When should you use cross join?

When should you use cross join?

The CROSS JOIN is used to generate a paired combination of each row of the first table with each row of the second table. This join type is also known as cartesian join. Suppose that we are sitting in a coffee shop and we decide to order breakfast.

What are the best practices to improve Hive query performance?

Hive Performance – 10 Best Practices for Apache Hive

• Partitioning Tables: Hive partitioning is an effective method to improve the query performance on larger tables.
• De-normalizing data:
• Compress map/reduce output:
• Map join:
• Input Format Selection:
• Parallel execution:
• Vectorization:
• Unit Testing:

How will you improve the performance of a program in Hive?

Types of Performance Tuning Techniques

1. 1 Avoid locking of tables.
2. 2 Use the Hive execution engine as TEZ.
3. 3 Use Hive Cost Based Optimizer (CBO)
4. 4 Parallel execution at a Mapper & Reducer level.
5. 5 Use STREAMTABLE option.
6. 6 Use Map Side JOIN Option.
7. 7 Avoid Calculated Fields in JOIN and WHERE clause.

How do you optimize a join in Hive?

Physical Optimizations:

1. Partition Pruning.
2. Scan pruning based on partitions and bucketing.
3. Scan pruning if a query is based on sampling.
4. Apply Group By on the map side in some cases.
5. Optimize Union so that union can be performed on map side only.
6. Decide which table to stream last, based on user hint, in a multiway join.

Why is Hive slow?

Hive tables are linked to directories on HDFS or S3 with files in them interpreted by the metadata stored with Hive. Without partitioning, Hive reads all the data in the directory and applies the query filters to it. This is slow and expensive since all data has to be read.

How Hadoop improve Hive query performance?

How to Improve Hive Query Performance With Hadoop

1. Use Tez Engine. Apache Tez Engine is an extensible framework for building high-performance batch processing and interactive data processing.
2. Use Vectorization.
3. Use ORCFile.
4. Use Partitioning.
5. Use Bucketing.
6. Cost-Based Query Optimization.

Which type of join is most resource intensive and slowest?

Broadcast Join
Most resource-‐ intensive and slowest join type. Broadcast Join Small tables are loaded into memory in all nodes, mapper scans through the large table and joins. Very fast, single scan through largest table. All but one table must be small enough to fit in RAM.

Why do we use cross join?

The CROSS JOIN is used to show every possible combination between two or more sets of data. You can do a cross join with more than 2 sets of data. Cross Joins are typically done without join criteria.