Weitere AWS SDK-Beispiele sind im GitHub Repo [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) verfügbar.

Die vorliegende Übersetzung wurde maschinell erstellt. Im Falle eines Konflikts oder eines Widerspruchs zwischen dieser übersetzten Fassung und der englischen Fassung (einschließlich infolge von Verzögerungen bei der Übersetzung) ist die englische Fassung maßgeblich.

# Erste Schritte mit Amazon EMR
<a name="ec2_example_emr_GettingStarted_037_section"></a>

Wie das aussehen kann, sehen Sie am nachfolgenden Beispielcode:
+ Erstellen Sie ein EC2-Schlüsselpaar
+ Richten Sie den Speicher ein und bereiten Sie Ihre Anwendung vor
+ Bereinigen von Ressourcen

------
#### [ Bash ]

**AWS CLI mit Bash-Skript**  
 Es gibt noch mehr dazu. GitHub Hier finden Sie das vollständige Beispiel und erfahren, wie Sie es im [Tutorials und Beispiele](https://github.com/aws-samples/sample-developer-tutorials/tree/main/tuts/037-emr-gs)-Repository für Entwickler einrichten und ausführen. 

```
#!/bin/bash

# EMR Getting Started Tutorial Script
# This script automates the steps in the Amazon EMR Getting Started tutorial

set -euo pipefail

# Security: Set strict mode and trap errors
trap 'handle_error "Script interrupted or command failed"' ERR

# Set up logging with secure permissions
LOG_FILE="emr-tutorial.log"
touch "$LOG_FILE"
chmod 600 "$LOG_FILE"
exec > >(tee -a "$LOG_FILE") 2>&1

echo "Starting Amazon EMR Getting Started Tutorial Script"
echo "Logging to $LOG_FILE"

# Function to handle errors
handle_error() {
    echo "ERROR: $1"
    echo "Resources created so far:"
    if [ -n "${BUCKET_NAME:-}" ]; then echo "- S3 Bucket: $BUCKET_NAME"; fi
    if [ -n "${CLUSTER_ID:-}" ]; then echo "- EMR Cluster: $CLUSTER_ID"; fi
    
    echo "Attempting to clean up resources..."
    cleanup
    exit 1
}

# Function to clean up resources
cleanup() {
    echo ""
    echo "==========================================="
    echo "CLEANUP IN PROGRESS"
    echo "==========================================="
    echo "Starting cleanup process..."
    
    # Terminate EMR cluster if it exists
    if [ -n "${CLUSTER_ID:-}" ]; then
        echo "Terminating EMR cluster: $CLUSTER_ID"
        aws emr terminate-clusters --cluster-ids "$CLUSTER_ID" 2>/dev/null || true
        
        echo "Waiting for cluster to terminate..."
        aws emr wait cluster-terminated --cluster-id "$CLUSTER_ID" 2>/dev/null || true
        echo "Cluster terminated successfully."
    fi
    
    # Delete S3 bucket and contents if it exists and is not shared
    if [ -n "${BUCKET_NAME:-}" ] && [ "${BUCKET_IS_SHARED:-false}" != "true" ]; then
        echo "Deleting S3 bucket contents: $BUCKET_NAME"
        aws s3 rm "s3://$BUCKET_NAME" --recursive 2>/dev/null || true
        
        echo "Deleting S3 bucket: $BUCKET_NAME"
        aws s3 rb "s3://$BUCKET_NAME" 2>/dev/null || true
    fi
    
    # Remove temporary key pair file if created by this script
    if [ -f "${KEY_NAME_FILE:-}" ]; then
        rm -f "$KEY_NAME_FILE"
        echo "Removed temporary key pair file."
    fi
    
    echo "Cleanup completed."
}

# Validate AWS CLI is installed and configured
if ! command -v aws &> /dev/null; then
    handle_error "AWS CLI is not installed"
fi

# Test AWS credentials
if ! aws sts get-caller-identity > /dev/null 2>&1; then
    handle_error "AWS credentials are not configured or invalid"
fi

# Generate a random identifier for S3 bucket
RANDOM_ID=$(openssl rand -hex 6)

# Check for shared prereq bucket
PREREQ_BUCKET=$(aws cloudformation describe-stacks --stack-name tutorial-prereqs-bucket \
    --query 'Stacks[0].Outputs[?OutputKey==`BucketName`].OutputValue' --output text 2>/dev/null || true)

if [ -n "$PREREQ_BUCKET" ] && [ "$PREREQ_BUCKET" != "None" ]; then
    BUCKET_NAME="$PREREQ_BUCKET"
    BUCKET_IS_SHARED=true
    echo "Using shared bucket: $BUCKET_NAME"
else
    BUCKET_IS_SHARED=false
    BUCKET_NAME="emr-${RANDOM_ID}"
fi
echo "Using bucket name: $BUCKET_NAME"

# Create S3 bucket with security best practices
echo "Creating S3 bucket: $BUCKET_NAME"
aws s3 mb "s3://$BUCKET_NAME" --region "${AWS_REGION:-us-east-1}" || handle_error "Failed to create S3 bucket"

# Tag the bucket
aws s3api put-bucket-tagging --bucket "$BUCKET_NAME" \
    --tagging 'TagSet=[{Key=project,Value=doc-smith},{Key=tutorial,Value=emr-gs}]'

# Enable bucket versioning for safety
aws s3api put-bucket-versioning --bucket "$BUCKET_NAME" --versioning-configuration Status=Enabled || true

# Block public access to bucket
aws s3api put-public-access-block --bucket "$BUCKET_NAME" \
    --public-access-block-configuration \
    "BlockPublicAcls=true,IgnorePublicAcls=true,BlockPublicPolicy=true,RestrictPublicBuckets=true" || true

# Enable encryption on bucket
aws s3api put-bucket-encryption --bucket "$BUCKET_NAME" \
    --server-side-encryption-configuration '{
        "Rules": [{
            "ApplyServerSideEncryptionByDefault": {
                "SSEAlgorithm": "AES256"
            }
        }]
    }' || true

echo "S3 bucket created successfully with security best practices."

# Create PySpark script
echo "Creating PySpark script: health_violations.py"
cat > health_violations.py << 'EOL'
import argparse

from pyspark.sql import SparkSession

def calculate_red_violations(data_source, output_uri):
    """
    Processes sample food establishment inspection data and queries the data to find the top 10 establishments
    with the most Red violations from 2006 to 2020.

    :param data_source: The URI of your food establishment data CSV, such as 's3://emr-tutorial-bucket/food-establishment-data.csv'.
    :param output_uri: The URI where output is written, such as 's3://emr-tutorial-bucket/restaurant_violation_results'.
    """
    with SparkSession.builder.appName("Calculate Red Health Violations").getOrCreate() as spark:
        # Load the restaurant violation CSV data
        if data_source is not None:
            restaurants_df = spark.read.option("header", "true").csv(data_source)

        # Create an in-memory DataFrame to query
        restaurants_df.createOrReplaceTempView("restaurant_violations")

        # Create a DataFrame of the top 10 restaurants with the most Red violations
        top_red_violation_restaurants = spark.sql("""SELECT name, count(*) AS total_red_violations 
          FROM restaurant_violations 
          WHERE violation_type = 'RED' 
          GROUP BY name 
          ORDER BY total_red_violations DESC LIMIT 10""")

        # Write the results to the specified output URI
        top_red_violation_restaurants.write.option("header", "true").mode("overwrite").csv(output_uri)

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--data_source', help="The URI for you CSV restaurant data, like an S3 bucket location.")
    parser.add_argument(
        '--output_uri', help="The URI where output is saved, like an S3 bucket location.")
    args = parser.parse_args()

    calculate_red_violations(args.data_source, args.output_uri)
EOL

# Secure the script file
chmod 600 health_violations.py

# Upload PySpark script to S3
echo "Uploading PySpark script to S3"
aws s3 cp health_violations.py "s3://$BUCKET_NAME/" --sse AES256 || handle_error "Failed to upload PySpark script"
echo "PySpark script uploaded successfully."

# Download and prepare sample data
echo "Downloading sample data"
curl -sS -o food_establishment_data.zip "https://docs.aws.amazon.com/emr/latest/ManagementGuide/samples/food_establishment_data.zip" || handle_error "Failed to download sample data"

# Verify downloaded file
if [ ! -f food_establishment_data.zip ] || [ ! -s food_establishment_data.zip ]; then
    handle_error "Downloaded file is empty or missing"
fi

unzip -o food_establishment_data.zip || handle_error "Failed to unzip sample data"
echo "Sample data downloaded and extracted successfully."

# Secure the sample data file
chmod 600 food_establishment_data.csv

# Upload sample data to S3
echo "Uploading sample data to S3"
aws s3 cp food_establishment_data.csv "s3://$BUCKET_NAME/" --sse AES256 || handle_error "Failed to upload sample data"
echo "Sample data uploaded successfully."

# Clean up sensitive local files
rm -f food_establishment_data.zip health_violations.py

# Create IAM default roles for EMR
echo "Creating IAM default roles for EMR"
aws emr create-default-roles 2>/dev/null || true
echo "IAM default roles created successfully."

# Check if EC2 key pair exists
echo "Checking for EC2 key pair"
KEY_PAIRS=$(aws ec2 describe-key-pairs --query "KeyPairs[*].KeyName" --output text 2>/dev/null || true)

if [ -z "$KEY_PAIRS" ]; then
    echo "No EC2 key pairs found. Creating a new key pair..."
    KEY_NAME="emr-tutorial-key-${RANDOM_ID}"
    KEY_NAME_FILE="${KEY_NAME}.pem"
    aws ec2 create-key-pair --key-name "$KEY_NAME" \
        --tag-specifications 'ResourceType=key-pair,Tags=[{Key=project,Value=doc-smith},{Key=tutorial,Value=emr-gs}]' \
        --query "KeyMaterial" --output text > "$KEY_NAME_FILE"
    chmod 400 "$KEY_NAME_FILE"
    echo "Created new key pair: $KEY_NAME"
else
    # Use the first available key pair
    KEY_NAME=$(echo "$KEY_PAIRS" | awk '{print $1}')
    echo "Using existing key pair: $KEY_NAME"
fi

# Launch EMR cluster with security best practices
echo "Launching EMR cluster with Spark"
CLUSTER_RESPONSE=$(aws emr create-cluster \
  --name "EMR Tutorial Cluster" \
  --release-label emr-6.10.0 \
  --applications Name=Spark \
  --ec2-attributes KeyName="$KEY_NAME" \
  --instance-type m5.xlarge \
  --instance-count 3 \
  --use-default-roles \
  --log-uri "s3://$BUCKET_NAME/logs/" \
  --ebs-root-volume-size 100 \
  --tags Key=project,Value=doc-smith Key=tutorial,Value=emr-gs \
  --security-configuration "EMR-Tutorial-SecurityConfig" 2>/dev/null || true)

# Check for errors in the response
if echo "$CLUSTER_RESPONSE" | grep -i "error" > /dev/null; then
    handle_error "Failed to create EMR cluster: $CLUSTER_RESPONSE"
fi

# Extract cluster ID using jq if available, otherwise use alternative parsing
if command -v jq &> /dev/null; then
    CLUSTER_ID=$(echo "$CLUSTER_RESPONSE" | jq -r '.ClusterId // empty')
else
    CLUSTER_ID=$(echo "$CLUSTER_RESPONSE" | grep -o '"ClusterId"[[:space:]]*:[[:space:]]*"[^"]*' | grep -o 'j-[A-Z0-9]*' || true)
fi

if [ -z "$CLUSTER_ID" ] || [ "$CLUSTER_ID" == "null" ]; then
    handle_error "Failed to extract cluster ID from response: $CLUSTER_RESPONSE"
fi

echo "EMR cluster created with ID: $CLUSTER_ID"

# Wait for cluster to be ready
echo "Waiting for cluster to be ready (this may take several minutes)..."
aws emr wait cluster-running --cluster-id "$CLUSTER_ID" || handle_error "Cluster failed to reach running state"

# Check if cluster is in WAITING state
CLUSTER_STATE=$(aws emr describe-cluster --cluster-id "$CLUSTER_ID" --query "Cluster.Status.State" --output text)
if [ "$CLUSTER_STATE" != "WAITING" ]; then
    echo "Waiting for cluster to reach WAITING state..."
    WAIT_COUNT=0
    MAX_WAIT=120
    while [ "$CLUSTER_STATE" != "WAITING" ]; do
        if [ $WAIT_COUNT -ge $MAX_WAIT ]; then
            handle_error "Cluster did not reach WAITING state within timeout period"
        fi
        sleep 30
        CLUSTER_STATE=$(aws emr describe-cluster --cluster-id "$CLUSTER_ID" --query "Cluster.Status.State" --output text)
        echo "Current cluster state: $CLUSTER_STATE"
        
        # Check for error states
        if [[ "$CLUSTER_STATE" == "TERMINATED_WITH_ERRORS" || "$CLUSTER_STATE" == "TERMINATED" ]]; then
            handle_error "Cluster entered error state: $CLUSTER_STATE"
        fi
        WAIT_COUNT=$((WAIT_COUNT + 1))
    done
fi

echo "Cluster is now in WAITING state and ready to accept work."

# Submit Spark application as a step
echo "Submitting Spark application as a step"
STEP_RESPONSE=$(aws emr add-steps \
  --cluster-id "$CLUSTER_ID" \
  --steps Type=Spark,Name="Health Violations Analysis",ActionOnFailure=CONTINUE,Args=["s3://$BUCKET_NAME/health_violations.py","--data_source","s3://$BUCKET_NAME/food_establishment_data.csv","--output_uri","s3://$BUCKET_NAME/results/"])

# Check for errors in the response
if echo "$STEP_RESPONSE" | grep -i "error" > /dev/null; then
    handle_error "Failed to submit step: $STEP_RESPONSE"
fi

# Extract step ID using appropriate method
if command -v jq &> /dev/null; then
    STEP_ID=$(echo "$STEP_RESPONSE" | jq -r '.StepIds[0] // empty')
else
    STEP_ID=$(echo "$STEP_RESPONSE" | grep -o 's-[A-Z0-9]*' | head -1 || true)
fi

if [ -z "$STEP_ID" ] || [ "$STEP_ID" == "null" ]; then
    echo "Full step response: $STEP_RESPONSE"
    handle_error "Failed to extract valid step ID from response"
fi

echo "Step submitted with ID: $STEP_ID"

# Wait for step to complete with timeout
echo "Waiting for step to complete (this may take several minutes)..."
aws emr wait step-complete --cluster-id "$CLUSTER_ID" --step-id "$STEP_ID" || handle_error "Step failed to complete"

# Check step status
STEP_STATE=$(aws emr describe-step --cluster-id "$CLUSTER_ID" --step-id "$STEP_ID" --query "Step.Status.State" --output text)
if [ "$STEP_STATE" != "COMPLETED" ]; then
    handle_error "Step did not complete successfully. Final state: $STEP_STATE"
fi

echo "Step completed successfully."

# View results
echo "Listing output files in S3"
aws s3 ls "s3://$BUCKET_NAME/results/" || handle_error "Failed to list output files"

# Download results
echo "Downloading results file"
RESULT_FILE=$(aws s3 ls "s3://$BUCKET_NAME/results/" | grep -o "part-[0-9]*\.csv" | head -1 || true)
if [ -z "$RESULT_FILE" ]; then
    echo "No result file found with pattern 'part-[0-9]*.csv'. Trying to find any CSV file..."
    RESULT_FILE=$(aws s3 ls "s3://$BUCKET_NAME/results/" | grep -o "part-.*\.csv" | head -1 || true)
    if [ -z "$RESULT_FILE" ]; then
        echo "Listing all files in results directory:"
        aws s3 ls "s3://$BUCKET_NAME/results/"
        handle_error "No result file found in the output directory"
    fi
fi

aws s3 cp "s3://$BUCKET_NAME/results/$RESULT_FILE" ./results.csv --sse AES256 || handle_error "Failed to download results file"
chmod 600 ./results.csv

echo "Results downloaded to results.csv"
echo "Top 10 establishments with the most red violations:"
cat results.csv

# Display SSH connection information
echo ""
echo "To connect to the cluster via SSH, use the following command:"
echo "aws emr ssh --cluster-id $CLUSTER_ID --key-pair-file ${KEY_NAME_FILE:-./${KEY_NAME}.pem}"

# Display summary of created resources
echo ""
echo "==========================================="
echo "RESOURCES CREATED"
echo "==========================================="
echo "- S3 Bucket: $BUCKET_NAME"
echo "- EMR Cluster: $CLUSTER_ID"
echo "- Results file: results.csv"
if [ -f "${KEY_NAME_FILE:-}" ]; then
    echo "- EC2 Key Pair: $KEY_NAME (saved to ${KEY_NAME_FILE})"
fi

# Perform cleanup
cleanup

echo "Script completed successfully."
```
+ Weitere API-Informationen finden Sie in den folgenden Themen der *AWS CLI -Befehlsreferenz*.
  + [AddSteps](https://docs.aws.amazon.com/goto/aws-cli/elasticmapreduce-2009-03-31/AddSteps)
  + [Cp](https://docs.aws.amazon.com/goto/aws-cli/s3-2006-03-01/Cp)
  + [CreateCluster](https://docs.aws.amazon.com/goto/aws-cli/elasticmapreduce-2009-03-31/CreateCluster)
  + [CreateDefaultRoles](https://docs.aws.amazon.com/goto/aws-cli/elasticmapreduce-2009-03-31/CreateDefaultRoles)
  + [CreateKeyPair](https://docs.aws.amazon.com/goto/aws-cli/ec2-2016-11-15/CreateKeyPair)
  + [DescribeCluster](https://docs.aws.amazon.com/goto/aws-cli/elasticmapreduce-2009-03-31/DescribeCluster)
  + [DescribeKeyPairs](https://docs.aws.amazon.com/goto/aws-cli/ec2-2016-11-15/DescribeKeyPairs)
  + [DescribeStep](https://docs.aws.amazon.com/goto/aws-cli/elasticmapreduce-2009-03-31/DescribeStep)
  + [Ls](https://docs.aws.amazon.com/goto/aws-cli/s3-2006-03-01/Ls)
  + [Mb](https://docs.aws.amazon.com/goto/aws-cli/s3-2006-03-01/Mb)
  + [Rb](https://docs.aws.amazon.com/goto/aws-cli/s3-2006-03-01/Rb)
  + [Rm](https://docs.aws.amazon.com/goto/aws-cli/s3-2006-03-01/Rm)
  + [SSH](https://docs.aws.amazon.com/goto/aws-cli/elasticmapreduce-2009-03-31/Ssh)
  + [TerminateClusters](https://docs.aws.amazon.com/goto/aws-cli/elasticmapreduce-2009-03-31/TerminateClusters)
  + [Wait](https://docs.aws.amazon.com/goto/aws-cli/elasticmapreduce-2009-03-31/Wait)

------