to recover from. Setting your message expiration to your client timeout helps avoid that.
Regarding RabbitMQ for RPC. Normally we use a message broker for decoupling and durability. Seeing as RPC is a synchronous communication, that is, we are waiting for a response, then durability is not a consideration. That leaves us decoupling. The question is does that decoupling buy you anything over the decoupling you can do with HTTP via a gateway or Docker service names?
I have two microservices into Docker and I want to connect one with other, but I don´t know to do it. The two (and the future apps) are API Rest with Spring-boot, I am searching info, tutorials... but I don`t see nothing. My idea is have an main app that it is be able to connect with the other microservices that they are API Rest and afterwards this main app publish and all this I want to have it inside of the container (Docker).
Is it possible?
Anyone knows any tutorial that explain this?
Thanks so much!
What you are describing could be an API Gateway. Here is a great tutorial explaining this pattern.
Implement an API gateway that is the single entry point for all clients. The API gateway handles requests in one of two ways. Some requests are simply proxied/routed to the appropriate service. It handles other requests by fanning out to multiple services.
A variation of this pattern is the Backend for Front-End pattern. It defines a separate API gateway for each kind of client.
Using an API gateway has the following benefits:
Insulates the clients from how the application is partitioned into microservices
Insulates the clients from the problem of determining the locations of service instances
Provides the optimal API for each client
Reduces the number of requests/roundtrips. For example, the API gateway enables clients to retrieve data from multiple services with a single round-trip. Fewer requests also means less overhead and improves the user experience. An API gateway is essential for mobile applications.
Simplifies the client by moving logic for calling multiple services from the client to API gateway
Translates from a “standard” public web-friendly API protocol to whatever protocols are used internally
The API gateway pattern has some drawbacks:
Increased complexity - the API gateway is yet another moving part that must be developed, deployed and managed
Increased response time due to the additional network hop through the API gateway - however, for most applications the cost of an extra roundtrip is insignificant.
How implement the API gateway?
An event-driven/reactive approach is best if it must scale to scale to handle high loads. On the JVM, NIO-based libraries such as Netty, Spring Reactor, etc. make sense. NodeJS is another option.
Just give you the simplest answer:
In general containers can communicate among each others with any protocols (http,ftp,tcp,udp) not limit to only rest(http/s)
using the internal/ external IPs and ports
using the internal/ external names (dns):
in your Micro-service is in the same cluster on multi-host -> you should be able to write the program in your Springboot to call http://{{container service name}} , It's the built-in feature of containers
if you have more microservices in different cluster or hosts or the internet , you can use APIM (API management) or reverse-proxy(NGINX,HAProxy) to manages the service name eg.
microservice1.yourdomain.com —> container1 or service1(cluster)
microservice2.yourdomain.com —> container2 or service 2(cluster)
yourdomain.com/microservice1—> container2 or service 2(cluster)
yourdomain.com/microservice2—> container1 or service1(cluster)
PS . there are more sophisticated techniques out there but it fundamentally come down above approaches.
I m studying Microservices architecture and I m actually wondering something.
I m quite okay with the fact of using (back) service discovery to make request able on REST based microservices. I need to know where's the service (or at least the front of the server cluster) to make requests. So it make sense to be able to discover an ip:port in that case.
But I was wondering what could be the aim of using service registry / discovery when dealing with AMQP (based only, without HTTP possible calls) ?
I mean, using AMQP is just like "I need that, and I expect somebody to answer me", I dont have to know who's the server that sent me back the response.
So what is the aim of using service registry / discovery with AMQP based microservice ?
Thanks for your help
AMQP (any MOM, actually) provides a way for processes to communicate without having to mind about actual IP addresses, communication security, routing, among other concerns. That does not necessarily means that any process can trust or even has any information about the processes it communicates with.
Message queues do solve half of the process: how to reach the remote service. But they do not solve the other half: which service is the right one for me. In other words, which service:
has the resources I need
can be trusted (is hosted on a reliable server, has a satisfactory service implementation, is located in a country where the local laws are compatible with your requirements, etc)
charges what you want to pay (although people rarely discuss cost when it comes to microservices)
will be there during the whole time window needed to process your service -- keep in mind that servers are becoming more and more volatile. Some servers are actually containers that can last for a couple minutes.
Those two problems are almost linearly independent. To solve the second kind of problems, you have resource brokers in Grid computing. There is also resource allocation in order to make sure that the last item above is correctly managed.
There are some alternative strategies such as multicasting the intention to use a service and waiting for replies with offers. You may have reverse auction in such a case, for instance.
In short, the rule of thumb is that if you do not have an a priori knowledge about which service you are going to use (hardcoded or in some configuration file), your agent will have to negotiate, which includes dynamic service discovery.
I have built a WebSockets server that acts as a chat message router (i.e. receiving messages from clients and pushing them to other clients according to a client ID).
It is a requirement that the service be able to scale to handle many millions of concurrent open socket connections, and I wish to be able to horizontally scale the server.
The architecture I have had in mind is to put the websocket server nodes behind a load balancer, which will create a problem because clients connected to different nodes won't know about each other. While both clients A and B enter via the LoadBalancer, client A might have an open connection with node 1 while client B is connected to node 2 - each node holds it's own dictionary of open socket connections.
To solve this problem, I was thinking of using some MQ system like ZeroMQ or RabbitMQ. All of the websocket server nodes will be subscribers of the MQ server, and when a node gets a request to route a message to a client which is not in the local connections dictionary, it will pub-lish a message to the MQ server, which will tell all the sub-scriber nodes to look for this client and issue the message if it's connected to that node.
Q1: Does this architecture make sense?
Q2: Is the pub-sub pattern described here really what I am looking for?
ZeroMQ would be my option - both architecture-wise & performance-wise
-- fast & low latency ( can measure your implementation performance & overheads, down to sub [usec] scale )
-- broker-less ( does not introduce another point-of-failure, while itself can have { N+1 | N+M } self-healing architecture )
-- smart Formal Communication Pattern primitives ready to be used ( PUB / SUB is the least cardinal one )
-- fair-queue & load balancing architectures built-in ( invisible for external observer )
-- many transport Classes for server-side internal multi-process / multi-threading distributed / parallel processing
-- ready to almost linear scaleability
Adaptive node re-discovery
This is a bit more complex subject. Your intention to create a feasible architecture will have to drill down into more details to solve.
Node authentication vs. peer-to-peer messaging
Node (re)-discovery vs. legal & privacy issues
Node based autonomous self-organising Agents vs. needs for central policy enforcement
To update this for 2021, we just solved this problem where we needed to design a system that could handle millions of simultaneous WS connections from IoT devices. The WS server just relays messages to our Serverless API backend that handles the actual logic. We chose to use docker and the node ws package using an auto-scaling AWS ECS Fargate cluster with an ALB in front of it.
This solved the main problem of routing messages, but then we had the same issue of how do we route response messages from the server. We initially thought of just keeping a central DB of connections, but routing messages to a specific Fargate instance behind an ALB didn't seem feasible.
Instead, we set up a simple sub/pub pattern using AWS SNS (https://aws.amazon.com/pub-sub-messaging/). Every WS server receives the response and then searches its own WS connections. Since each Fargate instance handles just routing (no logic), they can handle a lot of connections when we vertically scale them.
Update: To make this even more performant, you can use a persistent connection like Redis Pub/Sub to allow the response message to only go to one single server instead of every server.