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Add documentation for creating a parquet schema.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,274 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Unequal schema problems\n", | ||
| "\n", | ||
| "There are a few ways in which parquet files written with slightly different schema can cause issues in the import pipeline. They have a similar correction mechanism, so we discuss both here." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Error Demonstration\n", | ||
| "\n", | ||
| "Here, we attempt an import with some unequal schema, and see that the attempt fails in the reducing stage, when we're trying to combine partial parquet files into a single file with common metadata." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import tempfile\n", | ||
| "import os\n", | ||
| "from dask.distributed import Client\n", | ||
| "\n", | ||
| "from hipscat_import.pipeline import pipeline_with_client\n", | ||
| "from hipscat_import.catalog.arguments import ImportArguments\n", | ||
| "from hipscat_import.catalog.file_readers import get_file_reader\n", | ||
| "\n", | ||
| "mixed_schema_csv_dir = \"../../tests/hipscat_import/data/mixed_schema\"\n", | ||
| "tmp_path = tempfile.TemporaryDirectory()\n", | ||
| "\n", | ||
| "args = ImportArguments(\n", | ||
| " output_catalog_name=\"mixed_csv_bad\",\n", | ||
| " input_path=mixed_schema_csv_dir,\n", | ||
| " input_format=\"csv\",\n", | ||
| " output_path=tmp_path.name,\n", | ||
| " highest_healpix_order=1,\n", | ||
| ")\n", | ||
| "\n", | ||
| "client = Client(n_workers=1, threads_per_worker=1)\n", | ||
| "\n", | ||
| "try:\n", | ||
| " pipeline_with_client(args, client)\n", | ||
| "except:\n", | ||
| " pass # we know it's going to fail!!" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "We can overcome may of these issues by using a *parquet schema* file. This is a special kind of parquet file that only contains information on the columns (their names, data types, and additional key-value metadata).\n", | ||
| "\n", | ||
| "Let's take a look inside the schema structure and see the field types it expects to see:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import pyarrow.parquet as pq\n", | ||
| "\n", | ||
| "mixed_schema_csv_parquet = \"../../tests/hipscat_import/data/mixed_schema/schema.parquet\"\n", | ||
| "\n", | ||
| "parquet_file = pq.ParquetFile(mixed_schema_csv_parquet)\n", | ||
| "print(parquet_file.schema)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "We already have one for this data set, but we'll show you how to create one of your own later in this notebook." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "tmp_path = tempfile.TemporaryDirectory()\n", | ||
| "args = ImportArguments(\n", | ||
| " output_catalog_name=\"mixed_csv_good\",\n", | ||
| " input_path=mixed_schema_csv_dir,\n", | ||
| " input_format=\"csv\",\n", | ||
| " output_path=tmp_path.name,\n", | ||
| " highest_healpix_order=1,\n", | ||
| " file_reader=get_file_reader(\"csv\", schema_file=mixed_schema_csv_parquet),\n", | ||
| " use_schema_file=mixed_schema_csv_parquet,\n", | ||
| " overwrite=True,\n", | ||
| ")\n", | ||
| "pipeline_with_client(args, client)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Making a new parquet schema file\n", | ||
| "\n", | ||
| "There are a few different strategies we can use to create a schema file:\n", | ||
| "\n", | ||
| "* using some string representations of pandas datatypes\n", | ||
| "* using an explicit list of pyarrow data types\n", | ||
| "* and many more!\n", | ||
| "\n", | ||
| "We'll stick to these two, since they exercise the most common code paths through schema generation." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Using pandas type strings\n", | ||
| "\n", | ||
| "Something like the `tic_types.csv` file contains a list of the columns that the TIC data will contain, in a table like:\n", | ||
| "\n", | ||
| "```\n", | ||
| "name,type\n", | ||
| "ID,Int64\n", | ||
| "version,str\n", | ||
| "HIP,Int32\n", | ||
| "TYC,str\n", | ||
| "etc...\n", | ||
| "```\n", | ||
| "\n", | ||
| "Such files are a common way to send type information when the data files have no header.\n", | ||
| "\n", | ||
| "In this method, we will use pandas' type parsing to convert these strings into understood data types, and create the relevant parquet metadata." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import pandas as pd\n", | ||
| "\n", | ||
| "## Fetch the name/type information from a file.\n", | ||
| "type_list_frame = pd.read_csv(\"../static/tic_types.csv\")\n", | ||
| "\n", | ||
| "## For each row, add to a dictionary with name and a pandas series with the parsed data type.\n", | ||
| "## \"str\" is not understood as \"string\", so add a special case.\n", | ||
| "type_map = {\n", | ||
| " row[\"name\"]: pd.Series(dtype=(\"string\" if row[\"type\"] == \"str\" else row[\"type\"]))\n", | ||
| " for _, row in type_list_frame.iterrows()\n", | ||
| "}\n", | ||
| "dtype_frame = pd.DataFrame(type_map)\n", | ||
| "\n", | ||
| "## Now write our empty data frame to a parquet file.\n", | ||
| "schema_file = os.path.join(tmp_path.name, \"schema_from_csv_list.parquet\")\n", | ||
| "dtype_frame.to_parquet(schema_file)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Let's look at the parquet file's metadata and see if it matches what we'd expect.\n", | ||
| "\n", | ||
| "You'll notice that that there are A LOT of fields, and this is why you might not want to deal with column-by-column type discrepancies." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "parquet_file = pq.ParquetFile(schema_file)\n", | ||
| "print(parquet_file.schema)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Explict list of pyarrow data types\n", | ||
| "\n", | ||
| "Here, we know what pyarrow types we want to use for each column. This is helpful if you know you want nullable, or you know you DON'T want to use nullable types, but it requires some deeper knowledge of pyarrow data types." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import pyarrow as pa\n", | ||
| "\n", | ||
| "## List all of our columns as pyarrow fields.\n", | ||
| "schema_from_pyarrow = pa.schema(\n", | ||
| " [\n", | ||
| " pa.field(\"id\", pa.int64()),\n", | ||
| " pa.field(\"ra\", pa.float64()),\n", | ||
| " pa.field(\"dec\", pa.float64()),\n", | ||
| " pa.field(\"ra_error\", pa.float64()),\n", | ||
| " pa.field(\"dec_error\", pa.float64()),\n", | ||
| " pa.field(\"comment\", pa.string()),\n", | ||
| " pa.field(\"code\", pa.string()),\n", | ||
| " ]\n", | ||
| ")\n", | ||
| "schema_file = os.path.join(tmp_path.name, \"schema_from_pyarrow.parquet\")\n", | ||
| "pq.write_metadata(schema_from_pyarrow, schema_file)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Again, we'll check that the generated parquet metadata is what we expect:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "parquet_file = pq.ParquetFile(schema_file)\n", | ||
| "print(parquet_file.schema)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Finally, let's clean up." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "client.shutdown()\n", | ||
| "tmp_path.cleanup()" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "hipscatenv", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.11.4" | ||
| }, | ||
| "orig_nbformat": 4 | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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