Add EMR deploy scripts

.gitignore

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+.idea
+*~

bootstrap.sh

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+#!/bin/bash -xe
+
+sudo python3 -m pip uninstall numpy -y
+sudo python3 -m pip uninstall numpy -y
+sudo python3 -m pip uninstall numpy -y
+
+sudo python3 -m pip install boto3==1.19.7 botocore==1.22.7 jusText==3.0.0 langdetect==1.0.9 \
+    lxml==4.6.3 numpy==1.21.3 pandas==1.2.5 pyarrow==6.0.0 spacy==2.3.5 \
+    warcio==1.7.4 zstandard==0.16.0
+
+sudo python3 -m spacy download en_core_web_sm
+
+echo "========================"
+echo "Normal python pip freeze"
+python3 -m pip freeze

deploy.sh

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+cat hn-top-domains-filtered.py extract.py > runextract.py
+
+aws s3 cp runextract.py s3://tinysearch/code/
+aws s3 cp bootstrap.sh s3://tinysearch/code/
+
+
+aws emr create-cluster \
+    --applications Name=Spark Name=Zeppelin \
+    --ec2-attributes '{"InstanceProfile":"EMR_EC2_DefaultRole","SubnetId":"subnet-03c33360c68f73a48"}' \
+    --service-role EMR_DefaultRole \
+    --enable-debugging \
+    --release-label emr-5.33.1 \
+    --log-uri 's3n://tinysearch/pyspark-logs/' \
+    --bootstrap-actions '{"Path": "s3://tinysearch/code/bootstrap.sh"}' \
+    --steps '[{"Args":["spark-submit","--deploy-mode","cluster","s3n://tinysearch/code/runextract.py"],"Type":"CUSTOM_JAR","ActionOnFailure":"CONTINUE","Jar":"command-runner.jar","Properties":"","Name":"Spark application"}]' \
+    --name 'TinySearch' \
+    --instance-groups '[{"InstanceCount":5,"EbsConfiguration":{"EbsBlockDeviceConfigs":[{"VolumeSpecification":{"SizeInGB":32,"VolumeType":"gp2"},"VolumesPerInstance":1}]},"InstanceGroupType":"CORE","InstanceType":"m4.large","Name":"Core Instance Group"},{"InstanceCount":1,"EbsConfiguration":{"EbsBlockDeviceConfigs":[{"VolumeSpecification":{"SizeInGB":32,"VolumeType":"gp2"},"VolumesPerInstance":1}]},"InstanceGroupType":"MASTER","InstanceType":"m4.large","Name":"Master Instance Group"}]' \
+    --configurations '[{"Classification":"spark","Properties":{}}]' \
+    --scale-down-behavior TERMINATE_AT_TASK_COMPLETION --region us-east-1 \
+    --auto-terminate

domains.py

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+"""
+Extract top domains from BigQuery result.
+"""
+import json
+import os
+import sys
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+DATA_DIR = Path(os.environ['HOME']) / 'data' / 'tinysearch'
+ALL_DOMAINS_PATH = DATA_DIR / 'hn-top-domains.csv'
+TOP_DOMAINS_PATH = 'hn-top-domains-filtered.py'
+
+MIN_COUNT = 10
+PROBABILITY_THRESHOLD = 0.8
+
+
+def get_top_domains():
+    data = pd.read_csv(ALL_DOMAINS_PATH, index_col='domain')
+    data = data[data.index.notnull()]
+
+    frequent = data[data['total'] >= MIN_COUNT]
+    scores = frequent['mean_score'] * np.log(frequent['total']) ** 2
+    median_score = np.median(scores)
+    print("Median score", median_score)
+    probabilities = scores / (scores + median_score)
+
+    top_probabilities = probabilities[probabilities > PROBABILITY_THRESHOLD]
+    top_probabilities.sort_values(ascending=False, inplace=True)
+    with open(TOP_DOMAINS_PATH, 'w') as output_file:
+        probabilities_str = str(top_probabilities.to_dict()).replace(', ', ',\n')
+        output_file.write("DOMAINS = " + probabilities_str + '\n\n')
+        # json.dump(probabilities.to_dict(), output_file, indent=2)
+
+        # for row in probabilities.iterrows():
+        #     output_file.write(json.dumps(row.to_dict()) + '\n')
+
+
+if __name__ == '__main__':
+    get_top_domains()