These are the info logs
"2022-08-18T07:47:14.333850Z 32263817 [Note] [MY-010914] [Server] Aborted connection 32263817 to db: 'unconnected' user: 'xyz' host: '10.x.x.x' (Got an error reading communication packets)."
[Note] [MY-010914] [Server] Got packets out of order"
[Server] Got an error reading communication packets
I don't understand why I am getting this continuously on cloud sql logs also is this the reason why my db got crashed.
There are a lot of reasons that could perhaps lead to the MySQL connection packet issues.According to this thread MySQL Error Reading Communication Packets
MySQL network communication code was written under the assumption that queries are always reasonably short,and therefore can be sent to and processed by the server in one chunk, which is called a packet in MySQL terminology. The server allocates the memory for a temporary buffer to store the packet, and it requests enough to fit it entirely. This architecture requires a precaution to avoid having the server run out of memory---a cap on the size of the packet, which this option accomplishes.
The code of interest in relation to this option is found in sql/net_serv.cc. Take a look at my_net_read(), then follow the call to my_real_read() and pay particular attention to net_realloc().
This variable also limits the length of a result of many string functions. See sql/field.cc and sql/intem_strfunc.cc for details.
According to the MySQL Documentation
You can also get these errors if you send a query to the server that is incorrect or too large.you can increase the query limit by setting the server's max_allowed_packet variable, which has a default value of 1MB. You may also need to increase the maximum packet size on the client end. More information on setting the packet size is given in Section C.5.2.10, “Packet too large”.
An INSERT or REPLACE statement that inserts a great many rows can also cause these sorts of errors. Either one of these statements sends a single request to the server irrespective of the number of rows to be inserted; thus, you can often avoid the error by reducing the number of rows sent per INSERT or REPLACE.
Additionally you may have a look at this link1 & link2
Related
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.
Is there a way for a replication client to say whenever they it was able to successfully store the data, or is it that PostgreSQL is streaming pending data to the client and the moment data leave network interface it is considered delivered?
I'd think that client has a chance to say "ACK - I got the data", but I can't seem to find this anywhere... I'm simply wondering what if the client fails to store the data (e.g. due to power failure) - isn't there a way to get it again from Postgres?
General info here https://www.postgresql.org/docs/9.5/static/logicaldecoding.html
I'll answer my own Q.
After doing much more reading, I can say there is ACK-like mechanism there.
Under some conditions (e.g. on interval) server will ask logical replication consumer to report what was the last piece of data that was persisted (i.e. flushed to disk or similar). Then and only then server will treat data up to that reported point delivered for given replication channel.
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.
Does this value only affect incoming traffic? If i set this value to say 4MB on zookeeper server as well as zookeeper client and I start my client, will I still get data > 4MB when I do a request for a path /abc/asyncMultiMap/subs. If /subs has data greater than 4MB is the server going to break it up in chunks <= 4MB and send it in pieces to the client?
I am using zookeeper 3.4.6 on both client (via vertx-zookeeper) and server. I see errors on clients where it complains that packet length is greater than 4MB.
java.io.IOException: Packet len4194374 is out of range!
at org.apache.zookeeper.ClientCnxnSocket.readLength(ClientCnxnSocket.java:112) ~[zookeeper-3.4.6.jar:3.4.6-1569965]
"This is a server-side setting"
This statement is incorrect, jute.maxbuffer is evaluated on client as well by Record implementing classes that receive InputArchive. Each time a field is read and stored into an InputArchive the value is checked against jute.maxbuffer. Eg ClientCnxnSocket.readConnectResult
I investigated it in ZK 3.4
There is no chunking in the response.
This is a server-side setting. You will get this error if the entirety of the response is greater than the jute.maxbuffer setting. This response limit includes the list of children of znodes as well so even if subs does not have a lot of data but has enough children such that their length of their paths exceeds the max buffer size you will get the error.
I have problem reading from socket. There is asterisk instance running with plenty of calls (10-60 in a minute) and I'm trying to read and process CDR events related to those calls (connected to AMI).
Here is library which I'm using (not mine, but was pushed to fork because of bugs) https://github.com/warik/gami
Its pretty straightforward, main action goes in gami.go - readDispatcher.
buf := make([]byte, _READ_BUF) // read buffer
for {
rc, err := (*a.conn).Read(buf)
So, there is TCPConn (a.conn) and buffer with size 1024 to which I'm reading messages from socket. So far so good, but eventually, from time to time (this time may vary from 10 minutes to 5 hours independently of data amount which comes through socket) Read operation fails with io.EOF error. I was trying to reconnect and relogin immediately, but its also impossible - connection times out, so i was pushed to wait for about 40-60sec, and this time is very crucial to me, I'm losing a lot of data because of delay. I was googling, reading sources and trying a lot of stuff - nothing. The most strange thing, that simple socket opened in python or php does not fail.
Is it possible that problem because of lack of file descriptors to represent socket on mine machine or on asterisk server?
Is it possible that problem in asterisk configuration (because i have another asterisk on which this problem doesn't reproduce, but also, i have time less calls on last one)?
Is it possible that problem in my way to deal with socket connection or with Go in general?
go version go1.2.1 linux/amd64
asterisk 1.8
Update to latest asterisk. There was bug like that when AMI send alot of data.
For check issue, you have send via ami command like "COMMAND sip show peers"(or any other long output command) and see result.
Ok, problem was in OS socket buffer overflow. As appeared there were to much data to handle.
So, were are three possible ways to fix this:
increase socket buffer volume
increase somehow speed of process which reeds data from socket
lower data volume or frequency
The thing that gami is by default reading all data from asterisk. And i was reading all of them and filter them after actual read operation. According that AMI listening application were running on pretty poor PC it appeared that it simply cannot read all the data before buffer capacity will be exposed.But its possible to receive only particular events, by sending "Events" action to AMI and specifying desired "EventMask".
So, my decision was to do that. And create different connections for different events type.