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Advanced Index Compression works well on all supported indexes, including those indexes that are not good candidates for the existing prefix compression feature; including indexes with no, or few, duplicate values in the leading columns of the index.
Advanced Index Compression improves the compression ratios significantly while still providing efficient access to the index. Processing of large volumes of data is significantly faster than the exact aggregation, especially for data sets with a large number of distinct values, with negligible deviation from the exact result.
In Oracle RAC systems, for well-partitioned applications, this feature can be used when the combined buffer caches of all instances, with some extra space to handle duplicate cached blocks between instances, is greater than the database size.
Caching the entire database provides significant performance benefits, especially for workloads that were previously limited by I/O throughput or response time.
Companies are adopting JSON as a way of storing unstructured and semi-structured data.
As the volume of JSON data increases, it becomes necessary to be able to store and query this data in a way that provides similar levels of security, reliability and availability as are provided for relational data.
A DBA simply has to allocate memory to In-Memory Column Store.
The optimizer is aware of In-Memory Column Store, so whenever a query accesses objects that reside in the column store and would benefit from its columnar format, they are sent there directly.
This directive applies to any kind of direct path operation, such as a bulk insert or a move operation.
In-Memory Column Store enables objects (tables or partitions) to be stored in memory in a columnar format.
The columnar format enables scans, joins and aggregates to perform much faster than the traditional on-disk formats for analytical style queries.
The improved performance also allows more ad-hoc analytic queries to be run directly on the real-time transaction data without impacting the existing workload.
The last few years have witnessed a surge in the concept of in-memory database objects to achieve improved query response times.