5 Commits (9befbe9b406574e8d8b687799701bd8cebf5163b)

Author	SHA1	Message	Date
anand76	6c7b1a0cc7	Batched MultiGet API for multiple column families (#5816) ... Summary: Add a new API that allows a user to call MultiGet specifying multiple keys belonging to different column families. This is mainly useful for users who want to do a consistent read of keys across column families, with the added performance benefits of batching and returning values using PinnableSlice. As part of this change, the code in the original multi-column family MultiGet for acquiring the super versions has been refactored into a separate function that can be used by both, the batching and the non-batching versions of MultiGet. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5816 Test Plan: make check make asan_check asan_crash_test Differential Revision: D18408676 Pulled By: anand1976 fbshipit-source-id: 933e7bec91dd70e7b633be4ff623a1116cc28c8d	5 years ago
Vijay Nadimpalli	4c49e38f15	MultiGet batching in memtable (#5818) ... Summary: RocksDB has a MultiGet() API that implements batched key lookup for higher performance (https://github.com/facebook/rocksdb/blob/master/include/rocksdb/db.h#L468). Currently, batching is implemented in BlockBasedTableReader::MultiGet() for SST file lookups. One of the ways it improves performance is by pipelining bloom filter lookups (by prefetching required cachelines for all the keys in the batch, and then doing the probe) and thus hiding the cache miss latency. The same concept can be extended to the memtable as well. This PR involves implementing a pipelined bloom filter lookup in DynamicBloom, and implementing MemTable::MultiGet() that can leverage it. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5818 Test Plan: Existing tests Performance Test: Ran the below command which fills up the memtable and makes sure there are no flushes and then call multiget. Ran it on master and on the new change and see atleast 1% performance improvement across all the test runs I did. Sometimes the improvement was upto 5%. TEST_TMPDIR=/data/users/$USER/benchmarks/feature/ numactl -C 10 ./db_bench -benchmarks="fillseq,multireadrandom" -num=600000 -compression_type="none" -level_compaction_dynamic_level_bytes -write_buffer_size=200000000 -target_file_size_base=200000000 -max_bytes_for_level_base=16777216 -reads=90000 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 -statistics -memtable_whole_key_filtering=true -memtable_bloom_size_ratio=10 Differential Revision: D17578869 Pulled By: vjnadimpalli fbshipit-source-id: 23dc651d9bf49db11d22375bf435708875a1f192	5 years ago
anand76	e10570331d	Support row cache with batched MultiGet (#5706) ... Summary: This PR adds support for row cache in ```rocksdb::TableCache::MultiGet```. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5706 Test Plan: 1. Unit tests in db_basic_test 2. db_bench results with batch size of 2 (```Get``` is faster than ```MultiGet``` for single key) - Get - readrandom : 3.935 micros/op 254116 ops/sec; 28.1 MB/s (22870998 of 22870999 found) MultiGet - multireadrandom : 3.743 micros/op 267190 ops/sec; (24047998 of 24047998 found) Command used - TEST_TMPDIR=/dev/shm/multiget numactl -C 10 ./db_bench -use_existing_db=true -use_existing_keys=false -benchmarks="readtorowcache,[read|multiread]random" -write_buffer_size=16777216 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -row_cache_size=4194304000 -batch_size=2 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=131072 Differential Revision: D17086297 Pulled By: anand1976 fbshipit-source-id: 85784378da913e05f1baf31ec1b4e7c9345e7f57	5 years ago
Zhongyi Xie	5d27d65bef	multiget: fix memory issues due to vector auto resizing (#5279) ... Summary: This PR fixes three memory issues found by ASAN * in db_stress, the key vector for MultiGet is created using `emplace_back` which could potentially invalidates references to the underlying storage (vector<string>) due to auto resizing. Fix by calling reserve in advance. * Similar issue in construction of GetContext autovector in version_set.cc * In multiget_context.h use T[] specialization for unique_ptr that holds a char array Pull Request resolved: https://github.com/facebook/rocksdb/pull/5279 Differential Revision: D15202893 Pulled By: miasantreble fbshipit-source-id: 14cc2cda0ed64d29f2a1e264a6bfdaa4294ee75d	6 years ago
anand76	fefd4b98c5	Introduce a new MultiGet batching implementation (#5011) ... Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b	6 years ago