Redshift Query Performance to reduce CPU utilisation

Question

I want to take a general Idea of how I can optimise the query performance in redshift Database, I have Huge queries with lots of joins, I do understand using sort and Dist key it can be achieved but is there a method which we can follow in order to get some optimal results.

What to look in a table and how to approach query optimisation in redshift? What are the necessary steps to look for or approach in order to have a certain plan for optimisation?

Any guidance will help a lot

Answer 1

Having improved many queries on Redshift there are a few things I can point you towards. First let me list a few tools / techniques to make sure you have these in your toolbox.

• Ability to read and EXPLAIN plan and find expected costly points
• Know where to find the query "actual" execution report
• Know the system tables to find join, distribution, and disk io reports

So with those understood let's look at where many queries go sideways on Redshift. I will try to list these out in pareto order but any of these, or combos, can create significant issue.

#1 - Fat in the middle queries. When joining it is possible to expand the number of rows being operated upon many fold. Cross joining is a clear way this can happen but isn't how this usually happens. If the join on conditions create a many to many join pattern the number of rows can expand. When the table sizes are very large and the "multiplication" can make absurd data sizes. The explain plan can show this but not always - use of DISTINCT and GROUP BY can "hide" the true size of the dataset in play. Performing a SELECT COUNT(*) on your join tree can help show how big this is. You may also may need to look a pieces of the join tree if a later join is collapsing the rows (failure of the query optimizer?). Redshift is a columnar database and not well set up for the creation of data - this includes during the execution of query.

#2 - Distribution of large amounts of data. Redshift is a cluster and the node are connected together by ethe.net cables and these connections are the slowest part of the cluster. A lot of work is done by the query optimizer to minimize the amount of data that needs to move around the.network. However, it doesn't know your data as well as you do and doesn't always do this well. Look at the type of joins you are getting - is distribution needed? how much data is being distributed? Also, group by (and window functions) need to combine rows and therefore may need redistribution to complete. How big are the data sets entering your aggregation steps?

Moving a lot of data around the.network will be slow. The difficulty is that it isn't always clear how to reduce this movement. Large join trees like you say you have can do "odd" things when it comes to the resulting distribution of the "joined" data. Joins are performed one at a time and the order these happen can matter. The query optimizer is making a number of decisions about the order of joins and how to organize the resulting data from each join. The choices it makes is based on what it sees in the table metadata so completeness of metadata matters. WHERE conditions can also impact the optimizer's choices. There are just way to many interactions to itemize them out here. Best advice is to look at the performance per step and see if data distribution is a factor. Then work to control how data is distributed in the query's execution. This may mean changing the join trees or even decomposing the query into several with temp table that have distribution set so that data movement is minimized.

#3 Excessive IO traffic - While not as slow as the.networks, the disk IO subsystem is often a bottleneck. This shows up in a few ways. Are you reading more data from disk than is needed? (Metadata up to date?) Do you need a redundant WHERE clause to eliminate data? (Redundant WHERE clause is one that isn't needed functionally but is added so Redshift can perform the metadata comparisons that will reduce data read at scan.) Data spill is another way that disk IO can be strained (this goes back to #1). If data needs to spill to disk it can bring the disk IO performance down considerably. Use your metadata and Where clauses well.

Now these 3 areas often team up to kill your performance. Read too many rows from your tables, join all these extra rows together across the.network while also making many new rows. This data doesn't fit in memory so now Redshift needs to spill to disk to complete the query. Things slow down real fast in these conditions.

Lastly these factors I've listed are cluster wide "resources" of Redshift. If one query take up a lot of one of these then there is less for other queries running at the same time. What often happens is that the query writers on a cluster follow similar patterns (good or bad) and when their pattern is costly on one axis then many of their queries are costly on the same axis. This shows up as queries that work "ok" when run in isolation but very badly when others are using the cluster. This generally means that many queries are contributing to pushing the cluster "over the edge" on some limited resource. There are system tables that you can look at to see aggregated IO or.network traffic to see these effects.

Good queries are:

• Don't make a lot of new "rows" during execution (not fat in the middle)
• Keep large data sets "on node" and only redistribute data once the data has been pared down significantly
• Don't read more data from disk than is necessary and don't spill

The problem is that doing all of these isn't always possible the trick is to not over subscribe the cluster resources you have.