Abstract: Techniques for automatically migrating documents from a document database to a relational database are provided. In one technique, it is determined whether a set of documents, from a document database system, can be stored in a relational database system. If so, one or more entities to be normalized are identified based on a hierarchical structure of the set of documents. One or more scripts are generated based on the identified one or more entities. In a related technique, a set of documents from a document database system is stored. It is validated that the set of documents can be converted to one or more duality views. Data of the set of documents is normalized for storing in a relational database system. A script is generated that, when executed, generates the one or more duality views.

Abstract: JSON Duality Views are object views that return JDV objects. JDV objects are virtual because they are not stored in a database as JSON objects. Rather, JDV objects are stored in shredded form across tables and table attributes (e.g. columns) and returned by a DBMS in response to database commands that request a JDV object from a JSON Duality View. Through JSON Duality Views, changes to the state of a JDV object may be specified at the level of a JDV object. JDV objects are updated in a database using optimistic lock.

Abstract: One or more engine instances are executed on each host to form an engine cluster. A plurality of control instances are executed on a first set of hosts to form a control cluster and comprise a control instance leader and one or more control instance followers. In response to a first host indicating a failure of a neighbor host, a pair-wise focused investigation is initiated to check peer-to-peer connections between the first host and the neighbor host. In response to one or more additional hosts indicating failures of neighbor hosts while the pair-wise focused investigation is being performed, a wide investigation is performed to check connections between the control cluster and the plurality of hosts. One or more hosts are added to an eviction list and an eviction protocol is performed to evict the one or more hosts from the engine cluster using the eviction list.

Abstract: Techniques are described for Transaction Guard to impose at-most-once execution by generating and using the database's native transaction identifier, DB XID. In an implementation, DB XID is unique within a (pluggable) database instance (with local undo) and uniquely identifies a transaction in the database. The Transaction Guard that is extended to use native transaction information determines the commit outcome using the native transaction identifier of the transaction instead of relying on the persistence of the Logical Transaction Identifier (LTXID) in a separate table. Using the native transaction identifier, the Transaction Guard significantly improves performance by eliminating the extra write(s) incurred during commit operations.

Abstract: One or more engine instances are executed on each host to form an engine cluster. A plurality of control instances are executed on a first set of hosts to form a control cluster and comprise a control instance leader and one or more control instance followers. In response to a first host indicating a failure of a neighbor host, a pair-wise focused investigation is initiated to check peer-to-peer connections between the first host and the neighbor host. In response to one or more additional hosts indicating failures of neighbor hosts while the pair-wise focused investigation is being performed, a wide investigation is performed to check connections between the control cluster and the plurality of hosts. One or more hosts are added to an eviction list and an eviction protocol is performed to evict the one or more hosts from the engine cluster using the eviction list.

Abstract: One or more engine instances are executed on each host to form an engine cluster. A plurality of control instances are executed on a first set of hosts to form a control cluster and comprise a control instance leader and one or more control instance followers. In response to a first host indicating a failure of a neighbor host, a pair-wise focused investigation is initiated to check peer-to-peer connections between the first host and the neighbor host. In response to one or more additional hosts indicating failures of neighbor hosts while the pair-wise focused investigation is being performed, a wide investigation is performed to check connections between the control cluster and the plurality of hosts. One or more hosts are added to an eviction list and an eviction protocol is performed to evict the one or more hosts from the engine cluster using the eviction list.

Abstract: A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

Abstract: JSON Duality Views are object views that return JDV objects. JDV objects are virtual because they are not stored in a database as JSON objects. Rather, JDV objects are stored in shredded form across tables and table attributes (e.g. columns) and returned by a DBMS in response to database commands that request a JDV object from a JSON Duality View. Through JSON Duality Views, changes to the state of a JDV object may be specified at the level of a JDV object. JDV objects are updated in a database using optimistic lock.

Abstract: JSON Duality Views are object views that return JDV objects. JDV objects are virtual because they are not stored in a database as JSON objects. Rather, JDV objects are stored in shredded form across tables and table attributes (e.g. columns) and returned by a DBMS in response to database commands that request a JDV object from a JSON Duality View. Through JSON Duality Views, changes to the state of a JDV object may be specified at the level of a JDV object. JDV objects are updated in a database using optimistic lock.

Abstract: JSON Duality Views are object views that return JDV objects. JDV objects are virtual because they are not stored in a database as JSON objects. Rather, JDV objects are stored in shredded form across tables and table attributes (e.g. columns) and returned by a DBMS in response to database commands that request a JDV object from a JSON Duality View. Through JSON Duality Views, changes to the state of a JDV object may be specified at the level of a JDV object. JDV objects are updated in a database using optimistic lock.

Abstract: One or more engine instances are executed on each host to form an engine cluster. A plurality of control instances are executed on a first set of hosts to form a control cluster and comprise a control instance leader and one or more control instance followers. In response to a first host indicating a failure of a neighbor host, a pair-wise focused investigation is initiated to check peer-to-peer connections between the first host and the neighbor host. In response to one or more additional hosts indicating failures of neighbor hosts while the pair-wise focused investigation is being performed, a wide investigation is performed to check connections between the control cluster and the plurality of hosts. One or more hosts are added to an eviction list and an eviction protocol is performed to evict the one or more hosts from the engine cluster using the eviction list.

Abstract: Techniques related to query execution against an in-memory standby database are disclosed. A first database includes PF data stored on persistent storage in a persistent format. The first database is accessible to a first database server that converts the PF data to a mirror format to produce MF data that is stored within volatile memory. The first database server receives, from a second database server, one or more change records indicating one or more transactions performed against a second database. The one or more change records are applied to the PF data, and a reference timestamp is advanced from a first to a second timestamp. The first database server invalidates any MF data that is changed by a subset of the one or more transactions that committed between the first and second timestamps.

Abstract: A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

Abstract: A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

Abstract: A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

Abstract: A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

Abstract: A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

Abstract: A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

Abstract: A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to “slices”, and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

Abstract: Embodiments store transaction metadata in dedicated pools of allocated memory chunks. Portions of the pools of allocated memory chunks are dedicated to the respective apply slave processes that mine and process change records. Also, the pools of allocated memory chunks are anchored within the structure of a transaction log such that buffering and application of metadata for one transaction does not block required buffering and application of metadata for other transactions. The standby database system pre-processes transaction metadata in preparation for application of the metadata to invalidate appropriate portions of MF data. Further, embodiments divide the work of pre-processing invalidation records among the many apply slave processes that record the invalidation records. A garbage collection selects memory chunks for garbage collection in reverse order of how the chunks were allocated.