Overview
The article discusses the new capabilities of ClickHouse Cloud to query Iceberg and Delta Lake tables through the DataLakeCatalog engine. It highlights the integration with AWS Glue Catalog and Databricks Unity Catalog, enabling users to discover and query lakehouse tables seamlessly.
What You'll Learn
1
How to connect ClickHouse Cloud to AWS Glue Catalog for querying Iceberg tables
2
How to utilize the DataLakeCatalog engine for querying Delta Lake tables
3
Why ClickHouse's parallel query scaling enhances performance across large datasets
4
When to use federated queries across different catalogs in ClickHouse
Key Questions Answered
How can ClickHouse query Iceberg and Delta Lake tables?
ClickHouse can query Iceberg and Delta Lake tables directly through the DataLakeCatalog engine, which connects to catalogs like AWS Glue and Databricks Unity Catalog. This integration allows for automatic discovery and querying of lakehouse tables at high performance.
What are the benefits of using the DataLakeCatalog engine in ClickHouse Cloud?
The DataLakeCatalog engine enables users to query metadata from lakehouse catalogs, allowing for efficient querying of Iceberg and Delta Lake tables as if they were native ClickHouse tables. This enhances the performance and flexibility of data analytics across various data sources.
What is the significance of the distributed cache layer in ClickHouse Cloud?
The distributed cache layer in ClickHouse Cloud provides shared, low-latency access to frequently accessed data across compute nodes, significantly reducing read times from S3 and enabling stateless, elastic compute capabilities. This enhances overall query performance.
How does ClickHouse handle parallel query scaling?
ClickHouse efficiently scales analytical queries across all CPU cores and compute nodes, delivering sub-second results even on datasets with tens or hundreds of billions of rows. This is achieved through a partial-aggregation-state execution model that distributes work by Parquet file.
Key Statistics & Figures
Average Parquet query speed improvement
1.8×
This improvement is observed across ClickBench when using the new native Parquet reader in ClickHouse.
Row processing speed
977.46 million rows/s.
This speed was achieved during a query on the Iceberg table, demonstrating ClickHouse's high-performance capabilities.
Data processed in a single query
339.04 million rows
This was processed in under 0.35 seconds during a demonstration query on the Iceberg table.
Technologies & Tools
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Database
Clickhouse
Used as the high-performance lakehouse query engine.
Data Catalog
AWS Glue Catalog
Provides metadata for querying Iceberg tables.
Data Catalog
Databricks Unity Catalog
Manages Delta Lake tables and provides metadata for querying.
Data Format
Apache Iceberg
Open table format supported by ClickHouse for querying.
Data Format
Delta Lake
Another open table format supported by ClickHouse for querying.
Key Actionable Insights
1Leverage the DataLakeCatalog engine to connect ClickHouse with your existing data catalogs for seamless querying.This integration allows for immediate access to your Iceberg and Delta Lake tables, enhancing your data analytics capabilities without the need for extensive data migrations.
2Utilize the distributed cache feature in ClickHouse Cloud to improve query performance on frequently accessed data.By caching hot data, you can significantly reduce latency and improve the responsiveness of your analytics applications, especially when dealing with large datasets.
3Explore federated queries to combine data from multiple catalogs in a single query.This capability allows for more comprehensive analytics, enabling insights that span across different data sources without the need for data duplication.
Common Pitfalls
1
Failing to properly configure the DataLakeCatalog engine can lead to issues in querying external tables.
Ensure that the correct catalog type and credentials are specified when setting up the connection to avoid access errors.
Related Concepts
Datalakecatalog Engine
Federated Queries
Performance Optimization In Clickhouse