Ignite分布式计算

call

call和funcation都是发送到分布式节点执行的代码。
是实现了IgniteCallable接口的算子，被ignite.compute()发送到节点去执行。
call和funcation可以同步或者异步执行，大多数情况下，我们会使用异步执行。

this.compute.broadcast(() -> System.out.println("Hello Node: " + ignite.cluster().localNode().id()));

private Collection<IgniteCallable<Integer>> createCalls(){
    Collection<IgniteCallable<Integer>> calls = new ArrayList<>();
    for(String word : "How many characters".split(" ")) {
        calls.add(() -> {
            return word.length();
        });
    }
    return calls;
}

public boolean call(){
    Collection<Integer> res = this.compute.call(createCalls());
    int total = res.stream().mapToInt(Integer::intValue).sum();
    logger.info("call: the total lengths of all words is = " + total);
    return true;
}

public boolean asyncCall(){
    IgniteFuture<Collection<Integer>> future = this.compute.callAsync(createCalls());
    future.listen(fut -> {
        int total = fut.get().stream().mapToInt(Integer::intValue).sum();
        logger.info("asyncCall: Total number of characters = " + total);
    });
    return true;
}

map-reduce

call和map-reduce的场景比较适合replicated的方式，当所有的节点通过复制模式拿到数据之后，使用call和map-redice可以从local快速获得数据.
但是“Replicated caches are ideal when data sets are small and updates are infrequent.”，这个就有点搞笑。

但当由于时钟同步差异，节点的数据不一致时会怎样？

public boolean mapReduce(){
    String text = "Hello Ignite Enable World!";
    int cnt = ignite.compute().execute(MapExampleCharacterCountTask.class, text);
    logger.info("mapReduce: text length without spaces = " + cnt);
    return true;
}

private static class MapExampleCharacterCountTask extends ComputeTaskAdapter<String, Integer> {
    @Override
    public Map<? extends ComputeJob, ClusterNode> map(List<ClusterNode> nodes, String arg) throws IgniteException {
        Map<ComputeJob, ClusterNode> map = new HashMap<>();
        Iterator<ClusterNode> it = nodes.iterator();
        for (final String word : arg.split(" ")) {
            if (!it.hasNext()) {
                it = nodes.iterator();
            }
            ClusterNode node = it.next();
            map.put(new ComputeJobAdapter() {
                @Override
                public Object execute() throws IgniteException {
                    System.out.println("** node map reduce call **>" + word);
                    return word.length();
                }
            }, node);
        }
        return map;
    }

    @Override
    public Integer reduce(List<ComputeJobResult> results) throws IgniteException {
        int sum = 0;
        for (ComputeJobResult res : results) {
            sum += res.<Integer>getData();
        }
        return sum;
    }
}

affinity compute

当cache使用partition方式部署时，affinity compute使用cache对象相同的算法调度compute到指定的节点，这样算子的执行和cache的位置一致，可以取得本地的查询速度。假设cache中的对象包含一个1000个随机数数组，我们的计算是对这个数据进行sum。

import org.apache.ignite.*;
import org.apache.ignite.binary.BinaryObject;
import org.apache.ignite.lang.IgniteCallable;
import org.apache.ignite.resources.IgniteInstanceResource;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.util.List;

public class AffinityComputeExample {
    Ignite ignite;
    IgniteCache<Long, Organization> cache;
    final static int COUNT_ORG = 1000;
    Logger logger = LoggerFactory.getLogger(getClass());
    final static String cacheName = "organization";
    final static int QUERY_TIMES = 10;
    Long[] idxes;

    public AffinityComputeExample(){
        idxes = new Long[QUERY_TIMES];
        for (int i = 0; i < QUERY_TIMES; i++) {
            idxes[i] = Long.valueOf(i + 1);
        }
    }

    public void setUp() {
        String path = AffinityKeyExample.class.getResource("/example-affinitykey.xml").getFile();
        this.ignite = Ignition.start(path);
        this.cache = this.ignite.getOrCreateCache(cacheName);
        this.cache.clear();
        IgniteDataStreamer<Long, Organization> streamerOrg = ignite.dataStreamer(cacheName);
        logger.info("load data ...");
        for (int i = 1; i <= COUNT_ORG; i++) {
            Organization r = new Organization("org_" + i);
            streamerOrg.addData(r.id, r);
        }
        streamerOrg.flush();
        streamerOrg.close();
    }

    public void run() {
        setUp();
        IgniteCompute compute = this.ignite.compute();

        for (Long k:idxes) {
            Long sum = compute.affinityCall(cacheName, k, new SumTask(k));
            logger.info(k + " sum = " + sum);
        }
    }
    
    private static class SumTask implements IgniteCallable<Long> {
        Long key;
        public SumTask(Long k) {
            this.key = k;
        }

        @IgniteInstanceResource
        private Ignite ignite;

        @Override
        public Long call() throws Exception {
            IgniteCache<Long, BinaryObject> cache = ignite.cache(cacheName).withKeepBinary();
            System.out.println(this.key);
            BinaryObject obj = cache.get(this.key);
            if (obj != null) {
                List<Long> data = obj.field("data");
                Long sum = data.stream().mapToLong(Long::longValue).sum();
                return sum;
            }
            return null;
        }
    }

}

https://ignite.apache.org/docs/latest/data-modeling/affinity-collocation

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