[🍏 0x3d11] [11 May 20:30:00.001679] [DEBUG] [🚀 MultiFetcher.initialize_sources] Begin initialization of 2 children.
[🍏 0x3d11] [11 May 20:30:00.001837] [INFO] [✅ MemoryFetcher.initialize_sources] Finished initialization of 'MemoryFetcher' in 4 Ξs: MemoryFetcher(sources=['category'], return_all=True)
[🍏 0x3d11] [11 May 20:30:00.073236] [DEBUG] [id_translation.fetching] Metadata for 'postgresql+pg8000://postgres:***@localhost:5002/sakila' created in 71 ms.
[🍏 0x3d11] [11 May 20:30:00.074350] [INFO] [✅ SqlFetcher.initialize_sources] Finished initialization of 'SqlFetcher' in 72 ms: SqlFetcher('postgresql+pg8000://postgres:***@localhost:5002/sakila', sources=['address', 'film', 'language', 'customer', 'country', 'staff', 'actor', 'city'])
[🍏 0x3d11] [11 May 20:30:00.074425] [DEBUG] [✅ MultiFetcher.initialize_sources] Finished initialization 2 children and 9 sources in 73 ms.
[🎃 0x73c4] [11 May 20:30:00.217945] [DEBUG] [id_translation.dio] Using rank-0 (priority=1999) implementation 'id_translation.dio.integration.pandas.PandasIO' for translatable of type='pandas.DataFrame'.
[🎃 0x73c4] [11 May 20:30:00.218200] [DEBUG] [🚀 Translator.translate] Begin translation of 'DataFrame'-type data. Names to translate: Derive based on type.
[🎃 0x73c4] [11 May 20:30:00.218286] [DEBUG] [id_translation.Translator] Name extraction complete. Found names=['customer_id', 'film_id', 'category_id', 'staff_id', 'rental_date', 'return_date'] for 'DataFrame'-type data.
[🎃 0x73c4] [11 May 20:30:00.218335] [DEBUG] [🚀 Translator.map] Begin name-to-source mapping of names=['customer_id', 'film_id', 'category_id', 'staff_id', 'rental_date', 'return_date'] in 'DataFrame' against sources=['category', 'address', 'film', 'language', 'customer', 'country', 'staff', 'actor', 'city'].
[🎃 0x73c4] [11 May 20:30:00.219851] [DEBUG] [id_translation.Translator.map] Computed 6x9 match scores in context=None in 146 Ξs:
candidates   category  address  film  language  customer  country  staff  actor  city
values                                                                               
customer_id      0.12     0.06 -0.00     -0.00      1.00    -0.01  -0.01   0.04 -0.01
film_id         -0.00     0.00  1.00     -0.00     -0.00    -0.01  -0.01  -0.01  0.24
category_id      1.00     0.00 -0.00      0.12      0.12     0.14  -0.01  -0.01  0.04
staff_id        -0.00     0.00  0.05      0.05     -0.00    -0.01   0.99  -0.01 -0.01
rental_date      -inf     -inf  -inf      -inf      -inf     -inf   -inf   -inf  -inf
return_date      -inf     -inf  -inf      -inf      -inf     -inf   -inf   -inf  -inf
[🎃 0x73c4] [11 May 20:30:00.220095] [INFO] [✅ Translator.map] Finished mapping of 4/6 names in 'DataFrame' in 2 ms: {'customer_id': 'customer', 'film_id': 'film', 'category_id': 'category', 'staff_id': 'staff', 'rental_date': None, 'return_date': None}.
[🎃 0x73c4] [11 May 20:30:00.220429] [DEBUG] [🚀 MultiFetcher.fetch] Dispatch FETCH jobs for 4 sources using 2 different fetchers on 2 threads.
[🎃 0x73c4] [11 May 20:30:00.220712] [DEBUG] [id_translation.fetching.MultiFetcher] Begin FETCH job for 3 sources using rank-1 fetcher SqlFetcher('postgresql+pg8000://postgres:***@localhost:5002/sakila', sources=['address', 'film', 'language', 'customer', 'country', 'staff', 'actor', 'city']) at 0x74426f9d1450.
[🎃 0x73c4] [11 May 20:30:00.220871] [DEBUG] [🚀 SqlFetcher.fetch] Begin fetching placeholders=('id', 'name', 'last_name') for 22 IDs from 3 sources: {'staff': 2, 'customer': 10, 'film': 10}.
[🎃 0x73c4] [11 May 20:30:00.220955] [DEBUG] [🚀 SqlFetcher.map_placeholders] Begin mapping of wanted placeholders={'last_name', 'id', 'name'} to actual placeholders={'staff_id', 'first_name', 'address_id', 'username', 'picture', 'email', 'password', 'store_id', 'active', 'last_name', 'last_update'} for source='staff'.
[🎃 0x73c4] [11 May 20:30:00.221694] [DEBUG] [id_translation.fetching.MultiFetcher] Begin FETCH job for 1 sources using rank-0 fetcher MemoryFetcher(sources=['category'], return_all=True) at 0x74426f9d0910.
[🎃 0x73c4] [11 May 20:30:00.221792] [DEBUG] [🚀 MemoryFetcher.fetch] Begin fetching placeholders=('id', 'name', 'last_name') for 7 IDs from 1 sources: {'category': 7}.
[🎃 0x73c4] [11 May 20:30:00.221854] [DEBUG] [🚀 MemoryFetcher.map_placeholders] Begin mapping of wanted placeholders={'last_name', 'id', 'name'} to actual placeholders={'id', 'name'} for source='category'.
[🎃 0x73c4] [11 May 20:30:00.223078] [DEBUG] [id_translation.fetching.map] Computed 3x2 match scores in context='category' in 30 Ξs:
candidates   id  name
values               
last_name  0.00  0.17
id          inf  -inf
name       -inf   inf
[🎃 0x73c4] [11 May 20:30:00.224228] [DEBUG] [✅ MemoryFetcher.map_placeholders] Finished placeholder mapping for source='category': {'id': 'id', 'name': 'name', 'last_name': None}.
[🎃 0x73c4] [11 May 20:30:00.224312] [DEBUG] [🚀 MemoryFetcher.fetch_translations] Begin fetching 7 IDs from source='category'. Placeholders: ('id', 'name', 'last_name').
[🎃 0x73c4] [11 May 20:30:00.224371] [DEBUG] [✅ MemoryFetcher.fetch_translations] Finished fetching 16 IDs from source='category' in 59 Ξs. Placeholders: ('id', 'name').
[🎃 0x73c4] [11 May 20:30:00.224441] [INFO] [✅ MemoryFetcher.fetch] Finished fetching from 1 sources in 3 ms: ['category' x ('id', 'name') x 16/7 IDs].
[🎃 0x73c4] [11 May 20:30:00.224545] [DEBUG] [id_translation.fetching.map] Computed 3x11 match scores in context='staff' in 129 Ξs:
candidates  staff_id  first_name  address_id  username  picture  email  password  store_id  active  last_name  last_update
values                                                                                                                    
last_name       -inf        -inf        -inf      -inf     -inf   -inf      -inf      -inf    -inf        inf         -inf
id              0.99       -0.00        0.16     -0.00     0.06   0.12      0.24      0.61    0.04      -0.01        -0.01
name            0.00        0.99        0.02      0.20     0.06   0.18      0.03     -0.01    0.08       0.37         0.09
[🎃 0x73c4] [11 May 20:30:00.224730] [DEBUG] [✅ SqlFetcher.map_placeholders] Finished placeholder mapping for source='staff': {'last_name': 'last_name', 'name': 'first_name', 'id': 'staff_id'}.
[🎃 0x73c4] [11 May 20:30:00.224805] [DEBUG] [🚀 SqlFetcher.fetch_translations] Begin fetching 2 IDs from source='staff'. Placeholders: ('staff_id', 'first_name', 'last_name').
[🎃 0x73c4] [11 May 20:30:00.227016] [DEBUG] [✅ SqlFetcher.fetch_translations] Finished fetching 2 IDs from source='staff' in 2 ms. Placeholders: ('staff_id', 'first_name', 'last_name').
[🎃 0x73c4] [11 May 20:30:00.227093] [DEBUG] [🚀 SqlFetcher.map_placeholders] Begin mapping of wanted placeholders={'last_name', 'id', 'name'} to actual placeholders={'customer_id', 'first_name', 'address_id', 'activebool', 'email', 'store_id', 'active', 'last_name', 'create_date', 'last_update'} for source='customer'.
[🎃 0x73c4] [11 May 20:30:00.228364] [DEBUG] [id_translation.fetching.map] Computed 3x10 match scores in context='customer' in 114 Ξs:
candidates  customer_id  first_name  address_id  activebool  email  store_id  active  last_name  create_date  last_update
values                                                                                                                   
last_name          -inf        -inf        -inf        -inf   -inf      -inf    -inf        inf         -inf         -inf
id                 0.99       -0.00        0.16        0.09   0.12      0.36    0.04      -0.01         0.07         0.16
name               0.00        0.99       -0.00       -0.00   0.12     -0.01    0.08       0.38         0.14         0.09
[🎃 0x73c4] [11 May 20:30:00.228532] [DEBUG] [✅ SqlFetcher.map_placeholders] Finished placeholder mapping for source='customer': {'last_name': 'last_name', 'name': 'first_name', 'id': 'customer_id'}.
[🎃 0x73c4] [11 May 20:30:00.228586] [DEBUG] [🚀 SqlFetcher.fetch_translations] Begin fetching 10 IDs from source='customer'. Placeholders: ('customer_id', 'first_name', 'last_name').
[🎃 0x73c4] [11 May 20:30:00.230394] [DEBUG] [✅ SqlFetcher.fetch_translations] Finished fetching 10 IDs from source='customer' in 2 ms. Placeholders: ('customer_id', 'first_name', 'last_name').
[🎃 0x73c4] [11 May 20:30:00.230476] [DEBUG] [🚀 SqlFetcher.map_placeholders] Begin mapping of wanted placeholders={'last_name', 'id', 'name'} to actual placeholders={'title', 'special_features', 'description', 'language_id', 'rating', 'replacement_cost', 'original_language_id', 'rental_rate', 'release_year', 'rental_duration', 'fulltext', 'film_id', 'length', 'last_update'} for source='film'.
[🎃 0x73c4] [11 May 20:30:00.232072] [DEBUG] [id_translation.fetching.map] Computed 3x14 match scores in context='film' in 141 Ξs:
candidates  title  special_features  description  language_id  rating  replacement_cost  original_language_id  rental_rate  release_year  rental_duration  fulltext  film_id  length  last_update
values                                                                                                                                                                                           
last_name    0.04             -0.00        -0.00        -0.00   -0.01              0.06                 -0.01         0.10          0.07            -0.01     -0.01    -0.01    0.03         0.07
id          -0.00              0.01         0.02         0.10    0.10             -0.01                  0.05        -0.01         -0.01             0.02      0.23     0.98   -0.01         0.00
name          inf              -inf         -inf         -inf    -inf              -inf                  -inf         -inf          -inf             -inf      -inf     -inf    -inf         -inf
[🎃 0x73c4] [11 May 20:30:00.232289] [DEBUG] [✅ SqlFetcher.map_placeholders] Finished placeholder mapping for source='film': {'name': 'title', 'id': 'film_id', 'last_name': None}.
[🎃 0x73c4] [11 May 20:30:00.232345] [DEBUG] [🚀 SqlFetcher.fetch_translations] Begin fetching 10 IDs from source='film'. Placeholders: ('film_id', 'title').
[🎃 0x73c4] [11 May 20:30:00.234065] [DEBUG] [✅ SqlFetcher.fetch_translations] Finished fetching 10 IDs from source='film' in 2 ms. Placeholders: ('film_id', 'title').
[🎃 0x73c4] [11 May 20:30:00.234141] [INFO] [✅ SqlFetcher.fetch] Finished fetching from 3 sources in 13 ms: ['staff' x ('id', 'name', 'last_name') x 2/2 IDs], ['customer' x ('id', 'name', 'last_name') x 10/10 IDs], ['film' x ('id', 'name') x 10/10 IDs].
[🎃 0x73c4] [11 May 20:30:00.234433] [DEBUG] [✅ MultiFetcher.fetch] Finished FETCH jobs for 4 sources using 2 different fetchers in 14 ms.
[🎃 0x73c4] [11 May 20:30:00.236320] [DEBUG] [id_translation.Translator] Performance counters [ms]: {'map': 1.821, 'extract': 0.244, 'fetch': 14.354, 'verify': 0.337, 'insert': 1.414, 'overhead': 0.543}
[🎃 0x73c4] [11 May 20:30:00.236399] [INFO] [✅ Translator.translate] Finished translation of 29 unique IDs (4 names) in 'DataFrame' in 19 ms.