redshift alter table rename takes forever

#Redshift alter table rename takes forever mod#

However, one bright spot, you can add columns to the internal tables with zero impact to existing materialized views. Adding columns: There are more DDL ( Data Definition Language) limitations on creating materialized views.This is similar to reading data from a table and helps avoid duplicating expensive table joins and aggregations. Materialized view on materialized view: Redshift lets you create materialized views based on materialized views you already created.In redshift you can create a materialized view to refer data in external tables (AWS S3) and even define one in terms of an existing view.

More than just tables: Do you have files in AWS S3 you would like to reference? Or maybe you already have a materialized view and need a new one with some additional data?.

Materialized views in Redshift have some noteworthy features. Think of a materialized view as the best of a table ( data storage) and a view ( stored sql query).Ī Redshift materialized views save us the most expensive resource of all – time. So, when you call the materialized view, all its doing is extracting data from the stored results. When a materialized view is created, the underlying SQL query gets executed right away and the output data stored. This is where materialized views come in handy. In other words, if a complex sql query takes forever to run, a view based on the same SQL will do the same. However, a view does not generate output data until it is executed. A view by the way, is nothing more than a stored SQL query you execute as frequently as needed. If this task needs to be repeated, you save the SQL script and execute it or may even create a SQL view. Sometimes this might require joining multiple tables, aggregating data and using complex SQL functions. We do this by writing SQL against database tables.

#Redshift alter table rename takes forever mod#

While this type of result can be produced in a number of ways, it feels much cleaner using the MOD function to get a whole number remainder.To derive information from data, we need to analyze it. You could then define the training and test group using these simple numbers. One approach would be to divide each User ID by 3 using the modulo operation MOD(, 3) which would produce one of the 3 different remainders (0, 1, and 2) for each of the users. So why is this valuable? Think about creating training and testing bins for an experiment that were a) replicable and b) more randomly selected than just the splitting my (ordered) dataset into two parts by a date cutoff. The MOD command in Redshift lets you perform this function MOD(3,2) will equal 1. 3/2 gives us a remainder of 1 - this would be the modulus. Modulo math is all about determining the remainder of dividing two numbers. Modulo math is usually reserved for more advanced programming languages but can be useful inside of SQL as well.

SELECT catid, catname FROM category WHERE mod ( catid, 2 ) = 1 ORDER BY 1, 2 catid There are so many other uses for row numbers when trying to clean or organize your data based on some ordinal parameter. If you want to see the most recent transaction for each customer for each product they’ve bought, just partition by both customer and the product, and your row numbering will restart with each new customer/product combination. Then filter your results to only include the rows that are in the position you care about - in this case, 1. Here we can assign an order to the transactions, grouped however you choose (in this case, by customer) and ordered however you choose (in this case, by transaction date descending, so the most recent orders for each customer are the same number: 1). The more elegant way is to use the ROW_NUMBER function. With no single date or date range to attach to all customers, we could find the most recent transaction date for each customer and then join the same transactions table to itself where the transaction date equals the customer’s most recent transaction date and the customer ID matches. We want the most recent purchase for each customer, even if it didn’t happen today. Think about customers with multiple purchases. SELECT salesid, sellerid, qty, ROW_NUMBER () OVER ( PARTITION BY sellerid ORDER BY qty asc ) AS row FROM winsales ORDER BY 2, 4 salesid