Order of in Java - O(n) O(1) O(log n)

Below is a list of the Big O complexities in order of how well they scale relative to the dataset.
O(1)/Constant Complexity: Constant.  This means irrelevant of the size of the data set the algorithm will always take a constant time. 1 item takes 1 second, 10 items takes 1 second, 100 items takes 1 second. It always takes the same amount of time.
O(log n)/Logarithmic Complexity: Not as good as constant, but still pretty good.  The time taken increases with the size of the data set, but not proportionately so. This means the algorithm takes longer per item on smaller datasets relative to larger ones.   1 item takes 1 second, 10 items takes 2 seconds, 100 items takes 3 seconds. If your dataset has 10 items, each item causes 0.2 seconds latency. If your dataset has 100, it only takes 0.03 seconds extra per item. This makes log n algorithms very scalable.
O(n)/Linear Complexity: The larger the data set, the time taken grows proportionately.  1 item takes 1 second, 10 items takes 10 seconds, 100 items takes 100 seconds.
O(n log n): A nice combination of the previous two.  Normally there’s 2 parts to the sort, the first loop is O(n), the second is O(log n), combining to form O(n log n) 1 item takes 2 seconds, 10 items takes 12 seconds, 100 items takes 103 seconds.
O(n^2)/Quadratic Complexity: Things are getting extra slow. 1 item takes 1 second, 10 items takes 100, 100 items takes 10000.  
O(2^n): Exponential Growth! The algorithm takes twice as long for every new element added.  1 item takes 1 second, 10 items takes 1024 seconds, 100 items takes 1267650600228229401496703205376 seconds.

Common Data Structures and Relative functions:

Lists and Sets:
TreeSetO(log n)O(log n)O(log n)O(log n)

TreeMapO(log n)O(log n)O(log n)O(log n)