57.12 ClickHouse

Give an example DB schema of clickhouse doing quick aggregation

To create a ClickHouse schema optimized for quick aggregation, you can consider the following example:

CREATE TABLE example_table
    hotel String,
    staff_member String,
    task_number Float64,
    date DateTime
) ENGINE = MergeTree()
ORDER BY (hotel, date)
PRIMARY KEY (hotel, date);

In this schema example:

This schema structure is optimized for quick aggregation in ClickHouse, especially for scenarios where you need to aggregate data based on specific columns like hotel and date.

[1] https://altinity.com/blog/clickhouse-aggregation-fun-part-2-exploring-and-fixing-performance
[2] https://www.restack.io/docs/signoz-knowledge-clickhouse-query-examples-signoz
[3] https://www.youtube.com/watch?v=-Ma2ohy_6lM
[4] https://clickhouse.com/blog/clickhouse-faster-queries-with-projections-and-primary-indexes
[5] https://stackoverflow.com/questions/66618762/optimal-primary-key-on-this-clickhouse-schema-for-aggregation

What are some best practices for designing a clickhouse schema for quick aggregation

Some best practices for designing a ClickHouse schema optimized for quick aggregation include:

  1. Column Selection: Choose columns that are frequently used in aggregation queries. Limit the number of columns to those essential for analysis to reduce storage costs and improve query performance[1].
  2. Primary Key Design: Define a primary key that aligns with the columns used in aggregations. This helps ClickHouse efficiently locate and aggregate data based on the primary key columns[4].
  3. Data Types: Utilize appropriate data types for columns to ensure efficient storage and processing. ClickHouse offers specialized data types like LowCardinality, UUID, Maps, and Nested that can enhance performance[4].
  4. Partitioning: Consider partitioning your data based on time durations like months, days, or weeks. Partitioning can help optimize data retrieval and aggregation operations by minimizing the data read[4].
  5. Indexing: Carefully index key columns in your tables to enhance query performance. This includes setting up primary keys for unique identification and secondary indexes for fast filtering[5].
  6. Materialized Views: Leverage materialized views in ClickHouse to pre-aggregate data and speed up query processing. Materialized views act as insert triggers and can be used for various analytical use cases[4].
  7. Distributed Setup: Implement a distributed ClickHouse setup for horizontal scalability. This involves configuring multiple ClickHouse shards and a coordination mechanism like Zookeeper for efficient data processing across nodes[5].
  8. Query Optimization: Write custom SQL queries that leverage ClickHouse's functions and operators to minimize data scanned. Use WHERE clauses effectively to filter data early in queries and aggregate data to reduce network load and improve query response times[5].

By following these best practices, you can design a ClickHouse schema that is well-optimized for quick aggregation, enabling efficient data management and analytics.

[1] https://stackoverflow.com/questions/50342296/clickhouse-schema-design-predefined-set-of-columns
[2] https://kb.altinity.com/altinity-kb-schema-design/
[3] https://www.youtube.com/watch?v=-Ma2ohy_6lM
[4] https://docs.gitlab.com/ee/development/database/clickhouse/
[5] https://www.restack.io/docs/signoz-knowledge-signoz-clickhouse-schema

What are some common mistakes to avoid when designing a clickhouse schema for quick aggregation?

Some common mistakes to avoid when designing a ClickHouse schema for quick aggregation include:

  1. Overly Granular Partitioning: Avoid using overly granular partitioning keys, as this can lead to too many partitions, impacting query performance. ClickHouse benefits from having partitions aligned with the data access patterns to optimize query execution[1].

  2. Inefficient Primary Key Selection: Carefully select the primary key as it directly impacts query performance and data compression. While ClickHouse doesn't require a unique primary key per row, having multiple primary keys can negatively affect insertion performance. Once a table is created, the primary key cannot be updated, so it's crucial to get it right from the start[1].

  3. Lack of Data Skipping Indexes: Not utilizing data skipping indexes can result in slower queries, especially when querying columns that are not part of the primary key. Data skipping indexes, which use bloom filters, help ClickHouse skip reading chunks of data that are guaranteed to have no match, improving query performance[1].

  4. Memory Limits and Query Performance: Be cautious of hitting memory limits when loading dashboards over large datasets. Consider using techniques like Adaptive Bit Rate (ABR) to manage memory usage efficiently and speed up query processing for identifying anomalies without requiring exact numbers[1].

  5. Suboptimal Table Design: Ensure that the table design is optimized for fast data retrieval and aggregation. Utilize appropriate column types, indexing strategies, and partitioning keys to enhance query performance and storage efficiency[3].

By avoiding these common mistakes and following best practices in ClickHouse schema design, you can create a schema that is well-suited for quick aggregation and efficient data analytics.

[1] https://blog.cloudflare.com/log-analytics-using-clickhouse
[2] https://clickhouse.com/blog/clickhouse-faster-queries-with-projections-and-primary-indexes
[3] https://www.restack.io/docs/signoz-knowledge-signoz-clickhouse-schema
[4] https://stackoverflow.com/questions/66618762/optimal-primary-key-on-this-clickhouse-schema-for-aggregation
[5] https://docs.gitlab.com/ee/development/database/clickhouse/

Thoughts 🤔 by Soumendra Kumar Sahoo is licensed under CC BY 4.0