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One concern for the data cache is that the LRU eviction algorithm is suceptible to being flushed by large scans of low priority data. This implements the LIRS algorithm described in "LIRS: An Efficient Low Inter-reference Recency Set Replacement Policy to Improve Buffer Cache Performance" by Song Jiang / Xiaodon Xhang 2002. LIRS is a scan-resistent eviction algorithm with low performance penalty to LRU. This introduces the startup flag data_cache_eviction_policy to control which eviction policy to use. The only two options are LRU and LIRS, with the default continuing to be LRU. To accomodate the new algorithm and associated tests, some code moved around: 1. The RLCacheShard implementation moved from util/cache/cache.cc to util/cache/rl-cache.cc. 2. The backend cache tests were split into multiple files. util/cache/cache-test.h contains shared cache testing code. util/cache/cache-test.cc contains generic tests that should work for any algorithm. util/cache/rl-cache-test.cc are RLCacheShard specific tests util/cache/lirs-cache-test.cc are LIRS specific tests 3. To make it easy for clients of the cache code to customize the cache eviction algorithm, the public interface changed from using a template to taking the policy as an argument. 4. Cache::MemoryType is removed. 5. Cache adds an Init() method to verify the validity of startup flags Testing: - Added LIRS specific backend cache tests (lirs-cache-test) - Ran TPC-DS with a very small cache and concurrency to test corner cases with the LIRS eviction policy - Parameterized data-cache-test to run for both LRU and LIRS - Added LIRS equivalents for tests in custom_cluster/test_data_cache.py - Ran cache-bench with LRU and LIRS. The results are: Test case | Algorithm | Lookups / sec | Hit rate ZIPFIAN ratio=1.00x | LRU | 11.31M | 99.9% ZIPFIAN ratio=1.00x | LIRS | 10.09M | 99.8% ZIPFIAN ratio=3.00x | LRU | 11.36M | 95.9% ZIPFIAN ratio=3.00x | LIRS | 9.27M | 96.4% UNIFORM ratio=1.00x | LRU | 7.46M | 99.8% UNIFORM ratio=1.00x | LIRS | 6.93M | 99.8% UNIFORM ratio=3.00x | LRU | 5.63M | 33.3% UNIFORM ratio=3.00x | LIRS | 3.24M | 33.3% The takeaway is that LIRS is a bit slower on lookups and quite a bit slower on inserts. However, they both are still doing millions of operations per second, so it should not be a bottleneck for the data cache. Change-Id: I670fa4b2b7c93998130dc4e8b2546bb93e9a84f8 Reviewed-on: http://gerrit.cloudera.org:8080/15306 Reviewed-by: Joe McDonnell <joemcdonnell@cloudera.com> Tested-by: Impala Public Jenkins <impala-public-jenkins@cloudera.com>