Abstract: Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.

Abstract: Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.

Abstract: Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.

Abstract: Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.

Abstract: Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.

Abstract: Techniques are described herein for leveraging and extending relational data sharding infrastructure to provide comprehensive support for sharding semi-structured and unstructured data, in addition to the relational data, in a single sharded database system. Specifically, techniques are described in which a catalog server pre-gathers all data it needs to answer fetches from a dynamically created temporary lob. In addition, techniques are provided in which a catalog server encodes shard identifiers in lob locators sent to the client. Techniques are provided for pushing down various operators to shard servers.

Abstract: Techniques are described herein for leveraging and extending relational data sharding infrastructure to provide comprehensive support for sharding semi-structured and unstructured data, in addition to the relational data, in a single sharded database system. Specifically, techniques are described in which a catalog server pre-gathers all data it needs to answer fetches from a dynamically created temporary lob. In addition, techniques are provided in which a catalog server encodes shard identifiers in lob locators sent to the client. Techniques are provided for pushing down various operators to shard servers.

Abstract: Techniques are described herein for leveraging and extending relational data sharding infrastructure to provide comprehensive support for sharding semi-structured and unstructured data, in addition to the relational data, in a single sharded database system. Specifically, techniques are described in which a catalog server pre-gathers all data it needs to answer fetches from a dynamically created temporary lob. In addition, techniques are provided in which a catalog server encodes shard identifiers in lob locators sent to the client. Techniques are provided for pushing down various operators to shard servers.

Abstract: Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.

Abstract: Techniques are described herein for leveraging and extending relational data sharding infrastructure to provide comprehensive support for sharding semi-structured and unstructured data, in addition to the relational data, in a single sharded database system. Specifically, techniques are described in which a catalog server pre-gathers all data it needs to answer fetches from a dynamically created temporary lob. In addition, techniques are provided in which a catalog server encodes shard identifiers in lob locators sent to the client. Techniques are provided for pushing down various operators to shard servers.

Abstract: Techniques are described herein for leveraging and extending relational data sharding infrastructure to provide comprehensive support for sharding semi-structured and unstructured data, in addition to the relational data, in a single sharded database system. Specifically, techniques are described in which a catalog server pre-gathers all data it needs to answer fetches from a dynamically created temporary lob. In addition, techniques are provided in which a catalog server encodes shard identifiers in lob locators sent to the client. Techniques are provided for pushing down various operators to shard servers.

Abstract: Techniques are described herein for leveraging and extending relational data sharding infrastructure to provide comprehensive support for sharding semi-structured and unstructured data, in addition to the relational data, in a single sharded database system. Specifically, techniques are described in which a catalog server pre-gathers all data it needs to answer fetches from a dynamically created temporary lob. In addition, techniques are provided in which a catalog server encodes shard identifiers in lob locators sent to the client. Techniques are provided for pushing down various operators to shard servers.

Abstract: Techniques are described herein for leveraging and extending relational data sharding infrastructure to provide comprehensive support for sharding semi-structured and unstructured data, in addition to the relational data, in a single sharded database system. Specifically, techniques are described in which a catalog server pre-gathers all data it needs to answer fetches from a dynamically created temporary lob. In addition, techniques are provided in which a catalog server encodes shard identifiers in lob locators sent to the client. Techniques are provided for pushing down various operators to shard servers.

Abstract: Techniques are described herein for leveraging and extending relational data sharding infrastructure to provide comprehensive support for sharding semi-structured and unstructured data, in addition to the relational data, in a single sharded database system. Specifically, techniques are described in which a catalog server pre-gathers all data it needs to answer fetches from a dynamically created temporary lob. In addition, techniques are provided in which a catalog server encodes shard identifiers in lob locators sent to the client. Techniques are provided for pushing down various operators to shard servers.