Clickhouse and iceberg

Is common for ClickHouse to use S3 for data storage. And while it's not as common (yet) for ClickHouse to directly integrate with Iceberg as a table format, the integration is evolving and becoming increasingly relevant in modern data architectures.

Let's break down each part:

1. ClickHouse and S3 (Common and Yes):

Yes, it's very common for ClickHouse to interact with and utilize S3. In cloud deployments, especially on AWS (where S3 is native), it's a highly prevalent pattern.

Why is it common?

Scalability and Cost-Effectiveness: S3 provides highly scalable and cost-effective object storage. ClickHouse, designed for massive datasets, benefits greatly from this.

Separation of Compute and Storage: Using S3 allows for decoupling compute (ClickHouse servers) from storage. This enables independent scaling of both and is a hallmark of modern cloud architectures.

Cloud-Native Architecture: For organizations adopting cloud-native approaches, leveraging cloud-native storage like S3 alongside a cloud-native database like ClickHouse is a natural fit.

Data Lake Integration: S3 often forms the basis of data lakes. ClickHouse can be used to query data residing in these data lakes, making S3 integration crucial.

Backup and Restore: S3 is commonly used for ClickHouse backups and restores, providing durable and reliable storage for database snapshots.

How ClickHouse uses S3:

S3 Table Engine: ClickHouse provides the S3 table engine, which allows you to create tables that directly store data on S3. You can write and read data from these tables.

s3 Table Function: The s3 table function allows you to query data directly from files in S3 on-demand, without needing to create a permanent table. This is useful for ad-hoc queries and data exploration.

Materialized Views on S3 Tables: You can create materialized views based on S3 tables to pre-process and optimize data for faster querying.

Storage Configuration: ClickHouse can be configured to use S3 as an external or tiered storage for less frequently accessed data, extending the capacity of local disks and optimizing storage costs.

Backup and Restore Operations: ClickHouse's backup and restore tools can be configured to use S3 as the storage location for backups.

2. ClickHouse and Iceberg (Less Common Currently, but Evolving):

Currently, direct native integration of ClickHouse as an Iceberg-compatible query engine is less mature compared to systems like Spark, Flink, and Trino. However, this landscape is changing.

Why less common currently?

ClickHouse's Strengths Focused on Internal Data Management: ClickHouse is designed to be a high-performance database by managing its own data storage and indexing very efficiently. Iceberg is designed to manage external data lakes. Historically, ClickHouse users might have prioritized its native capabilities over adopting an external table format.

Maturity of Iceberg Integration: While there are efforts to integrate ClickHouse with Iceberg, the ecosystem around Iceberg and query engines is still evolving. Integrations are becoming stronger, but might not be as fully featured or widely adopted as native ClickHouse table engines or S3 integration.

Performance Considerations: Adding a layer of metadata management (like Iceberg) can introduce some overhead compared to directly querying data formats in S3. Users might have initially prioritized raw performance when using ClickHouse with S3 and avoided additional layers.

Why increasingly relevant and evolving?

Data Lake Architectures are Becoming Standard: Organizations are increasingly adopting data lake architectures built on object storage like S3. Managing these data lakes effectively becomes crucial.

Iceberg Solves Data Lake Governance Challenges: Iceberg addresses key challenges in data lakes, such as schema evolution, transactional consistency, and query planning. These benefits are valuable even when using a powerful engine like ClickHouse on top of a data lake.

Demand for Interoperability: Organizations often use multiple query engines (Spark, Flink, Trino, ClickHouse) on the same data lake. Iceberg provides a common table format that allows these engines to work together consistently on the same data.

Developing Integrations: There are ongoing efforts and projects to enhance ClickHouse's integration with Iceberg. This includes developing connectors and features to make ClickHouse a more first-class citizen in an Iceberg-managed data lake environment. You can find projects and discussions around using ClickHouse with Iceberg, indicating growing interest.

Current State of Iceberg and ClickHouse:

You might see ClickHouse used in architectures alongside systems that fully manage Iceberg tables (like Spark or Trino). ClickHouse could then query data within that data lake, potentially accessing it via mechanisms that leverage Iceberg metadata indirectly or through emerging connectors.

Direct, deeply integrated Iceberg support within ClickHouse as a native table engine is still developing, though the ecosystem is moving in that direction. Keep an eye on ClickHouse roadmap and community discussions for the latest developments.

3. Most Common Data Format Used by ClickHouse (External Data):

When ClickHouse interacts with external data sources (like files in S3, or data from other systems), the most common data formats used are:

CSV (Comma-Separated Values) and TSV (Tab-Separated Values): These are simple, text-based formats and are very widely supported. They are often used for initial data loading or simpler data exchange scenarios.

JSON (JavaScript Object Notation): ClickHouse has good support for parsing and processing JSON data. JSON is increasingly popular for semi-structured data.

Parquet: Parquet is a highly favored and increasingly common format for analytical workloads in data lakes, and thus for ClickHouse.

Columnar Format: Parquet is a columnar format, which aligns perfectly with ClickHouse's columnar database nature, allowing for efficient data reading.

Compression and Encoding: Parquet supports efficient compression and encoding schemes, reducing storage space and improving query performance.

Schema Evolution: Parquet supports schema evolution features.

Ecosystem Standard: Parquet is widely adopted in the big data ecosystem and is supported by many data processing tools and engines.

Avro: Avro is another row-based data serialization system that is also used, particularly in Hadoop and streaming data contexts. It offers schema evolution and is binary, making it more efficient than text-based formats like CSV.

Native ClickHouse Format: While not for general external data exchange, ClickHouse also has its own efficient binary format. This is primarily used for replication between ClickHouse servers and for very efficient data transfer when you control both ends of the data flow.

In Summary:

ClickHouse commonly uses S3 for scalable and cost-effective storage, especially in cloud environments. This integration is mature and well-established.

ClickHouse's direct integration with Iceberg as a native table format is less mature currently but is an evolving area of interest and development. The benefits of Iceberg for data lake governance are becoming increasingly relevant even for ClickHouse users.

The most common external data format for ClickHouse, particularly for analytical workloads and data lakes, is Parquet. CSV, TSV, JSON, and Avro are also frequently used, depending on the specific use case and data source.