Oracle International Corporation

Jesse Kamp

Amit Ganesh

Vineet Marwah

Tirthankar Lahiri

Shasank Kisan Chavan

Allison Holloway

Michael J Gleeson

Juan R Loaiza

Roger MacNicol

Kam Shergill

Garret Frederick Swart

Anindya Chandra Patthak

Niloy Mukherjee

Vikram Kapoor

Manosiz Bhattacharyya

Hideaki Kimura

Krishna Kunchithapadam

Sachin Kulkarni

Teck Hua Lee

Sanket Hase

Avneesh Pant

Vivekanandhan Raja

Lawrence J Ellison

Ajit Mylavarapu

Vasudha Krishnaswamy

Aurosish Mishra

Neil J S MacNaughton

Kartik Kulkarni

Prashant Gaharwar

Maria Colgan

Siddharth Teotia

Meichun Hsu

Zhen Hua Liu

Yunrui Li

Boris Klots

Sukhada Pendse

Xia Hua

Victor Chen

Roger Dermot Macnicol

Jiaqi Yan

Kothanda Umamageswaran

Beda Christoph Hammerschmidt

Krishnan Meiyyappan

Dinesh Das

Sujatha Muthulingam

Ramesh Kumar

Atrayee Mullick

Hariharan Lakshmanan

Viral Shah

Dina Thomas

Adrian Tsz Him Ng

Adrian Ng

Spyros Blanas

Srikrishnan Suresh

Dhruvil Shah

Adrian Daniel Popescu

Yiran Qin

Aditi Gupta

Satyanarayana R Valluri

Maheswaran Venkatachalam

Sachin Kulkami

Roger D Macnicol

Roger Macnicol

Allison L Holloway

Neil JS Macnaughton

A computer analyzes a relational schema of a database to generate a data entry schema and encodes the data entry schema as JSON. The data entry schema is sent to a database client so that the client can validate entered data before the entered data is sent for storage. From the client, entered data is received that conforms to the data entry schema because the client used the data entry schema to validate the entered data before sending the data. Into the database, the entered data is stored that conforms to the data entry schema. The data entry schema and the relational schema have corresponding constraints on a datum to be stored, such as a range limit for a database column or an express set of distinct valid values. A constraint may specify a format mask or regular expression that values in the column should conform to, or a correlation between values of multiple columns.

Framework and method for consistent cross-tier data validation

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

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)

Techniques herein use in-memory column vectors to process data that is external to a database management system (DBMS) and logically join the external data with data that is native to the DBMS. In an embodiment, a computer maintains a data dictionary for native data that is durably stored in an DBMS and external data that is not durably stored in the DBMS. From a client through a connection to the DBMS, the computer receives a query. The computer loads the external data into an in-memory column vector that resides in random access memory of the DBMS. Based on the query and the data dictionary, the DBMS executes a data join of the in-memory column vector with the native data. To the client through said connection, the computer returns results of the query based on the data join.

Performing in-memory columnar analytic queries on externally resident data

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.

Techniques are provided to allow more sophisticated operations to be performed remotely by machines that are not fully functional. Operations that can be performed reliably by a machine that has experienced a hardware and/or software error are referred to herein as Remote Direct Memory Operations or 'RDMOs'. Unlike RDMAs, which typically involve trivially simple operations such as the retrieval of a single value from the memory of a remote machine, RDMOs may be arbitrarily complex. The techniques described herein can help applications run without interruption when there are software faults or glitches on a remote system with which they interact.

One-sided reliable remote direct memory operations

A database server stores compressed units in data blocks of a database. A table (or data from a plurality of rows thereof) is first compressed into a 'compression unit' using any of a wide variety of compression techniques. The compression unit is then stored in one or more data block rows across one or more data blocks. As a result, a single data block row may comprise compressed data for a plurality of table rows, as encoded within the compression unit. Storage of compression units in data blocks maintains compatibility with existing data block-based databases, thus allowing the use of compression units in preexisting databases without modification to the underlying format of the database. The compression units may, for example, co-exist with uncompressed tables. Various techniques allow a database server to optimize access to data in the compression unit, so that the compression is virtually transparent to the user.

Storing compression units in relational tables

A hashing scheme includes a cache-friendly, latchless, non-blocking dynamically resizable hash index with constant-time lookup operations that is also amenable to fast lookups via remote memory access. Specifically, the hashing scheme provides each of the following features: latchless reads, fine grained lightweight locks for writers, non-blocking dynamic resizability, cache-friendly access, constant-time lookup operations, amenable to remote memory access via RDMA protocol through one sided read operations, as well as non-RDMA access.

Latchless, non-blocking dynamically resizable segmented hash index

Techniques are provided for enabling a requesting entity to retrieve data that is managed by a database server instance from the volatile memory of a server machine that is executing the database server instance. The techniques allow the requesting entity to retrieve the data from the volatile memory of the host server machine without involving the database server instance in the retrieval operation. Because the retrieval does not involve the database server instance, the retrieval may succeed even when the database server instance has stalled or become unresponsive. In addition, direct retrieval of data using the techniques described herein will often be faster and more efficient than retrieval of the same information through conventional interaction with the database server instance.

Method for efficient primary key based queries using atomic RDMA reads on cache friendly in-memory hash index

Techniques are provided for storing in in-memory unit (IMU) in a lower-storage tier and copying the IMU to DRAM when needed for query processing. Techniques are also provided for copying IMUs to lower tiers of storage when evicted from the cache of higher tiers of storage. Techniques are provided for implementing functionality of IMUs within a storage system, to enable database servers to push tasks, such as filtering, to the storage system where the storage system may access IMUs within its own memory to perform the tasks. Metadata associated with a set of data may be used to indicate whether an IMU for the data should be created by the database server machine or within the storage system.

Columnar caching in tiered storage

Techniques are described for encoding join columns that belong to the same domain with a common dictionary. The tables are encoded with dictionary indexes that make the comparison operation of a join query a quick equality check of two integers and there is no need to compute any hashes during execution. Additionally, the techniques described herein minimize the bloom filter creation and evaluation cost as well because the dictionary indexes serve as hash values into the bloom filter. If the bloom filter is as large as the range of dictionary indexes, then the filter is no longer a probabilistic structure and can be used to filter rows in the probe phase with full certainty without any significant overhead.

Using shared dictionaries on join columns to improve performance of joins in relational databases

Techniques are described herein for distributing distinct portions of a database object across volatile memories of selected nodes of a plurality of nodes in a clustered database system. The techniques involve storing a unit-to-service mapping that associates a unit (a database object or portion thereof) to one or more database services. The one or more database services are mapped to one or more nodes. The nodes to which a service is mapped may include nodes in disjoint database systems, so long as those database systems have access to a replica of the unit. The database object is treated as in-memory enabled by nodes that are associated with the service, and are treated as not in-memory enabled by nodes that are not associated with the service.

Flexible in-memory column store placement

Growing community of inventors

Castro Valley, CA, United States of America

Jesse Kamp