Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In some embodiments, the disclosed subject matter involves an entity routing service to route user requests for an application service to a particular data center based on the user's entity status. The user's entity status is defined by at least the application service requested, and may include the user's organization, geographic area and other criteria. The routing may be effected at the frontend application server level rather than at the backend. Other embodiments are described and claimed.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A database may facilitate zero-downtime upgrades by concurrently maintaining multiple editions of database objects for use by both pre-upgrade and post-upgrade clients of a database application. Operations performed within the database are associated with an edition based on, for example, an initiating client or transaction. When an operation references an object or data, the database automatically performs the operation using the object or data associated with the edition with which the operation is itself associated. The database may determine the associated edition without explicit identification of the associated edition in a query or in code. Thus, no client or stored procedure code changes are necessary to reflect a new edition added during an update. Data changes in one edition may be automatically and immediately propagated to the other edition through the use of cross-edition triggers, thereby allowing both pre-upgrade and post-upgrade clients to remain fully functional throughout an upgrade.

Abstract: A database may facilitate zero-downtime upgrades by concurrently maintaining multiple editions of database objects for use by both pre-upgrade and post-upgrade clients of a database application. Operations performed within the database are associated with an edition based on, for example, an initiating client or transaction. When an operation references an object or data, the database automatically performs the operation using the object or data associated with the edition with which the operation is itself associated. The database may determine the associated edition without explicit identification of the associated edition in a query or in code. Thus, no client or stored procedure code changes are necessary to reflect a new edition added during an update. Data changes in one edition may be automatically and immediately propagated to the other edition through the use of cross-edition triggers, thereby allowing both pre-upgrade and post-upgrade clients to remain fully functional throughout an upgrade.