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Investigate differences between Parquet files generated by PySpark & … #6

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1 change: 1 addition & 0 deletions .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -135,3 +135,4 @@ dmypy.json
*.parquet

data/
tmp/
246 changes: 246 additions & 0 deletions notebooks/pyarrow-vs-pyspark-parquet-metadata.ipynb
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Original file line number Diff line number Diff line change
@@ -0,0 +1,246 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "8dcbcfc2-79be-4e36-9633-f16b384bbb84",
"metadata": {},
"outputs": [],
"source": [
"import pyarrow.parquet as pq"
]
},
{
"cell_type": "markdown",
"id": "678fec87-1c2c-4ee9-8862-3148adff1a1c",
"metadata": {},
"source": [
"## Dask Parquet file"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "429c1c5e-15fd-4eb7-b4a1-5b3199dc580b",
"metadata": {},
"outputs": [],
"source": [
"parquet_file = pq.ParquetFile('../tmp/mrpowers-h2o/groupby-1e7/parquet-dask/part.0.parquet')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "257f078c-4fec-4b70-807a-59e3a4dfa1ac",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<pyarrow._parquet.FileMetaData object at 0x16b19b270>\n",
" created_by: parquet-cpp-arrow version 6.0.1\n",
" num_columns: 10\n",
" num_rows: 20000000\n",
" num_row_groups: 1\n",
" format_version: 1.0\n",
" serialized_size: 5299"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parquet_file.metadata"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "680cd102-991b-4ae9-bc5d-d44d876e3db7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<pyarrow._parquet.RowGroupMetaData object at 0x16b1ed450>\n",
" num_columns: 10\n",
" num_rows: 20000000\n",
" total_byte_size: 728526800"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parquet_file.metadata.row_group(0)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "c8b636f9-9818-4f09-8bda-de8ad8f1b22e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<pyarrow._parquet.ParquetSchema object at 0x16b1fa900>\n",
"required group field_id=-1 schema {\n",
" optional binary field_id=-1 id1 (String);\n",
" optional binary field_id=-1 id2 (String);\n",
" optional binary field_id=-1 id3 (String);\n",
" optional int64 field_id=-1 id4;\n",
" optional int64 field_id=-1 id5;\n",
" optional int64 field_id=-1 id6;\n",
" optional int64 field_id=-1 v1;\n",
" optional int64 field_id=-1 v2;\n",
" optional double field_id=-1 v3;\n",
" optional int64 field_id=-1 __null_dask_index__;\n",
"}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parquet_file.schema"
]
},
{
"cell_type": "markdown",
"id": "06f7cb7d-678f-499f-aa2e-0771dd8780d7",
"metadata": {},
"source": [
"## PySpark Parquet file"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "cdda39b1-5b55-44fc-9ea1-58b32b9294a7",
"metadata": {},
"outputs": [],
"source": [
"parquet_file = pq.ParquetFile('../tmp/mrpowers-h2o/groupby-1e7/parquet-pyspark/part-00000-c0fc14dc-8165-4db1-941e-9df62834f14c-c000.snappy.parquet')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "e0b9146d-76be-4f53-b47e-9c326aaa9635",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<pyarrow._parquet.FileMetaData object at 0x10ca928b0>\n",
" created_by: parquet-mr version 1.12.1 (build 2a5c06c58fa987f85aa22170be14d927d5ff6e7d)\n",
" num_columns: 9\n",
" num_rows: 20000000\n",
" num_row_groups: 4\n",
" format_version: 1.0\n",
" serialized_size: 4926"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parquet_file.metadata"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "b5ad7225-cd00-4562-84ea-699b134eebb9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<pyarrow._parquet.RowGroupMetaData object at 0x10c9dd220>\n",
" num_columns: 9\n",
" num_rows: 6610100\n",
" total_byte_size: 196985924"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parquet_file.metadata.row_group(0)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "7058fc96-e78b-4149-919b-c8043f72ea71",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<pyarrow._parquet.ParquetSchema object at 0x16b1bd200>\n",
"required group field_id=-1 spark_schema {\n",
" optional binary field_id=-1 id1 (String);\n",
" optional binary field_id=-1 id2 (String);\n",
" optional binary field_id=-1 id3 (String);\n",
" optional int32 field_id=-1 id4;\n",
" optional int32 field_id=-1 id5;\n",
" optional int32 field_id=-1 id6;\n",
" optional int32 field_id=-1 v1;\n",
" optional int32 field_id=-1 v2;\n",
" optional double field_id=-1 v3;\n",
"}"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parquet_file.schema"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "48c14e79-f6fe-418a-a0e6-347b25711ac2",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:mr-pyspark] *",
"language": "python",
"name": "conda-env-mr-pyspark-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
10 changes: 9 additions & 1 deletion scripts/create_groupby_data_parquet.py
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@@ -1,7 +1,15 @@
import dask.dataframe as dd
import sys

dataset = sys.argv[1]
# dataset = sys.argv[1]

ddf = dd.read_csv("./data/mrpowers-h2o/groupby-1e8/csv/*.csv")
ddf.repartition(5).to_parquet(
"./tmp/mrpowers-h2o/groupby-1e7/parquet-dask",
engine="pyarrow",
compression="snappy",
)


if dataset == "1e7":
# 1e7
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7 changes: 6 additions & 1 deletion scripts/create_groupby_data_parquet_pyspark.py
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@@ -1,11 +1,16 @@
import sys

dataset = sys.argv[1]
# dataset = sys.argv[1]

from pyspark.sql import SparkSession

spark = SparkSession.builder.master("local").appName("mrpowers").getOrCreate()

df = spark.read.csv("./data/mrpowers-h2o/groupby-1e8/csv", header='true', inferSchema=True)
df.repartition(5).write.parquet(
"./tmp/mrpowers-h2o/groupby-1e7/parquet-pyspark"
)

if dataset == "1e7":
df = spark.read.csv("./data/mrpowers-h2o/groupby-1e7/csv", header='true', inferSchema=True)
df.repartition(26).write.parquet(
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