Develop Reference

SQLAlchemy sqlite3 remove value from JSON column on multiple rows with different JSON values

Say I have an id column that is saved as ids JSON NOT NULL using SQLAlchemy, and now I want to delete an id from this column. I'd like to do several things at once:
query only the rows who have this specific ID
delete this ID from all rows it appears in
a bonus, if possible - delete the row if the ID list is now empty.
For the query, something like this:
db.query(models.X).filter(id in list(models.X.ids)) should work.
now, I'd rather avoid iterating over each query and then send an update request as it can be multiple rows. Is there any elegant way to do this?
Thanks!

For the search and remove remove part you can use json_remove function (from SQLLite built-in functions)
from sqlalchemy import func
db.query(models.X).update({'ids': func.json_remove(models.X.ids,f'$[{TARGET_ID}]') })
Here replace TARGET_ID by the targeted id.
Now this will update the row 'silently' (wether or not this id is present in the array).
If you want to first check if target id is in the column: you can query first all rows containing the target id with json_extract query (calling .all() method and then remove those ids with an .update() call.
But this will cost you double amount of queries (less performant).
For the delete part, you can use the json_array_length built-in function
from sqlalchemy import func
db.query(models.X).filter(func.json_array_length(models.X.ids) == 0).delete()
FYI : Not sure that you can do both in one query, and even if possible, I would not do it for clean syntax, logging and monitoring reasons.

How do I upsert all the rows from one table into another table using Postgres?

I am working in Python, using Pandas to pull data from a TSV, convert it to a data frame, then syncing that data frame to a temp table in postgres using df.to_sql. That process works great.
However, once that table exists, I want to move all the rows from that table to the permanent table. The two tables will always be identical. The permanent table has a unique index, so if the id already exists it should update the row instead.
Here is my attempt to upsert all rows from one table to another:
WITH moved_rows AS (
DELETE FROM my_table_tmp a
RETURNING a.*
)
INSERT INTO my_table
SELECT * FROM moved_rows
ON CONFLICT ("unique_id") DO
UPDATE SET
Field_A = excluded.Field_A,
Field_B = excluded.Field_B,
Field_C = excluded.Field_C
Unfortunately, when I run this, I get this error:
psycopg2.errors.UndefinedColumn: column excluded.field_a does not exist
LINE 10: Field_A = excluded.Field_A,
^
HINT: Perhaps you meant to reference the column "excluded.Field_A".
But in fact, that column does exist. What am I missing here? I've tried removing Field_A from the set and then I get the same error with Field_B.

Answering my own question here - the issue is that Postgres ignores capitalization unless it's quoted.
This was not clear in the example I posted because I obscured the naming of the fields I was working with. I've updated them now to show the issue.
In order to fix this you need to wrap your field names with double quotes. E.g. "Field_A" = excluded."Field_A"

sqlalchemy.exc.IntegrityError: (psycopg2.errors.UniqueViolation) duplicate key value violates unique constraint "product_pkey" [duplicate]

I have a tabled called products
which has following columns
id, product_id, data, activity_id
What I am essentially trying to do is copy bulk of existing products and update it's activity_id and create new entry in the products table.
Example:
I already have 70 existing entries in products with activity_id 2
Now I want to create another 70 entries with same data except for updated activity_id
I could have thousands of existing entries that I'd like to make a copy of and update the copied entries activity_id to be a new id.
products = self.session.query(model.Products).filter(filter1, filter2).all()
This returns all the existing products for a filter.
Then I iterate through products, then simply clone existing products and just update activity_id field.
for product in products:
product.activity_id = new_id
self.uow.skus.bulk_save_objects(simulation_skus)
self.uow.flush()
self.uow.commit()
What is the best/ fastest way to do these bulk entries so it kills time, as of now it's OK performance, is there a better solution?

You don't need to load these objects locally, all you really want to do is have the database create these rows.
You essentially want to run a query that creates the rows from the existing rows:
INSERT INTO product (product_id, data, activity_id)
SELECT product_id, data, 2 -- the new activity_id value
FROM product
WHERE activity_id = old_id
The above query would run entirely on the database server; this is far preferable over loading your query into Python objects, then sending all the Python data back to the server to populate INSERT statements for each new row.
Queries like that are something you could do with SQLAlchemy core, the half of the API that deals with generating SQL statements. However, you can use a query built from a declarative ORM model as a starting point. You'd need to
Access the Table instance for the model, as that then lets you create an INSERT statement via the Table.insert() method.
You could also get the same object from models.Product query, more on that later.
Access the statement that would normally fetch the data for your Python instances for your filtered models.Product query; you can do so via the Query.statement property.
Update the statement to replace the included activity_id column with your new value, and remove the primary key (I'm assuming that you have an auto-incrementing primary key column).
Apply that updated statement to the Insert object for the table via Insert.from_select().
Execute the generated INSERT INTO ... FROM ... query.
Step 1 can be achieved by using the SQLAlchemy introspection API; the inspect() function, applied to a model class, gives you a Mapper instance, which in turn has a Mapper.local_table attribute.
Steps 2 and 3 require a little juggling with the Select.with_only_columns() method to produce a new SELECT statement where we swapped out the column. You can't easily remove a column from a select statement but we can, however, use a loop over the existing columns in the query to 'copy' them across to the new SELECT, and at the same time make our replacement.
Step 4 is then straightforward, Insert.from_select() needs to have the columns that are inserted and the SELECT query. We have both as the SELECT object we have gives us its columns too.
Here is the code for generating your INSERT; the **replace keyword arguments are the columns you want to replace when inserting:
from sqlalchemy import inspect, literal
from sqlalchemy.sql import ClauseElement
def insert_from_query(model, query, **replace):
# The SQLAlchemy core definition of the table
table = inspect(model).local_table
# and the underlying core select statement to source new rows from
select = query.statement
# validate asssumptions: make sure the query produces rows from the above table
assert table in select.froms, f"{query!r} must produce rows from {model!r}"
assert all(c.name in select.columns for c in table.columns), f"{query!r} must include all {model!r} columns"
# updated select, replacing the indicated columns
as_clause = lambda v: literal(v) if not isinstance(v, ClauseElement) else v
replacements = {name: as_clause(value).label(name) for name, value in replace.items()}
from_select = select.with_only_columns([
replacements.get(c.name, c)
for c in table.columns
if not c.primary_key
])
return table.insert().from_select(from_select.columns, from_select)
I included a few assertions about the model and query relationship, and the code accepts arbitrary column clauses as replacements, not just literal values. You could use func.max(models.Product.activity_id) + 1 as a replacement value (wrapped as a subselect), for example.
The above function executes steps 1-4, producing the desired INSERT SQL statement when printed (I created a products model and query that I thought might be representative):
>>> print(insert_from_query(models.Product, products, activity_id=2))
INSERT INTO products (product_id, data, activity_id) SELECT products.product_id, products.data, :param_1 AS activity_id
FROM products
WHERE products.activity_id != :activity_id_1
All you have to do is execute it:
insert_stmt = insert_from_query(models.Product, products, activity_id=2)
self.session.execute(insert_stmt)

How to avoid explicit casting NULL during INSERT in Postgresql

I am writing python scripts to sychronize tables from a MSSQL database to a Postgresql DB. The original author tends to use super wide tables with a lot of regional consecutive NULL holes in them.
For insertion speed, I serialized the records in bulk to string in the following form before execute()
INSERT INTO A( {col_list} )
SELECT * FROM ( VALUES (row_1), (row_2),...) B( {col_list} )
During the row serialization, its not possbile to determin the data type of NULL or None in python. This makes the job complicated. All NULL values in timestamp columns, integer columns etc need explicit type cast into proper types, or Pg complains about it.
Currently I am checking the DB API connection.description property and compare column type_code, for every column and add type casting like ::timestamp as needed.
But this feels cumbersome, with the extra work: the driver already converted the data from text to proper python data type, now I have to redo it for column with those many Nones.
Is there any better way to work around this with elegancy & simplicity ?

If you don't need the SELECT, go with #Nick's answer.
If you need it (like with a CTE to use the input rows multiple times), there are workarounds depending on the details of your use case.
Example, when working with complete rows:
INSERT INTO A -- complete rows
SELECT * FROM (
VALUES ((NULL::A).*), (row_1), (row_2), ...
) B
OFFSET 1;
{col_list} is optional noise in this particular case, since we need to provide complete rows anyway.
Detailed explanation:
Casting NULL type when updating multiple rows

Instead of inserting from a SELECT, you can attach a VALUES clause directly to the INSERT, i.e.:
INSERT INTO A ({col_list})
VALUES (row_1), (row_2), ...
When you insert from a query, Postgres examines the query in isolation when trying to infer the column types, and then tries to coerce them to match the target table (only to find out that it can't).
When you insert directly from a VALUES list, it knows about the target table when performing the type inference, and can then assume that any untyped NULL matches the corresponding column.

You could try to create json from data and then rowset from json using json_populate_record(..).
postgres=# create table js_test (id int4, dat timestamp, val text);
CREATE TABLE
postgres=# insert into js_test
postgres-# select (json_populate_record(null::js_test,
postgres(# json_object(array['id', 'dat', 'val'], array['5', null, 'test']))).*;
INSERT 0 1
postgres=# select * from js_test;
id | dat | val
----+-----+------
5 | | test
You can use json_populate_recordset(..) to do the same with multiple rows in one go. You just pass json value that is array of json. Make sure it isn't array of json.
So this is OK: '[{"id":1,"dat":null,"val":6},{"id":3,"val":"tst"}]'::json
This is not: array['{"id":1,"dat":null,"val":6}'::json,'{"id":3,"val":"tst"}'::json]
select *
from json_populate_recordset(null::js_test,
'[{"id":1,"dat":null,"val":6},{"id":3,"val":"tst"}]')

creating blank field and receving the INTEGER PRIMARY KEY with sqlite, python

I am using sqlite with python. When i insert into table A i need to feed it an ID from table B. So what i wanted to do is insert default data into B, grab the id (which is auto increment) and use it in table A. Whats the best way receive the key from the table i just inserted into?

As Christian said, sqlite3_last_insert_rowid() is what you want... but that's the C level API, and you're using the Python DB-API bindings for SQLite.
It looks like the cursor method lastrowid will do what you want (search for 'lastrowid' in the documentation for more information). Insert your row with cursor.execute( ... ), then do something like lastid = cursor.lastrowid to check the last ID inserted.
That you say you need "an" ID worries me, though... it doesn't matter which ID you have? Unless you are using the data just inserted into B for something, in which case you need that row ID, your database structure is seriously screwed up if you just need any old row ID for table B.

Check out sqlite3_last_insert_rowid() -- it's probably what you're looking for:
Each entry in an SQLite table has a
unique 64-bit signed integer key
called the "rowid". The rowid is
always available as an undeclared
column named ROWID, OID, or _ROWID_ as
long as those names are not also used
by explicitly declared columns. If the
table has a column of type INTEGER
PRIMARY KEY then that column is
another alias for the rowid.
This routine returns the rowid of the
most recent successful INSERT into the
database from the database connection
in the first argument. If no
successful INSERTs have ever occurred
on that database connection, zero is
returned.
Hope it helps! (More info on ROWID is available here and here.)

Simply use:
SELECT last_insert_rowid();
However, if you have multiple connections writing to the database, you might not get back the key that you expect.