Join index in PAP smoothly

Aug 6th, 2022
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How to join index in PAP

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How to Join index in PAP

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hello and welcome to this quarters firebolt tech tips today were going to talk about join indexes lets jump right in so im going to look at a query this is a query thats joining the fact table to a dimension table so were going to run this query notice that were doing a where predicate on the dimension table were also grouping by that dimension table so you can tell this query is taking a fair amount of time to run which is quite unacceptable so 11.88 seconds so lets look at why that query ran that long so lets do an explain plan on it and lets take a look so the first thing you notice is the explain plan is actually doing an inner join from the fact table to the dimension table so you can tell were doing a left outer join here but because of the wear predicate firebolt is converting that to an inner join fireball always prefers outer joins compared to inner joints the other thing to note here is that we are using the aggregating index on the fact table so theres not a lo

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Sometimes, MySQL does not only use existing indices to join two tables, It also generates auto indices to join temporary tables. The cost of generating an auto index on a temporary table is negligible compared to the cost of execution without the index. This process is called materialization [3].
Definition. A join index is a collection of pairs {(r, s)} such that the record in table R with record ID (RID) r joins with the record in table S with RID s, ing to the join predicate which defines the index.
Join indexes are materialized views that improve access times for cross-table queries, and which are automatically updated when changed are made to the underlying tables.
The purpose of a join index is to accelerate common joins, even equijoins. One of the advantages of join indices is that they can be represented in a very compact way, allowing for highly efficient access.
The indexes must include all the join columns, in the same key order on both tables. A merge join on (col1, col2) can use indexes on (col1, col2) or (col2, col1) , but the key order must be the same for both tables.
The join index is comparable to a secondary index because it provides the Optimizer with additional access paths to the data. Teradata stores join indexes as separate physical database objects, unlike secondary indexes stored as internal subtables. The user can decide to store a join index in any database.
Creating a secondary index causes Teradata Database to build a separate internal subtable to contain the index rows, thus adding another set of rows that requires updating each time a table row is inserted, deleted, or updated.
The indexes must include all the join columns, in the same key order on both tables. A merge join on (col1, col2) can use indexes on (col1, col2) or (col2, col1) , but the key order must be the same for both tables.
The primary index deteriorates performance only if the distribution is skewed. No maintenance is needed. A secondary index deteriorates performance because it is mapped in a sub table, which must be maintained with every UPDATE and INSERT statement. This means we should only create a secondary index if it is also used!
The indexes must include all the join columns, in the same key order on both tables. A merge join on (col1, col2) can use indexes on (col1, col2) or (col2, col1) , but the key order must be the same for both tables.

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