java的Guava工具包介绍

List<String> list = Lists.newArrayList();
Set<String> set = Sets.newHashSet();
Map<String, String> map = Maps.newHashMap();

HashSet<Integer> setA = Sets.newHashSet(1, 2, 3, 4, 5);
HashSet<Integer> setB = Sets.newHashSet(4, 5, 6, 7, 8);
Sets.SetView<Integer> uNIOn = Sets.union(setA, setB);
System.out.println("union:" + union);
Sets.SetView<Integer> difference = Sets.difference(setA, setB);
System.out.println("difference:" + difference);
Sets.SetView<Integer> intersection = Sets.intersection(setA, setB);
System.out.println("intersection:" + intersection);

HashMap<String, Integer> mapA = Maps.newHashMap();
mapA.put("a", 1);
mapA.put("b", 2);
mapA.put("c", 3);
HashMap<String, Integer> mapB = Maps.newHashMap();
mapB.put("b", 20);
mapB.put("c", 3);
mapB.put("d", 4);
MapDifference<String, Integer> differenceMap = Maps.difference(mapA, mapB);
Map<String, MapDifference.ValueDifference<Integer>> entriesDiffering = differenceMap.entriesDiffering();
//左边差集
Map<String, Integer> entriesOnlyLeft = differenceMap.entriesOnlyOnLeft();
//右边差集
Map<String, Integer> entriesOnlyRight = differenceMap.entriesOnlyOnRight();
//交集
Map<String, Integer> entriesInCommon = differenceMap.entriesInCommon();
System.out.println(entriesDiffering);   // {b=(2, 20)}
System.out.println(entriesOnlyLeft);    // {a=1}
System.out.println(entriesOnlyRight);   // {d=4}
System.out.println(entriesInCommon);    // {c=3}

ImmutableList<Integer> iList = ImmutableList.of(12,54,87);
ImmutableSet<Integer> iSet = ImmutableSet.of(354,54,764,354);
ImmutableMap<String, Integer> iMap = ImmutableMap.of("k1", 453, "k2", 534);

Multiset<Integer> set = HashMultiset.create();
set.add(3);
set.add(3);
set.add(4);
set.add(5);
set.add(4);

set.add(7, 3);

[3 x 2, 4 x 2, 5, 7 x 3]

int count = set.count(3);

Multimap<String, String> map = LinkedHashMultimap.create();
map.put("key", "haha");
map.put("key", "haha1");
Collection<String> key = map.get("key");
System.out.println(key);

public static void main(String[] args) {
  BiMap<String, String> biMap = HashBiMap.create();
  biMap.put("key", "haha");
  biMap.put("key", "haha1");
  biMap.put("key1", "haha");
  String value = biMap.get("key");
  System.out.println(value);
}

public static void main(String[] args) {
  Table<String, String, List<Object>> tables = HasHBasedTable.create();
  tables.put("财务部", "总监", Lists.newArrayList());
  tables.put("财务部", "职员",Lists.newArrayList());
  tables.put("法务部", "助理",Lists.newArrayList());
  System.out.println(tables);
}

List<Integer> list = Lists.newArrayList();
list.add(34);
list.add(64);
list.add(267);
list.add(865);
String result = Joiner.skipNulls().on("-").join(list);
System.out.println(result);
输出：34-64-267-865

Map<String, Integer> map = Maps.newHashMap();
map.put("key1", 45);
map.put("key2",234);
String result = Joiner.on(",").withKeyValueSeparator("=").join(map);
System.out.println(result);
输出：
key1=45,key2=234

String str = "1-2-3-4-5-6";
List<String> list = Splitter.on("-").splitToList(str);
System.out.println(list);
输出：
[1, 2, 3, 4, 5, 6]

String str = "1-2-3-4-  5-  6   ";
List<String> list = Splitter.on("-").omitEmptyStrings().trimResults().splitToList(str);
System.out.println(list);

String str = "key1=54,key2=28";
Map<String,String> map = Splitter.on(",").withKeyValueSeparator("=").split(str);
System.out.println(map);
输出：
{key1=54, key2=28}

UserInfo build = UserInfo.builder().uid(234L).gender(1).build();
UserInfo build1 = UserInfo.builder().uid(4354L).gender(0).build();
Ordering<UserInfo> byOrdering = Ordering.natural().nullsFirst().onResultOf((Function<UserInfo, Comparable<Integer>>) input -> input.getGender());
System.out.println(byOrdering.compare(build1, build));

Stopwatch stopwatch = Stopwatch.createStarted();
try {
  //TODO 模拟业务逻辑
  Thread.sleep(2000L);
} catch (InterruptedException e) {
  e.printStackTrace();
}
long nanos = stopwatch.elapsed(TimeUnit.SECONDS);
System.out.println(nanos);

// LoadinGCache是Cache的缓存实现
LoadingCache<String, Object> cache = CacheBuilder.newBuilder()
  //设置缓存大小
  .maximumSize(1000)
  //设置到期时间
  .expireAfterWrite(10, TimeUnit.MINUTES)
  //设置缓存里的值两分钟刷新一次
  .refreshAfterWrite(2, TimeUnit.MINUTES)
  //开启缓存的统计功能
  .recordStats()
  //构建缓存
  .build(new CacheLoader<String, Object>() {
    //此处实现如果根据key找不到value需要去如何获取
    @Override
    public Object load(String s) throws Exception {
      return new Object();
    }
    //如果批量加载有比反复调用load更优的方法则重写这个方法
    @Override
    public Map<String, Object> loadAll(Iterable<? extends String> keys) throws Exception {
      return super.loadAll(keys);
    }
  });

cache.invalidateAll();//清除所有缓存项
//清理的时机：在写操作时顺带做少量的维护工作，或者偶尔在读操作时做——如果写操作实在太少的话
//如果想自己维护则可以调用Cache.cleanUp();
cache.cleanUp();
//另外有时候需要缓存中的数据做出变化重载一次,这个过程可以异步执行
cache.refresh("key");

RateLimiter r = RateLimiter.create(5);
while (true) {
  System.out.println("get 1 tokens: " + r.acquire() + "s");
}
输出：
get 1 tokens: 0.0s
get 1 tokens: 0.197059s
get 1 tokens: 0.195338s
get 1 tokens: 0.196918s
get 1 tokens: 0.19955s
get 1 tokens: 0.199062s
get 1 tokens: 0.195589s
get 1 tokens: 0.195061s
......  

RateLimiter r = RateLimiter.create(2, 3, TimeUnit.SECONDS);
while (true) {
  System.out.println("get 1 tokens: " + r.acquire(1) + "s");
  System.out.println("get 1 tokens: " + r.acquire(1) + "s");
  System.out.println("end");
}
输出：
get 1 tokens: 0.0s
get 1 tokens: 1.33068s
end
get 1 tokens: 0.995792s
get 1 tokens: 0.662838s
end
get 1 tokens: 0.494775s
get 1 tokens: 0.497293s
end
get 1 tokens: 0.49966s
get 1 tokens: 0.49625s
end