Helper methods to easily return saved search parameters available to the user in the workspace, such as fields, search owners, search indexes are also provided. The Services API supports the create, read, update, delete, and query operations on a dtSearch DTO. For more information, see dtSearch on the Relativity Documentation site. If an index is not frequently updated in small batches, then a simple rule of thumb is to compress after initial creation and then to compress whenever the amount of data indexed increases by 10-20%, or the amount of obsolete data exceeds 20%.Relativity's dtSearch engine provides advanced search functionality such as proximity, stemming, fuzzy searches, and Boolean operators. For a rough estimate of the time needed to compress an index, divide the total size of the index by 150 Mb to get compression time in minutes.įor frequently-updated indexes, compression is usually done on a schedule (once a day during off-hours, or once a week over the weekend).
The time required to compress an index is proportional to the size of the index, and will depend on both CPU and hard disk speed. For example, if you start with a 50 Gb index that is fully compressed, and then update it 10 times, each time adding a one page document, fragmentation will be 11 but only a tiny percentage of the index data will be fragmented. On each update, dtSearch appends data to what is already in the index, without affecting fragmentation of the existing data. It might be more, because some updates might involve more than one commit (for example, if the update involves a lot of data).įragmentation is also affected by the amount of data that is fragmented. For example, if you build an index and then update it 5 times, fragmentation would be at least 6. Each time an index update commits, fragmentation of the index increases. In both cases, the data is not removed from the index but is tagged as "obsolete" for removal the next time the index is compressed.įragmentation occurs when an index is updated. Obsolete data comes from documents that are reindexed and from documents that are removed from the index. Compressing an index optimizes the index structure, removing obsolete data and defragmenting search structures for better performance.
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