Abstract: The concepts relate to data handling, and more specifically to data handling scenarios where data is revised on one computer and stored on another computer. One example can obtain a set of blobs relating to revisions of a file. The example can determine a target size of datastore blobs. In an instance where a total size of the set of blobs is less than the target size, this example can aggregate the set of blobs into an individual datastore blob. Otherwise, the example can identify new or edited individual blobs of the set and aggregate the new or edited individual blobs into first datastore blobs. The example can also aggregate other individual blobs of the set into second datastore blobs.

Abstract: The concepts relate to data handling, and more specifically to data handling scenarios where data is revised on one computer and stored on another computer. One example can obtain a set of blobs relating to revisions of a file. The example can determine a target size of datastore blobs. In an instance where a total size of the set of blobs is less than the target size, this example can aggregate the set of blobs into an individual datastore blob. Otherwise, the example can identify new or edited individual blobs of the set and aggregate the new or edited individual blobs into first datastore blobs. The example can also aggregate other individual blobs of the set into second datastore blobs.

Abstract: The concepts relate to data handling, and more specifically to data handling scenarios where data is revised on one computer and stored on another computer. One example can obtain a set of blobs relating to revisions of a file. The example can determine a target size of datastore blobs. In an instance where a total size of the set of blobs is less than the target size, this example can aggregate the set of blobs into an individual datastore blob. Otherwise, the example can identify new or edited individual blobs of the set and aggregate the new or edited individual blobs into first datastore blobs. The example can also aggregate other individual blobs of the set into second datastore blobs.

Abstract: The concepts relate to data handling, and more specifically to data handling scenarios where data is revised on one computer and stored on another computer. One example can obtain a set of blobs relating to revisions of a file. The example can determine a target size of datastore blobs. In an instance where a total size of the set of blobs is less than the target size, this example can aggregate the set of blobs into an individual datastore blob. Otherwise, the example can identify new or edited individual blobs of the set and aggregate the new or edited individual blobs into first datastore blobs. The example can also aggregate other individual blobs of the set into second datastore blobs.

Abstract: A technology for efficiently filtering using a web site is disclosed. In one method approach, distinct filtering values based on a type of data selected from a web page associated with a web site are received without requiring retrieval of filtering values that are unrelated to the selected type of data.

Abstract: A dynamically configurable list management system that supports list elements of multiple content types is presented. Each list element is associated with an element type via an element type identifier. The element type identifier uniquely identifies the element type to the list management system. Each property associated with each element type is included in the list. Each property is identified by a unique property identifier. When adding a list element of a new element type to an existing list that does not include any elements of the new element type, the list management system identifies every property associated with the new element type. For each property associated with the new element type, the list management system determines which properties are not already included in the list, and adds those properties that are not already including to the list.