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fjetter/example_parquet_dask.ipynb

Created October 12, 2021 17:34
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example_parquet_dask.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 32,
"id": "e57d7139-a175-4671-a294-c70cf7353b97",
"metadata": {},
"outputs": [],
"source": [
"from dask.datasets import timeseries\n",
"\n",
"ddf_jan = timeseries(\n",
" start='2000-01-01',\n",
" end='2000-02-01',\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "6a4295b1-fd87-4202-88fa-772ea711f762",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>name</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" </tr>\n",
" <tr>\n",
" <th>timestamp</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01 00:00:00</th>\n",
" <td>1063</td>\n",
" <td>Hannah</td>\n",
" <td>0.276926</td>\n",
" <td>0.058821</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-01 00:00:01</th>\n",
" <td>1012</td>\n",
" <td>Yvonne</td>\n",
" <td>0.472681</td>\n",
" <td>-0.218207</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-01 00:00:02</th>\n",
" <td>971</td>\n",
" <td>Zelda</td>\n",
" <td>-0.364523</td>\n",
" <td>-0.519655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-01 00:00:03</th>\n",
" <td>1023</td>\n",
" <td>Dan</td>\n",
" <td>-0.282011</td>\n",
" <td>0.794970</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-01 00:00:04</th>\n",
" <td>1030</td>\n",
" <td>Quinn</td>\n",
" <td>0.215620</td>\n",
" <td>0.043371</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id name x y\n",
"timestamp \n",
"2000-01-01 00:00:00 1063 Hannah 0.276926 0.058821\n",
"2000-01-01 00:00:01 1012 Yvonne 0.472681 -0.218207\n",
"2000-01-01 00:00:02 971 Zelda -0.364523 -0.519655\n",
"2000-01-01 00:00:03 1023 Dan -0.282011 0.794970\n",
"2000-01-01 00:00:04 1030 Quinn 0.215620 0.043371"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ddf_jan.head()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "5bf42ecd-da81-4220-a501-2ac7c2a63a2c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div><strong>Dask DataFrame Structure:</strong></div>\n",
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>name</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" </tr>\n",
" <tr>\n",
" <th>npartitions=31</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>int64</td>\n",
" <td>object</td>\n",
" <td>float64</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-31</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-02-01</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
"<div>Dask Name: make-timeseries, 31 tasks</div>"
],
"text/plain": [
"Dask DataFrame Structure:\n",
" id name x y\n",
"npartitions=31 \n",
"2000-01-01 int64 object float64 float64\n",
"2000-01-02 ... ... ... ...\n",
"... ... ... ... ...\n",
"2000-01-31 ... ... ... ...\n",
"2000-02-01 ... ... ... ...\n",
"Dask Name: make-timeseries, 31 tasks"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ddf_jan"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "155251e1-e39a-4da7-8461-93482996a058",
"metadata": {},
"outputs": [],
"source": [
"ddf_jan.to_parquet(\n",
" path=\"my_first_dataset\", # File path\n",
" engine=\"pyarrow\", # parquet implementation\n",
" \n",
" # some file format specific stuff\n",
" compression={\"name\": \"snappy\"},\n",
" \n",
" # Format specific\n",
" partition_on=[\"name\"],\n",
" \n",
" # Compute engine specifics (i.e. stuff for basic dask)\n",
" compute=True,\n",
" \n",
" # storage backend stuff (S3, GCS, ...)\n",
" storage_options={},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "9906910a-840e-4d03-aa3a-c9f0e014c936",
"metadata": {},
"outputs": [],
"source": [
"import dask.dataframe as dd"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "859aad0d-5567-4ea8-adf2-95d8413274c3",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>name</th>\n",
" </tr>\n",
" <tr>\n",
" <th>timestamp</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2000-01-01 00:00:07</th>\n",
" <td>1010</td>\n",
" <td>0.284375</td>\n",
" <td>-0.963815</td>\n",
" <td>Alice</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-01 00:00:13</th>\n",
" <td>1034</td>\n",
" <td>0.551905</td>\n",
" <td>0.383799</td>\n",
" <td>Alice</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-01 00:00:15</th>\n",
" <td>1025</td>\n",
" <td>-0.611963</td>\n",
" <td>0.314208</td>\n",
" <td>Alice</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-01 00:00:33</th>\n",
" <td>1042</td>\n",
" <td>0.075010</td>\n",
" <td>-0.713647</td>\n",
" <td>Alice</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-01 00:00:40</th>\n",
" <td>952</td>\n",
" <td>0.079303</td>\n",
" <td>0.389080</td>\n",
" <td>Alice</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id x y name\n",
"timestamp \n",
"2000-01-01 00:00:07 1010 0.284375 -0.963815 Alice\n",
"2000-01-01 00:00:13 1034 0.551905 0.383799 Alice\n",
"2000-01-01 00:00:15 1025 -0.611963 0.314208 Alice\n",
"2000-01-01 00:00:33 1042 0.075010 -0.713647 Alice\n",
"2000-01-01 00:00:40 952 0.079303 0.389080 Alice"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# minimal example\n",
"ddf = dd.read_parquet(\n",
" path=\"my_first_dataset\",\n",
")\n",
"ddf.head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "ab84529c-1e49-45d4-b86d-1153aadae59a",
"metadata": {},
"outputs": [],
"source": [
"ddf = dd.read_parquet(\n",
" path=\"my_first_dataset\", # File path\n",
" columns=[\"name\", \"x\"],\n",
" # Fitlers have to be in disjunctive normal form\n",
" filters=[\n",
" [\n",
" (\"x\", \">\", 0.0), # OR\n",
" (\"y\", \"<\", 1.0),\n",
" ],\n",
" # AND\n",
" [\n",
" (\"x\", \">\", 5.0),\n",
" (\"name\", \"==\", \"Ingrid\"),\n",
" ]\n",
" ],\n",
" \n",
" # Hints for sizes of splits/tasks. Task per RG, per file, multiple files?\n",
" split_row_groups=None,\n",
" chunksize=None,\n",
" aggregate_files=None,\n",
" \n",
" # Other hints for query planner. Ugly implementation specifics\n",
" gather_statistics=None,\n",
" ignore_metadata_file=False,\n",
"\n",
"\n",
" # How to materialize. Impacts the output data dtype (e.g. dictionary encoding vs plain, etc.)\n",
" categories=None,\n",
" index=None,\n",
" \n",
" # storage backend stuff (S3, GCS, ...)\n",
" storage_options={},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "561e8fee-93dc-48ec-aff3-13b3e312897b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div><strong>Dask DataFrame Structure:</strong></div>\n",
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>x</th>\n",
" </tr>\n",
" <tr>\n",
" <th>npartitions=806</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th></th>\n",
" <td>category[known]</td>\n",
" <td>float64</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
"<div>Dask Name: read-parquet, 806 tasks</div>"
],
"text/plain": [
"Dask DataFrame Structure:\n",
" name x\n",
"npartitions=806 \n",
" category[known] float64\n",
" ... ...\n",
"... ... ...\n",
" ... ...\n",
" ... ...\n",
"Dask Name: read-parquet, 806 tasks"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ddf"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "4b4d1947-8595-47bd-a1c3-4db5d6cea993",
"metadata": {},
"outputs": [],
"source": [
"timeseries(\n",
" start='2000-02-01',\n",
" end='2000-03-01',\n",
").to_parquet(\n",
" path=\"my_first_dataset\", # File path\n",
" engine=\"pyarrow\", # parquet implementation\n",
" # Format specific\n",
" partition_on=[\"name\"],\n",
" append=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "591456d4-4c42-45a8-a099-307250434d8b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5184000"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(\n",
" dd.read_parquet(\n",
" path=\"my_first_dataset\",\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "3cf6fa24-aca8-4198-9e16-2e67339ec10e",
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Appended columns not the same.\nPrevious: ['id', 'x', 'y', 'timestamp'] | New: ['timestamp', 'name', 'x']",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/var/folders/h0/kd1gdptx7gzb6cbywplfrx1r0000gn/T/ipykernel_12127/535303701.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m timeseries(\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'2000-02-01'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'2000-03-01'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"name\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"x\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_parquet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"my_first_dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# File path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/workspace/dask/dask/dataframe/core.py\u001b[0m in \u001b[0;36mto_parquet\u001b[0;34m(self, path, *args, **kwargs)\u001b[0m\n\u001b[1;32m 4558\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mto_parquet\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4560\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mto_parquet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4561\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4562\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mto_orc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/workspace/dask/dask/dataframe/io/parquet/core.py\u001b[0m in \u001b[0;36mto_parquet\u001b[0;34m(df, path, engine, compression, write_index, append, overwrite, ignore_divisions, partition_on, storage_options, custom_metadata, write_metadata_file, compute, compute_kwargs, schema, **kwargs)\u001b[0m\n\u001b[1;32m 642\u001b[0m \u001b[0;31m# Engine-specific initialization steps to write the dataset.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 643\u001b[0m \u001b[0;31m# Possibly create parquet metadata, and load existing stuff if appending\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 644\u001b[0;31m meta, schema, i_offset = engine.initialize_write(\n\u001b[0m\u001b[1;32m 645\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 646\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/workspace/dask/dask/dataframe/io/parquet/arrow.py\u001b[0m in \u001b[0;36minitialize_write\u001b[0;34m(cls, df, fs, path, append, partition_on, ignore_divisions, division_info, schema, index_cols, **kwargs)\u001b[0m\n\u001b[1;32m 847\u001b[0m \u001b[0mdtypes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_pyarrow_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrow_schema\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcategories\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 848\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpartition_on\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 849\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 850\u001b[0m \u001b[0;34m\"Appended columns not the same.\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 851\u001b[0m \u001b[0;34m\"Previous: {} | New: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Appended columns not the same.\nPrevious: ['id', 'x', 'y', 'timestamp'] | New: ['timestamp', 'name', 'x']"
]
}
],
"source": [
"# Value error due to schema errors\n",
"timeseries(\n",
" start='2000-02-01',\n",
" end='2000-03-01',\n",
")[[\"name\", \"x\"]].to_parquet(\n",
" path=\"my_first_dataset\", # File path\n",
" engine=\"pyarrow\", # parquet implementation\n",
" # Format specific\n",
" partition_on=[\"name\"],\n",
" append=True,\n",
")"
]
}
],
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"kernelspec": {
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"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.12"
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"nbformat": 4,
"nbformat_minor": 5
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