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! >> >> -- >> You received this message because you are subscribed to the Google Groups >> "sqlalchemy" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to sqlalchemy+...@googlegroups.com. >> To post to this group, send email to sqlal...@googlegroups.com. >> Visit this group at http://groups.google.com/group/sqlalchemy. >> For more options, visit https://groups.google.com/d/optout. >> >> >> -- You received this message because you are subscribed to the Google Groups "sqlalchemy" group. 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