#
#  This demonstrates scalability of libabac.
#  A basic chaining attribute rule, Ai+1.likes <- Ai.likes 
#  and the borderline case of Ao.likes <- Ao are created in
#  ATTR.py. X of these attribute rules are made, where the 
#  matching ID principals are made in SETUP.py.
#  A query of Ax.likes <-?- Ao will let us know how fast
#  do the prolog db runs down the X attribute rules to arrive
#  at the solution and also Ao.likes <-?- Ax tells us
#  how long it takes to not validate the assumption

# borderline Credential
# [keyid:John0].role:likes <- [keyid:John0]
#
# [keyid:John1].role:likes <- [keyid:John0].likes
# John1 likes everything John0 likes
#
# [keyid:John2].role:likes <- [keyid:John1].likes
#
# and so on

# good query,   Does JohnN likes John0 ?
#   [keyid:JohnN].role:likes <-?- [keyid:John0]
#
# bad query,    Does John0 likes JohnN?
#   [keyid:John0].role:likes <-?- [keyid:JohnN]