So, the first command under DDL is the Create command. The Create command is used to create a new database or a new component in the database like tables, stored procedure, index, etc. They are used to modify the tables to add, rename, edit or delete a column. Alter command can also rename the table itself. The Alter Table command allows us to modify an existing column in an existing table. This is useful when modifying the type or the size of the column.
An important thing to keep in mind is that we cannot rename a column with the modify sub-command. To rename, there is a separate sub-command. Run the below lines of command in sequence to add a new column to the table, modify the type of the column, rename the column, drop the column and finally rename the table itself.
The DROP commands are used to delete objects from the database or the schema. It can delete a table, view, stored procedure, index or even a database. However, the partition you create makes a pseudocolumn on which you can query, so you must rename your table column to something else that users should not query on! Now you want to partition on date. Your Hive definition could use "dtDontQuery" as a column name so that "date" can be used for partitioning and querying.
The table is also partitioned and data is stored in sequence files. The data format in the files is assumed to be field-delimited by ctrl-A and row-delimited by newline. Specify a value for the key hive. This comes in handy if you already have data generated. Starting Hive 4. For another example of creating an external table, see Loading Data in the Tutorial. Tables can also be created and populated by the results of a query in one create-table-as-select CTAS statement.
The table created by CTAS is atomic, meaning that the table is not seen by other users until all the query results are populated. So other users will either see the table with the complete results of the query or will not see the table at all. Starting with Hive 3. Starting with Hive 0. For an example, see Common Table Expression. Being able to select data from one table to another is one of the most powerful features of Hive.
Hive handles the conversion of the data from the source format to the destination format as the query is being executed. The new table contains no rows. Before Hive 0. In Hive 0. Such an organization allows the user to do efficient sampling on the clustered column - in this case userid. The sorting property allows internal operators to take advantage of the better-known data structure while evaluating queries, also increasing efficiency.
There is also an example of creating and populating bucketed tables. As of Hive 0. This feature can be used to improve performance for tables where one or more columns have skewed values. By specifying the values that appear very often heavy skew Hive will split those out into separate files or directories in case of list bucketing automatically and take this fact into account during queries so that it can skip or include the whole file or directory in case of list bucketing if possible.
A table that has been created as a temporary table will only be visible to the current session. Data will be stored in the user's scratch directory, and deleted at the end of the session.
The user will not be able to access the original table within that session without either dropping the temporary table, or renaming it to a non-conflicting name. S tarting in Hive 1. As of Hive 4. A table that supports operations with ACID semantics.
See this for more details about transactional tables. As of Hive 2. Hive includes support for non-validated primary and foreign key constraints. Some SQL tools generate more efficient queries when constraints are present. Since these constraints are not validated, an upstream system needs to ensure data integrity before it is loaded into Hive. As of Hive 3. The data is actually moved to the. The metadata is completely lost. When dropping a table referenced by views, no warning is given the views are left dangling as invalid and must be dropped or recreated by the user.
Otherwise, the table information is removed from the metastore and the raw data is removed as if by 'hadoop dfs -rm'. In many cases, this results in the table data being moved into the user's. Trash folder in their home directory; users who mistakenly DROP TABLEs may thus be able to recover their lost data by recreating a table with the same schema, recreating any necessary partitions, and then moving the data back into place manually using Hadoop.
This solution is subject to change over time or across installations as it relies on the underlying implementation; users are strongly encouraged not to drop tables capriciously. The purge option can also be specified with the table property auto. See the Alter Partition section below for how to drop partitions.
Removes all rows from a table or partition s. The rows will be trashed if the filesystem Trash is enabled, otherwise they are deleted as of Hive 2. Starting with HIVE 2. This is applicable only for managed tables see managed tables. This behavior can be turned off if the "auto. Starting with Hive 4. Alter table statements enable you to change the structure of an existing table.
Similarly, alter table partition statements allow you change the properties of a specific partition in the named table. As of version 0. Rename has been changed as of version 2. Hive versions prior to 0. You can use this statement to add your own metadata to the tables. Users can add their own properties to this list.
These statements enable you to change a table's SerDe or add user-defined metadata to the table's SerDe object. The SerDe properties are passed to the table's SerDe when it is being initialized by Hive to serialize and deserialize data. So users can store any information required for their custom SerDe here.
Remove SerDe Properties is supported as of Hive 4. Users should make sure the actual data layout conforms with the metadata definition. As of Hive release 2. As of Hive 1. The values can be number literals. Partition values should be quoted only if they are strings.
The location must be a directory inside of which data files reside. If the data does not exist in the partition's location, queries will not return any results. That is, every query specifying a partition will always use only the first partition. See these documents for details and examples:. This statement lets you change the value of a partition column.
One of use cases is that you can use this statement to normalize your legacy partition column value to conform to its type. Resource Group Management Statements. Table Maintenance Statements. Component, Plugin, and Loadable Function Statements. Other Administrative Statements. InnoDB Cluster. InnoDB ReplicaSet. Error Messages and Common Problems. MySQL 8. See Section 5. The mysql. Like other hidden data dictionary tables, the mysql. See Section The structure of the mysql.
Used by DDL operations that rename tablespace files. Data Definition Statements. Atomic Data Definition Statement Support. LIKE Statement. Silent Column Specification Changes.
Secondary Indexes and Generated Columns. Data Manipulation Statements. Parenthesized Query Expressions. The Subquery as Scalar Operand. Comparisons Using Subqueries. Restrictions on Subqueries. Transactional and Locking Statements. Statements That Cause an Implicit Commit. Restrictions on XA Transactions. Replication Statements. Functions which Configure the Source List.
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