Chapter 7. Hints for program developers

7.1. Ignore return values
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When you’re not interested in returning value of the #put(k, v) or #remove(k) method, use Flag.IGNORE_RETURN_VALUES flag as shown below:

Using Flag.IGNORE_RETURN_VALUES

Cache noPreviousValueCache = cache.getAdvancedCache().withFlags(Flag.IGNORE_RETURN_VALUES);
noPreviousValueCache.put(k, v);

Cache noPreviousValueCache = cache.getAdvancedCache().withFlags(Flag.IGNORE_RETURN_VALUES);
noPreviousValueCache.put(k, v);

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It is also possible to set this flag using ConfigurationBuilder

Using ConfigurationBuilder settings

ConfigurationBuilder cb = new ConfigurationBuilder();
cb.unsafe().unreliableReturnValues(true);

ConfigurationBuilder cb = new ConfigurationBuilder();
cb.unsafe().unreliableReturnValues(true);

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7.2. Use Externalizer for marshalling
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Red Hat Data Grid uses JBoss Marshalling to transfer objects over the wire. The most efficient way to marshall user data is to provide an AdvancedExternalizer. This solutions prevents JBoss Marshalling from sending class name over the network and allows to save some bandwidth:

User entity with Externalizer

import org.infinispan.marshall.AdvancedExternalizer;

public class Book {

   final String name;
   final String author;

   public Book(String name, String author) {
      this.name = name;
      this.author = author;
   }

   public static class BookExternalizer
            implements AdvancedExternalizer<Book> {

      @Override
      public void writeObject(ObjectOutput output, Book book)
            throws IOException {
         output.writeObject(book.name);
         output.writeObject(book.author);
      }

      @Override
      public Person readObject(ObjectInput input)
            throws IOException, ClassNotFoundException {
         return new Person((String) input.readObject(), (String) input.readObject());
      }

      @Override
      public Set<Class<? extends Book>> getTypeClasses() {
         return Util.<Class<? extends Book>>asSet(Book.class);
      }

      @Override
      public Integer getId() {
         return 2345;
      }
   }
}

import org.infinispan.marshall.AdvancedExternalizer;

public class Book {

   final String name;
   final String author;

   public Book(String name, String author) {
      this.name = name;
      this.author = author;
   }

   public static class BookExternalizer
            implements AdvancedExternalizer<Book> {

      @Override
      public void writeObject(ObjectOutput output, Book book)
            throws IOException {
         output.writeObject(book.name);
         output.writeObject(book.author);
      }

      @Override
      public Person readObject(ObjectInput input)
            throws IOException, ClassNotFoundException {
         return new Person((String) input.readObject(), (String) input.readObject());
      }

      @Override
      public Set<Class<? extends Book>> getTypeClasses() {
         return Util.<Class<? extends Book>>asSet(Book.class);
      }

      @Override
      public Integer getId() {
         return 2345;
      }
   }
}

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The Externalizer must be registered in cache configuration. See configuration examples below:

Adding Externalizer using XML

<cache-container>
   <serialization>
      <advanced-externalizer class="Book$BookExternalizer"/>
   </serialization>
</cache-container>

<cache-container>
   <serialization>
      <advanced-externalizer class="Book$BookExternalizer"/>
   </serialization>
</cache-container>

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Adding Externalizer using Java

GlobalConfigurationBuilder builder = ...
builder.serialization().addAdvancedExternalizer(new Book.BookExternalizer());

GlobalConfigurationBuilder builder = ...
builder.serialization().addAdvancedExternalizer(new Book.BookExternalizer());

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7.3. Storing Strings efficiently
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If your strings are mostly ASCII, convert them to UTF-8 and store them as byte[]:

Using String#getBytes("UTF-8") allows to decrease size of the object
Consider using G1 GC with additional JVM flag -XX:+UseStringDeduplication. This allows to decrease memory footprint (see JEP 192 for details).

7.4. Use simple cache for local caches
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When you don’t need the full feature set of caches, you can set local cache to "simple" mode and achieve non-trivial speedup while still using Red Hat Data Grid API.

This is an example comparison of the difference, randomly reading/writing into cache with 2048 entries as executed on 2x8-core Intel® Xeon® CPU E5-2640 v3 @ 2.60GHz:

Expand

Table 7.1. Number of operations per second (± std. dev.)
Cache type	single-threaded cache.get(…)	single-threaded cache.put(…)	32 threads cache.get(…)	32 threads cache.put(…)
Local cache	14,321,510 ± 260,807	1,141,168 ± 6,079	236,644,227 ± 2,657,918	2,287,708 ± 100,236
Simple cache	38,144,468 ± 575,420	11,706,053 ± 92,515	836,510,727 ± 3,176,794	47,971,836 ± 1,125,298
CHM	60,592,770 ± 924,368	23,533,141 ± 98,632	1,369,521,754 ± 4,919,753	75,839,121 ± 3,319,835

The CHM shows comparison for ConcurrentHashMap from JSR-166 with pluggable equality/hashCode function, which is used as the underlying storage in Red Hat Data Grid.

Even though we use JMH to prevent some common pitfals of microbenchmarking, consider these results only aproximative. Your mileage may vary.

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7.1. Ignore return values
复制链接

7.2. Use Externalizer for marshalling
复制链接

7.3. Storing Strings efficiently
复制链接

7.4. Use simple cache for local caches
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Chapter 7. Hints for program developers

7.1. Ignore return values复制链接链接已复制到粘贴板!

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7.3. Storing Strings efficiently复制链接链接已复制到粘贴板!

7.4. Use simple cache for local caches复制链接链接已复制到粘贴板!

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关于红帽文档

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7.1. Ignore return values
复制链接

7.2. Use Externalizer for marshalling
复制链接

7.3. Storing Strings efficiently
复制链接

7.4. Use simple cache for local caches
复制链接