  The Ruelle thermodynamic formalism for dynamical trajectories over the large
time $T$ corresponds to the large deviation theory for the information per unit
time of the trajectories probabilities. The microcanonical analysis consists in
evaluating the exponential growth in $T$ of the number of trajectories with a
given information per unit time, while the canonical analysis amounts to
analyze the appropriate non-conserved $\beta$-deformed dynamics in order to
obtain the scaled cumulant generating function of the information, the first
cumulant being the famous Kolmogorov-Sinai entropy. This framework is described
in detail for discrete-time Markov chains and for continuous-time Markov jump
processes converging towards some steady-state, where one can also construct
the Doob generator of the associated $\beta$-conditioned process. The
application to the Directed Random Trap model on a ring of $L$ sites allows to
illustrate this general framework via explicit results for all the introduced
notions. In particular, the glassy phase is characterized by anomalous scaling
laws with the size $L$ and by non-self-averaging properties of the
Kolmogorov-Sinai entropy and of the higher cumulants of the trajectory
information.
