Stories with Tag columnar database

• ClickHouse gets lazier (and faster): Introducing lazy materialization

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  Posted: 2025-04-22 16:03:32

  Verbose tl;dr

  ClickHouse's new "lazy materialization" feature improves query performance by deferring the calculation of intermediate result sets until absolutely necessary. Instead of eagerly computing and storing each step of a complex query, ClickHouse now analyzes the entire query plan and identifies opportunities to skip or combine calculations, especially when dealing with filtering conditions or aggregations. This leads to significant reductions in memory usage and processing time, particularly for queries involving large intermediate data sets that are subsequently filtered down to a smaller final result. The blog post highlights performance improvements of up to 10x, and this optimization is automatically applied without any user intervention.

  The ClickHouse blog post, "ClickHouse gets lazier (and faster): Introducing lazy materialization," details a significant performance optimization implemented in the ClickHouse database system leveraging a technique called "lazy materialization." This technique fundamentally alters how ClickHouse handles intermediate data during query processing, leading to substantial improvements in speed, particularly for complex queries involving multiple transformations.

  Traditionally, ClickHouse, like many database systems, materialized, meaning physically stored, the intermediate results of each step in a multi-stage query. For instance, if a query involved filtering, aggregating, and then sorting data, the results of the filtering stage would be fully computed and stored before the aggregation commenced, and the aggregated results would be materialized before sorting began. This approach, while straightforward, can be inefficient, especially when subsequent stages drastically reduce the data volume or when specific intermediate results become unnecessary due to later filtering. It involves unnecessary writing and reading data, consuming both time and storage resources.

  Lazy materialization, as introduced in ClickHouse, optimizes this process by delaying the computation and materialization of intermediate results until absolutely necessary. Instead of fully computing and storing each stage's output, ClickHouse now constructs a logical representation of the transformations required. This representation, referred to in the post as a "pipeline," describes the series of operations to be performed without immediately executing them. Only when the final result set is requested, perhaps for display to the user or for further processing, does ClickHouse traverse this pipeline, effectively "pulling" the data through the necessary transformations.

  This on-demand execution allows ClickHouse to apply multiple operations simultaneously, essentially fusing them together. Imagine a query that filters, aggregates, and then filters again. With lazy materialization, ClickHouse can combine these filtering steps, processing each row only once and applying both filter conditions concurrently. This eliminates the overhead of storing and retrieving intermediate results, reducing I/O operations and significantly speeding up the overall query execution.

  Furthermore, the blog post highlights the intelligent optimization potential unlocked by lazy materialization. Because the entire query plan is available before execution begins, ClickHouse can analyze the pipeline and identify further optimizations. For instance, it might rearrange operations for better efficiency, eliminate redundant computations, or leverage specific data structures suited to the combined operations.

  The post emphasizes that this lazy materialization approach represents a fundamental shift in ClickHouse's query execution engine and that it is designed to be transparent to the user. Existing queries should benefit automatically without requiring any modification. The developers highlight various benchmark results demonstrating substantial performance gains, particularly in complex queries involving multiple transformations. These improvements translate to faster query responses, reduced resource consumption, and enhanced overall system efficiency.

  • ClickHouse
  • Database
  • columnar database
  • Query Optimization
  • performance
  • Lazy Materialization
  • Materialization
  • data processing
  • big data
  • Analytics

  Summary of Comments ( 8 )
  https://news.ycombinator.com/item?id=43763688

  Verbose tl;dr

  HN commenters generally praised ClickHouse's lazy materialization feature. Several noted the cleverness of deferring calculations until absolutely necessary, highlighting potential performance gains, especially with larger datasets. Some questioned the practical impact compared to existing optimizations, wondering about specific scenarios where it shines. Others pointed out similarities to other database systems and languages like SQL Server and Haskell, suggesting that this approach, while not entirely novel, is a valuable addition to ClickHouse. One commenter expressed concern about potential debugging complexity introduced by this lazy evaluation model.

  The Hacker News post discussing ClickHouse's lazy materialization feature has a moderate number of comments, mostly focusing on the technical implications and potential benefits of this new functionality.

  Several commenters express enthusiasm for the performance improvements promised by lazy materialization, particularly in scenarios involving complex queries and large datasets. They appreciate the ability to defer computations until absolutely necessary, avoiding unnecessary work and potentially speeding up query execution. The concept of pushing projections down the query plan is also highlighted as a key advantage, optimizing data processing by only calculating the necessary columns.

  Some users delve deeper into the technical details, discussing how lazy materialization interacts with other database features like vectorized execution and query optimization. They speculate about the potential impact on memory usage and execution time, noting the trade-offs involved in deferring computations. One commenter mentions the potential for further optimization by intelligently deciding which parts of the query to materialize eagerly versus lazily, hinting at the complexity of implementing such a feature effectively.

  A few comments touch on the broader implications of lazy materialization for database design and query writing. They suggest that this feature could encourage users to write more complex queries without worrying as much about performance penalties, potentially leading to more sophisticated data analysis. However, there's also some caution expressed about the potential for unexpected behavior or performance regressions if lazy materialization isn't handled carefully.

  Some users share their experience with similar features in other database systems, drawing comparisons and contrasting the approaches taken by different vendors. This provides valuable context and helps to understand the unique aspects of ClickHouse's implementation.

  While there isn't overwhelming discussion, the existing comments demonstrate a clear interest in the technical aspects of lazy materialization and its potential impact on ClickHouse's performance and usability. They highlight the trade-offs involved in this optimization technique and offer insightful perspectives on its potential benefits and drawbacks.

• Bulk inserts on ClickHouse: How to avoid overstuffing your instance

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  Posted: 2025-02-11 14:43:45

  Verbose tl;dr

  ClickHouse excels at ingesting large volumes of data, but improper bulk insertion can overwhelm the system. To optimize performance, prioritize using the native clickhouse-client with the INSERT INTO ... FORMAT command and appropriate formatting like CSV or JSONEachRow. Tune max_insert_threads and max_insert_block_size to control resource consumption during insertion. Consider pre-sorting data and utilizing clickhouse-local for larger datasets, especially when dealing with multiple files. Finally, merging small inserted parts using optimize table after the bulk insert completes significantly improves query performance by reducing fragmentation.

  This blog post, titled "Bulk inserts on ClickHouse: How to avoid overstuffing your instance," delves into the intricacies of efficiently inserting large volumes of data into ClickHouse, a column-oriented database management system renowned for its analytical performance. While ClickHouse excels at ingesting and querying vast datasets, improper bulk insertion techniques can lead to performance degradation and resource exhaustion. The article provides a comprehensive guide to optimizing these bulk operations.

  The author begins by highlighting the common pitfalls of naive bulk insertion approaches. Specifically, they caution against inserting data too frequently with excessively small batch sizes. This approach, they explain, overburdens ClickHouse's merge process, a critical background operation that consolidates smaller data parts into larger, more efficiently queried segments. Excessive merging consumes significant system resources, impacting query performance and overall system responsiveness.

  The post then introduces the concept of "parts" and "merges" within ClickHouse's architecture. Parts represent the initial units of data ingested by ClickHouse. These parts are then asynchronously merged in the background to create larger, optimized segments for querying. Too many small parts lead to an excessive number of merges, thus hindering performance.

  To mitigate these issues, the author recommends several strategies for optimizing bulk insertions. They emphasize the importance of carefully selecting an appropriate batch size. Larger batches reduce the number of parts created, consequently reducing the merge overhead. The post suggests experimenting with different batch sizes to find the optimal balance between insertion speed and merge efficiency.

  Furthermore, the author discusses the use of clickhouse-client's --max_insert_block_size setting, which controls the size of blocks sent to ClickHouse during insertion. This setting, when combined with appropriate batching, can significantly improve ingestion performance. They elaborate on how this parameter impacts memory usage on both the client and server sides, recommending adjustments based on available resources.

  The article also explores the advantages of using a buffer table, essentially a temporary staging area for data before it's merged into the main table. This technique allows for greater control over the merging process, as data can be accumulated in the buffer table and then inserted into the main table in larger, optimized batches. The post provides practical examples of using buffer tables and outlines the benefits in terms of reduced merge operations and improved query performance.

  Finally, the author touches upon the trade-offs between insertion speed and resource consumption. While faster insertions might seem desirable, they can negatively impact query performance if not managed properly. The post encourages readers to carefully consider their specific use case and prioritize either raw insertion speed or overall system performance, adjusting their bulk insertion strategy accordingly. The ultimate goal, as highlighted by the author, is to balance the speed of data ingestion with the efficiency of query processing to achieve optimal ClickHouse performance.

  • ClickHouse
  • bulk inserts
  • data ingestion
  • database performance
  • performance optimization
  • data loading
  • ETL
  • big data
  • columnar database
  • Database Management
  • overloading
  • Resource Management
  • best practices

  Summary of Comments ( 4 )
  https://news.ycombinator.com/item?id=43013248

  Verbose tl;dr

  HN users generally agree that ClickHouse excels at ingesting large volumes of data. Several commenters caution against using clickhouse-client for bulk inserts due to its single-threaded nature and recommend using a client library or the HTTP interface for better performance. One user highlights the importance of adjusting max_insert_block_size for optimal throughput. Another points out that ClickHouse's performance can vary drastically based on hardware and schema design, suggesting careful benchmarking. The discussion also touches upon alternative tools like DuckDB for smaller datasets and the benefit of using a message queue like Kafka for asynchronous ingestion. A few users share their positive experiences with ClickHouse's performance and ease of use, even with massive datasets.

  The Hacker News post titled "Bulk inserts on ClickHouse: How to avoid overstuffing your instance" has a moderate number of comments discussing various aspects of ClickHouse performance and bulk loading strategies.

  Several commenters focused on the importance of using clickhouse-client's --max_insert_threads option to control concurrent inserts and prevent overwhelming the server. This setting is crucial for maximizing ingestion throughput while maintaining server stability. Discussion around this point included optimal thread counts and their relationship to server resources. One user emphasized the diminishing returns of excessively high thread counts, highlighting the need to find a balance based on specific hardware and data volume.

  The complexities of ClickHouse's merge process were also brought up, with commenters noting its resource intensiveness and potential impact on query performance. The blog post's suggestion of managing merges and avoiding small parts was reiterated in the comments, with some users offering their own experiences and best practices for merge management. One commenter mentioned the potential for "merge storms" and suggested strategies for mitigation, like spreading out ingestion tasks over time.

  Another commenter shared a contrasting experience where they found individual INSERT statements to be more efficient for their specific use case. This highlighted the fact that optimal bulk loading strategies can be highly dependent on data characteristics, ingestion patterns, and specific ClickHouse configurations. The discussion included speculation about the reasons for this counterintuitive observation, with possibilities like network overhead and internal ClickHouse optimizations being suggested.

  The topic of schema design and data types also emerged, with a commenter emphasizing the impact of appropriate data type choices on ClickHouse performance. This comment underscored the importance of considering factors like cardinality and data distribution when designing tables for ClickHouse.

  Finally, a commenter suggested investigating alternative ingestion methods, such as using the native protocol or leveraging Kafka for streaming data into ClickHouse. This broadened the discussion beyond the blog post's focus, offering additional avenues for optimizing bulk ingestion workflows. Another comment suggested looking into "MaterializedMySQL" engine for simplifying integration with existing MySQL databases.

  Overall, the comments provided valuable insights and practical advice regarding ClickHouse bulk insertion, expanding on the points raised in the original blog post and offering a more nuanced perspective on the complexities of optimizing ingestion performance.