What is the best solution for database connection pooling in python?

Question

I have developed some custom DAO-like classes to meet some very specialized requirements for my project that is a server-side process that does not run inside any kind of framework.

The solution works great except that every time a new request is made, I open a new connection via MySQLdb.connect.

What is the best "drop in" solution to switch this over to using connection pooling in python? I am imagining something like the commons DBCP solution for Java.

The process is long running and has many threads that need to make requests, but not all at the same time... specifically they do quite a lot of work before brief bursts of writing out a chunk of their results.

Edited to add: After some more searching I found anitpool.py which looks decent, but as I'm relatively new to python I guess I just want to make sure I'm not missing a more obvious/more idiomatic/better solution.

Answer 1

In MySQL?

I'd say don't bother with the connection pooling. They're often a source of trouble and with MySQL they're not going to bring you the performance advantage you're hoping for. This road may be a lot of effort to follow--politically--because there's so much best practices hand waving and textbook verbiage in this space about the advantages of connection pooling.

Connection pools are simply a bridge between the post-web era of stateless applications (e.g. HTTP protocol) and the pre-web era of stateful long-lived batch processing applications. Since connections were very expensive in pre-web databases (since no one used to care too much about how long a connection took to establish), post-web applications devised this connection pool scheme so that every hit didn't incur this huge processing overhead on the RDBMS.

Since MySQL is more of a web-era RDBMS, connections are extremely lightweight and fast. I have written many high volume web applications that don't use a connection pool at all for MySQL.

This is a complication you may benefit from doing without, so long as there isn't a political obstacle to overcome.

Answer 2

Wrap your connection class.

Set a limit on how many connections you make. Return an unused connection. Intercept close to free the connection.

Update: I put something like this in dbpool.py:

import sqlalchemy.pool as pool
import MySQLdb as mysql
mysql = pool.manage(mysql)

Answer 3

IMO, the "more obvious/more idiomatic/better solution" is to use an existing ORM rather than invent DAO-like classes.

It appears to me that ORM's are more popular than "raw" SQL connections. Why? Because Python is OO, and the mapping from a SQL row to an object is absolutely essential. There aren't many use cases where you deal with SQL rows that don't map to Python objects.

I think that SQLAlchemy or SQLObject (and the associated connection pooling) are the more idiomatic Pythonic solutions.

Pooling as a separate feature isn't very common because pure SQL (without object mapping) isn't very popular for the kind of complex, long-running processes that benefit from connection pooling. Yes, pure SQL is used, but it's always used in simpler or more controlled applications where pooling isn't helpful.

I think you might have two alternatives:

1. Revise your classes to use SQLAlchemy or SQLObject. While this appears painful at first (all that work wasted), you should be able to leverage all the design and thought. It's merely an exercise in adopting a widely-used ORM and pooling solution.
2. Roll out your own simple connection pool using the algorithm you outlined -- a simple Set or List of connections that you cycle through.

Answer 4

Old thread, but for general-purpose pooling (connections or any expensive object), I use something like:

def pool(ctor, limit=None):
    local_pool = multiprocessing.Queue()
    n = multiprocesing.Value('i', 0)
    @contextlib.contextmanager
    def pooled(ctor=ctor, lpool=local_pool, n=n):
        # block iff at limit
        try: i = lpool.get(limit and n.value >= limit)
        except multiprocessing.queues.Empty:
            n.value += 1
            i = ctor()
        yield i
        lpool.put(i)
    return pooled

Which constructs lazily, has an optional limit, and should generalize to any use case I can think of. Of course, this assumes that you really need the pooling of whatever resource, which you may not for many modern SQL-likes. Usage:

# in main:
my_pool = pool(lambda: do_something())
# in thread:
with my_pool() as my_obj:
    my_obj.do_something()

This does assume that whatever object ctor creates has an appropriate destructor if needed (some servers don't kill connection objects unless they are closed explicitly).

Answer 5

I've just been looking for the same sort of thing.

I've found pysqlpool and the sqlalchemy pool module

Answer 6

Replying to an old thread but the last time I checked, MySQL offers connection pooling as part of its drivers.

You can check them out at :

https://dev.mysql.com/doc/connector-python/en/connector-python-connection-pooling.html

From TFA, Assuming you want to open a connection pool explicitly (as OP had stated):

dbconfig = {  "database": "test", "user":"joe" }
cnxpool = mysql.connector.pooling.MySQLConnectionPool(pool_name = "mypool",pool_size = 3, **dbconfig)

This pool is then accessed by requesting from the pool through the get_connection() function.

cnx1 = cnxpool.get_connection()
cnx2 = cnxpool.get_connection()

Answer 7

Making your own connection pool is a BAD idea if your app ever decides to start using multi-threading. Making a connection pool for a multi-threaded application is much more complicated than one for a single-threaded application. You can use something like PySQLPool in that case.

It's also a BAD idea to use an ORM if you're looking for performance.

If you'll be dealing with huge/heavy databases that have to handle lots of selects, inserts, updates and deletes at the same time, then you're going to need performance, which means you'll need custom SQL written to optimize lookups and lock times. With an ORM you don't usually have that flexibility.

So basically, yeah, you can make your own connection pool and use ORMs but only if you're sure you won't need anything of what I just described.

Answer 8

Use DBUtils, simple and reliable.

pip install DBUtils

Answer 9

i did it for opensearch so you can refer it.

    from opensearchpy import OpenSearch

    def get_connection():
                    connection = None
                    try:
                        connection = OpenSearch(
                            hosts=[{'host': settings.OPEN_SEARCH_HOST, 'port': settings.OPEN_SEARCH_PORT}],
                            http_compress=True,
                            http_auth=(settings.OPEN_SEARCH_USER, settings.OPEN_SEARCH_PASSWORD),
                            use_ssl=True,
                            verify_certs=True,
                            ssl_assert_hostname=False,
                            ssl_show_warn=False,
                        )
                    except Exception as error:
                        print("Error: Connection not established {}".format(error))
                    else:
                        print("Connection established")
                    return connection

    class OpenSearchClient(object):
        connection_pool = []
        connection_in_use = []

        def __init__(self):
            if OpenSearchClient.connection_pool:
                pass
            else:

                OpenSearchClient.connection_pool = [get_connection() for i in range(0, settings.CONNECTION_POOL_SIZE)]

        def search_data(self, query="", index_name=settings.OPEN_SEARCH_INDEX):
            available_cursor = OpenSearchClient.connection_pool.pop(0)
            OpenSearchClient.connection_in_use.append(available_cursor)
            response = available_cursor.search(body=query, index=index_name)
            available_cursor.close()
            OpenSearchClient.connection_pool.append(available_cursor)
            OpenSearchClient.connection_in_use.pop(-1)
            return response