Microsoft Technology Licensing, LLC

Other

Daniel Georg Schall

Craig Steven Freedman

Hanumantha Rao Kodavalla

Panagiotis Antonopoulos

Naveen Prakash

Alejandro Hernandez Saenz

Raghavendra Thallam Kodandaramaih

Adrian-Leonard Radu

Chandrashekhar Kadiam

Devin Leo Rider

Nitish Jindal

Larry Bowar

Gregory L Schleining

Devin L Rider

Methods, systems, and computer program products are provided that construct and execute multi-step query plans in a database application. A database application accepts a query such as, for example, a graph query. The database application generates a physical query plan comprising a set of execution steps that perform the query. The set of execution steps comprises at least an initial step, an intermediate step and a final step. The database executes the query by passing control to the initial step that, when complete, passes execution control to some other step. The intermediate step executes and when complete, may pass execution control to any other step including itself. Steps may be configured to communicate arbitrary data between steps. The generated query plan may also comprise multiple multi-step sequences, and such sequences may be configured to execute in the alternative based on intermediate query results, or a parameter included in the query.

Multi-step query execution in SQL server

Methods for operation fragmentation with metadata serialization in query processing pushdowns are performed by systems and devices. A compute node receives a query directed to database data, and generates query text fragments. Portions of metadata of the database are read from different page servers, and are serialized by the compute node. Page identities of data pages in a page server that stores the data are determined from a page index at the compute node, and the compute node provides the text fragments, the serialized metadata, and the page identities to the page server storing the data. The page server compiles the text fragments based on the serialized metadata to generate an executable query plan for the query. The page server initializes and performs execution of the executable query plan against the data as a pushdown query operation, and a result of pushdown query operation is provided to the compute node.

Operation fragmentation with metadata serialization in query processing pushdowns

Distributed database systems including compute nodes and page servers are described herein that enable compute nodes to pushdown certain query processing compute tasks to the page servers to take advantage of otherwise idle compute resources at the page servers, and to reduce the quantity of data that moves between compute nodes and page servers. A distributed database system includes a page server and a compute node, wherein the page server is configured to maintain multiple versions of stored data objects. The compute node is configured to receive a query and generate a transaction context (TC) and modified table schemas (MTS) scoped to the query, and pushdown the query, TC and MTS to the page server that is configured to determine which data objects at the page server satisfy the query, and for each such object, which version of the object should be returned based on the TC.

Schema and data modification concurrency in query processing pushdown

Methods for page split detection and affinity in query processing pushdowns are performed by systems and devices. Page servers perform pushdown operations based on specific, and specifically formatted or generated, information, instructions, and data provided thereto from a compute node. Page servers also determine that page splits have occurred during reading of data pages maintained by page servers during pushdown operations, and also during fulfillment of compute node data requests. To detect a data page has split, page servers utilize information from a compute node of an expected next data page which is compared to a next data page in the page server page index. A mismatch in the comparison by page servers indicates data page was split. Compute nodes and page servers store and maintain off-row data generated during data operations via page affinity considerations where the off-row data is stored at the same page server as the data.

Page split detection and affinity in query processing pushdowns

Examples described herein generally relate to executing a received graph database query. The received graph database query can be converted into a recursive common table expression (CTE). Multiple alternative processes for executing the recursive CTE can be generated based on the recursive CTE. A cost associated with each of the multiple alternative processes can be determined. One of the multiple alternative processes can be converted into a multi-step sequence based on the associated cost. The multi-step sequence can be executed on a database to retrieve a set of results in response to the received graph database query.

Techniques for optimizing graph database queries

An apparatus for installing a pipeline slipliner, by pushing rather than drawing the slipliner into the host pipe. The gripper assembly is mounted on the boom of an earthworking machine or other vehicle, such as a track excavator. The gripper assembly includes gripper tracks that engage the exterior of the slipliner and advance it in a linear fashion. The gripper assembly is lowered on a boom into a excavation so as to level with the opening of the host pipe. The gripper assembly is then operated to advance the slipliner into the host pipe, with the slipliner being feed down to the gripper assembly on the surface of the excavation. The gripper assembly resiliently compresses the slipliner to establish a grip that enables it to push the slipliner great distances into the host pipe.

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Woodinville, WA, United States of America

Daniel Georg Schall