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Hash Natural Join

To perform hash join, Oracle follows these steps: 1.      Oracle chooses the smallest of two tables as the hash table (otherwise called as driving table). Oracle built the hash table in RAM after applying the hash function on the joining column(s) of the driving table. 2.      Oracle chooses the other [big] table as the probe table (otherwise called as driven table or probing table). It traverse through all the records of this probe table, applies the same hash function on the joining column(s) [column(s) used to join these two tables] and will hit the corresponding entry in the hash table. 3.      Oracle returns the output if a record from driving table is already present in the same hash key, else no record will be returned. It may look like Nested loop join & Hash join have the same architecture since both these have the concept of driving & driven tables but they have entirely different design if you closely look a...
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External Table

External Table is the another type of table which is supported by Oracle apart from IOT and Cluster table. External tables allow Oracle to query data that is stored outside the database in flat files. The ORACLE_LOADER (an in-built driver provided by Oracle itself) can be used to access any data stored in any format that can be loaded by invoking SQL*Loader (an in-built utility provided by Oracle itself to load the data from the flat file into the data table). External tables enable us to read flat-files (stored on the O/S) using SQL. They have been introduced in Oracle 9i as an alternative to SQL*Loader. External tables are essentially stored SQL*Loader control files, but because they are defined as tables, we can access our flat-file data using all available read-only SQL and PL/SQL operations. We can also read flat-files in parallel and join files to other files or tables, views and so on. In this example, the data file (emp.txt) is placed in the location, “C:\test”. Then you ...
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Bitmap index

Bitmap index is one of the index types that is supported by oracle. Unlike B*tree index, this is very compressed index. It means if I create a bitmap index on a column, then the generated index table will be smaller when compared the index table which is generated by the binary index on the same column. The reason is, in bitmap index, ROWIDs won’t be stored in the index table, instead index values will be mapped against bitmaps. Bitmap is nothing but it stores either of this values, 1(Match) or 0(No Match). For each distinct values in the column, a separate index record will be created. Unlike b*tree index, bitmap index can accept NULL and it creates separate index entry for this. Bitmap index will work better if number of distinct values are relatively less when compared to the total number of records. Why is it referred as “relatively less”? this index will work fine if table of 1,000,000,000 records contains 5000 distinct values and if table of 1,000 records contains 5 distinct va...
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