Dictionary-Encoded Hash Join Cache Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing hash join operations in database systems are inefficient due to excessive duplicate hash value calculations and key comparisons, especially when the number of unique key values is small, leading to wasteful use of computing resources.
Innovation Solution
The implementation of a dictionary-encoded hash join operation that caches hash table lookup results in an array, reducing the number of hash lookups from the number of rows per micro-partition file to the number of unique join keys per micro-partition file, thereby avoiding duplicate computations during the probe phase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional hash join operations are performed without caching, then complete hash table lookups are conducted for every row, but excessive duplicate hash value calculations and key comparisons occur, wasting computing resources
Solution Approach 1:
The patent performs hash table lookups in advance during the build phase and stores results in a cache. During the probe phase, pre-computed hash values and lookup results are reused, eliminating the need to perform the same hash calculations and comparisons multiple times for duplicate keys, thus reducing processor cycles and improving efficiency
Solution Approach 2:
The patent creates a cached copy of hash table lookup results that can be reused across multiple probe operations. Instead of repeatedly accessing the original hash table and performing identical calculations, the system uses replicated cache entries to serve subsequent lookup requests, reducing computational overhead
2Productivity
If hash table lookups are performed for every row in the probe phase, then all probe rows are processed, but the number of hash lookups increases unnecessarily when many rows share the same join keys
Solution Approach 1:
The system pre-computes hash values and performs lookup results caching before the probe phase. During probing, it checks the cache first to determine if a row's hash lookup has already been performed, avoiding redundant hash calculations and lookups for rows with duplicate join keys, thus reducing both the number of lookups and time consumed
Solution Approach 2:
The patent implements selective caching where hash lookup results are cached based on local characteristics of the data - specifically when join keys repeat within the probe side. The cache mechanism adapts to the local data distribution pattern, providing optimization only where duplicate keys exist, rather than uniformly processing all rows
3Reliability
If duplicate hash calculations and key comparisons are performed, then thorough lookup is ensured, but computing resources including memory usage and network bandwidth are wasted
Solution Approach 1:
The patent performs hash calculations and lookups in advance during the build phase, storing results in a cache before the probe phase begins. This preliminary action ensures lookup accuracy is maintained while avoiding redundant calculations during probing, as the pre-computed results are stored in memory for efficient retrieval without re-calculation
Solution Approach 2:
The system creates cached copies of hash table entries and lookup results that can be reused during the probe phase. These copies are stored in memory with appropriate data structures that maintain lookup accuracy while reducing the need for repeated access to the original hash table, thus optimizing memory usage patterns
Data Source
AI summary
In one aspect, a computer-implemented method includes detecting, by a server includes one or more processors, a request to perform a hash join operation on a data structure stored in a data storage device, forming a hash lookup dictionary based on lookup results in a hash table, storing the hash lookup dictionary in a cache, and probing, during a probing phase of the hash join operation, the cache.