Mysql slave invalidating query cache entries table
Users can then add invalidation patterns to an invalidation group that manipulates the same table and column.
Scale Arc then has a cache invalidation group consisting of read queries or stored procedures as well invalidation queries or stored procedures all using a common column.
Once the cache rules are created and grouped, Scale Arc will add cache objects created by the select calls with the metadata value extracted from the column location.
When the application modifies data with the update or insert queries, Scale Arc will extract the metadata values from the column location.
Scale Arc supports string, boolean, long, double, short, byte, binary, decimal, byte Array, date, time, timestamp, and Character Streams as column types.
For example, consider a product catalog table with a column of “product_id.” The application issues select or update queries to retrieve or update data from the table using “product_id.” Scale Arc uses the “product_id” value as the metadata to tag the cache objects internally.
The page load performance and the accuracy of auction data are extremely important for these sites.
Database resources are heavily taxed towards the end of the auction when many more users are watching an item and starting a bidding war.
The reason for its disabling, is that the query cache does not scale with high-throughput workloads on multi-core machines.
This feature significantly increases the number of use cases where you can apply caching without the risk of data inconsistency challenges that can result from using TTL-based invalidation.
The Scale Arc analytics UI helps identify the queries that belong to the same table and access the same column.
Up to 40% of web users abandon an e Commerce site if page load times exceed 3 seconds.
Shopping cart data, despite being unique for each user, can now be cached with auto cache invalidation since Scale Arc ensures data consistency and reflects the new items in Scale Arc’s cache as soon as the cart is modified.