ClickHouse^® Kafka Engine vs Tinybird Kafka Connector

Both ClickHouse® Kafka Engine and Tinybird's Kafka connector can ingest data from Kafka into ClickHouse. They solve the same problem but with different approaches and tradeoffs.

ClickHouse Kafka Engine is a native feature that runs inside your ClickHouse cluster. Tinybird's connector is a managed service that handles Kafka consumption separately from your ClickHouse cluster.

While there are other vendors offering Kafka to ClickHouse solutions, this comparison focuses on Tinybird's value proposition versus the open source ClickHouse Kafka Engine. This comparison covers the practical differences, failure modes and when each solution makes sense.

There's no universal "best" choice, it depends on your priorities, team size and operational requirements.

Overview

ClickHouse Kafka Engine runs as part of your ClickHouse cluster. It uses Kafka consumers that pull messages directly into ClickHouse tables.

Configuration is done via SQL DDL statements and it requires managing the ClickHouse cluster itself. Operating Kafka Engine effectively requires a dedicated engineering team with ClickHouse expertise to handle monitoring, scaling and troubleshooting.

Tinybird Kafka Connector is a managed service that consumes from Kafka and writes to Tinybird's ClickHouse infrastructure. It's serverless, auto-scales and includes built-in observability and failure handling.

Tinybird also offers self-hosted options for teams that need on-premises or self-managed deployments.

Pricing and Support

ClickHouse Engine: Gives you full control but requires managing infrastructure yourself. You pay for what you use, controlling costs but handling capacity planning. No enterprise support unless you buy ClickHouse Cloud or enterprise licenses.

Tinybird's connector: Managed service included in Tinybird plans with no extra cost. Enterprise support is available with direct access to the team. Self-hosted options available for on-premises deployments.

Tradeoff: ClickHouse Engine gives you full control but requires you to manage infrastructure and rely on community support. Tinybird's connector is managed but requires a Tinybird account.

Scaling and Performance

Auto scaling and Resource Management

ClickHouse Kafka Engine: Runs on your cluster and requires manual scaling. You scale by adjusting cluster size or changing kafka_num_consumers, but this requires restarts and causes downtime.

Kafka consumption competes with queries for cluster resources. Heavy queries slow down ingestion and heavy ingestion slows down queries. You need to balance CPU, memory and I/O between ingestion and queries, which gets complex at scale.

Tinybird's connector: Automatically scales based on load. It runs separately from queries, so ingestion doesn't affect query performance. No configuration or restarts needed.

Data Ingestion Model

ClickHouse Kafka Engine: Uses a pull based model. It pulls messages from Kafka in batches at intervals, not true streaming. There's inherent latency in the polling mechanism.

The kafka_max_block_size and kafka_poll_timeout_ms settings control how many messages are pulled per batch and how long to wait, but you're always working within a polling model.

Sub second flushing is configurable via kafka_flush_interval_ms, but this affects cluster performance. More frequent flushing means more I/O operations and potential contention with queries.

Tinybird's connector: Uses a push based model with low latency for faster processing. Sub second flushing is available on demand without affecting queries since ingestion runs separately.

High Scale Throughput

ClickHouse Kafka Engine: Works well for moderate throughput, but petabyte scale requires careful tuning. You manage consumer groups, partitions and resources yourself. The pull based model and resource contention add complexity at scale.

Tinybird's connector: Battle-tested at petabyte scale. Used by companies processing billions of events daily. Automatic scaling handles high throughput without manual tuning.

Tradeoff: ClickHouse Engine gives you direct control over scaling but requires manual management. Tinybird's connector handles scaling automatically but you have less direct control over the underlying mechanisms.

Operations and Failure Handling

Failure Handling and Resilience

ClickHouse Kafka Engine: No built-in circuit breaker. Backpressure is implicit via block size settings, but it can still overwhelm the cluster. If writes are slow, messages accumulate in memory and can cause out-of-memory errors.

There's no dead letter queue. Broken messages are skipped and lost, making debugging difficult. When you restart ClickHouse, Kafka consumption stops and you accumulate lag.

Failure recovery is manual. System tables provide information, but you build monitoring, alerting and recovery yourself.

Tinybird's connector: Built-in circuit breaker and backpressure protection. If writes are slow, it automatically throttles to prevent overwhelming the system.

Problematic messages go to quarantine for inspection. The connector handles deployments without interrupting ingestion. Failure recovery is automatic with clear guidance.

Configuration Management

ClickHouse Kafka Engine: Configuration updates require table recreation or cluster restarts, causing downtime.

Tinybird's connector: Supports hot reloading. You can change topics, schemas and settings through the CLI or UI with no downtime.

Tradeoff: ClickHouse Engine requires you to build failure handling yourself. Tinybird's connector includes these features but you have less visibility into the underlying mechanisms.

Schema and Serialization

Schema Evolution

ClickHouse Kafka Engine: Requires manual schema management. Changing schemas requires ALTER TABLE statements. If new messages arrive before you update, they may fail or be skipped.

The process involves detecting changes, planning migrations, coordinating with producers, running ALTER TABLE statements, updating dependencies and resuming ingestion. This requires careful coordination and can cause downtime or data loss.

Tinybird's connector: Handles schema evolution automatically via FORWARD_QUERY. Schema changes happen without downtime and backward compatibility is handled automatically. You define how to transform old data and the system applies transformations automatically.

Tradeoff: Both support the same formats, but Tinybird provides more tooling for schema management. ClickHouse Engine requires more manual work but gives you full control.

Observability

Monitoring and Metrics

ClickHouse Kafka Engine: Provides observability through system tables, but you build the monitoring yourself. Lag metrics are in system.kafka_consumers, but you write queries to track them. Insert latency isn't exposed natively, you calculate it from query logs.

You set up dashboards and alerts yourself. You can integrate with Grafana/Prometheus, but you configure everything yourself.

Tinybird's connector: Includes pre configured observability. Lag metrics are in kafka_ops_log with real-time visibility per partition. Insert latency is exposed in datasources_ops_log. Pre configured Grafana/Prometheus integrations are available.

Tradeoff: Both provide observability, but Tinybird's is pre configured and more comprehensive. ClickHouse Engine requires more setup but gives you direct access to system tables.

Developer Experience

Development Workflow

ClickHouse Kafka Engine: Configured via SQL DDL statements. No CLI, you write SQL to create and configure tables. This is SQL-native but requires more manual work.

Data as code requires manual SQL management. You version control DDL statements yourself and manage migrations across environments. Environment branching requires manual management of consumer group IDs and secrets.

Schema evolution requires manual ALTER TABLE statements. You plan, test and execute migrations yourself with no built-in tools.

Tinybird's connector: Includes a full CLI with tb commands. You can create, update and manage connectors from the command line, making it easy to automate.

The Tinybird code approach enables data as code. Configurations, schemas and transformations are stored as files you commit to git. Built-in branching lets you test changes safely in separate environments.

Schema evolution happens automatically via FORWARD_QUERY without manual migrations.

Tradeoff: ClickHouse Engine is more SQL-native but requires more manual work. Tinybird's connector provides more tooling but adds abstraction.

Choose ClickHouse Kafka Engine when

• You need full control over Kafka consumer settings and cluster resources
• You have a dedicated ops team to manage ClickHouse clusters and monitoring
• Cost optimization is critical and you can operate infrastructure efficiently
• You're already running ClickHouse and want to keep everything in one place
• You need to customize Kafka consumption behavior beyond what managed services offer
• You have strict compliance requirements that require on-premises or self-managed infrastructure

Choose Tinybird Kafka Connector when

• You want to focus on data pipelines rather than infrastructure management
• You need automatic scaling without manual intervention
• You have scaling problems with your current Kafka to ClickHouse setup
• Schema evolution is frequent and you want it handled automatically
• You need enterprise support and SLA guarantees
• You want built-in observability without setting up monitoring yourself
• You're building new pipelines and want to move fast with less operational overhead
• You need high availability without managing cluster failover yourself

Conclusion

Both solutions work well for Kafka to ClickHouse ingestion. The choice depends on your priorities:

• ClickHouse Kafka Engine gives you full control and keeps everything in your infrastructure, but requires more operational work.
• Tinybird Kafka Connector provides managed scaling, built-in observability and easier schema evolution, but adds a dependency on Tinybird's service.

There's no universal best choice. Teams with strong ops capabilities and cost sensitivity often choose Kafka Engine. Teams prioritizing developer velocity and operational simplicity often choose Tinybird's connector.

The good news: both use standard Kafka protocols, so you can migrate between them if your needs change.

Additional resources:

• ClickHouse Kafka Engine documentation
• Tinybird Kafka connector guide
• Kafka to ClickHouse monitoring guide