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SQL query of Concatenating Client last names

I'm trying to create an sql query that takes records from a File table and a Customer table. A file can have multiple customers. I want to show only one record per File.id and Concatenate the last names based on alphabetical order of the clients if the names are different or only show one if they are the same.
Below is a picture of the Relationship.
Table Relationship
The results from my query look like this currently.
enter image description here
I would like the query to look like this.
File ID
Name
1
Dick Dipe
2
Bill
3
Lola
Originally I had tried doing a subquery but I had issues that there were multiple results and it couldn't list more than one. If I could do a loop and add to an array, I feel like that would work.
If I were to do it in Python, I would write this but when I try to translate that into SQL, I get errors that either the subquery can only display one result or the second name under file two gets cut off.
clients = ['Dick','Dipe','Bill','Lola', 'Lola']
files = [1,2,3]
fileDetails = [[1,0],[1,1],[2,2],[3,3],[3,4]]
file_clients = {}
for file_id, client_index in fileDetails:
if file_id not in file_clients:
file_clients[file_id] = []
client_name = clients[client_index]
file_clients[file_id].append(client_name)
for file_id, client_names in file_clients.items():
client_names = list(dict.fromkeys(client_names))
client_names_string = " ".join(client_names)
print(f"File {file_id}: {client_names_string}")

Set Cache in Django, but a data must dynamically

I have an issue, i want to save data to cache so when i need to get data i dont need it to get from database. But there is a data i need to get from database no matter what which is stock.
Illustration i need to save to cache from db:
ProductDetails:{
'product_name': ....,
'categories':....,
etc
}
but the stock product which i need to get is from the same db,i try to use multiple loop like this:
'''
products = queryset()
cache_key =f'test_detail_{parameter}'
information = {}
details = []
if not cache.get(cache_key):
for product in products:
information[product.id] = {
'product_name': product.name,
'date_input': product.date,
'weight':product.weight
}
cache.set(cache_key, information, duration)
information = cache.get(cache_key)
for key, value in information.items():
information[key][stock] = numpy.sum([x.stock for x in products if key == x.id ])
details.append(information[key])
return details
'''
is there any method more efficient and effective using only 1 Queryset because i'm using 2 Queryset (the first time is when i get the data to set cache, the second time is when i get the stock data)?
Thankss

How to query conditional attributes in DynamoDB

I'm trying to query all the Food values in the "Categories" attribute and the "review_count" attribute values that are at least 100. My first time working with scanning tables in DynamoDB through python. I need to use the table.scan function as well. This is what I have tried so far.
resp = table.scan(FilterExpression='(categories = cat1) AND' + '(review_count >= 100)',
ExpressionAttributeValues={
':cat1': 'Food',
})
Any help would be greatly appreciated. Thanks

Assuming table name is test
FilterExpression can't contain constants, should only have either table attributes like categories, review_count and placeholders like :cat1, :rc . So, 100 can be replaced with a variable :rc.
All placeholders should start with : , so, cat1 should be :cat1
table = dynamodb.Table('test')
response = table.scan(
FilterExpression= 'categories=:cat1 AND review_count>=:rc',
ExpressionAttributeValues= {
':cat1': "Food" ,
':rc': 100,
}
)
data = response['Items']
Important point to note on scan , from documentation
A single Scan operation reads up to the maximum number of items set
(if using the Limit parameter) or a maximum of 1 MB of data and then
apply any filtering to the results using FilterExpression.

How to have the possibility to call name of columns in db.session.query with 2 tables in Flask Python?

I am developing a web application with Flask, Python, SQLAlchemy, and Mysql.
I have 2 tables:
TaskUser:
- id
- id_task (foreign key of id column of table Task)
- message
Task
- id
- id_type_task
I need to extract all the tasksusers (from TaskUser) where the id_task is in a specific list of Task ids.
For example, all the taskusers where id_task is in (1,2,3,4,5)
Once I get the result, I do some stuff and use some conditions.
When I make this request :
all_tasksuser=TaskUser.query.filter(TaskUser.id_task==Task.id) \
.filter(TaskUser.id_task.in_(list_all_tasks),Task.id_type_task).all()
for item in all_tasksuser:
item.message="something"
if item.id_type_task == 2:
#do some stuff
if item.id_task == 7 or item.id_task == 7:
#do some stuff
I get this output error:
if item.id_type_task == 2:
AttributeError: 'TaskUser' object has no attribute 'id_type_task'
It is normal as my SQLAlchemy request is calling only one table. I can't access to columns of table Task.
BUT I CAN call the columns of TaskUser by their names (see item.id_task).
So I change my SQLAlchemy to this:
all_tasksuser=db_mysql.session.query(TaskUser,Task.id,Task.id_type_task).filter(TaskUser.id_task==Task.id) \
.filter(TaskUser.id_task.in_(list_all_tasks),Task.id_type_task).all()
This time I include the table Task in my query BUT I CAN'T call the columns by their names. I should use the [index] of columns.
I get this kind of error message:
AttributeError: 'result' object has no attribute 'message'
The problem is I have many more columns (around 40) on both tables. It is too complicated to handle data with index numbers of columns.
I need to have a list of rows with data from 2 different tables and I need to be able to call the data by column name in a loop.
Do you think it is possible?

The key point leading to the confusion is the fact that when you perform a query for a mapped class like TaskUser, the sqlalchemy will return instances of that class. For example:
q1 = TaskUser.query.filter(...).all() # returns a list of [TaskUser]
q2 = db_mysql.session.query(TaskUser).filter(...).all() # ditto
However, if you specify only specific columns, you will receive just a (special) list of tuples:
q3 = db_mysql.session.query(TaskUser.col1, TaskUser.col2, ...)...
If you switch your mindset to completely use the ORM paradigm, you will work mostly with objects. In your specific example, the workflow could be similar to below, assuming you have relationships defined on your models:
# model
class Task(...):
id = ...
id_type_task = ...
class TaskUser(...):
id = ...
id_task = Column(ForeignKey(Task.id))
message = ...
task = relationship(Task, backref="task_users")
# query
all_tasksuser = TaskUser.query ...
# work
for item in all_tasksuser:
item.message = "something"
if item.task.id_type_task == 2: # <- CHANGED to navigate the relationship ...
#do some stuff
if item.task.id_task == 7 or item.task.id_task == 7: # <- CHANGED
#do some stuff

First error message is the fact that query and filter without join (our any other joins) cannot give you columns from both tables. You need to either put both tables into session query, or join those two tables in order to gather column values from different tables, so this code:
all_tasksuser=TaskUser.query.filter(TaskUser.id_task==Task.id) \
.filter(TaskUser.id_task.in_(list_all_tasks),Task.id_type_task).all()
needs to look more like this:
all_tasksuser=TaskUser.query.join(Task) \
.filter(TaskUser.id_task.in_(list_all_tasks),Task.id_type_task).all()
or like this:
all_tasksuser=session.query(TaskUser, Task).filter(TaskUser.id_task==Task.id) \
.filter(TaskUser.id_task.in_(list_all_tasks),Task.id_type_task).all()
Another thing is that the data will be structured differently so in the first example, you will need two for loops:
for taskuser in all_taskuser:
for task in taskuser.task:
# to reference to id_type_task : task.id_type_task
and in the second example your result is the tuple, so for loop should look like this
for taskuser, task in all_taskuser:
# to reference to id_type_task : task.id_type_task
NOTE: I haven't check all these examples, so there may be errors, but concepts are there. For more info, please refer yourself to this page:
https://www.tutorialspoint.com/sqlalchemy/sqlalchemy_orm_working_with_joins.htm

How to dynamically check postgresql constraints in psycopg2?

I'm generating random data to fill a database (without knowing how is the database before runtime). I can fill it if it has no constraints, but when it has i can't differenciate between values passing the check and values that don't.
Let's see an example. Table definition:
CREATE TABLE test (
id INT,
age INT CONSTRAINT adult CHECK (age > 18),
PRIMARY KEY (id)
);
The data of that table that i have during runtime is:
Table and columns names
Columns types
Column UNIQUE, and NOT NULL
Column constraint definition as a string
Foreign keys
I can get more data from postgresql internal tables preferably from the information squema
I want to check the constraint before making an insert with that data. It's valid for me to do so using the database to check it, or to check it in code.
Here is a short snippet, try to detect when the check is False before the execution of the insert query:
# Data you have access to:
t_name = 'test'
t_col_names = ['id', 'age']
col_constraints = {
'id': '',
'age': 'age > 18'}
# you can access more data,
# but you have to query the database to do so
id_value = 1
#I want to check values HERE
age_value = 17
#I want to check values HERE
values = (id_value, age_value)
#I could want to check HERE
query = "INSERT INTO test (id, age) VALUES (%s, %s);"
db_cursor.execute(query, values)
db_cursor.close()
Because of how data is generated in my application, managing the error thrown is not an option if it's done while/after executing the insert query, it would increment the cost of generating random data dramatically.
EDIT to explain why try: is not an option:
If I wait for the exception, the problematic element that provoke a thrown error would already be in multiple queries.
Let's see in the previous example how this could happen. I generate a random data pool to pick from and generate tuples of insert values:
age_pool = (7, 19, 23, 48)
id_pool = (0,2,3,...,99) #It's not that random for better understanding
Now if I generate 100 insert queries and supposing 25% of them has a 7 in them (an age < 18). From a single value i have 25 invalid queries that will try to execute in the database (a costly operation by the way) to fail hopelessly. After that i would have to generate more random data in this case 25 more insert queries that could have the same problem if i generate a 8 for example.
On the other hand if i check just after generating the element, i check if it's a valid value and for one single element i have multiple valid combinations of values.

You could use eval():
def constraint_check(constraints, keys, values):
vals = dict(zip(keys, values))
for k, v in constraints.items():
if v and not eval(v.replace(k, str(vals[k]))):
return False
return True
t_name = 'test'
t_col_names = ['id', 'age']
col_constraints = {
'id': '',
'age': 'age > 18'}
id_value = 1
age_value = 17
values = (id_value, age_value)
if constraint_check(col_constraints, ('id', 'age'), values):
query = "INSERT INTO test (id, age) VALUES (%s, %s);"
db_cursor.execute(query, values)
However, this will work well only for very simple constraints. A Postgres check expression may include constructs specific for Postgres and not known in Python. For example, the app fails with this obviously valid constraint:
create table test(
id int primary key,
age int check(age between 18 and 60));
I do not think you can implement the complete Postgres expression parser in Python in an easy way and whether this would be profitable to achieve the intended effect.

It's not clear why a try...except clause is not desired. You test for the precise exception and keep going.
How about:
problem_inserts = []
try:
db_cursor.execute(query, values)
db_cursor.close()
except <your exception here>:
problem_inserts.append(query)
In this snippet, you keep a list of all queries that didn't go through properly. I don't know what else you can do. I don't think you want to change the data to make it fit into the table.