INSTalytics: Cluster Filesystem Co-design for Big-data Analytics


Muthian Sivathanu, Midhul Vuppalapati, Bhargav S. Gulavani, Kaushik Rajan, and Jyoti Leeka, Microsoft Research India; Jayashree Mohan, Univ. of Texas Austin; Piyus Kedia, IIIT Delhi


We present the design, implementation, and evaluation of Instalytics, a co-designed stack of a cluster file system and the compute layer, for efficient big data analytics in large-scale data centers. Instalytics amplifies the well-known benefits of data partitioning in analytics systems; instead of traditional partitioning on one dimension, Instalytics enables data to be simultaneously partitioned on four different dimensions at the same storage cost, enabling a larger fraction of queries to benefit from partition filtering and joins without network shuffle.

To achieve this, Instalytics uses compute-awareness to customize the 3-way replication that the cluster file system employs for availability. A new heterogeneous replication layout enables Instalytics to preserve the same recovery cost and availability as traditional replication. Instalytics also uses compute-awareness to expose a new {\em sliced-read} API that improves performance of joins by enabling multiple compute nodes to read slices of a data block efficiently via co-ordinated request scheduling and selective caching at the storage nodes.

We have implemented Instalytics in a production analytics stack, and show that recovery performance and availability is similar to physical replication, while providing significant improvements in query performance, suggesting a new approach to designing cloud-scale big-data analytics systems.

FAST '19 Open Access Sponsored by NetApp

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@inproceedings {227792,
author = {Muthian Sivathanu and Midhul Vuppalapati and Bhargav S. Gulavani and Kaushik Rajan and Jyoti Leeka and Jayashree Mohan and Piyus Kedia},
title = {{INSTalytics}: Cluster Filesystem Co-design for Big-data Analytics},
booktitle = {17th USENIX Conference on File and Storage Technologies (FAST 19)},
year = {2019},
isbn = {978-1-939133-09-0},
address = {Boston, MA},
pages = {235--248},
url = {},
publisher = {USENIX Association},
month = feb

Presentation Video