Why is APPROX_COUNT_DISTINCT() Faster in SparkSQL?

When working with large datasets in SparkSQL, APPROX_COUNT_DISTINCT() is a much faster alternative to COUNT(DISTINCT col). This is because it uses approximate counting instead of exact counting. Let’s break it down step by step. SparkSQL approx_count_distinct aggregate function

The Problem with `COUNT(DISTINCT col)`

By default, COUNT(DISTINCT col) scans all data and removes duplicates, which causes:

High memory usage – it stores all unique values.

High computational cost – requires full deduplication.

Performance bottlenecks – when the dataset is large, Spark needs to shuffle data between nodes, slowing down the query.

Why is `APPROX_COUNT_DISTINCT(col)` Faster?

Instead of exactly counting distinct values, APPROX_COUNT_DISTINCT() uses an efficient HyperLogLog (HLL) algorithm.

How HyperLogLog (HLL) Works

Converts input values into hash values.

Tracks the maximum number of leading zeros in the hash values.

Uses a mathematical formula to estimate the number of unique values.

Advantages of HLL:

Requires very little memory.

Handles billions of unique values efficiently.

Has O(1) time complexity (constant time, very fast).

Comparing Performance

Method	How It Works	Performance	Best for
`COUNT(DISTINCT col)`	Full scan + shuffle + deduplication	Slow (high memory & CPU usage)	Small datasets, where exact values are needed
`APPROX_COUNT_DISTINCT(col)`	Approximate counting using HyperLogLog	Fast (low memory & CPU usage)	Large datasets, when a small error (1-2%) is acceptable

SQL Example

Slow Method (Full Deduplication)


SELECT COUNT(DISTINCT player_id) FROM Activity;

Optimized Method (Faster with Approximation)


SELECT APPROX_COUNT_DISTINCT(player_id) FROM Activity;

Example Performance

Assume the Activity table has 10 million rows:

COUNT(DISTINCT player_id) → Takes 10 seconds (due to full deduplication and shuffle).

APPROX_COUNT_DISTINCT(player_id) → Takes 1 second (with only a ~1% error).

When to Use `APPROX_COUNT_DISTINCT()`

When working with big data (millions or billions of records).

When a small error (1-2%) is acceptable.

When you need faster query performance.

When doing analytics or trend analysis (e.g., unique user counts, event tracking).

For small datasets (thousands of rows), COUNT(DISTINCT col) is fine. But in distributed Spark computing, APPROX_COUNT_DISTINCT() significantly reduces shuffle overhead and improves speed.

Conclusion

Uses HyperLogLog to estimate distinct counts.

Much faster than COUNT(DISTINCT col).

Avoids expensive shuffle operations.

Ideal for large-scale data analysis.

🚀 If you're dealing with big data in SparkSQL, use APPROX_COUNT_DISTINCT() to speed up your queries!