Would NServiceBus or an equivalent ESB be appropriate for an application that has a bunch of different kinds of background maintenance-type tasks? For example:
Scanning databases for the occurence of certain words in user-generated content
Updating database tables that store the results of relatively expensive queries
Creating/maintaining external indexes for content
Sending event notification emails for a scheduled event.
My idea is to employ some kind of task scheduler (either the Windows builtin one, Quartz.NET, or my own database-based solution) to publish different kinds messages onto the bus periodically. The period may be as short as one minute or as long as a days. The reason I want to use the bus is so that I can scale out the number of subscribers as the system becomes larger and busier and the tasks become either more frequent or more resource-intensive. It would also provide redundancy as long as I always have at least two subscribers running.
The obvious alternative to this would be to write my own Windows Service that is triggered by the scheduler and performs the work, but I feel like making that scale beyond a single machine and provide fault tolerance might be more difficult than using the ESB as that plumbing.
Does this sound like a reasonable approach? Alternative suggestions?
TIA
As the author of NServiceBus, I'm quite probably biased, but there is a tradeoff between learning a new technology and writing (possibly a simpler version of) your own. I would recommend considering the longer term maintainance (and documentation) costs of your own solution as compared to one written in house.
In terms of the feature-set you described, NServiceBus does provide facilities for all of that.
Related
I am working on an article describing fundamentals of technologies used by scalable systems. I have worked on Erlang before in a self-learning excercise. I have gone through several articles but have not been able to answer the following questions:
What is in the implementation of Erlang that makes it scalable? What makes it able to run concurrent processes more efficiently than technologies like Java?
What is the relation between functional programming and parallelization? With the declarative syntax of Erlang, do we achieve run-time efficiency?
Does process state not make it heavy? If we have thousands of concurrent users and spawn and equal number of processes as gen_server or any other equivalent pattern, each process would maintain a state. With so many processes, will it not be a drain on the RAM?
If a process has to make DB operations and we spawn multiple instances of that process, eventually the DB will become a bottleneck. This happens even if we use traditional models like Apache-PHP. Almost every business application needs DB access. What then do we gain from using Erlang?
How does process restart help? A process crashes when something is wrong in its logic or in the data. OTP allows you to restart a process. If the logic or data does not change, why would the process not crash again and keep crashing always?
Most articles sing praises about Erlang citing its use in Facebook and Whatsapp. I salute Erlang for being scalable, but also want to technically justify its scalability.
Even if I find answers to these queries on an existing link, that will help.
Regards,
Yash
Shortly:
It's unmutable. You have no variables, only terms, tuples and atoms. Program execution can be divided by breakpoint at any place. Fully transactional model.
Processes are even lightweight than .NET threads and isolated.
It's made for communications. Millions of connections? Fully asynchronous? Maximum thread safety? Big cross-platform environment, which built only for one purpose — scale&communicate? It's all Ericsson language — first in this sphere.
You can choose some impersonators like F#, Scala/Akka, Haskell — they are trying to copy features from Erlang, but only Erlang born from and born for only one purpose — telecom.
Answers to other questions you can find on erlang.com and I'm suggesting you to visit handbook. Erlang built for other aims, so it's not for every task, and if you asking about awful things like php, Erlang will not be your language.
I'm no Erlang developer (yet) but from what I have read about it some of the features that makes it very scalable is that Erlang has its own lightweight processes that are using message passing to communicate with each other. Because of this there is no such thing as shared state and locking which is the case when using for example a multi threaded Java application.
Another difference compared to Java is that the Erlang VM does garbage collection on every little process that is running which does not take any time at all compared to Java which does garbage collection only per VM.
If you get problem with bottlenecks from database connection you could start by using a database pooling app running against maybe a replicated PostgreSQL cluster or if you still have bottlenecks use a multi replicated NoSQL setup with Mnesia, Riak or CouchDB.
I think process restarts can be very useful when you are experiencing rare bugs that only appear randomly and only when specific criteria is fulfilled. Bugs that cause the application to crash as soon as you restart the app should optimally be fixed or taken care of with a circuit breaker so that it does not spread further.
Here is one way process restart helps. By not having to deal with all possible error cases. Say you have a program that divides numbers. Some guy enters a zero to divide by. Instead of checking for that possible error (and tons more), just code the "happy case" and let process crash when he enters 3/0. It just restarts, and he can figure out what he did wrong.
You an extend this into an infinite number of situations (attempting to read from a non-existent file because the user misspelled it, etc).
The big reason for process restart being valuable is that not every error happens every time, and checking that it worked is verbose.
Error handling is verbose typically, so writing it interspersed with the logic handling doing a task can make it harder to understand the code. Moving that logic outside of the task allows you to more clearly distinguish between "doing things" code, and "it broke" code. You just let the thing that had a problem fail, and handle it as needed by a supervising party.
Since most errors don't mean that the entire program must stop, only that that particular thing isn't working right, by just restarting the part that broke, you can keep operating in a state of degraded functionality, instead of being down, while you repair the problem.
It should also be noted that the failure recovery is bounded. You have to lay out the limits for how much failure in a certain period of time is too much. If you exceed that limit, the failure propagates to another level of supervision. Each restart includes doing any needed process initialization, which is sometimes enough to fix the problem. For example, in dev, I've accidentally deleted a database file associated with a process. The crashes cascaded up to the level where the file was first created, at which point the problem rectified itself, and everything carried on.
I'm trying to write an Erlang application (OTP) that would parse a list of users and then launch workers that will work 24X7 to collect user-data (using three different APIs) from remote servers and store it in ets.
What would be the ideal architecture for this kind of application. Do I launch a bunch of workers - one for each user (assuming small number users)? What will happen if number of users increases very rapidly?
Also, to call different APIs I need to put up a Timer mechanism in the worker process.
Any hint will be really appreciated.
Spawning new process for each user is not a such bad idea. There are http servers that do this for each connection, and they doing quite fine.
First of all cost of creating new process is minimal. And cost of maintaining processes is even smaller. If one of the has nothing to do, it won't do anything; there is none (almost) runtime overhead from inactive processes, which in the end means that you are doing only the work you have to do (this is in fact the source of Erlang systems reactivity).
Some issue might be memory usage. Each process has it's own memory stack, and in use-case when they actually do not need to store any internal data, you might be allocating some unnecessary memory. But this also could be modified (even during runtime), and in most cases such memory will be garbage collected.
Actually I would not worry about such things too soon. Issues you might encounter might depend on many things, mostly amount of outside data or user activity, and you can not really design this. Most probably you won't encounter any of them for quite some time. There's no need for premature optimization, especially if you could bind yourself to design that would slow down rest of your development process. In Erlang, with processes being main source of abstraction you can easily swap this process-per-user with pool-of-workers, and ets with external service. But only if you really need it.
What's most important is fact that representing "user" as process would be closest to problem domain. "Users" are independent entities, and deserve separate processes (they have their own state, and they can act or react independent to each other). It is quite similar to using Objects and Classes in other languages (it is over-simplification, but it should get you going).
If you were writing this in Python or C++ would you worry about how many objects you were creating? Only in extreme cases. In Erlang the same general rule applies for processes. Don't worry about how many you are creating.
As for architecture, the only element that is an architectural issue in your question is whether you should design a fixed worker pool or a 1-for-1 worker pool. The shape of the supervision tree would be an outcome of whichever way you choose.
If you are scraping data your real bottleneck isn't going to be how many processes you have, it will be how many network requests you are able to make per second on each API you are trying to access. You will almost certainly get throttled.
(A few months ago I wrote a test demonstration of a very similar system to what you are describing. The limiting factor was API request limits from providers like fb, YouTube, g+, Yahoo, not number of processes.)
As always with Erlang, write some system first, and then benchmark it for real before worrying about performance. You will usually find that performance isn't an issue, and the times that it is you will discover that it is much easier to optimize one small part of an existing system than to design an optimized system from scratch. So just go for it and write something that basically does what you want right now, and worry about optimization tweaks after you have something that basically does what you want. After getting some concrete performance data (memory, request latency, etc.) is the time to start thinking about performance.
Your problem will almost certainly be on the API providers' side or your network latency, not congestion within the Erlang VM.
In my project
We crawls x number of server.
Number of user for each server varies from 1 to n.
We crawls 1 to z item for each user.
Currently we are monitoring QOS using graphite. We are storing time taken to crawl the item.
x.time_taken
Problem with this approach is that if only single user is affected we get false alert about QOS.
What will be the correct tool/technique to answer/monitor following points:
Alert only if minimum k user are affected. [Not number of events]
List of user which were affected.
I think graphite and statsd is not correct tool for this. What will be better tool for answering those two question ?
What you are asking for is often called Service Monitoring. For very good reasons you want to know the service impact of an event, rather than just that an event has happened.
The advantage of this approach is exactly as you state in your requirements - you can focus on events which impact a large part of your user base and you have a list of the users affected right away.
The main drawback, IMHO, is that Service Monitoring is usually much more complex than simple performance or event/alert monitoring. It also often relies on a service model, which in my experience is something that is hard to build and even harder to keep up to date.
For example if a server in your system shows a significant slow down or failure, depending on your architecture this may impact all users who use a service that relies on that server, or it may impact a very small subset, or even none at all initially, if there is a load balancing mechanism or redundancy mechanism in place.
You would need to reflect this architecture in your service monitoring model, and also change it every time you update your system architecture or deployment.
If your system is static enough or critical enough to warrant the investment then this may be worth your while. If not then a simple compromise may be just to update the graphing and alerting you are doing to alert when the average response time over a set number of users, or over all users on a server increases by a significant amount.
This may give you most of the benefits you are after without having to invest in the extra complexity of a service monitoring solution.
If you definitely are looking to expand your monitoring approach and want to stick with open source tools then I would start by looking at NAGIOS if your focus is on infrastructure, or there are quite a few web service monitoring solutions with Free Tiers such as pingdom:
http://www.nagios.org
https://www.pingdom.com
When you hit a roof on reading from a database, you have two choices, scale vertically by putting more hardware in the server, or scale horizontally by putting a second server to help offload the reads.
Offloading reads to a second server, means that all writes will hit both servers, while read only hits one.
Problem is when you hit a roof with writing, since writing has to happen to all servers, it means that all servers will be overloaded with write requests, and the server comes unusable. Adding more servers to the problem doesn't help, since it only adds more servers that will be overloaded. So you have to scale vertically.
Is this something that is specific to RDBMS'? or is it something that happens with all DBMS'?
I know you can do things on software side, and split the database in two, eg. all entries starting with 0-m in one db while n-z in another, but IMHO it is more of a workaround than a solution to the problem.
I can't see that this would be specific to the relational model. All databases that have to read and write (and that's most of them) will have a similar problem.
For what it's worth, most databases are read far more than written so the write roof occurs less frequently than you might think. In addition, load balancing databases as per your method tends to be an immediate write to the primary with queued writes to all secondaries (at least in my experience).
In that case, you're not actually waiting around for multiple writes as a user, you just wait for the first. The DBMS itself manages the synchronisation between instances. This of course means that secondary databases might not be totally up-to-date but this can be controlled. Technically, this breaks the ACID properties of the system as a whole but this can be architected around.
I think this is the case with any DBMS, although some handle it better than others. Like you mention, partitioning the database in software seems to be the most common solution to this.
In many applications though, partitioning the database like that makes sense anyways if you are at such a huge scale that it becomes necessary. For example, if you had a social networking app, it would probably make sense to partition your database by country or other geographical regions. This would allow you to have your servers located geographically close to the regions they serve. It would also help mitigate any problems with a cross-database "social graph" since peoples friends tend to live nearby.
You're hardly going to "hit a roof with writing, since writing has to happen to all server" because in most of RDBMS installations:
1) Reads are overwhelming more frequent than writes
2) Modern RDBMs have Multi-Version Concurrency Control able to reduce blocking when reading/writing
Does anybody knows if there is a sort of 'load-balancer' in the erlang standard library? I mean, if I have some really simple operations on a really large set of data, the overhead of constructing a process for every item will be larger than perform the operation sequentially. But if I can balance the work in the 'right number' of process, it will perform better, so I'm basically asking if there is an easy way to accomplish this task.
By the way, does anybody knows if an OTP application does some kind of balance load? I mean, in an OTP application there is the concept of a "worker process" (like a java-ish thread worker)?
See modules pg2 and pool.
pg2 implements quite simple distributed process pool. pg2:get_closest_pid/1 returns "closest" pid, i.e. random local process if available, otherwise random remote process.
pool implements load balancing between nodes started with module slave.
The plists module probably does what you want. It is basically a parallel implementation of the lists module, design to be used as a drop-in replacement. However, you can also control how it parallelizes its operations, for example by defining how many worker processes should be spawned etc.
You probably would do it by calculating some number of workers depending on the length of the list or the load of the system etc.
From the website:
plists is a drop-in replacement for
the Erlang module lists, making most
list operations parallel. It can
operate on each element in parallel,
for IO-bound operations, on sublists
in parallel, for taking advantage of
multi-core machines with CPU-bound
operations, and across erlang nodes,
for parallizing inside a cluster. It
handles errors and node failures. It
can be configured, tuned, and tweaked
to get optimal performance while
minimizing overhead.
There is no, in my view, usefull generic load-balancing tool in otp. And perhaps it only usefull to have one in specific cases. It is easy enough to implement one yourself. plists may be useful in the same cases. I do not believe in parallel-libraries as a substitute to the real thing. Amdahl will haunt you forever if you walk this path.
The right number of worker processes is equal to the number of schedulers. This may vary depending of what other work is done on the system. Use,
erlang:system_info(schedulers_online) -> NS
to get the number of schedulers.
The notion of overhead when flooding the system with an abundance of worker processes is somewhat faulty. There is overhead with new processes but not as much as with os-threads. The main overhead is message copying between processes, this can be alleviated with the use of binaries since only the reference to the binary is sent. With eterms the structure is first expanded then copied to the other process.
There is no way how to predict cost of work mechanically without measure it e.g do it. Some person must determine how to partition work for some class of tasks. In load balancer word I understand something very different than in your question.