Elastic Flash Inc.

Pradeep Jnana Madhavarapu

Anurag Windlass Gupta

Neal Fachan

Laurion Darrell Burchall

Darshan Bharatkumar Rawal

Naoki Iwakami

Samuel James Mckelvie

Christopher Richard Newcombe

Benjamin Tobler

James Mcclellan Corey

Yan Valerie Leshinsky

Systems and methods for optimizing distributed computing systems are disclosed, such as for processing raw data from data sources (e.g., structured, semi-structured, key-value paired, etc.) in applications of big data. A process for utilizing multiple processing cores for data processing can include receiving raw input data and a first portion of digested input data from a data source client through an input/output bus at a first processor core, receiving, from the first processor core, the raw input data and first portion of digested input data by a second processor core, digesting the received raw input data by the second processor core to create a second portion of digested input data, receiving the second portion of digested input data by the first processor core, and writing, by the first processor core, the first portion of digested input data and the second portion of digested input data to a storage medium.

Systems and methods for optimizing distributed computing systems including server architectures and client drivers

Implementations described herein relate to methods, systems, and computer-readable media to obtain snapshots used for analysis of streaming data. In some implementations, a computer-implemented method includes receiving initial data that includes a plurality of identifiers and corresponding timestamps, generating and storing a snapshot based on the initial data, wherein the snapshot includes the identifiers and a corresponding status, receiving a data stream that includes a subset of the identifiers, activity information for each identifier in the subset, and corresponding timestamps. The method further includes periodically analyzing the data stream to obtain a delta that includes an updated status for each identifier in the subset, storing the delta separate from the snapshot. The method further includes receiving a request for identifiers that are active in a particular time period, and based on the particular time period, retrieving active identifiers from the data stream, the delta, or the snapshot.

Analysis of streaming data using deltas and snapshots

Implementations described herein relate to methods, systems, and computer-readable media to write data records. In some implementations, a method may include calculating a data rate of a data stream that includes a plurality of data records and determining if the data rate of the data stream is less than an ingest threshold. The method may further include, if the data rate of the data stream is less than the ingest threshold, calculating a number of write requests per time unit based on the data stream; determining a storage capacity per storage bucket; determining a read interval for the data stream; based on the number of write requests per time unit, the storage capacity, and the read interval, selecting a size of time window per storage bucket; and writing the plurality of data records to a particular storage bucket.

Data ingestion with spatial and temporal locality

Growing community of inventors

Cupertino, CA, United States of America

Pradeep Jnana Madhavarapu