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3 commits
| Author | SHA1 | Message | Date | |
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Richard Barnes
|
a8dd15ad41 |
Fix deprecated dynamic exception in internal_repo_rocksdb/repo/java/rocksjni/kv_helper.h +1
Summary: LLVM has detected a violation of `-Wdeprecated-dynamic-exception-spec`. Dynamic exceptions were removed in C++17. This diff fixes the deprecated instance(s). See [Dynamic exception specification](https://en.cppreference.com/w/cpp/language/except_spec) and [noexcept specifier](https://en.cppreference.com/w/cpp/language/noexcept_spec). Reviewed By: palmje Differential Revision: D58528375 fbshipit-source-id: 130fecd3aa556e4cdb955feea53c442bd9fbc864 |
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Alan Paxton
|
d9a441113e |
JNI get_helper code sharing / multiGet() use efficient batch C++ support (#12344)
Summary: Implement RAII-based helpers for JNIGet() and multiGet() Replace JNI C++ helpers `rocksdb_get_helper, rocksdb_get_helper_direct`, `multi_get_helper`, `multi_get_helper_direct`, `multi_get_helper_release_keys`, `txn_get_helper`, and `txn_multi_get_helper`. The model is to entirely do away with a single helper, instead a number of utility methods allow each separate JNI `Get()` and `MultiGet()` method to organise their parameters efficiently, then call the underlying C++ `db->Get()`, `db->MultiGet()`, `txn->Get()`, or `txn->MultiGet()` method itself, and use further utilities to retrieve results. Roughly speaking: * get keys into C++ form * Call C++ Get() * get results and status into Java form We achieve a useful performance gain as part of this work; by using the updated C++ multiGet we immediately pick up its performance gains (batch improvements to multiGet C++ were previously implemented, but not until now used by Java/JNI). multiGetBB already uses the batched C++ multiGet(), and all other benchmarks show consistent improvement after the changes: ## Before: ``` Benchmark (columnFamilyTestType) (keyCount) (keySize) (multiGetSize) (valueSize) Mode Cnt Score Error Units MultiGetNewBenchmarks.multiGetBB200 no_column_family 10000 1024 100 256 thrpt 25 5315.459 ± 20.465 ops/s MultiGetNewBenchmarks.multiGetBB200 no_column_family 10000 1024 100 1024 thrpt 25 5673.115 ± 78.299 ops/s MultiGetNewBenchmarks.multiGetBB200 no_column_family 10000 1024 100 4096 thrpt 25 2616.860 ± 46.994 ops/s MultiGetNewBenchmarks.multiGetBB200 no_column_family 10000 1024 100 16384 thrpt 25 1700.058 ± 24.034 ops/s MultiGetNewBenchmarks.multiGetBB200 no_column_family 10000 1024 100 65536 thrpt 25 791.171 ± 13.955 ops/s MultiGetNewBenchmarks.multiGetList10 no_column_family 10000 1024 100 256 thrpt 25 6129.929 ± 94.200 ops/s MultiGetNewBenchmarks.multiGetList10 no_column_family 10000 1024 100 1024 thrpt 25 7012.405 ± 97.886 ops/s MultiGetNewBenchmarks.multiGetList10 no_column_family 10000 1024 100 4096 thrpt 25 2799.014 ± 39.352 ops/s MultiGetNewBenchmarks.multiGetList10 no_column_family 10000 1024 100 16384 thrpt 25 1417.205 ± 22.272 ops/s MultiGetNewBenchmarks.multiGetList10 no_column_family 10000 1024 100 65536 thrpt 25 655.594 ± 13.050 ops/s MultiGetNewBenchmarks.multiGetListExplicitCF20 no_column_family 10000 1024 100 256 thrpt 25 6147.247 ± 82.711 ops/s MultiGetNewBenchmarks.multiGetListExplicitCF20 no_column_family 10000 1024 100 1024 thrpt 25 7004.213 ± 79.251 ops/s MultiGetNewBenchmarks.multiGetListExplicitCF20 no_column_family 10000 1024 100 4096 thrpt 25 2715.154 ± 110.017 ops/s MultiGetNewBenchmarks.multiGetListExplicitCF20 no_column_family 10000 1024 100 16384 thrpt 25 1408.070 ± 31.714 ops/s MultiGetNewBenchmarks.multiGetListExplicitCF20 no_column_family 10000 1024 100 65536 thrpt 25 623.829 ± 57.374 ops/s MultiGetNewBenchmarks.multiGetListRandomCF30 no_column_family 10000 1024 100 256 thrpt 25 6119.243 ± 116.313 ops/s MultiGetNewBenchmarks.multiGetListRandomCF30 no_column_family 10000 1024 100 1024 thrpt 25 6931.873 ± 128.094 ops/s MultiGetNewBenchmarks.multiGetListRandomCF30 no_column_family 10000 1024 100 4096 thrpt 25 2678.253 ± 39.113 ops/s MultiGetNewBenchmarks.multiGetListRandomCF30 no_column_family 10000 1024 100 16384 thrpt 25 1337.384 ± 19.500 ops/s MultiGetNewBenchmarks.multiGetListRandomCF30 no_column_family 10000 1024 100 65536 thrpt 25 625.596 ± 14.525 ops/s ``` ## After: ``` Benchmark (columnFamilyTestType) (keyCount) (keySize) (multiGetSize) (valueSize) Mode Cnt Score Error Units MultiGetBenchmarks.multiGetBB200 no_column_family 10000 1024 100 256 thrpt 25 5191.074 ± 78.250 ops/s MultiGetBenchmarks.multiGetBB200 no_column_family 10000 1024 100 1024 thrpt 25 5378.692 ± 260.682 ops/s MultiGetBenchmarks.multiGetBB200 no_column_family 10000 1024 100 4096 thrpt 25 2590.183 ± 34.844 ops/s MultiGetBenchmarks.multiGetBB200 no_column_family 10000 1024 100 16384 thrpt 25 1634.793 ± 34.022 ops/s MultiGetBenchmarks.multiGetBB200 no_column_family 10000 1024 100 65536 thrpt 25 786.455 ± 8.462 ops/s MultiGetBenchmarks.multiGetBB200 1_column_family 10000 1024 100 256 thrpt 25 5285.055 ± 11.676 ops/s MultiGetBenchmarks.multiGetBB200 1_column_family 10000 1024 100 1024 thrpt 25 5586.758 ± 213.008 ops/s MultiGetBenchmarks.multiGetBB200 1_column_family 10000 1024 100 4096 thrpt 25 2527.172 ± 17.106 ops/s MultiGetBenchmarks.multiGetBB200 1_column_family 10000 1024 100 16384 thrpt 25 1819.547 ± 12.958 ops/s MultiGetBenchmarks.multiGetBB200 1_column_family 10000 1024 100 65536 thrpt 25 803.861 ± 9.963 ops/s MultiGetBenchmarks.multiGetBB200 20_column_families 10000 1024 100 256 thrpt 25 5253.793 ± 28.020 ops/s MultiGetBenchmarks.multiGetBB200 20_column_families 10000 1024 100 1024 thrpt 25 5705.591 ± 20.556 ops/s MultiGetBenchmarks.multiGetBB200 20_column_families 10000 1024 100 4096 thrpt 25 2523.377 ± 15.415 ops/s MultiGetBenchmarks.multiGetBB200 20_column_families 10000 1024 100 16384 thrpt 25 1815.344 ± 11.309 ops/s MultiGetBenchmarks.multiGetBB200 20_column_families 10000 1024 100 65536 thrpt 25 820.792 ± 3.192 ops/s MultiGetBenchmarks.multiGetBB200 100_column_families 10000 1024 100 256 thrpt 25 5262.184 ± 20.477 ops/s MultiGetBenchmarks.multiGetBB200 100_column_families 10000 1024 100 1024 thrpt 25 5706.959 ± 23.123 ops/s MultiGetBenchmarks.multiGetBB200 100_column_families 10000 1024 100 4096 thrpt 25 2520.362 ± 9.170 ops/s MultiGetBenchmarks.multiGetBB200 100_column_families 10000 1024 100 16384 thrpt 25 1789.185 ± 14.239 ops/s MultiGetBenchmarks.multiGetBB200 100_column_families 10000 1024 100 65536 thrpt 25 818.401 ± 12.132 ops/s MultiGetBenchmarks.multiGetList10 no_column_family 10000 1024 100 256 thrpt 25 6978.310 ± 14.084 ops/s MultiGetBenchmarks.multiGetList10 no_column_family 10000 1024 100 1024 thrpt 25 7664.242 ± 22.304 ops/s MultiGetBenchmarks.multiGetList10 no_column_family 10000 1024 100 4096 thrpt 25 2881.778 ± 81.054 ops/s MultiGetBenchmarks.multiGetList10 no_column_family 10000 1024 100 16384 thrpt 25 1599.826 ± 7.190 ops/s MultiGetBenchmarks.multiGetList10 no_column_family 10000 1024 100 65536 thrpt 25 737.520 ± 6.809 ops/s MultiGetBenchmarks.multiGetList10 1_column_family 10000 1024 100 256 thrpt 25 6974.376 ± 10.716 ops/s MultiGetBenchmarks.multiGetList10 1_column_family 10000 1024 100 1024 thrpt 25 7637.440 ± 45.877 ops/s MultiGetBenchmarks.multiGetList10 1_column_family 10000 1024 100 4096 thrpt 25 2820.472 ± 42.231 ops/s MultiGetBenchmarks.multiGetList10 1_column_family 10000 1024 100 16384 thrpt 25 1716.663 ± 8.527 ops/s MultiGetBenchmarks.multiGetList10 1_column_family 10000 1024 100 65536 thrpt 25 755.848 ± 7.514 ops/s MultiGetBenchmarks.multiGetList10 20_column_families 10000 1024 100 256 thrpt 25 6943.651 ± 20.040 ops/s MultiGetBenchmarks.multiGetList10 20_column_families 10000 1024 100 1024 thrpt 25 7679.415 ± 9.114 ops/s MultiGetBenchmarks.multiGetList10 20_column_families 10000 1024 100 4096 thrpt 25 2844.564 ± 13.388 ops/s MultiGetBenchmarks.multiGetList10 20_column_families 10000 1024 100 16384 thrpt 25 1729.545 ± 5.983 ops/s MultiGetBenchmarks.multiGetList10 20_column_families 10000 1024 100 65536 thrpt 25 783.218 ± 1.530 ops/s MultiGetBenchmarks.multiGetList10 100_column_families 10000 1024 100 256 thrpt 25 6944.276 ± 29.995 ops/s MultiGetBenchmarks.multiGetList10 100_column_families 10000 1024 100 1024 thrpt 25 7670.301 ± 8.986 ops/s MultiGetBenchmarks.multiGetList10 100_column_families 10000 1024 100 4096 thrpt 25 2839.828 ± 12.421 ops/s MultiGetBenchmarks.multiGetList10 100_column_families 10000 1024 100 16384 thrpt 25 1730.005 ± 9.209 ops/s MultiGetBenchmarks.multiGetList10 100_column_families 10000 1024 100 65536 thrpt 25 787.096 ± 1.977 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 no_column_family 10000 1024 100 256 thrpt 25 6896.944 ± 21.530 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 no_column_family 10000 1024 100 1024 thrpt 25 7622.407 ± 12.824 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 no_column_family 10000 1024 100 4096 thrpt 25 2927.538 ± 19.792 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 no_column_family 10000 1024 100 16384 thrpt 25 1598.041 ± 4.312 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 no_column_family 10000 1024 100 65536 thrpt 25 744.564 ± 9.236 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 1_column_family 10000 1024 100 256 thrpt 25 6853.760 ± 78.041 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 1_column_family 10000 1024 100 1024 thrpt 25 7360.917 ± 355.365 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 1_column_family 10000 1024 100 4096 thrpt 25 2848.774 ± 13.409 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 1_column_family 10000 1024 100 16384 thrpt 25 1727.688 ± 3.329 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 1_column_family 10000 1024 100 65536 thrpt 25 776.088 ± 7.517 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 20_column_families 10000 1024 100 256 thrpt 25 6910.339 ± 14.366 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 20_column_families 10000 1024 100 1024 thrpt 25 7633.660 ± 10.830 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 20_column_families 10000 1024 100 4096 thrpt 25 2787.799 ± 81.775 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 20_column_families 10000 1024 100 16384 thrpt 25 1726.517 ± 6.830 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 20_column_families 10000 1024 100 65536 thrpt 25 787.597 ± 3.362 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 100_column_families 10000 1024 100 256 thrpt 25 6922.445 ± 10.493 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 100_column_families 10000 1024 100 1024 thrpt 25 7604.710 ± 48.043 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 100_column_families 10000 1024 100 4096 thrpt 25 2848.788 ± 15.783 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 100_column_families 10000 1024 100 16384 thrpt 25 1730.837 ± 6.497 ops/s MultiGetBenchmarks.multiGetListExplicitCF20 100_column_families 10000 1024 100 65536 thrpt 25 794.557 ± 1.869 ops/s MultiGetBenchmarks.multiGetListRandomCF30 no_column_family 10000 1024 100 256 thrpt 25 6918.716 ± 15.766 ops/s MultiGetBenchmarks.multiGetListRandomCF30 no_column_family 10000 1024 100 1024 thrpt 25 7626.692 ± 9.394 ops/s MultiGetBenchmarks.multiGetListRandomCF30 no_column_family 10000 1024 100 4096 thrpt 25 2871.382 ± 72.155 ops/s MultiGetBenchmarks.multiGetListRandomCF30 no_column_family 10000 1024 100 16384 thrpt 25 1598.786 ± 4.819 ops/s MultiGetBenchmarks.multiGetListRandomCF30 no_column_family 10000 1024 100 65536 thrpt 25 748.469 ± 7.234 ops/s MultiGetBenchmarks.multiGetListRandomCF30 1_column_family 10000 1024 100 256 thrpt 25 6922.666 ± 17.131 ops/s MultiGetBenchmarks.multiGetListRandomCF30 1_column_family 10000 1024 100 1024 thrpt 25 7623.890 ± 8.805 ops/s MultiGetBenchmarks.multiGetListRandomCF30 1_column_family 10000 1024 100 4096 thrpt 25 2850.698 ± 18.004 ops/s MultiGetBenchmarks.multiGetListRandomCF30 1_column_family 10000 1024 100 16384 thrpt 25 1727.623 ± 4.868 ops/s MultiGetBenchmarks.multiGetListRandomCF30 1_column_family 10000 1024 100 65536 thrpt 25 774.534 ± 10.025 ops/s MultiGetBenchmarks.multiGetListRandomCF30 20_column_families 10000 1024 100 256 thrpt 25 5486.251 ± 13.582 ops/s MultiGetBenchmarks.multiGetListRandomCF30 20_column_families 10000 1024 100 1024 thrpt 25 4920.656 ± 44.557 ops/s MultiGetBenchmarks.multiGetListRandomCF30 20_column_families 10000 1024 100 4096 thrpt 25 3922.913 ± 25.686 ops/s MultiGetBenchmarks.multiGetListRandomCF30 20_column_families 10000 1024 100 16384 thrpt 25 2873.106 ± 4.336 ops/s MultiGetBenchmarks.multiGetListRandomCF30 20_column_families 10000 1024 100 65536 thrpt 25 802.404 ± 8.967 ops/s MultiGetBenchmarks.multiGetListRandomCF30 100_column_families 10000 1024 100 256 thrpt 25 4817.996 ± 18.042 ops/s MultiGetBenchmarks.multiGetListRandomCF30 100_column_families 10000 1024 100 1024 thrpt 25 4243.922 ± 13.929 ops/s MultiGetBenchmarks.multiGetListRandomCF30 100_column_families 10000 1024 100 4096 thrpt 25 3175.998 ± 7.773 ops/s MultiGetBenchmarks.multiGetListRandomCF30 100_column_families 10000 1024 100 16384 thrpt 25 2321.990 ± 12.501 ops/s MultiGetBenchmarks.multiGetListRandomCF30 100_column_families 10000 1024 100 65536 thrpt 25 1753.028 ± 7.130 ops/s ``` Closes https://github.com/facebook/rocksdb/issues/11518 Pull Request resolved: https://github.com/facebook/rocksdb/pull/12344 Reviewed By: cbi42 Differential Revision: D54809714 Pulled By: pdillinger fbshipit-source-id: bee3b949720abac073bce043b59ce976a11e99eb |
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Alan Paxton
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5a063ecd34 |
Java API consistency between RocksDB.put() , .merge() and Transaction.put() , .merge() (#11019)
Summary:
### Implement new Java API get()/put()/merge() methods, and transactional variants.
The Java API methods are very inconsistent in terms of how they pass parameters (byte[], ByteBuffer), and what variants and defaulted parameters they support. We try to bring some consistency to this.
* All APIs should support calls with ByteBuffer parameters.
* Similar methods (RocksDB.get() vs Transaction.get()) should support as similar as possible sets of parameters for predictability.
* get()-like methods should provide variants where the caller supplies the target buffer, for the sake of efficiency. Allocation costs in Java can be significant when large buffers are repeatedly allocated and freed.
### API Additions
1. RockDB.get implement indirect ByteBuffers. Added indirect ByteBuffers and supporting native methods for get().
2. RocksDB.Iterator implement missing (byte[], offset, length) variants for key() and value() parameters.
3. Transaction.get() implement missing methods, based on RocksDB.get. Added ByteBuffer.get with and without column family. Added byte[]-as-target get.
4. Transaction.iterator() implement a getIterator() which defaults ReadOptions; as per RocksDB.iterator(). Rationalize support API for this and RocksDB.iterator()
5. RocksDB.merge implement ByteBuffer methods; both direct and indirect buffers. Shadow the methods of RocksDB.put; RocksDB.put only offers ByteBuffer API with explicit WriteOptions. Duplicated this with RocksDB.merge
6. Transaction.merge implement methods as per RocksDB.merge methods. Transaction is already constructed with WriteOptions, so no explicit WriteOptions methods required.
7. Transaction.mergeUntracked implement the same API methods as Transaction.merge except the ones that use assumeTracked, because that’s not a feature of merge untracked.
### Support Changes (C++)
The current JNI code in C++ supports multiple variants of methods through a number of helper functions. There are numerous TODO suggestions in the code proposing that the helpers be re-factored/shared.
We have taken a different approach for the new methods; we have created wrapper classes `JDirectBufferSlice`, `JDirectBufferPinnableSlice`, `JByteArraySlice` and `JByteArrayPinnableSlice` RAII classes which construct slices from JNI parameters and can then be passed directly to RocksDB methods. For instance, the `Java_org_rocksdb_Transaction_getDirect` method is implemented like this:
```
try {
ROCKSDB_NAMESPACE::JDirectBufferSlice key(env, jkey_bb, jkey_off,
jkey_part_len);
ROCKSDB_NAMESPACE::JDirectBufferPinnableSlice value(env, jval_bb, jval_off,
jval_part_len);
ROCKSDB_NAMESPACE::KVException::ThrowOnError(
env, txn->Get(*read_options, column_family_handle, key.slice(),
&value.pinnable_slice()));
return value.Fetch();
} catch (const ROCKSDB_NAMESPACE::KVException& e) {
return e.Code();
}
```
Notice the try/catch mechanism with the `KVException` class, which combined with RAII and the wrapper classes means that there is no ad-hoc cleanup necessary in the JNI methods.
We propose to extend this mechanism to existing JNI methods as further work.
### Support Changes (Java)
Where there are multiple parameter-variant versions of the same method, we use fewer or just one supporting native method for all of them. This makes maintenance a bit easier and reduces the opportunity for coding errors mixing up (untyped) object handles.
In order to support this efficiently, some classes need to have default values for column families and read options added and cached so that they are not re-constructed on every method call.
This PR closes https://github.com/facebook/rocksdb/issues/9776
Pull Request resolved: https://github.com/facebook/rocksdb/pull/11019
Reviewed By: ajkr
Differential Revision: D52039446
Pulled By: jowlyzhang
fbshipit-source-id: 45d0140a4887e42134d2e56520e9b8efbd349660
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