I see redis capable of 10s of thousands of connections. But why does it need so many? The connection(s) should be established by server, and only 1 server-redis connection should be sufficient for as many sessions as may be.
Is there something wrong with my logic?
You are right - one connection from server should be sufficient, BUT under "server" you have to imagine single instance of HTTP server running. On single machine there can me a lot of running server instances.
Then multiply this count of servers by count of individual machines using the same redis server and easily you are on very large numbers of connections.
Kacer is right about the scenario. However, assume one more scenario.. where application keeps connection pool for performance reason.
Assume you are a proud owner of a Travel agency. However your agency have only 1 car and 1 driver. But people are mad about your company and want to travel by your travel agency only. So you need to send 100 people from Destination A to B.
A ———— B
Now, when first person goes second will have to wait until car returns after dropping 1st person. then 2nd will go and then 3rd. Although you have the fastest car and fastest driver but still it will take some time
Now, assume you have 50 cars and 50 drivers… Would be much better right?
And what is unfortunately in first scenario your car met with an accident. You will have no other option. But if you manage connection pool you have 49 other alternates.
Related
I'm look recommendations on how to achieve low latency for the following network protocol:
Alice sends out a request for information to many peers selected at random from a very large pool.
Each peer responds with a small packet <20kb.
Alice aggregates the responses and selects a peer accordingly.
Alice and the selected peer then continue to the second phase of the protocol whereby a sequence of 2 requests and responses are performed.
Repeat from 1.
Given that steps 1 and 2 do not need to be reliable (as long as a percentage of responses arrive back we proceed to step 3) and that 1 is essentially a multicast, this part of the protocol seems to suit UDP - setting up a TCP connection to these peers would add an addition round trip.
However step 4 needs to be reliable - we can't tolerate packet loss during the subsequent requests/responses.
The conundrum I'm facing is that UDP suits 1 and 2 and TCP protocol suits 4. Connecting to every peer selected in 1 is slow especially since we aim to transmit just 20kb, however UDP cannot be tolerated for step 4. Handshaking the peer selected in 4. would require an additional round trip, which compared to the 3 round trips still is a considerable increase in total time.
Is there some hybrid scheme whereby you can do a TCP handshake while transmitting a small amount of data? (The handshake could be merged into 1 and 2 and hence doesn't add any additional round trip time.)
Is there a name for such protocols? What should I read to become more acquainted with such problems?
Additional info:
Participants are assumed to be randomly distributed around the globe and connected via the internet.
The pool selected from in step 1. is on the order of 1000 addresses and the the sample from the pool on the order of 10 to 100.
There's not enough detail here to do a well-informed criticism. If you were hiring me for advice, I'd want to know a lot more about the proposal, but since I'm doing this for free, I'll just answer the question as asked, and try to make it practical rather than ideal.
I'd argue that UDP is not suitable for the early part of your protocol. You can't just multicast a single packet to a large number of hosts on the Internet (although you can do it on typical LANs). A 20KB payload is not the sort of thing you can generally transmit in a single datagram in any case, and the moment messages fail to fit in a single datagram, UDP loses most of its attraction, because you start reinventing TCP (badly).
Probably the simplest thing you can do is base your system on HTTP, and work with implementations which incorporate all the various speed-ups that Google (mostly) has been putting into HTTP development. This includes TCP Fast Open, and things like it. Initiate connections out to your chosen servers; some will respond faster than others: use that to your advantage by going with the quickest ones. Don't underestimate the importance of efficient implementation relative to theoretical round-trip time, by the way.
For stage two, continue with HTTP as before. For efficiency, you could hold all the connections open at the end of phase one and then close all the ones except your chosen phase two partner. It's not clear from your description that the phase two exchange lends itself to the HTTP model, though, so I have to hand-wave this a bit.
It's also possible that you can simply hold TCP connections open to all available peers more or less permanently, thus dodging the cost of connection establishment nearly all the time. A thousand simultaneous open connections is large, but not outrageous in most contexts (although you may need to tweak OS settings to allow it). If you do that, you can just talk whatever protocol you like over TCP. If it's a truly peer-to-peer protocol, you only need one TCP connection per pair. Implementing this kind of thing is tricky, though: an average programmer will do a terrible job of it, in my experience.
I have trouble with establishing asynchronous point to point channel using ZeroMQ.
My approach to build point to point channel was that it generates as many ZMQ_PAIR sockets as possible up to the number of peers in the network. Because ZMQ_PAIR socket ensures an exclusive connection between two peers, it needs the same number of peers. My first attempt is realized as the following diagram that represents paring connections between two peers.
But the problem of the above approach is the fact that each pairing socket needs a distinct bind address. For example, if four peers are in the network, then each peer should have at least three ( TCP ) address to bind the rest of peers, which is very unrealistic and inefficient.
( I assume that peer has exactly one unique address among others. Ex. tcp://*:5555 )
It seems that there is no way other than using different patterns, which contain some set of message brokers, such as XREQ/XREP.
( I intentionally avoid broker based approach, because my application will heavily exchange message between peers, which it will often result in performance bottleneck at the broker processes. )
But I wonder that if there is anybody who uses ZMQ_PAIR socket to efficiently build point to point channel? Or is there a way to bypass to have distinct host IP addresses for multiple ZMQ_PAIR sockets to bind?
Q: How to effectively establish ... well,
Given the above narrative, the story of "How to effectively ..." ( where a metric of what and how actually measures the desired effectivity may get some further clarification later ), turns into another question - "Can we re-factor the ZeroMQ Signalling / Messaging infrastructure, so as to work without using as many IP-addresses:port#-s as would the tcp://-transport-class based topology actually need?"
Upon an explicitly expressed limit of having not more than a just one IP:PORT# per host/node ( being thus the architecture's / desing's the very, if not the most expensive resource ) one will have to overcome a lot troubles on such a way forward.
It is fair to note, that any such attempt will come at an extra cost to be paid. There will not be any magic wand to "bypass" such a principal limit expressed above. So get ready to indeed pay the costs.
It reminds me one Project in TELCO, where a distributed-system was operated in a similar manner with a similar original motivation. Each node had an ssh/sshd service setup, where local-port forwarding enabled to expose a just one publicly accessible IP:PORT# access-point and all the rest was implemented "inside" a mesh of all the topological links going through ssh-tunnels not just because the encryption service, but right due to the comfort of having the ability to maintain all the local-port-forwarding towards specific remote-ports as a means of how to setup and operate such exclusive peer-to-peer links between all the service-nodes, yet having just a single public access IP:PORT# per node.
If no other approach will seem feasible ( PUB/SUB being evicted for either traffic actually flowing to each terminal node in cases of older ZeroMQ/API versions, where Topic-filtering gets processed but on the SUB-side, which both security and network Departments will not like to support, or for concentrated workloads and immense resources needs on PUB-side, in cases of newer ZeroMQ/API versions, where Topic-filter is being processed on the sender's side. Adressing, dynamic network peer (re-)discovery, maintenance, resources planning, fault resilience, ..., yes, not any easy shortcut seems to be anywhere near to just grab and (re-)use ) the above mentioned "stone-age" ssh/sshd-port-forwarding with ZeroMQ, running against such local-ports only, may save you.
Anyway - Good Luck on the hunt!
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'm currently doing some research into connection and socket timeout settings but I'm fairly new to this stuff.
As a stab in the dark we are thinking of adding 40 seconds as a connection and sockettimeout when we run json over http calls to another server.
httpConnectionManagerParams.setConnectionTimeout(40000);
httpConnectionManagerParams.setSoTimeout(40000);
But really I don't know about how to understand what the ideal settings or best practices to use are. I would appreciate it if someone could give me some pointers on what to consider when or tips on how to come up with a good gestimate on these settings.
The sort of advice I'm looking for is something like ... 40 seconds is far too long because it might cause another issue.... or ... the higher you set this value the more chance you have of causing another isssue... or 40 seconds is not to high at all... or to work out an ideal figure multiply Y by T
thanks
EDIT
Adding firebug trace of server call.
There's no reason whatsoever why they should be equal. Considering each condition separately, you want to set it high enough that a timeout will indicate a genuine problem rather than just a temporary overload, and low enough that you maintain responsiveness of the application.
As a rule, 40 seconds is far too long for a connect timeout. I would view anything in double figures with suspicion. A server should be able to accept tens or hundreds of connections per second.
Read timeouts are a completely different matter. The 'correct' value, if there is such a thing, depends entirely on the average service time for the request and on its variance. As a starting point, you might want to set it to double the expected service time, or the average service time plus two or three standard deviations, depending entirely on your service level requirements and the performance of your server and its variance. There is no hard and fast rule about this. Many contractual service level agreements (SLAs) specify a 'normal' response time of two seconds, which may inform your deliberations. But it's your decision.
We monitor our mongoDB connection count using this:
http://godoc.org/labix.org/v2/mgo#GetStats
However, we have been facing a strange connection leak issue where the connectionCount creeps up consistently by 1 more open connection per 10 seconds. (That's regardless whether there is any requests). I can spin up a server in localhost, leave it there, do nothing, the conectionCount will still creep up. Connection count eventually creeps up to a few thousand and it kills the app/db then and we have to restart the app.
This might not be enough information for you to debug. Does anyone have any ideas, connection leaks that you have dealt with in the past. How did you debug it? What are some of the way that I can debug this.
We have tried a few things, we scanned our code base for any code that could open a connection and put counters/debugging statements there, and so far we have found no leak. It is almost like there is a leak in a library somewhere.
This is a bug in a branch that we have been working on and there have been a few hundred commits into it. We have done a diff between this and master and couldn't find why there is a connection leak in this branch.
As an example, there is the dataset that I am referencing:
Clusters: 1
MasterConns: 9936 <-- creeps up 1 per second
SlaveConns: -7359 <-- why is this negative?
SentOps: 42091780
ReceivedOps: 38684525
ReceivedDocs: 39466143
SocketsAlive: 78 <-- what is the difference between the socket count and the master conns count?
SocketsInUse: 1231
SocketRefs: 1231
MasterConns is the number that creeps up one per 10 second. I am not entirely sure what the other numbers can mean.
MasterConns cannot tell you whether there's a leak or not, because it does not decrease. The field indicates the number of connections made since the last statistics reset, not the number of sockets that are currently in use. The latter is indicated by the SocketsAlive field.
To give you some additional relief on the subject, every single test in the mgo suite is wrapped around logic that ensures that statistics show sane values after the test finishes, so that potential leaks don't go unnoticed. That's the main reason why such statistics collection system was introduced.
Then, the reason why you see this number increasing every 10 seconds or so is due to the internal activity that happens to learn the status of the cluster. That said, this behavior was recently changed so that it doesn't establish new connections and instead picks existent sockets from the pool, so I believe you're not using the latest release.
Having SlaveConns negative looks like a bug. There's a small edge case about statistics collection for connections made, because we cannot tell whether a given server is a master or a slave before we've talked to it, so there might be an uncovered path. If you still see that behavior after you upgrade, please report the issue and I'll be happy to look at it.
SocketsInUse is the number of sockets that are still being referenced by one or more sessions, whether they are alive (the connection is established) or not. SocketsAlive is, again, the real number of live TCP connections. The delta between the two indicates that a number of sessions were not closed. This may be okay, if they are still being held in memory by the application and will eventually be closed, or it may be a leak if a session.Close operation was missed by the application.