Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable storage media for implementing a scalable, secure, efficient, and adaptable distributed digital ledger transaction network. Indeed, the disclosed systems can reduce storage and processing requirements, improve security of implementing computing devices and underlying digital assets, accommodate a wide variety of different digital programs (or “smart contracts”), and scale to accommodate billions of users and associated digital transactions. For example, the disclosed systems can utilize a host of features that improve storage, account/address management, digital transaction execution, consensus, and synchronization processes. The disclosed systems can also utilize a new programming language that improves efficiency and security of the distributed digital ledger transaction network.

Abstract: A disclosed computer-implemented method may include partitioning, based on a social graph representative of social connections among a plurality of users of a social networking system, the plurality of users into a plurality of user buckets. Each user bucket may include at least one user of the social networking system. The method may further include selecting a set of user buckets from the plurality of user buckets, assigning the set of user buckets to a data center included in a networking infrastructure, and routing data sent from a user device associated with a user included in the set of user buckets to the data center. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable storage media for implementing a scalable, secure, efficient, and adaptable distributed digital ledger transaction network. Indeed, the disclosed systems can reduce storage and processing requirements, improve security of implementing computing devices and underlying digital assets, accommodate a wide variety of different digital programs (or “smart contracts”), and scale to accommodate billions of users and associated digital transactions. For example, the disclosed systems can utilize a host of features that improve storage, account/address management, digital transaction execution, consensus, and synchronization processes. The disclosed systems can also utilize a new programming language that improves efficiency and security of the distributed digital ledger transaction network.

Abstract: A disclosed computer-implemented method may include receiving, via a monitoring infrastructure that monitors an operational state of a networking infrastructure, data representative of the operational state of the networking infrastructure during a period of time. The networking infrastructure may include (1) a plurality of data centers, and (2) at least one point-of-presence (POP) edge node. The method may also include (1) accessing data representative of a set of predefined policies associated with the networking infrastructure, and (2) based on the data representative of the operational state (a) determining a set of edge load factors associated with the edge node, and (b) generating, via a linear solver and further based the set of edge load factors associated with the edge node and the set of predefined policies associated with the networking infrastructure, a routing table for the POP edge node. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A multi-shard database system receives a transaction including multiple actions directed to different shards of the database system. The database system creates a transaction record including a transaction identifier and a transaction status for the transaction in a transaction database. The database system then executes, in parallel, the multiple actions on the different shards by associating with each data item involved in the transaction a data structure that includes the transaction identifier and new data to be applied to the data item. The database system then updates the transaction status in the transaction record for the transaction from pending to completed when each of the multiple actions is successfully executed on the corresponding shard. Consistency is eventually implemented when the data structures associated with the data items involved in the transaction are evaluated. The evaluation of a data structure can be triggered by a read request or other events.

Abstract: A multi-shard database system receives a transaction including multiple actions directed to different shards of the database system. The database system creates a transaction record including a transaction identifier and a transaction status for the transaction in a transaction database. The database system then executes, in parallel, the multiple actions on the different shards by associating with each data item involved in the transaction a data structure that includes the transaction identifier and new data to be applied to the data item. The database system then updates the transaction status in the transaction record for the transaction from pending to completed when each of the multiple actions is successfully executed on the corresponding shard. Consistency is eventually implemented when the data structures associated with the data items involved in the transaction are evaluated. The evaluation of a data structure can be triggered by a read request or other events.

Abstract: A multi-shard database system receives a transaction including multiple actions directed to different shards of the database system. The database system creates a transaction record including a transaction identifier and a transaction status for the transaction in a transaction database. The database system then executes, in parallel, the multiple actions on the different shards by associating with each data item involved in the transaction a data structure that includes the transaction identifier and new data to be applied to the data item. The database system then updates the transaction status in the transaction record for the transaction from pending to completed when each of the multiple actions is successfully executed on the corresponding shard. Consistency is eventually implemented when the data structures associated with the data items involved in the transaction are evaluated. The evaluation of a data structure can be triggered by a read request or other events.

Abstract: A multi-shard database system receives a transaction including multiple actions directed to different shards of the database system. The database system creates a transaction record including a transaction identifier and a transaction status for the transaction in a transaction database. The database system then executes, in parallel, the multiple actions on the different shards by associating with each data item involved in the transaction a data structure that includes the transaction identifier and new data to be applied to the data item. The database system then updates the transaction status in the transaction record for the transaction from pending to completed when each of the multiple actions is successfully executed on the corresponding shard. Consistency is eventually implemented when the data structures associated with the data items involved in the transaction are evaluated. The evaluation of a data structure can be triggered by a read request or other events.

Abstract: A method for continuously updating a set of replicas. The method comprises storing a plurality of replicas of data in a current configuration of members from a plurality of nodes, receiving a reconfiguration command by a member of said current configuration, selecting at least one estimated configuration from said plurality of nodes, receiving at least one write command by a member of said current configuration, disseminating said at least one write command to each member of said at least one estimated configuration and validating said at least one estimated configuration. At least one estimated configuration processes at least one of the write commands before the validating is completed.

Abstract: Face-containing images within web pages are automatically annotated to identify the people having those faces. The annotation is based on faces detected in the images and named entities detected in text associated with the images. Each candidate named entity may be scored by the prominence of the named entity in the text relative to the other extracted named entities. Queries are sent to a search engine based on the extracted candidate named entities. Sample images are returned. Face similarity calculations may be computed based on the featured faces and sample faces detected in the search engine-returned sample images to associate a probability score between each featured face and each candidate named entity. A bipartite matching instance may be solved to arrive at a maximum likelihood assignment of named entities to featured faces.

Abstract: A method for continuously updating a set of replicas. The method comprises storing a plurality of replicas of data in a current configuration of members from a plurality of nodes, receiving a reconfiguration command by a member of said current configuration, selecting at least one estimated configuration from said plurality of nodes, receiving at least one write command by a member of said current configuration, disseminating said at least one write command to each member of said at least one estimated configuration and validating said at least one estimated configuration. At least one estimated configuration processes at least one of the write commands before the validating is completed.

Abstract: Face-containing images within web pages are automatically annotated to identify the people having those faces. The annotation is based on faces detected in the images and named entities detected in text associated with the images. Each candidate named entity may be scored by the prominence of the named entity in the text relative to the other extracted named entities. Queries are sent to a search engine based on the extracted candidate named entities. Sample images are returned. Face similarity calculations may be computed based on the featured faces and sample faces detected in the search engine-returned sample images to associate a probability score between each featured face and each candidate named entity. A bipartite matching instance may be solved to arrive at a maximum likelihood assignment of named entities to featured faces.