This is a continuation of a series of posts I have written about Hazelcast.  I highly suggest you read the other ones: Part 1, Part 2, Part 3 and Part 4.

Things That Makes One Go “Huh?”

This post will have no Hazelcast specific code in it.  Let me repeat that.  This post will have no Hazelcast specific code in it.  That is because the fine folks at Hazelcast produced a product that implements different standards.  This allows for a choice of clients.  One of those standards that Hazelcast implements is memcached.

What about JCache?

JCache (JSR 107) is just for Java.  Memcached protocol clients have been implemented across several languages so one is not nailed down to one language.  Implementing the memcached protocol was a smart move in my opinion because it makes Hazelcast more than a “Java thing.”

Why Use Hazelcast?

Excellent question!  If one can use any memcached server, why use Hazelcast.  Well, to tell you the truth, unless one is sharing a database between several servers, one may not even need caching!  If one does need a caching solution, here is why I would choose Hazelcast:

1. Automatic, real time backups – I have not read of one Hazelcast datatype that is not backed up at least once.  Just stand up two instances, one off machine from the other, to get the full benefit.
2. Security – If the servers that need to cache are across different networks, then the firewall rules can be easier with Hazelcast.  Lets say n servers are needing to cache data and n/2 of them are on the 192.168.1.x network and the other n/2 are on the 10.10.1.x network.  By setting one Hazelcast instance on either network, all n machines can be sharing a cache.  The Hazelcast instances can be configured to talk to just the instance on the other side.  That makes the firewall rule writer job easier because there only has to be a rule made for two servers rather than n machines  then the 192.168.1.x machines just talk to their Hazelcast node and the 10.10.1.x machines just talk to their Hazelcast node and let the Hazelcast instances do the rest of the work.

Example

I never like to show just a “ho hum” kind of example so I am going to show how a Java client can share data with a Python client.

Setup

I am using Java 1.7 and Python 3.4.  Unfortunately, neither language has memcached support out of the box so I went looking for already written clients.

Java

I found Spymemcached for Java.  I will be just skimming the surface of its abilities. It can be grabbed from Maven.  Here is the pom.xml file for the project:

 1 <?xml version="1.0" encoding="UTF-8"?>
 2 <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
 3     <modelVersion>4.0.0</modelVersion>
 4     <groupId>com.darylmathison</groupId>
 5     <artifactId>Memcache</artifactId>
 6     <version>1.0-SNAPSHOT</version>
 7     <packaging>jar</packaging>
 8     <properties>
 9         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
10         <maven.compiler.source>1.7</maven.compiler.source>
11         <maven.compiler.target>1.7</maven.compiler.target>
12     </properties>
13     <build>
14         <plugins>
15             <plugin>
16                 <groupId>org.apache.maven.plugins</groupId>
17                 <artifactId>maven-compiler-plugin</artifactId>
18                 <version>2.3.2</version>
19                 <configuration>
20                     <showDeprecation>true</showDeprecation>
21                 </configuration>
22             </plugin>
23             <plugin>
24                 <groupId>org.codehaus.mojo</groupId>
25                 <artifactId>exec-maven-plugin</artifactId>
26                 <version>1.3.2</version>
27                 <executions>
28                     <execution>
29                         <goals>
30                             <goal>java</goal>
31                         </goals>
32                     </execution>
33                 </executions>
34                 <configuration>
35                     <mainClass>com.darylmathison.memcache.Main</mainClass>
36                 </configuration>
37             </plugin>
38         </plugins>
39     </build>
40     <dependencies>
41         <dependency>
42             <groupId>net.spy</groupId>
43             <artifactId>spymemcached</artifactId>
44             <version>2.11.5</version>
45         </dependency>
46     </dependencies>
47 </project>

Python

Next, I found python3-memcached for Python. It uses the classic setup.py procedure to install.

Server

Not much of a cache if the server is missing. One can download Hazelcast at hazelcast.org/download, extract the contents, cd into the bin directory and run the server.bat or server script according to one’s OS. As setting up servers go, that is the easiest one I have ever done.

Situation

The “expensive” operation that I am trying to make cheaper is Fibonacci numbers. Because Python and Java can both understand unicode, the values are stored as unicode strings. The key is a unicode string of the number of the sequence or the number of rounds it takes to get there.

Code

Java

package com.darylmathison.memcache;

import java.io.IOException;
import java.net.InetSocketAddress;
import net.spy.memcached.MemcachedClient;

/**
 *
 * @author Daryl
 */
public class Main {

    /**
     * @param args the command line arguments
     */
    public static void main(String[] args) {
        try {
            MemcachedClient client = new MemcachedClient(new InetSocketAddress("localhost", 5701));
            for(int i = 2; i < 20; i++) {
                System.out.println("value of round " + i + " is " + fibonacci(i, client));
            }
            client.shutdown();
        } catch(IOException ioe) {
            ioe.printStackTrace();
        }
    }
    
    private static long fibonacci(int rounds, MemcachedClient client) {
        String cached = (String)client.get(String.valueOf(rounds));
        if(cached != null) {
            System.out.print("cached ");
            return Long.parseLong(cached);
        }
        
        long[] lastTwo = new long[] {1, 1};
        
        for(int i = 0; i < rounds; i++) {
            long last = lastTwo[1];
            lastTwo[1] = lastTwo[0] + lastTwo[1];
            lastTwo[0] = last;
        }
        
        client.set(String.valueOf(rounds), 360, String.valueOf(lastTwo[1]));
        return lastTwo[1];
     }
}

Python

Here is the Python client. As a pythonian, I tried to be as pythonic as possible.

import memcache

client = memcache.Client(['localhost:5701'])

def fibonacci(round):
    f = [1, 1, 1]
    
    for i in range(round):
        f[-1] = sum(f[:2])
        f[0], f[1] = f[1], f[2]
        
    return f[2]

def retrievefib(round):
    fib = client.get(str(round))
    if not fib:
        fib = fibonacci(round)
        client.set(str(round), str(fib))
    else:
        print("cached")
        
    return fib

def main():
    store = [ x for x in range(20) if x % 2 == 0]
    for i in store:
        retrievefib(i)
    
    for i in range(20):
        print(retrievefib(i))

if __name__ == "__main__":
    main()

Conclusion

Well, here is an example of Hazelcast as being the powerhouse behind the scenes. This is a place where I think it shines the most. One doesn’t have to create whole new crafty, distributed applications to take advantage of Hazelcast. All one has to do is use known practices and let Hazelcast do the hard work. The source for this post can be found here for the Java code and here for the Python code.

References

http://en.wikipedia.org/wiki/Fibonacci_number
https://code.google.com/p/spymemcached/
https://pypi.python.org/pypi/python3-memcached/1.51