Skip and Data Skipping Indices in ClickHouse

Skip indices and data skipping indices in ClickHouse are powerful features designed to improve query performance by skipping over unnecessary data during query execution. These indices allow ClickHouse to quickly determine which data blocks within a table do not contain relevant information for a query, thereby reducing the amount of data that needs to be read from disk.

Understanding Skip Indices

Skip indices are auxiliary structures that store aggregated information about data blocks. When a query is executed, ClickHouse uses these indices to identify and skip blocks that do not match the query conditions, significantly speeding up query processing.

Types of Skip Indices

ClickHouse supports several types of skip indices:

MinMax Index: Stores the minimum and maximum values for each column in a data block. If the query range doesn't overlap with the min-max range of the block, the block is skipped.
Set Index: Keeps a set of distinct values for a column in each block. Useful for queries with IN conditions.
Bloom Filter: A probabilistic data structure that can test whether an element is in a set. Especially effective for =, !=, LIKE, and IN operations on string columns.
ngrambf_v1 and tokenbf_v1: Variants of Bloom filters optimized for substring searches and token searches, respectively.

How to Use Skip Indices

Creating Tables with Skip Indices: To create a skip index, you must specify it at the time of table creation. Here's an example of creating a table with a MinMax index:

 CREATE TABLE example_table
 (
     date Date,
     event_name String,
     event_count Int32
 )
 ENGINE = MergeTree()
 PARTITION BY toYYYYMM(date)
 ORDER BY (date, event_name)
 SETTINGS index_granularity = 8192
 SKIP INDEX min_max_date(date);

Querying Data: When you run queries against a table with skip indices, ClickHouse automatically uses these indices to improve query performance. No special syntax is needed in the query; the optimization is applied under the hood.

Best Practices for Skip Indices

Selective Use: Apply skip indices to columns that are frequently used in query conditions. Overusing skip indices can increase storage and negatively impact insert performance.
Index Type Matching: Choose the type of skip index based on the column data type and the nature of the queries. For instance, use Bloom filters for columns involved in equality or pattern-matching conditions.
Monitoring: Use ClickHouse's monitoring tools to evaluate the effectiveness of your skip indices in improving query performance. Adjust as necessary.

Maintenance and Optimization

Alter Tables: If you need to add or remove skip indices after a table has been created, you can use the ALTER TABLEstatement to modify the indices.
Tuning Parameters: Experiment with parameters like index_granularity and the specific settings of each skip index type (e.g., Bloom filter length and hash functions) to optimize performance.

Conclusion

Skip indices and data skipping indices are essential tools in optimizing query performance in ClickHouse. By allowing ClickHouse to bypass irrelevant data blocks, these indices can lead to significant reductions in query execution time, especially for large datasets. Careful selection, creation, and maintenance of these indices can enhance the efficiency and speed of data retrieval operations in ClickHouse databases.

Optimizing Query Performance with Skip and Data Skipping Indices in ClickHouse