I am looking for an efficient method (which I can reuse for similar situations) to drop rows which have been updated.
My table has many columns, but the important ones are:
creation_timestamp, id, last_modified_timestamp
My primary key is the creation_timestamp and the id. However, after and id has been created, it can be modified by other users which is indicated by the last_modified_timestamp.
1) Read a daily file and add any new rows (based on creation_timestamp and id)
2) Remove old rows which have a different last_modified_timestamp and replace them with the latest versions.
I typically do most of my operations with Pandas (python library) and pyscopg2, so I am not extremely familiar with PostgreSQL 9.6 which is the database I am using. My initial approach is to just add the last_modified_timestamp to the primary key, and then just use a view to SELECT DISTINCT based on the latest changes. However, it seems like that is 'cheating' and I will be wasting space since I do not need to retain previous versions.
EDIT:
def create_update_query(df, table=FACT_TABLE):
columns = ', '.join([f'{col}' for col in DATABASE_COLUMNS])
constraint = ', '.join([f'{col}' for col in PRIMARY_KEY])
placeholder = ', '.join([f'%({col})s' for col in DATABASE_COLUMNS])
updates = ', '.join([f'{col} = EXCLUDED.{col}' for col in DATABASE_COLUMNS])
query = f"""
INSERT INTO {table} ({columns})
VALUES ({placeholder})
ON CONFLICT ({constraint})
DO UPDATE SET {updates};"""
query.split()
query = ' '.join(query.split())
return query
def load_updates(df, connection=DATABASE):
conn = connection.get_conn()
cursor = conn.cursor()
df1 = df.where((pd.notnull(df)), None)
insert_values = df1.to_dict(orient='records')
for row in insert_values:
cursor.execute(create_update_query(df), row)
conn.commit()
cursor.close()
del cursor
conn.close()
This appears to work. I was running into some issues, so right now i am looping through each row of the DataFrame as a dictionary, then inserting that row. Also, I had to figure out a way to fill in the nan columns with None, because I was getting errors with Timestamp dtypes with blank values, etc.
Related
I have a composite type:
CREATE TYPE mydata_t AS
(
user_id integer,
value character(4)
);
Also, I have a table, uses this composite type as an array of mydata_t.
CREATE TABLE tbl
(
id serial NOT NULL,
data_list mydata_t[],
PRIMARY KEY (id)
);
Here I want to update the mydata_t in data_list, where mydata_t.user_id is 100000
But I don't know which array element's user_id is equal to 100000
So I have to make a search first to find the element where its user_id is equal to 100000 ... that's my problem ... I don't know how to make the query .... in fact, I want to update the value of the array element, where it's user_id is equal to 100000 (Also where the id of tbl is for example 1) ... What will be my query?
Something like this (I know it's wrong !!!)
UPDATE "tbl" SET "data_list"[i]."value"='YYYY'
WHERE "id"=1 AND EXISTS (SELECT ROW_NUMBER() OVER() AS i
FROM unnest("data_list") "d" WHERE "d"."user_id"=10000 LIMIT 1)
For example, this is my tbl data:
Row1 => id = 1, data = ARRAY[ROW(5,'YYYY'),ROW(6,'YYYY')]
Row2 => id = 2, data = ARRAY[ROW(10,'YYYY'),ROW(11,'YYYY')]
Now i want to update tbl where id is 2 and set the value of one of the tbl.data elements to 'XXXX' where the user_id of element is equal to 11
In fact, the final result of Row2 will be this:
Row2 => id = 2, data = ARRAY[ROW(10,'YYYY'),ROW(11,'XXXX')]
If you know the value value, you can use the array_replace() function to make the change:
UPDATE tbl
SET data_list = array_replace(data_list, (11, 'YYYY')::mydata_t, (11, 'XXXX')::mydata_t)
WHERE id = 2
If you do not know the value value then the situation becomes more complex:
UPDATE tbl SET data_list = data_arr
FROM (
-- UPDATE doesn't allow aggregate functions so aggregate here
SELECT array_agg(new_data) AS data_arr
FROM (
-- For the id value, get the data_list values that are NOT modified
SELECT (user_id, value)::mydata_t AS new_data
FROM tbl, unnest(data_list)
WHERE id = 2 AND user_id != 11
UNION
-- Add the values to update
VALUES ((11, 'XXXX')::mydata_t)
) x
) y
WHERE id = 2
You should keep in mind, though, that there is an awful lot of work going on in the background that cannot be optimised. The array of mydata_t values has to be examined from start to finish and you cannot use an index on this. Furthermore, updates actually insert a new row in the underlying file on disk and if your array has more than a few entries this will involve substantial work. This gets even more problematic when your arrays are larger than the pagesize of your PostgreSQL server, typically 8kB. All behind the scene so it will work, but at a performance penalty. Even though array_replace sounds like changes are made in-place (and they indeed are in memory), the UPDATE command will write a completely new tuple to disk. So if you have 4,000 array elements that means that at least 40kB of data will have to be read (8 bytes for the mydata_t type on a typical system x 4,000 = 32kB in a TOAST file, plus the main page of the table, 8kB) and then written to disk after the update. A real performance killer.
As #klin pointed out, this design may be more trouble than it is worth. Should you make data_list as table (as I would do), the update query becomes:
UPDATE data_list SET value = 'XXXX'
WHERE id = 2 AND user_id = 11
This will have MUCH better performance, especially if you add the appropriate indexes. You could then still create a view to publish the data in an aggregated form with a custom type if your business logic so requires.
I have a table cusers with a primary key:
primary key(uid, lid, cnt)
And I try to insert some values into the table:
insert into cusers (uid, lid, cnt, dyn, ts)
values
(A, B, C, (
select C - cnt
from cusers
where uid = A and lid = B
order by ts desc
limit 1
), now())
on conflict do nothing
Quite often (with the possibility of 98%) a row cannot be inserted to cusers because it violates the primary key constraint, so hard select queries do not need to be executed at all. But as I can see PostgreSQL first counts the select query as a result of dyn column and only then rejects row because of uid, lid, cnt violation.
What is the best way to insert rows quickly in such situation?
Another explanation
I have a system where one row depends on another. Here is an example:
(x, x, 2, 2, <timestamp>)
(x, x, 5, 3, <timestamp>)
Two columns contain an absolute value (2 and 5) and relative value (2, 5 - 2). Each time I insert new row it should:
avoid same rows (see primary key constraint)
if new row differs, it should count a difference and put it into the dyn column (so I take the last inserted row for the user according to the timestamp and subtract values).
Another solution I've found is to use returning uid, lid, ts for inserts and get user ids which were really inserted - this is how I know they have differences from existing rows. Then I update inserted values:
update cusers
set dyn = (
select max(cnt) - min(cnt)
from (
select cnt
from cusers
where uid = A and lid = B
order by ts desc
limit 2) Table
)
where uid = A and lid = B and ts = TS
But it is not a fast approach either, as it seeks all over the ts column to find the two last inserted rows for each user. I need a fast insert query as I insert millions of rows at a time (but I do not write duplicates).
What the solution can be? May be I need a new index for this? Thanks in advance.
I have been trying to change all the values in a column. When I do it turns out to take my table and drop all the rows that match my criteria in the where clause.
Example
|_Month_|_Cost_|_Title_|
|___2___|_15.99|_hello_|
|___2___|_32.87|_John__|
|___2___|_32.87|_Neat__|
|___3___|_32.87|_Kelps_|
|___4___|_32.87|_Gulp__|
|___5___|_32.87|_Tried_|
I run this Query
UPDATE tableName SET Month = 'FEB' WHERE Month = 2;
The Change:
|_Month_|_Cost_|_Title_|
|__FEB__|_32.87|_Neat__|
|___3___|_32.87|_Kelps_|
|___4___|_32.87|_Gulp__|
|___5___|_32.87|_Tried_|
What I need:
|_Month_|_Cost_|_Title_|
|__FEB__|_15.99|_hello_|
|__FEB__|_32.87|_John__|
|__FEB__|_32.87|_Neat__|
|___3___|_32.87|_Kelps_|
|___4___|_32.87|_Gulp__|
|___5___|_32.87|_Tried_|
Where am I going wrong?
I have also tried to get the date changed from a timestamp with
SELECT TO_DATE(payment_timestamp, 'Mon") payment_month
from tablename;
but PostgreSQL doesn't recognize the TO_DATE()?? Can someone tell me why?
I have the following code in Postgres
select op.url from identity.legal_entity le
join identity.profile op on le.legal_entity_id =op.legal_entity_id
where op.global_id = '8wyvr9wkd7kpg1n0q4klhkc4g'
which returns 1 row.
Then I try to update the url field with the following:
update identity.profile
set url = 'htpp:sam'
where identity.profile.url in (
select op.url from identity.legal_entity le
join identity.profile op on le.legal_entity_id =op.legal_entity_id
where global_id = '8wyvr9wkd7kpg1n0q4klhkc4g'
);
But the above ends up updating more than 1 row, actually all of the rows of the identity table.
I would assume since the first postgres statement returns one row, only one row at most can be updated, but I am getting the wrong effect where all of the rows are being updated. Why ?? Please help a nubie fix the above update statement.
Instead of using profile.url to identify the row you want to update, use the primary key. That is what it is there for.
So if the primary key column is called id, the statement could be modified to:
UPDATE identity.profile
SET ...
WHERE identity.profile.id IN (SELECT op.id FROM ...);
But you can do this much simpler in PostgreSQL with
UPDATE identity.profile op
SET url = 'htpp:sam'
FROM identity.legal_entity le
WHERE le.legal_entity_id = op.legal_entity_id
AND le.global_id = '8wyvr9wkd7kpg1n0q4klhkc4g';
I have a postgresql database and in one particular table, with many rows. One column in this table, called data, is a float array, REAL[], and gets filled with an array of ~4500 elements. I want to access this table through some query via SQLAlchemy and the ORM.
How do I select all rows in the table where a subset of this column satisfies some condition, e.g.contains a range of values? Like I want to select all rows where the data contains values >= 10, or values between >=10 and <=20.
Can I do this with a straight session query like
rows = session.query(Table).filter(Table.data.(some conditional)).all()
where my conditional is something like "VALUES >= 10 and VALUES <= 20"?
Or do I need to define some special methods, or setup, when I'm defining my SQLAlchemy table class. For example, I have my table set up as
class Table(Base):
__tablename__ = 'table'
__table_args__ = {'autoload' : True, 'schema' : 'testdb', 'extend_existing':True}
data = deferred(Column(ARRAY(Float)))
def __repr__(self):
return '<Table (pk={0})>'.format(self.pk)
Ideally I'd like to set it up so I can just do simple filtering in my session.query calls. Is this possible? I'm not super familiar with the ORM, so maybe it is?
I've had a look at the ARRAY Comparator sqlalchemy docs but those only seem to work on exact values. My data is precise to 6 sigfigs, and I don't know the exact values ahead of time.
What's the best way to do this? Thanks.
EDIT:
Based on the below comment, here is the code I'm using in attempting to select all rows (out of 1000) that have data (from 1 column) >= 1.0. There should be 537 rows.
rows = session.query(datadb.Table).filter(datadb.Table.data.any(1.0,operator=operators.le)).all()
This gives the correct subset number. len(rows) = 537. However, I don't understand the logic of with this operator, where to select data >=1.0 , I use the le operator? Also, along those same lines, there should be 234 rows that have data between the values >=1.0 and <1.0, but this statement fails to give the correct subset..
rows = session.query(datadb.Table).filter(datadb.Table.data.any(1.0,operator=operators.le)).filter(datadb.Table.data.any(1.2,operator=operators.ge)).all()
* EDIT 2 *
Here's an example of my database Table with a few rows. pk is an integer, and data is a real[].
db datadb
schema Table
pk data
0 [0.0,0.0,0.5,0.3,1.3,1.9,0.3,0.0,0.0]
1 [0.1,0.0,1.0,0.7,1.1,1.5,1.2,0.3,1.4]
2 [0.0,0.6,0.4,0.3,1.6,1.7,0.4,1.3,0.0]
3 [0.0,0.1,0.2,0.4,1.0,1.1,1.2,0.9,0.0]
4 [0.0,0.0,0.5,0.3,0.2,0.1,0.7,0.3,0.1]
I have 5 rows, 4 of them have data with values >= 1.0, while just 2 have values in the range >= 1.0 and <= 1.2. The query I would do to grab the rows is in the first case
rows = session.query(datadb.Table).filter(datadb.Table.data.any(1.0,operator=operators.le)).all()
This should return the 4 rows, at pk=0,1,2,3. This query does what I expect. The second case
rows = session.query(datadb.Table).filter(datadb.Table.data.any(1.0,operator=operators.le)).filter(datadb.Table.data.any(1.2,operator=operators.ge)).all()
and should return the 2 rows at pk=1,3. However this query just returns the 4 rows from the first query. For the second query, I also tried
rows = session.query(datadb.Table).filter(datadb.Table.data.any(1.0,operator=operators.le),datadb.Table.data.any(1.2,operator=operators.ge)).all()
which also didn't work.
Please read documentation on ARRAY.Comparator, according to which you should be able to do the following:
rows = (session.query(Table)
.filter(Table.data.any(10, operator=operators.le))
.filter(Table.data.any(20, operator=operators.ge)
).all()
EDIT:
# combined filter does not work,
# but applying one or the other is still useful as it reduces the result set
q = (session.query(MyTable)
.filter(MyTable.data.any(1.0, operator=operators.le))
# .filter(MyTable.data.any(1.2, operator=operators.ge))
)
# filter in memory
items = [_row for _row in q.all()
if any(1.0 <= item <= 1.2 for item in _row.data)]
for item in items:
print(item)