Abstract: A method for managing log files for recording operations on data stored in a database is provided, wherein a set of log files having an allocated first portion of storage is updated, the allocated first portion of storage is monitored, and a second portion of storage is allocated in dependence on a determination that an available portion of storage is below a predetermined size. A method for generating a snapshot is provided, wherein data entries are included in the snapshot in dependence on a determined relative order of log records in a set of log files. A method of replicating a binary large object is provided, wherein the binary large object is sent to a second database in response to identifying a log record comprising data indicating the binary large object.

Abstract: A method for managing log files for recording operations on data stored in a database is provided, wherein a set of log files having an allocated first portion of storage is updated, the allocated first portion of storage is monitored, and a second portion of storage is allocated in dependence on a determination that an available portion of storage is below a predetermined size. A method for generating a snapshot is provided, wherein data entries are included in the snapshot in dependence on a determined relative order of log records in a set of log files. A method of replicating a binary large object is provided, wherein the binary large object is sent to a second database in response to identifying a log record comprising data indicating the binary large object.

Abstract: A method for managing log files for recording operations on data stored in a database is provided, wherein a set of log files having an allocated first portion of storage is updated, the allocated first portion of storage is monitored, and a second portion of storage is allocated in dependence on a determination that an available portion of storage is below a predetermined size. A method for generating a snapshot is provided, wherein data entries are included in the snapshot in dependence on a determined relative order of log records in a set of log files. A method of replicating a binary large object is provided, wherein the binary large object is sent to a second database in response to identifying a log record comprising data indicating the binary large object.

Abstract: The processing of transaction oriented data tends to be row-oriented, while the processing of analytical operations tends to be column-oriented. Various systems, sometimes referred to as operational data warehouses, may comprise mechanisms adapted for use in scenarios where both transactional data processing and analytical queries are to be performed efficiently. The operational data warehouse (ODW) may perform and update data efficiently by maintaining a table in structures comprising a column store, a delta store, a delete bitmap, and a delete buffer. In this environment, key values may be associated for each row such that the ODW may more efficiently seek rows. Further, rows may also be excluded from a column store based at least in part on a filter criterion. The filtering criterion may be used to filter out rows based on a created predicate set by a user or the system.

Abstract: Techniques for improved update performance in transactional and data warehouse applications are disclosed herein. A request to store a modified version of the row is received. A first record indicative of a logically deleted state for values in the column store corresponding to the row is added to a first plurality of records. The modified version of the row is added to the row store. The identifier is modified to indicate that the column store does not contain a location corresponding to the modified version of the row. A second record indicative of the modified version of the row not having been propagated to the column is added to a second plurality of records store.

Abstract: The processing of transaction oriented data tends to be row-oriented, while the processing of analytical operations tends to be column-oriented. Various systems, sometimes referred to as operational data warehouses, may comprise mechanisms adapted for use in scenarios where both transactional data processing and analytical queries are to be performed efficiently. The operational data warehouse (ODW) may perform and update data efficiently by maintaining a table in structures comprising a column store, a delta store, a delete bitmap, and a delete buffer. In this environment, key values may be associated for each row such that the ODW may more efficiently seek rows. Further, rows may also be excluded from a column store based at least in part on a filter criterion. The filtering criterion may be used to filter out rows based on a created predicate set by a user or the system.

Abstract: Embodiments are directed to efficiently determining which cache entries are to be evicted from memory and to incorporating a probability of reuse estimation in a cache entry eviction determination. A computer system with multiple different caches accesses a cache entry. The computer system determines an entry cost value for the accessed cache entry. The entry cost value indicates an amount of time the computer system is slowed down by to load the cache entry into cache memory. The computer system determines an opportunity cost value for the computing system caches. The opportunity cost value indicates an amount of time by which the computer system is slowed down while performing other operations that could have used the cache entry's cache memory space. Upon determining that the entry cost value is lower than the opportunity cost value, the computer system probabilistically evicts the cache entry from cache memory.

Abstract: Embodiments are directed to efficiently determining which cache entries are to be evicted from memory and to incorporating a probability of reuse estimation in a cache entry eviction determination. A computer system with multiple different caches accesses a cache entry. The computer system determines an entry cost value for the accessed cache entry. The entry cost value indicates an amount of time the computer system is slowed down by to load the cache entry into cache memory. The computer system determines an opportunity cost value for the computing system caches. The opportunity cost value indicates an amount of time by which the computer system is slowed down while performing other operations that could have used the cache entry's cache memory space. Upon determining that the entry cost value is lower than the opportunity cost value, the computer system probabilistically evicts the cache entry from cache memory.

Abstract: Efficient server-side query statement parameter bucketization is provided for a query language, such as the structured query language (SQL). A process is applied for replacing the parameters of a parameterized query with new parameters, whose types are normalized to enable more query execution plan sharing. In one embodiment, in response to receiving a parameterized query expression of a query language, the bucketization process operates to bucketize parameter(s) of the parameterized expression based on the type(s) of the parameter(s) by replacing the type(s) with new broader, or more specific, type(s). An existing server-side parameterization framework that includes a mechanism for determining when parameterization is “safe,” is extended, and is applied to bucketize parameters by using the mechanism to determine when replacing a parameter(s) of parameterized expressions with new parameter(s) of different type(s) is safe.

Abstract: Efficient server-side query statement parameter bucketization is provided for a query language, such as the structured query language (SQL). A process is applied for replacing the parameters of a parameterized query with new parameters, whose types are normalized to enable more query execution plan sharing. In one embodiment, in response to receiving a parameterized query expression of a query language, the bucketization process operates to bucketize parameter(s) of the parameterized expression based on the type(s) of the parameter(s) by replacing the type(s) with new broader, or more specific, type(s). An existing server-side parameterization framework that includes a mechanism for determining when parameterization is “safe,” is extended, and is applied to bucketize parameters by using the mechanism to determine when replacing a parameter(s) of parameterized expressions with new parameter(s) of different type(s) is safe.