Hive allows querying and managing large datasets stored in distributed systems like HDFS. It uses a schema-on-read approach where the schema is not enforced until query time, allowing flexible data storage. Hive supports built-in, user-defined, and aggregate functions. It performs equi-joins and supports various join strategies like map-side joins, reduce-side joins, shuffle joins, and bucket joins. Map-side joins perform the join during the map phase by joining records with the same key from sorted and partitioned tables.

2. Content
What all you need to
know about HIVE at very
high level:
✦ Architecture
✦ Workflow
✦ Read On Schema
Approach
✦ Functions
✦ Join Strategies

3. HIVE: Architecture
Refer: https://www.tutorialspoint.com/hive/hive_introduction.htm

4. HIVE: Workflow
Refer: https://www.tutorialspoint.com/hive/hive_introduction.htm

5. HIVE: Schema on Read
Approach
Let user to redefine tables to match the data without touching the data, unlike Mysql’s Schema on Write
approach. (Reference: https://www.marklogic.com/blog/schema-on-read-vs-schema-on-write/)
No predetermined structure so the data can be presented in a schema that is most relevant to the task at
hand.
Upfront modeling exercise disappears.

6. Hive has serialization and deserialization adapters to let the user do this, so it
isn’t intended for online tasks requiring heavy read/write traffic.
HIVE:
Seralization/Deserialization

7. HIVE: Functions
There are three types of function APIs in Hive:
Built-In Functions
UDF (User Defined Functions- Normal functions) is a function that takes one or
more columns from a row as argument and returns a single value or object. Eg-
concat(arg1, arg2)
UDTF (User Defined Table Functions) takes zero or more inputs and and produces
multiple columns or rows of output. Eg: explode()
UDAF (User Defined Aggregate Functions)
Macros a function that uses other Hive functions.
Reference:
https://www.qubole.com/resources/hive-function-cheat-sheet/

8. HIVE: Join
Hive allows only equi-join.
So ON clause can have only equal conditions
(=) combined with AND operator only.

9. HIVE: Join Strategies
Map-Reduce Join
Map Side Join (join during map phase)
Reduce Side Join (join during reduce phase)
Hive Shuffle Join
Hive Map-Side Join (Broadcast Join)
Hive Bucket Join

10. HIVE: Map-Side Join
Join the records by key during read
of the input files
Highly constraint
Both tables should be sorted on
same join key
Both tables should have same
number of partitions.
Usually achieved when both input
tables were created by (different)
MapReduce jobs having the same
amount of reducers using the same
(join) key.