This is more efficient even if the indexes must be copied to a network drive after they are created. It is better to build the index on an internal drive on the machine where the indexer is running, rather than generating an index on a remote drive or external drive. Document access speed is less important but can become significant if the multithreaded indexer is being used. Generating the index requires a high volume of read/write activity to and from the index, and SSD storage is much faster than non-SSD drives. Consider using the dtSearch document caching feature, particularly for use with web-based data that changes frequently or that may not be available in the future or for PST files.If you are accessing data across potentially unreliable network connections (for example, crawling a large variety of web sites), download the data prior to indexing.(Other more efficient forms of encryption such as BitLocker-encrypted drives affect indexing speed much less.) Do not generate or store an index on a compressed or encrypted NTFS folder.Avoid generating an index on an external drive (SAN, NAS, Firewire, or USB).Keep the indexes as close as possible to the machine where the indexer is executing-even if the data is remote.Use SSD storage for the index and, if possible, for the documents as well.Alternatively, you can set AutoCommitIntervalMB to zero, which requires dtSearch to commit only once at the end of an indexing job.ĭtSearch Desktop: This setting is not currently available in dtSearch Desktop.ĭtSearch Developer API: Set AutoCommitIntervalMB to a value of either 0 or greater than 64,000. For best performance, set AutoCommitIntervalMB to a value greater than 64,000. Higher values improve indexing performance. The dtSearch Engine API provides a setting, IndexJob.AutoCommitIntervalMB, that determines how often dtSearch must commit index updates. Make sure, however, that the final index holds no more than about a terabyte of text. Merging indexes into a new, empty index-rather than merging into an index that already contains data-results in a substantially faster and more efficient merge process. (A single dtSearch query can search any number of indexes.) Or, for optimal index structure and search efficiency, merge the multiple indexes into a single index. After creation of multiple individual indexes, you can run searches across all indexes at once. Merge multiple indexes into a new, empty index. For information on multithreaded use of the dtSearch Engine API and indexing using multiple threads, see Multithreaded operations. Multiple index updates can also run concurrently on the same machine, in separate processes or on multiple threads in the same process. Splitting up the indexing job is also a good strategy if disk space is insufficient to index all data at once. For very large indexing jobs, using multiple machines to simultaneously build indexes on different portions of a data collection is generally much faster than indexing on a single machine. Index on multiple machines running simultaneously. For optimal search speed, after many index updates, use the compress function to defragment the index. Use the compress function after multiple index updates. Indexing in small batches makes each update relatively slower and fragments the index structure. The dtSearch indexer is optimized for indexing large volumes of text at once. Do not require the indexer to “commit” index updates too often (dtSearch Engine users only).When merging, merge indexes into a new empty index, rather than merging into an index that already contains data.For very large indexing jobs, index on multiple machines running simultaneously, and then merge the indexes.Use the index compress function after multiple index updates.Document Storage and the NTFS File System. Why filtering improves accuracy when searching forensic data.
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