Amazon Technologies, Inc.

Google Inc.

Sandeep Bhatia

Ming-Chuan Wu

Andrew Whitaker

Kanishka Chaturvedi

Timothy Andrew Rath

Mustafa Ozan Ozen

Maruthi Manohar Reddy Devarenti

Gaurav Saxena

Lonnie J Princehouse

VamsiKrishna Chaitanya Manchem

Atilim Cetin

Gaurav Gupta

Rahul Sharma

Beryl Tomay

Vinoo Vasudevan

Kevin Lee Davenport, Jr

Stefano Stefani

Andrew Kadatch

Dumanshu Goyal

Lishi Jiang

Pravin Mittal

Andrey Arkharov

Abhijeet More

Dhruv Goel

Eric Coll

Haroldo Jose Level

Nirmesh Khandelwal

Rajesh Iyer

Hong Yang

Nilesh Shahdadpuri

Nilanjan Basu

Abhilash Reddy Koppula

Nitin Ahuja

Pushap Goyal

Anik Sarker

Sergey Korostelev

Justyna Ilczuk

Mohamed Ershad Junaid

Kimi Shah

Lynn Mendenhall

Damian Poznanski

Data log-base logical block devices are used to control parameter-based versioning at a block-device level. Memory in the block device stores data log indicating data and corresponding metadata, the metadata indicating a particular historical time. The block device may receive a request, such as a remote procedure call (RPC), for data from the logical block device, and identify data included in the data log having metadata that matches or satisfies a historical time parameter included in and extracted from the request.

Parameter-based versioning for log-based block devices

A non-relational database may be emulated using a relational database with a distributed data store. A request to access the non-relational database may be received at a frontend for a relational database engine that emulates an interface for a non-relational database engine. The request may be translated into the format for the relational database engine and performed by the relational database engine. The relational database engine may access data for the non-relational database at storage nodes that store the data in a relational table column of non-relational data type.

Emulating a non-relational database on a relational database with a distributed data store

Atomic partition scheme updates to partition items may be implemented by a time series database. A time threshold may be assigned to partition scheme update so that the time threshold may be applied across a set of ingestion nodes that may apply the partition scheme update the same. A request to store an item with a timestamp less than the time threshold may be stored in one partition of the time series database, while the item may be stored in a different partition of the time series database if the item has timestamp greater than or equal to the time threshold.

Atomic partition scheme updates to store items in partitions of a time series database

Methods, systems, and computer-readable media for partition splitting in a distributed database are disclosed. A partition of data is split into a first sub-partition and a second sub-partition. A first portion of the data is assigned to the first sub-partition, and a second portion of the data is assigned to the second sub-partition. One or more elements of the first portion of the data from the partition and an additional one or more elements of the first portion of the data from the first sub-partition are stored into a first node. One or more elements of the second portion of the data from the partition and an additional one or more elements of the second portion of the data from the second sub-partition are stored into a second node. The partition is prevented from receiving new data after the partition is split into the first and second sub-partitions.

Partition splitting in a distributed database

Replicated partitions of a database may be moved from one or more replica nodes to a different primary node. The replica nodes may maintain replicated partitions of one or more primary partitions of a primary node servicing access requests to the primary partitions. The replicated partitions may be copied to a different node in order to cause the processing of access requests directed to primary partitions to be serviced by the different node. Replicated partitions of a database may be moved from replica nodes to perform a split operation or a merger operation.

Moving database partitions from replica nodes

Techniques are described for clustering data at the point of ingestion for storage using scalable storage resources. To cluster data at the point of ingestion, a data ingestion and query service uses a multilevel hash tree (MLHT)-based index to map a hierarchy of attribute values associated with each data element onto a point of a MLHT (which itself conceptually maps onto a continuous range of values). The total range of the MLHT is divided into one or more data partitions, each of which is mapped to one or more physical storage resources. A mapping algorithm uses the hierarchy of attribute fields to calculate the position of each data element ingested and, consequently, a physical storage resource at which to store the data element.

Dynamic distributed data clustering using multi-level hash trees

Performance-based caching may be implemented for query plan caches that are used to provide query plans to perform queries instead of generating query plans to perform queries. Performance metrics for queries performed at a database may be collected and used to cache query plans generated for the queries. When a query is received that matches one of the queries, the cached plan corresponding to the matching query may be used to perform the query.

Performance-based query plan caching

Processing and storage responsibility for a data set may be split according to separately stored shards of the data set. As one or more loads associated with shards of the dataset grow a re-sharding operation may be performed to reduce loading of particular shards and nodes that host the particular shards. A re-sharding operation may cause only a sub-set of as set of shards of the dataset to be split and only cause second portions of the split shards to be stored in additional computing nodes. In some embodiments, a number of shards to be included in the sub-set of shards to be split may be selected based on an overall number of shards in the set and a largest number in the Fibonacci sequence that is less than the overall number of shards in the set.

Data re-sharding

Multi-type attribute indexes may be implemented for document databases. When a request to perform a query at a document database is received that is directed toward in indexed attribute that has multiple data types for values stored in the multi-type attribute index, a predicate in the query may be replaced with a different predicate that is applicable to search the multi-type attribute index according to a sort order for the multiple data types stored in the at multi-type attribute index. A plan that includes the different predicate may be performed in order to provide a result of the query to a user.

Multi-type attribute index for a document database

A data storage and retrieval system receives data points for a time series. The data storage and retrieval system stores the data points for a first portion of the time series using a first data format. Based at least in part on an analysis of queries performed on the first portion of the time series, the data storage and retrieval system determines to store a second portion of the time series using a second data format. The data storage and retrieval system stores subsequently received data points of the time series in the second data format.

Dynamic data layouts for time-series data

Disclosed are various embodiments of systems, methods, and computer programs that generate filters that can be used to filter offerings in a user interface. The effectiveness of filters can be evaluated to generate an effectiveness metric. The effectiveness metric can be used to weight the filters. The effectiveness metric can be based on a probability analysis of filters that are based on the probability that a filter was active when a transaction in the transaction history occurred.

Generating filters based upon item attributes

Disclosed are approaches for using overlapping geo-fences to confirm delivery of a shipment. A first client computing device and a second client computing device may be in data communication with a server computing device. The server computing device may receive a delivery notification from the first client computing device. The server computing device may receive a first position of the first client computing device and a second position of the second client computing device. The server computing device may then determine that the second position is within a threshold distance of the first position or vice versa. Finally, the server computing device may generate a delivery confirmation in response to a first determination that the second position is within a threshold distance of the first position and a second determination that the first position is within a threshold distance of the second position.

Delivery confirmation using overlapping geo-fences

Processing responsibility for a data set may be split according to separately stored partitions of the data set. A data set may be stored in different storage units that separately store different partitions of the data set. Mapping information may be maintained indicating the assignment of a node to process access requests to multiple partitions of a data set. A split event may be detected for the node, and one or more partitions assigned to the node may be reassigned to a different node. The storage units for the reassigned partitions may be made accessible to the different node. Then mapping information can be updated to indicate change in assignment from the original node to the different node so that the different node can begin processing access requests for the reassigned partitions.

Splitting processing responsibility for separately stored data partitions

Techniques for offering an item may be provided. For example, an item may be available for ordering from a network-based resource based on available offers. A request for an unavailable offer may be received. The request may be matched with a new offer that may have been generated after the request may have been received. Based on the matching, the item may be ordered from the network-based resource according to the new offer.

Requesting an item available from a network-based resource

Techniques for ordering data associated with an item may be provided. For example, a first set of the data may be ordered based on predefined values of a first attribute. Based on receiving a request to further order the first set based on a second attribute, a second set of the data may be selected based on the first set and on a sizing window. The sizing window may be configured to define a size of the second set. Values corresponding to the data in the second set may be computed. These computed values may be used to order the second set of data.

Growing community of inventors

Bothell, WA, United States of America

Sandeep Bhatia