Oracle International Corporation

Shrikumar Hariharasubrahmanian

Nipun Agarwal

Sanjay Jinturkar

Jian Wen

Hamed Ahmadi

Michael Delorimier

Jacco Draaijer

Jarod Wen

Lijian Wan

Jean-Pierre Dijcks

Herein are spatially scalable techniques for anisotropic compression of shared entropy between alternate representations of same data. In an embodiment, a computer compresses an uncompressed independent column into a compressed independent column. Based on the compressed independent column, an uncompressed dependent column is compressed into a compressed dependent column. The compressed independent column and the compressed dependent column are stored in a same file. In an embodiment, a computer stores, in metadata, an encrypted private key for decrypting an encrypted column. The encrypted column and the metadata are stored in a file. A request to read the encrypted column is received. Based on a public key and the file, the encrypted private key is decrypted into a decrypted private key. The public key is contained in the request and/or the file. The request is executed by decrypting, based on the decrypted private key and the file, the encrypted column.

Anisotropic compression as applied to columnar storage formats

Herein is a probabilistic indexing technique for searching semi-structured text documents in columnar storage formats such as Parquet, using columnar input/output (I/O) avoidance, and needing minimal storage overhead. In an embodiment, a computer associates columns with text strings that occur in semi-structured documents. Text words that occur in the text strings are detected. Respectively for each text word, a bitmap, of a plurality of bitmaps, that contains a respective bit for each column is generated. Based on at least one of the bitmaps, some of the columns or some of the semi-structured documents are accessed.

Probabilistic text index for semi-structured data in columnar analytics storage formats

Techniques are described herein for indexing personal information in columnar data storage format based files. In an embodiment, row groups of rows that comprise a plurality of columns are stored in a set of files. Each column of a row group is stored in a chunk of column pages in the set of files. A regular expression index that indexes a particular column in the set of files is stored for each row group. The regular expression index identifies column pages in the chunk of the particular column that include a particular column value that satisfies a regular expression specified in a query. The regular expression specified in the query in evaluated against the particular column using the regular expression index.

Personal information indexing for columnar data storage format

Techniques are described for generating a map of operations to ingest external data. In an embodiment, DBMS receives a query schema that specifies an external data source and client-defined columns to arrange queried data from data fields of the external data source. DBMS generates an ingest schema for the queried data of the external data source such that the ingest schema specifies ingest data fields from the data fields of the external data source to be mapped to the one or more client-defined columns. Additionally, the DBMS identifies surplus data in the external data source, which has not been specified by the ingest schema and is different from the queried data defined by the client-requested query schema. The process not only ingests the ingest data fields from the external data source but also the surplus data from the external data source. In response to receiving a query according to a different query schema, DBMS generates a result set that based on the surplus data of the external data source. The result generation is based on the DBMS generating a map of operations, which when performed, generates a result set for the received query regardless whether the query is based on the different query schema.

Map of operations for ingesting external data

According to one or more embodiments, multiple related data streams are compressed jointly, such that substantive similarities between the multiple related streams are leveraged to reduce the overall size of the resulting compressed data. Specifically, given the compressed version of a primary data stream, a secondary data stream may be highly compressed by utilizing portions of the compressed version of the primary data stream to represent similar portions of the secondary data stream. A compression application is configured to receive (either concurrently or separately) information identifying multiple related input streams. For each input stream provided to the compression application, the application outputs one output stream. The computing system writes the output streams of the compression application to storage. Embodiments allow the size of the compressed version of a secondary data stream to be greatly reduced compared to the size of the stream compressed without reference to a primary data stream.

Growing community of inventors

Palo Alto, CA, United States of America

Shrikumar Hariharasubrahmanian