I am trying to learn architecting an business application adhering microservices fundamentals and its considerations. I have come across a question to which I am bit confused.
In a microservice architecture having multiple microservices with their own DB if data needs to be shared among each others then what should be the proffered way, service bus or calling them via HttpClient ?
I know that with message queue through service bus whenever a message is needed to be shared with others one micro service can publish this message and all subscriber then can retrieve the same, but in this case if that information needs to be stored in other microservice application's DB too, would that not become the redundant data?
So isn't enough to read the data simply via HttpClient whenever needed.
Looking forward to see the replies, thanks for the help in advance.
It depends upon the other factor like latency, redundancy and availability. Both options works keeping redundant data or REST call whenever we need data.
Points that work against direct HTTP Clients calls are -
It impact availability. It reduce overall availability if the system.
It impact performance and latency. Support there is an operation from service A that need data from service B. Frequency of the operation is very high. In that case, it reduce performance and increase latency as well as response time.
It doesn't support JOINs. So, you have to manipulate data. That also impact performance.
Points that work against message bus approach/event driven -
Duplicate data - So, increase complexity of the system to keep the same in sync.
It reduce consistency of the system. Now, system is eventual consistent.
In system design, no option is incorrect. All options have some pros and some cons so choose wisely according to your requirement and system.
Related
In a world of microservices, often one microservice needs to invoke another, synchronous or asynchronous way.
In the case of synchronous way of communication, I have understood that it affects the availbility of services, as both services need to be available during calls.
To minimize this synchronous way of communication, one possible solution is to have DATA REPLICATION at client service. The client service also up-to-date data by listening to events published by services.
According to me, this is not a good choice as we are duplicating data and it might become stale and also database overhead.
what will be the best suitable scenario when the above pattern will be the best suit?
Microservices are distributed systems. This means that they are constrained by the CAP theorem, which basically means you have a choice between:
Sacrifice availability to preserve consistency: this would (among other things) lead to one service invoking functionality in another in a synchronous way. If the other service is unavailable, so is all functionality in this service which depends on that service's functionality.
Sacrifice consistency to preserve availability: you build services to be autonomous and not depend on other services being up. This leads in fairly short order to services not sharing databases and to asynchronous replication of data (because if service A has synchronously replicated data from service B, then service B being down doesn't affect A's availability, but A being down affects B's availability): with asynchronous replication, the best you can hope for is eventual consistency.
The choice between those two (if you happen to have the ability to freeze the entire universe if there's a network partition, you might be able to sacrifice partition tolerance for consistency and availability) is ultimately a business question (it's worth noting that there's a continuum of approaches between those extremes). How much are you spending on storage and on designing an (arguably) more complex system vs. how much are you losing by being unavailable?
It should be noted that the universe is inherently eventually consistent: the sun could have gone supernova a few minutes ago and we can't know it for a few minutes more.
As for the concern about duplicated data: chances are the data is already duplicated (backups) and in any database worth using the data is duplicated (the write-ahead log).
As for situations, it's a lot harder to think of a situation where aiming for strong consistency is strictly the most suitable option.
But for an example, consider a chain of coffee shops. We have a cash register service and we have a loyalty/rewards service. Data from the loyalty/rewards service is needed by the cash register (if a customer is redeeming a "50% off a latte" reward you'd want the register to know that it's valid), and every transaction (at least those with a loyalty ID) at the register should be known by the rewards service.
If we want the reward redemptions to be consistent, then it implies that if the loyalty/rewards service is inaccessible from the register, no rewards can be redeemed. There's a nonzero chance that a customer who can't redeem a reward just walks out (and a further nonzero chance that they never get coffee from you again).
Conversely, if we want both services to have a consistent view then we're demanding that if the power's out at any store we can't determine new rewards, or if the loyalty/rewards service is inaccessible from the register, no new sales can be made.
The solution is for both services to maintain the data they need to function, even if another service controls updates to that data. They'll eventually catch up. In the case of reward redemption, assuming the unavailability happens rarely enough, it may even be desirable to have the cash register perform a preliminary validation and if that passes, assume that the reward is valid and submit it later to the loyalty/reward service.
One of the benefits of Microservice architecture is one can scale heavily used parts of the application without scaling the other parts. This supposedly provides benefits around cost.
However, my question is, if a heavily used microservice is dependent on other microservice to do it's work wouldn't you have to scale the other services as well seemingly defeating the purpose. If a microservice is calling other micro service at real time to do it's job, does it mean that Micro service boundaries are not established correctly.
There's no rule of thumb for that.
Scaling usually depends on some metrics and when some thresholds are reached then new instances are created. Same goes for the case when they are not needed anymore.
Some services are doing simple, fast tasks, like taking an input and writing it to the database and others may be longer running task which can take any amount of time.
If a service that needs scale is calling a service that can easily handle heavy loads in a reliable way then there is no need to scale that service.
That idea behind scaling is to scale up when needed in order to support the loads and then scale down whenever loads get in the regular metrics ranges in order to reduce the costs.
There are two topics to discuss here.
First is that usually, it is not a good practice to communicate synchronously two microservices because you are coupling them in time, I mean, one service has to wait for the other to finish its task. So normally it is a better approach to use some message queue to decouple the producer and consumer, this way the load of one service doesn't affect the other.
However, there are situations in which it is necessary to do synchronous communication between two services, but it doesn't mean necessarily that both have to scale the same way, for example: if a service has to make several calls to other services, queries to database, or other kind of heavy computational tasks, and one of the service called only do an array sorting, probably the first service has to scale much more than the second in order to process the same number of request because the threads in the first service will be occupied longer time than the second
We are using Spring boot application with Maria DB database. We are getting data from difference services and storing in our database. And while calling other service we need to fetch data from db (based on mapping) and call the service.
So to avoid database hit, we want to cache all mapping data in cache and use it to retrieve data and call service API.
So our ask is - Add data in Cache when it gets created in database (could add up-to millions records) and remove from cache when status of one of column value is "xyz" (for example) or based on eviction policy.
Should we use in-memory cache using Hazelcast/ehCache or Redis/Couch base?
Please suggest.
Thanks
I mostly agree with Rick in terms of don't build it until you need it, however it is important these days to think early of where this caching layer would fit later and how to integrate it (for example using interfaces). Adding it into a non-prepared system is always possible but much more expensive (in terms of hours) and complicated.
Ok to the actual question; disclaimer: Hazelcast employee
In general for caching Hazelcast, ehcache, Redis and others are all good candidates. The first question you want to ask yourself though is, "can I hold all necessary records in the memory of a single machine. Especially in terms for ehcache you get replication (all machines hold all information) which means every single node needs to keep them in memory. Depending on the size you want to cache, maybe not optimal. In this case Hazelcast might be the better option as we partition data in a cluster and optimize the access to a single network hop which minimal overhead over network latency.
Second question would be around serialization. Do you want to store information in a highly optimized serialization (which needs code to transform to human readable) or do you want to store as JSON?
Third question is about the number of clients and threads that'll access the data storage. Obviously a local cache like ehcache is always the fastest option, for the tradeoff of lots and lots of memory. Apart from that the most important fact is the treading model the in-memory store uses. It's either multithreaded and nicely scaling or a single-thread concept which becomes a bottleneck when you exhaust this thread. It is to overcome with more processes but it's a workaround to utilize todays systems to the fullest.
In more general terms, each of your mentioned systems would do the job. The best tool however should be selected by a POC / prototype and your real world use case. The important bit is real world, as a single thread behaves amazing under low pressure (obviously way faster) but when exhausted will become a major bottleneck (again obviously delaying responses).
I hope this helps a bit since, at least to me, every answer like "yes we are the best option" would be an immediate no-go for the person who said it.
Build InnoDB with the memcached Plugin
https://dev.mysql.com/doc/refman/5.7/en/innodb-memcached.html
I am a software developer but wannabe architect new to the server scalability world.
In the context of multiple services working with the same data set, aiming to scale for redundancies and load balancing.
The question is: In a idealistic system, should services try to optimize their internal processing to reduce the amount of queries done to the remote server cache for better performance and less bandwidth at the cost of some local memory and code base or is it better to just go all-in and query the remote cache as the single transaction point every time any transaction need processing done on the data?
When I read about Redis and even general database usage online, the later seems to be the common option. Every nodes of the scaled application have no memory and read and write directly to the remote cache on every transactions.
But as a developer, I ask if this isn't a tremendous waste of resources? Whether you are designing at electronic chips level, at inter-thread, inter-process or inter-machine, I do believe it's the responsibility of each sub-system to do whatever it can to optimize its processing without depending on the external world if it can and hence reduce overall operation time.
I mean, if the same data is read over hundreds or time from the same service without changes (write), isn't it just more logical to keep a local cache and wait for notifications of changes (pub/sub) and only read only these changes to update the cache instead reading the bigger portion of data every time a transaction require it? On the other hand, I understand that this method implies that the same data will be duplicated at multiple place (more ram usage) and require some sort of expiration system not to keep the cache from filling up.
I know Redis is built to be fast. But however fast it is, in my opinion there's still a massive difference between reading directly from local memory versus querying an external service, transfer data over network, allocating memory, deserialize into proper objects and garbage collect it when you are finished with it. Anyone have benchmark numbers between in-process dictionaries query versus a Redis query on the localhost? Is it a negligible time in the bigger scheme of things or is it an important factor?
Now, I believe the real answer to my question until now is "it depends on your usage scenario", so let's elaborate:
Some of our services trigger actions on conditions of data change, others periodically crunch data, others periodically read new data from external network source and finally others are responsible to present data to users and let them trigger some actions and bring in new data. So it's a bit more complex than a single web pages deserving service. We already have a cache system codebase in most services, and we have a message broker system to notify data changes and trigger actions. Currently only one service of each type exist (not scaled). They transfer small volatile data over messages and bigger more persistent (changing less often) data over SQL. We are in process of moving pretty much all data to Redis to ease scalability and performances. Now some colleagues are having a heated discussion about whether we should abandon the cache system altogether and use Redis as the common global cache, or keep our notification/refresh system. We were wondering what the external world think about it. Thanks
(damn that's a lot of text)
I would favor utilizing in-process memory as much as possible. Any remote query introduces latency. You can use a hybrid approach and utilize in-process cache for speed (and it is MUCH faster) but put a significantly shorter TTL on it, and then once expired, reach further back to Redis.
I need to design a system which has these basic components:
A Webserver which will be getting ~100 requests/sec. The webserver only needs to dump data into raw data repository.
Raw data repository which has a single table which gets 100 rows/s from the webserver.
A raw data processing unit (Simple processing, not much. Removing invalid raw data, inserting missing components into damaged raw data etc.)
Processed data repository
Does it make sense in such a system to have a service layer on which all components would be built? All inter-component interaction will go through the service layers. While this would make the system easily upgradeable and maintainable, would it not also have a significant performance impact since I have so much traffic to handle?
Here's what can happen unless you guard against it.
In the communication between layers, some format is chosen, like XML. Then you build it and run it and find out the performance is not satisfactory.
Then you mess around with profilers which leave you guessing what the problem is.
When I worked on a problem like this, I used the stackshot technique and quickly found the problem. You would have thought it was I/O. NOT. It was that converting data to XML, and parsing XML to recover data structure, was taking roughly 80% of the time. It wasn't too hard to find a better way to do that. Result - a 5x speedup.
What do you see as the costs of having a separate service layer?
How do those costs compare with the costs you must incur? In your case that seems to be at least
a network read for the request
a database write for raw data
a database read of raw data
a database write of processed data
Plus some data munging.
What sort of services do you have a mind? Perhaps
saveRawData()
getNextRawData()
writeProcessedData()
why is the overhead any more than a procedure call? Service does not need to imply "separate process" or "web service marshalling".
I contend that structure is always of value, separation of concerns in your application really matters. In comparison with database activities a few procedure calls will rarely cost much.
In passing: the persisting of Raw data might best be done to a queuing system. You can then get some natural scaling by having many queue readers on separate machines if you need them. In effect the queueing system is naturally introducing some service-like concepts.
Personally feel that you might be focusing too much on low level implementation details when designing the system. Before looking at how to lay out the components, assemblies or services you should be thinking of how to architect the system.
You could start with the following high level statements from which to build your system architecture around:
Confirm the technical skill set of the development team and the operations/support team.
Agree on an initial finite list of systems that will integrate to your service, the protocols they support and some SLAs.
Decide on the messaging strategy.
Understand how you will deploy your service/system.
Decide on the choice of middleware (ESBs, Message Brokers, etc), databases (SQL, Oracle, Memcache, DB2, etc) and 3rd party frameworks/tools.
Decide on your caching and data latency strategy.
Break your application into the various areas of business responsibility - This will allow you to split up the work and allow easier communication of milestones during development/testing and implementation.
Design each component as required to meet the areas of responsibility. The areas of responsibility should automatically lead you to decide on how to design component, assembly or service.
Obviously not all of the above will match your specific case but I would suggest that they should at least be given some thought.
Good luck.
Abstraction and tiering will introduce latency, but the real question is, what are you GAINING to make the cost(s) worthwhile? Loose coupling, governance, scalability, maintainability are worth real $.
Even the best-designed layered app will exhibit more latency than an app talking directly to a DB. Users who know the original system will feel the difference. They may not like it, so this can be a political issue as much as a technical one.