  Sheer increase in volume of data over the last decade has triggered research
in cluster computing frameworks that enable web enterprises to extract big
insights from big data. While Apache Spark is gaining popularity for exhibiting
superior scale-out performance on the commodity machines, the impact of data
volume on the performance of Spark based data analytics in scale-up
configuration is not well understood. We present a deep-dive analysis of Spark
based applications on a large scale-up server machine. Our analysis reveals
that Spark based data analytics are DRAM bound and do not benefit by using more
than 12 cores for an executor. By enlarging input data size, application
performance degrades significantly due to substantial increase in wait time
during I/O operations and garbage collection, despite 10\% better instruction
retirement rate (due to lower L1 cache misses and higher core utilization). We
match memory behaviour with the garbage collector to improve performance of
applications between 1.6x to 3x.
