This Python program processes Apache web server log files using Spark (RDD API) and stores structured, uniquely identified records in Elasticsearch. Here’s a detailed breakdown of what it does:

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1. Import Libraries

• json: For encoding structured data as JSON.
• hashlib: For generating unique document IDs using the SHA-224 hash.
• re: For regular expressions to parse log lines.

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2. Functions

• addId(data)

  • Serializes a Python dictionary to JSON bytes.
  • Computes a SHA-224 hash as a unique identifier (doc_id) for the record.
  • Adds doc_id to the dictionary and returns a tuple: (doc_id, serialized_json).

• parse(str)

  • Uses a precompiled regex to extract fields from an Apache log line:
    • IP address
    • Access date
    • HTTP operation/method
    • Request URI
  • Returns a dictionary with these keys: ip, date, operation, uri.

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3. Log Pattern

• Defines a regex pattern matching the standard Apache combined log format.
• Compiles it with re.compile for use in parsing.

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4. Spark RDD Pipeline

• Reads logs: sc.textFile("/home/ubuntu/walker/apache_logs") loads the raw log file into an RDD, one string per line.
• Parses logs: rdd.map(parse) maps each line to a parsed dictionary with the required fields.
• Adds IDs: rdd2.map(addID) generates a unique hash-based document ID for each record and serializes it as JSON.

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5. Elasticsearch Configuration

• Sets up configuration for the Elasticsearch Hadoop connector, specifying:
  • Elasticsearch host and port
  • Index/resource to write to
  • JSON input and mapping id

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6. Saving to Elasticsearch

• Calls saveAsNewAPIHadoopFile() with special output classes and the configuration to store the transformed records in Elasticsearch, using the generated doc_id as the index key.

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Summary

• Purpose: Efficiently transform and index large-scale Apache log data into Elasticsearch for querying and analysis.

• Process:

  1. Parse each log line into a fielded dictionary,
  2. Generate a strong, unique document identifier,
  3. Serialize each record to JSON,
  4. Bulk load these records into Elasticsearch using Spark RDD’s Hadoop API.

• Use Case: Enables scalable indexing and searching of log data for analytics, monitoring, or troubleshooting in distributed systems.

Note: The code assumes execution within a PySpark environment and uses the elasticsearch-hadoop connector for Elasticsearch integration.