How to optimize the join of two tables?
It can be activated by executing set hive.optimize.bucketmapjoin=true; before the query. If the tables don’t meet the conditions, Hive will simply perform the normal Inner Join. If both tables have the same amount of buckets and the data is sorted by the bucket keys, Hive can perform the faster Sort-Merge Join.
How do you split data into related tables?
It’s important to understand how to use SQL to deal with data that has been split up into multiple related tables, and bring the data back together across the tables when you need it. We do that using a concept called “join”s and that’s what I’ll show you next.
When is a single table better than multiple tables?
Basically a single table is good when data is one-to-one. When you have thousands of rows and columns of data, where the data is one-to-many, multiple tables are better to reduce duplicate data. Comment on Nicholas’s post “Here’s a good discussion: http://stackoverflow.co…”
How to perform a hive join of two tables?
To perform a bucketjoin “the amount of buckets in one table must be a multiple of the amount of buckets in the other table” as stated before and in addition hive.optimize.bucketmapjoin must be set to true.
Do you need to inner join two massive tables?
I have two massive tables with about 100 million records each and I’m afraid I needed to perform an Inner Join between the two. Now, both tables are very simple; here’s the description:
How to improve SQL Server table insert performance?
The execution speed of this query is too slow to be useful. Because the query takes too long to process, I tried out following solutions: Split the 20 joins into 4 joins on 5 tables. The query performance remains low however. Put indexes on the foreign key columns.
How to improve SQL Server efficient join technique?
However this performs quite badly due to the 5 usages of “exists” i.e. for 1 million rows in the main table, the query runs for 5 seconds and the STATISTICS IO output shows a looooot of reads, even if the query only returns a small subset of data. Can you give me any hint how we could improve this query dramatically?