Index B-Tree VS Bitmap

Hi all

I was studying all internal Index and am bit stuck in understanding under point.

Basically B-Tree and Bitmap uses access ROWID (Root-branch leaves) a row of the table. Now the question is if B-Tree and Bitmap using ROWID to access the value then what is the advantage of using Bitmap indexes on index B-Tree, if column has low cardinality.

Kind regards

I was studying all internal Index and am bit stuck in understanding under point.

The starting point when you are studying is the Oracle documentation.

Basically B-Tree and Bitmap uses access ROWID (Root-branch leaves) a row of the table. Now the question is if B-Tree and Bitmap use ROWID to access the value then what is the advantage of using Bitmap indexes on index B-Tree, if the column is to have a low cardinality.

For this issue of language SQL doc and the Data Warehousing Guide answers your question

http://docs.Oracle.com/CD/B28359_01/server.111/b28286/statements_5011.htm

BITMAP IMAGE

Specify BITMAP to indicate that index must be created with a bitmap for each separate key, rather than indexing each line separately. Bitmap indexes store the ROWID associated with a key in the form of bitmap value. Each bit in the bitmap corresponds to a possible rowid. If the bit is set, then it means that the line with the corresponding rowid contains the value of the key. The internal representation of bitmaps is best suited for applications with a low level of concurrent transactions, such as data warehousing.

That first sentence is the first clue: "a bitmap for each separate key, rather than indexing each line separately. An index of "MALE" (e.g., MALE/FEMALE) bitmap has the AN index entry pointing to "MALE", then a bitmap representing the ROWID for any lines that have the "MASCULINE" attribute

A lot index is a separate index for EACH line entry. So the difference would pretty much look the same if rows 2,4,6,8,10 are male:

bunch - "male: 2", "man: 4", "male: 6", "male: 8", "male: 10»

bitmap - ' male: 2, 4, 6, 8, 10'

The foregoing is not a faithful but close - image segment index memory is essentially repeat the key value ("male") for each entry, but the bitmap index only indicates "male" once. Oracle can also store this list of ROWID (bitmap) very efficiently and compactly. This saves space and improves the performance.

The Data Warehousing Guide gives some details:

http://docs.Oracle.com/CD/B28359_01/server.111/b28313/indexes.htm#sthref85

Cardinality

The benefits of using bitmap indexes are the most important for the columns in which the relationship between the number of distinct values for the number of rows in the table is small.

We refer to this ratio, such as the degree of cardinality. A type column, which has only two distinct values (male and female), is optimal for a bitmap index. However, data warehouse administrators also build indexes on columns with higher cardinalities bitmap.

For example, on a table with 1 million rows, a column with 10,000 distinct values is a candidate for a bitmap index. A bitmap on this column index can surpass a

B-tree indexes, especially when this column is often queried in conjunction with other indexed columns. In fact, in a typical data warehouse environments, a bitmap index can be considered for any non-unique column.

B-tree indexes are most effective for high-cardinality data: that is, for data with several possible values, such as customer_name or phone_number . In a data warehouse, the B-tree indexes should only be used for unique columns or other with very high cardinalities (that is, the columns that are almost unique). The majority of the clues in a data warehouse should be bitmap indexes.

In ad-hoc queries and similar situations, bitmap indexes can significantly improve query performance. ANDand OR conditions in the WHERE clause of a query can be quickly resolved by performing the corresponding Boolean operations directly on the bitmaps before converting the resulting bitmap to the ROWID. If the number of lines is small, the query can be answered quickly without resorting to a table scan complete.

As suggested by this text index bitmap can be really powerful when multiple bitmap indexes can be combined to perform AND and OR operations.

bitmap - ' male: 2, 4, 6, 8, 10'

bitmap - 'age20': 1,2,5,7,8,9,10'

It should be easy to see that a predicate of 'MÂLE' OR "AGE20" combine the two bitmaps and produce

bitmap - ' male | years20 '-1,2,4,5,6,7,8,9,10'

A string of ROWID values compact nice. Then Oracle can just go GET each of these rows by rowid.

A predicate of 'MALE' AND 'Years20' would be similar

bitmap - 'male & years20' - 2,8,10

Again, YOU, a human, can easily start at the beginning of these 'bitmaps' and select values answering this predicate.

Just as easily Oracle can combine MUCH these bitmaps at the same time as the ROWID values are SORTED in each group.

Tags: Database

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    OracleGuy777 wrote:
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  • for bitmap or not...

    First of all, I would like to apologize if this question has been answered, but I couldn't find the answer to my related question.

    I am running 11.2.0.2 EE on Solaris 10.

    My problem is that we have a table very heavily used in an OLTP database that includes the columns that are regularly used in queries.

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    Bitmap indexes are not suitable for tables that have a large number of small insertions, deletions or changes that change the indexed columns - so your main table of 5 M of lines is not a good candidate.  However, OLTP systems tend to have queries more predictable than DSS or DWH, if the fact that you have a lot of columns 'low cardinality' (i.e. columns with a relatively important lines by the key value) may not be a problem if you can identify a few multi-column indexes and build them with compression on the main columns.  It is also interesting to note that even if you create the index b-tree on individual columns (even once, you would use compression), the optimizer can do "tree / conversion of bitmap" to optimize the use of these indices.

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    Concerning

    Jonathan Lewis

    http://jonathanlewis.WordPress.com

    Now on Twitter: @jloracle

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