Kartik Kulkarni

Techniques are described herein for distributing distinct portions of a database object across volatile memories of selected nodes of a plurality of nodes in a clustered database system. The techniques involve storing a unit-to-service mapping that associates a unit (a database object or portion thereof) to one or more database services. The one or more database services are mapped to one or more nodes. The nodes to which a service is mapped may include nodes in disjoint database systems, so… Show more

Techniques are described herein for distributing distinct portions of a database object across volatile memories of selected nodes of a plurality of nodes in a clustered database system. The techniques involve storing a unit-to-service mapping that associates a unit (a database object or portion thereof) to one or more database services. The one or more database services are mapped to one or more nodes. The nodes to which a service is mapped may include nodes in disjoint database systems, so long as those database systems have access to a replica of the unit. The database object is treated as in-memory enabled by nodes that are associated with the service, and are treated as not in-memory enabled by nodes that are not associated with the service. Show less

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… Show more

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. Also, a deduplication algorithm ensures that typically only a single invalidation message per block is applied to invalidate MF data. Show less

Techniques are described herein for distributing distinct portions of a database object across the volatile memories of a plurality of nodes in a clustered database system. The techniques involve establishing a single database server instance located on a node in a multi-node cluster as a load-operation master for a particular data set. The load-operation master determines how the data set may be separated into chunks using a hash function. The load-operation master then broadcasts a small… Show more

Techniques are described herein for distributing distinct portions of a database object across the volatile memories of a plurality of nodes in a clustered database system. The techniques involve establishing a single database server instance located on a node in a multi-node cluster as a load-operation master for a particular data set. The load-operation master determines how the data set may be separated into chunks using a hash function. The load-operation master then broadcasts a small payload of consistency information to other database servers, so each database server may independently execute the hash function and independently load their respectively assigned chunks of data. Show less

A minimum value (MV) is computed for start timestamps that each correspond to an uncommitted transaction. In an embodiment, the MV is computed for a pluggable database that is open on at least first and second instances of a database. The MV is computed for the first instance as of a first current timestamp (CT). The MV and the first CT are communicated to a second instance that has a second CT. If the first and second CTs are equal, the second instance store the MV. If the first CT is bigger… Show more

A minimum value (MV) is computed for start timestamps that each correspond to an uncommitted transaction. In an embodiment, the MV is computed for a pluggable database that is open on at least first and second instances of a database. The MV is computed for the first instance as of a first current timestamp (CT). The MV and the first CT are communicated to a second instance that has a second CT. If the first and second CTs are equal, the second instance store the MV. If the first CT is bigger, the second CT also becomes equal to the first CT. If the first CT is smaller, the MV is discarded, and the first CT becomes equal to the second CT. In an embodiment, if the MV remains unchanged for a predetermined time period, a start timestamp corresponding to the MV is advanced to current or future timestamp. Show less

Techniques are described herein for executing queries on distinct portions of a database object that has been separate into chunks and distributed across the volatile memories of a plurality of nodes in a clustered database system. The techniques involve redistributing the in-memory database object portions on changes to the clustered database system. Each node may maintain a mapping indicating which nodes in the clustered database system store which chunks, and timestamps indicating when each… Show more

Techniques are described herein for executing queries on distinct portions of a database object that has been separate into chunks and distributed across the volatile memories of a plurality of nodes in a clustered database system. The techniques involve redistributing the in-memory database object portions on changes to the clustered database system. Each node may maintain a mapping indicating which nodes in the clustered database system store which chunks, and timestamps indicating when each mapping entry was created or updated. A query coordinator may use the timestamps to select a database server instance with local in memory access to data required by a portion of a query to process that portion of the query. Show less

Techniques are described herein for distributing data from one or more partitioned tables across the volatile memories of a cluster. In memory copies of data from partitioned tables are grouped based on the data falling within the same partition criteria. These groups are used for assigning data from corresponding partitions to the same node when distributing data from partitioned tables across the volatile memories of a multi-node cluster. When a query requires a join between rows of… Show more

Techniques are described herein for distributing data from one or more partitioned tables across the volatile memories of a cluster. In memory copies of data from partitioned tables are grouped based on the data falling within the same partition criteria. These groups are used for assigning data from corresponding partitions to the same node when distributing data from partitioned tables across the volatile memories of a multi-node cluster. When a query requires a join between rows of partitioned tables, the work for the join query is divided into work granules that correspond to partition-wise join operations. Those partition-wise join operations are assigned to nodes by a query coordinator based on the partition-to-node mapping located in the node of the query coordinator. Show less

Techniques are described herein for executing queries on distinct portions of a database object that has been separate into chunks and distributed across the volatile memories of a plurality of nodes in a clustered database system. The techniques involve receiving a query that requires work to be performed on data that resides in a plurality of on disk extents. A parallel query coordinator that is aware of the in-memory distribution divides the work into granules that align with the in-memory… Show more

Techniques are described herein for executing queries on distinct portions of a database object that has been separate into chunks and distributed across the volatile memories of a plurality of nodes in a clustered database system. The techniques involve receiving a query that requires work to be performed on data that resides in a plurality of on disk extents. A parallel query coordinator that is aware of the in-memory distribution divides the work into granules that align with the in-memory separation. The parallel query coordinator then sends each granule to the database server instance with local in memory access to the data required by the granule and aggregates the results to respond to the query. Show less

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… Show more

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. Show less