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ClickHouse Learnings

Challenges of Connecting Flink to ClickHouse

Why integrating Flink with ClickHouse is difficult – key challenges explained

Written by

Armend Avdijaj

Mar 17, 2025

Real-time data processing has become common practice for organizations working with time-sensitive insights or products. Apache Flink has been the industry-standard stream processing framework used for this purpose for some time now. ClickHouse, a high-performance analytical database, is frequently chosen alongside Flink for building end-to-end real-time data platforms. However, connecting these two services systems has presented a significant challenge to the community.

The fundamental issue lies in the absence of a native connector between Flink and ClickHouse. Unlike databases such as MySQL, PostgreSQL, or Elasticsearch, which all have official Flink connectors, ClickHouse lacks dedicated integration support. This has forced data engineers to develop custom solutions that often compromise on performance, reliability, or processing guarantees.

In this article, we'll examine the architectural differences between each platform that make a connector development challenging, illustrate some of the custom solutions that have been developed, and analyze their respective limitations.

We'll be using the open-source version of ClickHouse, which can be installed by following the official documentation.

Understanding Flink and ClickHouse

To address the integration challenges, we first need to understand the core architectures of both systems and why organizations want to combine them despite the difficulties.

Both technologies have distinct designs that excel at different aspects of data processing. Let us examine their key components and how their fundamental differences create integration challenges.

Apache Flink Architecture

Apache Flink was designed for processing unbounded data streams with consistent state management. Its distributed architecture consists of JobManagers for coordination and TaskManagers for data processing. The JobManager orchestrates execution, while TaskManagers run the actual processing logic across multiple nodes.

Flink's checkpoint mechanism enables true exactly-once processing semantics, critical for applications requiring data accuracy. The framework handles both stream and batch processing through a unified model, treating batch datasets as bounded streams.

Figure 1: Apache Flink architecture diagram showing JobManager, TaskManagers, and checkpoint mechanism

ClickHouse Architecture

ClickHouse organizes data by columns rather than rows to accelerate analytical queries. Its MergeTree engine family provides efficient storage and querying capabilities, while the Distributed engine enables horizontal scaling across multiple servers.

The database achieves exceptional query performance through vectorized execution, code generation, and effective compression. These techniques allow ClickHouse to scan billions of rows in seconds, performance that traditional databases can't match for analytical workloads:

Figure 2: ClickHouse architecture diagram showing distributed table engines and columnar storage

However, ClickHouse makes deliberate trade-offs for performance. Most notably, it lacks support for full ACID transactions, instead focusing on append-only operations with eventual consistency across its distributed architecture. This design choice creates significant challenges when integrating with systems like Flink that rely on transactional guarantees.

Architectural Disparities

Now that we have established how both frameworks are designed, it should be no surprise that there are significant architectural disparities between them:

Figure 3: Diagram illustrating the architectural mismatch between systems

This diagram highlights the key architectural mismatches between Flink's processing model and ClickHouse's storage approach that complicate integration efforts:

Processing Paradigm: Flink processes continuous streams with stateful operators, while ClickHouse is designed for analytical queries over large datasets.
Execution Model: Flink maintains a persistent dataflow graph with continuous execution, whereas ClickHouse executes discrete queries with their own execution plans.
Distribution Architecture: Flink uses centralized coordination through JobManagers, while ClickHouse employs a more loosely coupled architecture with independent nodes.
Transaction Support: Flink's exactly-once guarantees rely on a two-phase commit protocol, but ClickHouse lacks full ACID transaction capabilities required for this approach. Exactly-once processing guarantees that each record is processed exactly one time, even in the presence of failures. This is a critical requirement for many applications handling financial transactions, user activity tracking, or business metrics. Flink achieves this through its checkpoint mechanism and two-phase commit protocol when writing to supported external systems.

These fundamental architectural differences explain why creating a native connector between these systems has been challenging and why no official connector exists in the Flink ecosystem.

As we'll explore in the next section, data engineering teams have developed several workarounds to address these challenges, each with its own limitations and trade-offs.

Current Workarounds and Their Limitations

There are some approaches organizations use to connect Flink with ClickHouse, each addressing the architectural disparities in different ways. On this article we will focus on the three most popular methods.

To illustrate these, let us set up a practical example with ClickHouse. We'll create a simple event tracking table that might be used in a real-time analytics pipeline.

First, we establish a connection to ClickHouse and create our sample table using the clickhouse-connect library:

# 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")

Next we insert some random data points:

# 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)

Which should output the following:

JDBC Connector Approach

This approach Involves configuring external JDBC driver dependencies and using Flink's JDBC connector. Organizations must obtain and configure both the Flink JDBC connector and ClickHouse JDBC driver to make this work.

In this example, we want to set up a Flink pipeline that processes streaming data and writes the results to our ClickHouse user_events table.

In concept, a Flink-to-ClickHouse JDBC integration would follow the following pattern:

# 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.

This code defines a table sink using Flink's SQL API, configuring a JDBC connector pointed at ClickHouse.

Even if the dependencies were properly configured, this approach would still face significant limitations:

Limited transaction support: The JDBC connector doesn't integrate with Flink's checkpointing mechanism, making exactly-once semantics impossible to guarantee.
Throughput bottlenecks: Connection pooling and statement execution overhead can severely limit performance at scale.
Failure handling challenges: Network issues or connection failures can result in lost or duplicated data.

HTTP Interface Integration

This approach bypasses JDBC overhead by communicating directly with ClickHouse's native HTTP API.

In this example, we want to implement a custom Flink sink function that batches records and sends them to ClickHouse using HTTP requests.

In concept, the approach would follow the following pattern:

# 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

The code demonstrates a custom MapFunction that collects records into a batch and periodically sends them to ClickHouse using the HTTP interface. This conceptual approach gives us direct control over batch sizes, error handling, and retry logic, however the implementation as shown would not execute successfully in a Flink pipeline because it doesn't properly integrate as a sink operator in Flink's execution graph.

This method attempts to provide better performance than the JDBC alternative but still carries several challenges:

Improper Flink integration: The implementation must be modified to properly register as a sink operator rather than a simple map function.
Manual batch management: Developers must handle batching, retries, and failure scenarios.
No integration with checkpoints: HTTP requests aren't coordinated with Flink's checkpointing, compromising exactly-once semantics.
Error recovery complexity: Partial batch failures require complex logic to avoid data loss or duplication.

Two-Phase Commit with Temporary Tables

For applications requiring stronger consistency guarantees, we can implement a two-phase commit pattern using temporary tables in ClickHouse. This approach attempts to bridge the gap between Flink's transactional expectations and ClickHouse's limited transaction support.

In the following example, we demonstrate a simplified version of the approach, though it lacks integration with Flink's checkpointing mechanism due to API limitations in the current environment:

# 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

While this pattern can approximate exactly-once semantics, it comes with significant trade-offs:

Increased storage requirements: Temporary tables consume additional storage.
Higher latency: Multi-stage processing increases end-to-end latency.
Complex implementation: Error handling and cleanup logic add substantial complexity.
Resource overhead: Additional queries and table operations impact overall system performance.

Kafka as an Intermediary Layer

A fourth approach avoids direct integration between Flink and ClickHouse by introducing Apache Kafka as an intermediary message broker. Flink processes data and writes results to Kafka topics, while ClickHouse consumes those topics separately.

Let us introduce an example where we have a conceptual Flink job that writes to Kafka, and then illustrates how ClickHouse can consume from that Kafka topic using the same user_events table structure we've been working with:

# 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)

And then on ClickHouse Side, we can create a Kafka Engine Table to consume the topic:

-- Create a ClickHouse table using Kafka engine to act as a queue for incoming data
CREATE TABLE user_events_queue (
    event_id String,
    user_id String,
    event_type String,
    event_time DateTime,
    properties String
) ENGINE = Kafka
SETTINGS kafka_broker_list = 'kafka:9092',
         kafka_topic_list = 'user-events',
         kafka_group_name = 'clickhouse_consumer_group',
         kafka_format = 'JSONEachRow';

-- Create a materialized view to transform incoming Kafka data and store in our existing user_events table
CREATE MATERIALIZED VIEW user_events_mv TO user_events AS
SELECT
    event_id,
    user_id,
    event_type,
    event_time,
    properties
FROM

This approach decouples the writing (Flink) and reading (ClickHouse) components, allowing each system to operate independently. Kafka serves as a buffer that can handle backpressure and provide exactly-once delivery semantics when configured properly. The ClickHouse Kafka engine continuously polls the Kafka topic, and the materialized view transfers the data to our target user_events table.

While this is a valid approach, there is one significant challenge, and that is the introduction of a third component to the architecture:

Increased architectural complexity: Instead of managing two systems, teams must now operate and maintain three interconnected distributed systems.
Coordination overhead: When issues appear, debugging becomes much more complex as data flows through multiple independent services.
Consistency model mismatch: Ensuring proper exactly-once semantics requires precise configurations around Kafka consumer offsets, Flink checkpoints, and ClickHouse's materialized views, with failure in any component potentially causing duplication or data loss.
Cost implications: Running a production-grade Kafka cluster significantly increases infrastructure costs beyond a direct integration.

Practical Impact on Data Pipelines

We have introduced four potential approaches for connecting Flink with ClickHouse. Each method is feasible in practice, but also has its own drawbacks. This has forced engineering teams to carefully evaluate their pipeline requirements since one solution might not work for another use case.

Approach	Implementation Complexity	Performance	Consistency Guarantees	Error Recovery	Operational Overhead	Maintenance Effort
JDBC Connector	Medium Requires external dependencies but uses standard APIs	Moderate Limited by connection pooling and JDBC overhead	At-least-once No integration with checkpointing	Limited Basic retry capabilities	Medium Requires dependencymanagement	Medium Requires updating JDBC drivers and monitoring connection pool metrics
HTTP Interface	High Requires custom implementation	High Direct use of ClickHouse's native API	At-least-once No checkpoint integration	Manual Requires custom error handling	High Custom code maintenance	High Custom code must be maintained and updated with ClickHouse API changes
Two-Phase Commit	Very High Requires deep knowledge of both systems	Low Multiple stages add latency	Approximate exactly-once Through careful temporary table management	Complex Requires sophisticated failure handling	Very High Temporary table management, monitoring	Very High Requires constant monitoring of temporary tables and cleanup procedures
Kafka Intermediary	Medium Uses standard integration patterns	Moderate Additional network hop but good throughput	Exactly-once With proper configuration	Robust Built-in recovery mechanisms	Medium Requires Kafka cluster management	Medium-High Requires maintaining multiple systems and monitoring the Kafka cluster

Table 1: Comparison of three common approaches with their strengths and limitations

Moreover, a "perfect solution" might not even be possible for a given use case, leaving organizations in an uncomfortable position where performance, consistency, simplicity, and/or robustness need to be sacrificed.

Final Thoughts

The absence of a native connector between Apache Flink and ClickHouse creates significant challenges for real‑time analytics pipelines. Architectural differences around transactions, consistency, and write patterns make integrations non‑trivial without custom solutions.

When teams do manage to connect Flink and ClickHouse, the combination is powerful: Flink’s stream processing + ClickHouse’s analytical performance. Getting there, however, requires careful design and a clear understanding of trade‑offs across consistency, performance, complexity, and operations.

For a deeper dive into limitations and hands‑on details, see:

If you’re struggling with these integration challenges and want something more streamlined, consider our open‑source approach: GlassFlow for ClickHouse.