  Yellow elephants are slow. A major reason is that they consume their inputs
entirely before responding to an elephant rider's orders. Some clever riders
have trained their yellow elephants to only consume parts of the inputs before
responding. However, the teaching time to make an elephant do that is high. So
high that the teaching lessons often do not pay off. We take a different
approach. We make elephants aggressive; only this will make them very fast. We
propose HAIL (Hadoop Aggressive Indexing Library), an enhancement of HDFS and
Hadoop MapReduce that dramatically improves runtimes of several classes of
MapReduce jobs. HAIL changes the upload pipeline of HDFS in order to create
different clustered indexes on each data block replica. An interesting feature
of HAIL is that we typically create a win-win situation: we improve both data
upload to HDFS and the runtime of the actual Hadoop MapReduce job. In terms of
data upload, HAIL improves over HDFS by up to 60% with the default replication
factor of three. In terms of query execution, we demonstrate that HAIL runs up
to 68x faster than Hadoop. In our experiments, we use six clusters including
physical and EC2 clusters of up to 100 nodes. A series of scalability
experiments also demonstrates the superiority of HAIL.
