Abstract: Working set ratio estimations of data items in a sliding time window are determined to dynamically allocate storage for the data items. A working set ratio may be determined by accessing a fixed-size array that stores respective timestamps of last accesses of data items to determine which data items are useful to determine an estimate of a working set for the application within a range of time. The working set ratio is then determined from an estimated working set and an amount of computing resources allocated to the application by the estimated working set. The amount of the computing resources allocated to the application may then be automatically scaled according to the determine working set ratio.

Abstract: A request is received associated with a review. Within first content, a first field of interest and a second field of interest are identified and within second content, a third field of interest and a fourth field of interest are identified. A review is generated that includes a first indication of the first field of interest and a second indication of the second field of interest within the first content, as well as a third indication of the third field of interest and a fourth indication of the fourth field of interest within the second content. The review is transmitted to a device of a reviewer for reviewing the content.

Abstract: Methods, systems, and computer-readable media for dynamic prefetching for database queries are disclosed. A query of a database is started according to a first prefetch policy. Before completing the query, the first prefetch policy is changed to a second prefetch policy. A portion of the query is performed according to the second prefetch policy.

Abstract: A request is received associated with reviewing content. As part of the request, one or more conditions are received and the content is analyzed to identify a first field of interest and a second field of interest. The first field of interest and the second field of interest represent fields of interest associated with the review of the content. At least one of the first field of interest or the second field of interest may not satisfy the one or more conditions and the content, or a portion thereof, may be sent for review.

Abstract: A system that implements a scalable data storage service may maintain tables in a data store on behalf of storage service clients. The service may maintain table data in multiple replicas of partitions that are stored on respective computing nodes in the system. In response to detecting an anomaly in the system, detecting a change in data volume on a partition or service request traffic directed to a partition, or receiving a service request from a client to split a partition, the data storage service may create additional copies of a partition replica using a physical copy mechanism. The data storage service may issue a split command defined in an API for the data store to divide the original and additional replicas into multiple replica groups, and to configure each replica group to maintain a respective portion of the table data that was stored in the partition before the split.

Abstract: Distributed database management systems may maintain collections of items spanning multiple partitions. Index structures may correspond to items on one partition or to items on multiple partitions. Item collections and indexes may be replicated. Changes to the data maintained by the distributed database management system may result in updates to multiple index structures. The changes may be compiled into an instruction set applicable to the index structures. In-memory buffers may contain the instructions prior to transmission to affected partitions. Replication logs may be combined with an acknowledgment mechanism for reliable transmission of the instructions to the affected partitions.

Abstract: Methods, systems, and computer-readable media for dynamic prefetching for database queries are disclosed. A query of a database is started according to a first prefetch policy. Before completing the query, the first prefetch policy is changed to a second prefetch policy. A portion of the query is performed according to the second prefetch policy.

Abstract: Techniques for batch mode execution for calls to remote services are described. A method of batch mode execution for calls to remote services may include generating, by a query service of a provider network, a query plan to optimize a query for batch processing of data, the query plan including at least a function reference to a function provided by at least one service of the provider network, executing the query plan to invoke the function associated with the function reference, wherein a batch function generates a request including a batch of service calls to be processed by the at least one service, sends the request including the batch of service calls to the at least one service, and obtains a plurality of machine learning responses from the at least one service, and generating a query response based on the plurality of responses.

Abstract: A network-accessible machine learning service is provided herein. For example, the network-accessible machine learning service provider can operate one or more physical computing devices accessible to user devices via a network. These physical computing device(s) can host virtual machine instances that are configured to train machine learning models using training data referenced by a user device. These physical computing device(s) can further host virtual machine instances that are configured to execute trained machine learning models in response to user-provided inputs, generating outputs that are stored and/or transmitted to user devices via the network.

Abstract: A database system may associate functions with a database table. A request to associate a function with a table in a database system may be received. An association between the table and the function may be created. The function may include parameters that are determined from values within the table which are then invoked by a request to perform the function. The associated function may cause the collection of the values prior to performance of the function.

Abstract: A system that implements a scaleable data storage service may maintain tables in a non-relational data store on behalf of clients. Each table may include multiple items. Each item may include one or more attributes, each containing a name-value pair. Attribute values may be scalars or sets of numbers or strings. The system may provide an API usable to request that values of one or more of an item's attributes be updated. An update request may be conditional on expected values of one or more item attributes (e.g., the same or different item attributes). In response to a request to update the values of one or more item attributes, the previous values and/or updated values may be optionally returned for the updated item attributes or for all attributes of an item targeted by an update request. Items stored in tables may be indexed using a simple or composite primary key.

Abstract: Techniques for making machine learning inference calls for database query processing are described. In some embodiments, a method of making machine learning inference calls for database query processing may include generating a first batch of machine learning requests based at least on a query to be performed on data stored in a database service, wherein the query identifies a machine learning service, sending the first batch of machine learning requests to an input buffer of an asynchronous request handler, the asynchronous request handler to generate a second batch of machine learning requests based on the first batch of machine learning requests, and obtaining a plurality of machine learning responses from an output buffer of the asynchronous request handler, the machine learning responses generated by the machine learning service using a machine learning model in response to receiving the second batch of machine learning requests.

Abstract: Techniques for implementing a multi-wire protocol and multi-dialect database engine are described. A database engine exposes multiple interfaces in the form of ports that support different database wire protocols. The database engine supports multiple query dialects that can be passed over any one of the supported wire protocols. The database engine can support multiple different query dialects within a single database session.

Abstract: A system that implements a scaleable data storage service may maintain tables in a data store on behalf of storage service clients. The service may maintain data in partitions stored on respective computing nodes in the system. The service may support multiple throughput models, including a committed throughput model and a best effort throughput model. A service request to create a table may specify that requests directed to the table should be serviced under a committed throughput model and may specify the committed throughput level in terms of logical service request units. The service may reserve low-latency storage and other resources sufficient to meet the specified committed throughput level. A client/user may request a modification to the committed throughput level in anticipation of workload changes, such as an increase or decrease in traffic or data volume. In response, the system may increase or decrease the resources reserved for the table.

Abstract: Techniques for automated machine learning (ML) pipeline exploration and deployment are described. An automated ML pipeline generation system allows users to easily construct optimized ML pipelines by providing a dataset, identifying a target column in the dataset, and providing an exploration budget. Multiple candidate ML pipelines can be identified and evaluated through an exploration process, and a best ML pipeline can be provided to the requesting user or deployed for production inference. Users can configure, monitor, and adapt the exploration at multiple points in time throughout.

Abstract: A system that implements a scalable data storage service may maintain tables in a data store on behalf of storage service clients. The service may maintain table data in multiple replicas of partitions that are stored on respective computing nodes in the system. In response to detecting an anomaly in the system, detecting a change in data volume on a partition or service request traffic directed to a partition, or receiving a service request from a client to split a partition, the data storage service may create additional copies of a partition replica using a physical copy mechanism. The data storage service may issue a split command defined in an API for the data store to divide the original and additional replicas into multiple replica groups, and to configure each replica group to maintain a respective portion of the table data that was stored in the partition before the split.

Abstract: Techniques and technologies for providing a fully managed datastore for clients to securely store, discover, retrieve, remove, and share curated data, or features, to develop machine learning (ML) models in an efficient manner. The feature store service may provide clients with the ability to create and store feature groups that include features and associated metadata providing clients with a quick understanding of features so that they may determine which features are suitable for training ML models and/or use with ML models. The feature store service may provide first a data store configured to store the most recent values associated with a feature group, such that client can access the features and utilize ML models to make real-time predictions with low latency and high throughput, and a second datastore configured to store historical values associated with a feature group, such that a client can utilize the features to train ML models.

Abstract: Querying databases may be performed with references to machine learning models. A database query may be received that references a machine learning model and database. In response to the query, the machine learning model may provide information which may be returned as part of a result of the query or may be used to generate a result of the query. The machine learning model may be generated in response to a request to generate a machine learning model that includes a database query that identifies the data upon which a machine learning technique may be applied to generate the machine learning model.

Abstract: A system that implements a scaleable data storage service may maintain tables in a data store on behalf of storage service clients. The service may maintain data in partitions stored on respective computing nodes in the system. The service may support multiple throughput models, including a committed throughput model and a best effort throughput model. A service request to create a table may specify that requests directed to the table should be serviced under a committed throughput model and may specify the committed throughput level in terms of logical service request units. The service may reserve low-latency storage and other resources sufficient to meet the specified committed throughput level. A client/user may request a modification to the committed throughput level in anticipation of workload changes, such as an increase or decrease in traffic or data volume. In response, the system may increase or decrease the resources reserved for the table.

Abstract: Techniques for hosting adding and warming a host are described. In some instances, a method of determining that at least one group of hosts is to be increased by adding an additional host to the group of hosts; sending a request to the group of hosts for a list of machine learning models loaded per host of the group of hosts; receiving, from each host, the list of loaded machine learning models; loading at least a proper subset of list of loaded machine learning models into random access memory of the at least one group; receiving a request to perform an inference; routing the request to the additional host of the group of hosts; performing an inference using the additional host of the group of hosts; and providing a result of the inference to an external entity is described.