Hello I don't think this is in the right place for this question but I don't know where to ask it. I want to make a website and an api for that website using the same SQLAlchemy database would just running them at the same time independently be safe or would this cause corruption from two write happening at the same time.
SQLA is a python wrapper for SQL. It is not it's own database. If you're running your website (perhaps flask?) and managing your api from the same script, you can simply use the same reference to your instance of SQLA. Meaning, when you use SQLA to connect to a database and save to a variable, what is really happening is it saves the connection to a variable, and you continually reference that variable, as opposed to the more inefficient method of creating a new connection every time. So when you say
using the same SQLAlchemy database
I believe you are actually referring to the actual underlying database itself, not the SQLA wrapper/connection to it.
If your website and API are not running in the same script (or even if they are, depending on how your API handles simultaneous requests), you may encounter a race condition, which, according to Wikipedia, is defined as:
the condition of an electronics, software, or other system where the system's substantive behavior is dependent on the sequence or timing of other uncontrollable events. It becomes a bug when one or more of the possible behaviors is undesirable.
This may be what you are referring to when you mentioned
would this cause corruption from two write happening at the same time.
To avoid such situations, when a process accesses a file, (depending on the OS,) check is performed to see if there is a "lock" on that file, and if so, the OS refuses to open that file. A lock is created when a process accesses a file (and there is no other process holding a lock on that file), such as by using with open(filename): and is released when the process no longer holds an open reference to the file (such as when python execution leaves the with open(filename): indentation block.) This may be the real issue you might encounter when using two simultaneous connections to a SQLite db.
However, if you are using something like MySQL, where you connect to a SQL server process, and NOT a file, since there is no direct access to a file, there will be no lock on the database, and you may run in to that nasty race condition in the following made up scenario:
Stack Overflow queries the reputation an account to see if it should be banned due to negative reputation.
AT THE EXACT SAME TIME, Someone upvotes an answer made by that account that sets it one point under the account ban threshold.
The outcome is now determined by the speed of execution of these 2 tasks.
If the upvoter has, say, a slow computer, and the "upvote" does not get processed by StackOverflow before the reputation query completes, the account will be banned. However, if there is some lag on Stack Overflow's end, and the upvote processes before the account query finishes, the account will not get banned.
The key concept behind this example is that all of these steps can occur within fractions of a second, and the outcome depends of the speed of execution on both ends.
To address the issue of data corruption, most databases have a system in place that properly order database read and writes, however, there are still semantic issues that may arise, such as the example given above.
Two applications can use the same database as the DB is a separate application that will be accessed by each flask app.
What you are asking can be done and is the methodology used by many large web applications, specially when the API is written in a different framework than the main application.
Since SQL databases are ACID compliant, they have a system in place to queue the multiple read/write requests put to it and perform them in the correct order while ensuring data reliability.
One question to ask though is whether it is useful to write two separate applications. For most flask-only projects the best approach would be to separate the project using blueprints, having a “main” blueprint and a “api” blueprint.
I am using python and sqlalchemy to manage a sqlite database (in the future I plan to replace sqlite with postgres).
The operations I do are INSERT, SELECT and DELETE and all these operations are part of a python script that runs every hour.
Each one of these operation can take a considerable amount of time due to the large amount of data.
Now in certain circumstances the python script may be killed by an external process. How can I make sure that my database is not corrupted if the script is killed while reading / writing from the DB?
Well, you use a database.
Databases implement ACID properties (see here). To the extent possible, these guarantee the integrity of the data, even when transactions are not complete.
The issue that you are focusing on is dropped connections. I think dropped connections usually result in a transaction being rolled back (I'm not sure if there are exceptions). That is, the database ignores everything since the last commit.
So, the database protects you against internal corruption. Your data model might become invalid, if the sequence of operations is stopped at an arbitrary place. The solution to this is to wrap such operations into a transaction, so the transaction is rolled back.
There is a (small) danger of databases getting corrupted when the hardware or software they are running on suddenly "disappears". This is rare and there are safeguards. And, this is not the problem that you are concerned with (unless your SQLite instance is part of your python process).
I am developing an application that uses CherryPy. Now I need to implement a database, and I would very much like it to be embedded with the app, to save users some headache. The obvious first choice is of course SQLite, seeing how it's part of the standard library.
There seems to be a lot of different takes on this. Some saying that you should never use SQLite in a threaded application, some saying it's ok, and with wildly differing estimates of how many writes per second I can expect.
Is using SQLite in this way viable, and how slow can I expect writing to the database will be?
If viable, what is the best method of implementing it? Subscribing a connection to each start_thread? Start a connection every time a page is exposed, as some seem to do?
I've read that turning PRAGMA synchronous=OFF in SQLite can improve performance at the cost of "if you lose power in the middle of a transaction, your database file might go corrupt." What are the probabilities here? Is this an acceptable choice perhaps in conjunction with some sort of backup system?
Are there any other embedded databases that would be a better choice?
Should I just give up on this and use a PostgreSQL database at the cost of user convenience?
Thanks in advance.
I have a twisted daemon that does some xml feed parsing.
I store my data in PostgreSQL via twisted.enterprise.adbapi , which IIRC is wrapping psycopg2
I've run into a few problems with storing data into database -- with duplicate data periodically getting in there.
To be honest, there are some underlying issues with my implementation which should be redone and designed much better. I lack the time and resources to do that though - so we're in 'just keep it running' mode for now.
I think the problem may happen from either my usage of deferToThread or how I've spawned the server at the start.
As a quick overview of the functionality I think is at fault:
Twisted queries Postgres for Accounts that should be analyzed , and sets a block on them
SELECT
id
FROM
xml_sources
WHERE
timestamp_last_process < ( CURRENT_TIMESTAMP AT TIME ZONE 'UTC' - INTERVAL '4 HOUR' )
AND
is_processing_block IS NULL ;
lock_ids = [ i['id'] for i in results ]
UPDATE xml_sources SET is_processing_block = True WHERE id IN %(lock_ids)s
What I think is happening, is (accidentally) having multiple servers running or various other issues results in multiple threads processing this data.
I think this would likely be fixed - or at least ruled out as an issue - if I wrap this quick section in an exclusive table lock.
I've never done table locking through twisted before though. can anyone point me in the right direction ?
You can do a SELECT FOR UPDATE instead of a plain SELECT, and that will lock the rows returned by your query. If you actually want table locking you can just issue a LOCK statement, but based on the rest of your question I think you want row locking.
If you are using adbapi, then keep in mind that you will need to use runInteraction if you want to run more than one statement in a transaction. Functions passed to runInteraction will run in a thread, so you may need to use callFromThread or blockingCallFromThread to reach from the database interaction back into the reactor.
However, locking may not be your problem. For one thing, if you are mixing deferToThread and adbapi, something's likely wrong. adbapi is already doing the equivalent of deferToThread for you. You should be able to do everything on the main thread.
You'll have to include a representative example for a more specific answer though. Consider your question: it's basically "Sometimes I get duplicate data, with a self-admittedly problematic implementation, that is big and I can't fix and I also can't show you." This is not a question which it is possible to answer.
I have a Python program that uses the "threading" module. Once every second, my program starts a new thread that fetches some data from the web, and stores this data to my hard drive. I would like to use sqlite3 to store these results, but I can't get it to work. The issue seems to be about the following line:
conn = sqlite3.connect("mydatabase.db")
If I put this line of code inside each thread, I get an OperationalError telling me that the database file is locked. I guess this means that another thread has mydatabase.db open through a sqlite3 connection and has locked it.
If I put this line of code in the main program and pass the connection object (conn) to each thread, I get a ProgrammingError, saying that SQLite objects created in a thread can only be used in that same thread.
Previously I was storing all my results in CSV files, and did not have any of these file-locking issues. Hopefully this will be possible with sqlite. Any ideas?
Contrary to popular belief, newer versions of sqlite3 do support access from multiple threads.
This can be enabled via optional keyword argument check_same_thread:
sqlite.connect(":memory:", check_same_thread=False)
You can use consumer-producer pattern. For example you can create queue that is shared between threads. First thread that fetches data from the web enqueues this data in the shared queue. Another thread that owns database connection dequeues data from the queue and passes it to the database.
The following found on mail.python.org.pipermail.1239789
I have found the solution. I don't know why python documentation has not a single word about this option. So we have to add a new keyword argument to connection function
and we will be able to create cursors out of it in different thread. So use:
sqlite.connect(":memory:", check_same_thread = False)
works out perfectly for me. Of course from now on I need to take care
of safe multithreading access to the db. Anyway thx all for trying to help.
Switch to multiprocessing. It is much better, scales well, can go beyond the use of multiple cores by using multiple CPUs, and the interface is the same as using python threading module.
Or, as Ali suggested, just use SQLAlchemy's thread pooling mechanism. It will handle everything for you automatically and has many extra features, just to quote some of them:
SQLAlchemy includes dialects for SQLite, Postgres, MySQL, Oracle, MS-SQL, Firebird, MaxDB, MS Access, Sybase and Informix; IBM has also released a DB2 driver. So you don't have to rewrite your application if you decide to move away from SQLite.
The Unit Of Work system, a central part of SQLAlchemy's Object Relational Mapper (ORM), organizes pending create/insert/update/delete operations into queues and flushes them all in one batch. To accomplish this it performs a topological "dependency sort" of all modified items in the queue so as to honor foreign key constraints, and groups redundant statements together where they can sometimes be batched even further. This produces the maxiumum efficiency and transaction safety, and minimizes chances of deadlocks.
You shouldn't be using threads at all for this. This is a trivial task for twisted and that would likely take you significantly further anyway.
Use only one thread, and have the completion of the request trigger an event to do the write.
twisted will take care of the scheduling, callbacks, etc... for you. It'll hand you the entire result as a string, or you can run it through a stream-processor (I have a twitter API and a friendfeed API that both fire off events to callers as results are still being downloaded).
Depending on what you're doing with your data, you could just dump the full result into sqlite as it's complete, cook it and dump it, or cook it while it's being read and dump it at the end.
I have a very simple application that does something close to what you're wanting on github. I call it pfetch (parallel fetch). It grabs various pages on a schedule, streams the results to a file, and optionally runs a script upon successful completion of each one. It also does some fancy stuff like conditional GETs, but still could be a good base for whatever you're doing.
Or if you are lazy, like me, you can use SQLAlchemy. It will handle the threading for you, (using thread local, and some connection pooling) and the way it does it is even configurable.
For added bonus, if/when you realise/decide that using Sqlite for any concurrent application is going to be a disaster, you won't have to change your code to use MySQL, or Postgres, or anything else. You can just switch over.
You need to use session.close() after every transaction to the database in order to use the same cursor in the same thread not using the same cursor in multi-threads which cause this error.
Use threading.Lock()
I could not find any benchmarks in any of the above answers so I wrote a test to benchmark everything.
I tried 3 approaches
Reading and writing sequentially from the SQLite database
Using a ThreadPoolExecutor to read/write
Using a ProcessPoolExecutor to read/write
The results and takeaways from the benchmark are as follows
Sequential reads/sequential writes work the best
If you must process in parallel, use the ProcessPoolExecutor to read in parallel
Do not perform any writes either using the ThreadPoolExecutor or using the ProcessPoolExecutor as you will run into database locked errors and you will have to retry inserting the chunk again
You can find the code and complete solution for the benchmarks in my SO answer HERE Hope that helps!
Scrapy seems like a potential answer to my question. Its home page describes my exact task. (Though I'm not sure how stable the code is yet.)
I would take a look at the y_serial Python module for data persistence: http://yserial.sourceforge.net
which handles deadlock issues surrounding a single SQLite database. If demand on concurrency gets heavy one can easily set up the class Farm of many databases to diffuse the load over stochastic time.
Hope this helps your project... it should be simple enough to implement in 10 minutes.
I like Evgeny's answer - Queues are generally the best way to implement inter-thread communication. For completeness, here are some other options:
Close the DB connection when the spawned threads have finished using it. This would fix your OperationalError, but opening and closing connections like this is generally a No-No, due to performance overhead.
Don't use child threads. If the once-per-second task is reasonably lightweight, you could get away with doing the fetch and store, then sleeping until the right moment. This is undesirable as fetch and store operations could take >1sec, and you lose the benefit of multiplexed resources you have with a multi-threaded approach.
You need to design the concurrency for your program. SQLite has clear limitations and you need to obey them, see the FAQ (also the following question).
Please consider checking the value of THREADSAFE for the pragma_compile_options of your SQLite installation. For instance, with
SELECT * FROM pragma_compile_options;
If THREADSAFE is equal to 1, then your SQLite installation is threadsafe, and all you gotta do to avoid the threading exception is to create the Python connection with checksamethread equal to False. In your case, it means
conn = sqlite3.connect("mydatabase.db", checksamethread=False)
That's explained in some detail in Python, SQLite, and thread safety
The most likely reason you get errors with locked databases is that you must issue
conn.commit()
after finishing a database operation. If you do not, your database will be write-locked and stay that way. The other threads that are waiting to write will time-out after a time (default is set to 5 seconds, see http://docs.python.org/2/library/sqlite3.html#sqlite3.connect for details on that).
An example of a correct and concurrent insertion would be this:
import threading, sqlite3
class InsertionThread(threading.Thread):
def __init__(self, number):
super(InsertionThread, self).__init__()
self.number = number
def run(self):
conn = sqlite3.connect('yourdb.db', timeout=5)
conn.execute('CREATE TABLE IF NOT EXISTS threadcount (threadnum, count);')
conn.commit()
for i in range(1000):
conn.execute("INSERT INTO threadcount VALUES (?, ?);", (self.number, i))
conn.commit()
# create as many of these as you wish
# but be careful to set the timeout value appropriately: thread switching in
# python takes some time
for i in range(2):
t = InsertionThread(i)
t.start()
If you like SQLite, or have other tools that work with SQLite databases, or want to replace CSV files with SQLite db files, or must do something rare like inter-platform IPC, then SQLite is a great tool and very fitting for the purpose. Don't let yourself be pressured into using a different solution if it doesn't feel right!