Abstract:
Herein are high throughput techniques for incorporating cryptographic blockchains into relational data. In an embodiment, a computer indicates, in a database dictionary in a database, that a relational table is for blockchain storage. The relational table contains application column(s). In response to that indication, the relational table is associated with system column(s) that are unmodifiable by administrators and clients of the database. The system column(s) include a cryptographic hash column. A request to store a particular value in a particular application column is received from a client. In response to receiving the request, a cryptographic hash value is calculated for a new row for the relational table. In the relational table, the computer stores the new row that contains: the particular value in the particular application column, and the cryptographic hash value in the cryptographic hash column.
Abstract:
Distributed ledgered data is stored within a distributed persistent storage system comprising multiple persistent storage systems as distributed ledgered participants. The distributed ledgered data is maintained using the native capabilities of a persistent storage system. The distributed ledgered data is replicated as persistent data objects in a "ledgered repository of objects" that are replicated at each of the persistent storage systems. Changes at one persistent storage system are recorded within a block in a distributed blockchain that is distributed across each of the other distributed ledgered participants. The other distributed ledgered participants read the changes from the blockchain and apply the changes to the respective replicas at each of the other distributed ledgered participants.
Abstract:
Techniques are provided to allow more sophisticated operations to be performed remotely by machines that are not fully functional. Operations that can be performed reliably by a machine that has experienced a hardware and/or software error are referred to herein as Remote Direct Memory Operations or "RDMOs". Unlike RDMAs, which typically involve trivially simple operations such as the retrieval of a single value from the memory of a remote machine, RDMOs may be arbitrarily complex. The techniques described herein can help applications run without interruption when there are software faults or glitches on a remote system with which they interact.
Abstract:
Techniques are provided for enabling a requesting entity to retrieve data that is managed by a database server instance from the volatile memory of a server machine that is executing the database server instance. The techniques allow the requesting entity to retrieve the data from the volatile memory of the host server machine without involving the database server instance in the retrieval operation. Because the retrieval does not involve the database server instance, the retrieval may succeed even when the database server instance has stalled or become unresponsive. In addition, direct retrieval of data using the techniques described herein will often be faster and more efficient than retrieval of the same information through conventional interaction with the database server instance.
Abstract:
Techniques are provided for using an intermediate cache between the shared cache of a database server and the non-volatile storage of a storage system. The intermediate cache may be local to the machine upon which the database server is executing, or may be implemented within the storage system. In one embodiment, the database system includes both a DB server-side intermediate cache, and a storage-side intermediate cache. The caching policies used to populate the intermediate cache are intelligent, taking into account factors that may include which database object an item belongs to, the item type of the item, a characteristic of the item; or the database operation in which the item is involved.
Abstract:
A shared-nothing database system is provided in which the rows of each table are assigned to "slices", and multiple copies ("duplicas") of each slice are stored across the persistent storage of multiple nodes. 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 are performed by the node that has the primary duplica of the slice to which the target row is assigned. The changes are then propagated other duplicas ("secondary duplicas") of the same slice.