Capital One Services, LLC

Behdad Forghani

Abhijit Chitnis

Aditya Relangi

Aniketh Bothra

Parvesh Kumar

Naga Venkata Sriram Vadakattu

Anurag Jain

Janardhan Deepak Prabhakara

Mayur Jagtap

Mark Assousa

Rahul Surendra Nath

Methods and systems described herein may include processing a change data capture stream in parallel and optimizing the write operations contained in the change data capture stream such that the write operations may be written to a change table in parallel. Processing the change data capture stream may include decoding the transaction data contained in the change data capture stream in parallel. After the transaction data in the change data capture stream has been decoded, the transaction data may be analyzed to identify write operations that write to the same row. Write operations that write to the same row may be merged into a single write operation. The merging of write operations may be performed until each write operation writes to a different row. The write operations may then be written to the change table in parallel.

Parallel stream processing of change data capture

Methods and systems disclosed herein may optimize write operations in a transaction to reduce the number of operations to the point where each of the write operations may be performed in parallel. The writer optimizer may review a first write operation and a second write operation to determine whether the first write operation and the second write operation write to the same row in the same table. When the first write operation and the second write operation write to the same row in the same table, the first write operation and the second write operation may be combined to generate a functionally-equivalent third write operation. The third write operation may reduce the number of operations in the transaction by replacing both the first write operation and the second write operation.

Optimization of database write operations by combining and parallelizing operations based on a hash value of primary keys

The exemplary embodiments provide real-time data capture and processing which improves data processing performance and speed and facilitate passing of the processed data to various analytical sources, while maintaining superior data quality checks, particularly with respect to data elements associated with multiple data types. The proposed system and process can be used to continuously consume and listen to multiple events while mapping the events to appropriate schemas provided in a separate schema stream. The schema stream is provided once and cached to minimize bandwidth consumed by the transaction stream. The schema information is then further enriched with information from a metadata registry. The event data may then be compressed and aligned in memory tables based on the enriched schema. Once events are decoded and sorted into memory tables in accordance to the identified schema, each memory table can be processed in parallel.

System, method and computer-accessible medium for capturing data changes

Optimization of database write operations by combining and parallelizing operations based on hashed

A device may receive source code and identify, based on the source code, an abstract syntax tree representing an abstract syntactic structure of the source code. Based on the abstract syntax tree, the device may identify a closure, the closure implementing a function based on at least a portion of the abstract syntax tree. In addition, the device may perform an action based on the closure.

Generating closures from abstract representation of source code

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Allen, TX, United States of America

Behdad Forghani