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    <p>Horizontal scaling for any application will eventually run into limits as demand for the data increases. Those limits are determined by load and server/database performance. At some point, if demand and load increase with scaling, the number of servers/databases will have to increase as well. Depending on the data that is being stored, the servers/databases will either have to be duplicated and synchronized, or some sort of hashing algorithm will need to be employed to split data across multiple servers. As you increase the number of synchronized data sources the cost of replicating/synchronizing those servers increases as well. That is why the hashed approach may be more appealing to minimize cost.</p> <p>True High Availability solutions are very expensive to implement. I've seen various degrees of HA as well, but by definition it means absolute minimal or no downtime of, or lose of access to the data source. To achieve this requires a lot of redundant hardware, networking, and software that is able to utilize redundant hardware without losing the ability to get to the data when one of the data sources fails. Hardware failure is inevitable, it will happen, as well as power outages and other random acts of nature. Depending on how critical this data is an HA solution will also require multiple data centers on multiple independent power grids. Which is obviously going to be <em>very</em> expensive, so it all depends on how critical this data is to the end-user.</p> <p>So, HA is an extreme scenario requiring an expensive architecture. I find that most of the time people are interested in just minimizing downtime, and depending on the size of the data source this can be achieved fairly inexpensively with adding hot-spares of the data sources.</p>
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A row detail

Detail views are divided into sections. All the information in the data section comes from columns in the selected row. The other sections display data from other, related rows.

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