MapReduce is a famous model for data-intensive parallel computing in shared-nothing clusters. One of the main issues in MapReduce is the fact of depending its performance mainly on data distribution. MapReduce contains simple load balance technique based on FIFO job scheduler that serves the jobs in their submission order but unfortunately it is insufficient in real world cases as it missed many factors that impact the performance such as heterogeneity factor and data skewness, so Load balancing is important to make all resources utilized evenly and more efficiently. There are two main schemes in load balancing a-Static Load Balancing Schemes b-Dynamic load balancing. The main aim of this work is to study and compare existing Load Balance algorithms also to illustrate the features of Load Balance algorithms