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Using DuckDB in Python to access Parquet data
Using DuckDB in Python to access Parquet data#
Did a quick experiment with DuckDB today, inspired by the bmschmidt/hathi-binary repo.
That repo includes 3GB of data in 68 parquet files. Those files are 45MB each.
DuckDB can run queries against Parquet data really fast.
I checked out the repo like this:
1cd /tmp2git clone https://github.com/bmschmidt/hathi-binary3cd hathi-binaryTo install it:
1pip install duckdbThen in a Python console:
1>>> import duckdb2>>> db = duckdb.connect() # No need to pass a file name, we will use a VIEW3>>> db.execute("CREATE VIEW hamming AS SELECT * FROM parquet_scan('parquet/*.parquet')")4<duckdb.DuckDBPyConnection object at 0x110eab530>5>>> db.execute("select count(*) from hamming").fetchall()6[(17123746,)]7>>> db.execute("select sum(A), sum(B), sum(C) from hamming").fetchall()8[(19478990546114240096822710, 16303362475198894881395004, 43191807707832192976154883)]There are 17,123,746 records in the 3GB of Parquet data.
I switched to iPython so I could time a query. First I ran a query to see what a record looks like, using .df().to_dict() to convert the result into a DataFrame and then a Python dictionary:
1In [17]: db.execute("select * from hamming limit 1").df().to_dict()2Out[17]:3{'htid': {0: 'uc1.b3209520'},4 'A': {0: -3968610387004385723},5 'B': {0: 7528965001168362882},6 'C': {0: 5017761927246436345},7 'D': {0: 2866021664979717155},8 'E': {0: -8718458467632335109},9 'F': {0: 3783827906913154091},10 'G': {0: -883843087282811188},11 'H': {0: 4045142741717613284},12 'I': {0: -9144138405661797607},13 'J': {0: 3285280333149952194},14 'K': {0: -3352555231043531556},15 'L': {0: 2071206943103704211},16 'M': {0: -5859914591541496612},17 'N': {0: -4209182319449999971},18 'O': {0: 2040176595216801886},19 'P': {0: 860910514658882647},20 'Q': {0: 3505065119653024843},21 'R': {0: -3110594979418944378},22 'S': {0: -8591743965043807123},23 'T': {0: -3262129165685658773}}Then I timed an aggregate query using %time:
1In [18]: %time db.execute("select sum(A), sum(B), sum(C) from hamming").fetchall()2CPU times: user 1.13 s, sys: 488 ms, total: 1.62 s3Wall time: 206 ms4Out[18]:5[(19478990546114240096822710,6 16303362475198894881395004,7 43191807707832192976154883)]206ms to sum three columns across 17 million records is pretty fast!
Using DuckDB in Python to access Parquet data
https://mranv.pages.dev/posts/using-duckdb-in-python-to-access-parquet-data/