RxLMDB provide a RxJava API to LMDB (through lmdbjni) which is an ultra-fast, ultra-compact key-value embedded data store developed by Symas for the OpenLDAP Project. LMDB uses memory-mapped files, so it has the read performance of a pure in-memory database while still offering the persistence of standard disk-based databases. Transactional with full ACID semantics and crash-proof by design. No corruption. No startup time. Zero-config cache tuning.
RxLMDB will also provide a remote API built on Aeron in the near future.
Java 8 and RxJava is a pleasure to work with but since the LMDB API is a bit low level it make sense to raise the abstraction level to modern standards without scarifying too much (??) performance. So extending LMDB with RxJava makes it possible for asynchronous and event-based programs to process data from LMDB as sequences and adds operators that allow you to compose sequences together declaratively while abstracting away concerns about things like low-level threading, synchronization, thread-safety and concurrent data structures.
- If you want to run slow, copy-parse everything, like protobuf.
- If you want to run fast, zero-copy-parse only what you need.
- If you want to run faster, also use parallel range scans.
- If you want to run fastest, do not use RxLMDB, but plain LMDB.
- Better hardware obviously matters, zero-copy-parse scales well, copy-parse scales badly.
1 Thread
Benchmark Mode Cnt Score Error Units
BigKeyValueForwardRangeScan.plain thrpt 10 1178232.202 ± 81015.649 ops/s
BigKeyValueForwardRangeScan.rx thrpt 10 1162131.060 ± 112057.128 ops/s
BigZeroCopyForwardRangeScan.plain thrpt 10 9299859.225 ± 2529503.812 ops/s
KeyValueForwardRangeScan.plain thrpt 10 6674117.744 ± 1067856.172 ops/s
KeyValueForwardRangeScan.rx thrpt 10 5323064.014 ± 1061179.864 ops/s
KeyValueForwardSkipRangeScan.plain thrpt 10 8789483.189 ± 768294.614 ops/s
KeyValueForwardSkipRangeScan.rx thrpt 10 6453558.501 ± 903252.457 ops/s
ProtoForwardRangeScan.plain thrpt 10 977556.340 ± 263740.090 ops/s
ProtoForwardRangeScan.rx thrpt 10 842469.488 ± 170672.957 ops/s
SbeForwardRangeScan.plain thrpt 10 5924733.706 ± 1985892.580 ops/s
SbeForwardRangeScan.rx thrpt 10 4570195.110 ± 500547.365 ops/s
ValsForwardRangeScan.plain thrpt 10 5365088.191 ± 2345685.548 ops/s
ValsForwardRangeScan.rx thrpt 10 3627839.672 ± 1284540.222 ops/s4 Threads
Benchmark Mode Cnt Score Error Units
BigKeyValueForwardRangeScan.plain thrpt 10 1978242.823 ± 174190.990 ops/s
BigKeyValueForwardRangeScan.rx thrpt 10 1699797.802 ± 147330.769 ops/s
BigZeroCopyForwardRangeScan.plain thrpt 10 18631395.953 ± 7500005.892 ops/s
KeyValueForwardRangeScan.plain thrpt 10 13384190.029 ± 2015610.137 ops/s
KeyValueForwardRangeScan.rx thrpt 10 8646695.332 ± 2026413.388 ops/s
KeyValueForwardSkipRangeScan.plain thrpt 10 14736089.587 ± 2432557.384 ops/s
KeyValueForwardSkipRangeScan.rx thrpt 10 12330989.000 ± 559894.869 ops/s
ProtoForwardRangeScan.plain thrpt 10 651203.480 ± 28715.405 ops/s
ProtoForwardRangeScan.rx thrpt 10 617451.737 ± 20311.644 ops/s
SbeForwardRangeScan.plain thrpt 10 8991860.431 ± 465302.254 ops/s
SbeForwardRangeScan.rx thrpt 10 4755629.167 ± 1821428.568 ops/s
ValsForwardRangeScan.plain thrpt 10 8546665.500 ± 1269468.808 ops/s
ValsForwardRangeScan.rx thrpt 10 5812951.172 ± 573829.010 ops/s8 threads
Benchmark Mode Cnt Score Error Units
BigKeyValueForwardRangeScan.plain thrpt 10 10933343.150 ± 89647.695 ops/s
BigKeyValueForwardRangeScan.rx thrpt 10 9537755.671 ± 79734.499 ops/s
BigZeroCopyForwardRangeScan.plain thrpt 10 67753109.315 ± 25043041.656 ops/s
KeyValueForwardRangeScan.plain thrpt 10 51957281.758 ± 1315119.210 ops/s
KeyValueForwardRangeScan.rx thrpt 10 37261517.010 ± 2339705.356 ops/s
KeyValueForwardSkipRangeScan.plain thrpt 10 72329999.694 ± 9638355.993 ops/s
KeyValueForwardSkipRangeScan.rx thrpt 10 49290830.102 ± 6230559.413 ops/s
ProtoForwardRangeScan.plain thrpt 10 2043454.082 ± 96951.493 ops/s
ProtoForwardRangeScan.rx thrpt 10 2129419.080 ± 199508.987 ops/s
SbeForwardRangeScan.plain thrpt 10 59222391.194 ± 8513022.888 ops/s
SbeForwardRangeScan.rx thrpt 10 43212267.029 ± 1891687.949 ops/s
ValsForwardRangeScan.plain thrpt 10 54333422.372 ± 2917837.551 ops/s
ValsForwardRangeScan.rx thrpt 10 39036264.187 ± 2346692.590 ops/s<dependency>
<groupId>org.deephacks.rxlmdb</groupId>
<artifactId>rxlmdb</artifactId>
<version>${rxlmdb.version}</version>
</dependency>
<!-- add lmdbjni platform of choice -->
<dependency>
<groupId>org.deephacks.lmdbjni</groupId>
<artifactId>lmdbjni-linux64</artifactId>
<version>${lmdbjni.version}</version>
</dependency>
<dependency>
<groupId>org.deephacks.lmdbjni</groupId>
<artifactId>lmdbjni-osx64</artifactId>
<version>${lmdbjni.version}</version>
</dependency>
<dependency>
<groupId>org.deephacks.lmdbjni</groupId>
<artifactId>lmdbjni-win64</artifactId>
<version>${lmdbjni.version}</version>
</dependency>
<dependency>
<groupId>org.deephacks.lmdbjni</groupId>
<artifactId>lmdbjni-android</artifactId>
<version>${lmdbjni.version}</version>
</dependency> RxLMDB lmdb = RxLMDB.builder()
.path("/tmp/rxlmdb")
.size(ByteUnit.GIGA, 1)
.build();
RxDB db = RxDB.builder()
.name("test")
.lmdb(lmdb)
.build();
KeyValue[] kvs = new KeyValue[] {
new KeyValue(new byte[] { 1 }, new byte[] { 1 }),
new KeyValue(new byte[] { 2 }, new byte[] { 2 }),
new KeyValue(new byte[] { 3 }, new byte[] { 3 })
};
// put
db.put(Observable.from(kvs));
// get
Observable<KeyValue> o = db.get(Observable.just(new byte[] { 1 }));
// scan forward
Observable<List<KeyValue<> o = db.scan();
// scan backward
Observable<List<KeyValue<> o = db.scan(KeyRange.backward());
// scan range forward
Observable<List<KeyValue<> o = db.scan(
KeyRange.range(new byte[]{ 1 }, new byte[]{ 2 }
);
// scan range backward
Observable<List<KeyValue<> o = db.scan(
KeyRange.range(new byte[]{ 2 }, new byte[]{ 1 }
);
// parallel range scans
Observable<List<KeyValue>> obs = db.scan(
KeyRange.range(new byte[]{ 1 }, new byte[]{ 1 }),
KeyRange.range(new byte[]{ 2 }, new byte[]{ 2 }),
KeyRange.range(new byte[]{ 3 }, new byte[]{ 3 })
);
// zero copy parallel range scans
Observable<List<Byte>> obs = db.scan(
(key, value) -> key.getByte(0),
KeyRange.range(new byte[]{ 1 }, new byte[]{ 1 }),
KeyRange.range(new byte[]{ 2 }, new byte[]{ 2 }),
KeyRange.range(new byte[]{ 3 }, new byte[]{ 3 })
);
// Cursor scans
Observable<List<byte[]>> obs = db.cursor((cursor, subscriber) -> {
cursor.first();
subscriber.onNext(cursor.keyBytes());
cursor.last();
subscriber.onNext(cursor.keyBytes());
});
// count rows
Integer count = db.scan()
.flatMap(Observable::from)
.count().toBlocking().first();
// delete
db.delete(Observable.just(new byte[] { 1 }));
// delete range
Observable<byte[]> keys = db.scan()
.flatMap(Observable::from)
.map(kv -> kv.key);
db.delete(keys);
The write amplification of LMDB's copy-on-write approach can sometimes become expensive. So for higher throughput, infinite data streams, RxLMDB provide effecient and asynchronous batching. Remember to use a SerializedSubject if multiple threads are writing concurrently.
SerializedSubject<KeyValue, KeyValue> subject = PublishSubject.<KeyValue>create().toSerialized();
db.batch(subject.buffer(10, TimeUnit.NANOSECONDS, 512));
subject.onNext(new KeyValue(new byte[] { 1 }, new byte[] { 1 }));
subject.onNext(new KeyValue(new byte[] { 2 }, new byte[] { 2 }));
subject.onCompleted();