The document discusses the importance of low-latency data access through effective collaboration between CPU, memory, and disk. It covers topics like aggregation processing, vectorization, and query compilation, highlighting the significance of latencies at scale. The author emphasizes that seemingly insignificant operations can become critical when processing large volumes of data.

1. © Kriti Kathuria 2023 HOSTED BY
Low-Latency Data Access:
The Required Synergy Between CPU, Memory & Disk
Kriti Kathuria
Database Researcher

2. © Kriti Kathuria 2023
Kriti Kathuria (she/her)
Database Researcher
■ Conceptualizing Eventual Durability
■ SQL-gen for Incremental View Maintenance
■ Data Engineer in a past life
■ Good mentorship is powerful and fundamental
■ At scale, the insignificant become significant!
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3. © Kriti Kathuria 2023
Motivation
● At scale, the insignificant becomes significant!
● A single IO takes insignificant time
● But when it is GBs of data and thousands of IO ops, the latencies become
significant.
● Thus, p99, at scale, matters.
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4. © Kriti Kathuria 2023
Outline
● Motivation
● Existing Techniques
○ Aggregation Processing
○ Vectorization
○ Query Compilation
● Closing Remarks
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Matrix Multiplication
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● Example from MIT 6.172, Fall 2018, Lecture 1

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Matrix Multiplication
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● 3 matrices: A x B = C
X
=
i
k j
k
i
j

7. © Kriti Kathuria 2023
Matrix Multiplication
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Matrix Multiplication
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Processing aggregate queries
in a database
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Aggregation during run-generation
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Aggregation during run-generation
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● tpch sf = 1, 6M rows
● filter: 5M rows fetched from disk
● output: 4 rows
Thanh Do, Goetz Graefe, and Jeffrey Naughton. 2023. Efficient
Sorting, Duplicate Removal, Grouping, and Aggregation. ACM Trans.
Database Syst. 47, 4, Article 16 (December 2022), 35 pages.
https://doi.org/10.1145/3568027

12. © Kriti Kathuria 2023
Aggregation during run-generation
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● tpch sf = 1, 6M rows
● filter: 5M rows fetched from disk
● output: 4 rows
● tpch sf = 1000, 6B rows
● filter: 5B rows fetched from disk
● output: 4 rows
Thanh Do, Goetz Graefe, and Jeffrey Naughton. 2023. Efficient
Sorting, Duplicate Removal, Grouping, and Aggregation. ACM Trans.
Database Syst. 47, 4, Article 16 (December 2022), 35 pages.
https://doi.org/10.1145/3568027

13. © Kriti Kathuria 2023
Aggregation during run-generation
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Run generation
for sorting
Sorted runs
Reduction of
sorted data
Thanh Do, Goetz Graefe, and Jeffrey Naughton. 2023. Efficient
Sorting, Duplicate Removal, Grouping, and Aggregation. ACM Trans.
Database Syst. 47, 4, Article 16 (December 2022), 35 pages.
https://doi.org/10.1145/3568027

14. © Kriti Kathuria 2023
Aggregation during run-generation
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In-memory
index
Unsorted data on disk
Thanh Do, Goetz Graefe, and Jeffrey Naughton. 2023. Efficient
Sorting, Duplicate Removal, Grouping, and Aggregation. ACM Trans.
Database Syst. 47, 4, Article 16 (December 2022), 35 pages.
https://doi.org/10.1145/3568027

15. © Kriti Kathuria 2023
Vectorized Query Processing
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Vectorized Query Processing
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Vectorized Query Processing
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JIT Query Compilation
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JIT Query Compilation
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JIT Query Compilation
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21. © Kriti Kathuria 2023
Kriti Kathuria
linkedin.com/in/kriti-kathuria/
twitter.com/kaykathuria
Thank you! Let’s connect.
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