SIGMOD'12 poster

1. Reducing Cache Misses in Hash Join
Probing Phase By Pre‐sorting Strategy
Gi-Hwan Oh, Jae-Myung Kim, Woon-Hak Kang, and Sang-Won Lee
Background and Motivation
• Evolution of In‐memory Hash Join, |R| < |S|
‐ Partitioning (Shatdal et al. VLDB`94) ‐ Multi‐core Radix‐clustering (Kim et al. VLDB`09)
‐ |R|>> cache size ‐ Hash join on multicore
=> |R.partition| < cache size ‐ No partitioning (revisited) (Blanas et al. SIGMOD`11)
‐ Radix‐clustering (Manegold et al. VLDB`00) ‐ Multicore and skewed data
‐ # partitions >> TLB entries ‐ Cons : High cache miss ( 99% in probe phase )
=> Multi‐pass partitioning
Proposed Scheme: Pre-sorting
 Cache Miss in Probe Phase S Hash
Table R
‐ Reference pattern on hash table is random
In‐Cache
‐ Cause cache misses
Sort
 Strategy: Sorting ‘S’ Relation by Join Attribute Probe
‐ Change reference pattern of a hash table of relation ‘R’ Build
‐ Global random ‐> clustering in local scope (better temporal Hash
Table
and spatial locality in cache access)
‐ Sorting all records of ‘S’ is unrealistic
‐ In‐cache sorting: sorting buffer size = the largest private cache  Benefits of This Strategy
‐ Maximize the number of records to sort ‐ Reduces cache misses
‐ Extract (key,rid) to reduce record size (like alpha sort) ‐ Can be applied on any hash
 Reduction cache miss >> Sorting overhead join algorithms
Cycles Cache Misses
Normal Data 183,850,677,579 105,341,310
Fully Sorted Data 27,370,971,180 389,938
Performance Evaluation
Cycles Execution Time  Environmental Setting
200 ‐ HW: Intel Core‐i7 860, 2.8GHz (private 256K,
Billions
shared 8M), 12GB RAM
150
‐ OS: Linux 2.6.32‐220.7 (CentOS)
100
‐ Dataset: |R| = 16M, |S| = 256M, hash
entries=1M, Schema R & S = (long, long),
50
Uniform distribution
0
IP IP+SP NP NP+SP
 Evaluation Result
PART BUILD PROBE SORT ‐ Outperforms all other algorithms
• IP=Independent Partitioning ‐ IP+SP is 30% faster than IP
• NP=No Partitioning ‐ NP+SP is 30% faster than NP
• SP=Sorting and Probing