Hive Going In-Memory

Kevin Feasel



Carter Shanklin and Nita Dembla discuss Hive memory-handling optimizations:

Let’s put this architecture to the test with a realistic dataset size and workload. Our previous performance blog, “Announcing Apache Hive 2.1: 25x Faster Queries and Much More”, discussed 4 reasons that LLAP delivers dramatically faster performance versus Hive on Tez. In that benchmark we saw 25+x performance boosts on ad-hoc queries with a dataset that fit entirely into the cluster’s memory.

In most cases, datasets will be far too large to fit in RAM so we need to understand if LLAP can truly tackle the big data challenge or if it’s limited to reporting roles on smaller datasets. To find out, we scaled the dataset up to 10 TB, 4x larger than aggregate cluster RAM, and we ran a number of far more complex queries.

Table 3 below shows how Hive LLAP is capable of running both At Speed and At Scale. The simplest query in the benchmark ran in 2.68 seconds on this 10 TB dataset while the most complex query, Query 64 performed a total of 37 joins and ran for more than 20 minutes.

Given how much faster memory is than disk, and given Spark’s broad adoption, this makes sense as a strategy for Hive’s continued value.

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