Microsoft Technology Licensing, LLC

Manoj A Syamala

Vivek Ravindranath Narasayya

Sudipto Das

Feng Li

Ajay Kalhan

John Morgan Oslake

Pankaj Arora

Shize Xu

Junfeng Dong

Jiaqi Liu

Changsong Li

Arnd Christian König

Surajit J Chaudhuri

Badrish Chandramouli

Hyunjung Park

Hideaki Kimura

Bolin Ding

Gaoxiang Xu

Yi Shan

Arjun Dasgupta

Jingjing Wang

Intelligent memory brokering for multiple process instances, such as relational databases (e.g., SQL servers), reclaims memory based on value, thereby minimizing cost across instances. An exemplary solution includes: based at least on a trigger event, determining a memory profile for each of a plurality of process instances at a computing node; determining an aggregate memory profile, the aggregate memory profile indicating a memory unit cost for each of a plurality of memory units; determining a count of memory units to be reclaimed; identifying, based at least on the aggregate memory profile and the count of memory units to be reclaimed, a count of memory units to be reclaimed within each process instance so that a total cost is minimized to reclaim the determined count; and communicating, to each process instance having identified memory units to be reclaimed, a count of memory units to be reclaimed within the process instance.

Memory management for multiple process instances

Techniques for differential overbooking on a cloud database. These techniques may include determining a reservation amount of a multi-tenant resource for a first service of a based upon an overbooking characteristic of the first service, and determining that a total usage value of the multi-tenant resource by a plurality of services is greater than a threshold value. In addition, the techniques may include determining a service usage value of the multi-tenant resource by the first service, determining a first overage value of the first service based on the service usage value and the reservation amount, and performing a resource reclamation process over the multi-tenant resource based on the first overage value of the first service.

Differential overbooking in a cloud computing environment

Methods for determining cache activity and for optimizing cache reclamation are performed by systems and devices. A cache entry access is determined at an access time, and a data object of the cache entry for a current time window is identified that includes a time stamp for a previous access and a counter index. A conditional counter operation is then performed on the counter associated with the index to increment the counter when the time stamp is outside the time window or to maintain the counter when the time stamp is within the time window. A counter index that identifies another counter for a previous time window where the other counter value was incremented for the previous cache entry access causes the other counter to be decremented. A cache configuration command to reclaim, or additionally allocate space to, the cache is generated based on the values of the counters.

System and method for determining cache activity and optimizing cache reclamation

A solution is disclosed for memory management of serverless databases that includes: based at least on detecting a trigger event, determining whether memory is to be reclaimed; based at least on determining that memory is to be reclaimed, determining an amount of memory to be reclaimed; identifying memory to be reclaimed; and reclaiming the identified memory. Disclosed solutions are flexible, enabling customization of the aggressiveness and manner of memory reclamation. This permits users to specify a tailored balance point between performance and cost, for arrangements that bill users based on resource usage (e.g., memory consumed by a serverless database). In some examples, users specify a ramp-down parameter that is used to determine how much memory can be evicted in a particular reclamation event, time intervals (or another criteria) for triggering a reclamation event, and a definition for whether a cache is active.

Memory management for serverless databases

Described herein are technologies relating to computing resource allocation among multiple tenants. Each tenant may have a respective absolute reservation for rate-based computing resources, which is independent of computing resource reservations of other tenants. The multiple tenants vie for the rate-based computing resources, and tasks are scheduled based upon which tenants submit the tasks and the resource reservations of such tenants.

Dynamic allocation of resources while considering resource reservations

Provided are methods and systems for generating physical database design tuning recommendations. Given a workload, the system analyzes the workload to identify and recommend a set of rowstore and columnstore indexes optimal for the performance of the workload. The system is designed to estimate the size of the columnstore index (at the granularity of each column) without actually building the index, estimate the improvement in query performance that each columnstore index would result in when built, and automatically derive the workload used for the physical design tuning task by analyzing stored query execution history data. This automatic workload derivation is orthogonal to columnstores and can be used even when columnstore indexes are not being used.

Workload-driven recommendations for Columnstore and Rowstore indexes in relational databases

Techniques for tenant performance isolation in a multiple-tenant database management system are described. These techniques may include providing a reservation of server resources. The server resources reservation may include a reservation of a central processing unit (CPU), a reservation of Input/Ouput throughput, and/or a reservation of buffer pool memory or working memory. The techniques may also include a metering mechanism that determines whether the resource reservation is satisfied. The metering mechanism may be independent of an actual resource allocation mechanism associated with the server resource reservation.

Isolating resources and performance in a database management system

A server system may include a cluster of multiple computers that are networked for high-speed data communications. Each of the computers has a remote direct memory access (RDMA) network interface to allow high-speed memory sharing between computers. A relational database engine of each computer is configured to utilize a hierarchy of memory for temporary storage of working data, including in order of decreasing access speed (a) local main memory, (b) remote memory accessed via RDMS, and (c) mass storage. The database engine uses the local main memory for working data, and additionally uses the RDMA accessible memory for working data when the local main memory becomes depleted. The server system may include a memory broker to which individual computers report their available or unused memory, and which leases shared memory to requesting computers.

Memory sharing for working data using RDMA

Techniques for tenant performance isolation in a multiple-tenant database management system are described. These techniques may include providing a reservation of server resources. The server resources reservation may include a reservation of a central processing unit (CPU), a reservation of Input/Output throughput, and/or a reservation of buffer pool memory or working memory. The techniques may also include a metering mechanism that determines whether the resource reservation is satisfied. The metering mechanism may be independent of an actual resource allocation mechanism associated with the server resource reservation.

A plurality of indicators representing a plurality of respective candidate database configurations may be obtained, each of the candidate database configurations including a plurality of database queries and a plurality of candidate database indexes associated with a database table. A portion of the candidate database indexes included in the plurality of database indexes may be selected based on skyline selection. An enumeration of the portion of the plurality of the candidate database indexes may be determined based on a greedy algorithm.

Compression aware physical database design

This patent application relates to granular and workload driven database index defragmentation techniques. These techniques allow for defragmenting individual index ranges, performing benefit analysis to estimate the impact of defragmenting indexes or index ranges, and leveraging such benefit analysis to provide automated workload-driven recommendations of index(es) or index range(s) to defragment.

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Issaquah, WA, United States of America

Manoj A Syamala