k-mers (nucleotide strings of length k) form the basis of several algorithms in computational genomics. In particular, k-mer abundance information in sequence data is useful in read error correction, parameter estimation for genome assembly, digital normalization etc. We give a streaming algorithm Kmerlight for computing the k-mer abundance histogram from sequence data. Our algorithm is fast and uses very small memory footprint. We provide analytical bounds on the error guarantees of our algorithm. Kmerlight can efficiently process genome scale and metagenome scale data using standard desktop machines. Few applications of abundance histograms computed by Kmerlight are also shown. We use abundance histogram for de novo estimation of repetitiveness in the genome based on a simple probabilistic model that we propose. We also show estimation of k-mer error rate in the sampling using abundance histogram. Our algorithm can also be used for abundance estimation in a general streaming setting. The Kmerlight tool is written in C++ and is available for download and use from https://github.com/nsivad/kmerlight.