# Import libraries
import clickhouse_connect
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

# Connect to the ClickHouse server
client = clickhouse_connect.get_client(host='localhost', port=8123)

# Create a sample table for user events
create_table_query = """
CREATE TABLE IF NOT EXISTS user_events (
    event_id UUID,
    user_id String,
    event_type String,
    event_time DateTime,
    properties String
) ENGINE = MergeTree()
ORDER BY (event_time, user_id)
"""

client.command(create_table_query)
print("Table created successfully")

# Check if table already has data
count_result = client.query('SELECT count() FROM user_events')
if count_result.result_rows[0][0] > 0:
    print(f"Table already contains {count_result.result_rows[0][0]} records. Skipping data insertion.")
else:
    # Generate sample data
    np.random.seed(42)
    events = []
    event_types = ['page_view', 'click', 'purchase', 'signup', 'login']
    user_ids = [f'user_{i}' for i in range(1, 11)]

    # Generate 1000 sample events
    start_date = datetime.now() - timedelta(days=7)
    for i in range(1000):
        event_id = i + 1
        user_id = np.random.choice(user_ids)
        event_type = np.random.choice(event_types)
        event_time = start_date + timedelta(
            seconds=np.random.randint(0, 7 * 24 * 60 * 60)
        )
        properties = f'{{"source": "example", "session_id": "{np.random.randint(1000, 9999)}"}}'

        events.append([
            event_id,
            user_id,
            event_type,
            event_time,
            properties
        ])

    # Create a DataFrame and insert data
    df = pd.DataFrame(events, columns=['event_id', 'user_id', 'event_type', 'event_time', 'properties'])

    # Insert data using clickhouse-connect
    client.insert('user_events', df.values.tolist(), column_names=df.columns.tolist())
    print(f"Inserted {len(df)} rows of sample data")

# Verify data was inserted correctly
result = client.query('SELECT count() FROM user_events')
print(f"Total records in table: {result.result_rows[0][0]}")

# Sample query to check the data
sample = client.query('SELECT * FROM user_events LIMIT 5')
print("\\nSample data:")
for row in sample.result_rows:
    print(row)

# Import libraries
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment, EnvironmentSettings

# Set up the execution environment
env = StreamExecutionEnvironment.get_execution_environment()
t_env = StreamTableEnvironment.create(env)

# This configuration is required but not sufficient - the JAR must actually be available
t_env.get_config().get_configuration().set_string(
    "pipeline.jars", "file:///path/to/clickhouse-jdbc-0.4.6-all.jar"
)

# Define source data (simulated stream for demonstration)
t_env.execute_sql("""
    CREATE TEMPORARY TABLE event_source (
        event_id STRING,
        user_id STRING,
        event_type STRING,
        event_time TIMESTAMP(3),
        properties STRING
    ) WITH (
        'connector' = 'datagen',
        'rows-per-second' = '100',
        'fields.event_id.kind' = 'sequence',
        'fields.event_id.start' = '1001',
        'fields.event_id.end' = '2000',
        'fields.user_id.length' = '8',
        'fields.event_type.length' = '10',
        'fields.properties.length' = '50'
    )
""")

# Define the ClickHouse sink (this requires the JDBC connector to be available)
t_env.execute_sql("""
    CREATE TABLE clickhouse_sink (
        event_id STRING,
        user_id STRING,
        event_type STRING,
        event_time TIMESTAMP(3),
        properties STRING
    ) WITH (
        'connector' = 'jdbc',
        'url' = 'jdbc:clickhouse://localhost:8123/default',
        'table-name' = 'user_events',
        'driver' = 'ru.yandex.clickhouse.ClickHouseDriver',
        'sink.batch-size' = '1000',
        'sink.flush-interval' = '1s'
    )
""")

# The insert statement that would transfer data (if the connector were available)
insert_statement = """
    INSERT INTO clickhouse_sink
    SELECT
        event_id,
        user_id,
        event_type,
        event_time,
        properties
    FROM event_source
"""

# Note: In a complete example, we would execute this statement with:
# t_env.execute_sql(insert_statement).wait()
# However, this returns an error since we're missing the dependencies abovementioned.

# Import libraries
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.datastream.functions import MapFunction, RuntimeContext
from pyflink.common.typeinfo import Types
import requests
import json
import uuid

# Define custom Flink sink
class ClickHouseHttpSink(MapFunction):
    """
    A custom Flink sink that uses ClickHouse's HTTP interface for data insertion.
    This approach provides better performance than JDBC but requires custom implementation.
    """
    def __init__(self, url, table, batch_size=1000):
        self.url = url
        self.table = table
        self.batch_size = batch_size
        self.batch = []

    def open(self, runtime_context: RuntimeContext):
        self.batch = []

    def map(self, value):
        # Add record to batch
        self.batch.append(value)

        # Flush when batch size is reached
        if len(self.batch) >= self.batch_size:
            self._flush()

        return value  # Pass through for downstream operators

    def _flush(self):
        if not self.batch:
            return

        # Format the data for ClickHouse insertion via HTTP
        data = '\\n'.join([json.dumps(record) for record in self.batch])
        query = f"INSERT INTO {self.table} FORMAT JSONEachRow"

        try:
            response = requests.post(
                f"{self.url}?query={query}",
                data=data,
                headers={'Content-Type': 'application/json'}
            )
            response.raise_for_status()
            print(f"Successfully inserted {len(self.batch)} records")
            self.batch = []
        except Exception as e:
            # In production, this error handling would need to be more robust
            print(f"Error sending data to ClickHouse: {e}")
            # No retry mechanism implemented here

# Example usage with our existing user_events table
def demonstrate_http_sink():
    """
    Setup a simple Flink job using the HTTP sink.
    Note: This implementation demonstrates the concept but does not
    properly integrate with Flink's execution graph.
    """

    # Create Flink execution environment
    env = StreamExecutionEnvironment.get_execution_environment()

    # For demonstration, generate synthetic events
    # In a real application, this would come from Kafka, files, or other sources
    def generate_sample_event():
        return {
            "event_id": str(uuid.uuid4()),
            "user_id": f"user_{uuid.uuid4().hex[:5]}",
            "event_type": "page_view",
            "event_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            "properties": json.dumps({"source": "http-example"})
        }

    # Generate 10 example events
    sample_events = [generate_sample_event() for _ in range(10)]

    # Create a data stream
    stream = env.from_collection(sample_events)

    # Apply the custom ClickHouse HTTP sink
    clickhouse_sink = ClickHouseHttpSink("<http://localhost:8123>", "user_events")
    stream.map(clickhouse_sink, output_type=Types.STRING)

    # Note: In a complete example, we would execute this with the following:
    # env.execute("ClickHouse HTTP Sink Example")
    # However, this returns an error without proper integration as a sink operator

# Import libraries
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.datastream.functions import SinkFunction, RuntimeContext
import clickhouse_connect
import uuid
import hashlib
import time

# Define Flink Two-Phase Commit Sink
class ClickHouseTwoPhaseCommitSink(SinkFunction):
    """
    A simplified two-phase commit sink for ClickHouse that demonstrates
    the temporary table approach for stronger consistency guarantees.
    """
    def __init__(self, host, port, target_table):
        self.host = host
        self.port = port
        self.target_table = target_table
        self.client = None
        self.temp_table = None
        self.batch = []
        self.batch_size = 1000

    def open(self, runtime_context: RuntimeContext):
        self.client = clickhouse_connect.get_client(host=self.host, port=self.port)
        self._create_temp_table()

    def invoke(self, value, context):
        # Add record to batch
        self.batch.append(value)

        # Insert into temporary table
        try:
            self.client.command(f"""
                INSERT INTO {self.temp_table} VALUES (
                    '{value["event_id"]}',
                    '{value["user_id"]}',
                    '{value["event_type"]}',
                    '{value["event_time"]}',
                    '{value["properties"]}'
                )
            """)

            # When batch size is reached, commit to target table
            if len(self.batch) >= self.batch_size:
                self._commit_batch()

        except Exception as e:
            print(f"Error in ClickHouse operation: {e}")

    def _create_temp_table(self):
        """Create a temporary table for staging data."""
        # Generate a unique name for the temporary table
        table_hash = hashlib.md5(f"{self.target_table}_{time.time()}".encode()).hexdigest()[:8]
        self.temp_table = f"temp_{self.target_table}_{table_hash}"

        # Create a temporary table with the same structure as the target
        self.client.command(f"""
            CREATE TABLE IF NOT EXISTS {self.temp_table} AS {self.target_table}
            ENGINE = MergeTree()
            ORDER BY (event_time, user_id)
        """)

    def _commit_batch(self):
        """Commit data from temporary table to target table."""
        if not self.batch:
            return

        try:
            # "Commit" by copying data from temporary table to target table
            self.client.command(f"""
                INSERT INTO {self.target_table}
                SELECT * FROM {self.temp_table}
            """)

            # Clear the temporary table for the next batch
            self.client.command(f"TRUNCATE TABLE {self.temp_table}")

            # Reset batch
            self.batch = []

        except Exception as e:
            print(f"Error committing batch: {e}")

    def close(self):
        """Clean up resources."""
        if self.client:
            # Commit any remaining records
            if self.batch:
                self._commit_batch()

            # Clean up the temporary table
            if self.temp_table:
                try:
                    self.client.command(f"DROP TABLE IF EXISTS {self.temp_table}")
                except:
                    pass

# Import modules
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment, EnvironmentSettings

# Set Up Flink Environment
env = StreamExecutionEnvironment.get_execution_environment()
settings = EnvironmentSettings.new_instance().in_streaming_mode().build()
t_env = StreamTableEnvironment.create(env, settings)

# Add Required Kafka Connector JAR
# Note: The actual JAR file needs to be downloaded manually:
# <https://repo1.maven.org/maven2/org/apache/flink/flink-connector-kafka/>
t_env.get_config().get_configuration().set_string(
    "pipeline.jars", "file:///path/to/flink-connector-kafka.jar"
)

# Define a Source Table
t_env.execute_sql("""
    CREATE TEMPORARY TABLE source_events (
        event_id STRING,
        user_id STRING,
        event_type STRING,
        event_time TIMESTAMP(3),
        properties STRING
    ) WITH (
        'connector' = 'datagen',
        'rows-per-second' = '100',
        'fields.user_id.kind' = 'random',
        'fields.user_id.length' = '10',
        'fields.event_type.kind' = 'random',
        'fields.event_type.length' = '10',
        'fields.properties.kind' = 'random',
        'fields.properties.length' = '30'
    )
""")

# Define a Kafka Sink Table
t_env.execute_sql("""
    CREATE TABLE kafka_events (
        event_id STRING,
        user_id STRING,
        event_type STRING,
        event_time TIMESTAMP(3),
        properties STRING
    ) WITH (
        'connector' = 'kafka',
        'topic' = 'user-events',
        'properties.bootstrap.servers' = 'kafka:9092',
        'format' = 'json'
    )
""")

# Insert Data From Source to Kafka
t_env.execute_sql("""
    INSERT INTO kafka_events
    SELECT event_id, user_id, event_type, event_time, properties
    FROM source_events
""")

# On ClickHouse Side, Create Kafka Engine Table (Consume Topic)