Elasticsearch源码分析—线程池(十一)

线程池

每个节点都有一些线程池来优化线程内存的消耗，按节点来配置管理。有些线程池还拥有与之关联的队列配置，用来允许挂住一些未处理的请求，而不是丢弃它。

Elasticsearch对线程池的处理的源码在org.elasticsearch.node.Node中,核心代码为:

final ThreadPool threadPool = new ThreadPool(settings, executorBuilders.toArray(new ExecutorBuilder[0]));

其具体实现为:

super(settings);assert Node.NODE_NAME_SETTING.exists(settings);final Map<String, ExecutorBuilder> builders = new HashMap<>();final int availableProcessors = EsExecutors.boundedNumberOfProcessors(settings); final int halfProcMaxAt5 = halfNumberOfProcessorsMaxFive(availableProcessors); final int halfProcMaxAt10 = halfNumberOfProcessorsMaxTen(availableProcessors); final int genericThreadPoolMax = boundedBy(4 * availableProcessors, 128, 512); builders.put(Names.GENERIC, new ScalingExecutorBuilder(Names.GENERIC, 4, genericThreadPoolMax, TimeValue.timeValueSeconds(30))); builders.put(Names.INDEX, new FixedExecutorBuilder(settings, Names.INDEX, availableProcessors, 200)); builders.put(Names.BULK, new FixedExecutorBuilder(settings, Names.BULK, availableProcessors, 200)); // now that we reuse bulk for index/delete ops builders.put(Names.GET, new FixedExecutorBuilder(settings, Names.GET, availableProcessors, 1000)); builders.put(Names.SEARCH, new FixedExecutorBuilder(settings, Names.SEARCH, searchThreadPoolSize(availableProcessors), 1000)); builders.put(Names.MANAGEMENT, new ScalingExecutorBuilder(Names.MANAGEMENT, 1, 5, TimeValue.timeValueMinutes(5))); // no queue as this means clients will need to handle rejections on listener queue even if the operation succeeded // the assumption here is that the listeners should be very lightweight on the listeners side builders.put(Names.LISTENER, new FixedExecutorBuilder(settings, Names.LISTENER, halfProcMaxAt10, -1)); builders.put(Names.FLUSH, new ScalingExecutorBuilder(Names.FLUSH, 1, halfProcMaxAt5, TimeValue.timeValueMinutes(5))); builders.put(Names.REFRESH, new ScalingExecutorBuilder(Names.REFRESH, 1, halfProcMaxAt10, TimeValue.timeValueMinutes(5))); builders.put(Names.WARMER, new ScalingExecutorBuilder(Names.WARMER, 1, halfProcMaxAt5, TimeValue.timeValueMinutes(5))); builders.put(Names.SNAPSHOT, new ScalingExecutorBuilder(Names.SNAPSHOT, 1, halfProcMaxAt5, TimeValue.timeValueMinutes(5))); builders.put(Names.FETCH_SHARD_STARTED, new ScalingExecutorBuilder(Names.FETCH_SHARD_STARTED, 1, 2 * availableProcessors, TimeValue.timeValueMinutes(5))); builders.put(Names.FORCE_MERGE, new FixedExecutorBuilder(settings, Names.FORCE_MERGE, 1, -1)); builders.put(Names.FETCH_SHARD_STORE, new ScalingExecutorBuilder(Names.FETCH_SHARD_STORE, 1, 2 * availableProcessors, TimeValue.timeValueMinutes(5))); for (final ExecutorBuilder<?> builder : customBuilders) { if (builders.containsKey(builder.name())) { throw new IllegalArgumentException("builder with name [" + builder.name() + "] already exists"); } builders.put(builder.name(), builder); } this.builders = Collections.unmodifiableMap(builders); threadContext = new ThreadContext(settings); final Map<String, ExecutorHolder> executors = new HashMap<>(); for (@SuppressWarnings("unchecked") final Map.Entry<String, ExecutorBuilder> entry : builders.entrySet()) { final ExecutorBuilder.ExecutorSettings executorSettings = entry.getValue().getSettings(settings); final ExecutorHolder executorHolder = entry.getValue().build(executorSettings, threadContext); if (executors.containsKey(executorHolder.info.getName())) { throw new IllegalStateException("duplicate executors with name [" + executorHolder.info.getName() + "] registered"); } logger.debug("created thread pool: {}", entry.getValue().formatInfo(executorHolder.info)); executors.put(entry.getKey(), executorHolder); } executors.put(Names.SAME, new ExecutorHolder(DIRECT_EXECUTOR, new Info(Names.SAME, ThreadPoolType.DIRECT))); this.executors = unmodifiableMap(executors); this.scheduler = new ScheduledThreadPoolExecutor(1, EsExecutors.daemonThreadFactory(settings, "scheduler"), new EsAbortPolicy()); this.scheduler.setExecuteExistingDelayedTasksAfterShutdownPolicy(false); this.scheduler.setContinueExistingPeriodicTasksAfterShutdownPolicy(false); this.scheduler.setRemoveOnCancelPolicy(true); TimeValue estimatedTimeInterval = ESTIMATED_TIME_INTERVAL_SETTING.get(settings); this.cachedTimeThread = new CachedTimeThread(EsExecutors.threadName(settings, "[timer]"), estimatedTimeInterval.millis()); this.cachedTimeThread.start(); 

从源码中可以看到,Elasticsearch的线程池基本有许多不同名称的线程池,这些线程池的命名都缓存在一个常量静态内部类ThreadPool.Names中,源码如下:~~~java
public static class Names { public static final String SAME = "same"; public static final String GENERIC = "generic"; public static final String LISTENER = "listener"; public static final String GET = "get"; public static final String INDEX = "index"; public static final String BULK = "bulk"; public static final String SEARCH = "search"; public static final String MANAGEMENT = "management"; public static final String FLUSH = "flush"; public static final String REFRESH = "refresh"; public static final String WARMER = "warmer"; public static final String SNAPSHOT = "snapshot"; public static final String FORCE_MERGE = "force_merge"; public static final String FETCH_SHARD_STARTED = "fetch_shard_started"; public static final String FETCH_SHARD_STORE = "fetch_shard_store"; } 

而且Elasticsearch还将这些线程池分成了三个类型,分别为direct,fixed,scaling,这些类别也缓存在改常量类中,源码为:

public enum ThreadPoolType {DIRECT("direct"),FIXED("fixed"),SCALING("scaling"); private final String type; //省略getter/setter 

默认地,Elasticsearch将上述的各个线程池采用不同的类型,源码如下:

static {HashMap<String, ThreadPoolType> map = new HashMap<>();map.put(Names.SAME, ThreadPoolType.DIRECT);map.put(Names.GENERIC, ThreadPoolType.SCALING);map.put(Names.LISTENER, ThreadPoolType.FIXED);map.put(Names.GET, ThreadPoolType.FIXED);map.put(Names.INDEX, ThreadPoolType.FIXED);map.put(Names.BULK, ThreadPoolType.FIXED);map.put(Names.SEARCH, ThreadPoolType.FIXED);map.put(Names.MANAGEMENT, ThreadPoolType.SCALING);map.put(Names.FLUSH, ThreadPoolType.SCALING);map.put(Names.REFRESH, ThreadPoolType.SCALING);map.put(Names.WARMER, ThreadPoolType.SCALING);map.put(Names.SNAPSHOT, ThreadPoolType.SCALING);map.put(Names.FORCE_MERGE, ThreadPoolType.FIXED);map.put(Names.FETCH_SHARD_STARTED, ThreadPoolType.SCALING);map.put(Names.FETCH_SHARD_STORE, ThreadPoolType.SCALING);THREAD_POOL_TYPES = Collections.unmodifiableMap(map);}

各线程池功能说明

• GENERIC

  用于通用的操作（例如：后台节点发现），线程池类型为 scaling

• INDEX

  用于index/delete操作，线程池类型为 fixed， 大小的为处理器数量，队列大小为200，最大线程数为 1 + 处理器数量

• BULK

  用于bulk操作，线程池类型为 fixed， 大小的为处理器数量，队列大小为200，该池的最大线程数为 1 + 处理器数量

• GET

  用于get操作。线程池类型为 fixed，大小的为处理器数量，队列大小为1000。

• SEARCH

  用于count/search/suggest操作。线程池类型为 fixed， 大小的为 int((处理器数量 3) / 2) +1，队列大小为1000

• MANAGEMENT

  官方暂未说明(新版本才有)

• LISTENER

  主要用于Java客户端线程监听器被设置为true时执行动作。线程池类型为 scaling，最大线程数为min(10, (处理器数量)/2)

• FLUSH

  用于flush操作。线程池类型为 scaling，线程空闲保持存活时间为5分钟，最大线程数为min(10, (处理器数量)/2)

• REFRESH

  用于refresh操作。线程池类型为 scaling，线程空闲保持存活时间为5分钟，最大线程数为min(10, (处理器数量)/2)

• WARMER

  用于segment warm-up操作。线程池类型为 scaling，线程保持存活时间为5分钟，最大线程数为min(5, (处理器数量)/2)

• SNAPSHOT

  用于snaphost/restore操作。线程池类型为 scaling，线程保持存活时间为5分钟，最大线程数为min(5, (处理器数量)/2)

• FETCH_SHARD_STARTED

  官方暂未说明(新版本才有)

• FORCE_MERGE

  官方暂未说明(新版本才有)

• FETCH_SHARD_STORE

  官方暂未说明(新版本才有)

• SAME

  官方暂未说明(新版本才有)

各线程类型说明

• direct

  此类线程是一种不支持关闭的线程,就意味着一旦使用,则会一直存活下去.

• fixed

  此类线程池拥有固定数量的线程来处理请求，在没有空闲线程时请求将被挂在队列中（可选配）

• scaling

  此类线程池拥有的线程数量是动态的。这个数字介于core和max参数的配置之间变化

这些线程池的创建如果在调试源码的时候日志级别更改为DEBUG,也是可以看出的,如下:

[2017-09-27T14:31:47,558][DEBUG][o.e.t.ThreadPool         ] [x2LMQHg] created thread pool: name [force_merge], size [1], queue size [unbounded]
[2017-09-27T14:31:47,560][DEBUG][o.e.t.ThreadPool ] [x2LMQHg] created thread pool: name [fetch_shard_started], core [1], max [16], keep alive [5m] [2017-09-27T14:31:47,561][DEBUG][o.e.t.ThreadPool ] [x2LMQHg] created thread pool: name [listener], size [4], queue size [unbounded] [2017-09-27T14:31:47,565][DEBUG][o.e.t.ThreadPool ] [x2LMQHg] created thread pool: name [index], size [8], queue size [200] [2017-09-27T14:31:47,565][DEBUG][o.e.t.ThreadPool ] [x2LMQHg] created thread pool: name [refresh], core [1], max [4], keep alive [5m] [2017-09-27T14:31:47,566][DEBUG][o.e.t.ThreadPool ] [x2LMQHg] created thread pool: name [generic], core [4], max [128], keep alive [30s] [2017-09-27T14:31:47,566][DEBUG][o.e.t.ThreadPool ] [x2LMQHg] created thread pool: name [warmer], core [1], max [4], keep alive [5m] [2017-09-27T14:31:47,566][DEBUG][o.e.t.ThreadPool ] [x2LMQHg] created thread pool: name [search], size [13], queue size [1k] [2017-09-27T14:31:47,567][DEBUG][o.e.t.ThreadPool ] [x2LMQHg] created thread pool: name [flush], core [1], max [4], keep alive [5m] [2017-09-27T14:31:47,567][DEBUG][o.e.t.ThreadPool ] [x2LMQHg] created thread pool: name [fetch_shard_store], core [1], max [16], keep alive [5m] [2017-09-27T14:31:47,567][DEBUG][o.e.t.ThreadPool ] [x2LMQHg] created thread pool: name [management], core [1], max [5], keep alive [5m] [2017-09-27T14:31:47,568][DEBUG][o.e.t.ThreadPool ] [x2LMQHg] created thread pool: name [get], size [8], queue size [1k] [2017-09-27T14:31:47,568][DEBUG][o.e.t.ThreadPool ] [x2LMQHg] created thread pool: name [bulk], size [8], queue size [200] [2017-09-27T14:31:47,568][DEBUG][o.e.t.ThreadPool ] [x2LMQHg] created thread pool: name [snapshot], core [1], max [4], keep alive [5m] 

参考

• Thread Pool