Overview
The article discusses how ClickHouse efficiently queries Parquet files, a key storage format for Lakehouse architectures, without requiring data ingestion. It highlights ClickHouse's long-standing optimization for Parquet, its unique querying capabilities, and upcoming enhancements aimed at improving performance further.
What You'll Learn
1
How to query Parquet files directly using ClickHouse without ingestion
2
Why ClickHouse is optimized for Lakehouse architectures
3
When to apply parallelism and I/O reduction techniques in ClickHouse
Prerequisites & Requirements
- Understanding of data lake and Lakehouse concepts
- Familiarity with ClickHouse and Parquet file format(optional)
Key Questions Answered
How does ClickHouse query Parquet files without ingestion?
ClickHouse can directly query Parquet files by utilizing its query engine, which reads and processes data without requiring prior ingestion. This capability allows for faster query execution compared to many systems that necessitate data loading into native formats.
What performance improvements are expected with the new Parquet reader?
The upcoming native Parquet reader aims to enhance performance by eliminating the Arrow dependency, enabling better parallelism and I/O efficiency. It will support dictionary filtering and page-level min/max statistics, which will further reduce unnecessary data processing.
What are the key factors affecting Parquet query performance in ClickHouse?
Parquet query performance in ClickHouse is influenced by the level of parallelism and the degree of I/O reduction. Efficiently managing file reads and minimizing irrelevant data processing are crucial for optimizing query execution times.
Key Statistics & Figures
Number of file formats ClickHouse can query directly
70+
This capability allows users to run SQL queries on various data formats without needing to ingest them first.
Cold runtime for ClickBench query 41 on Parquet
170 milliseconds
This demonstrates the efficiency of ClickHouse when querying Parquet files directly, compared to other formats.
Total data processed for all 43 ClickBench queries on Parquet
468 GiB
This indicates the effectiveness of ClickHouse's optimizations, processing significantly less data than would be required without filtering.
Technologies & Tools
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Database
Clickhouse
Used for querying Parquet files directly and optimizing Lakehouse analytics.
File Format
Parquet
Serves as the primary storage format for data being queried in ClickHouse.
Library
Arrow
Used in the current Parquet reader implementation for data parsing.
Key Actionable Insights
1Utilize ClickHouse's ability to query Parquet files directly for faster analytics.By leveraging ClickHouse's direct querying capabilities, teams can significantly reduce the time spent on data ingestion, allowing for quicker insights and more agile data analysis.
2Implement parallelism strategies when querying large datasets in ClickHouse.Understanding how to configure parallel processing can lead to substantial performance improvements, especially when dealing with extensive data stored in Parquet format.
3Take advantage of I/O reduction techniques to optimize query performance.Applying techniques like predicate pushdown and column projection can help minimize the amount of data scanned, thus enhancing the overall efficiency of queries executed on ClickHouse.
Common Pitfalls
1
Overlooking the importance of configuring parallelism settings in ClickHouse.
Failing to optimize parallel processing can lead to suboptimal performance, especially when querying large datasets. It is crucial to understand how to adjust settings like max_threads and max_parsing_threads to fully utilize available resources.
Related Concepts
Data Lake
Lakehouse Architecture
Open Table Formats Like Apache Iceberg And Delta Lake