[sqlalchemy] Bulk Inserts and Unique Constraints
I'm having a few issues with unique constraints and bulk inserts. The
software I'm writing takes data from an external source (a lot of it,
anywhere from 1,000 rows per minute to 100-200k+), crunches it down into
its hierarchy and saves it to the DB, to be aggregated in the background.
The function handling the initial DB save is designed to work with about
20-50k rows at a time - very little modification takes place, it's pretty
much just grabbed and thrown into the table. Obviously the amount of data
being saved somewhat excludes the use of the ORM in this particular table,
but there are a number of other tables that benefit from using the ORM.
Hence, the small stuff uses the ORM and the big stuff uses the Core.
The main problem I'm having is with the initial save. The data comes in
unordered and sometimes contains duplicates, so there's a UniqueConstraint
on Entry on sub, division, created. Unfortunately, this hampers the bulk
insert - if there's a duplicate, it rolls back the entire insert and hence
the entries aren't available to be referenced by the segments later.
Obviously, capturing it in a try/catch would skip the whole block as well.
Both Entry and Segment have the same problem - there are often duplicate
segments. Since there's a large amount of data being pushed through it, I
assume it's impractical to insert the elements individually - while there's
only 100-200 entries per block, there's usually 20-50k segments.
Is there any way of forcing the engine to skip over duplicates and not
rollback the transaction on exception? Code's below. Using Postgres, with
psycopg2 as the driver.
engine.execute(Entry.__table__.insert(), entries)
segment_list = []
for sub, entry in entry.items():
segments = entry.pop('segments')
e = db.query(Entry)\
.filter(Entry.sub==entry['sub'])\
.filter(Entry.division==entry['division'])\
.filter(Entry.created==entry['created']).first()
for segment in segments:
segment['entry_id'] = e.id
segment_list.append(segment)
engine.execute(Segment.__table__.insert(), segment_list)
In addition, is there some way to pre-fetch data? Rather than query for
each Entry, it'd be nice to pre-load all entries and save a couple hundred
queries.
Thanks!
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Re: [sqlalchemy] Bulk Inserts and Unique Constraints
On Mar 23, 2014, at 11:33 AM, James Meneghello murod...@gmail.com wrote:
I'm having a few issues with unique constraints and bulk inserts. The
software I'm writing takes data from an external source (a lot of it,
anywhere from 1,000 rows per minute to 100-200k+), crunches it down into its
hierarchy and saves it to the DB, to be aggregated in the background. The
function handling the initial DB save is designed to work with about 20-50k
rows at a time - very little modification takes place, it's pretty much just
grabbed and thrown into the table. Obviously the amount of data being saved
somewhat excludes the use of the ORM in this particular table, but there are
a number of other tables that benefit from using the ORM. Hence, the small
stuff uses the ORM and the big stuff uses the Core.
The main problem I'm having is with the initial save. The data comes in
unordered and sometimes contains duplicates, so there's a UniqueConstraint on
Entry on sub, division, created. Unfortunately, this hampers the bulk insert
- if there's a duplicate, it rolls back the entire insert and hence the
entries aren't available to be referenced by the segments later. Obviously,
capturing it in a try/catch would skip the whole block as well. Both Entry
and Segment have the same problem - there are often duplicate segments. Since
there's a large amount of data being pushed through it, I assume it's
impractical to insert the elements individually - while there's only 100-200
entries per block, there's usually 20-50k segments.
Is there any way of forcing the engine to skip over duplicates and not
rollback the transaction on exception? Code's below. Using Postgres, with
psycopg2 as the driver.
engine.execute(Entry.__table__.insert(), entries)
segment_list = []
for sub, entry in entry.items():
segments = entry.pop('segments')
e = db.query(Entry)\
.filter(Entry.sub==entry['sub'])\
.filter(Entry.division==entry['division'])\
.filter(Entry.created==entry['created']).first()
for segment in segments:
segment['entry_id'] = e.id
segment_list.append(segment)
engine.execute(Segment.__table__.insert(), segment_list)
In addition, is there some way to pre-fetch data? Rather than query for each
Entry, it'd be nice to pre-load all entries and save a couple hundred queries.
you have all the entries in segment_list. Seems like you'd just want to dedupe
entries as they enter that list, that's a pretty simple thing to do with a
dictionary. but additionally you're also emitting a SELECT for every item
individually so whatever time you're saving on that INSERT is just being
expended with that huge number of SELECT statements anyway.
so yes you certainly want to pre-load everything, since the criteria here is
three different things you can use a tuple-based IN clause.
below is an example that uses this and also batches so that it won't run out of
memory under any circumstances, it starts with 50K rows in the database and
then adds another 50K with 20K overlapping. the whole thing runs in about 28
seconds on my mac.
from sqlalchemy import *
from sqlalchemy.orm import *
from sqlalchemy.ext.declarative import declarative_base
import random
import itertools
Base = declarative_base()
class Entry(Base):
__tablename__ = 'a'
id = Column(Integer, primary_key=True)
sub = Column(Integer)
division = Column(Integer)
created = Column(Integer)
__table_args__ = (UniqueConstraint('sub', 'division', 'created'), )
e = create_engine(postgresql://scott:tiger@localhost/test, echo=True)
Base.metadata.drop_all(e)
Base.metadata.create_all(e)
a_bunch_of_fake_unique_entries = list(set(
(random.randint(1, 10), random.randint(1, 10), random.randint(1,
10))
for i in range(10)
))
entries_we_will_start_with = a_bunch_of_fake_unique_entries[0:5]
entries_we_will_merge = a_bunch_of_fake_unique_entries[3:10]
sess = Session(e)
counter = itertools.count(1)
sess.add_all([Entry(id=next(counter), sub=sub, division=division,
created=created)
for sub, division, created in entries_we_will_start_with])
sess.commit()
# here's where your example begins... This will also batch it
# to ensure it can scale arbitrarily
while entries_we_will_merge:
batch = entries_we_will_merge[0:1000]
entries_we_will_merge = entries_we_will_merge[1000:]
existing_entries = dict(
((entry.sub, entry.division, entry.created), entry)
for entry in sess.query(Entry).filter(
tuple_(Entry.sub, Entry.division,
Entry.created).in_([
tuple_(sub, division, created)
for sub, division, created in
[sqlalchemy] Re: Bulk Inserts and Unique Constraints
Anyway, I don't think you should issue so many SELECT's, performance
suffers from it most, I think. INSERT statement will return the inserted
id's for you, maybe you want to read this
http://docs.sqlalchemy.org/en/latest/core/tutorial.html#executing
Skip duplicates... well if you turn off the constraint check and that's
what you want, it works but... I think the best strategy for database is to
ROLLBACK it. It's YOUR duty to filter the duplicates before send them to
the database. set in python works well in this situation. You may want
learn more about stored procedure if you want the database do the
filtering.
And, if you really cares much about performance and you know the weakness
of ORM, why do you use ORM?
Cosmia
On Sunday, March 23, 2014 11:33:32 PM UTC+8, James Meneghello wrote:
I'm having a few issues with unique constraints and bulk inserts. The
software I'm writing takes data from an external source (a lot of it,
anywhere from 1,000 rows per minute to 100-200k+), crunches it down into
its hierarchy and saves it to the DB, to be aggregated in the background.
The function handling the initial DB save is designed to work with about
20-50k rows at a time - very little modification takes place, it's pretty
much just grabbed and thrown into the table. Obviously the amount of data
being saved somewhat excludes the use of the ORM in this particular table,
but there are a number of other tables that benefit from using the ORM.
Hence, the small stuff uses the ORM and the big stuff uses the Core.
The main problem I'm having is with the initial save. The data comes in
unordered and sometimes contains duplicates, so there's a UniqueConstraint
on Entry on sub, division, created. Unfortunately, this hampers the bulk
insert - if there's a duplicate, it rolls back the entire insert and hence
the entries aren't available to be referenced by the segments later.
Obviously, capturing it in a try/catch would skip the whole block as well.
Both Entry and Segment have the same problem - there are often duplicate
segments. Since there's a large amount of data being pushed through it, I
assume it's impractical to insert the elements individually - while there's
only 100-200 entries per block, there's usually 20-50k segments.
Is there any way of forcing the engine to skip over duplicates and not
rollback the transaction on exception? Code's below. Using Postgres, with
psycopg2 as the driver.
engine.execute(Entry.__table__.insert(), entries)
segment_list = []
for sub, entry in entry.items():
segments = entry.pop('segments')
e = db.query(Entry)\
.filter(Entry.sub==entry['sub'])\
.filter(Entry.division==entry['division'])\
.filter(Entry.created==entry['created']).first()
for segment in segments:
segment['entry_id'] = e.id
segment_list.append(segment)
engine.execute(Segment.__table__.insert(), segment_list)
In addition, is there some way to pre-fetch data? Rather than query for
each Entry, it'd be nice to pre-load all entries and save a couple hundred
queries.
Thanks!
--
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