Home Box Office, Inc.

Allen Arthur Gay

Sata Busayarat

Jonathan David Lutz

Dylan Carney

Brandon C Furtwangler

Steven N Furtwangler

Gregory John Bellingham

Jinjiang Zeng

Jack Song

Mei Qi

Brandon S Furtwangler

The described technology is generally directed towards maintaining content selection graphs in an in-memory content selection graph data store in association with respective start times that indicates when the respective graphs become active. When a request to return content selection data is received, an active graph that corresponds to the request and the current time is accessed to obtain the requested content selection data. The response data can be prebuilt, e.g., in a set of active graphs for different client types, so that the response can be returned generally as is from the active graph in the set for that particular client type. A Redis cache can be used to maintain the various graph sets, including the active graph sets and graph sets that will become active at a future time.

Preloaded content selection graph for rapid retrieval

The described technology is directed towards obtaining and returning time offset data instead of current data in response to a data request. The time offset data may be limited to privileged clients only, and only provided thereto when desired, using a time offset value set by the client, for example. For example, a privileged user may request time offset data corresponding to a future time so as to preview how the data may be presented at a future time. Time offset data may be used by a system entity to fill a cache, e.g., as secondary cached data that may be used once primary cached data expires.

Time offset data request handling

The described technology is directed towards returning expanded data, beyond the data that was specifically requested, to a client, in order to reduce or eliminate likely subsequent requests for more data. Client requests for data items are processed by expansion rule logic to find an expansion rule set (e.g., file) corresponding to the identified data item. The rule set is used to expand requests for certain data items into expanded requests that return more data items than those requested. Client devices receive, cache and (often) use the cached expanded data items, which thereby reduces the overall number of requests to the data service. Expansion rules may be based upon observations (e.g., machine learned and/or manual) of request patterns, so as to reasonably predict which data item or items clients will likely next need, and return those as expanded data items in anticipation of their need.

Resource response expansion

The described technology is generally directed towards coordinating the generation, validation and enabling of content selection graphs in an in-memory content selection graph data store. When a set of content selection graphs is requested, a coordinator starts the generation of the relevant graphs. Upon successful generation, the coordinator starts a validation of the generated graphs against rules for the nodes/response data in the graphs. If the generated graphs pass validation, the coordinator enables the graph set for use in an in-memory cache, whereby when a request to return content selection data is received, an active graph that corresponds to the request and the current time is accessed to obtain and return the response data as the requested content selection data.

Coordinator for preloading time-based content selection graphs

The described technology is directed towards maintaining and using a version-based hierarchy of software resources (e.g., file system files) to return version-specific responses to clients. A client sends its version information with each data request, and gets back a response based upon that version. Version changes are made by maintaining the current version of each software code resource and overriding the current version with a previous version for clients as needed. The technology allows updates (e.g., for new devices and new software resource versions) to be supported by inserting resources into the resource hierarchy and moving resources therein based upon versioning. A system based on deltas is also contemplated, in which only parts of a file may be changed relative to a different version, instead of overriding the entire file.

Maintaining and updating software versions via hierarchy

The described technology is generally directed towards generating content selection graphs in an in-memory content selection graph data store in association with respective start times that indicate when the respective graphs become active. When a content selection graph is needed for a given timepoint, a node identifier is used to obtain response data that is cached for that node identifier and timepoint. Edge data associated with the node identifier is used to determine a next node for obtaining its response data for caching, and so on. The response data can be prebuilt, e.g., generated in a set of active graphs for different client types, so that a response can be returned generally as is from the active graph in the set for the node identifier and that particular client type. For efficiency, operations can be used to avoid processing a node identifier more than once when generating a graph.

Preloaded content selection graph generation

The described technology is generally directed towards causing the generation of a content selection graph (or set of graphs) upon receipt of a notification that a new graph or replacement graph is needed with respect to a starting timepoint. For a new graph, a timepoint for that new graph indicates the need for the new graph at a given starting time. For a replacement graph, a notification (subscribed from a monitoring service) can indicate that an existing graph has changed; a replacement graph is generated with a graph identifier of the replacement graph, which then replaces the graph identifier of the existing graph in the mappings of valid graphs to start times. A Redis cache can be used to maintain the various graph sets, including the active graph sets and graph sets that will become active at a future time.

Collection of timepoints and mapping preloaded graphs

The described technology is generally directed towards garbage collecting content selection graphs and related data from in an in-memory content selection graph data store. When a set of content selection graphs expire, a more current content selection graph set becomes active, and the storage space (e.g., in a Redis cache) used by the expired content selection graphs is reclaimed via garbage collection. Some graphs can be replaced before use, referred to as orphaned graphs, and the storage space for any such orphaned graphs is also reclaimed during garbage collection. Also garbage collected is storage space including related data structures used to generate and validate graphs.

Garbage collection of preloaded time-based graph data

The described technology is directed towards repurposing expired cached data when no unexpired data is available. Cached, unexpired data is used in response to a request when such data exists. If such data does not exist, e.g., at a front-end data service, then an attempt to obtain the requested data from another (e.g., back-end data service) is made. If the attempt is unsuccessful, and expired cached data exists, the expired cached data is returned in response to the request, e.g., instead of returning an error. A back-end data service may similarly return expired cached data when the back-end data service is unable to obtain unexpired requested data elsewhere. An emergency mode may be entered in which data, whether expired or not, is returned from a cache when such data exists, such as when an attempt to obtain the data elsewhere is known in advance to be futile.

Cached data repurposing

The described technology is generally directed towards validating content selection graphs for use in an in-memory content selection graph data store. When a content selection graph data is generated, the graph nodes can correspond to prebuilt response data. The response data for any of the nodes can be verified against rules based on the type of a node, for example, as well as based on client-specific information for types of clients. The overall validation process can validate the content selection graph for subsequent use, can result in errors or warnings being logged (which may or may not render the graph unusable, e.g., depending on severity), or can fail the content selection graph. If validated, the content selection graph can be used to rapidly return response data when content from the graph is requested.

Preloaded content selection graph validation

The described technology is directed towards returning less data than is available for a data item in response to a request to a data service. A virtual graph node is returned in response to client requests, in which the virtual node comprises a relatively lightweight set of information relative to the full set of information for the data item, e.g., maintained in a main (graph) node. A requesting client indicates that a virtual node is desired, and receives a response comprising the virtual node, generally processed from the main node's data into a reduced subset of the main node. The main node may be cached at the data service, and returned if and when requested.

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Shoreline, WA, United States of America

Allen Arthur Gay