How to Implement Grace Hash Join in C++ for Partition and Probe Assignments

Record Handling and Hashing Mechanisms

The Record structure or class usually encapsulates the data unit that the entire system will process. It includes two key attributes: a key (for identification or joining) and data (representing payload). It provides at least two critical functions:

1. partition_hash(): This function is used during the partitioning stage. It determines which bucket a record belongs to based on a hashing strategy. Typically, a modulo operation on the hash output distributes records evenly.
2. probe_hash(): Used during the probing phase to help efficiently match records from the two relations being joined. This hash ensures uniform distribution in a hash table.
3. operator== Overload: Record comparisons should not rely only on hash values due to possible collisions. Hence, a strict equality operator is usually overloaded to check actual data equality after hash-based filtering.

Proper understanding of when to use which function is vital to the join operation's accuracy and efficiency.

Page and Memory Architecture

A database system doesn't operate on individual records but on pages, each capable of holding multiple records. Memory (Mem) typically consists of multiple such pages.

• Page: Each page handles loading, writing, and flushing of records. It includes methods such as loadRecord(), loadPair(), full(), and reset().
• Mem: Represents the in-memory buffer of the system, composed of a finite number of pages (controlled by a constant like MEM_SIZE_IN_PAGE).

Efficient use of memory and pages is essential. If your logic doesn't flush a full page on time or resets it correctly, it may result in memory leaks or overwritten data.

Disk and Data Loading

The Disk abstraction simulates persistent storage. It includes functionalities like:

• read_data(): To load relations from a .txt file into disk pages.
• loadFromDisk(): To move data from disk to memory.
• flushToDisk(): To write memory pages back to disk.

Understanding how the disk and memory interact is key to building both the partitioning and probing logic efficiently.

Structuring Your Generalized Hash Join Implementation

Implementing GHJ involves several coordinated steps: partitioning the relations into manageable subsets, and then probing those subsets to produce joined records. Let’s look at how each stage should be designed and implemented.

Building a Robust Partition Function

The partition function divides each relation (left and right) into buckets based on the hash of their keys. This step reduces the join's memory footprint and sets the stage for efficient probing.

Deciding the Number of Buckets and Allocation

A common mistake students make is choosing an inappropriate number of buckets. A typical strategy is to use MEM_SIZE_IN_PAGE - 1 buckets to leave one page for intermediate operations.

Each Bucket will track a set of page IDs for flushed data during partitioning. These are later used during the probe phase.

Streaming and Sorting the Records

For each relation:

1. Iterate through disk pages using their ID range.
2. Load each page into memory.
3. Extract records and hash them using partition_hash().
4. Based on hash value, determine the target bucket.
5. Use in-memory page buffers to hold bucketed records.
6. Once a page buffer is full, flush it to disk and log the page ID in the corresponding bucket.

Repeat this for both left and right relations.

Final Flush and Cleanup

Once all records are read, ensure any non-empty page buffers are flushed to disk. This final flush avoids data loss.

Designing an Efficient Probe Function

In the probe phase, you take each pair of left and right buckets and find matching records based on probe_hash().

Choosing the Build Side Wisely

Always choose the smaller of the two bucket groups (left or right) as the build side. This minimizes memory consumption when loading the hash table.

Constructing the Hash Table

Load each disk page of the build bucket into memory and create a hash table using probe_hash() as the key.

Probing for Matches and Outputting Pairs

Now, load each page from the probe side and search the hash table for matching entries.

Don’t forget to flush the final result page if it contains any remaining data.

Debugging, Testing, and Performance Optimization

Testing with Edge Case Scenarios

Start by using the provided left_rel.txt and right_rel.txt files. Then test for:

• No matches between relations.
• All records with the same key.
• Disproportionate sizes between left and right relations.

Use logs and assertions to verify intermediate states, like bucket sizes and flushed page counts.

Memory and Disk Management Checks

If records seem missing from the output:

• Ensure all pages are flushed after full or final usage.
• Reset pages after flushing.
• Use assertions to check that no buffer overflows or underflows occur.

assert(!page.full() || page.empty());

Profiling and Hash Optimization

Hash collisions can bottleneck performance. If you find excessive collisions, experiment with different hash functions or increase the bucket count (within memory limits). Also, avoid unnecessary memory copies during loading and flushing operations.

Conclusion: Key Takeaways and Final Tips

Implementing Generalized Hash Join in C++ is a valuable learning experience that offers insights into how real-world databases operate under constrained resources. Here are the top strategies for success:

• Understand every component: From records to disk and memory, know how each module works.
• Design before coding: Flowcharts or pseudocode help in building a sound logic before implementation.
• Test rigorously: Edge cases can expose logic flaws that typical datasets won’t.
• Optimize gradually: Once it works, profile and refactor.

If you can confidently implement GHJ logic in a modular, bug-free, and optimized manner, you’re already several steps ahead in mastering database internals. Such assignments are not just academic tasks—they're training grounds for building scalable systems in the real world.