I'm working on a project to extract books from Hacker News, and want to link the books to records from Open Library. The Open Library data dumps are several gigabytes of compressed TSV, so are too big to fit in memory on a standard machine. The Libraries Hacked repository imports it into a PostgreSQL database, but I don't want to set up a Postgres database for an analysis job if I can avoid it. Instead I want to use an SQLite database to store the data.

The SQLite CLI has an import statement, but when I tried to run it with .mode tabs it kept complaining about unescaped quotes and wrong number of fields. Instead I wrote a little Python script to iterate over all the data and yield tuples of the 5 columns.

from pathlib import Path
import gzip

ol_dump_date = '2022-06-06'
data_path = Path('../data/01_raw')

def ol_path(segment):
return data_path / f'ol_dump_{segment}_{ol_dump_date}.txt.gz'

def ol_data(segment):
with gzip.open(ol_path(segment), 'rt') as f:
for line in f:
yield tuple(line.split('\t', 5))

It turns out it's a bit slower inserting one row at a time, so we'll make minibatches from the sequence to write. If the batch size is too large it uses a lot of memory, if it's very small then it's slower due to the overhead of parsing the statement. In practice 1000 seems like a good tradeoff.

def minibatch(seq, size):
items = []
for x in seq:
items.append(x)
if len(items) >= size:
yield items
items = []
if items:
yield items

We can then create the table and insert the rows a minibatch at a time. Wrapping this in a transaction makes it slightly faster. It would be reasonable to put a PRIMARY KEY constraint on the key column; but in this use case it won't make much difference if the incoming data is valid.

from tqdm.auto import tqdm

def create_segment(cur, segment, batch_size=10_000):
cur.execute(f'CREATE TABLE {segment} (type TEXT, key TEXT, revision INT, last_modified TEXT, json TEXT);')

with con:
for batch in minibatch(tqdm(ol_data(segment)), batch_size):
con.executemany('INSERT INTO authors VALUES (?,?,?,?,?)', batch)

Finally we actually create all the tables. We set some PRAGMA to make bulk inserts faster (as per this blog and this Stackoverflow), and then create each segment. This completes in about 25 minutes on my laptop, leaving a 62GB SQLite file (since we now have all that uncompressed JSON).

con = sqlite3.connect('openlibrary.sqlite')

con.execute("PRAGMA synchronous=OFF")
con.execute("PRAGMA count_changes=OFF")
con.execute("PRAGMA journal_mode=MEMORY")
con.execute("PRAGMA temp_store=MEMORY")

for segment in ['authors', 'works', 'editions']:
create_segment(con, segment)

This is just a start; we'll need to do some more work to make this usable for looking up entries such as extracting names and adding indexes.

With SQLite's json support we can now query the json column. For example to get all the authors of the book Regression and Other Stories we can run:

SELECT works.key,
authors.key,
json_extract(authors.json, '$.name') as author_name FROM works JOIN json_each(( SELECT json FROM works as w WHERE w.key = works.key ), '$.authors') as work_authors
JOIN authors ON authors.key = json_extract(work_authors.value,
'$.author.key') WHERE json_extract(works.json, '$.title') = 'Regression and Other Stories';

This is pretty slow; it takes around 4 minutes to return the 3 authors. To make this more usable we're going to need to create indexes, and likely change the tables.