Fast Query Processing by Distributing an Index over CPU Caches

TL;DR

This paper introduces a distributed index approach for cluster-based data access, leveraging network communication to reduce cache misses and improve query processing speed for large tree structures.

Contribution

It proposes splitting a large tree index across cluster nodes and using network messages to access cache-resident parts, reducing cache misses compared to local traversal.

Findings

Distributed index reduces cache misses and improves query speed.

Network-based access often outperforms local cache traversal.

Approach is effective when the index fits in cluster-wide CPU caches.

Abstract

Data intensive applications on clusters often require requests quickly be sent to the node managing the desired data. In many applications, one must look through a sorted tree structure to determine the responsible node for accessing or storing the data. Examples include object tracking in sensor networks, packet routing over the internet, request processing in publish-subscribe middleware, and query processing in database systems. When the tree structure is larger than the CPU cache, the standard implementation potentially incurs many cache misses for each lookup; one cache miss at each successive level of the tree. As the CPU-RAM gap grows, this performance degradation will only become worse in the future. We propose a solution that takes advantage of the growing speed of local area networks for clusters. We split the sorted tree structure among the nodes of the cluster. We assume that the structure will fit inside the aggregation of the CPU caches of the entire cluster. We then send a word over the network (as part of a larger packet containing other words) in order to examine the tree structure in another node's CPU cache. We show that this is often faster than the standard solution, which locally incurs multiple cache misses while accessing each successive level of the tree.