HashMap源码-使用说明部分

    /*
     * Implementation notes.
     * 使用说明
     *
     * This map usually acts as a binned (bucketed) hash table, but
     * when bins get too large, they are transformed into bins of
     * TreeNodes, each structured similarly to those in
     * java.util.TreeMap. Most methods try to use normal bins, but
     * relay to TreeNode methods when applicable (simply by checking
     * instanceof a node).  Bins of TreeNodes may be traversed and
     * used like any others, but additionally support faster lookup
     * when overpopulated. However, since the vast majority of bins in
     * normal use are not overpopulated, checking for existence of
     * tree bins may be delayed in the course of table methods.
     *
     * HashMap常被描述为带bins的Hash表。但是bins变大的时候将装换成红黑树，结构像java.util.TreeMap。
     * 绝大多数方法使用普通的扁平的bins。但节点的数到达一定的阀值之后，变成红黑树的方法。
     * 红黑树的bins跟普通的扁平的bins没有差别，只是在数据量多的时候能够快速查找。
     * 大多数情况下，bins的数量不会很多。所以在内部实现上也对于bins数量的检查也会滞后。
     *
     * Tree bins (i.e., bins whose elements are all TreeNodes) are
     * ordered primarily by hashCode, but in the case of ties, if two
     * elements are of the same "class C implements Comparable<C>",
     * type then their compareTo method is used for ordering. (We
     * conservatively check generic types via reflection to validate
     * this -- see method comparableClassFor).  The added complexity
     * of tree bins is worthwhile in providing worst-case O(log n)
     * operations when keys either have distinct hashes or are
     * orderable, Thus, performance degrades gracefully under
     * accidental or malicious usages in which hashCode() methods
     * return values that are poorly distributed, as well as those in
     * which many keys share a hashCode, so long as they are also
     * Comparable. (If neither of these apply, we may waste about a
     * factor of two in time and space compared to taking no
     * precautions. But the only known cases stem from poor user
     * programming practices that are already so slow that this makes
     * little difference.)
     *
     * 红黑树的bins主要是根据该bin的hashCode排序，但是当两个元素是同一个实现了Comparable接口的对象，
     * 那么排序方式是通过该对象的compareTo方法决定排序。（每一个对象都会进行映射检查）
     * 在理想情况下（元素有不同的hashCode或者排序的）转化成红黑树的复杂运算是值得的。 
     *
     * Because TreeNodes are about twice the size of regular nodes, we
     * use them only when bins contain enough nodes to warrant use
     * (see TREEIFY_THRESHOLD). And when they become too small (due to
     * removal or resizing) they are converted back to plain bins.  In
     * usages with well-distributed user hashCodes, tree bins are
     * rarely used.  Ideally, under random hashCodes, the frequency of
     * nodes in bins follows a Poisson distribution
     * (http://en.wikipedia.org/wiki/Poisson_distribution) with a
     * parameter of about 0.5 on average for the default resizing
     * threshold of 0.75, although with a large variance because of
     * resizing granularity. Ignoring variance, the expected
     * occurrences of list size k are (exp(-0.5) * pow(0.5, k) /
     * factorial(k)). The first values are:
     *
     * 0:    0.60653066
     * 1:    0.30326533
     * 2:    0.07581633
     * 3:    0.01263606
     * 4:    0.00157952
     * 5:    0.00015795
     * 6:    0.00001316
     * 7:    0.00000094
     * 8:    0.00000006
     * more: less than 1 in ten million
     *
     * The root of a tree bin is normally its first node.  However,
     * sometimes (currently only upon Iterator.remove), the root might
     * be elsewhere, but can be recovered following parent links
     * (method TreeNode.root()).
     *
     * All applicable internal methods accept a hash code as an
     * argument (as normally supplied from a public method), allowing
     * them to call each other without recomputing user hashCodes.
     * Most internal methods also accept a "tab" argument, that is
     * normally the current table, but may be a new or old one when
     * resizing or converting.
     * 
     * 内部方法中都接受一个hash code的参数，避免每次重复计算
     *
     * When bin lists are treeified, split, or untreeified, we keep
     * them in the same relative access/traversal order (i.e., field
     * Node.next) to better preserve locality, and to slightly
     * simplify handling of splits and traversals that invoke
     * iterator.remove. When using comparators on insertion, to keep a
     * total ordering (or as close as is required here) across
     * rebalancings, we compare classes and identityHashCodes as
     * tie-breakers.
     *
     * The use and transitions among plain vs tree modes is
     * complicated by the existence of subclass LinkedHashMap. See
     * below for hook methods defined to be invoked upon insertion,
     * removal and access that allow LinkedHashMap internals to
     * otherwise remain independent of these mechanics. (This also
     * requires that a map instance be passed to some utility methods
     * that may create new nodes.)
     * 
     * 当bin树化，拆分，非树化，都会保持相同的访问顺序，
     * 通过LinkedHashMap实现树化和扁平化的转换，在插入、删除、访问都会回调LinkedHashMap的实现方法
     *
     * The concurrent-programming-like SSA-based coding style helps
     * avoid aliasing errors amid all of the twisty pointer operations.
     */