I would like to use the web server library ESPAsyncWebServer to create an API on my ESP32. But I was wondering, since the web server most likely runs on the secondary processor, do I need to make accessing data thread safe? So that the main loop and the web server can both access the data?
Or are they actually running on the same thread an I don't need to worry about it?
Yes, you need to implement thread safe access to any shared data. Asynchronous processing means it runs in its own thread. See the documentation for clarification.
Related
Nodejs can not have a built-in thread API like java and .net
do. If threads are added, the nature of the language itself will
change. It’s not possible to add threads as a new set of available
classes or functions.
Nodejs 10.x added worker threads as an experiment and now stable since 12.x. I have gone through the few blogs but did not understand much maybe due to lack of knowledge. How are they different than the threads.
Worker threads in Javascript are somewhat analogous to WebWorkers in the browser. They do not share direct access to any variables with the main thread or with each other and the only way they communicate with the main thread is via messaging. This messaging is synchronized through the event loop. This avoids all the classic race conditions that multiple threads have trying to access the same variables because two separate threads can't access the same variables in node.js. Each thread has its own set of variables and the only way to influence another thread's variables is to send it a message and ask it to modify its own variables. Since that message is synchronized through that thread's event queue, there's no risk of classic race conditions in accessing variables.
Java threads, on the other hand, are similar to C++ or native threads in that they share access to the same variables and the threads are freely timesliced so right in the middle of functionA running in threadA, execution could be interrupted and functionB running in threadB could run. Since both can freely access the same variables, there are all sorts of race conditions possible unless one manually uses thread synchronization tools (such as mutexes) to coordinate and protect all access to shared variables. This type of programming is often the source of very hard to find and next-to-impossible to reliably reproduce concurrency bugs. While powerful and useful for some system-level things or more real-time-ish code, it's very easy for anyone but a very senior and experienced developer to make costly concurrency mistakes. And, it's very hard to devise a test that will tell you if it's really stable under all types of load or not.
node.js attempts to avoid the classic concurrency bugs by separating the threads into their own variable space and forcing all communication between them to be synchronized via the event queue. This means that threadA/functionA is never arbitrarily interrupted and some other code in your process changes some shared variables it was accessing while it wasn't looking.
node.js also has a backstop that it can run a child_process that can be written in any language and can use native threads if needed or one can actually hook native code and real system level threads right into node.js using the add-on SDK (and it communicates with node.js Javascript through the SDK interface). And, in fact, a number of node.js built-in libraries do exactly this to surface functionality that requires that level of access to the nodejs environment. For example, the implementation of file access uses a pool of native threads to carry out file operations.
So, with all that said, there are still some types of race conditions that can occur and this has to do with access to outside resources. For example if two threads or processes are both trying to do their own thing and write to the same file, they can clearly conflict with each other and create problems.
So, using Workers in node.js still has to be aware of concurrency issues when accessing outside resources. node.js protects the local variable environment for each Worker, but can't do anything about contention among outside resources. In that regard, node.js Workers have the same issues as Java threads and the programmer has to code for that (exclusive file access, file locks, separate files for each Worker, using a database to manage the concurrency for storage, etc...).
It comes under the node js architecture. whenever a req reaches the node it is passed on to "EVENT QUE" then to "Event Loop" . Here the event-loop checks whether the request is 'blocking io or non-blocking io'. (blocking io - the operations which takes time to complete eg:fetching a data from someother place ) . Then Event-loop passes the blocking io to THREAD POOL. Thread pool is a collection of WORKER THREADS. This blocking io gets attached to one of the worker-threads and it begins to perform its operation(eg: fetching data from database) after the completion it is send back to event loop and later to Execution.
I am a beginner of Linux programming and I am not sure if what I expect is feasible. I would appreciate if someone could give me some tips.
What I want to do is to develop a shared object (.so file), which could be used by multiple applications. If one of those applications calls an initialize function in the shared object, a new thread will be created to run an infinite loop to accept incoming events. This thread will keep running even after the initialize function return. Thus all applications could keep sending events to this thread for processing.
I wonder if this could be achieved? Any idea will be appreciated.
As has been noted in the comments, this cannot be done: you cannot directly invoke a function in another process. This is why RPC, IPC, web services etc. were invented.
The following two diagrams are my understanding on how threads work in a event-driven web server (like Node.js + JavaScript) compared to a non-event driven web server (like IIS + C#)
From the diagram is easy to tell that on a traditional web server the number of threads used to perform 3 long running operations is larger than on a event-driven web server (3 vs 1.)
I think I got the "traditional web server" counts correct (3) but I wonder about the event-driven one (1). Here are my questions:
Is it correct to assume that only one thread was used in the event-driven scenario? That can't be correct, something must have been created to handle the I/O tasks. Right?
How did the evented server handled the I/O? Let's say that the I/O was to read from a database. I suspect that the web server had to create a thread to hand off the job of connecting to the database? Right?
If the event-driven web server indeed created threads to handle the I/O where is the gain?
A possible explanation for my confusion could be that on both scenarios, traditional and event-driven, three separate threads were indeed created to handle the I/O (not shown in the pictures) but the difference is really on the number of threads on the web server per-se, not on the I/O threads. Is that accurate?
Node may use threads for IO. The JS code runs in a single thread, but all the IO requests are running in parallel threads. If you want some JS code to run in parallel threads, use thread-a-gogo or some other packages out there which mitigate that behaviour.
Same as 1., threads are created by Node for IO operations.
You don't have to handle threading, unless you want to. Easier to develop. At least that's my point of view.
A node application can be coded to run like another web server. Typically, JS code runs in a single thread, but there are ways to make it behave differently.
Personally, I recommend threads-a-gogo (the package name isn't that revealing, but it is easy to use) if you want to experiment with threads. It's faster.
Node also supports multiple processes, you may run a completely separate process if you also want to try that out.
The best way to picture NodeJS is like a furious squirrel (i.e. your thread) running in a wheel with an infinite number of pigeons (your I/O) available to pass messages around.
I/O in node is "free". Your squirrel works to set up the connection and send the pigeon off, then can go on to do other things while the pigeon retrieves the data, only dealing with the data when the pigeon returns.
If you write bad code, you can end up having the squirrel waiting for each pigeon.
So always write non-blocking i/o code.
If you can encourage your Pigeons to promise to come back ;)
Promises and generators are probably the best approach you can take to this.
HOWEVER you can always use Node cluster to establish a master squirrel that will procreate child squirrels based on the number of CPUs the master squirrel can find to dole out the work.
Hope this helps and note the complete lack of a car analogy.
I'm following the boost-asio tutorial and don't know how to make a multi-threaded server using boost. I've compiled and tested the daytime client and daytime synchronous server and improved the communication (server asks the client for a command, processes it and then returns the result to the client). But this server can handle only one client at one time.
I would like to use boost to make a multi-threaded server. There is also daytime asynchronous server which executes
boost::asio::io_service io_service;
tcp_server server(io_service);
io_service.run();
in the main program function. The question is - is boost creating a thread for each client somewhere inside? Is this a multi-threaded solution? If not - how to make a multi-threaded server with boost? Thanks for any advice.
have a look at this tutorial. in short terms:
io_service.run() in multiple threads gives a thread pool
multiple io_services give completely separated threads
You don't need to explicitly work with threads when you want to support multiple clients. But for that you should use asynchronous calls (as opposed to synchronous, which are used in the tutorials you listed). Have a look at the asynchronous echo tcp server example, it serves multiple clients without using threads.
is boost creating a thread for each client somewhere inside?
When working with asynchronous calls, boost asio is doing these things behind the scenes. It could use threads, but it usually doesn't because there are other, preferred mechanisms for working with multiple sockets at once. For example on linux you have epoll, select and poll (in order of preference). I'm not sure what the situation is on windows, there might be other mechanisms or the preference order might be different. But in any case, boost asio takes care of this, chooses the best mechanism there is for your platform and hides it behind those asynchronous calls.
Why does being thread safe matter in a web app? Pylons (Python web framework) uses a global application variable which is not thread safe. Does this matter? Is it only a problem if I intend on using multi-threading? Or, does it mean that one user might not have updated state if another user... I'm just confusing myself. What's so important with this?
Threading errors can lead to serious and subtle problems.
Say your system has 10 members. One more user signs up to your system and the application adds him to the roster and increments the count of members; "simultaneously", another user quits and the application removes him from the roster and decrements the count of members.
If you don't handling threading properly, your member count (which should be 10) could easily be nine, 10, or 11, and you'll never be able to reproduce the bug.
So be careful.
You should care about thread safety. E.g in java you write a servlet that provides some functionality. The container will deploy an instance of your servlet, and as HTTP requests arrive from clients, over different TCP connections, each request is handled by a separate thread which in turn will call your servlet. As a result, you will have your servlet being call from multiple threads. So if it is not thread-safe, then erroneous result will be returned to the user, due to data corruption of access to shared data by threads.
It really depends on the application framework (which I know nothing about in this case) and how the web server handles it. Obviously, any good webserver is going to be responding to multiple requests simultaneously, so it will be operating with multiple threads. That web server may dispatch to a single instance of your application code for all of these requests, or it may spawn multiple instances of your web application and never use a given instance concurrently.
Even if the app server does use separate instances, your application will probably have some shared state--say, a database with a list of users. In that case, you need to make sure that state can be accessed safely from multiple threads/instances of your web app.
Then, of course, there is the case where you use threading explicitly in your application. In that case, the answer is obvious.
Your Web Application is almost always multithreading. Even though you might not use threads explicitly. So, to answer your questions: it's very important.
How can this happen? Usually, Apache (or IIS) will serve several request simultaneously, calling multiple times from multiple threads your python programs. So you need to consider that your programs run in multiple threads concurrently and act accordingly.
(This was too long to add a comment to the other fine answers.)
Concurrency problems (read: multiple access to shared state) is a super-set of threading problems. The (concurrency problems) can easily exist at an "above thread" level such as a process/server level (the global variable in the case you mention above is process-unique value, which in turn can lead to an inconsistent view/state if there are multiple processes).
Care must be taken to analyze the data consistency requirements and then implement the software to fulfill those requirements. I would always err on the side of safe, and only degrade in carefully analyzed areas where it is acceptable.
However, note that CPython runs only one thread context for Python code execution (to get true concurrent threads you need to write/use C extensions), so, while you can get a form of race condition upon expected data, you won't get (all) the same kind of partial-write scenarios and such that may plague C/C++ programs. But, once again. Err on the side of a consistent view.
There are a number of various existing methods of making access to a global atomic -- across threads or processes. Use them.