Stable Hashing For Maps In ClickHouse

Hey guys! Let's dive into a common challenge when working with ClickHouse: how to generate a stable hash for maps, regardless of the order of their elements. This is super important for data consistency and reliable comparisons. As you might know, ClickHouse is a powerhouse for handling massive datasets, and having a way to consistently hash maps is a must-have for a bunch of use cases. It's especially crucial when dealing with data that may arrive in different orders but represents the same underlying information. So, let's explore the problem and discuss some potential solutions.

The Problem: Order Matters (But Sometimes It Shouldn't)

In the world of data, sometimes the order of elements matters a lot. But other times, it's irrelevant. When we're talking about maps (also known as dictionaries or associative arrays), the order of key-value pairs shouldn't always affect how we identify or compare them. For instance, map('aa', 1, 'bb', 2) and map('bb', 2, 'aa', 1) represent the same data. Ideally, we want a hash function that produces the same output for both, so we can reliably compare them.

The Current Situation

Currently, ClickHouse's built-in hashing functions like cityHash64 are sensitive to the order of elements within a map. This is where the issue arises, and the example provided clearly illustrates this. You've probably already seen the problem if you've worked with maps and hash functions in other systems. The output of cityHash64 varies depending on the order in which the key-value pairs are stored. This means that if the same map is created from different sources or at different times, you might end up with different hash values, even though the data itself is the same. This can lead to all sorts of headaches: incorrect joins, failed comparisons, and a general lack of trust in your data.

The Ideal Solution: A Stable Hash Function

The most straightforward solution is a function or setting that provides a stable hash for maps. This function would take a map as input and produce a hash value that is consistent, regardless of the order of the map's elements. Think of it like this: for any two maps that contain the same key-value pairs, the function must generate the same hash. It's a fundamental requirement for a reliable system. Ideally, we want a solution that:

• Is Efficient: Hashing large maps can be computationally expensive, so speed is important. The solution should be performant.
• Is Deterministic: The hash function should always produce the same output for the same input. This is non-negotiable.
• Is Collision-Resistant: While perfect collision resistance is impossible, the function should minimize the chances of different maps producing the same hash.
• Is Easy to Use: The function should be simple to integrate into existing ClickHouse queries. No complex setups or workarounds should be necessary.

Exploring Possible Approaches: Functions and Settings

Now, let's explore some potential solutions, and pros and cons:

Function-Based Approach

One approach is to create a dedicated function for generating stable hashes. This function would take a map as input and internally sort the map's elements before calculating the hash. The general idea is to normalize the map by putting its elements in a predictable order before hashing them. This normalization step is key to stability.

For example, the function might:

1. Sort the keys: Sort the keys alphabetically or numerically. This ensures a consistent order. Some languages or libraries have built-in sorting capabilities, and ClickHouse also offers a mapSort function that can be utilized to make this possible.
2. Iterate and Concatenate: Iterate through the sorted key-value pairs and concatenate them into a string. The format of the concatenated string must be consistent. Use a delimiter (e.g., a comma or a special character) to separate keys and values.
3. Hash the String: Apply a standard hash function (e.g., cityHash64, xxHash) to the concatenated string.

Pros of Function-Based Approach

• Flexibility: You can customize the hashing algorithm to your specific needs. The function can be designed to handle different data types in the map, as well as deal with nested maps.
• Maintainability: Easier to modify and maintain compared to changing core ClickHouse functionality.

Cons of Function-Based Approach

• Performance: Sorting the map's elements can introduce overhead, especially for large maps. It's generally less efficient than an optimized, built-in solution.
• Needs to be created for each hashing algorithm: Creating a new function for each hash function can be time-consuming and cumbersome. Also, it's not ideal.

Setting-Based Approach

Another approach is to introduce a setting or a modification to the existing hash functions in ClickHouse. The user could enable an option (e.g., stable_map_hashing) that tells ClickHouse to sort the map elements before calculating the hash. The logic would be implemented within the hash function itself, so the user wouldn't need to manually sort the maps first. Essentially, the setting would modify the behavior of the hash function.

Pros of Setting-Based Approach

• Efficiency: The internal implementation could be highly optimized for performance, potentially outperforming a user-defined function.
• Convenience: Users could easily enable stable hashing with a simple setting change. No need to rewrite queries or create custom functions.
• Consistency: The feature could be consistently applied across all relevant ClickHouse functions.

Cons of Setting-Based Approach

• Less Flexible: Modifying existing functions could be tricky, and there might be compatibility issues. Changes to core ClickHouse functionality require more effort.

The mapSort Alternative and Its Limitations

As mentioned in the original request, using mapSort is an alternative. This function sorts the map's elements by key. Then, you could hash the result. This approach has the potential to solve the stable-hashing problem. However, this is not optimal for several reasons:

• Creates a New Map: mapSort creates a new map. This means extra memory allocation and overhead, which may not be desirable if you're working with very large maps.
• Readability: Requires an extra step in your query, which can make the code less readable and more complex.

Additional Considerations and Context

Here are some other things to think about:

• Data Types: The function or setting should handle different data types for both keys and values within the map (e.g., strings, numbers, dates). This will make it more flexible and useful.
• Nested Maps: Consider how nested maps should be handled. Should the function recursively apply stable hashing to nested maps? This decision is important for consistent behavior.
• Performance Testing: Thoroughly test the performance of any solution, especially with large maps. Measure the impact of sorting and hashing on query execution time.
• Use Cases: Think through all the use cases where stable hashing is important. This helps you to identify and prioritize features and optimize the implementation.

Conclusion: The Path Forward

In conclusion, ensuring stable hashing for maps in ClickHouse is a critical requirement for maintaining data consistency and accuracy. Whether it's a function or a setting, a solution that consistently hashes maps regardless of element order is vital. While using mapSort is one possible workaround, it comes with limitations. The optimal solution would provide a performant, reliable, and user-friendly way to achieve this, helping you trust your data even more.