Querying Hive Data
Now that you have connected to Hive, you can start querying data stored in the Hive data warehouse. Hive uses a SQL-like language called HiveQL, which is similar to standard SQL, making it easy for SQL developers to work with Hadoop.
Basic HiveQL Queries
Here are some examples of basic HiveQL queries:
-
Creating a Table:
CREATE TABLE IF NOT EXISTS my_table (
id INT,
name STRING,
age INT
)
STORED AS PARQUET;
This creates a new table named my_table
with three columns: id
, name
, and age
. The data is stored in the Parquet file format.
-
Inserting Data:
INSERT INTO my_table
VALUES (1, 'John', 30), (2, 'Jane', 25), (3, 'Bob', 35);
This inserts three rows of data into the my_table
table.
-
Querying Data:
SELECT * FROM my_table;
This query selects all rows and columns from the my_table
table.
SELECT name, age FROM my_table WHERE age > 25;
This query selects the name
and age
columns from the my_table
table where the age
is greater than 25.
Advanced HiveQL Queries
Hive also supports more advanced SQL features, such as:
- Joins: Hive supports various types of joins, including inner, left, right, and full outer joins.
- Aggregations: Hive provides a wide range of aggregate functions, such as
SUM
, AVG
, COUNT
, and GROUP BY
.
- Partitioning: Hive supports partitioning data by one or more columns, which can improve query performance.
- Views: Hive allows you to create views, which are virtual tables that can be used in subsequent queries.
- User-Defined Functions (UDFs): Hive supports the creation of custom functions to extend the functionality of HiveQL.
Here's an example of a more advanced HiveQL query that performs a join and aggregation:
SELECT
department,
AVG(salary) AS avg_salary
FROM
employee_table
GROUP BY
department
ORDER BY
avg_salary DESC;
This query calculates the average salary for each department and orders the results by the average salary in descending order.
By mastering HiveQL, you can efficiently query and analyze large datasets stored in Hive, leveraging the power of the Hadoop ecosystem.