I am trying to parse all the queries executed by users (within a period of time) in PostgreSQL DB (by querying the pg_stat_statements table) and trying to create a report of which tables are used by users to run either a Select or an Insert or a Delete query. Basically running something like Select query, queryid, userid from pg_stat_state and then parsing each query to check if it was a Select or an Insert or a Delete query and also extract the table_Name from the query.
I am using sqlparse python module but very new to it so need help.
I am able to get the table name by using something like:
import sqlparse
from sqlparse.sql import Where, Comparison, Parenthesis, Identifier
for tokens in sqlparse.parse(sql_statement)[0]:
if isinstance(tokens, Identifier):
print(str(tokens))
but not sure how to get the type of statement (Select/Insert/Delete) together with the name of the table. Also, need to incorporate COPY statements as Selects too.
I tried using psqlparse but I did not see much info/help online regarding this module.
Please suggest.
Thanks.
This is not trivial, and I don't think sqlparse really helps very much. INSERT and DELETE are pretty easy, because they usually start out "INSERT INTO table" and "DELETE FROM table", but "SELECT" is the wild wild west. Clearly the tables will be mentioned in a FROM clause, but it could be "FROM table1 t1, table t2, table t3 WHERE" or "FROM table t1 LEFT INNER JOIN table t2 LEFT INNER JOIN table t3 WHERE".
You might have nested queries, and a SELECT doesn't even have to have a table. Plus, there could be UNIONs that mention further tables. And, of course, "SELECT INTO" is just another way of doing "INSERT". I believe you should start out just doing text processing, looking for the key words. You might get far enough.
I need to write an SQL query for MS-Access 2000 so that a row is updated if it exists, but inserted if it does not. (I believe this is called an "upsert")
i.e.
If row exists...
UPDATE Table1 SET (...) WHERE Column1='SomeValue'
If it does not exist...
INSERT INTO Table1 VALUES (...)
Can this be done in one query?
You can simulate an upsert in an Access by using an UPDATE query with a LEFT JOIN.
update b
left join a on b.id=a.id
set a.f1=b.f1
, a.f2=b.f2
, a.f3=b.f3
Assuming a unique index on Column1, you can use a DCount expression to determine whether you have zero or one row with Column1 = 'SomeValue'. Then INSERT or UPDATE based on that count.
If DCount("*", "Table1", "Column1 = 'SomeValue'") = 0 Then
Debug.Print "do INSERT"
Else
Debug.Print "do UPDATE"
End If
I prefer this approach to first attempting an INSERT, trapping the 3022 key violation error, and doing an UPDATE in response to the error. However I can't claim huge benefits from my approach. If your table includes an autonumber field, avoiding a failed INSERT would stop you from expending the next autonumber value needlessly. I can also avoid building an INSERT string when it's not needed. The Access Cookbook told me string concatenation is a moderately expensive operation in VBA, so I look for opportunities to avoid building strings unless they're actually needed. This approach will also avoid creating a lock for an unneeded INSERT.
However, none of those reasons may be very compelling for you. And in all honesty I think my preference in this case may be about what "feels right" to me. I agree with this comment by #David-W-Fenton to a previous Stack Overflow question: "It's better to write your SQL so you don't attempt to append values that already exist -- i.e., prevent the error from happening in the first place rather than depending on the database engine to save you from yourself."
An "upsert" is possible, if the tables have a unique key.
This old tip from Smart Access is one of my favourites:
Update and Append Records with One Query
By Alan Biggs
Did you know that you can use an update query in Access to both update
and add records at the same time? This is useful if you have two
versions of a table, tblOld and tblNew, and you want to integrate the
changes from tblNew into tblOld.
Follow these steps:
Create an update query and add the two tables. Join the two tables by
dragging the key field of tblNew onto the matching field of tblOld.
Double-click on the relationship and choose the join option that includes all records from tblNew and only those that match from
tblOld.
Select all the fields from tblOld and drag them onto the QBE grid.
For each field, in the Update To cell type in tblNew.FieldName, where FieldName matches the field name of tblOld.
Select Query Properties from the View menu and change Unique Records to False. (This switches off the DISTINCTROW option in the SQL
view. If you leave this on you'll get only one blank record in your
results, but you want one blank record for each new record to be added
to tblOld.)
Run the query and you'll see the changes to tblNew are now in tblOld.
This will only add records to tblOld that have been added to tblNew.
Records in tblOld that aren't present in tblNew will still remain in
tblOld.
I usually run the insert statement first and then I check to see if error 3022 occurred, which indicates the row already exists. So something like this:
On Error Resume Next
CurrentDb.Execute "INSERT INTO Table1 (Fields) VALUES (Data)", dbFailOnError
If Err.Number = 3022 Then
Err.Clear
CurrentDb.Execute "UPDATE Table1 SET (Fields = Values) WHERE Column1 = 'SomeValue'", dbFailOnError
ElseIf Err.Number <> 0 Then
'Handle the error here
Err.Clear
End If
Edit1:
I want to mention that what I've posted here is a very common solution but you should be aware that planning on errors and using them as part of the normal flow of your program is generally considered a bad idea, especially if there are other ways of achieving the same results. Thanks to RolandTumble for pointing this out.
You don't need to catch the error. Instead, just run the INSERT statement and then check
CurrentDb.RecordsAffected
It will either be 1 or 0, depending.
Note: It's not good practice to execute against CurrentDB. Better to capture the database to a local variable:
Dim db As DAO.Database
Set db = CurrentDb
db.Execute(INSERT...)
If db.RecordsAffected = 0 Then
db.Execute(UPDATE...)
End If
As others have mentioned, You can UPSERT with an UPDATE LEFT JOIN using the new table as the left hand side. This will add all missing records and update matching records, leaving deleted records intact.
If we follow the Create and run an update query Article we will end up with SQL that looks like this:
UPDATE Table1
INNER JOIN NewTable1 ON Table1.ID = NewTable1.ID
SET Table1.FirstName = [NewTable1].[FirstName]
but an inner join will only update matching records, it won't add new records. So let's change that INNER to a LEFT:
UPDATE Table1
LEFT JOIN NewTable1 ON Table1.ID = NewTable1.ID
SET Table1.FirstName = [NewTable1].[FirstName]
Now save a copy of the DB. Run a test on the copy before you run this on your primary DB.
I am trying to update many records at a time using SQLAlchemy, but am finding it to be very slow. Is there an optimal way to perform this?
For some reference, I am performing an update on 40,000 records and it took about 1 hour.
Below is the code I am using. The table_name refers to the table which is loaded, the column is the single column which is to be updated, and the pairs refer to the primary key and new value for the column.
def update_records(table_name, column, pairs):
table = Table(table_name, db.MetaData, autoload=True,
autoload_with=db.engine)
conn = db.engine.connect()
values = []
for id, value in pairs:
values.append({'row_id': id, 'match_value': str(value)})
stmt = table.update().where(table.c.id == bindparam('row_id')).values({column: bindparam('match_value')})
conn.execute(stmt, values)
Passing a list of arguments to execute() essentially issues 40k individual UPDATE statements, which is going to have a lot of overhead. The solution for this is to increase the number of rows per query. For MySQL, this means inserting into a temp table and then doing an update:
# assuming temp table already created
conn.execute(temp_table.insert().values(values))
conn.execute(table.update().values({column: temp_table.c.match_value})
.where(table.c.id == temp_table.c.row_id))
Or, alternatively, you can use INSERT ... ON DUPLICATE KEY UPDATE to avoid creating the temp table, but SQLAlchemy does not support that natively, so you'll need to use a custom compiled construct for that (e.g. this gist).
According to document fast-execution-helpers, batch update statements can be issued as one statement. In my experiments, this trick reduce update or deletion time from 30 mins to 1 mins.
engine = create_engine(
"postgresql+psycopg2://scott:tiger#host/dbname",
executemany_mode='values_plus_batch',
executemany_values_page_size=5000, executemany_batch_page_size=5000)
A very frequently asked question here is how to do an upsert, which is what MySQL calls INSERT ... ON DUPLICATE UPDATE and the standard supports as part of the MERGE operation.
Given that PostgreSQL doesn't support it directly (before pg 9.5), how do you do this? Consider the following:
CREATE TABLE testtable (
id integer PRIMARY KEY,
somedata text NOT NULL
);
INSERT INTO testtable (id, somedata) VALUES
(1, 'fred'),
(2, 'bob');
Now imagine that you want to "upsert" the tuples (2, 'Joe'), (3, 'Alan'), so the new table contents would be:
(1, 'fred'),
(2, 'Joe'), -- Changed value of existing tuple
(3, 'Alan') -- Added new tuple
That's what people are talking about when discussing an upsert. Crucially, any approach must be safe in the presence of multiple transactions working on the same table - either by using explicit locking, or otherwise defending against the resulting race conditions.
This topic is discussed extensively at Insert, on duplicate update in PostgreSQL?, but that's about alternatives to the MySQL syntax, and it's grown a fair bit of unrelated detail over time. I'm working on definitive answers.
These techniques are also useful for "insert if not exists, otherwise do nothing", i.e. "insert ... on duplicate key ignore".
9.5 and newer:
PostgreSQL 9.5 and newer support INSERT ... ON CONFLICT (key) DO UPDATE (and ON CONFLICT (key) DO NOTHING), i.e. upsert.
Comparison with ON DUPLICATE KEY UPDATE.
Quick explanation.
For usage see the manual - specifically the conflict_action clause in the syntax diagram, and the explanatory text.
Unlike the solutions for 9.4 and older that are given below, this feature works with multiple conflicting rows and it doesn't require exclusive locking or a retry loop.
The commit adding the feature is here and the discussion around its development is here.
If you're on 9.5 and don't need to be backward-compatible you can stop reading now.
9.4 and older:
PostgreSQL doesn't have any built-in UPSERT (or MERGE) facility, and doing it efficiently in the face of concurrent use is very difficult.
This article discusses the problem in useful detail.
In general you must choose between two options:
Individual insert/update operations in a retry loop; or
Locking the table and doing batch merge
Individual row retry loop
Using individual row upserts in a retry loop is the reasonable option if you want many connections concurrently trying to perform inserts.
The PostgreSQL documentation contains a useful procedure that'll let you do this in a loop inside the database. It guards against lost updates and insert races, unlike most naive solutions. It will only work in READ COMMITTED mode and is only safe if it's the only thing you do in the transaction, though. The function won't work correctly if triggers or secondary unique keys cause unique violations.
This strategy is very inefficient. Whenever practical you should queue up work and do a bulk upsert as described below instead.
Many attempted solutions to this problem fail to consider rollbacks, so they result in incomplete updates. Two transactions race with each other; one of them successfully INSERTs; the other gets a duplicate key error and does an UPDATE instead. The UPDATE blocks waiting for the INSERT to rollback or commit. When it rolls back, the UPDATE condition re-check matches zero rows, so even though the UPDATE commits it hasn't actually done the upsert you expected. You have to check the result row counts and re-try where necessary.
Some attempted solutions also fail to consider SELECT races. If you try the obvious and simple:
-- THIS IS WRONG. DO NOT COPY IT. It's an EXAMPLE.
BEGIN;
UPDATE testtable
SET somedata = 'blah'
WHERE id = 2;
-- Remember, this is WRONG. Do NOT COPY IT.
INSERT INTO testtable (id, somedata)
SELECT 2, 'blah'
WHERE NOT EXISTS (SELECT 1 FROM testtable WHERE testtable.id = 2);
COMMIT;
then when two run at once there are several failure modes. One is the already discussed issue with an update re-check. Another is where both UPDATE at the same time, matching zero rows and continuing. Then they both do the EXISTS test, which happens before the INSERT. Both get zero rows, so both do the INSERT. One fails with a duplicate key error.
This is why you need a re-try loop. You might think that you can prevent duplicate key errors or lost updates with clever SQL, but you can't. You need to check row counts or handle duplicate key errors (depending on the chosen approach) and re-try.
Please don't roll your own solution for this. Like with message queuing, it's probably wrong.
Bulk upsert with lock
Sometimes you want to do a bulk upsert, where you have a new data set that you want to merge into an older existing data set. This is vastly more efficient than individual row upserts and should be preferred whenever practical.
In this case, you typically follow the following process:
CREATE a TEMPORARY table
COPY or bulk-insert the new data into the temp table
LOCK the target table IN EXCLUSIVE MODE. This permits other transactions to SELECT, but not make any changes to the table.
Do an UPDATE ... FROM of existing records using the values in the temp table;
Do an INSERT of rows that don't already exist in the target table;
COMMIT, releasing the lock.
For example, for the example given in the question, using multi-valued INSERT to populate the temp table:
BEGIN;
CREATE TEMPORARY TABLE newvals(id integer, somedata text);
INSERT INTO newvals(id, somedata) VALUES (2, 'Joe'), (3, 'Alan');
LOCK TABLE testtable IN EXCLUSIVE MODE;
UPDATE testtable
SET somedata = newvals.somedata
FROM newvals
WHERE newvals.id = testtable.id;
INSERT INTO testtable
SELECT newvals.id, newvals.somedata
FROM newvals
LEFT OUTER JOIN testtable ON (testtable.id = newvals.id)
WHERE testtable.id IS NULL;
COMMIT;
Related reading
UPSERT wiki page
UPSERTisms in Postgres
Insert, on duplicate update in PostgreSQL?
http://petereisentraut.blogspot.com/2010/05/merge-syntax.html
Upsert with a transaction
Is SELECT or INSERT in a function prone to race conditions?
SQL MERGE on the PostgreSQL wiki
Most idiomatic way to implement UPSERT in Postgresql nowadays
What about MERGE?
SQL-standard MERGE actually has poorly defined concurrency semantics and is not suitable for upserting without locking a table first.
It's a really useful OLAP statement for data merging, but it's not actually a useful solution for concurrency-safe upsert. There's lots of advice to people using other DBMSes to use MERGE for upserts, but it's actually wrong.
Other DBs:
INSERT ... ON DUPLICATE KEY UPDATE in MySQL
MERGE from MS SQL Server (but see above about MERGE problems)
MERGE from Oracle (but see above about MERGE problems)
Here are some examples for insert ... on conflict ... (pg 9.5+) :
Insert, on conflict - do nothing.
insert into dummy(id, name, size) values(1, 'new_name', 3)
on conflict do nothing;`
Insert, on conflict - do update, specify conflict target via column.
insert into dummy(id, name, size) values(1, 'new_name', 3)
on conflict(id)
do update set name = 'new_name', size = 3;
Insert, on conflict - do update, specify conflict target via constraint name.
insert into dummy(id, name, size) values(1, 'new_name', 3)
on conflict on constraint dummy_pkey
do update set name = 'new_name', size = 4;
I am trying to contribute with another solution for the single insertion problem with the pre-9.5 versions of PostgreSQL. The idea is simply to try to perform first the insertion, and in case the record is already present, to update it:
do $$
begin
insert into testtable(id, somedata) values(2,'Joe');
exception when unique_violation then
update testtable set somedata = 'Joe' where id = 2;
end $$;
Note that this solution can be applied only if there are no deletions of rows of the table.
I do not know about the efficiency of this solution, but it seems to me reasonable enough.
SQLAlchemy upsert for Postgres >=9.5
Since the large post above covers many different SQL approaches for Postgres versions (not only non-9.5 as in the question), I would like to add how to do it in SQLAlchemy if you are using Postgres 9.5. Instead of implementing your own upsert, you can also use SQLAlchemy's functions (which were added in SQLAlchemy 1.1). Personally, I would recommend using these, if possible. Not only because of convenience, but also because it lets PostgreSQL handle any race conditions that might occur.
Cross-posting from another answer I gave yesterday (https://stackoverflow.com/a/44395983/2156909)
SQLAlchemy supports ON CONFLICT now with two methods on_conflict_do_update() and on_conflict_do_nothing():
Copying from the documentation:
from sqlalchemy.dialects.postgresql import insert
stmt = insert(my_table).values(user_email='a#b.com', data='inserted data')
stmt = stmt.on_conflict_do_update(
index_elements=[my_table.c.user_email],
index_where=my_table.c.user_email.like('%#gmail.com'),
set_=dict(data=stmt.excluded.data)
)
conn.execute(stmt)
http://docs.sqlalchemy.org/en/latest/dialects/postgresql.html?highlight=conflict#insert-on-conflict-upsert
MERGE in PostgreSQL v. 15
Since PostgreSQL v. 15, is possible to use MERGE command. It actually has been presented as the first of the main improvements of this new version.
It uses a WHEN MATCHED / WHEN NOT MATCHED conditional in order to choose the behaviour when there is an existing row with same criteria.
It is even better than standard UPSERT, as the new feature gives full control to INSERT, UPDATE or DELETE rows in bulk.
MERGE INTO customer_account ca
USING recent_transactions t
ON t.customer_id = ca.customer_id
WHEN MATCHED THEN
UPDATE SET balance = balance + transaction_value
WHEN NOT MATCHED THEN
INSERT (customer_id, balance)
VALUES (t.customer_id, t.transaction_value)
WITH UPD AS (UPDATE TEST_TABLE SET SOME_DATA = 'Joe' WHERE ID = 2
RETURNING ID),
INS AS (SELECT '2', 'Joe' WHERE NOT EXISTS (SELECT * FROM UPD))
INSERT INTO TEST_TABLE(ID, SOME_DATA) SELECT * FROM INS
Tested on Postgresql 9.3
Since this question was closed, I'm posting here for how you do it using SQLAlchemy. Via recursion, it retries a bulk insert or update to combat race conditions and validation errors.
First the imports
import itertools as it
from functools import partial
from operator import itemgetter
from sqlalchemy.exc import IntegrityError
from app import session
from models import Posts
Now a couple helper functions
def chunk(content, chunksize=None):
"""Groups data into chunks each with (at most) `chunksize` items.
https://stackoverflow.com/a/22919323/408556
"""
if chunksize:
i = iter(content)
generator = (list(it.islice(i, chunksize)) for _ in it.count())
else:
generator = iter([content])
return it.takewhile(bool, generator)
def gen_resources(records):
"""Yields a dictionary if the record's id already exists, a row object
otherwise.
"""
ids = {item[0] for item in session.query(Posts.id)}
for record in records:
is_row = hasattr(record, 'to_dict')
if is_row and record.id in ids:
# It's a row but the id already exists, so we need to convert it
# to a dict that updates the existing record. Since it is duplicate,
# also yield True
yield record.to_dict(), True
elif is_row:
# It's a row and the id doesn't exist, so no conversion needed.
# Since it's not a duplicate, also yield False
yield record, False
elif record['id'] in ids:
# It's a dict and the id already exists, so no conversion needed.
# Since it is duplicate, also yield True
yield record, True
else:
# It's a dict and the id doesn't exist, so we need to convert it.
# Since it's not a duplicate, also yield False
yield Posts(**record), False
And finally the upsert function
def upsert(data, chunksize=None):
for records in chunk(data, chunksize):
resources = gen_resources(records)
sorted_resources = sorted(resources, key=itemgetter(1))
for dupe, group in it.groupby(sorted_resources, itemgetter(1)):
items = [g[0] for g in group]
if dupe:
_upsert = partial(session.bulk_update_mappings, Posts)
else:
_upsert = session.add_all
try:
_upsert(items)
session.commit()
except IntegrityError:
# A record was added or deleted after we checked, so retry
#
# modify accordingly by adding additional exceptions, e.g.,
# except (IntegrityError, ValidationError, ValueError)
db.session.rollback()
upsert(items)
except Exception as e:
# Some other error occurred so reduce chunksize to isolate the
# offending row(s)
db.session.rollback()
num_items = len(items)
if num_items > 1:
upsert(items, num_items // 2)
else:
print('Error adding record {}'.format(items[0]))
Here's how you use it
>>> data = [
... {'id': 1, 'text': 'updated post1'},
... {'id': 5, 'text': 'updated post5'},
... {'id': 1000, 'text': 'new post1000'}]
...
>>> upsert(data)
The advantage this has over bulk_save_objects is that it can handle relationships, error checking, etc on insert (unlike bulk operations).
I'd like to retrieve the fully referenced column name from a PyOdbc Cursor. For example, say I have 2 simple tables:
Table_1(Id, < some other fields >)
Table_2(Id, < some other fields >)
and I want to retrieve the joined data
select * from Table_1 t1, Table2 t2 where t1.Id = t2.Id
using pyodbc, like this:
query = 'select * from Table_1 t1, Table2 t2 where t1.Id = t2.Id'
import pyodbc
conn_string = '<removed>'
connection = pyodbc.connect(conn_string)
cursor = connection.cursor()cursor.execute(query)
I then want to get the column names:
for row in cursor.description:
print row[0]
BUT if I do this I'll get Id twice which I don't want. Ideally I could get t1.Id and t2.Id in the output.
Some of the solutions I've thought of (and why I don't really want to implement them):
re-name the columns in the query - in my real-world use case there are dozens of tables, some with dozens of rows that are changed far too often
parse my query and automate my SQL query generation (basically checking the query for tables, using the cursor.tables function to get the columns and then replacing the select * with a set of named columns) - If I have too I'll do this, but it seems like overkill for a testing harness
Is there a better way? Any advice would be appreciated.
The PyOdbc docs offer
# columns in table x
for row in cursor.columns(table='x'):
print(row.column_name)
www.PyOdbc wiki The API docs are useful
Here's how I do it.
import pyodbc
connection = pyodbc.connect('DSN=vertica_standby', UID='my_user', PWD='my_password', ansi=True)
cursor = connection.cursor()
for row in cursor.columns(table='table_name_in_your_database'):
print(row.column_name)
You have to have your DSN (data source name) set up via two files. odbc.ini and odbcinst.ini
It doesn't seem to be possible to do what I want without writing a decent amount of code to wrap it up. None of the other answers actually answered the question of returning different column names by the table they originate from in some relatively automatic fashion.