Implementing HyperLogLog for Efficient Cardinality Estimation in ClickHouse

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Implementing HyperLogLog for Efficient Cardinality Estimation in ClickHouse

How HyperLogLog Enhances Cardinality Estimation Efficiency in ClickHouse


2 min read

Implementing HyperLogLog in ClickHouse for approximate distinct count calculations is straightforward due to the native support for this probabilistic data structure. HyperLogLog is used to estimate the cardinality of a dataset—i.e., the number of distinct elements in a set—while using significantly less memory than would be required for an exact count. This makes it highly useful in scenarios involving large volumes of data where perfect accuracy is not critical.

Here’s a step-by-step guide on how to implement HyperLogLog in ClickHouse:

1. Create a Table with HyperLogLog Aggregate Function

First, you need to create a table that will use HyperLogLog to aggregate data. ClickHouse provides an aggregate function called uniqHLL12 that implements HyperLogLog. Here's how you can create a table using this function:

CREATE TABLE my_table (
    event_date Date,
    user_id UInt32,
    HLL AggregateFunction(uniqHLL12, UInt32)
) ENGINE = AggregatingMergeTree()
ORDER BY (event_date);

In this example, user_id is the field for which we want to estimate the distinct count, and HLL is a column of type AggregateFunction that stores the HyperLogLog data structure.

2. Insert Data Using the HyperLogLog Function

When inserting data, use the uniqHLL12 function to process data into the HyperLogLog format:

INSERT INTO my_table (event_date, user_id, HLL)
VALUES ('2020-01-01', 1, uniqHLL12(1)),
       ('2020-01-01', 2, uniqHLL12(2)),
       ('2020-01-01', 1, uniqHLL12(1));  -- duplicate user_id to demonstrate uniqueness

3. Query to Estimate Distinct Counts

To retrieve the estimated count of unique user_ids, use the merge function associated with the uniqHLL12 stored in the HLL column:

    uniqMerge(HLL) AS unique_users_count
FROM my_table
GROUP BY event_date;

This query will return the number of unique user_ids per event_date, estimated using the HyperLogLog algorithm.

4. Considerations and Use Cases

  • Accuracy vs. Performance: HyperLogLog provides a good balance between accuracy and performance, typically offering a standard error of about 1% for the uniqHLL12 version, which uses 12 bits per element. This is usually sufficient for large-scale analytics where exact counts are less critical.

  • Storage Efficiency: HyperLogLog significantly reduces the amount of memory required to estimate cardinalities, especially beneficial in high-cardinality scenarios.

  • Use Cases: Suitable for real-time analytics, log analysis, and any application where a quick, space-efficient estimation of distinct counts is valuable.

5. Updating Data

If your application requires updates to data already inserted (e.g., inserting data for past dates), ensure that your data insertion logic accounts for merging existing HyperLogLog structures with new data, which ClickHouse handles automatically when using uniqHLL12 in combination with the AggregatingMergeTree engine.

By following these steps, you can effectively implement HyperLogLog in ClickHouse, leveraging its capabilities for efficient approximate counting in large-scale data environments.