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5 Commits

Author SHA1 Message Date
Alan Paxton
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
2023-12-11 11:03:17 -08:00
Alan Paxton
f8969ad7d4 Improve Java API get() performance by reducing copies (#10970)
Summary:
Performance improvements for `get()` paths in the RocksJava API (JNI).
Document describing the performance results.

Replace uses of the legacy `DB::Get()` method wrapper returning data in a `std::string` with direct calls to `DB::Get()` passing a pinnable slice to receive this data. Copying from a pinned slice direct to the destination java byte array, without going via an intervening std::string, is a major performance gain for this code path.

Note that this gain only comes where `DB::Get()` is able to return a pinned buffer; where it has to copy into the buffer owned by the slice, there is still the intervening copy and no performance gain. It may be possible to address this case too, but it is not trivial.

Pull Request resolved: https://github.com/facebook/rocksdb/pull/10970

Reviewed By: pdillinger

Differential Revision: D42125567

Pulled By: ajkr

fbshipit-source-id: b7a4df7523b0420cadb1e9b6c7da3ec030a8da34
2022-12-21 11:54:24 -08:00
Alan Paxton
c1ec0b28eb java / jni io_uring support (#9224)
Summary:
Existing multiGet() in java calls multi_get_helper() which then calls DB::std::vector MultiGet(). This doesn't take advantage of io_uring.

This change adds another JNI level method that runs a parallel code path using the DB::void MultiGet(), using ByteBuffers at the JNI level. We call it multiGetDirect(). In addition to using the io_uring path, this code internally returns pinned slices which we can copy out of into our direct byte buffers; this should reduce the overall number of copies in the code path to/from Java. Some jmh benchmark runs (100k keys, 1000 key multiGet) suggest that for value sizes > 1k, we see about a 20% performance improvement, although performance is slightly reduced for small value sizes, there's a little bit more overhead in the JNI methods.

Closes https://github.com/facebook/rocksdb/issues/8407

Pull Request resolved: https://github.com/facebook/rocksdb/pull/9224

Reviewed By: mrambacher

Differential Revision: D32951754

Pulled By: jay-zhuang

fbshipit-source-id: 1f70df7334be2b6c42a9c8f92725f67c71631690
2021-12-15 18:09:25 -08:00
Adam Retter
7242dae7fe Improve RocksJava Comparator (#6252)
Summary:
This is a redesign of the API for RocksJava comparators with the aim of improving performance. It also simplifies the class hierarchy.

**NOTE**: This breaks backwards compatibility for existing 3rd party Comparators implemented in Java... so we need to consider carefully which release branches this goes into.

Previously when implementing a comparator in Java the developer had a choice of subclassing either `DirectComparator` or `Comparator` which would use direct and non-direct byte-buffers resepectively (via `DirectSlice` and `Slice`).

In this redesign there we have eliminated the overhead of using the Java Slice classes, and just use `ByteBuffer`s. The `ComparatorOptions` supplied when constructing a Comparator allow you to choose between direct and non-direct byte buffers by setting `useDirect`.

In addition, the `ComparatorOptions` now allow you to choose whether a ByteBuffer is reused over multiple comparator calls, by setting `maxReusedBufferSize > 0`. When buffers are reused, ComparatorOptions provides a choice of mutex type by setting `useAdaptiveMutex`.

 ---
[JMH benchmarks previously indicated](https://github.com/facebook/rocksdb/pull/6241#issue-356398306) that the difference between C++ and Java for implementing a comparator was ~7x slowdown in Java.

With these changes, when reusing buffers and guarding access to them via mutexes the slowdown is approximately the same. However, these changes offer a new facility to not reuse mutextes, which reduces the slowdown to ~5.5x in Java. We also offer a `thread_local` mechanism for reusing buffers, which reduces slowdown to ~5.2x in Java (closes https://github.com/facebook/rocksdb/pull/4425).

These changes also form a good base for further optimisation work such as further JNI lookup caching, and JNI critical.

 ---
These numbers were captured without jemalloc. With jemalloc, the performance improves for all tests, and the Java slowdown reduces to between 4.8x and 5.x.

```
ComparatorBenchmarks.put                                                native_bytewise  thrpt   25  124483.795 ± 2032.443  ops/s
ComparatorBenchmarks.put                                        native_reverse_bytewise  thrpt   25  114414.536 ± 3486.156  ops/s
ComparatorBenchmarks.put              java_bytewise_non-direct_reused-64_adaptive-mutex  thrpt   25   17228.250 ± 1288.546  ops/s
ComparatorBenchmarks.put          java_bytewise_non-direct_reused-64_non-adaptive-mutex  thrpt   25   16035.865 ± 1248.099  ops/s
ComparatorBenchmarks.put                java_bytewise_non-direct_reused-64_thread-local  thrpt   25   21571.500 ±  871.521  ops/s
ComparatorBenchmarks.put                  java_bytewise_direct_reused-64_adaptive-mutex  thrpt   25   23613.773 ± 8465.660  ops/s
ComparatorBenchmarks.put              java_bytewise_direct_reused-64_non-adaptive-mutex  thrpt   25   16768.172 ± 5618.489  ops/s
ComparatorBenchmarks.put                    java_bytewise_direct_reused-64_thread-local  thrpt   25   23921.164 ± 8734.742  ops/s
ComparatorBenchmarks.put                              java_bytewise_non-direct_no-reuse  thrpt   25   17899.684 ±  839.679  ops/s
ComparatorBenchmarks.put                                  java_bytewise_direct_no-reuse  thrpt   25   22148.316 ± 1215.527  ops/s
ComparatorBenchmarks.put      java_reverse_bytewise_non-direct_reused-64_adaptive-mutex  thrpt   25   11311.126 ±  820.602  ops/s
ComparatorBenchmarks.put  java_reverse_bytewise_non-direct_reused-64_non-adaptive-mutex  thrpt   25   11421.311 ±  807.210  ops/s
ComparatorBenchmarks.put        java_reverse_bytewise_non-direct_reused-64_thread-local  thrpt   25   11554.005 ±  960.556  ops/s
ComparatorBenchmarks.put          java_reverse_bytewise_direct_reused-64_adaptive-mutex  thrpt   25   22960.523 ± 1673.421  ops/s
ComparatorBenchmarks.put      java_reverse_bytewise_direct_reused-64_non-adaptive-mutex  thrpt   25   18293.317 ± 1434.601  ops/s
ComparatorBenchmarks.put            java_reverse_bytewise_direct_reused-64_thread-local  thrpt   25   24479.361 ± 2157.306  ops/s
ComparatorBenchmarks.put                      java_reverse_bytewise_non-direct_no-reuse  thrpt   25    7942.286 ±  626.170  ops/s
ComparatorBenchmarks.put                          java_reverse_bytewise_direct_no-reuse  thrpt   25   11781.955 ± 1019.843  ops/s
```
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6252

Differential Revision: D19331064

Pulled By: pdillinger

fbshipit-source-id: 1f3b794e6a14162b2c3ffb943e8c0e64a0c03738
2020-02-03 12:30:13 -08:00
Adam Retter
6477075f2c JMH microbenchmarks for RocksJava (#6241)
Summary:
This is the start of some JMH microbenchmarks for RocksJava.

Such benchmarks can help us decide on performance improvements of the Java API.

At the moment, I have only added benchmarks for various Comparator options, as that is one of the first areas where I want to improve performance. I plan to expand this to many more tests.

Details of how to compile and run the benchmarks are in the `README.md`.

A run of these on a XEON 3.5 GHz 4vCPU (QEMU Virtual CPU version 2.5+) / 8GB RAM KVM with Ubuntu 18.04, OpenJDK 1.8.0_232, and gcc 8.3.0 produced the following:

```
# Run complete. Total time: 01:43:17

REMEMBER: The numbers below are just data. To gain reusable insights, you need to follow up on
why the numbers are the way they are. Use profilers (see -prof, -lprof), design factorial
experiments, perform baseline and negative tests that provide experimental control, make sure
the benchmarking environment is safe on JVM/OS/HW level, ask for reviews from the domain experts.
Do not assume the numbers tell you what you want them to tell.

Benchmark                                         (comparatorName)   Mode  Cnt       Score       Error  Units
ComparatorBenchmarks.put                           native_bytewise thrpt   25   122373.920 ±  2200.538  ops/s
ComparatorBenchmarks.put              java_bytewise_adaptive_mutex thrpt   25    17388.201 ±  1444.006  ops/s
ComparatorBenchmarks.put          java_bytewise_non-adaptive_mutex thrpt   25    16887.150 ±  1632.204  ops/s
ComparatorBenchmarks.put       java_direct_bytewise_adaptive_mutex thrpt   25    15644.572 ±  1791.189  ops/s
ComparatorBenchmarks.put   java_direct_bytewise_non-adaptive_mutex thrpt   25    14869.601 ±  2252.135  ops/s
ComparatorBenchmarks.put                   native_reverse_bytewise thrpt   25   116528.735 ±  4168.797  ops/s
ComparatorBenchmarks.put      java_reverse_bytewise_adaptive_mutex thrpt   25    10651.975 ±   545.998  ops/s
ComparatorBenchmarks.put  java_reverse_bytewise_non-adaptive_mutex thrpt   25    10514.224 ±   930.069  ops/s
```

Indicating a ~7x difference between comparators implemented natively (C++) and those implemented in Java. Let's see if we can't improve on that in the near future...
Pull Request resolved: https://github.com/facebook/rocksdb/pull/6241

Differential Revision: D19290410

Pulled By: pdillinger

fbshipit-source-id: 25d44bf3a31de265502ed0c5d8a28cf4c7cb9c0b
2020-01-07 15:46:09 -08:00