Real-time data processing solutions continue to evolve as organizations face increasingly complex data pipeline challenges. GlassFlow for ClickHouse Streaming ETL has emerged as a specialized tool for managing data flows between Kafka and ClickHouse, with a particular focus on solving the persistent problem of data duplication in streaming pipelines.
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| The GitHub repository for GlassFlow, showcasing a real-time data processing solution for Kafka and ClickHouse |
Deduplication Approach Sparks Technical Curiosity
The community has shown significant interest in GlassFlow's deduplication mechanism, with several experts questioning how it compares to existing solutions. One commenter raised a direct comparison with ClickHouse's built-in ReplacingMergeTree engine, which already provides deduplication capabilities, albeit with potential read-time costs and schema design considerations.
How is this better than using ReplacingMergeTree in ClickHouse? RMT dedups automatically albeit with a potential cost at read time and extra work to design schema for performance.
This highlights a key consideration for potential users: whether to handle deduplication at the database level or earlier in the data pipeline. GlassFlow's approach performs deduplication before data reaches ClickHouse, potentially offering performance advantages but requiring additional infrastructure.
Implementation Details Under Scrutiny
Data engineers with experience in building deduplication systems have expressed skepticism about the lack of technical details provided about GlassFlow's implementation. Scalable deduplication presents numerous challenges including handling network latencies, managing partitioned data streams, and ensuring fault tolerance. These concerns reflect the complexity of building reliable deduplication systems that maintain high throughput.
The project documentation describes configurable time windows for deduplication of up to 7 days and simple configuration of deduplication keys, but the underlying mechanisms that make this possible at scale remain unclear to the community. This has led to comparisons with other established deduplication systems like Segment's exactly-once delivery pipeline.
Flexibility and Performance Questions
Representatives from ClickHouse itself have shown interest in understanding the scope of GlassFlow's deduplication capabilities, particularly whether it works only for entire duplicate rows or can handle partial column conflicts. The creator confirmed that the current implementation focuses on deduplicating before ingestion into ClickHouse, suggesting a whole-row approach rather than column-level deduplication.
Performance testing has been conducted, with the developers reporting throughput of approximately 15,000 requests per second on a MacBook Pro M2 running in Docker. However, community members have requested more comprehensive load testing information, which would help potential users evaluate the solution's suitability for production environments.
Potential for Broader Application
While GlassFlow currently targets the specific Kafka-to-ClickHouse pipeline, community discussion has revealed interest in expanding its capabilities. Questions about supporting additional data sources beyond Kafka and destinations beyond ClickHouse suggest there's demand for a more versatile solution.
The project creators have indicated that the architecture is designed to be extensible, with the potential to add more sources and sinks. They noted that the initial focus on Kafka and ClickHouse was driven by the needs of early users who already had Kafka in their data stack and were building real-time analytics with ClickHouse.
The community has also expressed interest in direct integration with NATS, which would be possible given that GlassFlow already uses the NATS Kafka Bridge internally.
In an increasingly complex data engineering landscape, tools like GlassFlow represent specialized solutions to specific pain points. While the community has raised valid questions about implementation details and comparative advantages, the focus on solving real-world streaming deduplication challenges addresses a genuine need for many organizations building real-time data pipelines.
Reference: GlassFlow for ClickHouse Streaming ETL