How do I optimize hive joins?
Physical Optimizations:
- Partition Pruning.
- Scan pruning based on partitions and bucketing.
- Scan pruning if a query is based on sampling.
- Apply Group By on the map side in some cases.
- Optimize Union so that union can be performed on map side only.
- Decide which table to stream last, based on user hint, in a multiway join.
How do I optimize multiple JOINs query?
It’s vital you optimize your queries for minimum impact on database performance.
- Define business requirements first.
- SELECT fields instead of using SELECT *
- Avoid SELECT DISTINCT.
- Create joins with INNER JOIN (not WHERE)
- Use WHERE instead of HAVING to define filters.
- Use wildcards at the end of a phrase only.
How to optimize multiple left joins in SQL?
Eventually you’ll want a few more attributes and you might exceed some architectural limit of the database on the number of joins it can do. The solution is: don’t reconstruct the row in SQL. Instead, query the attributes as multiple rows, instead of trying to combine them onto a single row.
How many left joins are there in a query?
The query has 50+ left joins between 10 or so tables. To give a brief overview of the database model, the tables joined are tables that store data for a particular data type (ex: date_fields, integer_fields, text_fields, etc.) and each has a column for the value, a “datafield” id, and a ticket id.
What should you consider when optimizing a query?
Any comments or questions are appreciated. There are always 2 things to consider when optimising queries: You are performing date manipulations before you join your dates. As a general rule this will prevent a query optimser from using an index even if it exists.
Do you have to make one join per attribute?
You’re using a variation of the terrible antipattern called Entity-Attribute-Value. You’re storing attributes on separate rows, so if you want to reconstruct something that looks like a conventional row of data, you need to make one join per attribute.