  In this paper, we discuss the laws of the iterated logarithm (LIL) for
occupation times of Markov processes $Y$ in general metric measure space both
near zero and near infinity under some minimal assumptions. We first establish
LILs of (truncated) occupation times on balls $B(x,r)$ of radii $r$ up to an
function $\Phi (r)$, which is an iterated logarithm of mean exit time of $Y$,
by showing that the function $\Phi$ is optimal. Our first result on LILs of
occupation times covers both near zero and near infinity regardless of
transience and recurrence of the process. Our assumptions are truly local in
particular at zero and the function $\Phi$ in our truncated occupation times $r
\mapsto\int_0^{ \Phi (x,r)} {\bf 1}_{B(x,r)}(Y_s)ds$ depends on space variable
$x$ too. We also prove that a similar LIL for total occupation times $r
\mapsto\int_0^\infty {\bf 1}_{B(x,r)}(Y_s)ds$ holds when the process is
transient. Then we establish LIL concerning large time behaviors of occupation
times $t \mapsto \int_0^t {\bf 1}_{A}(Y_s)ds$
  under an additional condition that guarantees the recurrence of the process.
Our results cover a large class of Feller (Levy-like) processes, random
conductance models with long range jumps, jump processes with mixed polynomial
local growths and jump processes with singular jumping kernels.
