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    System and method for facilitating replication in a distributed database
    1.
    发明授权
    System and method for facilitating replication in a distributed database 有权

    公开(公告)号：US11106538B2

    公开(公告)日：2021-08-31

    申请号：US15177001

    申请日：2016-06-08

    Applicant: MongoDB, Inc.

    Inventor： Dwight Merriman

    IPC: G06F11/14 ,  G06F16/23 ,  G06F11/20

    Abstract: According to one aspect, a distributed database system is configured to manage multi-writer operations on a distributed database by implementing one or more catamorphic database operators. Catamorphic operators can be architected on the system, and executed with little or no reconciliation logic. Catamorphic operators define sets of catamorphic operations and respective execution logic where the order of execution of catamorphic operations is not relevant to a final result.

    Systems and methods for durable database operations in a memory-mapped environment
    2.
    发明授权
    Systems and methods for durable database operations in a memory-mapped environment 有权
    在内存映射环境中持久数据库操作的系统和方法

    公开(公告)号：US08990159B2

    公开(公告)日：2015-03-24

    申请号：US13666266

    申请日：2012-11-01

    Applicant: MongoDB, Inc.

    Inventor： Dwight Merriman ,  Eliot Horowitz ,  Mathias Stearn

    IPC: G06F17/30 ,  G06F11/14

    CPC classification number: G06F17/30174 ,  G06F11/1471 ,  G06F12/02 ,  G06F17/30368 ,  G06F2212/163 ,  G06F2212/206

    Abstract: A durable memory-mapped database system includes a first memory-mapped view of a database, a second memory-mapped view of the database, a journal buffer and a journal. The first memory-mapped view of the database is a protected view and includes copies of a plurality of datafiles from the database. The second memory-mapped view of the database is a write view and includes copies of the plurality of datafiles. The journal buffer is a buffer in random access memory configured to record datafile updates. The journal is configured to periodically receive recorded datafile updates from the journal buffer.

    Abstract translation: 持久的内存映射数据库系统包括数据库的第一内存映射视图，数据库的第二内存映射视图，日志缓冲区和日志。 数据库的第一个内存映射视图是受保护的视图，并且包括来自数据库的多个数据文件的副本。 数据库的第二存储器映射视图是写入视图，并且包括多个数据文件的副本。 日志缓冲区是随机访问存储器中的一个缓冲区，用于记录数据文件更新。 日志被配置为定期从日志缓冲区接收记录的数据文件更新。

    Aggregation framework system architecture and method
    3.
    发明授权
    Aggregation framework system architecture and method 有权

    公开(公告)号：US10990590B2

    公开(公告)日：2021-04-27

    申请号：US15706593

    申请日：2017-09-15

    Applicant: MongoDB, Inc.

    Inventor： Dwight Merriman ,  Eliot Horowitz ,  Chris Todd Westin

    IPC: G06F16/00 ,  G06F16/242 ,  G06F16/27 ,  G06F16/2453 ,  G06F16/9535 ,  G06F16/2455 ,  H04L1/00 ,  G06F16/248 ,  G06F16/951 ,  G06F16/958

    Abstract: Database systems and methods that implement a data aggregation framework are provided. The framework can be configured to optimize aggregate operations over non-relational distributed databases, including, for example, data access, data retrieval, data writes, indexing, etc. Various embodiments are configured to aggregate multiple operations and/or commands, where the results (e.g., database documents and computations) captured from the distributed database are transformed as they pass through an aggregation operation. The aggregation operation can be defined as a pipeline which enables the results from a first operation to be redirected into the input of a subsequent operation, which output can be redirected into further subsequent operations. Computations may also be executed at each stage of the pipeline, where each result at each stage can be evaluated by the computation to return a result. Execution of the pipeline can be optimized based on data dependencies and re-ordering of the pipeline operations.

    AGGREGATION FRAMEWORK SYSTEM ARCHITECTURE AND METHOD
    6.
    发明申请
    AGGREGATION FRAMEWORK SYSTEM ARCHITECTURE AND METHOD 审中-公开

    公开(公告)号：US20160246861A1

    公开(公告)日：2016-08-25

    申请号：US15042297

    申请日：2016-02-12

    Applicant: MongoDB, Inc.

    Inventor： Dwight Merriman ,  Eliot Horowitz ,  Chris Todd Westin

    IPC: G06F17/30

    CPC classification number: G06F17/30569 ,  G06F17/30283 ,  G06F17/30345 ,  G06F17/30489 ,  G06F17/30545 ,  G06F17/30554 ,  G06F17/30563 ,  G06F17/30595 ,  G06F17/30864 ,  G06F17/3089

    Abstract: Database systems and methods that implement a data aggregation framework are provided. The framework can be configured to optimize aggregate operations over non-relational distributed databases, including, for example, data access, data retrieval, data writes, indexing, etc. Various embodiments are configured to aggregate multiple operations and/or commands, where the results (e.g., database documents and computations) captured from the distributed database are transformed as they pass through an aggregation operation. The aggregation operation can be defined as a pipeline which enables the results from a first operation to be redirected into the input of a subsequent operation, which output can be redirected into further subsequent operations. Computations may also be executed at each stage of the pipeline, where each result at each stage can be evaluated by the computation to return a result. Execution of the pipeline can be optimized based on data dependencies and re-ordering of the pipeline operations.

    System and method for optimizing data migration in a partitioned database
    7.
    发明授权
    System and method for optimizing data migration in a partitioned database 有权

    公开(公告)号：US10740355B2

    公开(公告)日：2020-08-11

    申请号：US15654590

    申请日：2017-07-19

    Applicant: MongoDB, Inc.

    Inventor： Eliot Horowitz ,  Dwight Merriman

    IPC: G06F17/00 ,  G06F16/27

    Abstract: According to one aspect, provided is a horizontally scaled database architecture. Partition a database enables efficient distribution of data across a number of systems reducing processing costs associated with multiple machines. According to some aspects, the partitioned database can be managed as a single source interface to handle client requests. Further, it is realized that by identifying and testing key properties, horizontal scaling architectures can be implemented and operated with minimal overhead. In one embodiment, databases can be partitioned in an order preserving manner such that the overhead associated with moving the data for a given partition can be minimized during management of the data and/or database. In one embodiment, splits and migrations operations prioritize zero cost partitions, thereby, reducing computational burden associated with managing a partitioned database.

    System and method for determining exact location results using hash encoding of multi-dimensioned data
    8.
    发明授权
    System and method for determining exact location results using hash encoding of multi-dimensioned data 有权

    公开(公告)号：US10572465B2

    公开(公告)日：2020-02-25

    申请号：US15482419

    申请日：2017-04-07

    Applicant: MongoDB, Inc.

    Inventor： Dwight Merriman ,  Eliot Horowitz

    IPC: G06F17/30 ,  G06F16/22

    Abstract: Aspects of the present invention are directed to system and methods for optimizing identification of locations within a search area using hash values. A hash value represents location information in a single dimension format. Computing points around some location includes calculating an identification boundary that surrounds the location of interest based on the location's hash value. The identification boundary is expanded until it exceeds a search area defined by the location and a distance. Points around the location can be identified based on having associated hash values that fall within the identification boundary. Hashing operations let a system reduce the geometric work (i.e. searching inside boundaries) and processing required, by computing straightforward operations on hash quantities (e.g. searching a linear range of geohashes), instead of, for example, point to point comparisons.

    LARGE DISTRIBUTED DATABASE CLUSTERING SYSTEMS AND METHODS
    9.
    发明申请
    LARGE DISTRIBUTED DATABASE CLUSTERING SYSTEMS AND METHODS 审中-公开

    公开(公告)号：US20180096045A1

    公开(公告)日：2018-04-05

    申请号：US15721176

    申请日：2017-09-29

    Applicant: MongoDB, Inc.

    Inventor： Dwight Merriman ,  Eliot Horowitz

    IPC: G06F17/30

    CPC classification number: G06F16/278 ,  G06F16/273

    Abstract: Systems and methods are provided for managing asynchronous replication in a distributed database environment, while providing for scaling of the distributed database. A cluster of nodes can be assigned roles for managing partitions of data within the database and processing database requests. In one embodiment, each cluster includes a node with a primary role to process write operations and mange permit asynchronous replication of the operations to at least one secondary node. Each cluster or set of nodes can host one or more partitions of database data, and grouping of the clusters can be implemented a shard cluster that hosts the data of the distributed database. Each shard can be configured to manage the size of any hosted partitions, splitting database partitions, migrated partitions, and managing expansion of shard clusters to encompass new systems.

    AGGREGATION FRAMEWORK SYSTEM ARCHITECTURE AND METHOD
    10.
    发明申请
    AGGREGATION FRAMEWORK SYSTEM ARCHITECTURE AND METHOD 审中-公开

    公开(公告)号：US20180004804A1

    公开(公告)日：2018-01-04

    申请号：US15706593

    申请日：2017-09-15

    Applicant: MongoDB, Inc.

    Inventor： Dwight Merriman ,  Eliot Horowitz ,  Chris Todd Westin

    IPC: G06F17/30 ,  H04L1/00

    CPC classification number: G06F16/244 ,  G06F16/2453 ,  G06F16/24556 ,  G06F16/248 ,  G06F16/27 ,  G06F16/951 ,  G06F16/9535 ,  G06F16/958 ,  H04L1/0076

    Abstract: Database systems and methods that implement a data aggregation framework are provided. The framework can be configured to optimize aggregate operations over non-relational distributed databases, including, for example, data access, data retrieval, data writes, indexing, etc. Various embodiments are configured to aggregate multiple operations and/or commands, where the results (e.g., database documents and computations) captured from the distributed database are transformed as they pass through an aggregation operation. The aggregation operation can be defined as a pipeline which enables the results from a first operation to be redirected into the input of a subsequent operation, which output can be redirected into further subsequent operations. Computations may also be executed at each stage of the pipeline, where each result at each stage can be evaluated by the computation to return a result. Execution of the pipeline can be optimized based on data dependencies and re-ordering of the pipeline operations.

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