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Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
Open HFT libraries in @Java
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Open HFT libraries in @Java

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Java libraries which are used for trading systems which can be valuable infrastructure in any Java application which needs scale and performance.

Java libraries which are used for trading systems which can be valuable infrastructure in any Java application which needs scale and performance.

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  • 1. Low latency persistence, logging, IPC and more Peter Lawrey CEO, Principal Consultant Higher Frequency Trading
  • 2. Agenda Who are we? Libraries designed to be ultra-low GC. When would you use them? Sample code.
  • 3. Who are we Higher Frequency Trading is a small consulting and software development house specialising in  Low latency, high throughput software  8 developers in Europe and USA.  Sponsor HFT related open source projects  Core Java engineering
  • 4. Who am I? Peter Lawrey - CEO and Principal Consultant - 3rd on Stackoverflow for Java, most Java Performance answers. - Founder of the Performance Java User's Group - An Australian, based in the U.K.
  • 5. What is our OSS Key OpenHFT projects  Chronicle, low latency logging, event store and IPC. (record / log everything)  HugeCollections, cross process embedded persisted data stores. (only need the latest) Millions of operations per second. Micro-second latency.
  • 6. What is HFT?  No standard definition.  Trading faster than a human can see.  Being fast can make the difference between making and losing money.  For different systems this means typical latencies of between − 10 micro-seconds and − 10 milli-second. (Latencies external to the provider)
  • 7. Event driven processing Trading system use event driven processing to minimise latency in a system.  Any data needed should already be loaded in memory, not go off to a slow SQL database.  Each input event triggers a response, unless there is a need to limit the output.
  • 8. Simple Trading System
  • 9. Critical Path A trading system is designed around the critical path. This has to be as short in terms of latency as possible.  Critical path has a tight latency budget which excludes many traditional databases.  Even the number of network hops can be minimised.  Non critical path can use tradition databases
  • 10. What is Chronicle? Very fast embedded persistence for Java. Functionality is simple and low level by design
  • 11. Where does Chronicle come from  Low latency, high frequency trading – Applications which are sub 100 micro-second external to the system.
  • 12. Where does Chronicle come from  High throughput trading systems – Hundreds of thousand of events per second
  • 13. Where does Chronicle come from  Modes of use – GC free – Lock-less – Shared memory – Text or binary – Replicated over TCP – Supports thread affinity
  • 14. Use for Chronicle  Synchronous text logging – support for SLF4J coming.  Synchronous binary data logging
  • 15. Use for Chronicle  Messaging between processes via shared memory  Messaging across systems
  • 16. Use for Chronicle  Supports recording micro-second timestamps across the systems  Replay for production data in test
  • 17. Writing to Chronicle IndexedChronicle ic = new IndexedChronicle(basePath); Appender excerpt = ic.createAppender(); for (int i = 1; i <= runs; i++) { excerpt.startExcerpt(); excerpt.writeUnsignedByte('M'); // message type excerpt.writeLong(i); // e.g. time stamp excerpt.writeDouble(i); excerpt.finish(); } ic.close();
  • 18. Reading from Chronicle IndexedChronicle ic = new IndexedChronicle(basePath); ic.useUnsafe(true); // for benchmarks Tailer excerpt = ic.createTailer(); for (int i = 1; i <= runs; i++) { while (!excerpt.nextIndex()) { // busy wait } char ch = (char) excerpt.readUnsignedByte(); long l = excerpt.readLong(); double d = excerpt.readDouble(); assert ch == 'M'; assert l == i; assert d == i; excerpt.finish(); } ic.close();
  • 19. Chronicle code VanillaChronicle chronicle = new VanillaChronicle(baseDir); // one per thread ExcerptAppender appender = chronicle.createAppender(); // once per message appender.startExcerpt(); appender.appendDateMillis(System.curren tTimeMillis()) .append(" - ").append(finalT)
  • 20. Chronicle and replication Replication is point to point (TCP) Server A records an event – replicates to Server B Server B reads local copy – B processes the event Server B stores the result. – replicates to Server A Server A replies. Round trip 25 micro-seconds 99% of the time GC-free Lock less Off heap Unbounded
  • 21. How does it recover? Once finish() returns, the OS will do the rest. If an excerpt is incomplete, it will be pruned.
  • 22. Cache friendly Data is laid out continuously, naturally packed. You can compress some types. One entry starts in the next byte to the previous one.
  • 23. Consumer insensitive No matter how slow the consumer is, the producer never has to wait. It never needs to clean messages before publishing (as a ring buffer does) You can start a consumer at the end of the day e.g. for reporting. The consumer can be more than the main memory size behind the producer as a Chronicle is not limited by main memory.
  • 24. How does it collect garbage? There is an assumption that your application has a daily or weekly maintenance cycle. This is implemented by closing the files and creating new ones. i.e. the whole lot is moved, compressed or deleted. Anything which must be retained can be copied to the new Chronicle
  • 25. Is there a lower level API? Chronicle 2.0 is based on OpenHFT Java Lang library which supports access to 64-bit native memory.  Has long size and offsets.  Support serialization and deserialization  Thread safe access including locking
  • 26. Is there a higher level API? You can hide the low level details with an interface.
  • 27. Is there a higher level API? There is a demo program with a simple interface. This models a “hub” process which take in events, processes them and publishes results.
  • 28. HugeCollections HugeCollections provides key-value storage.  Persisted (by the OS)  Embedded in multiple processes  Concurrent reads and writes  Off heap accessible without serialization.
  • 29. Creating a SharedHashMap  Uses a builder to create the map as there are a number of options.
  • 30. Updating an entry in the SHM  Create an off heap reference from an interface and update it as if it were on the heap
  • 31. Accessing a SHM entry  Accessing an entry looks like normal Java code, except arrays use a method xxxAt(n)
  • 32. Why use SHM?  Shared between processes  Persisted, or “written” to tmpfs e.g. /dev/shm  Can be GC-less, so not impact on pause times.  As little as 1/5th of the memory of ConcurrentHashMap  TCP/UDP multi-master replication planned.
  • 33. HugeCollections and throughput SharedHashMap tested on a machine with 128 GB, 16 cores, 32 threads. String keys, 64-bit long values.  10 million key-values updated at 37 M/s  500 million key-values updated at 23 M/s  On tmpfs, 2.5 billion key-values at 26 M/s
  • 34. HugeCollections and latency For a Map of small key-values (both 64-bit longs) With an update rate of 1 M/s, one thread. Percentile 100K entries 1 M entries 10 M entries 50% (typical) 0.1 μsec 0.2 μsec 0.2 μsec 90% (worst 1 in 10) 0.4 μsec 0.5 μsec 0.5 μsec 99% (worst 1 in 100) 4.4 μsec 5.5 μsec 7 μsec 99.9% 9 μsec 10 μsec 10 μsec 99.99% 10 μsec 12 μsec 13 μsec worst 24 μsec 29 μsec 26 μsec
  • 35. Performance of CHM With a 30 GB heap, 12 updates per entry
  • 36. Performance of SHM With a 64 MB heap, 12 updates per entry, no GCs
  • 37. Bonus topic: Units A peak times an application writes 49 “mb/s” to a disk which supports 50 “mb/s” and is replicated over a 100 “mb/s” network. What units were probably intended and where would you expect buffering if any?
  • 38. Bonus topic: Units A peak times an application writes 49 MiB/s to a disk which supports 50 MB/s and is replicated over a 100 Mb/s network. MiB = 1024^2 bytes MB = 1000^2 bytes Mb = 125,000 bytes The 49 MiB/s is the highest rate and 100 Mb/s is the lowest.
  • 39. Bonus topic: Units Unit bandwidth Used for mb - miili-bit mb/s – milli-bits per second ? mB - milli-byte mB/s – milli-bytes per second ? kb – kilo-bit (1000) kb/s – kilo-bits (baud) per second Dial up bandwidth kB – kilo-byte (1000) kB/s – kilo-bytes per second ? Mb – mega-bit (1000^2) Mb/s – mega-bits (baud) per second Cat 5 ethernet MB - mega-byte (1000^2) MB/s – mega bytes per second Disk bandwidth Mib – mibi-bit (1024^2) Mib – Mibi-bits per second ? MiB – mibi-byte (1024^2) MiB – Mibi-bytes per second Memory bandwidth Gb – giga-bit (1000^3) Gb/s – giga-bit (baud) per second High speed networks GB – giga-byte (1000^3) GB/s – giga-byte per second - Gib – gibi-bit (1024^3) Gib/s – gibi-bit per second - GiB – gibi-byte (1024^3) GiB/s – gibi-byte per second. Memory Bandwidth
  • 40. Q & A https://github.com/OpenHFT/OpenHFT @PeterLawrey peter.lawrey@higherfrequencytrading.com

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