Dell Products Inc.

Emc IP Holding Company LLC

Pratheek Veluswamy

Shibi Panikkar

Robert A Lincourt

Sisir Samanta

Ravi Prakash Kulkarni

Sekar Ponnayan

Kwong Lung Yong

Methods, apparatus, and processor-readable storage media for managing transaction consistency in distributed databases are provided herein. An example computer-implemented method includes determining a first one of a plurality of consistency levels to be applied for a transaction in a distributed database comprising a set of database nodes; and in response to determining that the first one of the plurality of consistency levels is to be applied: configuring at least two connections with the distributed database; releasing a first one of the at least two connections in response to detecting that the transaction completed on a first node in the set of database nodes; and in response to one or more of the other database nodes in the set of database nodes being updated to reflect changes on the first database node resulting from the transaction, releasing at least a second one of the at least two connections.

Managing transaction consistency in distributed databases

An apparatus comprises at least one processing device comprising a processor coupled to a memory. The at least one processing device is configured to simulate a first data set stored in a first database to create a second data set, wherein the second data set maintains one or more query impacting characteristics of the first data set such that performing a query on the second data set yields a performance result similar to another performance result that would occur when the query is performed on the first data set. The at least one processing device is further configured to load the second data set into a second database, and then perform the query on the second data set stored in the second database to obtain the performance result.

Intelligent data simulator for performance testing

An apparatus comprises a processing device configured to receive, from a given client at a single sign-on manager coupled to a database cluster comprising a plurality of databases, an access request comprising an identifier of a given one of the plurality of databases in the database cluster and single sign-on credentials for the given client to access the database cluster. The processing device is also configured to authenticate, at the single sign-on manager, the single sign-on credentials in the access request and, responsive to authenticating the single sign-on credentials in the access request, to establish a connection between the given client and the given one of the plurality of databases in the database cluster utilizing a session established between the single sign-on manager and the given one of the plurality of databases in the database cluster.

Single sign-on services for database clusters

An apparatus comprises a processing device configured to monitor logs of a database system, to analyze the logs to collect ongoing transaction information for one or more applications utilizing the database system, and to maintain the ongoing transaction information for each of the one or more applications in a corresponding one of a set of one or more queues of a data store external to the database system. The processing device is also configured to detect one or more designated events affecting operation of the database system and, responsive to detecting at least one of the one or more designated events, to process one or more unsaved transactions of the database system utilizing the information stored in the set of one or more queues of the data store external to the database system.

Maintaining ongoing transaction information of a database system in an external data store for processing unsaved transactions in response to designated events

Data protection techniques for data structures in an information processing system are provided. For example, a method comprises the following steps. A request is received to create a data structure with a given data structure name and one or more given parameter names. A pair of data structures is generated in response to the request. Each of the pair of data structures is assigned a different randomly-generated data structure name derived from the given data structure name in the request, and the one or more given parameter names are assigned different one or more randomly-generated parameter names in each of the pair of data structures.

Secure data structure for database system

An apparatus comprises at least one processing device configured to, in a monitoring system in which at any given time at least one monitoring node has a primary monitoring role responsible for monitoring operation of system nodes of a distributed system and other monitoring nodes have a secondary monitoring role responsible for monitoring operation of the at least one monitoring node having the primary monitoring role, identify a first monitoring node having the primary monitoring role at a current time. The processing device is also configured to determine, based at least in part on a time-based ranking of the monitoring nodes, a second monitoring node having the secondary monitoring role at the current time to transition to the primary monitoring role, and to transition the second monitoring node to the primary monitoring role at a subsequent time.

Controlling monitoring roles of nodes in a monitoring system utilizing a time-based ranking of the nodes

A method comprises dividing a plurality of operations of a microservice between a plurality of databases, and synchronizing data corresponding to the plurality of operations between the plurality of databases. The microservice is a create, read, update, delete (CRUD) microservice, and the plurality of operations comprise creating, reading, updating and deleting the data.

Intelligent datastore determination for microservice

A machine learning (ML) module that intelligently stores data in an online backup system based on the criticality of a business process. For a critical business process, all pre- and post-operation data are stored in the backup. For a non-critical business process, the ML module does not collect all data, but collects that data which deviates from a pre-defined target data pattern. Such deviating data is stored for a given time period and then discarded if no recovery request is received for it within the time period. Subsequent receipt of similar type of data may trigger the ML module to not store it. Thus, online backup storage is maintained at a manageable size without any loss of data for a critical business process. During data recovery, data is retrieved from the online backup with referential integrity to restore the original database without downtime to give a truly online recovery experience.

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Bengaluru, India

Pratheek Veluswamy