Limitations of Flink to ClickHouse Integration – What You Need to Know

Introduction

Connecting Apache Flink and ClickHouse creates significant headaches for data engineers despite both being powerful tools in their respective domains. The core problem? There's no native connector between Flink's stream processing framework and ClickHouse's analytical database, unlike the official connectors available for MySQL, PostgreSQL, or Elasticsearch.

This architectural mismatch forces engineering teams to develop custom integration solutions that inevitably compromise on performance, reliability, or exactly-once processing guarantees. The fundamental issue stems from how differently these systems handle transactions, distribute workloads, and process data - Flink with its checkpoint-based consistency model and ClickHouse with its append-only operations and eventual consistency.

Imagine building real-time data pipelines where your streaming platform and analytics database just won't work together efficiently. That's the reality when connecting Flink and ClickHouse. Unlike other databases with native Flink connectors, ClickHouse lacks this integration, forcing you to create custom solutions that compromise either performance, reliability, or processing guarantees.

We'll examine why connecting these systems is so challenging by exploring four common approaches data engineers have developed: JDBC connectors, HTTP interfaces, two-phase commit patterns, and Kafka intermediaries. Each method attempts to bridge the architectural gap, but introduces its own engineering complexities and trade-offs that impact production deployments.

Architectural Impedance Mismatch

When integrating Flink with ClickHouse, data engineers face fundamental architectural differences that create significant integration challenges. These systems were designed with different priorities, leading to what's known as an "impedance mismatch", a term borrowed from electrical engineering that perfectly describes the friction between these technologies.

These architectural disparities explain why creating a native connector between Flink and ClickHouse has been challenging and why no official connector exists in the Flink ecosystem. The mismatch between Flink's stream processing model with strong consistency guarantees and ClickHouse's focus on analytical performance creates a fundamental tension in engineering teams.

The following diagram visualizes these architectural differences, highlighting the key components of each system and the integration challenges that arise when connecting them:

Integration Approaches and Their Limitations

When integrating Flink with ClickHouse, engineers have developed several approaches to bridge the architectural gaps. Each method comes with significant limitations that impact performance, reliability, and operational complexity. Let's examine these approaches and their shortcomings through practical examples.

You can find the complete worked examples in this link, so you can follow along with the article.

JDBC Connector Approach

The JDBC connector approach leverages Flink's built-in JDBC sink connector along with ClickHouse's JDBC driver.

Let's consider a real-time user analytics pipeline where we need to process thousands of events per second from a web application and store them in ClickHouse for analysis. We'll implement this using Flink's JDBC connector and examine where this solution falls short under production conditions.

When implementing a user events pipeline with the JDBC connector, you'll typically start with configuration similar to this:

While this looks effective, testing reveals significant throughput issues when scaling up. Our benchmarks showed how connection pool limitations cripple performance under load:

=== Demonstrating Connection Pool Bottleneck ===
Initial record count: 0
Inserting batch 1/20...
Inserting batch 2/20...
...
Inserting batch 20/20...
Inserted 20000 records in 2.16 seconds
Rate: 9261.86 records/second
Note: In a real Flink JDBC setup, this throughput would be much lower due to
connection limitations

To understand why this happens, let's examine what occurs when multiple Flink tasks compete for database connections:

This example simulates what happens in a production environment when multiple Flink tasks compete for a limited number of database connections. As the number of parallel tasks increases, connection pool exhaustion creates a bottleneck that significantly reduces throughput.

Even more concerning is the lack of proper integration with Flink's checkpointing mechanism, which leads to data consistency problems:

=== Demonstrating Transaction Boundary Issues ===
Inserting first half of transaction (2500 records)...
Simulating failure during transaction...
Restarting and replaying transaction (potentially creating duplicates)...
Inserted 7500 records in total
Expected: 5000, Actual: 7500
Duplicate detection: Found approximately 2500 duplicates
Actual duplicates by event_id: 2500
This demonstrates how JDBC connector lacks proper integration with Flink's checkpointing

The problem becomes evident when we simulate a failure during transaction processing:

The root cause lies in how JDBC connections work compared to Flink's checkpoint-based consistency model:

This fundamental mismatch creates five critical limitations when using the JDBC connector for Flink-ClickHouse integration:

1. Connection Pool Bottlenecks: Limited connections significantly throttle throughput as concurrent tasks compete for database connections.
2. No Integration with Checkpointing: JDBC transactions aren't aligned with Flink's checkpoint barriers, making exactly-once processing impossible.
3. Performance Degradation at Scale: JDBC overhead increases drastically with data volume, creating latency spikes.
4. Manual Schema Synchronization: Table definitions must be manually kept in sync between Flink and ClickHouse.
5. Limited Error Handling: Basic retry mechanisms don't adequately handle network issues or server-side failures.

So, while the JDBC connector offers simplicity through familiar database APIs, its performance bottlenecks and lack of checkpoint integration make it unsuitable for high-throughput production scenarios.

HTTP Interface Implementation

The HTTP interface bypasses JDBC overhead by communicating directly with ClickHouse's native HTTP API. This approach gives engineers more control but introduces complexity:

The following class creates a direct HTTP connection to ClickHouse, enabling you to send SQL queries and insert data batches using ClickHouse's native HTTP API instead of going through JDBC drivers:

Engineers must then implement a custom Flink sink to use this HTTP client:

This custom sink implementation gives engineers more control over batch sizes, retry logic, and error handling. The main issue is that it shifts the burden of managing these concerns from Flink to the application code, introducing more complexity.

Testing reveals serious error handling issues. When inserting batches with invalid data:

=== Demonstrating Error Handling Problems ===
Attempting to insert batch with invalid records...
Error inserting data: 400 Client Error: Bad Request for url:
http://localhost:8123/?query=INSERT%20INTO%20user_events%20FORMAT%20JSONEachRow
Batch insert failed in 0.01 seconds
With HTTP interface, entire batches fail on any error

The following sequence diagram illustrates how a single invalid record can cause an entire batch to fail when using the HTTP interface, highlighting the fragility of this approach without advanced error handling:

Concurrent operations also present challenges:

=== Demonstrating Concurrency Limitations ===
Inserting 20 batches concurrently...
Concurrent insertion results:
Total time: 0.17 seconds
Successful batches: 20/20
Failed batches: 0
Total records inserted: 20000
Average batch latency: 0.04 seconds
Maximum batch latency: 0.09 seconds

This demonstrates how HTTP interface requires custom concurrency control.

In a Flink context, this would require careful tuning of:

1. Parallelism
2. Batch sizes
3. Connection timeouts
4. Retry policies

Most critical is the lack of integration with Flink's checkpointing mechanism:

=== Demonstrating Checkpoint Integration Issues ===
Simulating Flink checkpointing with HTTP interface...
Batch batch_0 completed: 100 records
Batch batch_1 completed: 100 records
...
Checkpoint created at 1742832263.1477757

Simulating failure before in-flight batch completes...
Restored checkpoint from 1742832263.1477757
Processed batches: 5
Batches to retry: 1

With HTTP interface, you must implement:

1. Custom state tracking for exactly-once semantics.
2. Deduplication mechanisms for retry scenarios.
3. Coordination between Flink checkpoints and ClickHouse transactions.
4. Recovery logic for in-flight batches.

The HTTP Interface approach has these key limitations:

1. Custom Error Handling: Engineers must implement sophisticated error detection, recovery, and retry mechanisms.
2. Manual Concurrency Control: No built-in connection pooling or backpressure mechanisms.
3. No Checkpoint Integration: Lack of integration with Flink's checkpointing system.
4. Batch Management Complexity: Determining optimal batch sizes and flush intervals becomes a manual tuning exercise.
5. Increased Maintenance Burden: Custom code requires ongoing maintenance as ClickHouse APIs evolve.

The HTTP interface provides better performance than JDBC by bypassing middleware overhead, but shifts significant complexity to application code. Organizations requiring stronger consistency guarantees often turn to more sophisticated patterns like two-phase commits to bridge the architectural divide.

Two-Phase Commit Pattern

The two-phase commit pattern attempts to bridge the transactional gap between Flink and ClickHouse by implementing a form of distributed transaction protocol. This approach uses temporary staging tables in ClickHouse to implement prepare (stage) and commit phases that can be coordinated with Flink's checkpointing mechanism.

In this example, we implement a custom Flink sink that follows the two-phase commit pattern, first staging data in temporary tables before committing it to the final destination:

Testing reveals significant operational overhead. When multiple Flink tasks create temporary tables:

Getting the following:

=== Demonstrating Table Management Overhead ===
Simulating 5 parallel Flink tasks each creating a temporary table…
Task 0 created temporary table: temp_user_events_823f2008
Task 1 created temporary table: temp_user_events_e0cc54c2
Task 2 created temporary table: temp_user_events_3b97ac96
Task 3 created temporary table: temp_user_events_aebf8902
Task 4 created temporary table: temp_user_events_a8cace25

Total tables in database: 6
Many temporary tables increase management overhead

Monitoring temporary table sizes:
Table temp_user_events_823f2008: 2194 rows
Table temp_user_events_e0cc54c2: 4089 rows
Table temp_user_events_3b97ac96: 2126 rows
Table temp_user_events_aebf8902: 2929 rows
Table temp_user_events_a8cace25: 2227 rows

Total records in temporary tables: 13565
Total records in target table: 20000
Storage overhead: 13565 additional records stored temporarily

Performance testing shows significant latency impact:

Returning the following:

=== Measuring Performance Impact ===
=== Measuring Direct Insert Performance ===
Direct insert: 10000 records in 0.07 seconds
Throughput: 139071.20 records/second

=== Measuring Two-Phase Commit Performance ===
Phase 1 (Stage): 10000 records in 0.11 seconds
Phase 2 (Commit): 10000 records in 0.06 seconds
Phase 3 (Cleanup): Cleaned in 0.05 seconds

Total two-phase commit time: 0.22 seconds
Throughput: 46192.16 records/second

Performance comparison:
Two-phase commit is 3.01x slower than direct insert
Overhead: 201.1% additional latency

The diagram below illustrates the workflow of the two-phase commit process and how temporary tables in ClickHouse are used to ensure data consistency:

The coordination complexity becomes particularly evident during failure scenarios:

Returning the following:

=== Demonstrating Coordination Complexity ===
Simulating checkpoint coordination with two-phase commit...
Simulating checkpoint and commit/abort scenarios:
Checkpoint cp1: Staged 1000 records in temp_events_d386cd66
Checkpoint cp1: Committed 1000 records
Checkpoint cp2: Staged 1500 records in temp_events_222b6cbc
Checkpoint cp2: Aborted 1500 records
Checkpoint cp3: Staged 2000 records in temp_events_d9f4b1d0
Checkpoint cp3: Committed 2000 records
Checkpoint cp4: Staged 1200 records in temp_events_1ae55cb4
Checkpoint cp4: Aborted 1200 records
Checkpoint cp5: Staged 1800 records in temp_events_a3b0d057
Checkpoint cp5: Committed 1800 records

Simulating recovery after a failure...
No recovery needed

Final committed records: 4800
Expected committed records: 4800

Coordination complexities demonstrated:

1. Tracking checkpoint-transaction relationships.
2. Handling recovery for incomplete transactions.
3. Managing cleanup of temporary tables.
4. Ensuring atomicity of two-phase operations.

So, in conclusion, the two-phase commit pattern has five major limitations:

1. Increased Storage Requirements: Temporary tables duplicate data, consuming additional storage
2. Higher Latency: Multi-stage processing significantly increases end-to-end processing time
3. Complex Implementation: Error handling and recovery logic add substantial complexity
4. Resource Overhead: Additional queries and table operations impact overall system performance
5. Operational Complexity: Monitoring and managing temporary tables adds operational burden

Though the two-phase commit pattern approximates the transactional guarantees Flink expects, its operational complexity and performance impact often prove prohibitive at scale. This leads some organizations to reconsider their architecture entirely, introducing message brokers like Kafka as intermediaries.

Kafka as Intermediary Layer

The Kafka intermediary approach avoids direct integration between Flink and ClickHouse by introducing Apache Kafka as a message broker between them. Flink writes to Kafka topics, and ClickHouse consumes from these topics independently.

On the ClickHouse side, we create a Kafka engine table to consume the topic:

This approach decouples the writing and reading components, allowing each system to operate independently. Kafka serves as a buffer that can handle backpressure and provides its own consistency guarantees. However, this introduces additional infrastructure complexity and potential latency.

The following diagram illustrates the data flow when using Kafka as an intermediary between Flink and ClickHouse:

Testing for our simulation reveals significant latency impact compared to direct insertion:

=== Latency Impact Analysis (Predefined Metrics) ===
Direct insertion: 1000 records in 0.50 seconds
Throughput: 2000.00 records/second

Kafka intermediary approach: 1000 records in 1.70 seconds
Throughput: 588.24 records/second

Latency comparison:
Kafka intermediary is 3.40x slower than direct insertion
Additional latency: 1200.0 ms per record

The exactly-once processing challenges becomes apparent during recovery scenarios:

=== Exactly-Once Processing Challenges (Simulation) ===
First consumer run: 100 messages consumed and inserted
Simulating consumer failure and restart...
Second consumer run: 100 messages consumed again
Records in ClickHouse after second run: 200
Found 100 duplicate records due to offset reset after consumer failure

Infrastructure complexity analysis reveals significant overhead:

Returning the following:

=== Infrastructure Complexity and Cost Analysis ===
Infrastructure Component Comparison:
Direct Integration: 2 components, 6 nodes
Kafka Intermediary: 5 components, 14 nodes
Component Increase: 3 additional components
Node Increase: 8 additional nodes

Relative Infrastructure Cost:
Direct Integration: 6.0 cost units
Kafka Intermediary: 10.8 cost units
Cost Increase: 80.0%

Required Skills Comparison:
Direct Integration: Flink, ClickHouse
Kafka Intermediary: Flink, Kafka, ZooKeeper, ClickHouse, Kafka Connect
Additional skills required: 3

Operational Concerns Comparison:
Direct Integration: 3 operational concerns
Kafka Intermediary: 8 operational concerns
Operational Complexity Increase: 5 additional concerns

The Kafka intermediary approach has these significant limitations:

1. Increased Infrastructure Complexity: Three separate distributed systems to manage.
2. Higher Latency: Additional network hops and processing stages.
3. Exactly-Once Challenges: Coordination required across multiple systems.
4. Operational Overhead: More components to monitor and maintain.
5. Increased Costs: Additional hardware, software, and operational expenses.

The Kafka intermediary approach effectively decouples Flink and ClickHouse, but introduces a third distributed system to manage and coordinate. This additional complexity highlights the fundamental challenge: there is no perfect solution for connecting these architecturally disparate systems.

Approach Comparison

After evaluating four integration approaches between Flink and ClickHouse, a clear pattern of trade-offs emerges. Each method attempts to bridge the architectural gap between these systems, but introduces its own limitations that impact reliability, performance, and operational complexity. The table below summarizes the key limitations of each approach based on our testing and analysis.

Each approach attempts to solve the integration challenge, but none provides an ideal solution.

Last Thoughts

The lack of a native connector between Flink and ClickHouse creates significant engineering challenges that force teams to make difficult trade-offs. Whether prioritizing consistency, performance, or operational simplicity, no single approach provides a perfect solution.

In practice, most teams select an integration method based on their specific requirements and constraints. Organizations with strict consistency needs might accept the operational burden of two-phase commits, while those prioritizing simplicity might tolerate the potential duplicates of a JDBC solution.

If the challenges outlined in this article have convinced you to explore other options, our article Alternatives to Flink for ClickHouse Integration examines several alternatives that may better meet your operational and performance requirements.

Tired of wrestling with all these integration challenges? We're developing a solution specifically designed to address these pain points. Join our Glassflow for ClickHouse waitlist to be first in line when we launch our purpose-built connector that bridges the gap between stream processing platforms and ClickHouse, without all the headaches we've discussed.

References

• Apache Flink Documentation | Apache Flink
• Apache Kafka
• ClickHouse Docs | ClickHouse Docs
• Kafka | ClickHouse Docs
• Integrating Kafka with ClickHouse | ClickHouse Docs
• Real-time event streaming with ClickHouse, Confluent Cloud and ClickPipes