Performance Engineering for Multi-Tenant Analytic Workloads on Snowflake: An Empirical Study of Clustering, Materialized Views, Query Tuning, and Virtual Warehouse Sizing Across Production Reference Deployments at Billion-Row Scale
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I1P120Keywords:
Cloud Data Warehouse, Performance Engineering, Clustering Keys, Materialized Views, Query Optimization, Elastic Compute SizingAbstract
Cloud data warehouses that separate storage from compute, such as Snowflake, deliver competitive baseline performance through automatic micro-partitioning, result caching, and elastic compute. However, workloads that scale to billions of rows or that shift in access pattern over time can drift into configurations where automatic mechanisms no longer suffice. This paper presents an empirical performance engineering study of multi-tenant analytic deployments on Snowflake, drawing on observations synthesized from production reference deployments that support recurring reporting alongside ad hoc analysis. The study evaluates four classes of optimization: clustering key selection on tables with billions of rows, materialized view design for high-frequency aggregations, query rewriting informed by query-profile inspection, and virtual warehouse sizing across a power-of-two compute matrix. The measurement protocol uses repeated warm- and cold-cache runs of a representative workload mix, with median and p95 latency, partitions scanned, and credits consumed reported as the primary metrics. Across the reference deployments we examined, clustering reduced p95 latency on the targeted large tables from 18.4 s to 3.2 s, materialized views reduced dashboard aggregation latency from 9.7 s to 0.42 s, and right-sizing the warehouse matrix lowered cost-per-query by approximately 38% at equivalent throughput. The contribution is a reproducible operational framing connecting diagnostic workflow, cost-benefit analysis, and regression prevention; the implications extend to other elastic cloud data platforms whose performance depends on the alignment of data layout, query shape, and compute sizing.
References
1. Dageville, B., Cruanes, T., Zukowski, M., Antonov, V., Avanes, A., Bock, J., et al. The Snowflake Elastic Data Warehouse. Proc. ACM SIGMOD, 2016. https://scholar.google.com/scholar?q=Dageville, B., Cruanes, T., Zukowski, M., Antonov, V., Avanes, A., Bock, J., et al. The Snowflake Elastic Data Warehouse. Proc. ACM SIGMOD, 2016.., | https://dl.acm.org/doi/10.1145/2882903.2903741
2. Abadi, D., Boncz, P., and Harizopoulos, S. The Design and Implementation of Modern Column-Oriented Database Systems. Foundations and Trends in Databases, vol. 5, no. 3, 2013. https://scholar.google.com/scholar?q=Abadi, D., Boncz, P., and Harizopoulos, S. The Design and Implementation of Modern Column-Oriented Database Systems. Foundations and Trends in Databases, vol. 5, no. 3, 2013.
3. Stonebraker, M. and Cetintemel, U. One Size Fits All: An Idea Whose Time Has Come and Gone. Proc. ICDE, 2005. https://scholar.google.com/scholar?q=Stonebraker, M. and Cetintemel, U. One Size Fits All: An Idea Whose Time Has Come and Gone. Proc. ICDE, 2005.
4. Kimball, R. and Ross, M. The Data Warehouse Toolkit, Third Edition. Wiley, 2013. https://scholar.google.com/scholar?q=Kimball, R. and Ross, M. The Data Warehouse Toolkit, Third Edition. Wiley, 2013.
5. Inmon, W. H. Building the Data Warehouse, Fourth Edition. Wiley, 2005. https://scholar.google.com/scholar?q=Inmon, W. H. Building the Data Warehouse, Fourth Edition. Wiley, 2005.
6. Armbrust, M., Das, T., Sun, L., Yavuz, B., Zhu, S., Murthy, M., et al. Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores. Proc. VLDB, 2020. https://scholar.google.com/scholar?q=Armbrust, M., Das, T., Sun, L., Yavuz, B., Zhu, S., Murthy, M., et al. Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores. Proc. VLDB, 2020.
7. Melnik, S., Gubarev, A., Long, J. J., Romer, G., Shivakumar, S., Tolton, M., and Vassilakis, T. Dremel: Interactive Analysis of Web-Scale Datasets. Proc. VLDB, 2010. | https://scholar.google.com/scholar?q=Melnik, S., Gubarev, A., Long, J. J., Romer, G., Shivakumar, S., Tolton, M., and Vassilakis, T. Dremel: Interactive Analysis of Web-Scale Datasets. Proc. VLDB, 2010.
8. Goldstein, J. and Larson, P. Optimizing Queries Using Materialized Views: A Practical, Scalable Solution. Proc. ACM SIGMOD, 2001. |1. https://scholar.google.com/scholar?q=Goldstein, J. and Larson, P. Optimizing Queries Using Materialized Views: A Practical, Scalable Solution. Proc. ACM SIGMOD, 2001.
9. Halevy, A. Y. Answering Queries Using Views: A Survey. The VLDB Journal, vol. 10, no. 4, 2001. | https://scholar.google.com/scholar?q=Halevy, A. Y. Answering Queries Using Views: A Survey. The VLDB Journal, vol. 10, no. 4, 2001.
10. Mistry, H., Roy, P., Sudarshan, S., and Ramamritham, K. Materialized View Selection and Maintenance Using Multi-Query Optimization. Proc. ACM SIGMOD, 2001. | https://scholar.google.com/scholar?q=Mistry, H., Roy, P., Sudarshan, S., and Ramamritham, K. Materialized View Selection and Maintenance Using Multi-Query Optimization. Proc. ACM SIGMOD, 2001.
11. Leis, V., Gubichev, A., Mirchev, A., Boncz, P., Kemper, A., and Neumann, T. How Good Are Query Optimizers, Really? Proc. VLDB, 2015. | https://scholar.google.com/scholar?q=Leis, V., Gubichev, A., Mirchev, A., Boncz, P., Kemper, A., and Neumann, T. How Good Are Query Optimizers, Really? Proc. VLDB, 2015.
12. Selinger, P. G., Astrahan, M. M., Chamberlin, D. D., Lorie, R. A., and Price, T. G. Access Path Selection in a Relational Database Management System. Proc. ACM SIGMOD, 1979. | https://scholar.google.com/scholar?q=Selinger, P. G., Astrahan, M. M., Chamberlin, D. D., Lorie, R. A., and Price, T. G. Access Path Selection in a Relational Database Management System. Proc. ACM SIGMOD, 1979.
13. Graefe, G. Query Evaluation Techniques for Large Databases. ACM Computing Surveys, vol. 25, no. 2, 1993. | https://scholar.google.com/scholar?q=Graefe, G. Query Evaluation Techniques for Large Databases. ACM Computing Surveys, vol. 25, no. 2, 1993.
14. Stonebraker, M., Abadi, D. J., Batkin, A., Chen, X., Cherniack, M., Ferreira, M., et al. C-Store: A Column-Oriented DBMS. Proc. VLDB, 2005. | https://scholar.google.com/scholar?q=Stonebraker, M., Abadi, D. J., Batkin, A., Chen, X., Cherniack, M., Ferreira, M., et al. C-Store: A Column-Oriented DBMS. Proc. VLDB, 2005.
15. Boncz, P. A., Zukowski, M., and Nes, N. MonetDB/X100: Hyper-Pipelining Query Execution. Proc. CIDR, 2005. | https://scholar.google.com/scholar?q=Boncz, P. A., Zukowski, M., and Nes, N. MonetDB/X100: Hyper-Pipelining Query Execution. Proc. CIDR, 2005.
16. Gupta, A., Agarwal, D., Tan, D., Kulesza, J., Pathak, R., Stefani, S., and Srinivasan, V. Amazon Redshift and the Case for Simpler Data Warehouses. Proc. ACM SIGMOD, 2015. | https://scholar.google.com/scholar?q=Gupta, A., Agarwal, D., Tan, D., Kulesza, J., Pathak, R., Stefani, S., and Srinivasan, V. Amazon Redshift and the Case for Simpler Data Warehouses. Proc. ACM SIGMOD, 2015.
17. Snowflake Inc. Snowflake Documentation. | https://scholar.google.com/scholar?q=Snowflake%20Inc.%20Snowflake%20Documentation. | https://docs.snowflake.com/
18. Snowflake Inc. Clustering Keys and Clustered Tables documentation. | https://scholar.google.com/scholar?q=Snowflake Inc. Clustering Keys and Clustered Tables documentation. | https://docs.snowflake.com/en/user-guide/tables-clustering-keys
19. Snowflake Inc. Working with Materialized Views documentation. | https://scholar.google.com/scholar?q=Snowflake Inc. Working with Materialized Views documentation. | https://docs.snowflake.com/en/user-guide/views-materialized
20. Snowflake Inc. Query Profile documentation. | https://scholar.google.com/scholar?q=Snowflake Inc. Query Profile documentation. | https://docs.snowflake.com/en/user-guide/ui-query-profile
21. Snowflake Inc. ACCOUNT_USAGE Schema documentation. | https://scholar.google.com/scholar?q=Snowflake Inc. ACCOUNT_USAGE Schema documentation. | https://docs.snowflake.com/en/sql-reference/account-usage
22. Snowflake Inc. Virtual Warehouses documentation. | https://scholar.google.com/scholar?q=Snowflake Inc. Virtual Warehouses documentation. | https://docs.snowflake.com/en/user-guide/warehouses
23. Cloud FinOps Foundation. FinOps Framework documentation. | https://scholar.google.com/scholar?q=Cloud FinOps Foundation. FinOps Framework documentation. | https://www.finops.org/framework/
