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.
Related
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.
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've been struggling with "sqlite3.OperationalError database is locked" all day....
Searching around for answers to what seems to be a well known problem I've found that it is explained most of the time by the fact that sqlite does not work very nice in multithreading where a thread could potentially timeout waiting for more than 5 (default timeout) seconds to write into the db because another thread has the db lock .
So having more threads that play with the db , one of them using transactions and frequently writing I've began measuring the time it takes for transactionns to complete. I've found that no transaction takes more than 300 ms , thus rendering as not plausible the above explication. Unless the thread that uses transactions makes ~21 (5000 ms / 300 ms) consecutive transactions while any other thread desiring to write gets ignored all this time
So what other hypothesis could potentially explain this behavior ?
I have had a lot of these problems with Sqlite before. Basically, don't have multiple threads that could, potentially, write to the db. If you this is not acceptable, you should switch to Postgres or something else that is better at concurrency.
Sqlite has a very simple implementation that relies on the file system for locking. Most file systems are not built for low-latency operations like this. This is especially true for network-mounted filesystems and the virtual filesystems used by some VPS solutions (that last one got me BTW).
Additionally, you also have the Django layer on top of all this, adding complexity. You don't know when Django releases connections (although I am pretty sure someone here can give that answer in detail :) ). But again, if you have multiple concurrent writers, you need a database layer than can do concurrency. Period.
I solved this issue by switching to postgres. Django makes this very simple for you, even migrating the data is a no-brainer with very little downtime.
In case anyone else might find this question via Google, here's my take on this.
SQLite is a database engine that implements the "serializable" isolation level (see here). By default, it implements this isolation level with a locking strategy (although it seems to be possible to change this to a more MVCC-like strategy by enabling the WAL mode described in that link).
But even with its fairly coarse-grained locking, the fact that SQLite has separate read and write locks, and uses deferred transactions (meaning it doesn't take the locks until necessary), means that deadlocks might still occur. It seems SQLite can detect such deadlocks and fail the transaction almost immediately.
Since SQLite does not support "select for update", the best way to grab the write lock early, and therefore avoid deadlocks, would be to start transactions with "BEGIN IMMEDIATE" or "BEGIN EXCLUSIVE" instead of just "BEGIN", but Django currently only uses "BEGIN" (when told to use transactions) and does not currently have a mechanism for telling it to use anything else. Therefore, locking failures become almost unavoidable with the combination of Django, SQLite, transactions, and concurrency (unless you issue the "BEGIN IMMEDIATE" manually, but that's pretty ugly and SQLite-specific).
But anyone familiar with databases knows that when you're using the "serializable" isolation level with many common database systems, then transactions can typically fail with a serialization error anyway. That happens in exactly the kind of situation this deadlock represents, and when a serialization error occurs, then the failing transaction must simply be retried. And, in fact, that works fine for me.
(Of course, in the end, you should probably use a less "lite" kind of database engine anyway if you need a lot of concurrency.)
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()
HI，i got a multi-threading program which all threads will operate on oracle
DB. So, can sqlalchemy support parallel operation on oracle?
tks!
OCI (oracle client interface) has a parameter OCI_THREADED which has the effect of connections being mutexed, such that concurrent access via multiple threads is safe. This is likely the setting the document you saw was referring to.
cx_oracle, which is essentially a Python->OCI bridge, provides access to this setting in its connection function using the keyword argument "threaded", described at http://cx-oracle.sourceforge.net/html/module.html#cx_Oracle.connect . The docs state that it is False by default due to its resulting in a "10-15% performance penalty", though no source is given for this information (and performance stats should always be viewed suspiciously as a rule).
As far as SQLAlchemy, the cx_oracle dialect provided with SQLAlchemy sets this value to True by default, with the option to set it back to False when setting up the engine via create_engine() - so at that level there's no issue.
But beyond that, SQLAlchemy's recommended usage patterns (i.e. one Session per thread, keeping connections local to a pool where they are checked out by a function as needed) prevent concurrent access to a connection in any case. So you can likely turn off the "threaded" setting on create_engine() and enjoy the possibly-tangible performance increases provided regular usage patterns are followed.
As long as each concurrent thread has it's own session you should be fine. Trying to use one shared session is where you'll get into trouble.