High Performance Joins: Hash Joins

Overview

• Lecture Structure: The first of a three-part lecture series on high-performance joins. This lecture focuses on hash joins.
• Upcoming Lectures: After spring break, will cover sort-merge joins and multi-way joins.
• Primary Takeaway: For most cases, hash joins are preferred.

Project Announcements

• Project 2: Ensure you've signed up for a database system; approvals are pending.
• Project 3: Details to be posted tonight; proposals due on Wednesday, consisting of high-level slides.
• General Info: Assistance in forming groups, on-campus availability for discussions.
• Project 1: Submissions and grades discussed; reminder to complete if not done.

Topics Covered

1. Background on Joins
2. Phases of Hash Joins
3. Building a Hash Table
4. Hash Functions and Schemes
5. Evaluation of Hash Join Algorithms

Background on Joins

• Main Join Types:
  • Hash Joins (focus of today's lecture)
  • Sort-Merge Joins
  • Multi-Way Joins
• Avoiding Nested Loops: More efficient join algorithms avoid brute force sequential scans.
• OLAP Systems: Prefer hash joins over nested loop joins due to large datasets and lack of indexes.

Key Components of Hash Joins

1. Minimize Synchronization Overhead: Reduce coordination between multiple worker threads.
2. Minimize Memory Access Costs: Optimize locality and efficient memory usage, reduce cache and TLB misses.

Phases of the Hash Join Algorithm

1. Partitioning Phase: (Optional)
   • Split data into disjoint subsets for parallel processing.
   • Methods: Implicit (pre-partitioned data) vs. Explicit (manual partitioning via hash functions).
2. Build Phase: Create a hash table from the outer table/relation based on the join key.
3. Probe Phase: Scan the inner table, hash the join key, and probe the hash table to find matches.

Hash Table Construction

• Hash Function: Maps join keys to hash values (preferably 32 or 64-bit integers).
• Collision Handling: Strategies to handle multiple keys mapping to the same hash bucket.
  • Linear Probing: Continuous array with sequential checks for free space.
  • Chained Hashing: Linked-list structure to store multiple elements in the same bucket.
  • Other Methods: Robin Hood Hashing, Hopscotch Hashing, Cuckoo Hashing.

Optimizations in Hash Joins

• Software Write-Combining Buffers: Use local buffers to reduce memory access costs for writes.
• Streaming Writes: Bypass CPU caches for data not needed again immediately.
• Bloom Filters: An auxiliary data structure to quickly check if a key is likely present in the hash table before actual probing.

Modern Debates and Developments

• Evolution of Joins: History from the 1970s to 2020s showcasing favorability shifts between sort-merge and hash joins.
• Current Best Practices: Paper analyses showing non-partitioning hash joins generally outperform partitioned ones due to simplicity and fewer implementation costs.
• Micro Benchmarking: XX Hash emerges as the fastest hash function in benchmarks.
• Real-World Performance: Hash joins show significant but not overbearing contribution to overall query processing time.

Key Takeaways

• High-Performance Joins: Vital in query processing, with hash joins often being superior due to efficiency in large-scale datasets.
• System Implementations: Most database systems standardize on one hash join algorithm for simplicity and maintaining performant operations.
• Optimization and Implementation: Focus on critical system paths and avoid overcomplicating with multiple join strategies.

Conclusion

• Final Note: Focus on a single efficient hash join implementation, considering the overall system performance impacts and engineering feasibility.