Re: [sqlalchemy] Issuing Raw SQL and Returning a List of Objects
Hi there,
This is great. Thanks for adding to the discussion.
Rgds
mjg
On Thursday, November 2, 2023 at 11:13:07 AM UTC-4 mkmo...@gmail.com wrote:
> Hi Mike,
>
> If I understand correctly, you want to work with raw sql and don't want
> any ORM getting in your way. I'm the same way, and it is trivial to use
> SQLAlchemy Core for this purpose.
>
> results = conn.execute(text('select foo, bar from
> baz')).mappings().fetchall() # mappings().fetchall() returns a list of
> dict like objects
> for row in results:
> print(row['foo'], row['bar'])
>
> result = conn.execute(text('select foo, bar from baz')).fetchall() #
> fetchall() without mappings() returns a list of named tuple like objects
> for row in results:
> print(row.foo, row.bar)
> print(row[0], row[1])
>
> On Thursday, August 24, 2023 at 5:06:11 AM UTC-7 Mike Graziano wrote:
>
>> Hi Simon,
>>
>> Thanks for responding to my post. It turns out that MyBatis can do
>> exactly what you are saying which essentially sounds like a bulk ETL
>> process. Again, the key difference is that MyBatis doesn’t require that
>> the mapping be done with all the DB-specific definitions which I frankly
>> prefer. There is a tool, the MyBatis generator, that does exactly this and
>> I have used it when I didn’t want to write my own mapping files since the
>> tables had hundreds of fields.
>>
>> In many cases, you are correct in that I was only loading data. The data
>> was retrieved by raw SQL and could involve joins with other tables much as
>> in a view. I just needed a data transfer mechanism to translate the SQL
>> results to a POJO. Your experience differed in that you did need to create
>> the tables with your Python code. I agree that SQLAlchemy is perfect for
>> that. I created the tables ahead of time usually with command-line psql
>> or, as you said, the tables already existed. In fact, I’d sometimes create
>> temp tables with the schema of existing tables and I also did that with
>> command-line psql in a Bash script.
>>
>> Thanks for your insights.
>>
>> Rgds
>>
>> mjg
>>
>> On Wednesday, August 23, 2023 at 12:49:23 PM UTC-4 Simon King wrote:
>>
>>> My perspective: the SQLAlchemy ORM really comes into its own when you
>>> are making use of its Unit of Work system to load a batch of objects from
>>> the database, manipulate those objects, and then flush your changes back to
>>> the database. If you are only *loading* data then you don't need a lot of
>>> the functionality of the ORM, and you might consider using SQLAlchemy Core
>>> instead.
>>>
>>> Using SQLAlchemy Core to execute SQL strings is very simple:
>>>
>>> https://docs.sqlalchemy.org/en/20/core/connections.html#basic-usage
>>>
>>> You can use the objects that come back from those calls directly (they
>>> have attributes named after the columns from the query), or you could
>>> trivially convert them into instances of some class that you've defined.
>>>
>>> It sounds like the sort of work you do involves writing code to access
>>> pre-existing databases, in which case writing SQL directly makes a lot of
>>> sense, and you have no need for the schema-definition parts of SQLAlchemy.
>>> But there are other classes of application for which the schema-definition
>>> tools are very useful. I have written many applications for which the
>>> database didn't already exist, so allowing SQLAlchemy to create the tables
>>> was the obvious way to go (with Alembic for migrations as the schema
>>> changed over time). SQLAlchemy also gives a certain amount of independence
>>> from the underlying database, meaning that I can run most of my tests using
>>> SQLite despite using Postgres or MySQL in production.
>>>
>>> In summary: use the right tool for the job :-)
>>>
>>> Simon
>>>
>>>
>>> On Mon, Aug 21, 2023 at 6:48 PM Mike Graziano wrote:
>>>
Hi Mike,
Thanks for that info. It was just what I needed. I also want to thank
you for your YouTube tutorials on SQLAlchemy. They are fantastic.
I don’t want to make this a huge post, but I have a real pet peeve
concerning ORMs. I come from a Java background where I used MyBatis as my
ORM. What I love about MyBatis was
- I could use raw SQL which I personally feel is superior. My
argument here is simple: Why learn another “language” for issuing SQL
statements when we have already spent a fair amount of time learning SQL.
Also, raw SQL is easily testable with either command line or GUI tools?
- The ORM should just use the mapped models in order to execute SQL
using mapping that in and of themselves doesn’t/shouldn’t care about the
tables. Unless you are creating a table with the ORM which I have found
to
be rare, the ORM shouldn’t care about the table structure other than field
names with the possibility of aliases and data types. Why define more
than
Re: [sqlalchemy] Postgresq Execute Many with Textual SQL Convenience Issue
On Thu, Nov 2, 2023, at 11:24 AM, mkmo...@gmail.com wrote:
> Hi Mike,
>
> When using Core, we can do a bulk insert and bulk return with Postgresql
> trivially:
>
> from sqlalchemy import table, column
> t = table('foo', column('id'), column('bar')
>
> ins = t.insert().values([{'bar': 'a'}, {'bar': 'b'}]).returning(foo.id)
>
> results = conn.execute(ins)
>
> ids = results.fetchall()
>
> However, with raw textual SQL, it is a bit more inconvenient.
>
> The following doesn't work:
>
> results = conn.execute(text(
> 'insert into foo values (:bar) returning id
> ), [{'bar': 'a'}, {'bar': 'b'}])
>
> # raises sqlalchemy.exc.ResourceClosedError: This result object does not
> return rows. It has been closed automatically.
> results.fetchall()
That's because you're using excecutemany. pep-249 executemany does not
support rows being returned, so if you send a list of params like that, it's an
automatic result close. there's nothing to fetch.
I would also note that the statement you have above might not be as useful as
you think since I would assume you would want those "RETURNING" rows to line up
with the dictionaries you are sending. This is not a guarantee for most
backends and the SQL Server backend will actually actively rearrange the rows.
Special (extremely inconvenient) forms must be taken to ensure this ordering.
SQLAlchemy 2.0 supports a new form of INSERT called insertmanyvalues
(https://docs.sqlalchemy.org/en/20/core/connections.html#insert-many-values-behavior-for-insert-statements
) which can receive a list of parameter dictionaries along with an insert()
construct and convert the operation into series of batched single statements
that are yielded as a single result set. That is, it does **not** use DBAPI
executemany (except on Oracle which has special support), it uses DBAPI
execute. It also does this very special and inconvenient formatting of the
INSERT statement to ensure to the greatest degree possible that RETURNING rows
are ordered the same way as the parameter sets.
>
> To get it working, we have to do it this way:
>
> results = conn.execute(text(
> 'insert into foo values (:bar0), (:bar1)
> ), {'bar0': 'x', 'bar1': 'y'})
>
> assert results.fetchall()
>
>
> This isn't convenient. For example you would have to convert a list of bars
> like [{'bar': 'a'}, {'bar': 'b'}] into a single dict with uniquely name keys
> {'bar0': 'a', 'bar1': 'b'}.
you do, because there's no result set implied from an executemany, you have to
use an execute.
if you want SQLAlchemy to convert your multiple parameters into a series of
"INSERT..VALUES", do the execution of them, and then with RETURNING work the
results back together, that's exactly what insertmanyvalues does.We spent
many months developing this feature plus figuring out the RETURNING / ordering
thing which took an extra month, so you'd be tapping into a great deal of
development efforts by using that feature.
However, that feature works only with insert() constructs. With text(), we
have no idea what your statement says and we don't parse SQL. you'd be on your
own there.
>
> I imagine sqlalchemy is doing that under the hood when using core. Is there
> some convenience function available in sqlalchemy core that I can use to
> simplify this?
The functions which do this start at
https://github.com/sqlalchemy/sqlalchemy/blob/b51cccec6a953555f39c16005cb5a2a49a6f4b21/lib/sqlalchemy/engine/default.py#L758
and then digs into compiler at
https://github.com/sqlalchemy/sqlalchemy/blob/b51cccec6a953555f39c16005cb5a2a49a6f4b21/lib/sqlalchemy/sql/compiler.py#L5306
, where you'll note these functions are now huge and complicated, as they must
accommodate all cases amongst a wide variety of statements scenarios, typing
issues on both the input and output side, and backends.
You can definitely write simple helpers to convert simple INSERT statements on
your own here, or maybe look at psycopg2's fast execution helpers also at
https://www.psycopg.org/docs/extras.html#fast-execution-helpers which are older
/ simpler versions of this kind of thing (however still probably not
generalizable).
--
SQLAlchemy -
The Python SQL Toolkit and Object Relational Mapper
http://www.sqlalchemy.org/
To post example code, please provide an MCVE: Minimal, Complete, and Verifiable
Example. See http://stackoverflow.com/help/mcve for a full description.
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[sqlalchemy] Postgresq Execute Many with Textual SQL Convenience Issue
Hi Mike,
When using Core, we can do a bulk insert and bulk return with Postgresql
trivially:
from sqlalchemy import table, column
t = table('foo', column('id'), column('bar')
ins = t.insert().values([{'bar': 'a'}, {'bar': 'b'}]).returning(foo.id)
results = conn.execute(ins)
ids = results.fetchall()
However, with raw textual SQL, it is a bit more inconvenient.
The following doesn't work:
results = conn.execute(text(
'insert into foo values (:bar) returning id
), [{'bar': 'a'}, {'bar': 'b'}])
# raises sqlalchemy.exc.ResourceClosedError: This result object does
not return rows. It has been closed automatically.
results.fetchall()
To get it working, we have to do it this way:
results = conn.execute(text(
'insert into foo values (:bar0), (:bar1)
), {'bar0': 'x', 'bar1': 'y'})
assert results.fetchall()
This isn't convenient. For example you would have to convert a list of bars
like [{'bar': 'a'}, {'bar': 'b'}] into a single dict with uniquely name
keys {'bar0': 'a', 'bar1': 'b'}.
I imagine sqlalchemy is doing that under the hood when using core. Is there
some convenience function available in sqlalchemy core that I can use to
simplify this?
--
SQLAlchemy -
The Python SQL Toolkit and Object Relational Mapper
http://www.sqlalchemy.org/
To post example code, please provide an MCVE: Minimal, Complete, and Verifiable
Example. See http://stackoverflow.com/help/mcve for a full description.
---
You received this message because you are subscribed to the Google Groups
"sqlalchemy" group.
To unsubscribe from this group and stop receiving emails from it, send an email
to sqlalchemy+unsubscr...@googlegroups.com.
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Re: [sqlalchemy] Issuing Raw SQL and Returning a List of Objects
Hi Mike,
If I understand correctly, you want to work with raw sql and don't want any
ORM getting in your way. I'm the same way, and it is trivial to use
SQLAlchemy Core for this purpose.
results = conn.execute(text('select foo, bar from
baz')).mappings().fetchall() # mappings().fetchall() returns a list of
dict like objects
for row in results:
print(row['foo'], row['bar'])
result = conn.execute(text('select foo, bar from baz')).fetchall() #
fetchall() without mappings() returns a list of named tuple like objects
for row in results:
print(row.foo, row.bar)
print(row[0], row[1])
On Thursday, August 24, 2023 at 5:06:11 AM UTC-7 Mike Graziano wrote:
> Hi Simon,
>
> Thanks for responding to my post. It turns out that MyBatis can do
> exactly what you are saying which essentially sounds like a bulk ETL
> process. Again, the key difference is that MyBatis doesn’t require that
> the mapping be done with all the DB-specific definitions which I frankly
> prefer. There is a tool, the MyBatis generator, that does exactly this and
> I have used it when I didn’t want to write my own mapping files since the
> tables had hundreds of fields.
>
> In many cases, you are correct in that I was only loading data. The data
> was retrieved by raw SQL and could involve joins with other tables much as
> in a view. I just needed a data transfer mechanism to translate the SQL
> results to a POJO. Your experience differed in that you did need to create
> the tables with your Python code. I agree that SQLAlchemy is perfect for
> that. I created the tables ahead of time usually with command-line psql
> or, as you said, the tables already existed. In fact, I’d sometimes create
> temp tables with the schema of existing tables and I also did that with
> command-line psql in a Bash script.
>
> Thanks for your insights.
>
> Rgds
>
> mjg
>
> On Wednesday, August 23, 2023 at 12:49:23 PM UTC-4 Simon King wrote:
>
>> My perspective: the SQLAlchemy ORM really comes into its own when you are
>> making use of its Unit of Work system to load a batch of objects from the
>> database, manipulate those objects, and then flush your changes back to the
>> database. If you are only *loading* data then you don't need a lot of the
>> functionality of the ORM, and you might consider using SQLAlchemy Core
>> instead.
>>
>> Using SQLAlchemy Core to execute SQL strings is very simple:
>>
>> https://docs.sqlalchemy.org/en/20/core/connections.html#basic-usage
>>
>> You can use the objects that come back from those calls directly (they
>> have attributes named after the columns from the query), or you could
>> trivially convert them into instances of some class that you've defined.
>>
>> It sounds like the sort of work you do involves writing code to access
>> pre-existing databases, in which case writing SQL directly makes a lot of
>> sense, and you have no need for the schema-definition parts of SQLAlchemy.
>> But there are other classes of application for which the schema-definition
>> tools are very useful. I have written many applications for which the
>> database didn't already exist, so allowing SQLAlchemy to create the tables
>> was the obvious way to go (with Alembic for migrations as the schema
>> changed over time). SQLAlchemy also gives a certain amount of independence
>> from the underlying database, meaning that I can run most of my tests using
>> SQLite despite using Postgres or MySQL in production.
>>
>> In summary: use the right tool for the job :-)
>>
>> Simon
>>
>>
>> On Mon, Aug 21, 2023 at 6:48 PM Mike Graziano wrote:
>>
>>> Hi Mike,
>>>
>>>
>>>
>>> Thanks for that info. It was just what I needed. I also want to thank
>>> you for your YouTube tutorials on SQLAlchemy. They are fantastic.
>>>
>>>
>>>
>>> I don’t want to make this a huge post, but I have a real pet peeve
>>> concerning ORMs. I come from a Java background where I used MyBatis as my
>>> ORM. What I love about MyBatis was
>>>
>>>
>>>
>>> - I could use raw SQL which I personally feel is superior. My
>>> argument here is simple: Why learn another “language” for issuing SQL
>>> statements when we have already spent a fair amount of time learning SQL.
>>> Also, raw SQL is easily testable with either command line or GUI tools?
>>>
>>> - The ORM should just use the mapped models in order to execute SQL
>>> using mapping that in and of themselves doesn’t/shouldn’t care about the
>>> tables. Unless you are creating a table with the ORM which I have found to
>>> be rare, the ORM shouldn’t care about the table structure other than field
>>> names with the possibility of aliases and data types. Why define more than
>>> what we need in order to populate a plain old object (POO – language
>>> agnostic). Why include characteristics like primary key, nullability,
>>> etc? Some Pydantic-like validation is handy, but can be table agnostic.
>>> Let’s extract the data via SQL and return POOs. In
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