As both the availability of internet access and the prominence of smart devices continue to increase, data is being generated at a rate faster than ever before. This massive increase in data production comes with many challenges, including efficiency concerns for the storage and retrieval of such large-scale data. However, users have grown to expect the sub-second response times that are common in most modern search engines, creating a problem --- how can such large amounts of data continue to be served efficiently enough to satisfy end users?
This dissertation investigates several issues regarding
tail latency
in large-scale information retrieval systems. Tail latency corresponds to the high percentile latency that is observed from a system --- in the case of search, this latency typically corresponds to how long it takes for a query to be processed. In particular, keeping tail latency as low as possible translates to a good experience for all users, as tail latency is directly related to the worst-case latency and hence, the worst possible user experience. The key idea in targeting tail latency is to move from questions such as
"what is the median latency of our search engine?"
to questions which more accurately capture user experience such as
"how many queries take more than
200
ms to return answers?"
or
"what is the worst case latency that a user may be subject to, and how often might it occur?"
While various strategies exist for efficiently processing queries over large textual corpora, prior research has focused almost entirely on improvements to the
average
processing time or cost of search systems. As a first contribution, we examine some state-of-the-art retrieval algorithms for two popular index organizations, and discuss the trade-offs between them, paying special attention to the notion of tail latency. This research uncovers a number of observations that are subsequently leveraged for improved search efficiency and effectiveness.
We then propose and solve a new problem, which involves processing a number of related query variations together, known as
multi-queries
, to yield higher quality search results. We experiment with a number of algorithmic approaches to efficiently process these multi-queries, and report on the cost, efficiency, and effectiveness trade-offs present with each.
Finally, we examine how
predictive models
can be used to improve the tail latency and end-to-end cost of a commonly used
multi-stage
retrieval architecture
without
impacting result effectiveness. By combining ideas from numerous areas of information retrieval, we propose a prediction framework which can be used for training and evaluating several efficiency/effectiveness trade-off parameters, resulting in improved trade-offs between cost, result quality, and tail latency.
A neural algorithm for a fundamental computing problem