To increase efficiency, many B-trees will limit the number of characters you can enter into an entry. In the example above the B-tree below limits entries to 4 characters. Clustered indexes are the unique index per table that uses the primary key to organize the data that is within the table. The clustered index ensures that the primary key is stored in increasing order, which is also the order the table holds in memory. Since the numbers are ordered, the search can navigate the B-tree allowing searches to happen in logarithmic time.
This is where non-clustered indexes become very useful. Non-clustered indexes are sorted references for a specific field, from the main table, that hold pointers back to the original entries of the table. The first example we showed is an example of a non-clustered table:.
They are used to increase the speed of queries on the table by creating columns that are more easily searchable. Note: Non-clustered indexes are not new tables. Non-clustered indexes hold the field that they are responsible for sorting and a pointer from each of those entries back to the full entry in the table.
You can think of these just like indexes in a book. The index points to the location in the book where you can find the data you are looking for. Non-clustered indexes point to memory addresses instead of storing data themselves. This makes them slower to query than clustered indexes but typically much faster than a non-indexed column. You can create many non-clustered indexes.
That is because indexes do not store all of the information from the original table. The pointer logic would look like this:. That means the query plan , the plan that SQL creates when determining the best way to perform a query, will begin to use the index when queries are being made. That is the clustered index that was referenced earlier in the article that is automatically created based off of the primary key. Even if you could hold the whole table in the buffer pool, that would make those buffers unavailable for other queries.
Either way, this will make for a sickly server. Appropriate indexing is the cure! SQL Server supports indexing for a variety of needs. Full-text, Spatial and XML indexes are outside the scope of this article.
Although not mentioned above, we will also not be looking at columnstore indexes in this article, nor in-memory tables. As described above, a clustered index affects how the data is actually stored. In a heap , the data rows are stored in no particular order. On the other hand, when you create a clustered index on a table, the organization of the data is changed so that it is now in order according to the keys specified. The result provides some guarantees regarding the asymptotic performance of the index.
Most operations search, insert and delete operate in O log n where n is the number of entries in the index. A nonclustered index shares the B-tree concept for the index nodes with the same performance guarantees.
However, such indexes do not affect the organization of the data pages, which may be clustered or not. Some optional features of nonclustered indexes are:. If you think through the implications of these descriptions, you should be able to see that a table is either a heap or a clustered index.
Other implications are that since the leaf node of a clustered index is a data page, there is no need for included columns since all columns are in the data page or filtered indexes since the clustered index is the whole table, by definition. A more subtle insight is this: Since B-Tree theory does not stipulate key uniqueness, a clustered index may have rows with duplicate keys. In that case, SQL Server will add a hidden uniqueifier a 4-byte integer to the index to enforce uniqueness.
As explained in the answer to question 3, above, when you create a clustered index, the order of the rows in the data pages is changed. Adding a clustered index to our working example, the command is a simple one:. The estimated plan shows just an operation to create an index. However, it is easy to deduce what must happen:. This is pretty much as expected! Reading from right-to-left, the table contents are read table scan , , rows are sent for sorting, then the sorted data set is sent to an IndexInsert operation, which creates the index nodes.
The parallelism operator indicates provision for a parallel IndexInsert operation, which can be run in many parallel streams depending on the number of available CPUs. The original table scan has been replaced with a clustered index seek.
The original reads have been reduced to just three. We can confirm this with a simple query:. What if we wanted to look up a customer by name instead? Something like this, perhaps:. Oh no! What can be done about this situation? Add a nonclustered index, of course! Now, we can see that the does an index seek, using the new index, followed by a key lookup. You would be forgiven for thinking that the nonclustered index contains a pointer to the pages containing the data row.
Instead of a pointer e. Of course, before we got here, we put a clustered index on the Customers table! As a consequence, we get a key lookup. Perhaps you were expecting just 3 reads as in the clustered index example we used before. However, there is no guarantee that there is just one Kaitlyn Myers in the table. What it is, is a B-tree built atop a table which may be clustered or a heap. So, if there is also a clustered index, the nonclustered index lives alongside the clustered index, and its entries point to the leaf level of that index — the data pages.
A nonclustered index has a structure like this:. You saw that a query using a nonclustered index had to lookup other columns with a key lookup into the clustered index. What if you frequently queried just a subset of the other columns? If I modify the nonclustered index like this:. So, we can see the positive benefit of included columns.
Lest there be any confusion, although a nonclustered index points to the data pages, its leaves are part of the B-tree itself. When an index contains all the columns needed to satisfy a query, it is called a covering index. Including strategic columns in a nonclustered index can ensure that the most frequent queries can be satisfied entirely from the index without the need for key lookups into the clustered index.
In that case, any lookups into the data pages are RID lookup. Too often clustered indexes and primary keys are conflated as if they are one and the same thing.
They are not! To be a relation in the formal, relational algebraic sense of the word, a table what most RDBMSs call relations must have a key — some column or collection of columns that, taken together, uniquely identify a row in a table. However, a key is not an index. It may be and usually is supported by an index, but at its heart, a key is a constraint — a condition that the database must maintain to preserve referential integrity.
Notice that the statement adds a constraint, not an index. It adds just such a constraint on the CustomerID column.
Now, since we already have both a clustered index and a nonclustered index, this produces an interesting result. The new one has been created as a backing index to implement the constraint. Note that it is nonclustered. Only one multi-valued key part can be used in a composite index. The multi-valued key part may be used in any order relative to the other parts of the key.
The optimizer uses a multi-valued index to fetch records when the following functions are specified in a WHERE clause:.
None of the three queries just shown are able to use any keys. To solve this problem, we can add a multi-valued index on the zipcode array in the JSON column custinfo , like this:. When we run the previous EXPLAIN statements again, we can now observe that the queries can and do use the index zips that was just created:. A multi-valued index can be defined as a unique key.
If defined as a unique key, attempting to insert a value already present in the multi-valued index returns a duplicate key error. If duplicate values are already present, attempting to add a unique multi-valued index fails, as shown here:. DML operations that affect multi-valued indexes are handled in the same way as DML operations that affect a normal index, with the only difference being that there may be more than one insert or update for a single clustered index record.
If multi-valued key part has an empty array, no entries are added to the index, and the data record is not accessible by an index scan. JSON null values are not permitted in indexed arrays. Because multi-valued indexes are virtual indexes on virtual columns, they must adhere to the same rules as secondary indexes on virtual generated columns. Multi-valued indexes are subject to the limitations and restrictions listed here:. Only one multi-valued key part is permitted per multi-valued index.
However, the CAST In this case, all values matching the JSON expression are stored in the index as a single flat array.
An index with a multi-valued key part does not support ordering and therefore cannot be used as a primary key. The maximum number of values per record for a multi-valued index is determined by the amount of data than can be stored on a single undo log page, which is bytes 64K minus bytes for overhead , which means that the maximum total length of key values is also bytes. The maximum number of keys depends on various factors, which prevents defining a specific limit.
Tests have shown a multi-valued index to permit as many as integer keys per record, for example. When the limit is reached, an error similar to the following is reported: ERROR HY : Exceeded max number of values per record for multi-valued index 'idx' by 1 value s. The only type of expression that is permitted in a multi-valued key part is a JSON expression.
The expression need not reference an existing element in a JSON document inserted into the indexed column, but must itself be syntactically valid. Because index records for the same clustered index record are dispersed throughout a multi-valued index, a multi-valued index does not support range scans or index-only scans. See Performance and Space Requirements. Character sets and collations other than the following two combinations of character set and collation are not supported for multi-valued indexes:.
The binary character set with the default binary collation. Section However, support for spatial column indexing varies among engines. Spatial and nonspatial indexes on spatial columns are available according to the following rules. Available for single spatial columns only. A spatial index cannot be created over multiple spatial columns. Column prefix lengths are prohibited. The full width of each column is indexed. Columns can be NULL unless the index is a primary key.
Currently, B-tree is used. Following the key part list, index options can be given. The value is treated as a hint; a different size could be used if necessary.
Some storage engines permit you to specify an index type when creating an index. For example:. Table Where multiple index types are listed, the first one is the default when no index type specifier is given.
Full-text index implementation is storage engine dependent. Spatial indexes are implemented as R-tree indexes. If you specify an index type that is not valid for a given storage engine, but another index type is available that the engine can use without affecting query results, the engine uses the available type. Prior to 8. USING HASH prevents the creation of an ordered index; otherwise, creating a unique index or primary key on an NDB table automatically results in the creation of both an ordered index and a hash index, each of which indexes the same set of columns.
The parser recognizes RTREE as a type name, but currently this cannot be specified for any storage engine. It associates a parser plugin with the index if full-text indexing and searching operations need special handling. Index definitions can include an optional comment of up to characters.
Specify index visibility. Indexes are visible by default. An invisible index is not used by the optimizer.
Specification of index visibility applies to indexes other than primary keys either explicit or implicit. For more information, see Section 8. The options are reserved for future use. Permitted values are a string literal containing a valid JSON document or an empty string ''. Invalid JSON is rejected.
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