Abstract: Certain example embodiments provide a single pass, reverse chronological approach to write-ahead log recovery, enabling space- and time-efficient the recovery of stored data from large write-ahead logs to a transient storage medium. The techniques described herein can in certain instances enable fast and efficient recovery, even in scenarios where at the time of a failure requiring such a recovery the live log is potentially multiple terabytes or larger in size. Certain example embodiments make use of a filtering mechanism (e.g., involving potentially stateful delete, skip, and/or transaction filters), a key/value property (allowing a live set of data, once identified, to be applied in any arbitrary order), etc. A simplified environment with a small closed set of mutative operations allows for the performing of recovery backwards by scanning the log from the most recent written record backwards in time (and, in other words, finishing with the oldest record).

Abstract: Certain example embodiments relate to a highly-concurrent, predictable, fast, self-managed, in-process space for storing data that is hidden away from the garbage collector and its related pauses. More particularly, certain example embodiments relate to improved memory management techniques for computer systems that leverage an off-heap direct-memory data store that is massively scalable and highly efficient. The off-heap store may be provided in connection with a Java-based environment, and garbage collection may be completely or nearly completely avoided for the off-heap store. The off-heap store may be integrated into a tiered storage solution in certain example embodiments.

Abstract: Database data is reliably maintained and invalidated based on actual changes to data in the database. The dependencies of a received query can be determined by submitting a version of the received query to the database through a native facility provided by the database to analyze how query structures are processed. The caching system can access the results of the facility to determine the tables, rows, or other partitions of data a received query is dependent upon or modifies. An abstracted form of the query can be cached with an indication of the tables, rows, etc. that queries of that structure access or modify. The tables a write or update query modifies can be cached with a time of last modification. A component can be implemented at or on the system of the database to directly detect changes to the database data. This component can monitor transactional information maintained by the database itself to determine when changes to the database occur.

Abstract: Database data is maintained reliably and invalidated based on actual changes to data in the database. Updates or changes to data are detected without parsing queries submitted to the database. The dependencies of a query can be determined by submitting a version of the received query to the database through a native facility provided by the database to analyze how query structures are processed. The caching system can access the results of the facility to determine the tables, rows, or other partitions of data a received query is dependent upon or modifies. An abstracted form of the query can be cached with an indication of the tables, rows, etc. that queries of that structure access or modify. The tables a write or update query modifies can be cached with a time of last modification.

Abstract: Database data is maintained reliably and invalidated based on actual changes to data in the database. Updates or changes to data are detected without parsing queries submitted to the database. Requests for stored procedures and their results can be cached without pre-existing knowledge of the stored procedure's operation. A caching system can open a session at the database instructing the database to monitor and log its processes. The caching system can pass the stored procedure request and access the log to determine how the stored procedure is processed and the queries executed as part of the stored procedure. The dependencies of a query can be determined by submitting a version of the received query to the database through a native facility provided by the database to analyze how query structures are processed. The results of the stored procedure can be cached the results of the query dependency analysis can be cached for subsequent request processing and invalidation.