Nong Li69 Prospect St, Newton, MA 02465

2021008, Mar 25, 2021

Sep 21, 2020

17/026772

- San Francisco CA, US
Nong Li - San Francisco CA, US

G06F 16/27
G06F 9/54
G06F 16/2455
G06F 16/25
G06F 16/28

Disclosed herein provides enhancements for operating a data access application service executing on a data access server system and an external computing system. In the data access server system, a request is received from a client device executing at least one of multiple application services for a dataset from one or more of multiple storage systems. In the data access server system, a data retrieval instruction is generated for the client device to access the dataset from the one or more of the multiple storage systems. The data retrieval instruction comprises task descriptions and a temporary credential. The data retrieval instruction is transferred to the external computing system via the client device and the requested dataset is retrieved and deployed based on the task descriptions and the temporary credential from the one or more of the multiple storage systems.

2021008, Mar 18, 2021

Jul 22, 2020

16/935654

- San Francisco CA, US
Nong Li - San Francisco CA, US

G06F 16/16
G06F 16/17
G06F 16/23
G06F 21/62
G06F 9/54

Various embodiments of the present technology generally relate to management of big data storage and the physical removal of data via data access systems for large data processing environments having multiple application services and multiple storage services. In some embodiments, a method of physically removing data from a storage system provides for identifying one or more files needing data removal treatment, determining that a file needing data removal treatment should be queued, and populating a queue with the file. Determining that a file should be queued is based, at least in part, on a staleness tolerance. The method further provides for treating the file and replacing a previous version of the file in storage with the updated file. In some implementations, treating the file includes removing data from the file to create an updated file and may further include additional changes to the file.

2020033, Oct 22, 2020

Jul 6, 2020

16/921558

- Palo Alto CA, US
Justin Erickson - San Francisco CA, US
Nong Li - San Francisco CA, US
Lenni Kuff - San Francisco CA, US
Henry Noel Robinson - San Francisco CA, US
Alan Choi - Palo Alto CA, US
Alex Behm - San Francisco CA, US

G06F 16/2458
G06F 16/25
G06F 16/27
G06F 16/2453

A format conversion engine for Apache Hadoop that converts data from its original format to a database-like format at certain time points for use by a low latency (LL) query engine. The format conversion engine comprises a daemon that is installed on each data node in a Hadoop cluster. The daemon comprises a scheduler and a converter. The scheduler determines when to perform the format conversion and notifies the converter when the time comes. The converter converts data on the data node from its original format to a database-like format for use by the low latency (LL) query engine.

2020033, Oct 22, 2020

Jul 6, 2020

16/921640

- Palo Alto CA, US
Justin Erickson - San Francisco CA, US
Nong Li - San Francisco CA, US
Lenni Kuff - San Francisco CA, US
Henry Noel Robinson - San Francisco CA, US
Alan Choi - Palo Alto CA, US
Alex Behm - San Francisco CA, US

G06F 16/2458
G06F 16/25
G06F 16/27
G06F 16/2453

A format conversion engine for Apache Hadoop that converts data from its original format to a database-like format at certain time points for use by a low latency (LL) query engine. The format conversion engine comprises a daemon that is installed on each data node in a Hadoop cluster. The daemon comprises a scheduler and a converter. The scheduler determines when to perform the format conversion and notifies the converter when the time comes. The converter converts data on the data node from its original format to a database-like format for use by the low latency (LL) query engine.

2019030, Oct 3, 2019

Mar 30, 2018

15/942150

- San Frnacisco CA, US
Nong Li - San Francisco CA, US

G06F 17/30
G06F 9/54
G06F 8/70

Disclosed herein provides enhancements for operating a data access system for large data processing environments. In one implementation, a method provides for maintaining a data structure comprising a plurality of customized code configurations each associated with a data request rule for each of the multiple application services. A code configuration query from a user is then received indicating a data request rule. The code configuration query requests code configurations for data retrieval from at least one of the multiple storage services over the data access system. The data structure is queried for one or more customized code configurations for each of the multiple application services associated with the indicated data request rule. The user is then provided with the one or more customized code configurations for each of the multiple application services associated with the indicated data request rule.

2019001, Jan 17, 2019

Aug 31, 2017

15/692861

- San Francisco CA, US
Nong Li - San Francisco CA, US

G06F 17/30

Disclosed herein provides enhancements for operating a data access system for large data processing environments. In one implementation, a method provides for receiving a data query from at least one of the multiple application services and identifying metadata that defines policies for deploying the queried data. The method further provides retrieving the queried data from at least one of the multiple storage services, generating a data configuration containing the retrieved data based on standardized parameters and the policies defined by the metadata, and deploying the data configuration to the at least one of the multiple application services.

2017013, May 11, 2017

May 13, 2016

15/154727

- Palo Alto CA, US
Justin Erickson - San Francisco CA, US
Nong Li - San Francisco CA, US
Lenni Kuff - San Francisco CA, US
Henry Noel Robinson - San Francisco CA, US
Alan Choi - Palo Alto CA, US
Alex Behm - San Francisco CA, US

G06F 17/30

A low latency query engine for APACHE HADOOP™ that provides real-time or near real-time, ad hoc query capability, while completing batch-processing of MapReduce. In one embodiment, the low latency query engine comprises a daemon that is installed on data nodes in a HADOOP™ cluster for handling query requests and all internal requests related to query execution. In a further embodiment, the low latency query engine comprises a daemon for providing name service and metadata distribution. The low latency query engine receives a query request via client, turns the request into collections of plan fragments and coordinates parallel and optimized execution of the plan fragments on remote daemons to generate results at a much faster speed than existing batch-oriented processing frameworks.

2017003, Feb 2, 2017

Oct 12, 2016

15/292053

- Palo Alto CA, US
Justin Erickson - San Francisco CA, US
Nong Li - San Francisco CA, US
Lenni Kuff - San Francisco CA, US
Henry Noel Robinson - San Francisco CA, US
Alan Choi - Palo Alto CA, US
Alex Behm - San Francisco CA, US

G06F 17/30

A format conversion engine for Apache Hadoop that converts data from its original format to a database-like format at certain time points for use by a low latency (LL) query engine. The format conversion engine comprises a daemon that is installed on each data node in a Hadoop cluster. The daemon comprises a scheduler and a converter. The scheduler determines when to perform the format conversion and notifies the converter when the time comes. The converter converts data on the data node from its original format to a database-like format for use by the low latency (LL) query engine.