Netflix Inc.

Gemini Ip, LLC

Ioannis Papapanagiotou

Shailesh Birari

Shashi Madappa

Sridhar Enugula

Jason Cacciatore

Minh Do

Christos Kalantzis

Jessica Destefano

Enrique Valenzuela

Mitchell Mccuiston

Andreas Andreakis

Christopher Karabats

Deva Jayaraman

Alex Edwards

Ahmad Wilson

Vinay Chella

Emirhan Akkaya

Kishore Udayashankar Kasi

Elaine Huang

Vikram Krishnamurthy

Prudhviraj Karumanchi

Systems and methods for providing users access to trusted custody and/or staking services for newly launched blockchain projects and/or their associated cryptocurrencies are described herein. For example, a host server including a system may send, receive, and stake cryptocurrency in a secure environment where the host server manages private keys associated with one or more wallets. In some cases, the host server may utilize an abstraction layer to translate user requests into commands that may be communicated to a third-party server associated with the blockchain and/or cryptocurrencies. The user requests may include actions associated with cryptocurrencies, such as checking a wallet address, generating new wallet, transferring cryptocurrency, and/or staking cryptocurrency.

Secured third-party wallet environment

Systems and methods for staking instrument cryptocurrency rewards and validator management are disclosed. The systems and methods include generating validator pods associated with cryptocurrency staking validation and generating validator keys configured to be utilized by the validator pods for validating proof of stake operations. A subsets of the validator keys may be assigned to the validator pods and a request to perform the proof of stake operations may be received. A validator pod may be provisioned for validating proof of stake operations, and the validator node may be utilized to validate proof of stake operations based on the selected validator node being a compliant validator node.

Cryptocurrency staking validator system

Various embodiments set forth systems and techniques for concurrent log and dump processing. The techniques include selecting, from a datastore, a chunk comprising one or more rows of data; comparing the one or more rows of data in the chunk with a first set of log events in a change log associated with the datastore, wherein each log event included in the first set of log events occurs after a first log event in the change log and prior to a second log event in the change log; selecting, based on the comparison, one or more non-conflicting rows in the chunk; and transmitting, to an output, one or more log events associated with the one or more non-conflicting rows in the chunk prior to processing a second set of log events in the change log, wherein the second set of log events occur after the second log event.

Watermark-based techniques for change-data-capture

Various embodiments of the present application set forth a computer-implemented method for accessing data comprising determining a manifest associated with a given user of an application, where the manifest identifies one or more assets that are accessible by the given user, for each of the one or more assets, one of a plurality of endpoint stores that stores data associated with the asset, and for each of the one or more assets, one of the plurality of endpoint stores that stores metadata associated with the asset, generating, based on the manifest, a user namespace that includes a unique reference for each of the one or more assets, and presenting the user namespace to the user.

File interface to dynamically manage access to remote files

In one embodiment, a data reconciliation engine works with data store nodes included in a distributed data store system to ensure consistency between the data store nodes. In operation, the data reconciliation receives a different data snapshot from each of the data store nodes. In response, the data reconciliation engine generates one or more recommendations designed to resolve inconsistencies between the data snapshots. The data reconciliation engine then transmits each recommendation to a different data store node. Because the data reconciliation engine performs many of the resource-intensive operations included in the data reconciliation process, the resources of the data store nodes may focus primarily on processing client requests instead of performing data reconciliation operations. Consequently, unlike conventional data store node based reconciliation applications, the data reconciliation engine may process large volumes of data without unacceptably increasing the time required for the distributed data store system to respond to client requests.

Techniques for performing data reconciliation in distributed data store systems

The disclosed computer-implemented method includes receiving an indication that cache data is to be copied from an originating cluster having a specified number of replica nodes to a destination cluster having an arbitrary number of replica nodes. The method further includes copying the cache data to a cache dump and creating a log that identifies where the cache data is stored in the cache dump. The method further includes copying the cache data from the cache dump to the replica nodes of the destination cluster. The copying includes writing the copied data in a distributed manner, such that at least a portion of the copied data is distributed over each of the replica nodes in the destination cluster. Various other methods, systems, and computer-readable media are also disclosed.

Cache warming: agility for a stateful service

In various embodiments, a benchmarking engine automatically tests a data store to assess functionality and/or performance of the data store. The benchmarking engine generates data store operations based on dynamically adjustable configuration data. As the benchmarking engine generates the data store operations, the data store operations execute on the data store. In a complementary fashion, as the data store operations execute on the data store, the benchmarking engine generates statistics based on the results of the executed data store operations. Advantageously, because the benchmarking engine adjusts the number and/or type of data store operations that the benchmarking engine generates based on any changes to the configuration data, the workload that executes on the data store may be fine-tuned as the benchmarking engine executes.

Techniques for dynamically benchmarking cloud data store systems

In various embodiments, a node manager configures a 'new' node as a replacement for an 'unavailable' node that was previously included in a distributed data store. First, the node manager identifies a source node that stores client data that was also stored in the unavailable node. Subsequently, the node manager configures the new node to operate as a slave of the source node and streams the client data from the source node to the new node. Finally, the node manager configures the new node to operate as one of multiple masters nodes in the distributed data store. Advantageously, by configuring the node to implement a hybrid of a master-slave replication scheme and a master-master replication scheme, the node manager enables the distributed data store to process client requests without interruption while automatically restoring the previous level of redundancy provided by the distributed data store.

Growing community of inventors

San Jose, CA, United States of America

Ioannis Papapanagiotou