Amazon Technologies, Inc.

Facebook, Inc.

Meta Platforms, Inc.

Jason George McHugh

Derek Ernest Denny-Brown, Ii

Praveen Kumar Gattu

Michael Arthur Ten-Pow

James Christopher Sorenson

Gunavardhan Kakulapati

Allan Henry Vermeulen

Abraham Martin Passaglia

Sohel R Ahuja

Paul Francis Dean Ferraro

Maximilian Francis Barrows

Andrew Jay Roths

Daniel Bibireata

Seth William Markle

Beryl Tomay

Nickolay Vladimirov Tchervenski

Eric Yves Theriault

Mark Warren McDuff

Ravi R Subramanian

Eric Allan Shell

Michael F Cohen

John David Cormie

Jonathan White Keljo

Johannes Peter Kopf

Tracy Dianne Schultz

Michal Maciej Bryc

Piotr Dollar

Qunshu Zhang

Daniel Robert Rashid

Omar Abdul Baki

Alyssa H Henry

Peng Shao

Michael Pechuk

Franklin Munoz Garcia

Paul F Ferraro

Phillip Hodgson

Svend Knarhoj Johannsen

Hao Zhu

Yin Yin

Michael A Uhlar

Sean Michael Snyder

Prasanna Srinivasan Venkatasubramanian

A distributed storage system may store data object instances in persistent storage and may store keymap information for those data object instances in a distributed hash table on multiple computing nodes. Each data object instance may include a composite key containing a user key. The keymap information for each data object instance may map the user key to a locator and the locator to the data object instance. A request to store or retrieve keymap information for a data object instance may be routed to a particular computing node based on a consistent hashing scheme in which a hash function is applied to a portion of the composite key of the data object instance. Thus, related entries may be clustered on the same computing nodes. The portion of the key to which the hash function is applied may include a pre-determined number of bits or be identified using a delimiter.

System and method for clustering distributed hash table entries

Systems and methods for providing object versioning in a storage system may support the logical deletion of stored objects. In response to a delete operation specifying both a user key and a version identifier, the storage system may permanently delete the specified version of an object having the specified key. In response to a delete operation specifying a user key, but not a version identifier, the storage system may create a delete marker object that does not contain object data, and may generate a new version identifier for the delete marker. The delete marker may be stored as the latest object version of the user key, and may be addressable in the storage system using a composite key comprising the user key and the new version identifier. Subsequent attempts to retrieve the user key without specifying a version identifier may return an error, although the object was not actually deleted.

System and method for logical deletion of stored data objects

Systems, methods, and non-transitory computer-readable media can obtain respective instances of verification information can be obtained from computing systems associated with a plurality of third-party vendors, wherein a computing system associated with a third-party vendor determines verification information based at least in part on an analysis of an identification document provided by a user to be authenticated. The respective instances of verification information can be interpreted, wherein interpreting an instance of verification information comprises assigning respective states to attribute fields included in the verification information. A set of interpreted instances of verification information can be evaluated in combination to determine whether to authenticate the user, wherein the set includes an interpreted instance of verification information from each of the plurality of third-party vendors.

Systems and methods for authenticating entities

The disclosed computer-implemented method may include (1) for each tenant in a plurality of tenants within a multi-tenant service system, assigning a probability factor to the tenant that indicates a likelihood that the tenant will be selected when a resource of the multi-tenant service system is available, (2) detecting that the resource of the multi-tenant service system is available, (3) probabilistically selecting a tenant from the plurality of tenants by using the probability factors assigned to the tenants in the plurality of tenants, and (4) directing the multi-tenant service system to allocate the resource to the selected tenant for execution of a work item received from the selected tenant. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for allocating shared resources in multi-tenant environments

Systems, methods, and non-transitory computer-readable media can generate an initial alpha mask for an image based on machine learning techniques. A plurality of uncertain pixels is defined in the initial alpha mask. For each uncertain pixel in the plurality of uncertain pixels, a binary value is assigned based on a nearest certain neighbor determination.

Systems and methods for image matting

A distributed storage system may store data object instances in persistent storage and may cache keymap information for those data object instances. The system may cache a latest symbolic key entry for some user keys of the data object instances. When a request is made for the latest version of stored data object instances having a specified user key, the latest version may be determined dependent on whether a latest symbolic key entry exists for the specified user key, and keymap information for the latest version may be returned. When storing keymap information, a flag may be set to indicate that a corresponding latest symbolic key entry should be updated. The system may delete a latest symbolic key entry for a particular user key from the cache in response to determining that no other requests involving the keymap information for data object instances having the particular user key are pending.

System and method for fetching the latest versions of stored data objects

An online system receives data and processes the data in a data processing pipeline. To data loss in the data processing pipeline, the online system determines a time interval during which each item of data is received and associates a set of counters with each time interval. For each time interval, an input counter is incremented for each data item received during the time interval and an output counter is incremented for each data item received during the time interval that was processed by the data processing pipeline. The online system compares an input number from the input counter and an output number from the output counter for each time interval. Based on a difference between the input number and output number for a time interval, the online system determines if a loss of data received during the time interval occurred. Lost Data are identified and processed.

Detecting loss of data received at a data processing pipeline of an online system during a specified time interval

An online system processes data in a distributed processing system. To detect loss and corruption of data, the online system periodically stores information describing states of data processed during various time intervals and updates the information to include changes occurring within a predetermined period. Based on states of data described by information stored at a time, the online system performs a modified process on data received or processed during a time interval. For each item of data on which the modified process was performed, the online system compares a modified state of the data item to a state described by information stored at an additional time to determine if data was lost or corrupted. Lost or corrupted data is identified and processed based on the state of data described by the information stored at the time.

Detecting data loss and corruption of data processed by a data processing system of an online system during a specified time interval

A report execution engine processes reports for evaluating advertising campaigns. The report execution engine maintains a plurality of generators, each generator including instructions to generate execution plans for one or more columns of a report. The report execution engine uses different generators for generating sub-plans for processing columns of the report. A generator generates a sub-plan for processing a column of the report and invokes other generators for generating sub-plans for remaining columns of the report. The generator receives a sub-plan for the remaining columns of the report from another generator and generates a combined execution plan based on the sub-plans. An execution plan comprises a hierarchical structure of nodes, each node representing a portion of computation of the report. The report execution engine generates the report based on the execution plan and sends the results for presentation.

Execution engine for generating reports for measuring effectiveness of advertising campaigns

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Seattle, WA, United States of America

Jason George McHugh