It's pretty much evident what's happening when you call getResultList() on a Query instance - the framework obtains a JDBC connection from the pool and returns it when the list is ready.
What is not clear to me is how JPA handles connections during the getResultStream() calls. Does it wait till I get to the end of the stream and then return the connection to the pool? What if I don't? What if I obtain a Spliterator from the stream and stop iterating somewhere in the middle?
My only guess is that such connections are returned to the pool after a timeout. Which would mean that, depending on the timeout value, I might need a much larger value of open DB connections. If I'm right, how do I configure the timeout value, particularly with Spring JPA?
The answer is it doesn't. You're supposed to wrap the returned stream in a 'try with resources' statement.
It seems that a lot of people don't realize that Streams are actually AutoCloseable and whenever they hold on to resources like JDBC connections or file handles, it's the developer's responsibility to properly close them.
Related
I am new to reactive world and trying to understand how the db-connections works under the hood with vertx-sql-clients.
I am using io.vertx:vertx-mysql-client, io.vertx:vertx-oracle-client and io.vertx:vertx-pg-client in my project.
As this is reactive database client, I can understand that one thread can handle multiple db-connections objects. I have set pool.maxSize to 1 and executed concurrently 5000 times a function, which gets the connection from the pool, executes a select query in the database and fetches some rows from the DB.
Now my question is, even though I have configured a single db-connection in the pool, still it can handle my 5000 requests concurrently, so how this works under the hood? is all the selects run over single database connection? if so, how it can handle a transaction management with single db-connection?
I don't understand some of Postgres mechanism and it makes me quite upset.
I usually use DBeaver as SQL client to query external pg base. If run create.. or insert.. queries and then connection for some reason is broken or invalidated, the pid is still running and finishes transaction.
But for some more complicated PL/pgSQL functions (with temp tables, loops, inserts, etc.) we wrote, breaking connection always causes process termination (it disappears from session list just before making next sql operation, eg. inserting a row in logtable). No matter if it's DBeaver editor or psql command.
I know that maybe disconnecting is critical problem, which should be eliminated and maybe I shouldn't expect process to successfully continue, but I do:) Or just to know why it happened and is it possible to prevent it?
If the network connection fails, the database server can detect that in two ways:
if it tries to send data to the client, it will figure out pretty quickly that the connection is down
if it tries to receive data from the client, it will only notice when the kernel's TCP keepalive mechanism has determined that the connection is down
When you say that sometimes execution of a function is terminated right away, I would say that is because the function returned data to the client.
In the case where a query keeps running, it is not attempting to return any data yet.
There is no cure for the former, but in PostgreSQL v14 you can prevent the latter by setting client_connection_check_interval. In addition, you have to set the PostgreSQL keepalive parameters so that the dead connection becomes known quickly.
See my article for more.
We use Spring integration for TCP socket communication with the hardware.
The client would be sending a sequence number to uniquely identify a message.
My requirement is to store these sequence numbers part of the socket message and validate them for non repetitive sequence numbers.
I went thru IdempotentReceiver, sounds like what i wanted.
But I need a durable and faster mechanism to store it, before unexpected shutdown of service and use the in memory cache for retrieving the latest sequence number.
Thank you in advance.!
You can use PropertiesPersistingMetadataStore for idempotent receiver:
The PropertiesPersistingMetadataStore is backed by a properties file and a PropertiesPersister.
By default, it only persists the state when the application context is closed normally. It implements Flushable so you can persist the state at will, be invoking flush().
See more in its JavaDocs.
Please look at this scotty app (it's taken directly from this old answer from 2014):
import Web.Scotty
import Database.MongoDB
import qualified Data.Text.Lazy as T
import Control.Monad.IO.Class
runQuery :: Pipe -> Query -> IO [Document]
runQuery pipe query = access pipe master "nutrition" (find query >>= rest)
main = do
pipe <- connect $ host "127.0.0.1"
scotty 3000 $ do
get "/" $ do
res <- liftIO $ runQuery pipe (select [] "stock_foods")
text $ T.pack $ show res
You see how the the database connection (pipe) is created only once when the web app launches. Subsequently, thousands if not millions of visitors will hit the "/" route simultaneously and read from the database using the same connection (pipe).
I have questions about how to properly use Database.MongoDB:
Is this the proper way of setting things up? As opposed to creating a database connection for every visit to "/". In this latter case, we could have millions of connections at once. Is that discouraged? What are the advantages and drawbacks of such an approach?
In the app above, what happens if the database connection is lost for some reason and needs to be created again? How would you recover from that?
What about authentication with the auth function? Should the auth function only be called once after creating the pipe, or should it be called on every hit to "/"?
Some say that I'm supposed to use a pool (Data.Pool). It looks like that would only help limit the number of visitors using the same database connection simultaneously. But why would I want to do that? Doesn't the MongoDB connection have a built-in support for simultaneous usages?
Even if you create connection per client you won't be able to create too many of them. You will hit ulimit. Once you hit that ulimit the client that hit this ulimit will get a runtime error.
The reason it doesn't make sense is because mongodb server will be spending too much time polling all those connections and it will have only as many meaningful workers as many CPUs your db server has.
One connection is not a bad idea, because mongodb is designed to send several requests and wait for responses. So, it will utilize as much resources as your mongodb can have with only one limitation - you have only one pipe for writing, and if it closes accidentally you will need to recreate this pipe yourself.
So, it makes more sense to have a pool of connections. It doesn't need to be big. I had an app which authenticates users and gives them tokens. With 2500 concurrent users per second it only had 3-4 concurrent connections to the database.
Here are the benefits connection pool gives you:
If you hit pool connection limit you will be waiting for the next available connection and will not get runtime error. So, you app will wait a little bit instead of rejecting your client.
Pool will be recreating connections for you. You can configure pool to close excess of connections and create more up until certain limit as you need them. If you connection breaks while you read from it or write to it, then you just take another connection from the pool. If you don't return that broken connection to the pool pool will create another connection for you.
If the database connection is closed then: mongodb listener on this connection will exit printing a error message on your terminal, your app will receive an IO error. In order to handle this error you will need to create another connection and try again. When it comes to handling this situation you understand that it's easier to use a db pool. Because eventually you solution to this will resemble connection pool very much.
I do auth once as part of opening a connection. If you need to auth another user later you can always do it.
Yes, mongodb handles simultaneous usage, but like I said it gives only one pipe to write and it soon becomes a bottle neck. If you create at least as many connections as your mongodb server can afford threads for handling them(CPU count), then they will be going at full speed.
If I missed something feel free to ask for clarifications.
Thank you for your question.
What you really want is a database connection pool. Take a look at the code from this other answer.
Instead of auth, you can use withMongoDBPool to if your MongoDB server is in secure mode.
Is this the proper way of setting things up? As opposed to creating a database connection for every visit to "/". In this latter case, we could have millions of connections at once. Is that discouraged? What are the advantages and drawbacks of such an approach?
You do not want to open one connection and then use it. The HTTP server you are using, which underpins Scotty, is called Warp. Warp has a multi-core, multi-green-thread design. You are allowed to share the same connection across all threads, since Database.MongoDB says outright that connections are thread-safe, but what will happen is that when one thread is blocked waiting for a response (the MongoDB protocol follows a simple request-response design) all threads in your web service will block. This is unfortunate.
We can instead create a connection on every request. This trivially solves the problem of one thread's blocking another but leads to its own share of problems. The overhead of setting up a TCP connection, while not substantial, is also not zero. Recall that every time we want to open or close a socket we have to jump from the user to the kernel, wait for the kernel to update its internal data structures, and then jump back (a context switch). We also have to deal with the TCP handshake and goodbyes. We would also, under high load, run out file descriptors or memory.
It would be nice if we had a solution somewhere in between. The solution should be
Thread-safe
Let us max-bound the number of connections so we don't exhaust the finite resources of the operating system
Quick
Share connections across threads under normal load
Create new connections as we experience increased load
Allow us to clean up resources (like closing a handle) as connections are deleted under reduced load
Hopefully already written and battle-tested by other production systems
It is this exactly problem that resource-pool tackles.
Some say that I'm supposed to use a pool (Data.Pool). It looks like that would only help limit the number of visitors using the same database connection simultaneously. But why would I want to do that? Doesn't the MongoDB connection have a built-in support for simultaneous usages?
It is unclear what you mean by simultaneous usages. There is one interpretation I can guess at: you mean something like HTTP/2, which has pipelining built into the protocol.
standard picture of pipelining http://research.worksap.com/wp-content/uploads/2015/08/pipeline.png
Above we see the client making multiple requests to the server, without waiting for a response, and then the client can receive responses back in some order. (Time flows from the top to the bottom.) This MongoDB does not have. This is a fairly complicated protocol design that is not that much better than just asking your clients to use connection pools. And MongoDB is not alone here: the simple request-and-response design is something that Postgres, MySQL, SQL Server, and most other databases have settled on.
And: it is true that connection pool limits the load you can take as a web service before all threads are blocked and your user just sees a loading bar. But this problem would exist in any of the three scenarios (connection pooling, one shared connection, one connection per request)! The computer has finite resources, and at some point something will collapse under sufficient load. Connection pooling's advantages are that it scales gracefully right up until the point it cannot. The correct solution to handling more traffic is to increase the number of computers; we should not avoid pooling simply due to this problem.
In the app above, what happens if the database connection is lost for some reason and needs to be created again? How would you recover from that?
I believe these kinds of what-if's are outside the scope of Stack Overflow and deserve no better answer than "try it and see." Buuuuuuut given that the server terminates the connection, I can take a stab at what might happen: assuming Warp forks a green thread for each request (which I think it does), each thread will experience an unchecked IOException as it tries to write to the closed TCP connection. Warp would catch this exception and serve it as an HTTP 500, hopefully writing something useful to the logs also. Assuming a single-connection model like you have now, you could either do something clever (but high in lines of code) where you "reboot" your main function and set up a second connection. Something I do for hobby projects: should anything odd occur, like a dropped connection, I ask my supervisor process (like systemd) to watch the logs and restart the web service. Though clearly not a great solution for a production, money-makin' website, it works well enough for small apps.
What about authentication with the auth function? Should the auth function only be called once after creating the pipe, or should it be called on every hit to "/"?
It should be called once after creating the connection. MongoDB authentication is per-connection. You can see an example here of how the db.auth() command mutates the MongoDB server's data structures corresponding to the current client connection.
I am using the Apache Http Client in a Scala application.
The application is fairly high throughput with high parallelism.
I am not sure but I think perhaps I am leaking connections. It seems that whenever the section of code that uses the client gets busy, the application become unresponsive. My suspicion is that I am leaking sockets or something which is then causing other aspects of the application to stop working. It may also not be leaking connections so much as not closing them fast enough.
For more context, occasionally, certain actions lead to this code being executed hundreds of times a minute in parallel. When this happens the Rest API (Spray) of the application becomes unresponsive. There are other areas of the application that operate in high parallelism as well and those never cause a problem with the applications responsiveness.
Cutting back on the parallelism of this section of code does seem to alleviate the problem but isn't a viable long term solution.
Am I forgetting to configure something, or configuring something incorrectly?
The code I am using is something like this:
class SomeClass {
val connectionManager = new PoolingHttpClientConnectionManager()
connectionManager.setDefaultMaxPerRoute(50)
connectionManager.setMaxTotal(500)
val httpClient = HttpClients.custom().setConnectionManager(connectionManager).build()
def postData() {
val post = new HttpPost("http://SomeUrl") // Typically this URL is fixed. It doesn't vary much if at all.
post.setEntity(new StringEntity("Some Data"))
try {
val response = httpClient.execute(post)
try {
// Check the response
} finally {
response.close()
}
} finally {
post.releaseConnection()
}
}
}
EDIT
I can see that I am building up a lot of connections in the TIME_WAIT state. I have tried adjusting the DefaultMaxPerRoute and the MaxTotal to a variety of values with no noticeable effect. It seems like I am missing something and as a result the connections are not being re-used, but I can't find any documentation that suggests what I am missing. It is critical that these connections get re-used.
EDIT 2
With further investigation, using lsof -p, I can see that if I set the MaxPerRoute to 10, there are in fact 10 connections being listed as "ESTABLISHED". I can see that the port numbers do not change. This seems to imply to me that in fact it is re-using the connections.
What that doesn't explain is why I am still leaking connections in this code? The reused connections and leaked connections (found with netstat -a) showing up in TIME_WAIT status share the same base url. So they are definitely related. Is it possible perhaps that I am re-using the connections but then somehow not properly closing the response?
EDIT 3
Located the source of the TIME_WAIT "leak". It was in an unrelated section of code. So it wasn't anything to do with the HttpClient. However after fixing up that code, all the TIME_WAITs went away, but the application is still becoming unresponsive when hitting the HttpClient code many times. Still investigating that portion.
You should really consider re-using HttpClient instance or at least the connection pool that underpins it instead of creating them for each new request execution. If you wish to continue doing the latter, you should also close the client or shut down the connection pool before they go out of scope.
As far as the leak is concerned, it should be relatively easy to track by running your application with context logging for connection management turned out as described here
IMO - you can use a much lower number of maxConnection per domain ( like 5 instead of 50 ) and still completely saturate your network bandwidth, if you use http efficiently.
im not a scala person ( android , java ) but have done lots and lots of optimization on http client side threadpools. IMO - blindly increasing connections per domain to 50 is masking some other serious issue with thruput.
2 points:
if you are using a shared "sharedPoolingClientConnManager" , correctly going to a small pool per domain and you conform to the recommended way of release your conn back to the pool ( you should be able to debug all this seeing a running metric of the state of connection per threadpool instance ) then u should be good.
whatever the parallelism feature of scala , you should understand something of how the 5 respective threads from the pool on a domain are sharing the socket?? IMO from the android/java experience is that even though each thread executor is supposedly doing blocking I/O to the server in the scope of that httpclient.exec statement, the actual channel management involved allows very high thruput without resorting to ASNyC client libs for http.
Android experience may not be relevant because client has only 4 threads. Having said that , even if you have 64 or more threads available , i just dont understand needing more than 10 connection per domain in order to keep your underlying http socket very , very busy with thruput.