#Redshift create view how to
#Redshift create view free

#Redshift create view free

This is still well within the free trial. In addition, you will also be charged for data storage. Serverless will try and optimise the computation by scaling to more RPUs which will increase cost.

Note that the queries would cost between $20-$30 as we are using the entire dataset with minimal filtering. In the second part, we will setup Quicksight and connect to our Redshift cluster, to access the data and build dashboards to generate some interesting insights. The other features provided by Redshift such as cluster performance monitoring, data recovery and guarding against surprise bills will also be touched upon. We will then run some queries and unload data to S3. You can also infer some info about intermediate result distribution from the explain plan but is doesn't provide a complete map of what it plans to do.In the first part of this blog, we will focus mainly on setting up Redshift serverless cluster and configuring access to external WorldWide Event Attendance Data Exchange via Redshift DataSharing Feature so it can be accessed from the database in the cluster provisioned. To sum up to see what Redshift is planning to do with your joins look at the EXPLAIN plan.

It does this to avoid having only 1 slice with data and all others with nothing to do as this would be very inefficient. If you DIST BOTH then Redshift is redistributing both sets of data to some new distribution (usually one of the join on columns). Here the EXPLAIN plan can give you hints about what Redshift is 'thinking' - if you see a DIST INNER join Redshift is moving the data of one table (or intermediate result set) to match the other.

The further join is away from the source table metadata (deep into the join tree) the more uncertain Redshift is about what the incoming and outgoing data of the join will look like. But here large and small are based on post WHERE clause filtering and the guesses Redshift can make based on metadata. For example it will usually join small tables to large tables first and keep the distribution of the large table. So Redshift is trying to make smart choices about the join order and intermediate result distribution. I've worked on clients' queries with dozens of joins and many nested levels of subquery and the EXPLAIN plan is often very different than the original query as written.

The more tables you are joining and the more complex your query, the more choices Redshift has and the less likely it is that the EXPLAIN plan will join in the order you specified. To see what order that Redshift plans to join your tables look at the EXPLAIN plan for the query. One of the choices Redshift makes is how the intermediate data of the query is distributed (your question) but remember that these intermediate results can be for a modified join order. The query plan (including join order and data distribution) is set at query compile, it is not dynamically determined during execution.

#Redshift create view how to

If you want Redshift to make smart choices on how to join your tables together you will want your table metadata to be up to date. Redshift makes these optimization choices based on the table metadata that is created / updated by ANALYZE. Your CTEs look like subqueries to the compile / optimization process and the order in which the joins are performed may have no relation to how you wrote the query. When you issue a query Redshift will compile and optimize that query as it sees fit to achieve the best performance and be logically equivalent.