I have a file called db.py with the following code:
from sqlalchemy import create_engine
from sqlalchemy.orm import scoped_session, sessionmaker
engine = create_engine('sqlite:///my_db.sqlite')
session = scoped_session(sessionmaker(bind=engine,autoflush=True))
I am trying to import this file in various subprocesses started using a spawn context (potentially important, since various fixes that worked for fork don't seem to work for spawn)
The import statement is something like:
from db import session
and then I use this session ad libitum without worrying about concurrency, assuming SQLite's internal locking mechanism will order transactions as to avoid concurrency error, I don't really care about transaction order.
This seems to result in errors like the following:
sqlite3.ProgrammingError: SQLite objects created in a thread can only be used in that same thread. The object was created in thread id 139813508335360 and this is thread id 139818279995200.
Mind you, this doesn't directly seem to affect my program, every transaction goes through just fine, but I am still worried about what's causing this.
My understanding was that scoped_session was thread-local, so I could import it however I want without issues. Furthermore, my assumption was that sqlalchemy will always handle the closing of connections and that sqllite will handle ordering (i.e. make a session wait for another seesion to end until it can do any transaction).
Obviously one of these assumptions is wrong, or I am misunderstanding something basic about the mechanism here, but I can't quite figure out what. Any suggestions would be useful.
The problem isn't about thread-local sessions, it's that the original connection object is in a different thread to those sessions. SQLite disables using a connection across different threads by default.
The simplest answer to your question is to turn off sqlite's same thread checking. In SQLAlchemy you can achieve this by specifying it as part of your database URL:
engine = create_engine('sqlite:///my_db.sqlite?check_same_thread=False')
I'm guessing that will do away with the errors, at least.
Depending on what you're doing, this may still be dangerous - if you're ensuring your transactions are serialised (that is, one after the other, never overlapping or simultaneous) then you're probably fine. If you can't guarantee that then you're risking data corruption, in which case you should consider a) using a database backend that can handle concurrent writes, or b) creating an intermediary app or service that solely manages sqlite reads and writes and that your other apps can communicate with. That latter option sounds fun but be warned you may end up reinventing the wheel when you're better off just spinning up a Postgres container or something.
Related
i have a real headache from trying to understand the cause of the following problem. We are using a combination of the following libraries:
pyTelegramBotAPI to process requests in a multi-threaded way
SQLAlchemy
sqlite
The SQLAlchemy was first using NullPool and now is configured to utilize QueuePool. I am also using the following idiom to have a new DB session firing up for each thread (as per my understanding)
Session = sessionmaker(bind=create_engine(classes.db_url, poolclass=QueuePool))
#contextmanager
def session_scope():
session = Session()
try:
yield session
session.commit()
except:
session.rollback()
raise
finally:
session.close()
#bot.message_handler(content_types=['document'])
def method_handler:
with session_scope() as session:
do_database_stuff_here(session)
Nevertheless I am still getting this annoying exception: (sqlite3.ProgrammingError) SQLite objects created in a thread can only be used in that same thread
Any ideas? ;) In particular, i don't get how it is possible for another tread to get somewhere in between the db operations...this is probably the reason of the pesky exception
update 1: if i change the poolclass to SingletonThreadPool, then there seems to be no more errors coming up. However, the documentation of SQLAlchemy tells that it's not production rife.
As you can see in the source, sqlite will raise this exception inside pysqlite_check_thread if the connection object is reused across any threads.
By using a QueuePool, you are telling SQLAchemy it is safe to reuse connections across multiple threads. It will therefore just pick a connection from the pool for any session no matter which thread it is on. This is why you're hitting the error. The first time you create and use a connection, you'll be fine; however the next use will probably be on a different thread and so fail the check.
This is why SQLAlchemy mandates the use of other pools such as SingletonThreadPool and NullPool.
Assuming you are using a file based database, you should use the NullPool. This will give you good concurrency on reads. Write access concurrency is always going to be an issue for sqlite; if you need this, you probably want a diffenet database.
Something that may be worth trying: use scoped_session instead of your contextmanager; scoped_session implicitly creates a thread-local session when it is accessed from a different thread. Be sure also to use NullPool.
from sqlalchemy.orm import scoped_session
sessionmaker(bind=create_engine(classes.db_url, poolclass=NullPool))
session = scoped_session()
Note that you can use this scoped session directly as if it were just a regular session, even though it is actually creating thread-local sessions behind the scenes when it is being used.
For scoped_session, should call session.remove() for after you're done (i.e., after each method_handler) and explicitly call session.commit() as needed.
In theory, your context manager should work in giving each thread its own session, but, for lack of better explanation, I wonder if there are multiple threads accessing that session within the context.
When my app runs, I'm very frequently getting issues around the connection pooling (one is "QueuePool limit of size 5 overflow 10 reached", another is "FATAL: remaining connection slots are reserved for non-replication superuser connections").
I have a feeling that it's due to some code not closing connections properly, or other code greedily trying to open new ones when it shouldn't, but I'm using the default SQL Alchemy settings so I assume the pool connection defaults shouldn't be unreasonable. We are using the scoped_session(sessionmaker()) way of creating the session so multiple threads are supported.
So my main question is if there is a tool or way to find out where the connections are going? Short of being able to see as soon as a new one is created (that is not supposed to be created), are there any obvious anti-patterns that might result in this effect?
Pyramid is very un-opinionated and with DB connections, there seem to be two main approaches (equally supported by Pyramid it would seem). In our case, the code base when I started the job used one approach (I'll call it the "globals" approach) and we've agreed to switch to another approach that relies less on globals and more on Pythonic idioms.
About our architecture: the application comprises one repo which houses the Pyramid project and then sources a number of other git modules, each of which had their own connection setup. The "globals" way connects to the database in a very non-ORM fashion, eg.:
(in each repo's __init__ file)
def load_database:
global tables
tables['table_name'] = Table(
'table_name', metadata,
Column('column_name', String),
)
There are related globals that are frequently peppered all over the code:
def function_needing_data(field_value):
global db, tables
select = sqlalchemy.sql.select(
[tables['table_name'].c.data], tables['table_name'].c.name == field_value)
return db.execute(select)
This tables variable is latched onto within each git repo which adds some more tables definitions and somehow the global tables manages to work, providing access to all of the tables.
The approach that we've moved to (although at this time, there are parts of both approaches still in the code) is via a centralised connection, binding all of the metadata to it and then querying the db in an ORM approach:
(model)
class ModelName(MetaDataBase):
__tablename__ = "models_table_name"
... (field values)
(function requiring data)
from models.db import DBSession
from models.model_name import ModelName
def function_needing_data(field_value):
return DBSession.query(ModelName).filter(
ModelName.field_value == field_value).all()
We've largely moved the code over to the latter approach which feels right, but perhaps I'm mistaken in my intentions. I don't know if there is anything inherently good or bad in either approach but could this (one of the approaches) be part of the problem so we keep running out of connections? Is there a telltale sign that I should look out for?
It appears that Pyramid functions best (in terms of handling the connection pool) when you use the Pyramid transaction manager (pyramid_tm). This excellent article by Jon Rosebaugh provides some helpful insight into both how Pyramid apps typically set up their database connections and how they should set them up.
In my case, it was necessary to include the pyramid_tm package and then remove a few occurrences where we were manually committing session changes since pyramid_tm will automatically commit changes if it doesn't see a reason not to.
[Update]
I continued to have connection pooling issues although much fewer of them. After a lot of debugging, I found that the pyramid transaction manager (if you're using it correctly) should not be the issue at all. The issue to the other connection pooling issues I had had to do with scripts that ran via cron jobs. A script will release it's connections when it's finished, but bad code design may result in situations where the same script can be opened up and starts running while the previous one is running (causing them both to run slower, slow enough to have both running while a third instance of the script starts and so on).
This is a more language- and database-agnostic error since it stems from poor job-scripting design but it's worth keeping in mind. In my case, the script had an "&" at the end so that each instance started as a background process, waited 10 seconds, then spawned another, rather than making sure the first job started AND completed, then waited 10 seconds, then started another.
Hope this helps when debugging this very frustrating and thorny issue.
I sometimes run python scripts that access the same database concurrently. This often causes database lock errors. I would like the script to then retry ASAP as the database is never locked for long.
Is there a better way to do this than with a try except inside a while loop and does that method have any problems?
Increase the timeout parameter in your calls to the connect function:
db = sqlite3.connect("myfile.db", timeout = 20)
If you are looking for concurrency, SQlite is not the answer. The engine doesn't perform good when concurrency is needed, especially when writing from different threads, even if the tables are not the same.
If your scripts are accessing different tables, and they have no relationships at DB level (i.e. declared FK's), you can separate them in different databases and then your concurrency issue will be solved.
If they are linked, but you can link them in the app level (script), you can separate them as well.
The best practice in those cases is implementing a lock mechanism with events, but honestly I have no idea how to implement such in phyton.
I noticed that sqlite3 isn´t really capable nor reliable when i use it inside a multiprocessing enviroment. Each process tries to write some data into the same database, so that a connection is used by multiple threads. I tried it with the check_same_thread=False option, but the number of insertions is pretty random: Sometimes it includes everything, sometimes not. Should I parallel-process only parts of the function (fetching data from the web), stack their outputs into a list and put them into the table all together or is there a reliable way to handle multi-connections with sqlite?
First of all, there's a difference between multiprocessing (multiple processes) and multithreading (multiple threads within one process).
It seems that you're talking about multithreading here. There are a couple of caveats that you should be aware of when using SQLite in a multithreaded environment. The SQLite documentation mentions the following:
Do not use the same database connection at the same time in more than
one thread.
On some operating systems, a database connection should
always be used in the same thread in which it was originally created.
See here for a more detailed information: Is SQLite thread-safe?
I've actually just been working on something very similar:
multiple processes (for me a processing pool of 4 to 32 workers)
each process worker does some stuff that includes getting information
from the web (a call to the Alchemy API for mine)
each process opens its own sqlite3 connection, all to a single file, and each
process adds one entry before getting the next task off the stack
At first I thought I was seeing the same issue as you, then I traced it to overlapping and conflicting issues with retrieving the information from the web. Since I was right there I did some torture testing on sqlite and multiprocessing and found I could run MANY process workers, all connecting and adding to the same sqlite file without coordination and it was rock solid when I was just putting in test data.
So now I'm looking at your phrase "(fetching data from the web)" - perhaps you could try replacing that data fetching with some dummy data to ensure that it is really the sqlite3 connection causing you problems. At least in my tested case (running right now in another window) I found that multiple processes were able to all add through their own connection without issues but your description exactly matches the problem I'm having when two processes step on each other while going for the web API (very odd error actually) and sometimes don't get the expected data, which of course leaves an empty slot in the database. My eventual solution was to detect this failure within each worker and retry the web API call when it happened (could have been more elegant, but this was for a personal hack).
My apologies if this doesn't apply to your case, without code it's hard to know what you're facing, but the description makes me wonder if you might widen your considerations.
sqlitedict: A lightweight wrapper around Python's sqlite3 database, with a dict-like interface and multi-thread access support.
If I had to build a system like the one you describe, using SQLITE, then I would start by writing an async server (using the asynchat module) to handle all of the SQLITE database access, and then I would write the other processes to use that server. When there is only one process accessing the db file directly, it can enforce a strict sequence of queries so that there is no danger of two processes stepping on each others toes. It is also faster than continually opening and closing the db.
In fact, I would also try to avoid maintaining sessions, in other words, I would try to write all the other processes so that every database transaction is independent. At minimum this would mean allowing a transaction to contain a list of SQL statements, not just one, and it might even require some if then capability so that you could SELECT a record, check that a field is equal to X, and only then, UPDATE that field. If your existing app is closing the database after every transaction, then you don't need to worry about sessions.
You might be able to use something like nosqlite http://code.google.com/p/nosqlite/
I want to make a Database Application Programming Interface written in Python and using SQLAlchemy (or any other database connectors if it is told that using SQLAlchemy for this kind of task is not the good way to go). The setup is a MySQL server running on Linux or BSD and a the Python software running on a Linux or BSD machine (Either foreign or local).
Basically what I want to do is spawn a new thread for each connections and the protocol would be custom and quite simple, although for each requests I would like to open a new transaction (or session as I have read) and then I need to commit the session. The problem I am facing right now is that there is high probability that another sessions happen at the same time from another connection.
My question here is what should I do to handle this situation?
Should I use a lock so only a single session can run at the same time?
Are sessions actually thread-safe and I am wrong about thinking that they are not?
Is there a better way to handle this situation?
Is threading the way not-to-go?
Session objects are not thread-safe, but are thread-local. From the docs:
"The Session object is entirely designed to be used in a non-concurrent fashion, which in terms of multithreading means "only in one thread at a time" .. some process needs to be in place such that mutltiple calls across many threads don’t actually get a handle to the same session. We call this notion thread local storage."
If you don't want to do the work of managing threads and sessions yourself, SQLAlchemy has the ScopedSession object to take care of this for you:
The ScopedSession object by default uses threading.local() as storage, so that a single Session is maintained for all who call upon the ScopedSession registry, but only within the scope of a single thread. Callers who call upon the registry in a different thread get a Session instance that is local to that other thread.
Using this technique, the ScopedSession provides a quick and relatively simple way of providing a single, global object in an application that is safe to be called upon from multiple threads.
See the examples in Contextual/Thread-local Sessions for setting up your own thread-safe sessions:
# set up a scoped_session
from sqlalchemy.orm import scoped_session
from sqlalchemy.orm import sessionmaker
session_factory = sessionmaker(bind=some_engine)
Session = scoped_session(session_factory)
# now all calls to Session() will create a thread-local session
some_session = Session()
# you can now use some_session to run multiple queries, etc.
# remember to close it when you're finished!
Session.remove()