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<javascript:>>
> 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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