This presentation was given at the Strata + Hadoop World, 2015 in San Jose.
Apache Hive is the most popular and most widely used SQL solution for Hadoop. To keep pace with Hadoop’s increasingly vital role in the Enterprise, Hive has transformed from a batch-only, high-latency system into a modern SQL engine capable of both batch and interactive queries over large datasets. Hive’s momentum is accelerating: With Spark integration and a shift to in-memory processing on the horizon, Hive continues to expand the boundaries of Big Data.
In this talk the speakers examined Hive performance, past, present and future. In particular they looked at Hive’s origins as a petabyte scale SQL engine.
Through some numbers and graphs, they showed how Hive became 100x faster by moving beyond MapReduce, by vectorizing execution and by introducing a cost-based optimizer.
They detailed and discussed the challenges of scalable SQL on Hadoop.
The looked into Hive’s sub-second future, powered by LLAP and Hive on Spark.
And showed just how fast Hive on Spark really is.