爲什麼要轉換？
因爲Map中桶的元素初始化是鏈表保存的，其查找性能是O(n)，而樹結構能將查找性能提升到O(log(n))。當鏈表長度很小的時候，即使遍歷，速度也非常快，但是當鏈表長度不斷變長，肯定會對查詢性能有一定的影響，所以才需要轉成樹。
爲什麼閾值是8？
轉換後存儲的數據結構TreeNodes佔用空間是普通Nodes的兩倍，只有當bin包含足夠多的節點時纔會轉成TreeNodes，而是否足夠多是由TREEIFY_THRESHOLD的值決定的。
在hashCode離散性很好的情況下，樹型bin（桶，即bucket，HashMap中hashCode值一樣的元素保存的地方）用到的概率非常小，因爲數據均勻分佈在每個bin中，幾乎不會有bin中鏈表長度會達到閾值。事實上，在隨機hashCode的情況下，在bin中節點的分佈頻率遵循如下的泊松分佈（http://en.wikipedia.org/wiki/Poisson_distribution）。
在擴容閾值爲0.75的情況下，（即使因爲擴容而方差很大）遵循着參數平均爲0.5的泊松分佈。忽略方差，按公式
計算，概率如下：
| 長度 | 概率 |
|---|---|
| 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 |
如上，一個bin中鏈表長度達到8個元素的概率爲0.00000006，幾乎是不可能事件。
大部分情況下，鏈表存儲能節約存儲空間同時有着良好的查找性能；極個別情況下，節點數達到8個，轉爲紅黑樹，能獲得更好的查找性能，同時因爲是個別情況，不需要大量的存儲空間。
所以，閾值8是時間和空間的權衡，是根據概率統計決定的。不得不感嘆，發展30年的Java每一項改動和優化都是非常嚴謹和科學的。
附. JDK(1.8.0_45)中的相關注釋
HashMap類第174～197行
* 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
ConcurrentHashMap中第327~349行也有關於此的說法，大同小異。
* The main disadvantage of per-bin locks is that other update
* operations on other nodes in a bin list protected by the same
* lock can stall, for example when user equals() or mapping
* functions take a long time. However, statistically, under
* random hash codes, this is not a common problem. Ideally, 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, given the 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