About ShitOps

# ShitOps Enterprise-Grade Word Frequency Analysis Framework
# Scalable Distributed Lexical Processing System v4.5.2

import asyncio
import functools
import hashlib
import json
import logging
import multiprocessing as mp
import os
import re
import sys
import time
import uuid
from collections import defaultdict, namedtuple
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
from dataclasses import dataclass, field
from enum import Enum, auto
from typing import Dict, List, Optional, Set, Tuple, Union, Any, Callable

# Kafka integration
from confluent_kafka import Producer, Consumer, KafkaError, KafkaException
from confluent_kafka.admin import AdminClient, NewTopic
from confluent_kafka.schema_registry import SchemaRegistryClient
from confluent_kafka.schema_registry.avro import AvroSerializer, AvroDeserializer
from confluent_kafka.serialization import StringSerializer, StringDeserializer

# Configure enterprise logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s [%(levelname)s] %(name)s::%(funcName)s:%(lineno)d - %(message)s'
)
logger = logging.getLogger("lexical-analyzer")

# Define domain models
class AnalysisStrategy(Enum):
    DISTRIBUTED_BLOCKCHAIN = auto()
    QUANTUM_APPROXIMATION = auto()
    NEURAL_EMBEDDING = auto()
    KAFKA_STREAMING = auto()

@dataclass
class TokenizationConfig:
    chunk_size: int = 100
    parallelism_factor: int = min(8, mp.cpu_count())
    persistence_path: str = field(default_factory=lambda: f"/tmp/lexical-{uuid.uuid4()}.json")
    checksum_algorithm: str = "sha256"
    retry_attempts: int = 3
    backoff_factor: float = 1.5

    # Kafka configuration
    kafka_bootstrap_servers: str = "kafka:9092"
    kafka_schema_registry_url: str = "http://schema-registry:8081"
    kafka_consumer_group: str = field(default_factory=lambda: f"word-counter-{uuid.uuid4()}")
    kafka_topic_prefix: str = "word-analysis"
    kafka_partitions: int = 12
    kafka_replication_factor: int = 3
    kafka_retention_ms: int = 604800000  # 7 days
    kafka_compaction_policy: bool = True

class LexicalBlock:
    def __init__(self, data: str, previous_hash: str = "0"):
        self.timestamp = time.time()
        self.data = data
        self.previous_hash = previous_hash
        self.nonce = 0
        self.hash = self._calculate_hash()

    def _calculate_hash(self) -> str:
        while True:
            hash_digest = hashlib.sha256(
                f"{self.timestamp}{self.data}{self.previous_hash}{self.nonce}".encode()
            ).hexdigest()
            if hash_digest.startswith("0"): # Simple proof-of-work
                return hash_digest
            self.nonce += 1

class DistributedLexicalChain:
    def __init__(self):
        self.chain = [LexicalBlock("Genesis Block")]
        self.pending_data = []
        self._lock = asyncio.Lock()

    async def add_block(self, data: str) -> None:
        async with self._lock:
            last_block = self.chain[-1]
            new_block = LexicalBlock(data, last_block.hash)
            self.chain.append(new_block)
            logger.info(f"Block added: {new_block.hash[:8]}... with data size {len(data)}")

    def validate_chain(self) -> bool:
        for i in range(1, len(self.chain)):
            current = self.chain[i]
            previous = self.chain[i-1]
            if current.hash != current._calculate_hash():
                return False
            if current.previous_hash != previous.hash:
                return False
        return True

# Kafka schema definitions
CHUNK_SCHEMA = '''
{
  "namespace": "com.shitops.lexical",
  "name": "ChunkAnalysisRequest",
  "type": "record",
  "fields": [
    {"name": "chunk_id", "type": "int"},
    {"name": "content", "type": "string"},
    {"name": "pattern", "type": "string"},
    {"name": "request_id", "type": "string"},
    {"name": "timestamp", "type": "long"}
  ]
}
'''

RESULT_SCHEMA = '''
{
  "namespace": "com.shitops.lexical",
  "name": "ChunkAnalysisResult",
  "type": "record",
  "fields": [
    {"name": "chunk_id", "type": "int"},
    {"name": "count", "type": "int"},
    {"name": "request_id", "type": "string"},
    {"name": "processing_time_ms", "type": "long"},
    {"name": "processor_id", "type": "string"},
    {"name": "timestamp", "type": "long"}
  ]
}
'''

class KafkaTopologyManager:
    def __init__(self, config: TokenizationConfig):
        self.config = config
        self.admin_client = AdminClient({'bootstrap.servers': config.kafka_bootstrap_servers})
        self.schema_registry = SchemaRegistryClient({'url': config.kafka_schema_registry_url})
        self.request_topic = f"{config.kafka_topic_prefix}-requests"
        self.result_topic = f"{config.kafka_topic_prefix}-results"
        self.error_topic = f"{config.kafka_topic_prefix}-errors"
        self.processor_id = str(uuid.uuid4())

    async def setup_topics(self) -> None:
        topics = [
            NewTopic(
                self.request_topic,
                num_partitions=self.config.kafka_partitions,
                replication_factor=self.config.kafka_replication_factor,
                config={
                    'retention.ms': str(self.config.kafka_retention_ms),
                    'cleanup.policy': 'delete',
                    'compression.type': 'lz4',
                    'max.message.bytes': '10485760'  # 10MB
                }
            ),
            NewTopic(
                self.result_topic,
                num_partitions=self.config.kafka_partitions,
                replication_factor=self.config.kafka_replication_factor,
                config={
                    'retention.ms': str(self.config.kafka_retention_ms),
                    'cleanup.policy': 'compact,delete' if self.config.kafka_compaction_policy else 'delete',
                    'compression.type': 'lz4',
                    'max.message.bytes': '5242880'  # 5MB
                }
            ),
            NewTopic(
                self.error_topic,
                num_partitions=max(1, self.config.kafka_partitions // 4),
                replication_factor=self.config.kafka_replication_factor,
                config={
                    'retention.ms': str(self.config.kafka_retention_ms * 2),  # Keep errors longer
                    'cleanup.policy': 'compact,delete',
                    'compression.type': 'lz4'
                }
            )
        ]

        # Create topics asynchronously
        futures = self.admin_client.create_topics(topics)

        # Wait for topic creation to complete
        for topic, future in futures.items():
            try:
                future.result()  # Wait for operation to complete
                logger.info(f"Topic {topic} created successfully")
            except KafkaException as e:
                if "already exists" in str(e):
                    logger.info(f"Topic {topic} already exists")
                else:
                    logger.error(f"Failed to create topic {topic}: {e}")
                    raise

        # Register schemas
        try:
            self.schema_registry.register_schema(f"{self.request_topic}-value", CHUNK_SCHEMA)
            self.schema_registry.register_schema(f"{self.result_topic}-value", RESULT_SCHEMA)
            logger.info("Schemas registered successfully")
        except Exception as e:
            logger.error(f"Schema registration error: {e}")
            # Continue anyway, might be already registered

    def create_producer(self) -> Producer:
        return Producer({
            'bootstrap.servers': self.config.kafka_bootstrap_servers,
            'acks': 'all',
            'enable.idempotence': True,
            'max.in.flight.requests.per.connection': 5,
            'compression.type': 'lz4',
            'linger.ms': 5,
            'batch.size': 65536,  # 64KB
            'delivery.timeout.ms': 30000,  # 30 seconds
            'request.timeout.ms': 15000,  # 15 seconds
        })

    def create_consumer(self) -> Consumer:
        return Consumer({
            'bootstrap.servers': self.config.kafka_bootstrap_servers,
            'group.id': self.config.kafka_consumer_group,
            'auto.offset.reset': 'earliest',
            'enable.auto.commit': False,
            'max.poll.interval.ms': 300000,  # 5 minutes
            'session.timeout.ms': 60000,  # 1 minute
            'fetch.min.bytes': 1,
            'fetch.max.bytes': 52428800,  # 50MB
            'max.partition.fetch.bytes': 1048576,  # 1MB
        })

class EnterpriseWordCounter:
    def __init__(self, config: TokenizationConfig = None):
        self.config = config or TokenizationConfig()
        self.lexical_chain = DistributedLexicalChain()
        self.result_store = defaultdict(int)
        self.metrics = {
            "start_time": None,
            "end_time": None,
            "chunks_processed": 0,
            "total_tokens": 0,
            "kafka_publish_time": 0,
            "kafka_consume_time": 0,
            "processing_errors": 0
        }
        self.kafka_manager = KafkaTopologyManager(self.config)
        self.request_id = str(uuid.uuid4())

    @staticmethod
    def _tokenize_chunk(chunk: str, pattern: str) -> int:
        try:
            return len(re.findall(pattern, chunk, re.IGNORECASE))
        except Exception as e:
            logger.error(f"Tokenization error: {e}")
            return 0

    async def _process_chunk(self, chunk_id: int, chunk: str, pattern: str) -> Tuple[int, int]:
        count = 0
        for attempt in range(self.config.retry_attempts):
            try:
                with ThreadPoolExecutor() as executor:
                    future = executor.submit(self._tokenize_chunk, chunk, pattern)
                    count = future.result(timeout=5.0)
                    break
            except Exception as e:
                logger.warning(f"Attempt {attempt+1} failed for chunk {chunk_id}: {e}")
                await asyncio.sleep(self.config.backoff_factor ** attempt)
        else:
            logger.error(f"All attempts failed for chunk {chunk_id}")
            self.metrics["processing_errors"] += 1

        # Record metrics
        self.metrics["chunks_processed"] += 1
        self.metrics["total_tokens"] += count

        # Add to blockchain for immutability
        await self.lexical_chain.add_block(
            json.dumps({"chunk_id": chunk_id, "count": count, "timestamp": time.time()})
        )

        return chunk_id, count

    async def _publish_chunks_to_kafka(self, chunks: List[str], pattern: str) -> None:
        logger.info(f"Publishing {len(chunks)} chunks to Kafka topic {self.kafka_manager.request_topic}")

        # Create Avro serializer for chunk requests
        chunk_serializer = AvroSerializer(
            schema_registry_client=self.kafka_manager.schema_registry,
            schema_str=CHUNK_SCHEMA,
            to_dict=lambda x: x
        )

        # Create producer
        producer = self.kafka_manager.create_producer()
        start_time = time.time()

        # Publish each chunk
        delivery_reports = []

        def delivery_callback(err, msg):
            if err is not None:
                logger.error(f"Failed to deliver message: {err}")
                self.metrics["processing_errors"] += 1
            else:
                logger.debug(f"Message delivered to {msg.topic()} [{msg.partition()}] at offset {msg.offset()}")

        for i, chunk in enumerate(chunks):
            # Create message payload
            message = {
                "chunk_id": i,
                "content": chunk,
                "pattern": pattern,
                "request_id": self.request_id,
                "timestamp": int(time.time() * 1000)
            }

            # Serialize and publish
            try:
                producer.produce(
                    topic=self.kafka_manager.request_topic,
                    key=str(i).encode(),
                    value=chunk_serializer(message),
                    on_delivery=delivery_callback
                )
                # Serve delivery callbacks
                producer.poll(0)
            except BufferError:
                # Queue is full, wait for some messages to be delivered
                logger.warning("Producer queue full, waiting for space...")
                producer.poll(1)
                # Retry this message
                producer.produce(
                    topic=self.kafka_manager.request_topic,
                    key=str(i).encode(),
                    value=chunk_serializer(message),
                    on_delivery=delivery_callback
                )

        # Wait for all messages to be delivered
        remaining = producer.flush(30)
        if remaining > 0:
            logger.warning(f"{remaining} messages were not delivered within timeout")
            self.metrics["processing_errors"] += remaining

        self.metrics["kafka_publish_time"] = time.time() - start_time
        logger.info(f"Published all chunks to Kafka in {self.metrics['kafka_publish_time']:.2f}s")

    async def _consume_results_from_kafka(self, expected_chunks: int) -> None:
        logger.info(f"Consuming results from Kafka topic {self.kafka_manager.result_topic}")

        # Create Avro deserializer for results
        result_deserializer = AvroDeserializer(
            schema_registry_client=self.kafka_manager.schema_registry,
            schema_str=RESULT_SCHEMA
        )

        # Create consumer
        consumer = self.kafka_manager.create_consumer()
        consumer.subscribe([self.kafka_manager.result_topic])

        start_time = time.time()
        results_received = 0
        timeout_ms = 30000  # 30 seconds

        while results_received < expected_chunks:
            # Check if we've been waiting too long
            if time.time() - start_time > timeout_ms / 1000:
                logger.warning(f"Timeout waiting for results, received {results_received}/{expected_chunks}")
                break

            # Poll for messages
            msg = consumer.poll(1.0)

            if msg is None:
                continue

            if msg.error():
                if msg.error().code() == KafkaError._PARTITION_EOF:
                    logger.debug(f"Reached end of partition {msg.partition()}")
                else:
                    logger.error(f"Consumer error: {msg.error()}")
                    self.metrics["processing_errors"] += 1
                continue

            # Process the message
            try:
                result = result_deserializer(msg.value())

                # Verify this result belongs to our request
                if result["request_id"] == self.request_id:
                    chunk_id = result["chunk_id"]
                    count = result["count"]

                    # Store the result
                    self.result_store[f"chunk_{chunk_id}"] = count
                    results_received += 1

                    # Commit offset
                    consumer.commit(msg)

                    logger.debug(f"Processed result for chunk {chunk_id}: count={count}")
            except Exception as e:
                logger.error(f"Error processing result: {e}")
                self.metrics["processing_errors"] += 1

        # Close the consumer
        consumer.close()

        self.metrics["kafka_consume_time"] = time.time() - start_time
        logger.info(f"Consumed {results_received}/{expected_chunks} results from Kafka in {self.metrics['kafka_consume_time']:.2f}s")

    async def analyze_text(self, text: str, word: str) -> int:
        self.metrics["start_time"] = time.time()

        # Prepare regex pattern
        pattern = r'\b' + re.escape(word) + r'\b'

        # Split text into microservices-compatible chunks
        chunks = [text[i:i+self.config.chunk_size] for i in range(0, len(text), self.config.chunk_size)]
        logger.info(f"Text split into {len(chunks)} chunks for distributed processing")

        # Set up Kafka infrastructure
        await self.kafka_manager.setup_topics()

        # Determine processing strategy based on chunk count
        if len(chunks) > 100:
            logger.info(f"Using Kafka streaming for {len(chunks)} chunks")
            # Use Kafka for very large workloads
            await self._publish_chunks_to_kafka(chunks, pattern)
            await self._consume_results_from_kafka(len(chunks))
        else:
            logger.info(f"Using direct processing for {len(chunks)} chunks")
            # Process chunks in parallel with asyncio for smaller workloads
            tasks = []
            for i, chunk in enumerate(chunks):
                tasks.append(self._process_chunk(i, chunk, pattern))

            results = await asyncio.gather(*tasks)

            # Store results
            for chunk_id, count in results:
                self.result_store[f"chunk_{chunk_id}"] = count

        # Persist results to JSON with atomic write
        tmp_file = f"{self.config.persistence_path}.tmp"
        with open(tmp_file, 'w') as f:
            json.dump(self.result_store, f)
        os.rename(tmp_file, self.config.persistence_path)

        # Calculate final result with blockchain verification
        if not self.lexical_chain.validate_chain():
            logger.critical("Blockchain integrity compromised! Results may be invalid.")
            raise RuntimeError("Data integrity validation failed")

        total_count = sum(self.result_store.values())

        self.metrics["end_time"] = time.time()
        logger.info(f"Analysis completed in {self.metrics['end_time'] - self.metrics['start_time']:.2f}s")
        logger.info(f"Processed {self.metrics['chunks_processed']} chunks with {self.metrics['total_tokens']} matches")

        # Generate detailed metrics report
        metrics_report = {
            "request_id": self.request_id,
            "total_processing_time_ms": int((self.metrics["end_time"] - self.metrics["start_time"]) * 1000),
            "chunks_processed": self.metrics["chunks_processed"],
            "total_matches": self.metrics["total_tokens"],
            "kafka_publish_time_ms": int(self.metrics["kafka_publish_time"] * 1000),
            "kafka_consume_time_ms": int(self.metrics["kafka_consume_time"] * 1000),
            "processing_errors": self.metrics["processing_errors"],
            "blockchain_blocks": len(self.lexical_chain.chain),
            "blockchain_valid": self.lexical_chain.validate_chain()
        }

        # Save metrics to file
        with open(f"{self.config.persistence_path}.metrics.json", 'w') as f:
            json.dump(metrics_report, f)

        return total_count

# Usage example (wrapped in async function)
async def count_word_occurrences(text: str, word: str) -> int:
    counter = EnterpriseWordCounter(
        TokenizationConfig(
            chunk_size=100,
            parallelism_factor=min(8, mp.cpu_count()),
            persistence_path=f"/tmp/wordcount-{uuid.uuid4()}.json",
            kafka_bootstrap_servers="kafka-broker-1:9092,kafka-broker-2:9092,kafka-broker-3:9092",
            kafka_schema_registry_url="http://schema-registry:8081",
            kafka_partitions=24,
            kafka_replication_factor=3
        )
    )
    return await counter.analyze_text(text, word)

# Synchronous wrapper for legacy systems
def count_word_occurrences_sync(text: str, word: str) -> int:
    return asyncio.run(count_word_occurrences(text, word))