  A Datalog program solves a constraint satisfaction problem (CSP) if and only
if it derives the goal predicate precisely on the unsatisfiable instances of
the CSP.
  There are three Datalog fragments that are particularly important for
finite-domain constraint satisfaction: arc monadic Datalog, linear Datalog, and
symmetric linear Datalog, each having good computational properties. We
consider the fragment of Datalog where we impose all of these restrictions
simultaneously, i.e., we study \emph{symmetric linear arc monadic (slam)
Datalog}. We characterise the CSPs that can be solved by a slam Datalog program
as those that have a gadget reduction to a particular Boolean constraint
satisfaction problem. We also present exact characterisations in terms of a
homomorphism duality (which we call \emph{unfolded caterpillar duality}), and
in universal-algebraic terms (using known minor conditions, namely the
existence of quasi Maltsev operations and $k$-absorptive operations of arity
$nk$}, for all $n,k \geq 1$). Our characterisations also imply that the
question whether a given finite-domain CSP can be expressed by a slam Datalog
program is decidable.
