How Flink and ClickHouse Combine to Build High‑Performance Real‑Time Data Warehouses

1. Overview

Apache Flink is a leading stream‑processing engine known for ease of use, high throughput, low latency, rich operators, and native state support. ClickHouse is an emerging OLAP database that offers exceptional query performance and a rich set of analytical functions. Neither component alone solves all real‑time data‑warehouse problems, but together they can achieve higher efficiency and happier development.

2. Factors That Impact Massive Data Query Efficiency

First factor – Diverse business requirements and complex analysis pipelines. In an e‑commerce scenario, growth analysts need user‑profile data for precise marketing, while risk‑control teams need the same raw data for machine‑learning models. Uncoordinated direct reads from source systems cause high load, duplicate development, and a "silo" architecture.

Second factor – Large and heterogeneous data volumes. Traditional OLTP databases (MySQL, PostgreSQL, Oracle) handle high‑concurrency writes well but perform poorly on complex analytical queries. OLAP engines such as ClickHouse excel at large‑scale aggregations but lack strong transactional guarantees, making them unsuitable as the sole data store.

Third factor – Variety of data sources and formats. Modern pipelines ingest click streams from Kafka, dimension tables from HBase, and transaction logs from relational databases. Normalizing and joining these heterogeneous sources is time‑consuming and error‑prone.

3. How a Real‑Time Data Warehouse Solves These Problems

A typical real‑time warehouse consists of five layers: ODS (raw source), DIM (dimension tables), DWD (detail fact tables), DWS (summary tables), and ADS (application‑specific tables). Data flows from ODS through CDC tools (Debezium, Canal) into ClickHouse for analytical queries, while Flink performs stream‑level joins and dynamic table creation before writing results to ClickHouse.

Key capabilities:

External applications (user profiling, recommendation, real‑time dashboards, risk monitoring) consume pre‑aggregated tables from the ADS layer.

The Application layer provides KV stores (HBase), relational services (PostgreSQL), OLAP services (ClickHouse), and search (Elasticsearch) for downstream apps.

The Summary layer (DWS) uses Flink to perform high‑throughput dynamic joins, then writes wide tables to ClickHouse for fast ad‑hoc queries.

The Detail layer (DWD) stores cleaned fact tables; the Dimension layer (DIM) holds slowly changing reference data.

4. Flink Is ClickHouse’s Best Companion

ClickHouse delivers column‑store compression, vectorized execution, and distributed processing, outperforming many other data‑processing systems on large‑scale queries (example shown with a 1‑billion‑row benchmark).

However, ClickHouse has limitations:

Not suited for high‑frequency single‑row writes (risk of "Too many parts" errors).

Poor at frequent updates/deletes, leading to temporary data inconsistency.

Weak at multi‑table joins across different storage engines.

Limited ecosystem support for diverse streaming sources.

These shortcomings align perfectly with Flink’s strengths:

High‑throughput, low‑latency stream processing.

Dynamic table‑mapping model that handles frequent updates via retract or upsert streams.

Rich connector ecosystem for Kafka, HBase, MySQL binlog, etc.

Powerful state management and windowing for precise join semantics.

5. Building a Stable Real‑Time Warehouse (v1.0)

When writing data from Flink to ClickHouse, two options exist: distributed tables (simpler but add latency) or local tables (lower latency, better load distribution). Strategies for local‑table writes include random node selection, round‑robin, or hash‑based routing to keep related keys on the same node.

For mutable streams, Flink’s Retract Stream or Upsert Stream can be mapped to ClickHouse’s CollapsingMergeTree engine, using a sign column to cancel outdated records and ensure accurate aggregates.

Additional considerations for a robust Flink‑ClickHouse connector include batch‑write buffering, fault‑retry policies, and SQL‑type mapping.

6. Evaluating Warehouse Quality

Key quality dimensions are consistency, accuracy, fault tolerance, and latency. Business teams must enforce schema standards; platform teams must provide metadata management, state snapshots, and watermark handling to mitigate out‑of‑order data.

When migrating from offline to real‑time warehouses, dual‑write verification and tools like Apache Griffin should be used to ensure result parity.

7. Architecture Evolution

Beyond the classic Lambda architecture (separate batch and stream layers), the Kappa architecture merges them into a single streaming pipeline, reducing development and operational overhead but requiring long‑term message retention.

To address Kappa’s drawbacks, modern table formats such as Apache Iceberg are introduced. Iceberg supports both streaming and batch reads/writes, provides efficient time‑travel for historical data, and integrates with Flink to build a unified real‑time warehouse.

8. Summary and Outlook

As data volumes grow, traditional OLTP databases become insufficient for analytical workloads, prompting the evolution from offline OLAP warehouses to Lambda and Kappa real‑time solutions. Combining Flink’s streaming capabilities with ClickHouse’s fast OLAP queries, and augmenting them with Iceberg’s unified table format, offers a scalable path toward batch‑stream fused warehouses (v2.0). Future work will focus on providing end‑to‑end solutions that maximize data value, accelerate digital transformation, and achieve efficiency gains.