CASE D: Already populated Tree
==============================
- Use A, B, C, D as subtree
- Sort the Keys in Buckets that share the initial part of the path
- For each subtree add there the new leafs
R
/ \
/ \
/ \
* *
/ | / \
/ | / \
/ | / \
L: A B C D
/\ /\ / \ / \
... ... ... ... ... ...
buildTreeBottomUp splits the key-values into n Buckets (where n is the
number of CPUs), in parallel builds a subtree for each bucket, once all
the subtrees are built, uses the subtrees roots as keys for a new tree,
which as result will have the complete Tree build from bottom to up,
where until the log2(nCPU) level it has been computed in parallel.
As result of this, the tree construction can be parallelized until
almost the top level, almost dividing the time by the number of CPUs.
CASE C: ALMOST CASE B --> if Tree has few Leafs (but numLeafs>=minLeafsThreshold)
==============================================================================
- Use A, B, G, F as Roots of subtrees
- Do CASE B for each subtree
- Then go from L to the Root
R
/ \
/ \
/ \
* *
/ | / \
/ | / \
/ | / \
L: A B G D
/ \
/ \
/ \
C *
/ \
/ \
/ \
... ... (nLeafs >= minLeafsThreshold)
CASE B: ALMOST CASE A, Almost empty Tree --> if Tree has numLeafs < minLeafsThreshold
==============================================================================
- Get the Leafs (key & value) (iterate the tree from the current root getting
the leafs)
- Create a new empty Tree
- Do CASE A for the new Tree, giving the already existing key&values (leafs)
from the original Tree + the new key&values to be added from the AddBatch call
R R
/ \ / \
A * / \
/ \ / \
B C * *
/ | / \
/ | / \
/ | / \
L: A B G D
/ \
/ \
/ \
C *
/ \
/ \
/ \
... ... (nLeafs < minLeafsThreshold)
arnaucube