Develop Reference

Can concurrent processes write to a shared database?

I'm using the Python multiprocessing library to generate several processes that each write to a shared (MongoDB) database. Is this safe, or will the writes overwrite each other?

So long as you make sure to create a separate database connection for each worker process, it's perfectly safe to have multiple processes accessing a database at the same time. Any queries they issue which make changes to the database will be applied individually, typically in the order they are received by the database. Under most situations this will be safe, but:
If your processes are all just inserting documents into the database, each insert will typically create a separate object.
The exception is if you explicitly specify an _id for a document, and that identifier has already been used within the collection. This will cause the insert to fail. (So don't do that: leave the _id out, and MongoDB will always generate a unique value for you.)
If your processes are deleting documents from the database, the operation will fail if another process has already deleted the same object. (This is not strictly a failure, though; it just means that someone else got there before you.)
If your processes are updating documents in the database, things get murkier.
So long as each process is updating a different document, you're fine.
If multiple processes are trying to update the same document at the same time, you start needing to be careful. Updates which replace values on an object will be applied in order, which may cause changes made by one process to inadvertently be overwritten by another. You should be careful to avoid specifying fields that you don't intend to change. Using MongoDB's update operators may be helpful to perform complex operations atomically, such as changing the numeric values of fields.
Note that "at the same time" doesn't necessarily mean that operations are occurring at exactly the same time. It means more generally that there's an "overlap" in the time two processes are working with the same document, e.g.
Process A Process B
--------- ---------
Reads object from DB ...
working... Reads object from DB
working... working...
updates object with changes working...
updates object with changes
In the above situation, it's possible for some of the changes made by process A to inadvertently be overwritten by process B.

In short, yes it is perfectly reasonable (and actually preferred) to let your database worry about the concurrency of your database operations.
Any relevant database driver (MongoDB included) will handle concurrent operations for you automatically.

django connections shared across threads causes ora-01000 maximum open cursors exeeded

I want to stop executing SQL statement if it takes too long to run.
To achieve this I hacked django.core.db.backends.oracle.base. In FormatStylePlaceholderCursor.execute and executemany instead of:
return self.cursor.execute(TIMEOUT, query, self._param_generator(params))
I do:
return timelimited(TIMEOUT, self.cursor.execute, query, self._param_generator(params))
And timelimited is a function from this recipe: http://code.activestate.com/recipes/576780-timeout-for-nearly-any-callable/. It wraps a function (i.e. cursor.execute) in separate thread and waits TIMEOUT. If function doesn't return the thread is stopped.
With this modification the application I'm running is throwing ora-01000 maximum cursors exceeded after some short period of time. I'm wandering why wrapping cursor.execute is causing this problem, how to fix it and what are other available solution to this problem.

I'm not familiar with Django nor Python. I can tell you what OCI drivers offer to users.
You must close the query handle - or whatever is it's name in Python. Otherwise you're leaking resources on database side
If the query is still active, you can interrupt it using OCIBreak call. This one is thread safe, and can be called from any thread, regardless what background thread is doing with the connection
Try to check, whether Python drivers for Oracle do allow you to call OCIBreak and OCIReset
This is what you need. Connection.cancel()

How to use simple sqlalchemy calls while using thread/multiprocessing

Problem
I am writing a program that reads a set of documents from a corpus (each line is a document). Each document is processed using a function processdocument, assigned a unique ID, and then written to a database. Ideally, we want to do this using several processes. The logic is as follows:
The main routine creates a new database and sets up some tables.
The main routine sets up a group of processes/threads that will run a worker function.
The main routine starts all the processes.
The main routine reads the corpus, adding documents to a queue.
Each process's worker function loops, reading a document from a queue, extracting the information from it using processdocument, and writes the information to a new entry in a table in the database.
The worker loops breaks once the queue is empty and an appropriate flag has been set by the main routine (once there are no more documents to add to the queue).
Question
I'm relatively new to sqlalchemy (and databases in general). I think the code used for setting up the database in the main routine works fine, from what I can tell. Where I'm stuck is I'm not sure exactly what to put into the worker functions for each process to write to the database without clashing with the others.
There's nothing particularly complicated going on: each process gets a unique value to assign to an entry from a multiprocessing.Value object, protected by a Lock. I'm just not sure whether what I should be passing to the worker function (aside from the queue), if anything. Do I pass the sqlalchemy.Engine instance I created in the main routine? The Metadata instance? Do I create a new engine for each process? Is there some other canonical way of doing this? Is there something special I need to keep in mind?
Additional Comments
I'm well aware I could just not bother with the multiprocessing but and do this in a single process, but I will have to write code that has several processes reading for the database later on, so I might as well figure out how to do this now.
Thanks in advance for your help!

The MetaData and its collection of Table objects should be considered a fixed, immutable structure of your application, not unlike your function and class definitions. As you know with forking a child process, all of the module-level structures of your application remain present across process boundaries, and table defs are usually in this category.
The Engine however refers to a pool of DBAPI connections which are usually TCP/IP connections and sometimes filehandles. The DBAPI connections themselves are generally not portable over a subprocess boundary, so you would want to either create a new Engine for each subprocess, or use a non-pooled Engine, which means you're using NullPool.
You also should not be doing any kind of association of MetaData with Engine, that is "bound" metadata. This practice, while prominent on various outdated tutorials and blog posts, is really not a general purpose thing and I try to de-emphasize this way of working as much as possible.
If you're using the ORM, a similar dichotomy of "program structures/active work" exists, where your mapped classes of course are shared between all subprocesses, but you definitely want Session objects to be local to a particular subprocess - these correspond to an actual DBAPI connection as well as plenty of other mutable state which is best kept local to an operation.

SQLite3 and Multiprocessing

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/

Google App Engine - design considerations about cron tasks

I'm developing software using the Google App Engine.
I have some considerations about the optimal design regarding the following issue: I need to create and save snapshots of some entities at regular intervals.
In the conventional relational db world, I would create db jobs which would insert new summary records.
For example, a job would insert a record for every active user that would contain his current score to the "userrank" table, say, every hour.
I'd like to know what's the best method to achieve this in Google App Engine. I know that there is the Cron service, but does it allow us to execute jobs which will insert/update thousands of records?

I think you'll find that snapshotting every user's state every hour isn't something that will scale well no matter what your framework. A more ordinary environment will disguise this by letting you have longer running tasks, but you'll still reach the point where it's not practical to take a snapshot of every user's data, every hour.
My suggestion would be this: Add a 'last snapshot' field, and subclass the put() function of your model (assuming you're using Python; the same is possible in Java, but I don't know the syntax), such that whenever you update a record, it checks if it's been more than an hour since the last snapshot, and if so, creates and writes a snapshot record.
In order to prevent concurrent updates creating two identical snapshots, you'll want to give the snapshots a key name derived from the time at which the snapshot was taken. That way, if two concurrent updates try to write a snapshot, one will harmlessly overwrite the other.
To get the snapshot for a given hour, simply query for the oldest snapshot newer than the requested period. As an added bonus, since inactive records aren't snapshotted, you're saving a lot of space, too.

Have you considered using the remote api instead? This way you could get a shell to your datastore and avoid the timeouts. The Mapper class they demonstrate in that link is quite useful and I've used it successfully to do batch operations on ~1500 objects.
That said, cron should work fine too. You do have a limit on the time of each individual request so you can't just chew through them all at once, but you can use redirection to loop over as many users as you want, processing one user at a time. There should be an example of this in the docs somewhere if you need help with this approach.

I would use a combination of Cron jobs and a looping url fetch method detailed here: http://stage.vambenepe.com/archives/549. In this way you can catch your timeouts and begin another request.
To summarize the article, the cron job calls your initial process, you catch the timeout error and call the process again, masked as a second url. You have to ping between two URLs to keep app engine from thinking you are in a accidental loop. You also need to be careful that you do not loop infinitely. Make sure that there is an end state for your updating loop, since this would put you over your quotas pretty quickly if it never ended.