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Challenges of Connecting Flink to ClickHouse

Why integrating Flink with ClickHouse is difficult – key challenges explained

Written by

Armend Avdijaj

Mar 17, 2025

Connecting Apache Flink to ClickHouse: Approaches, Trade-offs, and Practical Guidance

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 for this purpose for some time. ClickHouse, a high‑performance analytical database, is frequently chosen alongside Flink for building end‑to‑end real‑time data platforms. However, connecting these two 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 that make a connector challenging to build, illustrate common custom solutions, and analyze their 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’s 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 full ACID transactions, instead focusing on append‑only operations with eventual consistency across its distributed architecture. This design choice creates challenges when integrating with systems like Flink that rely on transactional guarantees.

Architectural Disparities

Figure 3: Diagram illustrating the architectural mismatch between systems

Key architectural mismatches that complicate a native connector:

Processing Paradigm: Flink processes continuous streams with stateful operators; ClickHouse is optimized for analytical queries over large datasets.
Execution Model: Flink maintains a persistent dataflow graph with continuous execution; ClickHouse executes discrete queries.
Distribution Architecture: Flink uses centralized coordination via JobManagers; ClickHouse employs a more loosely coupled, shard‑aware architecture.
Transaction Support: Flink’s exactly‑once guarantees rely on two‑phase commit; ClickHouse lacks full ACID transactions. Exactly‑once means each record is processed once—even during failures—which Flink achieves with checkpoints + 2PC with supported sinks.

These differences explain why no official connector exists in the Flink ecosystem and why workarounds are common.

Current Workarounds and Their Limitations

There are several approaches organizations use to connect Flink with ClickHouse, each addressing the disparities differently. Below we focus on four common methods and use a simple user_events table in ClickHouse to ground the examples.

Sample ClickHouse Setup (Python)

First, create a sample table using the clickhouse-connect client:

# Sample setup script (Python)
import clickhouse_connect
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

client = clickhouse_connect.get_client(host='localhost', port=8123)

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")

# Insert synthetic data if table is empty
count = client.query('SELECT count() FROM user_events').result_rows[0][0]
if count == 0:
    np.random.seed(42)
    events = []
    event_types = ['page_view', 'click', 'purchase', 'signup', 'login']
    user_ids = [f'user_{i}' for i in range(1, 11)]
    start = datetime.now() - timedelta(days=7)

    for _ in range(1000):
        event_id = '00000000-0000-0000-0000-' + f'{np.random.randint(0, 10**12):012d}'
        user_id = np.random.choice(user_ids)
        event_type = np.random.choice(event_types)
        event_time = start + timedelta(seconds=np.random.randint(0, 7*24*60*60))
        properties = '{"source": "example", "session_id": "' + str(np.random.randint(1000, 9999)) + '"}'
        events.append([event_id, user_id, event_type, event_time, properties])

    df = pd.DataFrame(events, columns=['event_id', 'user_id', 'event_type', 'event_time', 'properties'])
    client.insert('user_events', df.values.tolist(), column_names=df.columns.tolist())
    print(f"Inserted {len(df)} rows of sample data")

print("Total records in table:", client.query('SELECT count() FROM user_events').result_rows[0][0])
print("\nSample data:")
for row in client.query('SELECT * FROM user_events LIMIT 5').result_rows:
    print(row)

Example output (abridged):

Total records in table: 1000

Sample data:
('00000000-0000-0000-0000-000000000304', 'user_4', 'signup',  '2025-03-02 16:14:06', '{"source": "example", "session_id": "7555"}')
('00000000-0000-0000-0000-0000000000d2', 'user_10', 'login',   '2025-03-02 16:43:24', '{"source": "example", "session_id": "5234"}')
('00000000-0000-0000-0000-0000000003b7', 'user_5', 'page_view','2025-03-02 16:47:54', '{"source": "example", "session_id": "3330"}')
('00000000-0000-0000-0000-00000000003f', 'user_2', 'click',    '2025-03-02 16:59:16', '{"source": "example", "session_id": "1956"}')
('00000000-0000-0000-0000-000000000070', 'user_3', 'page_view','2025-03-02 17:05:54', '{"source": "example", "session_id": "1825"}')

1) JDBC Connector Approach

This approach involves using Flink’s JDBC connector with the ClickHouse JDBC driver. You must provide both the Flink JDBC connector and ClickHouse JDBC driver JARs on the classpath.

Conceptual pattern (Flink SQL API / PyFlink):

from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment, EnvironmentSettings

env = StreamExecutionEnvironment.get_execution_environment()
t_env = StreamTableEnvironment.create(env)

# Ensure the JAR is actually available at the path below
t_env.get_config().get_configuration().set_string(
    "pipeline.jars", "file:///path/to/clickhouse-jdbc-0.4.6-all.jar"
)

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'
    )
""")

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'
    )
""")

# Would be executed with t_env.execute_sql("INSERT INTO clickhouse_sink SELECT ...")
# if all dependencies are present.

Limitations

No tight integration with Flink checkpoints → no true exactly‑once.
Throughput bottlenecks from JDBC overhead.
Error handling and retries are limited.

2) HTTP Interface Integration

This approach uses ClickHouse’s HTTP API. It removes JDBC overhead but requires custom sink code.

Conceptual pattern (custom sink‑like map with batching, for illustration only):

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

class ClickHouseHttpSink(MapFunction):
    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):
        self.batch.append(value)
        if len(self.batch) >= self.batch_size:
            self._flush()
        return value

    def _flush(self):
        if not self.batch:
            return
        data = '\n'.join([json.dumps(rec) for rec in self.batch])
        query = f"INSERT INTO {self.table} FORMAT JSONEachRow"
        try:
            r = requests.post(f"{self.url}?query={query}", data=data, headers={'Content-Type': 'application/json'})
            r.raise_for_status()
            self.batch = []
        except Exception as e:
            print(f"Error sending to ClickHouse: {e}")

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"})
    }

Limitations

Must be implemented as a proper sink operator for Flink (not just a map).
Still no checkpoint integration → at‑least‑once semantics.
Error recovery, retries, and partial failures must be handled manually.

3) Two‑Phase Commit with Temporary Tables

This pattern approximates transactional behavior by staging records in a temporary table and committing in batches to the target.

Conceptual pattern (simplified, Python‑like pseudo‑sink):

from pyflink.datastream import StreamExecutionEnvironment
from pyflink.datastream.functions import SinkFunction, RuntimeContext
import clickhouse_connect, hashlib, time

class ClickHouseTwoPhaseCommitSink(SinkFunction):
    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):
        self.batch.append(value)
        self.client.command(f"""
            INSERT INTO {self.temp_table} VALUES (
                '{value["event_id"]}', '{value["user_id"]}', '{value["event_type"]}',
                '{value["event_time"]}', '{value["properties"]}'
            )
        """)
        if len(self.batch) >= self.batch_size:
            self._commit_batch()

    def _create_temp_table(self):
        suffix = hashlib.md5(f"{self.target_table}_{time.time()}".encode()).hexdigest()[:8]
        self.temp_table = f"temp_{self.target_table}_{suffix}"
        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):
        if not self.batch:
            return
        self.client.command(f"INSERT INTO {self.target_table} SELECT * FROM {self.temp_table}")
        self.client.command(f"TRUNCATE TABLE {self.temp_table}")
        self.batch = []

    def close(self):
        if self.batch:
            self._commit_batch()
        if self.temp_table:
            self.client.command(f"DROP TABLE IF EXISTS {self.temp_table}")

Limitations

Increased storage and latency (staging + commit).
Complex error handling and cleanup.
Still not integrated with Flink’s 2PC → approximate exactly‑once at best.

4) Kafka as an Intermediary Layer

Instead of writing directly to ClickHouse, Flink writes to Kafka; ClickHouse consumes from Kafka via the Kafka engine and a materialized view.

Flink (conceptual, PyFlink SQL API):

from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment, EnvironmentSettings

env = StreamExecutionEnvironment.get_execution_environment()
settings = EnvironmentSettings.new_instance().in_streaming_mode().build()
t_env = StreamTableEnvironment.create(env, settings)

# Ensure the Kafka connector JAR is on the classpath
t_env.get_config().get_configuration().set_string(
    "pipeline.jars", "file:///path/to/flink-connector-kafka.jar"
)

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'
    )
""")

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'
    )
""")

t_env.execute_sql("""
    INSERT INTO kafka_events
    SELECT event_id, user_id, event_type, event_time, properties
    FROM source_events
""")

ClickHouse side (Kafka engine + MV to persist to user_events):

-- Kafka queue table
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';

-- Materialized view to persist data
CREATE MATERIALIZED VIEW user_events_mv TO user_events AS
SELECT event_id, user_id, event_type, event_time, properties
FROM

Limitations

Adds Kafka to the stack (extra infra + cost).
Coordination of offsets/checkpoints still needed for end‑to‑end exactly‑once.

Practical Impact on Data Pipelines

The table below summarizes the trade‑offs of each approach. (All ratings are relative.)

Approach	Implementation Complexity	Performance	Consistency Guarantees	Error Recovery	Operational Overhead	Maintenance Effort
JDBC Connector	Medium — external deps; standard APIs	Moderate — JDBC overhead & pooling	At‑least‑once — no checkpoint integration	Limited — basic retries	Medium — manage drivers & pools	Medium — upgrade JDBC, monitor pool
HTTP Interface	High — custom sink implementation	High — direct HTTP; good batching	At‑least‑once — no checkpoint integration	Manual — custom retry/partial‑fail	High — custom code path	High — track API changes, custom logic
Two‑Phase Commit	Very High — deep knowledge; staging/commit logic	Low‑Mod — extra stages add latency	Approx. exactly‑once — not true 2PC	Complex — careful cleanup needed	Very High — temp tables, extra queries	Very High — ongoing cleanup/monitoring
Kafka Intermediary	Medium — standard pattern, extra component	Moderate — extra hop, good throughput	Exactly‑once (with correct configuration)	Robust — Kafka DLQ/offset mgmt	Medium — operate Kafka cluster	Medium‑High — multi‑system monitoring & upgrades

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

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:

Limitations of Flink to ClickHouse Integration – What You Need to Know (GlassFlow)
Alternatives to Flink for ClickHouse Integration (GlassFlow)

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