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Add simple Spark notebook and environment
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"id": "f8eafed2-77a1-4691-8e8b-aeb1187ce8f5", | ||
"metadata": { | ||
"tags": [] | ||
}, | ||
"source": [ | ||
"\n", | ||
"Spark on Coiled\n", | ||
"===============\n", | ||
"\n", | ||
"<img src=\"https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRLsyvblPuU1h0NRGoZiODTKqIYbTpCu3hrHoM1rXzt1A&s\"\n", | ||
" align=\"right\"\n", | ||
" width=\"40%\"/>\n", | ||
"\n", | ||
"Coiled can run Spark Jobs.\n", | ||
"\n", | ||
"You get all the same Coiled ease of use features:\n", | ||
"\n", | ||
"1. Quick startup\n", | ||
"2. Copies all of your local packages and code\n", | ||
"3. Runs in any region on any hardware\n", | ||
"4. Runs from your local notebook\n", | ||
"\n", | ||
"But now rather than just Dask you can run Spark too." | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"id": "0d130128-ac72-4ce6-87b1-b7a20337fd2a", | ||
"metadata": {}, | ||
"source": [ | ||
"### Read a little bit of data with pandas" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "09728a96-0c84-4198-ab52-4dcdfd704606", | ||
"metadata": { | ||
"tags": [] | ||
}, | ||
"outputs": [], | ||
"source": [ | ||
"import pandas as pd\n", | ||
"\n", | ||
"df = pd.read_parquet(\n", | ||
" \"s3://coiled-data/uber/part.0.parquet\",\n", | ||
")\n", | ||
"df" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"id": "3148ad8d-3de7-47b6-91a3-1d1f5a393f64", | ||
"metadata": {}, | ||
"source": [ | ||
"## Start Spark cluster to read lots of data" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "9a9e6076-c8b3-4282-90a4-0fe3ab49440d", | ||
"metadata": { | ||
"tags": [] | ||
}, | ||
"outputs": [], | ||
"source": [ | ||
"import coiled\n", | ||
"\n", | ||
"cluster = coiled.Cluster(\n", | ||
" n_workers=10,\n", | ||
" worker_memory=\"16 GiB\",\n", | ||
" region=\"us-east-2\",\n", | ||
")\n", | ||
"\n", | ||
"spark = cluster.get_spark()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "33b598a4-fe0a-43c5-8007-0e955ac193f9", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"df = spark.read.parquet(\"s3a://coiled-data/uber\")\n", | ||
"df.show()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "56e2b982-af1b-4140-8cc1-414343ba1f0a", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"df.count()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "829de2bc-ed09-4e10-b06f-268aa79ead59", | ||
"metadata": { | ||
"tags": [] | ||
}, | ||
"outputs": [], | ||
"source": [ | ||
"import dask\n", | ||
"import dask.dataframe as dd\n", | ||
"dask.config.set({\"dataframe.convert-string\": True}) # use PyArrow strings by default\n", | ||
"\n", | ||
"while True:\n", | ||
" client.restart()\n", | ||
"\n", | ||
" df = dd.read_parquet(\n", | ||
" \"s3://coiled-datasets/uber-lyft-tlc/\",\n", | ||
" storage_options={\"anon\": True},\n", | ||
" ).persist()\n", | ||
"\n", | ||
" for _ in range(10):\n", | ||
" df[\"tipped\"] = df.tips != 0\n", | ||
"\n", | ||
" df.groupby(\"hvfhs_license_num\").tipped.mean().compute()" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "f4b6b9f9-7ef3-4ca0-b769-0dd7e4ce6b0b", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python [conda env:spark]", | ||
"language": "python", | ||
"name": "conda-env-spark-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": "3.11.9" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 5 | ||
} |
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name: spark | ||
channels: | ||
- conda-forge | ||
dependencies: | ||
- python=3.11 | ||
- dask | ||
- coiled | ||
- ipykernel | ||
- pyspark==3.4.1 | ||
- pyarrow | ||
- grpcio | ||
- grpcio-status | ||
- openjdk~=11.0 | ||
- protobuf | ||
- jupyterlab | ||
- s3fs |