- 02 1月, 2014 3 次提交
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由 Igor Canadi 提交于
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由 Igor Canadi 提交于
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由 Igor Canadi 提交于
fix build bug
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- 31 12月, 2013 3 次提交
- 30 12月, 2013 2 次提交
- 27 12月, 2013 8 次提交
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由 dyu 提交于
fix build bug from recent commit:https://github.com/facebook/rocksdb/commit/43c386b72ee834c88a1a22500ce1fc36a8208277
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由 Siying Dong 提交于
Summary: I'm not sure what's the purpose of encoding file number to a new buffer for looking up the table cache. It seems to be unnecessary to me. With this patch, we point the lookup key to the address of the int64 of the file number. Test Plan: make all check Reviewers: dhruba, haobo, igor, kailiu Reviewed By: dhruba CC: leveldb Differential Revision: https://reviews.facebook.net/D14811
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由 Siying Dong 提交于
Summary: In some places we have NotFound status created with empty message, but it doesn't avoid a malloc. With this patch, the malloc is avoided for that case. The motivation of it is that I found in db_bench readrandom test when all keys are not existing, about 4% of the total running time is spent on malloc of Status, plus a similar amount of CPU spent on free of them, which is not necessary. Test Plan: make all check Reviewers: dhruba, haobo, igor Reviewed By: haobo CC: leveldb Differential Revision: https://reviews.facebook.net/D14691
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由 Kai Liu 提交于
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由 kailiu 提交于
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由 kailiu 提交于
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由 kailiu 提交于
Summary: A vector that leverages pre-allocated stack-based array to achieve better performance for array with small amount of items. Test Plan: Added tests for both correctness and performance Here is the performance benchmark between vector and autovector Please note that in the test "Creation and Insertion Test", the test case were designed with the motivation described below: * no element inserted: internal array of std::vector may not really get initialize. * one element inserted: internal array of std::vector must have initialized. * kSize elements inserted. This shows the most time we'll spend if we keep everything in stack. * 2 * kSize elements inserted. The internal vector of autovector must have been initialized. Note: kSize is the capacity of autovector ===================================================== Creation and Insertion Test ===================================================== created 100000 vectors: each was inserted with 0 elements total time elapsed: 128000 (ns) created 100000 autovectors: each was inserted with 0 elements total time elapsed: 3641000 (ns) created 100000 VectorWithReserveSizes: each was inserted with 0 elements total time elapsed: 9896000 (ns) ----------------------------------- created 100000 vectors: each was inserted with 1 elements total time elapsed: 11089000 (ns) created 100000 autovectors: each was inserted with 1 elements total time elapsed: 5008000 (ns) created 100000 VectorWithReserveSizes: each was inserted with 1 elements total time elapsed: 24271000 (ns) ----------------------------------- created 100000 vectors: each was inserted with 4 elements total time elapsed: 39369000 (ns) created 100000 autovectors: each was inserted with 4 elements total time elapsed: 10121000 (ns) created 100000 VectorWithReserveSizes: each was inserted with 4 elements total time elapsed: 28473000 (ns) ----------------------------------- created 100000 vectors: each was inserted with 8 elements total time elapsed: 75013000 (ns) created 100000 autovectors: each was inserted with 8 elements total time elapsed: 18237000 (ns) created 100000 VectorWithReserveSizes: each was inserted with 8 elements total time elapsed: 42464000 (ns) ----------------------------------- created 100000 vectors: each was inserted with 16 elements total time elapsed: 102319000 (ns) created 100000 autovectors: each was inserted with 16 elements total time elapsed: 76724000 (ns) created 100000 VectorWithReserveSizes: each was inserted with 16 elements total time elapsed: 68285000 (ns) ----------------------------------- ===================================================== Sequence Access Test ===================================================== performed 100000 sequence access against vector size: 4 total time elapsed: 198000 (ns) performed 100000 sequence access against autovector size: 4 total time elapsed: 306000 (ns) ----------------------------------- performed 100000 sequence access against vector size: 8 total time elapsed: 565000 (ns) performed 100000 sequence access against autovector size: 8 total time elapsed: 512000 (ns) ----------------------------------- performed 100000 sequence access against vector size: 16 total time elapsed: 1076000 (ns) performed 100000 sequence access against autovector size: 16 total time elapsed: 1070000 (ns) ----------------------------------- Reviewers: dhruba, haobo, sdong, chip Reviewed By: dhruba CC: leveldb Differential Revision: https://reviews.facebook.net/D14655
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由 Kai Liu 提交于
Only try to use fallocate if it's actually present on the system.
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- 24 12月, 2013 1 次提交
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由 Dhruba Borthakur 提交于
Summary: Add a pointer to the engineering design discussion forum. Test Plan: Reviewers: CC: Task ID: # Blame Rev:
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- 21 12月, 2013 2 次提交
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由 Igor Canadi 提交于
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由 Igor Canadi 提交于
Summary: Instead of locking and saving a DB state, we can cache a DB state and update it only when it changes. This change reduces lock contention and speeds up read operations on the DB. Performance improvements are substantial, although there is some cost in no-read workloads. I ran the regression tests on my devserver and here are the numbers: overwrite 56345 -> 63001 fillseq 193730 -> 185296 readrandom 771301 -> 1219803 (58% improvement!) readrandom_smallblockcache 677609 -> 862850 readrandom_memtable_sst 710440 -> 1109223 readrandom_fillunique_random 221589 -> 247869 memtablefillrandom 105286 -> 92643 memtablereadrandom 763033 -> 1288862 Test Plan: make asan_check I am also running db_stress Reviewers: dhruba, haobo, sdong, kailiu Reviewed By: haobo CC: leveldb Differential Revision: https://reviews.facebook.net/D14679
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- 20 12月, 2013 1 次提交
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由 Igor Canadi 提交于
fix missing gflags library
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- 19 12月, 2013 4 次提交
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由 Mark Callaghan 提交于
Summary: For some tests I want to cache the database prior to running other tests on the same invocation of db_bench. The readtocache test ignores --threads and --reads so those can be used by other tests and it will still do a full read of --num rows with one thread. It might be invoked like: db_bench --benchmarks=readtocache,readrandom --reads 100 --num 10000 --threads 8 Task ID: # Blame Rev: Test Plan: run db_bench Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: dhruba CC: leveldb Differential Revision: https://reviews.facebook.net/D14739
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由 Igor Canadi 提交于
Summary: db_test should be the first to execute because it finds the most bugs. Also, when third parties report issues, we don't want ldb error message, we prefer to have db_test error message. For example, see thread: https://github.com/facebook/rocksdb/issues/25 Test Plan: make check Reviewers: dhruba, haobo, kailiu Reviewed By: dhruba CC: leveldb Differential Revision: https://reviews.facebook.net/D14715
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由 Igor Canadi 提交于
Remove utilities/.DS_Store file.
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由 Igor Canadi 提交于
C bindings: add a bunch of the newer options
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- 18 12月, 2013 2 次提交
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由 Siying Dong 提交于
Summary: I realized that "D14409 Avoid sorting in Version::Get() by presorting them in VersionSet::Builder::SaveTo()" is not done in an optimized place. SaveTo() is usually inside mutex. Move it to Finalize(), which is called out of mutex. Test Plan: make all check Reviewers: dhruba, haobo, kailiu Reviewed By: dhruba CC: igor, leveldb Differential Revision: https://reviews.facebook.net/D14607
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由 Siying Dong 提交于
Summary: It seems to be a decision tradeoff in current codes: we make a malloc for every Get() to reduce one malloc for a flush inside mutex. It takes about 5% of CPU time in readrandom tests. We might consider the tradeoff to be the other way around. Test Plan: make all check Reviewers: dhruba, haobo, igor Reviewed By: dhruba CC: leveldb Differential Revision: https://reviews.facebook.net/D14697
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- 17 12月, 2013 1 次提交
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由 Josef Šimánek 提交于
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- 16 12月, 2013 1 次提交
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由 Mike Lin 提交于
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- 13 12月, 2013 3 次提交
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由 Igor Canadi 提交于
Summary: I realized that manifest will get deleted by PurgeObsoleteFiles in DBImpl, but it is sill cleaner to delete files before we restore the backup Test Plan: backupable_db_test Reviewers: dhruba Reviewed By: dhruba CC: leveldb Differential Revision: https://reviews.facebook.net/D14619
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由 Mark Callaghan 提交于
Summary: Adds these counters { WAL_FILE_SYNCED, "rocksdb.wal.synced" } number of writes that request a WAL sync { WAL_FILE_BYTES, "rocksdb.wal.bytes" }, number of bytes written to the WAL { WRITE_DONE_BY_SELF, "rocksdb.write.self" }, number of writes processed by the calling thread { WRITE_DONE_BY_OTHER, "rocksdb.write.other" }, number of writes not processed by the calling thread. Instead these were processed by the current holder of the write lock { WRITE_WITH_WAL, "rocksdb.write.wal" }, number of writes that request WAL logging { COMPACT_READ_BYTES, "rocksdb.compact.read.bytes" }, number of bytes read during compaction { COMPACT_WRITE_BYTES, "rocksdb.compact.write.bytes" }, number of bytes written during compaction Per-interval stats output was updated with WAL stats and correct stats for universal compaction including a correct value for write-amplification. It now looks like: Compactions Level Files Size(MB) Score Time(sec) Read(MB) Write(MB) Rn(MB) Rnp1(MB) Wnew(MB) RW-Amplify Read(MB/s) Write(MB/s) Rn Rnp1 Wnp1 NewW Count Ln-stall Stall-cnt -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 0 7 464 46.4 281 3411 3875 3411 0 3875 2.1 12.1 13.8 621 0 240 240 628 0.0 0 Uptime(secs): 310.8 total, 2.0 interval Writes cumulative: 9999999 total, 9999999 batches, 1.0 per batch, 1.22 ingest GB WAL cumulative: 9999999 WAL writes, 9999999 WAL syncs, 1.00 writes per sync, 1.22 GB written Compaction IO cumulative (GB): 1.22 new, 3.33 read, 3.78 write, 7.12 read+write Compaction IO cumulative (MB/sec): 4.0 new, 11.0 read, 12.5 write, 23.4 read+write Amplification cumulative: 4.1 write, 6.8 compaction Writes interval: 100000 total, 100000 batches, 1.0 per batch, 12.5 ingest MB WAL interval: 100000 WAL writes, 100000 WAL syncs, 1.00 writes per sync, 0.01 MB written Compaction IO interval (MB): 12.49 new, 14.98 read, 21.50 write, 36.48 read+write Compaction IO interval (MB/sec): 6.4 new, 7.6 read, 11.0 write, 18.6 read+write Amplification interval: 101.7 write, 102.9 compaction Stalls(secs): 142.924 level0_slowdown, 0.000 level0_numfiles, 0.805 memtable_compaction, 0.000 leveln_slowdown Stalls(count): 132461 level0_slowdown, 0 level0_numfiles, 3 memtable_compaction, 0 leveln_slowdown Task ID: #3329644, #3301695 Blame Rev: Test Plan: Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: dhruba CC: leveldb Differential Revision: https://reviews.facebook.net/D14583
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由 Igor Canadi 提交于
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- 12 12月, 2013 6 次提交
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由 Igor Canadi 提交于
Summary: @MarkCallaghan's tests indicate that performance with 8k rows in memtable is much worse than empty memtable. I wanted to add a regression tests that measures this effect, so we could optimize it. However, current config shows 634461 QPS on my devbox. Mark, any idea why this is so much faster than your measurements? Test Plan: Ran the regression test. Reviewers: MarkCallaghan, dhruba, haobo Reviewed By: MarkCallaghan CC: leveldb, MarkCallaghan Differential Revision: https://reviews.facebook.net/D14511
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由 Siying Dong 提交于
Summary: In get operations, merge_operands is only used in few cases. Lazily initialize it can reduce average latency in some cases Test Plan: make all check Reviewers: haobo, kailiu, dhruba Reviewed By: haobo CC: igor, nkg-, leveldb Differential Revision: https://reviews.facebook.net/D14415 Conflicts: db/db_impl.cc db/memtable.cc
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由 James Golick 提交于
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由 Siying Dong 提交于
[RocksDB Performance Branch] Avoid sorting in Version::Get() by presorting them in VersionSet::Builder::SaveTo() Summary: Pre-sort files in VersionSet::Builder::SaveTo() so that when getting the value, no need to sort them. It can avoid the costs of vector operations and sorting in Version::Get(). Test Plan: make all check Reviewers: haobo, kailiu, dhruba Reviewed By: dhruba CC: nkg-, igor, leveldb Differential Revision: https://reviews.facebook.net/D14409
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由 Siying Dong 提交于
Summary: creating new iterators of mem tables can be expensive. Move them out of mutex. DBImpl::WriteLevel0Table()'s mems seems to be a local vector and is only used by flushing. memtables to flush are also immutable, so it should be safe to do so. Test Plan: make all check Reviewers: haobo, dhruba, kailiu Reviewed By: dhruba CC: igor, leveldb Differential Revision: https://reviews.facebook.net/D14577 Conflicts: db/db_impl.cc
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由 Igor Canadi 提交于
Summary: I have ran a get benchmark where all the data is in the cache and observed that most of the time is spent on waiting for lock in LRUCache. This is an effort to optimize LRUCache. Test Plan: The data was loaded with fillseq. Then, I ran a benchmark: /db_bench --db=/tmp/rocksdb_stat_bench --num=1000000 --benchmarks=readrandom --statistics=1 --use_existing_db=1 --threads=16 --disable_seek_compaction=1 --cache_size=20000000000 --cache_numshardbits=8 --table_cache_numshardbits=8 I ran the benchmark three times. Here are the results: AFTER THE PATCH: 798072, 803998, 811807 BEFORE THE PATCH: 782008, 815593, 763017 Reviewers: dhruba, haobo, kailiu Reviewed By: haobo CC: leveldb Differential Revision: https://reviews.facebook.net/D14571
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- 11 12月, 2013 3 次提交
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由 James Golick 提交于
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由 Igor Canadi 提交于
Summary: We now delete backups with newer sequence number, so the clients don't have to handle confusing situations when they restore from backup. Test Plan: added a unit test Reviewers: dhruba Reviewed By: dhruba CC: leveldb Differential Revision: https://reviews.facebook.net/D14547
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由 Igor Canadi 提交于
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