In the case of sqlite, it is not clear whether we can easily commit right after each dataframe insert. (Assuming that auto-commit is off by default, following the python database wrapping convention).
Using the simplest sqlalchemy api flow ―
db_engine = db.create_engine()
for .....
# slowly compute some_df, takes a lot of time
some_df.to_sql(con = db_engine)
How can we make sure that every .to_sql is committed?
For motivation, imagine the particular use case being that each write reflects the result of a potentially very long computation and we do not want to lose a huge batch of such computations nor any single one of them, in case a machine goes down or in case the python sqlalchemy engine object is garbage collected before all its writes have actually drained in the database.
I believe auto-commit is off by default, and for sqlite, there is no way of changing that in the create_engine command. What might be the simplest, safest way for adding auto-commit behavior ― or explicitly committing after every dataframe write ― when using the simplistic .to_sql api?
Or must the code be refactored to use a different api flow to accomplish that?
You can set the connection to autocommit by:
db_engine = db_engine.execution_options(autocommit=True)
From https://docs.sqlalchemy.org/en/13/core/connections.html#understanding-autocommit:
The “autocommit” feature is only in effect when no Transaction has otherwise been declared. This means the feature is not generally used with the ORM, as the Session object by default always maintains an ongoing Transaction.
In your code you have not presented any explicit transactions, and so the engine used as the con is in autocommit mode (as implemented by SQLA).
Note that SQLAlchemy implements its own autocommit that is independent from the DB-API driver's possible autocommit / non-transactional features.
Hence the "the simplest, safest way for adding auto-commit behavior ― or explicitly committing after every dataframe write" is what you already had, unless to_sql() emits some funky statements that SQLA does not recognize as data changing operations, which it has not, at least of late.
It might be that the SQLA autocommit feature is on the way out in the next major release, but we'll have to wait and see.
Related
I have found that each session call .rollback() when closed.
So dose it meen that for best performance, if i need just single select (no transaction needed) than it is better to rewrite code from
session = init_session()
result = session.query(Currency.id, Currency.code).all()
session.close()
to version with conn.execute() ?
Due to Python's DB-API 2.0 spec you will be running everything in a transaction, even if you use the emulated "autocommit" feature of SQLAlchemy, unless you explicitly opt to setting the transaction isolation level to "AUTOCOMMIT" and your driver supports it. From a performance perspective running your query in a transaction should have negligible impact. Just remember to close the transaction when you are done, which you seem to be doing, so that any reserved resources are freed.
As a pet project I have been writing my own ORM to help me better understand the decisions made by production grade ORMs like Peewee or the more complex sqlalchemy.
In line with my titles question, is it better to spawn one cursor and reuse it for multiple SQL executions or spawn a new cursor for each transaction?
I've already guessed about avoid state issues (transactions with no commit) but is there another reason why it would be better to have one cursor for each operation (insert, update, select, delete, or create)?
Have you profiled and found that the creation of cursors is a significant source of overhead?
Cursors are a DB-API 2.0 artifact, not necessarily an actual "thing" that exists. They are designed to provide a common interface for executing queries and handling results/iteration. How they are implemented under-the-hood is up to the database driver. If you're aiming to support DB-API 2.0 compatible drivers, I suggest just use the cursor() method to create a cursor for every query execution. I would recommend to NEVER have a singleton or shared cursor.
In SQLite, for example, a cursor is essentially a wrapper around a sqlite3_stmt object, as there's no such thing as a "sqlite3_cursor". The stdlib sqlite3 driver maintains an internal cache of sqlite3_stmt objects to avoid the cost of compiling queries that are frequently used.
I am dealing with a doubt about sqlalchemy and objects refreshing!
I am in the situation in what I have 2 sessions, and the same object has been queried in both sessions! For some particular thing I cannot to close one of the sessions.
I have modified the object and commited the changes in session A, but in session B, the attributes are the initial ones! without modifications!
Shall I implement a notification system to communicate changes or there is a built-in way to do this in sqlalchemy?
Sessions are designed to work like this. The attributes of the object in Session B WILL keep what it had when first queried in Session B. Additionally, SQLAlchemy will not attempt to automatically refresh objects in other sessions when they change, nor do I think it would be wise to try to create something like this.
You should be actively thinking of the lifespan of each session as a single transaction in the database. How and when sessions need to deal with the fact that their objects might be stale is not a technical problem that can be solved by an algorithm built into SQLAlchemy (or any extension for SQLAlchemy): it is a "business" problem whose solution you must determine and code yourself. The "correct" response might be to say that this isn't a problem: the logic that occurs with Session B could be valid if it used the data at the time that Session B started. Your "problem" might not actually be a problem. The docs actually have an entire section on when to use sessions, but it gives a pretty grim response if you are hoping for a one-size-fits-all solution...
A Session is typically constructed at the beginning of a logical
operation where database access is potentially anticipated.
The Session, whenever it is used to talk to the database, begins a
database transaction as soon as it starts communicating. Assuming the
autocommit flag is left at its recommended default of False, this
transaction remains in progress until the Session is rolled back,
committed, or closed. The Session will begin a new transaction if it
is used again, subsequent to the previous transaction ending; from
this it follows that the Session is capable of having a lifespan
across many transactions, though only one at a time. We refer to these
two concepts as transaction scope and session scope.
The implication here is that the SQLAlchemy ORM is encouraging the
developer to establish these two scopes in his or her application,
including not only when the scopes begin and end, but also the expanse
of those scopes, for example should a single Session instance be local
to the execution flow within a function or method, should it be a
global object used by the entire application, or somewhere in between
these two.
The burden placed on the developer to determine this scope is one area
where the SQLAlchemy ORM necessarily has a strong opinion about how
the database should be used. The unit of work pattern is specifically
one of accumulating changes over time and flushing them periodically,
keeping in-memory state in sync with what’s known to be present in a
local transaction. This pattern is only effective when meaningful
transaction scopes are in place.
That said, there are a few things you can do to change how the situation works:
First, you can reduce how long your session stays open. Session B is querying the object, then later you are doing something with that object (in the same session) that you want to have the attributes be up to date. One solution is to have this second operation done in a separate session.
Another is to use the expire/refresh methods, as the docs show...
# immediately re-load attributes on obj1, obj2
session.refresh(obj1)
session.refresh(obj2)
# expire objects obj1, obj2, attributes will be reloaded
# on the next access:
session.expire(obj1)
session.expire(obj2)
You can use session.refresh() to immediately get an up-to-date version of the object, even if the session already queried the object earlier.
Run this, to force session to update latest value from your database of choice:
session.expire_all()
Excellent DOC about default behavior and lifespan of session
I just had this issue and the existing solutions didn't work for me for some reason. What did work was to call session.commit(). After calling that, the object had the updated values from the database.
TL;DR Rather than working on Session synchronization, see if your task can be reasonably easily coded with SQLAlchemy Core syntax, directly on the Engine, without the use of (multiple) Sessions
For someone coming from SQL and JDBC experience, one critical thing to learn about SQLAlchemy, which, unfortunately, I didn't clearly come across reading through the multiple documents for months is that SQLAlchemy consists of two fundamentally different parts: the Core and the ORM. As the ORM documentation is listed first on the website and most examples use the ORM-like syntax, one gets thrown into working with it and sets them-self up for errors and confusion - if thinking about ORM in terms of SQL/JDBC. ORM uses its own abstraction layer that takes a complete control over how and when actual SQL statements are executed. The rule of thumb is that a Session is cheap to create and kill, and it should never be re-used for anything in the program's flow and logic that may cause re-querying, synchronization or multi-threading. On the other hand, the Core is the direct no-thrills SQL, very much like a JDBC Driver. There is one place in the docs I found that "suggests" using Core over ORM:
it is encouraged that simple SQL operations take place here, directly on the Connection, such as incrementing counters or inserting extra rows within log
tables. When dealing with the Connection, it is expected that Core-level SQL
operations will be used; e.g. those described in SQL Expression Language Tutorial.
Although, it appears that using a Connection causes the same side effect as using a Session: re-query of a specific record returns the same result as the first query, even if the record's content in the DB was changed. So, apparently Connections are as "unreliable" as Sessions to read DB content in "real time", but a direct Engine execution seems to be working fine as it picks a Connection object from the pool (assuming that the retrieved Connection would never be in the same "reuse" state relatively to the query as the specific open Connection). The Result object should be closed explicitly, as per SA docs
What is your isolation level is set to?
SHOW GLOBAL VARIABLES LIKE 'transaction_isolation';
By default mysql innodb transaction_isolation is set to REPEATABLE-READ.
+-----------------------+-----------------+
| Variable_name | Value |
+-----------------------+-----------------+
| transaction_isolation | REPEATABLE-READ |
+-----------------------+-----------------+
Consider setting it to READ-COMMITTED.
You can set this for your sqlalchemy engine only via:
create_engine("mysql://<connection_string>", isolation_level="READ COMMITTED")
I think another option is:
engine = create_engine("mysql://<connection_string>")
engine.execution_options(isolation_level="READ COMMITTED")
Or set it globally in the DB via:
SET GLOBAL TRANSACTION ISOLATION LEVEL READ COMMITTED;
https://dev.mysql.com/doc/refman/8.0/en/innodb-transaction-isolation-levels.html
and
https://docs.sqlalchemy.org/en/14/orm/session_transaction.html#setting-transaction-isolation-levels-dbapi-autocommit
If u had added the incorrect model to the session, u can do:
db.session.rollback()
I've noticed this behavior in sqlite. When I re-use the cursor object, the working set memory in the task manager keeps increasing until my program throws a out of memory exception.
I refactored the code such that each time I query I open a connection to the sqlite file query what I want and then close the connection.
The latter somehow seems to be not so memory-hungry. It doesn't increase beyond a certain point.
All I do with my sqlite db is simple select (which contains two aggregations) against a table.
Is this a behavior we can somehow control? I'd want to reuse my cursor object, but not want memory to be eaten up...
See SQLite: PRAGMA cache_size
By default the cache size is fairly big (~2MB) and that is per-connection. You can set it smaller with the SQL statement:
PRAGMA cache_size=-KB
Use the negative '-' KB value to set it as a size in KiloBytes, otherwise it set the number of pages to use.
Also, if using multiple connections you might want to employ a shared cache to save memory:
SQLITE: Shared Cache
Yes, the sqlite3 module for Python uses a statement cache.
You can read about the cached_statements parameter here.
More info on this issue.
I'm using SQLAlchemy as the ORM within an application i've been building for some time.
So far, it's been quite a painless ORM to implement and use, however, a recent feature I'm working on requires a persistent & distributed queue (list & worker) style implementation, which I've built in MySQL and Python.
It's all worked quite well until I tested it in a scaled environment.
I've used InnoDB row level locking to ensure each row is only read once, while the row is locked, I update an 'in_use' value, to make sure that others don't grab at the entry.
Since MySQL doesn't offer a "NOWAIT" method like Postgre or Oracle does, I've run into locking issues where worker threads hang and wait for the locked row to become available.
In an attempt to overcome this limitation, I've tried to put all the required processing into a single statement, and run it through the ORM's execute() method, although, SQLAlchemy is refusing to return the query result.
Here's an example.
My SQL statement is:
SELECT id INTO #update_id FROM myTable WHERE in_use=0 ORDER BY id LIMIT 1 FOR UPDATE;
UPDATE myTable SET in_use=1 WHERE id=#update_id;
SELECT * FROM myTable WHERE id=#update_id;
And I run this code in the console:
engine = create_engine('mysql://<user details>#<server details>/myDatabase', pool_recycle=90, echo=True)
result = engine.execute(sqlStatement)
result.fetchall()
Only to get this result
[]
I'm certain the statement is running since I can see the update take effect in the database, and if I execute through the mysql terminal or other tools, I get the modified row returned.
It just seems to be SQLAlchemy that doesn't want to acknowledge the returned row.
Is there anything specific that needs to be done to ensure that the ORM picks up the response?
Cheers
You have executed 3 queries and MySQLdb creates a result set for each. You have to fetch first result, then call cursor.nextset(), fetch second and so on.
This answers your question, but won't be useful for you, because it won't solve locking issue. You have to understand how FOR UPDATE works first: it locks returned rows till the end of transaction. To avoid long lock wait you have to make it as short as possible: SELECT ... FOR UPDATE, UPDATE SET in_use=1 ..., COMMIT. You actually don't need to put them into single SQL statement, 3 execute() calls will be OK too. But you have have to commit before long computation, otherwise lock will be held too long and updating in_use (offline lock) is meaningless. And sure you can do the same thing using ORM too.