Many optimization problems arising in the domain of network design, communication and transportation etc. can be defined in terms of some metric space. Often for such problems, it is easier to give approximation algorithms with good performance bound for some classes of "simple" metric spaces. Then, one way to design good approximation algorithms for general metric spaces is to appoximate any metric space by a simple metric space and then using a good approximation algorithm for the simple metric space. The goal of approximating metric spaces has led to the notion of graph spanners, having many algorithmic applications. This paper by Yair Bartal (FOCS 96) provides a novel technique for the design of good randomized algorithms for optimization problems on metric spaces. It is shown that any metric space can be probabilistically approximated by a simple metric space with at most polylogarithm distortion. This metric space is simple in being a tree metric and very well suited for designing divide and conquer algorithms. The technique presented here has been used for solving at least two long-standing open problems: polylogarithmic competitive ratios for metrical task systems in the context of online algorithms and polylogarithmic approximation ratios for the group Steiner tree problem.