| Name | pgpq JSON |
| Version |
0.9.0
JSON |
| download |
| home_page | |
| Summary | Arrow -> PostgreSQL encoder |
| upload_time | 2023-10-18 15:16:34 |
| maintainer | |
| docs_url | None |
| author | Adrian Garcia Badaracco |
| requires_python | >=3.7 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# pgpq
Convert PyArrow RecordBatches to Postgres' native binary format.
## Usage
```python
"""Example for README.md"""
from tempfile import mkdtemp
import psycopg
import pyarrow.dataset as ds
import requests
from pgpq import ArrowToPostgresBinaryEncoder
# let's get some example data
tmpdir = mkdtemp()
with open(f"{tmpdir}/yellow_tripdata_2023-01.parquet", mode="wb") as f:
resp = requests.get(
"https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2023-01.parquet"
)
resp.raise_for_status()
f.write(resp.content)
# load an arrow dataset
# arrow can load datasets from partitioned parquet files locally or in S3/GCS
# it handles buffering, matching globs, etc.
dataset = ds.dataset(tmpdir)
# create an encoder object which will do the encoding
# and give us the expected Postgres table schema
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_name, 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")
```
Raw data
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"description": "# pgpq\n\nConvert PyArrow RecordBatches to Postgres' native binary format.\n\n## Usage\n\n```python\n\"\"\"Example for README.md\"\"\"\nfrom tempfile import mkdtemp\nimport psycopg\nimport pyarrow.dataset as ds\nimport requests\nfrom pgpq import ArrowToPostgresBinaryEncoder\n\n# let's get some example data\ntmpdir = mkdtemp()\nwith open(f\"{tmpdir}/yellow_tripdata_2023-01.parquet\", mode=\"wb\") as f:\n resp = requests.get(\n \"https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2023-01.parquet\"\n )\n resp.raise_for_status()\n f.write(resp.content)\n\n# load an arrow dataset\n# arrow can load datasets from partitioned parquet files locally or in S3/GCS\n# it handles buffering, matching globs, etc.\ndataset = ds.dataset(tmpdir)\n\n# create an encoder object which will do the encoding\n# and give us the expected Postgres table schema\nencoder = ArrowToPostgresBinaryEncoder(dataset.schema)\n# get the expected Postgres destination schema\n# note that this is _not_ the same as the incoming arrow schema\n# and not necessarily the schema of your permanent table\n# instead it's the schema of the data that will be sent over the wire\n# which for example does not have timezones on any timestamps\npg_schema = encoder.schema()\n# assemble ddl for a temporary table\n# it's often a good idea to bulk load into a temp table to:\n# (1) Avoid indexes\n# (2) Stay in-memory as long as possible\n# (3) Be more flexible with types\n# (you can't load a SMALLINT into a BIGINT column without casting)\ncols = [f'\"{col_name}\" {col.data_type.ddl()}' for col_name, col in pg_schema.columns]\nddl = f\"CREATE TEMP TABLE data ({','.join(cols)})\"\n\nwith psycopg.connect(\"postgres://postgres:postgres@localhost:5432/postgres\") as conn:\n with conn.cursor() as cursor:\n cursor.execute(ddl) # type: ignore\n with cursor.copy(\"COPY data FROM STDIN WITH (FORMAT BINARY)\") as copy:\n copy.write(encoder.write_header())\n for batch in dataset.to_batches():\n copy.write(encoder.write_batch(batch))\n copy.write(encoder.finish())\n # load into your actual table, possibly doing type casts\n # cursor.execute(\"INSERT INTO \\\"table\\\" SELECT * FROM data\")\n```\n\n",
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