pymysql.err.OperationalError - Lost connection to MySQL server during query - python

I am using Python script to insert records into MySQL database table.
The script fails with the following error message.
MySQL version is 8.0.17 ,Python version 3.6.5
(pymysql.err.OperationalError) (2013, 'Lost connection to MySQL server during query ([WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond)')
(Background on this error at: http://sqlalche.me/e/e3q8)
The issue is for only few tables.

MySQL automatically closes connections that have been idle for a specific period of time (wait_timeout for non-interactive connections). Therefore it may happen, that your connections are closed if there is too much idle time and connections are not renewed or connections are invalidated because of server restarts.
SQL-Alchemy mentions several strategies on how to tackle the issue of automatic disconnects and database restarts in its documentation on how to deal with pool disconnects.
Two options that you should have a look at are the pool_pre_ping parameter that adds a SELECT 1 before each query to check if the connection is still valid, otherwise the connection will be recycled.
The other option is pool_recycle time that should always be less then your mysql wait_timeout. After this time the connection is automatically recycled to not run in the wait_timeout.
You can check your connections in MySQL using the command
SHOW PROCESSLIST;
where you should see all open connection an the status they are in.

Related

mysql.connector to AWS RDS database timing out

I have an RDS database that a program I created using python and Mysql connect to, in order to keep track of usage of the program. Anytime the program is used, it adds 1 to a counter on the RDS database. Just this week the program has started throwing an error connecting to the RDS SQL database after about an hour of use. Previous to this, I could leave the software running for days without ever timing out. Closing the software and re-opening it, to re-establish the connection allows me to connect for approx another hour or so before it times out again.
I am connecting using the following parameters:
awsConn = mysql.connector.connect(host='myDatabase.randomStringofChars.us-east-1.rds.amazonaws.com', database='myDatabase', port=3306, user='username', password='password')
Did something recently change with AWS/RDS, do I just need to pass a different parameter into the connection string, or do I just need to add somewhere into my program to attempt to re-establish the connection every so often?
Thanks

Does the pre ping feature in SqlAlchemy db pools automatically reconnect and send the SQL command in case the pre ping check fails?

I want some clarification on how the pre ping feature exactly works with SqlAlchemy db pools. Let's say I try to make a SQL query to my database with the db pool. If the db pool sends a pre ping to check the connection and the connection is broken, does it automatically handle this? By handling I mean that it reconnects and then sends the SQL query? Or do I have to handle this myself in my code?
Thanks!
From the docs, yes stale connections are handled transparently:
The calling application does not need to be concerned about organizing operations to be able to recover from stale connections checked out from the pool.
... unless:
If the database is still not available when “pre ping” runs, then the
initial connect will fail and the error for failure to connect will be
propagated normally. In the uncommon situation that the database is
available for connections, but is not able to respond to a “ping”, the
“pre_ping” will try up to three times before giving up, propagating
the database error last received.

psycopg2.OperationalError: server closed the connection unexpectedly (Airflow in AWS, connection drops on both sides)

We have an Airflow instance running in AWS Fargate. It connects to an on-premise Postgres server (on Windows) and tries to load data from a (complicated) view. It uses a PostgresHook for that. However, the task in the DAG fails in Airflow with this error:
File "/usr/local/lib/python3.7/site-packages/airflow/hooks/dbapi_hook.py", line 120, in get_records
cur.execute(sql)
psycopg2.OperationalError: server closed the connection unexpectedly
This probably means the server terminated abnormally
before or while processing the request.
A while ago, the error occurred after some 10-15 minutes. Now, it occurs faster, after 5 minutes or even faster.
I have looked in the Postgres logs, that shows (confusingly) that it was the client that closed the connection:
LOG: could not send data to client: An existing connection was forcibly closed by the remote host.
FATAL: connection to client lost
I have tried a bunch of potential solutions already.
Without Airflow
Connnecting to the server outside of Airflow, using psycopg2 directly: works (using the complicated view).
Different table
Trying to load data from a different table from Airflow in the cloud: works, finishes quickly too. So this "timeout" only occurs because the query takes a while.
Running the Airflow container locally
At first I could reproduce this issue, but I (think I) solved it by adding some extra parameters in the postgres connection string: keepalives=1&keepalives_idle=60&keepalives_interval=60. However, I cannot reproduce this fix in the Airflow in the cloud, because when I add these parameters there, the error remains.
Increase timeouts
See above, I added keepalives, but I also tried to reason about other potential timeouts. I added a timeout execution_timeout to the DAG arguments, to no avail. We also checked networking timeouts, but given the irregular pattern of the connection failures, it doesn't really sound like such a hard timeout...
I am at a loss here. Any suggestions?
Update: we have solved this problem through a workaround. Instead of keeping the connection open while the complex view is being queried, we have turned the connection into an asynchronous connection (i.e., aconn = psycopg2.connect(database='test', async=1) from psycopg docs). Furthermore, we have turned the view into a materialized view, such that we only call a REFRESH MATERIALIZED VIEW through the asynchronous connection, and then we can just SELECT * on the materialized view a while later, which is very fast.

SQLAlchemy DatabaseError server close connection

I am running a several processes in python using multiprocessing. I am hitting a postgresql database and I keep getting this error:
(DatabaseError) server closed the connection unexpectedlyThis probably means the server terminated abnormallybefore or while processing the request.
The db admin tells he is not seeing any errors on his side and I can't figure out what is causings this.

Postgres psycopg2 DatabaseError on CHECK EXISTS on multiprocessing application

I'm using Postgres and psycopg2 as my driver in a multiprocessing application. In only 2 processes I am getting this error (I've tried 8 and it blows up pretty fast).
cursor.execute("SELECT EXISTS(SELECT * FROM users WHERE name='{0}');".format(name))
DatabaseError: error with no message from the libpq
LOG: unexpected EOF on client connection with an open transaction
Googling this error message was no help since there are several reasons why that error can occur. It is possible that other transactions are happening on other processes, but they each create their own database connection. I'm also closing the database connection after each process is complete and reconnecting when it is restarted.
My theory is that there are a lot of database commands happening at the same time and postgres doesn't like this for whatever reason. I'm not sure how to solve this since the application has to run this way.

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