Systems and method for processing timeseries data

    公开(公告)号:US12174847B2

    公开(公告)日:2024-12-24

    申请号:US17858957

    申请日:2022-07-06

    Applicant: MongoDB, Inc.

    Abstract: In some implementations, events measured at various points in time may be organized in a data structure that defines an event represented by a document. In particular, events can be organized in columns of documents referred to as buckets. These buckets may be indexed using B-trees by addressing metadata values or value ranges. Buckets may be defined by periods of time. Documents may also be geoindexed and stored in one or more locations in a distributed computer network. One or more secondary indexes may be created based on time and/or metadata values within documents.

    DISTRIBUTED DATABASE SYSTEMS AND METHODS WITH PLUGGABLE STORAGE ENGINES

    公开(公告)号:US20190303382A1

    公开(公告)日:2019-10-03

    申请号:US16294227

    申请日:2019-03-06

    Applicant: MongoDB, Inc.

    Abstract: According to one aspect, methods and systems are provided for selectively employing storage engines in a distributed database environment. The methods and systems can include a processor configured to execute a plurality of system components, wherein the system components comprise an operation prediction component configured to determine an expected set of operations to be performed on a portion of the database; a data format selection component configured to select, based on at least one characteristic of the expected set of operations, a data format for the portion of the database; and at least one storage engine for writing the portion of the database in the selected data format.

    Systems and methods for data conversion and comparison

    公开(公告)号:US10430433B2

    公开(公告)日:2019-10-01

    申请号:US15390351

    申请日:2016-12-23

    Applicant: MongoDB, Inc.

    Abstract: According to one embodiment, a translation component is configured to operate on document encoded data to translate the document encoded data into a canonical format comprising a plurality of canonical types that fold together into a byte stream. The translation component is configured to accept any storage format of data (e.g., column store, row store, LSM tree, etc. and/or data from any storage engine, WIREDTIGER, MMAP, AR tree, Radix tree, etc.) and translate that data into a byte stream to enable efficient comparison. When executing searches and using the translated data to provide comparisons there is necessarily a trade-off based on the cost of translating the data and how much the translated data can be leveraged to increase comparison efficiency.

    Systems and methods for data conversion and comparison

    公开(公告)号:US10423626B2

    公开(公告)日:2019-09-24

    申请号:US15390364

    申请日:2016-12-23

    Applicant: MongoDB, Inc.

    Abstract: According to one embodiment, a translation component is configured to operate on document encoded data to translate the document encoded data into a canonical format comprising a plurality of canonical types that fold together into a byte stream. The translation component is configured to accept any storage format of data (e.g., column store, row store, LSM tree, etc. and/or data from any storage engine, WIREDTIGER, MMAP, AR tree, Radix tree, etc.) and translate that data into a byte stream to enable efficient comparison. When executing searches and using the translated data to provide comparisons there is necessarily a trade-off based on the cost of translating the data and how much the translated data can be leveraged to increase comparison efficiency.

    Systems and methods for data conversion and comparison

    公开(公告)号:US10394822B2

    公开(公告)日:2019-08-27

    申请号:US15390345

    申请日:2016-12-23

    Applicant: MongoDB, Inc.

    Abstract: According to one embodiment, a translation component is configured to operate on document encoded data to translate the document encoded data into a canonical format comprising a plurality of canonical types that fold together into a byte stream. The translation component is configured to accept any storage format of data (e.g., column store, row store, LSM tree, etc. and/or data from any storage engine, WIREDTIGER, MMAP, AR tree, Radix tree, etc.) and translate that data into a byte stream to enable efficient comparison. When executing searches and using the translated data to provide comparisons there is necessarily a trade-off based on the cost of translating the data and how much the translated data can be leveraged to increase comparison efficiency.

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