Oracle International Corporation

International Business Machines Corporation

Garret Frederick Swart

Juan R Loaiza

Tirthankar Lahiri

Jesse Kamp

Hideaki Kimura

Vijayshankar Raman

Boris Klots

Ajit Mylavarapu

Vasudha Krishnaswamy

Sukhada Pendse

Kathirgamar Aingaran

Shasank Kisan Chavan

Roger J Bamford

Ashok Shivarudraiah

Avneesh Pant

Xun Xue

Neil J S MacNaughton

James Liam Finnie

Ankita Kumar

Solmaz Kolahi

Spyros Blanas

Meichun Hsu

Gopi K Attaluri

Yunrui Li

William Havinden Bridge, Jr

Aurosish Mishra

Sanjiv Kapil

Michael J Gleeson

Kam Shergill

Roger L Q Zheng

Krishna Kunchithapadam

Piotr M Plachta

Stewart L Palmer

F Ryan Johnson

Siddharth Teotia

Richard Sefton Sidle

Sumti Jairath

Sumanta Kumar Chatterjee

John Johnson

David Vengerov

Jean De Lavarene

Allison Holloway

Lin Qiao

Ravi Shankar Thammaiah

Teck Hua Lee

Nagarajan Muthukrishnan

Nikunj R Mehta

Weiwei Gong

Kartik Kulkarni

Frederick Ralph Reiss

Binoy Sukumaran

Robert Hanckel

Smriti R Ramakrishnan

Shuo Yang

Tinggang Wang

Harry M Yudenfriend

David Darden Chambliss

Matthew Albert Huras

Wei-Ming Hu

Mahesh Baburao Girkar

Gary C Ngai

James Alan Ruddy

Cetin Ozbutun

Douglas N Surber

J William Lee

Alan Paul Wood

Ronen Grosman

Prashant Pandey

Aamer U Sachedina

David Brower

Guillermo Alvarez

Keriley Kay Romanufa

Wesley Lin

Adam Y Lee

Suratna Budalakoti

Mohammad Faisal

Umesh Panchaksharaiah

Amrish Srivastava

Ekrem S C Soylemez

Adam Kociubes

Ravi Palakodety

Carlos Ruiz

Vinita Subramanian

Eugene Karichkin

Ashish Motivala

Roger Lq Zheng

Kangnyeon Kim

Colm Divilly

Aings Aingaran

Pablo Diaz-Gutierrez

Laleh Aghababaie Beni

Aida Vosoughi

Kuassi Mensah

Allison L Holloway

Neil J S Macnaughton

Neil JS Macnaughton

John G Johnson

William H Bridge

Techniques are described herein for an integrated in-front database cache ('IIDC') providing an in-memory, consistent, and automatically managed cache for primary database data. An IIDC comprises a database server instance that (a) caches data blocks from a source database managed by a second database server instance, and (b) performs recovery on the cached data using redo records for the database data. The IIDC instance implements relational algebra and is configured to run any complexity of query over the cached database data. Any cache miss results in the IIDC instance fetching the needed block(s) from a second database server instance managing the source database that provides the IIDC instance with the latest version of the requested data block(s) that is available to the second instance. Because redo records are used to continuously update the data blocks in an IIDC cache, the IIDC guarantees consistency of query results.

Diskless active data guard as cache

A computer assigns many threads to a hardware pipeline that contains a sequence of hardware stages that include a computing stage, a suspending stage, and a resuming stage. Each cycle of the hardware pipeline can concurrently execute a respective distinct stage of the sequence of hardware stages for a respective distinct thread. A read of random access memory (RAM) can be requested for a thread only during the suspending stage. While a previous state of a finite state machine (FSM) that implements a coroutine of the thread is in the suspending stage, a read of RAM is requested, and the thread is unconditionally suspended. While the coroutine of the thread is in the resuming stage, an asynchronous response from RAM is correlated to the thread and to a next state of the FSM. While in the computing stage, the next state of the FSM executes based on the asynchronous response from RAM.

Hardware-accelerated coroutines for linked data structures

One or more engine instances are executed on each host to form an engine cluster. A plurality of control instances are executed on a first set of hosts to form a control cluster and comprise a control instance leader and one or more control instance followers. In response to a first host indicating a failure of a neighbor host, a pair-wise focused investigation is initiated to check peer-to-peer connections between the first host and the neighbor host. In response to one or more additional hosts indicating failures of neighbor hosts while the pair-wise focused investigation is being performed, a wide investigation is performed to check connections between the control cluster and the plurality of hosts. One or more hosts are added to an eviction list and an eviction protocol is performed to evict the one or more hosts from the engine cluster using the eviction list.

System and method for transaction continuity across failures in a scale-out database

In an embodiment, a database management system (DBMS) hosted by a computer receives a request to execute a database statement and responsively generates an interpretable execution plan that represents the database statement. The DBMS decides whether execution of the database statement will or will not entail interpreting the interpretable execution plan and, if not, the interpretable execution plan is compiled into object code based on partial evaluation. In that case, the database statement is executed by executing the object code of the compiled plan, which provides acceleration. In an embodiment, partial evaluation and Turing-complete template metaprogramming (TMP) are based on using the interpretable execution plan as a compile-time constant that is an argument for a parameter of an evaluation template.

Producing natively compiled query plans by recompiling existing C code through partial evaluation

Techniques related to cache storage formats are disclosed. In some embodiments, a set of values is stored in a cache as a set of first representations and a set of second representations. For example, the set of first representations may be a set of hardware-level representations, and the set of second representations may be a set of non-hardware-level representations. Responsive to receiving a query to be executed over the set of values, a determination is made as to whether or not it would be more efficient to execute the query over the set of first representations than to execute the query over the set of second representations. If the determination indicates that it would be more efficient to execute the query over the set of first representations than to execute the query over the set of second representations, the query is executed over the set of first representations.

Storage formats for in-memory caches

Herein are resource-constrained techniques that plan ahead for resiliently moving pluggable databases between container databases after a failure in a high-availability database cluster. In an embodiment that has a database cluster that hierarchically contains many pluggable databases in many container databases in many virtual machines, a computer identifies many alternative placements that respectively assign each pluggable database instance (PDB) to a respective container database management system (CDBMS). For each alternative placement, a respective placement score is calculated based on the PDBs and the CDBMSs. Based on the placement scores of the alternative placements, a particular placement is selected with a best placement score that indicates optimal resilience for accommodating adversity such as failover and overcrowding.

Dynamic hierarchical placement of consolidated and pluggable databases in autonomous environments

The present invention relates to optimized access of a database. Herein are techniques to accelerate execution of any combination of ad hoc query, heterogenous hardware, and fluctuating workload. In an embodiment, a computer receives a data access request for data tuples and compiles the data access request into relational operators. A particular implementation of a particular relational operator is dynamically selected from multiple interchangeable implementations. Each interchangeable implementation contains respective physical operators. A particular hardware operator for a particular physical operator is selected from multiple interchangeable hardware operators that include: a first hardware operator that executes on first processing hardware, and a second hardware operator that executes on second processing hardware that is functionally different from the first processing hardware. A response to the data access request is generated based on: the data tuples, the particular implementation of the particular relational operator, and the particular hardware operator.

Techniques of heterogeneous hardware execution for SQL analytic queries for high volume data processing

Herein are resource-constrained techniques that plan ahead for resiliently moving pluggable databases between container databases after a failure in a high-availability database cluster. In an embodiment, a computer identifies many alternative placements that respectively assign each pluggable database to a respective container database. For each alternative placement, a respective resilience score is calculated for each pluggable database that is based on the container database of the pluggable database. Based on the resilience scores of the pluggable databases for the alternative placements, a particular placement is selected as an optimal placement that would maximize utilization of computer resources, minimize database latencies, maximize system throughput, and maximize the ability of the database cluster to avoid a service outage.

Resilience based database placement in clustered environment

A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to 'slices', and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

System and method for an ultra highly available, high performance, persistent memory optimized, scale-out database

Systems and methods for reducing latency of probing operations of remotely located linear hash tables are described herein. In an embodiment, a system receives a request to perform a probing operation on a remotely located linear hash table based on a key value. Prior to performing the probing operation, the system dynamically predicts a number of slots for a single read of the linear hash table to minimize total cost for an average probing operation. The system determines a hash value based on the key value and determines a slot of the linear hash table to which the hash value corresponds. After predicting the number of slots, the system issues an RDMA request to perform a read of the predicted number of slots from the linear hash table starting at the slot to which the hash value corresponds.

Efficient usage of one-sided RDMA for linear probing

Techniques are described for offloading remote direct memory operations (RDMOs) to 'execution candidates'. The execution candidates may be any hardware capable of performing the offloaded operation. Thus, the execution candidates may be network interface controllers, specialized co-processors, FPGAs, etc. The execution candidates may be on a machine that is remote from the processor that is offloading the operation, or may be on the same machine as the processor that is offloading the operation. Details for certain specific RDMOs, which are particularly useful in online transaction processing (OLTP) and hybrid transactional/analytical (HTAP) workloads, are provided.

Same-machine remote direct memory operations (RDMOS)

A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to 'slices', and storing multiple copies ('duplicas') of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the &#x201c;primary duplica&#x201d;. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (&#x201c;secondary duplicas&#x201d;) of the same slice.

Growing community of inventors

Palo Alto, CA, United States of America

Garret Frederick Swart