Baked Queries¶
baked
provides an alternative creational pattern for
Query
objects, which allows for caching of the object’s
construction and string-compilation steps. This means that for a
particular Query
building scenario that is used more than
once, all of the Python function invocation involved in building the query
from its initial construction up through generating a SQL string will only
occur once, rather than for each time that query is built up and executed.
The rationale for this system is to greatly reduce Python interpreter overhead for everything that occurs before the SQL is emitted. The caching of the “baked” system does not in any way reduce SQL calls or cache the return results from the database. A technique that demonstrates the caching of the SQL calls and result sets themselves is available in Dogpile Caching.
Deprecated since version 1.4: SQLAlchemy 1.4 and 2.0 feature an all-new direct query
caching system that removes the need for the BakedQuery
system.
Caching is now transparently active for all Core and ORM queries with no
action taken by the user, using the system described at SQL Compilation Caching.
Deep Alchemy
The sqlalchemy.ext.baked
extension is not for beginners. Using
it correctly requires a good high level understanding of how SQLAlchemy, the
database driver, and the backend database interact with each other. This
extension presents a very specific kind of optimization that is not ordinarily
needed. As noted above, it does not cache queries, only the string
formulation of the SQL itself.
Synopsis¶
Usage of the baked system starts by producing a so-called “bakery”, which represents storage for a particular series of query objects:
from sqlalchemy.ext import baked
bakery = baked.bakery()
The above “bakery” will store cached data in an LRU cache that defaults to 200 elements, noting that an ORM query will typically contain one entry for the ORM query as invoked, as well as one entry per database dialect for the SQL string.
The bakery allows us to build up a Query
object by specifying
its construction as a series of Python callables, which are typically lambdas.
For succinct usage, it overrides the +=
operator so that a typical
query build-up looks like the following:
from sqlalchemy import bindparam
def search_for_user(session, username, email=None):
baked_query = bakery(lambda session: session.query(User))
baked_query += lambda q: q.filter(User.name == bindparam("username"))
baked_query += lambda q: q.order_by(User.id)
if email:
baked_query += lambda q: q.filter(User.email == bindparam("email"))
result = baked_query(session).params(username=username, email=email).all()
return result
Following are some observations about the above code:
The
baked_query
object is an instance ofBakedQuery
. This object is essentially the “builder” for a real ormQuery
object, but it is not itself the actualQuery
object.The actual
Query
object is not built at all, until the very end of the function whenResult.all()
is called.The steps that are added to the
baked_query
object are all expressed as Python functions, typically lambdas. The first lambda given to thebakery()
function receives aSession
as its argument. The remaining lambdas each receive aQuery
as their argument.In the above code, even though our application may call upon
search_for_user()
many times, and even though within each invocation we build up an entirely newBakedQuery
object, all of the lambdas are only called once. Each lambda is never called a second time for as long as this query is cached in the bakery.The caching is achieved by storing references to the lambda objects themselves in order to formulate a cache key; that is, the fact that the Python interpreter assigns an in-Python identity to these functions is what determines how to identify the query on successive runs. For those invocations of
search_for_user()
where theemail
parameter is specified, the callablelambda q: q.filter(User.email == bindparam('email'))
will be part of the cache key that’s retrieved; whenemail
isNone
, this callable is not part of the cache key.Because the lambdas are all called only once, it is essential that no variables which may change across calls are referenced within the lambdas; instead, assuming these are values to be bound into the SQL string, we use
bindparam()
to construct named parameters, where we apply their actual values later usingResult.params()
.
Performance¶
The baked query probably looks a little odd, a little bit awkward and
a little bit verbose. However, the savings in
Python performance for a query which is invoked lots of times in an
application are very dramatic. The example suite short_selects
demonstrated in Performance illustrates a comparison
of queries which each return only one row, such as the following regular
query:
session = Session(bind=engine)
for id_ in random.sample(ids, n):
session.query(Customer).filter(Customer.id == id_).one()
compared to the equivalent “baked” query:
bakery = baked.bakery()
s = Session(bind=engine)
for id_ in random.sample(ids, n):
q = bakery(lambda s: s.query(Customer))
q += lambda q: q.filter(Customer.id == bindparam("id"))
q(s).params(id=id_).one()
The difference in Python function call count for an iteration of 10000 calls to each block are:
test_baked_query : test a baked query of the full entity.
(10000 iterations); total fn calls 1951294
test_orm_query : test a straight ORM query of the full entity.
(10000 iterations); total fn calls 7900535
In terms of number of seconds on a powerful laptop, this comes out as:
test_baked_query : test a baked query of the full entity.
(10000 iterations); total time 2.174126 sec
test_orm_query : test a straight ORM query of the full entity.
(10000 iterations); total time 7.958516 sec
Note that this test very intentionally features queries that only return one row. For queries that return many rows, the performance advantage of the baked query will have less and less of an impact, proportional to the time spent fetching rows. It is critical to keep in mind that the baked query feature only applies to building the query itself, not the fetching of results. Using the baked feature is by no means a guarantee to a much faster application; it is only a potentially useful feature for those applications that have been measured as being impacted by this particular form of overhead.
Rationale¶
The “lambda” approach above is a superset of what would be a more
traditional “parameterized” approach. Suppose we wished to build
a simple system where we build a Query
just once, then
store it in a dictionary for re-use. This is possible right now by
just building up the query, and removing its Session
by calling
my_cached_query = query.with_session(None)
:
my_simple_cache = {}
def lookup(session, id_argument):
if "my_key" not in my_simple_cache:
query = session.query(Model).filter(Model.id == bindparam("id"))
my_simple_cache["my_key"] = query.with_session(None)
else:
query = my_simple_cache["my_key"].with_session(session)
return query.params(id=id_argument).all()
The above approach gets us a very minimal performance benefit.
By re-using a Query
, we save on the Python work within
the session.query(Model)
constructor as well as calling upon
filter(Model.id == bindparam('id'))
, which will skip for us the building
up of the Core expression as well as sending it to Query.filter()
.
However, the approach still regenerates the full Select
object every time when Query.all()
is called and additionally this
brand new Select
is sent off to the string compilation step every
time, which for a simple case like the above is probably about 70% of the
overhead.
To reduce the additional overhead, we need some more specialized logic,
some way to memoize the construction of the select object and the
construction of the SQL. There is an example of this on the wiki
in the section BakedQuery,
a precursor to this feature, however in that system, we aren’t caching
the construction of the query. In order to remove all the overhead,
we need to cache both the construction of the query as well as the SQL
compilation. Let’s assume we adapted the recipe in this way
and made ourselves a method .bake()
that pre-compiles the SQL for the
query, producing a new object that can be invoked with minimal overhead.
Our example becomes:
my_simple_cache = {}
def lookup(session, id_argument):
if "my_key" not in my_simple_cache:
query = session.query(Model).filter(Model.id == bindparam("id"))
my_simple_cache["my_key"] = query.with_session(None).bake()
else:
query = my_simple_cache["my_key"].with_session(session)
return query.params(id=id_argument).all()
Above, we’ve fixed the performance situation, but we still have this string cache key to deal with.
We can use the “bakery” approach to re-frame the above in a way that looks less unusual than the “building up lambdas” approach, and more like a simple improvement upon the simple “reuse a query” approach:
bakery = baked.bakery()
def lookup(session, id_argument):
def create_model_query(session):
return session.query(Model).filter(Model.id == bindparam("id"))
parameterized_query = bakery.bake(create_model_query)
return parameterized_query(session).params(id=id_argument).all()
Above, we use the “baked” system in a manner that is
very similar to the simplistic “cache a query” system. However, it
uses two fewer lines of code, does not need to manufacture a cache key of
“my_key”, and also includes the same feature as our custom “bake” function
that caches 100% of the Python invocation work from the
constructor of the query, to the filter call, to the production
of the Select
object, to the string compilation step.
From the above, if we ask ourselves, “what if lookup needs to make conditional decisions as to the structure of the query?”, this is where hopefully it becomes apparent why “baked” is the way it is. Instead of a parameterized query building off from exactly one function (which is how we thought baked might work originally), we can build it from any number of functions. Consider our naive example, if we needed to have an additional clause in our query on a conditional basis:
my_simple_cache = {}
def lookup(session, id_argument, include_frobnizzle=False):
if include_frobnizzle:
cache_key = "my_key_with_frobnizzle"
else:
cache_key = "my_key_without_frobnizzle"
if cache_key not in my_simple_cache:
query = session.query(Model).filter(Model.id == bindparam("id"))
if include_frobnizzle:
query = query.filter(Model.frobnizzle == True)
my_simple_cache[cache_key] = query.with_session(None).bake()
else:
query = my_simple_cache[cache_key].with_session(session)
return query.params(id=id_argument).all()
Our “simple” parameterized system must now be tasked with generating cache keys which take into account whether or not the “include_frobnizzle” flag was passed, as the presence of this flag means that the generated SQL would be entirely different. It should be apparent that as the complexity of query building goes up, the task of caching these queries becomes burdensome very quickly. We can convert the above example into a direct use of “bakery” as follows:
bakery = baked.bakery()
def lookup(session, id_argument, include_frobnizzle=False):
def create_model_query(session):
return session.query(Model).filter(Model.id == bindparam("id"))
parameterized_query = bakery.bake(create_model_query)
if include_frobnizzle:
def include_frobnizzle_in_query(query):
return query.filter(Model.frobnizzle == True)
parameterized_query = parameterized_query.with_criteria(
include_frobnizzle_in_query
)
return parameterized_query(session).params(id=id_argument).all()
Above, we again cache not just the query object but all the work it needs to do in order to generate SQL. We also no longer need to deal with making sure we generate a cache key that accurately takes into account all of the structural modifications we’ve made; this is now handled automatically and without the chance of mistakes.
This code sample is a few lines shorter than the naive example, removes
the need to deal with cache keys, and has the vast performance benefits
of the full so-called “baked” feature. But
still a little verbose! Hence we take methods like BakedQuery.add_criteria()
and BakedQuery.with_criteria()
and shorten them into operators, and
encourage (though certainly not require!) using simple lambdas, only as a
means to reduce verbosity:
bakery = baked.bakery()
def lookup(session, id_argument, include_frobnizzle=False):
parameterized_query = bakery.bake(
lambda s: s.query(Model).filter(Model.id == bindparam("id"))
)
if include_frobnizzle:
parameterized_query += lambda q: q.filter(Model.frobnizzle == True)
return parameterized_query(session).params(id=id_argument).all()
Where above, the approach is simpler to implement and much more similar in code flow to what a non-cached querying function would look like, hence making code easier to port.
The above description is essentially a summary of the design process used to arrive at the current “baked” approach. Starting from the “normal” approaches, the additional issues of cache key construction and management, removal of all redundant Python execution, and queries built up with conditionals needed to be addressed, leading to the final approach.
Special Query Techniques¶
This section will describe some techniques for specific query situations.
Using IN expressions¶
The ColumnOperators.in_()
method in SQLAlchemy historically renders
a variable set of bound parameters based on the list of items that’s passed
to the method. This doesn’t work for baked queries as the length of that
list can change on different calls. To solve this problem, the
bindparam.expanding
parameter supports a late-rendered IN
expression that is safe to be cached inside of baked query. The actual list
of elements is rendered at statement execution time, rather than at
statement compilation time:
bakery = baked.bakery()
baked_query = bakery(lambda session: session.query(User))
baked_query += lambda q: q.filter(User.name.in_(bindparam("username", expanding=True)))
result = baked_query.with_session(session).params(username=["ed", "fred"]).all()
See also
bindparam.expanding
ColumnOperators.in_()
Using Subqueries¶
When using Query
objects, it is often needed that one Query
object is used to generate a subquery within another. In the case where the
Query
is currently in baked form, an interim method may be used to
retrieve the Query
object, using the BakedQuery.to_query()
method. This method is passed the Session
or Query
that is
the argument to the lambda callable used to generate a particular step
of the baked query:
bakery = baked.bakery()
# a baked query that will end up being used as a subquery
my_subq = bakery(lambda s: s.query(User.id))
my_subq += lambda q: q.filter(User.id == Address.user_id)
# select a correlated subquery in the top columns list,
# we have the "session" argument, pass that
my_q = bakery(lambda s: s.query(Address.id, my_subq.to_query(s).as_scalar()))
# use a correlated subquery in some of the criteria, we have
# the "query" argument, pass that.
my_q += lambda q: q.filter(my_subq.to_query(q).exists())
New in version 1.3.
Using the before_compile event¶
As of SQLAlchemy 1.3.11, the use of the QueryEvents.before_compile()
event against a particular Query
will disallow the baked query
system from caching the query, if the event hook returns a new Query
object that is different from the one passed in. This is so that the
QueryEvents.before_compile()
hook may be invoked against a particular
Query
every time it is used, to accommodate for hooks that
alter the query differently each time. To allow a
QueryEvents.before_compile()
to alter a sqlalchemy.orm.Query()
object, but
still to allow the result to be cached, the event can be registered
passing the bake_ok=True
flag:
@event.listens_for(Query, "before_compile", retval=True, bake_ok=True)
def my_event(query):
for desc in query.column_descriptions:
if desc["type"] is User:
entity = desc["entity"]
query = query.filter(entity.deleted == False)
return query
The above strategy is appropriate for an event that will modify a
given Query
in exactly the same way every time, not dependent
on specific parameters or external state that changes.
New in version 1.3.11: - added the “bake_ok” flag to the
QueryEvents.before_compile()
event and disallowed caching via
the “baked” extension from occurring for event handlers that
return a new Query
object if this flag is not set.
Disabling Baked Queries Session-wide¶
The flag Session.enable_baked_queries
may be set to False,
causing all baked queries to not use the cache when used against that
Session
:
session = Session(engine, enable_baked_queries=False)
Like all session flags, it is also accepted by factory objects like
sessionmaker
and methods like sessionmaker.configure()
.
The immediate rationale for this flag is so that an application which is seeing issues potentially due to cache key conflicts from user-defined baked queries or other baked query issues can turn the behavior off, in order to identify or eliminate baked queries as the cause of an issue.
New in version 1.2.
Lazy Loading Integration¶
Changed in version 1.4: As of SQLAlchemy 1.4, the “baked query” system is no longer part of the relationship loading system. The native caching system is used instead.