In versions of SQL Server earlier than SQL Server 2012 (11.x) and in Azure SQL Database, the UNICODE function returns a UCS-2 codepoint in the range 000000 through 00FFFF which is capable of representing the 65,535 characters in the Unicode Basic Multilingual Plane (BMP). As such, the index contains the hash keys but the table itself does not.To view Transact-SQL syntax for SQL Server 2014 and earlier, see Previous versions documentation. As stated above, my preferred solution is to “store” the hash keys in a calculated column that is then indexed.
SQL POUND KEY UPDATE
You might elect to fire an INSERT trigger, or use a stored procedure to create the hash key once the values of interest have been obtained, or even to execute an UPDATE query that creates the hash keys and populates the hash column retroactively (so that you can apply this technique to tables that already contain millions of rows). You have a number of choices as to how you create the hash key. This results in the following rows (clipped to 10 for brevity): USE AdventureWorksSELECT Name, GroupName, Checksum ( Name, GroupName ) AS HashKeyFROM Adventureworks.
SQL POUND KEY HOW TO
For example, the following query demonstrates how to obtain the hash key for any given value or combination of values: There are several algorithms available, the simplest of which is built into SQL Server in the form of the Checksum function. Hash Key Algorithms using the Checksum Function A search based on an integer column can be dramatically faster than a search based on a lengthy string key, and more so if it is a compound key. You isolate a small subset of rows using the hash key and then perform an exact-string match against the hits. The set of rows returned will be dramatically smaller than the set of rows the engine would have to visit in order to find an exact match on the original query values. In a 50 million row table, there will undoubtedly be hash collisions, but that isn’t the point. The database engine searches the index on the hashed column, returning the required row, or a small subset of matching rows.These values are then converted into a hash key.The user (whether a human or an application) queries the values of interest.Now, we index the hash key row and don’t bother with the index on the two columns mentioned above. We create a calculated column whose formula is the hash key of these two columns. Even better, we don’t have to store the hash keys themselves – or more accurately, we have to store them just once. A hash key based on these two columns is vastly smaller (4 bytes per row). Given 50 million rows, this is a problem. Now, how can we apply hash leys intelligently in our database designs? Suppose that we have these columns in the table of interest:Ī compound index on both these columns would consume 50 + 50 characters per row. Suppose that you fed this article into a hash algorithm, then changed one character in the article and fed the article back into the hashing algorithm: it would return a different integer. If this does occur, then it is known as a hash collision. If you use an efficient hash function then there will be only a small chance that two different strings will yield the same hash value. You feed said algorithm a string and you get back an integer. In brief, a hash is the integer result of an algorithm (known as a hash function) applied to a given string. There is a very slick alternative, using what are known alternatively as hash buckets or hash keys. But suppose the table of interest contains 50 million rows? Then you will notice the impact both in terms of storage requirements and search performance. In a small table, you might not notice the impact. Your application may require an index based on a lengthy string, or even worse, a concatenation of two strings, or of a string and one or two integers. Using Hash Keys instead of String Indexes
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