INSERT statement of postgresql supports RETURNING, read this
http://docs.sqlalchemy.org/en/rel_0_8/core/dml.html#sqlalchemy.sql.expression.Insert.returning
On Monday, March 24, 2014 2:43:46 PM UTC+8, James Meneghello wrote:
>
> Oops, I should add - the reason I can't use an itertools counter to
> pre-assign IDs is because the table is potentially being dumped to by
> multiple scripts, which is why I have to commit the parts prior to the
> segments (since engine.execute can't return multiple insert_ids).
>
> On Monday, 24 March 2014 14:40:52 UTC+8, James Meneghello wrote:
>>
>> Thanks for the quick reply!
>>
>> This seems to work pretty well. I took out the batching (as it's already
>> batched at a higher level) and modified it to suit the insertion of
>> children as well (and reducded the unique to a single field) , and it
>> appears to work.
>>
>> with db_session() as db:
>> existing_entries = dict(
>> ((entry.subject, entry) for entry in
>>
>> db.query(Entry).filter(Entry.subject.in_(entries.keys())).options(subqueryload('segments')).all()
>> )
>> )
>>
>> segment_inserts = []
>> for subject, entry in entries.items():
>> existing_entry = existing_entries.get(subject, None)
>> if existing_entry:
>> segments = dict(((s.segment, s) for s in
>> existing_entry.segments))
>> for segment_number, segment in entry['segments'].items():
>> if int(segment_number) not in segments:
>> segment['entry_id'] = existing_entry.id
>> segment_inserts.append(segment)
>> else:
>> entry_id = engine.execute(Entry.__table__.insert(),
>> entry).inserted_primary_key
>> for segment in entry['segments'].values():
>> segment['entry_id'] = entry_id[0]
>> segment_inserts.append(segment)
>>
>> engine.execute(Segment.__table__.insert(), segment_inserts)
>>
>>
>> For 20,000 segments, this ends up being about 45 seconds and 1650 queries
>> - 2 to select all the entries and segments, 1 to insert the segments and
>> the rest to insert parts. From here, however, I rewrote it a bit:
>>
>> with db_session() as db:
>> existing_entries = dict(
>> ((entry.subject, entry) for entry in
>>
>> db.query(Entry).filter(Entry.subject.in_(entries.keys())).all()
>> )
>> )
>>
>> entry_inserts = []
>> for subject, entry in entries.items():
>> existing_entry = existing_entries.get(subject, None)
>> if not existing_entry:
>> entry_inserts.append(entry)
>>
>> engine.execute(Entry.__table__.insert(), entry_inserts)
>>
>> existing_entries = dict(
>> ((entry.subject, entry) for entry in
>>
>> db.query(Entry).filter(Entry.subject.in_(entries.keys())).options(subqueryload('segments')).all()
>> )
>> )
>>
>> segment_inserts = []
>> for subject, entry in entries.items():
>> existing_entry = existing_entries.get(subject, None)
>> if existing_entry:
>> segments = dict(((s.segment, s) for s in
>> existing_entry.segments))
>> for segment_number, segment in entry['segments'].items():
>> if int(segment_number) not in segments:
>> segment['entry_id'] = existing_entry.id
>> segment_inserts.append(segment)
>> else:
>> log.error('i\'ve made a huge mistake')
>>
>> engine.execute(Segment.__table__.insert(), segment_inserts)
>>
>> This ends up being about 19 seconds, 6 queries for a clean dump, and a
>> bit less if the table is already populated. Removing the unique indexes on
>> both the entries and segments tables and replacing them with standard
>> indexes saves about a second in a full dump, and about 6 seconds for an
>> update. I'm pretty happy with where it is now, and I suspect most of the
>> time (aside from the two insert calls) is being spent in Python. That said,
>> if you have any tips for improvements I'd be all ears.
>>
>> Thanks for the help!
>>
>> On Monday, 24 March 2014 09:19:25 UTC+8, Michael Bayer wrote:
>>>
>>>
>>> On Mar 23, 2014, at 11:33 AM, James Meneghello <muro...@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, 100000), random.randint(1, 100000),
>>> random.randint(1, 100000))
>>> for i in range(100000)
>>> ))
>>>
>>> entries_we_will_start_with = a_bunch_of_fake_unique_entries[0:50000]
>>> entries_we_will_merge = a_bunch_of_fake_unique_entries[30000:100000]
>>>
>>> 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
>>> batch
>>> ])
>>> )
>>> )
>>>
>>> inserts = []
>>> for entry_to_merge in batch:
>>> existing_entry = existing_entries.get(entry_to_merge, None)
>>> if existing_entry:
>>> # do whatever to update existing
>>> pass
>>> else:
>>> inserts.append(
>>> dict(
>>> id=next(counter),
>>> sub=entry_to_merge[0],
>>> division=entry_to_merge[1],
>>> create_engine=entry_to_merge[2]
>>> )
>>> )
>>> if inserts:
>>> sess.execute(Entry.__table__.insert(), params=inserts)
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> Thanks!
>>>
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>>>
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