Managed search engines, such as Apache Solr and Elastic-
search, host huge inverted indices in main memory to offer
fast response times. This practice faces two challenges. First,
limited DRAM capacity necessitates search engines aggres-
sively compress indices to reduce their storage footprint.
Unfortunately, our analysis with a popular search library
shows that compression slows down queries (on average) by
up to 1.7× due to high decompression latency. Despite their
performance advantage, uncompressed indices require 10×
more memory capacity, making them impractical. Second,
indices today reside off-heap, encouraging unsafe memory
accesses and risking eviction from the page cache.

Emerging byte-addressable and scalable non-volatile mem-
ory (NVM) offers a good fit for storing uncompressed indices.
Unfortunately, NVM exhibits high latency. We rigorously
evaluate the performance of DRAM and NVM-backed com-
pressed/uncompressed indices to find that an uncompressed
index in a high-capacity managed heap memory-mapped
over NVM provides a 36% reduction in query response times
compared to a DRAM-backed compressed index in off-heap
memory. Also, it is only 11% slower than the uncompressed
index in a DRAM heap (fastest approach). DRAM and NVM-
backed compressed (off-heap) indices behave similarly.

We analyze the narrow response time gap between DRAM
and NVM-backed indices. We conclude that inverted indices
demand massive memory capacity, but search algorithms
exhibit a high spatial locality that modern cache hierarchies
exploit to hide NVM latency. We show the scalability of
uncompressed indices on the NVM-backed heap with large
core counts and index sizes. This work uncovers new space-
time tradeoffs in storing in-memory inverted indices.