公开(公告)号：US11599389B2

公开(公告)日：2023-03-07

申请号：US17463366

申请日：2021-08-31

Applicant: Snowflake Inc.

Inventor： Johan Harjono ,  Daniel Geoffrey Karp ,  Kunal Prafulla Nabar ,  Rares Radut ,  Arthur Kelvin Shi

IPC: G06F9/50

Abstract: Techniques described herein can optimize usage of computing resources in a data system. Dynamic throttling can be performed locally on a computing resource in the foreground and autoscaling can be performed in a centralized fashion in the background. Dynamic throttling can lower the load without overshooting while minimizing oscillation and reducing the throttle quickly. Autoscaling may involve scaling in or out the number of computing resources in a cluster as well as scaling up or down the type of computing resources to handle different types of situations.

公开(公告)号：US11537566B1

公开(公告)日：2022-12-27

申请号：US17806852

申请日：2022-06-14

Applicant: Snowflake Inc.

Inventor： Johan Harjono ,  Daniel Geoffrey Karp ,  Rares Radut ,  Samir Rehmtulla ,  Arthur Kelvin Shi ,  Thanakul Wattanawong

IPC: G06F16/00 ,  G06F16/182 ,  G06F16/28

Abstract: The subject technology determines an availability zone skew among multiple zones. The subject technology, based on the availability zone skew, determines a target skew to meet a global balancing of cluster instances. The subject technology, based on the target skew, selects a particular zone among multiple zones. The subject technology deploys a particular type of cluster instance to the particular zone. The subject technology, for each zone from the multiple zones, determining a respective number of cluster instances. The subject technology identifies a first zone that includes a highest number of cluster instances based on the respective number of cluster instances from each zone. The subject technology identifies a second zone that includes a lowest number of cluster instances based on the respective number of cluster instances from each zone.

公开(公告)号：US11461150B1

公开(公告)日：2022-10-04

申请号：US17462658

申请日：2021-08-31

Applicant: Snowflake Inc.

Inventor： Johan Harjono ,  Daniel Geoffrey Karp ,  Kunal Prafulla Nabar ,  Rares Radut ,  Arthur Kelvin Shi

IPC: G06F9/50 ,  G06F9/355

Abstract: Techniques described herein can optimize usage of computing resources in a data system. Dynamic throttling can be performed locally on a computing resource in the foreground and autoscaling can be performed in a centralized fashion in the background. Dynamic throttling can lower the load without overshooting while minimizing oscillation and reducing the throttle quickly. Autoscaling may involve scaling in or out the number of computing resources in a cluster as well as scaling up or down the type of computing resources to handle different types of situations.

公开(公告)号：US11372820B1

公开(公告)日：2022-06-28

申请号：US17461169

申请日：2021-08-30

Applicant: Snowflake Inc.

Inventor： Johan Harjono ,  Daniel Geoffrey Karp ,  Rares Radut ,  Samir Rehmtulla ,  Arthur Kelvin Shi ,  Thanakul Wattanawong

IPC: G06F16/00 ,  G06F16/182 ,  G06F16/28

Abstract: The subject technology determines, after a period of time elapses over a periodic segment of time, an imbalance of cluster instances deployed in multiple zones based on a threshold value, the cluster instances including different types of clusters associated with compute service manager instances. The subject technology identifies a particular type of cluster instance to include in a particular zone from the multiple zones. The subject technology adds the particular type of cluster instance to the particular zone to meet a global balancing of cluster instances in the multiple zones. The subject technology determines, after a second period of time elapses over the periodic segment of time, that a number of cluster instances deployed in the multiple zones is below the threshold value indicating a current balance of cluster instances in the multiple zones.