Amazon Redshift: Best Practices for Optimizing Query Performance

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Amazon Redshift Data Analytics

By Scott Peters, Lead Data Science Architect at Onica & Sudhir Gupta, Sr. Partner Solutions Architect at AWS. This post was originally published on the AWS Partner Network (APN) blog.

Across a plethora of industries, organizations look to utilize data analytics for operations and other functions that are critical to success. However, as data volumes grow, the management and value-extraction from data can get increasingly complex.

Amazon Redshift

Amazon Redshift is a powerful data warehouse service from Amazon Web Services (AWS) that simplifies data management and analytics. Let’s take a look at Amazon Redshift and best practices you can implement to optimize data querying performance.

Data Lakes vs. Data Warehouse

Before digging into Amazon Redshift, it is important to know the differences between data lakes and warehouses. A data lake, such as Amazon S3, is a centralized data repository that stores structured and unstructured data, at any scale and from multiple sources, without altering the data. Data warehouses on the other hand, store data in a reconciled state that is optimized to perform ongoing analytics and only the data needed for analytics is loaded into them from data lakes.

Amazon Redshift takes storage for data analytics one level further, amalgamating the qualities of data lakes and warehouses into a “lake house” approach. It allows the querying of large exabyte-scale data lakes while being cost-effective and minimizing data redundancy, as well as minimizing maintenance overhead and operational costs.

Amazon Redshift Architecture

In order to process complex queries on big data sets rapidly, Amazon Redshift architecture supports massively parallel processing (MPP) that distributes the job across multiple compute nodes for concurrent processing.

These nodes are grouped into clusters and each cluster consists of three types of nodes:

Leader Node manages connections, acts as the SQL endpoint, and coordinates parallel SQL processing.

Compute Nodes, composed of “slices”, execute queries in parallel on data that is stored in a columnar format, in 1MB immutable blocks. An Amazon Redshift cluster may contain between 1-128 compute nodes, portioned into slides that contain the table data and act as a local processing zone.

Amazon Redshift Spectrum Nodes execute queries against an Amazon S3 data lake.

Amazon Redshift: Best Practices for Optimizing Query Performance 1

Optimizing Query Performance

Extracting optimal querying performance mainly can be attributed to bringing the physical layout of data in the cluster in congruence with your query patterns. If Amazon Redshift is not performing to its potential, leverage the following changes.

Reconfiguring Workload Management (WLM)

Often left in its default setting, performance can be improved by tuning WLM, which can be automated or done manually. When going the automatic route, Amazon Redshift manages memory usage and concurrency based on cluster resource usage, and it allows you to set up eight priority-designated queues. When going the manual route, you can adjust the number of concurrent queries, memory allocation and targets.

Querying performance can also be optimized through some WLM configuration parameters such as:

Query monitoring rules that can help you manage expensive or runaway queries.

Short query acceleration which helps you prioritize short-running queries over longer-running queries, using machine learning algorithms to predict querying execution time.

Concurrency scaling, which helps you add multiple transient clusters ion seconds, to accelerate concurrent read queries.

WLM Best Practices

Some WLM tuning best practices include:

  • Creating different WLM queries for different types of workloads.
  • Limiting maximum total concurrency for the main cluster to 15 or less, to maximize throughput.
  • Enabling concurrency scaling.
  • Keeping the number of resources in a queue to a minimum.

Refining Data Distribution

The rows of a table are automatically distributed by Amazon Redshift across node slices, based on the following distribution styles:

  • AUTO – Starts with ALL and switches to EVEN as the table grows.
  • ALL – Consists of small, frequently joined and infrequently modified tables that are placed on the first slice of each compute node.
  • EVEN – Consists of large, standalone fact tables that are not frequently joined or aggregated in a round-robin distribution across the slices.
  • KEY – Consists of frequently joined, fact tables or large dimension tables. In this style, a column value is hashed and the same hash value is placed on the same slice.

Using the right distribution patterns can maximize the performance of JOIN, GROUP BY and INSERT INTO SELECT operations

Refining Data Sorting

The physical order of data on a disk is defined by sort keys. Table Columns used in WHERE clause predicates are a good choice for sort keys and date or time-related columns are used commonly. Zone maps, that are stored in memory and generated automatically, are used to define the value extremes for each block of data. Effectively using sort keys and zone maps together can help you restrict scans to the minimum required number of blocks.

The diagram below illustrates how table sorting focuses scanning targets for time-based queries, thereby improving query performance.

Amazon Redshift: Best Practices for Optimizing Query Performance 2

Optimal Query Performance Best Practices

Utilizing the aforementioned Amazon Redshift changes can help improve querying performance and improve cost and resource efficiency. Here are some more best practices you can implement for further performance improvement:

  • Using SORT keys on columns often used in WHERE clause filters
  • Using DISTKEY on columns that are often used in JOIN predicates
  • Compressing all columns except the first sort key column
  • Partitioning data in the data lake based upon query filters such as access pattern

To explore some more best practices, take a deeper dive into the Amazon Redshift changes and see an example of an in-depth query analysis, read the AWS Partner Network (APN) Blog here.

If you are embarking on a data journey and are looking to leverage AWS services to quickly, reliably and cost effectively develop your data platform, get in touch with our Data Engineering & Analytics team today!

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