International Business Machines Corporation

Couchbase, Inc.

Raghupathi Keshava Murthy

Sitaram Vemulapalli

Marco Greco

Bingjie Miao

John Frederic Miller

Prathibha Bisarahalli Prabhakar

Kevin Lynn Brown

Scott David Lashley

Karl Ostner

Stephen Ching-Sing Yen

Lynette D Adayilamuriyil

Frederick C Ho

Abhinav Dangeti

Naresh K Chainani

Liping Zhang

Ajaykumar B Gupte

Joaquim A Zuzarte

Lynette D Adyilamuriyil

Samskrith Keshav Raghav

A system manages time series data stored in association with documents. The system stores a time series in multiple documents. The time series data includes a series of data points indexed in time order, and each document stores a subset of the series of data points. Each document stores metadata about a time range of the subset of series of data points. The system indexes the documents based on their time ranges. The system may be used for storing and querying spatiotemporal data. Accordingly, each document further stores spatial data and metadata representing a spatial range of geohashes. Responsive to receiving a query for one or more data points, the system selects one or more documents from the multiple documents that contain data points in the particular spatial and time range based on the indexes. The system performs the query on the data points.

Processing time series and spatiotemporal data in document databases

A document-oriented database system generates an optimal query execution plan for database queries on an untyped field included in a collection of documents. Each untyped field is configured to store one or more scalar data values. The system generates histograms for scalar data values stored in untyped fields. Responsive to receiving a query statement for fetching or updating data value stored in one of the untyped fields, the system assigns a cost to each operator associated with the query statement based on the histogram, compares the costs assigned to the operators, and generates a query execution plan based on the comparison, and query the database using the query execution plan.

Cost-based query optimization for untyped fields in database systems

A database system performs queries on fields storing arrays of a database (i.e., array fields) using de-duplication indexes. The system generates de-duplication indexes for array fields. The de-duplication indexes include unique entries for corresponding distinct values stored by the array fields. The system uses the de-duplication indexes to perform efficient queries specifying corresponding array fields. The system may further generate de-duplication indexes corresponding one or more fields storing various types of values. In various embodiments, the system selects an optimal index from various indexes usable to execute a query, such as a de-duplication index and a conventional index.

Efficient indexing for querying arrays in databases

A distributed database system receives and executes a transaction including a first modification statement describing a first modification operation on a subset of records, a second modification statement describing a second modification operation on the subset of records, and a commit statement. Responsive to executing the first and second modification statements, the system receives a copy of the subset of records from data servers; performs the first modification operation to generate a first modified subset of records; and performs the second modification operation on the first modified subset of records. Responsive to executing the commit statement, the system stores the second modified subset of records in a commit table and causes the subset of records to be updated at the data servers.

Executing transactions on distributed databases

A document-oriented database system generates an optimal query execution plan for database queries on an untyped data field included in a collection of documents. The system generates histograms for multiple types of data stored by the untyped data field and uses the histograms to assign costs to operators usable to execute the database query. The system generates the optimal query execution plan by selecting operators based on the assigned costs. In various embodiments, the untyped data field stores scalars, arrays, and objects.

Cost-based query optimization for array fields in database systems

A distributed database system executes transactions on a distributed database. A received transaction includes statements describing modifications of records stored in the distributed database. The distributed database system executes the transaction at a query server by obtaining copies of records corresponding to the statements of the transaction and performing the modifications specified by the statements of the transaction on the record copies. The distributed database system stores the modified record copies at the query server during execution of the transaction. After the transaction has successfully been executed at the query server, the distributed database system attempts to perform a commit process to update the records stored in the distributed database based on the modified record copies.

Cost-based optimization for document-oriented database queries on arrays

According to embodiments of the present invention, machines, systems, computer-implemented methods and computer program products are provided for converting part or all of a document from an unstructured format into a structured format, and combining structured and unstructured data into an unstructured format. In some embodiments, documents are received in an unstructured format, and stored in a repository for querying (e.g., including updating and other operations (e.g., SQL SELECT, UPDATE, INSERT, DELETE, MERGE, etc.)). Responsive to detecting a predetermined number of the documents in an unstructured format stored in a repository for querying, an optimization criteria may be applied to identify one or more components of the stored documents for conversion, and the one or more components of the stored documents are converted from the unstructured format to a structured format.

Converting portions of documents between structured and unstructured data formats to improve computing efficiency and schema flexibility

Cost-based optimization for document-oriented database queries

A system for transforming time series data into data that is accessible by a data warehouse identifies a data table comprising the time series data. The system creates a virtual view of the data table where the time series data is represented as at least one standard relational table in the virtual view, where the virtual view is presented as a virtual table. The system partitions the virtual table into a plurality of virtual partitions according to a time interval. The virtual table is partitioned across a data time range, where the data time range comprises at least one time interval, and where each of the plurality of virtual partitions has a respective partition time range that spans the time interval. The virtual partitions are created to optimize loading of the data into the data warehouse by incrementally refreshing the data according to the respective partition time range.

Transforming timeseries and non-relational data to relational for complex and analytical query processing

Provided are techniques for cyclic based data partitioning policy with automatic physical schema management. A data partitioning policy for data is received, wherein the data partitioning policy identifies a condition for automatically implementing the data partitioning policy and criteria for modifying a set of partitions. In response to the condition occurring, the data partitioning policy is automatically applied to select at least one partition from the set of partitions based on the criteria. An operation is performed on the at least one partition to modify the set of partitions.

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Union City, CA, United States of America

Raghupathi Keshava Murthy