Transfer Batching Low-Level Communication Optimizations
12.8.3 Transfer Batching
Batching can be useful when your performance analysis indicates there are too many network transfers occurring. The standard batching technique uses two cutoff values: a timeout and a data limit. The technique is to catch and hold all data transfers at the batching level just above the real communication-transfer level and send all data transfers together in one transfer. The batched transfer is triggered either when the timeout is reached or when the data limit which is normally the batch buffer size is exceeded. Most message-queuing systems support this type of batching. The following code illustrates a simple example of adding batching to the communications layer. The bold type shows the extra code required: method synchronized since there will be another thread which sends the batched transfer if the timeout is reached void synchronized writeTransferbyte[] transferbuffer, int offset, int len { if len = 0 return; if len = batch.length - 4 - batchSize { batch is too full to take this chunk, so send off the last lot realWriteTransferbatchbuffer, 0, batchSize; batchSize = 0; lastSend = System.currentTimeMillis ; } addIntToBatchlen; System.arraycopytransferbuffer, offset, batchBuffer, batchSize, len; batchSize += len; } void realWriteTransferbyte[] transferbuffer, int offset, int len { communicationSocket.writelen; communicationSocket.writetransferbuffer, offset, len; communicationSocket.flush ; } batch timeout thread method void run { int elapsedTime; for;; { synchronizedthis { elapsedTime = System.currentTimeMillis - lastSend; if elapsedTime = timeout batchSize 0 { realWriteTransferbatchbuffer, 0, batchSize; - 279 - batchSize = 0; lastSend = System.currentTimeMillis ; } } try{Thread.sleeptimeout - elapsedTime;}catchInterruptedException e{} } } realReadTransfer throws IOException { Dont socket read until the buffer has been completely used if readBatchBufferlen - readBatchBufferOffset 0 return; otherwise read in the next batched communication readBatchBufferOffset = 0; int readBatchBufferlen = communicationSocket.read ; if readBatchBufferlen = 0 throw new IOExceptionblah blah; int readlen = 0; byte[] readBatchBuffer = new byte[readBatchBufferlen]; int n; whilereadlen readBatchBufferlen { n = communicationSocket.readreadBatchBuffer, readlen, readBatchBufferlen-readlen; if n = 0 readlen += n; else throw new IOExceptionblah blah again; } } byte[] readTransfer throws IOException { realReadTransfer ; int len = readIntFromBatch ; if len = 0 throw new IOExceptionblah blah; byte[] transferbuffer = new byte[len]; System.arraycopyreadBatchBuffer, readBatchBufferOffset, transferBuffer, 0, len; readBatchBufferOffset += len; return transferbuffer; }12.8.4 Multiplexing
Parts
» OReilly.Java.performance tuning
» The Tuning Game System Limitations and What to Tune
» A Tuning Strategy Introduction
» Threading to Appear Quicker Streaming to Appear Quicker
» User Agreements Starting to Tune
» Setting Benchmarks Starting to Tune
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» Optimizations Performed When Using the -O Option
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» Performance Checklist Underlying JDK Improvements
» Object-Creation Statistics Object Creation
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» The Cost of try-catch Blocks Without an Exception
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» no JIT 1.3 Loops and Switches
» Recursion Loops and Switches
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» no JIT HotSpot 1.0 no JIT HotSpot 1.0 Serialization
» no IO, Logging, and Console Output
» Clustering Objects and Counting IO Operations
» Compression IO, Logging, and Console Output
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» Avoiding Unnecessary Sorting Overhead
» An Efficient Sorting Framework
» no HotSpot Better Than Onlogn Sorting
» User-Interface Thread and Other Threads
» Desynchronization and Synchronized Wrappers
» Avoiding Serialized Execution HotSpot 1.0
» no JIT no JIT HotSpot 1.0 Timing Multithreaded Tests
» Atomic Access and Assignment
» Free Load Balancing from TCPIP
» Load-Balancing Classes Load Balancing
» A Load-Balancing Example Load Balancing
» Threaded Problem-Solving Strategies Threading
» Collections Appropriate Data Structures and Algorithms
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» Cached Access Appropriate Data Structures and Algorithms
» Caching Example I Appropriate Data Structures and Algorithms
» Caching Example II Appropriate Data Structures and Algorithms
» Finding the Index for Partially Matched Strings
» Search Trees Appropriate Data Structures and Algorithms
» Comparing Communication Layers Distributed Computing
» Batching I Application Partitioning
» Compression Caching Low-Level Communication Optimizations
» Transfer Batching Low-Level Communication Optimizations
» Batching II Distributed Garbage Collection
» Performance Checklist Distributed Computing
» When Not to Optimize Tuning Class Libraries and Beans
» Scaling Design and Architecture
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» Use simulations and benchmarks Consider the total work done and the design overhead
» Tuning After Deployment When to Optimize
» User Interface Usability Training Server Downtime
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» Clustering Files Cached Filesystems RAM Disks, tmpfs, cachefs
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» Network Bottlenecks Network IO
» Performance Checklist Underlying Operating System and Network Improvements
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