Arrow -> PostgreSQL encoder

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Project description

pgpq

Convert PyArrow RecordBatches to Postgres' native binary format.

Usage

import psycopg
import pyarrow.dataset as ds
from pgpq import ArrowToPostgresBinaryEncoder


dataset = ds.dataset("tests/testdata/")

encoder = ArrowToPostgresBinaryEncoder(dataset.schema)
# get the expected postgres destination schema
# note that this is _not_ the same as the incoming arrow schema
# and not necessarily the schema of your permanent table
# instead it's the schema of the data that will be sent over the wire
# which for example does not have timezones on any timestamps
pg_schema = encoder.schema()
# assemble ddl for a temporary table
# it's often a good idea to bulk load into a temp table to:
# (1) Avoid indexes
# (2) Stay in-memory as long as possible
# (3) Be more flexible with types (you can't load a SMALLINT into a BIGINT column without casting)
cols = [f"\"{col['name']}\" {col['data_type']['ddl']}" for col in pg_schema["columns"]]
ddl = f"CREATE TEMP TABLE data ({','.join(cols)})"

with psycopg.connect("postgres://postgres:postgres@localhost:5432/postgres") as conn:
    with conn.cursor() as cursor:
        cursor.execute(ddl)  # type: ignore
        with cursor.copy("COPY data FROM STDIN WITH (FORMAT BINARY)") as copy:
            copy.write(encoder.write_header())
            for batch in dataset.to_batches():
                copy.write(encoder.write_batch(batch))
            copy.write(encoder.finish())
        # load into your actual table, possibly doing type casts
        # cursor.execute("INSERT INTO \"table\" SELECT * FROM data")

Project details

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Release history Release notifications | RSS feed

0.9.0

Oct 18, 2023

0.8.0

Sep 21, 2023

0.7.3

Jun 15, 2023

0.7.2

Jun 15, 2023

0.7.1

Mar 31, 2023

This version

0.7.0

Mar 31, 2023

0.6.1

Mar 31, 2023

0.5.0

Feb 20, 2023

0.4.2

Feb 16, 2023

0.4.1

Feb 15, 2023

0.2.0

Feb 14, 2023

0.1.2

Feb 11, 2023

0.1.1

Feb 6, 2023

0.1.0

Feb 4, 2023

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