Amazon Redshift is one of the leading big data management services that any business can use to extract, transform and load data for various business uses. Amazon’s AWS platform is designed to help with that by providing access to Amazon Redshift with scalable AWS services. Redshift is complex, which gives you a lot of customization options but can also be harder to optimize without help. Here are 10 Amazon Redshift performance tips to maximize your Amazon Redshift experience.
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- Pre-Build Materialized Views
- Use Federated Queries
- Compress Data Where Possible
- Optimize Memory Management
- Combine Processes in a Transaction
- Capitalize on Concurrency Processes
- Make Improvements with Amazon Redshift Advisor
- Get Familiar With Redshift’s Data Types
- Repair Tables to Reduce Fragmentation
- Streamline the ETL Process with Integrate.io
Pre-Build Materialized Views
Many people don't know this, but Redshift can use pre-built materialized views to improve query performance and ease of use for data management purposes. Materialized views show the results of a query in a specific way. You can create templates for the data that you want to retrieve using AWS services so that you can speed up the ETL process.
Quickly Organize Data as it Comes In
Materialized views are so effective at speeding up the ETL process because they make data easier to work with.
For example, use cases where a company runs a query to find customer names by their email address are made easier by creating a materialized view to hold the results.
It can be one table that holds all of the results separating it from the rest of the data with distribution keys to make similar PostgreSQL queries faster in the future.
Use Federated Queries
Another Amazon Redshift performance tip is to use federated queries. Federated queries are designed to let you extract, transform and load live data in outside databases from within your Amazon Redshift Spectrum database.
Pull Together Resources From Other Databases
Put simply, you can use federated queries to aggregate data from other compute nodes. This can be helpful when building business intelligence that requires real-time updates to essential information.
For example, a company that needs to have updated stock information from other companies’ data sources, like NASDAQ, can aggregate real-time data to support investment decisions using Amazon S3.
Compress Data Where Possible
Amazon Redshift is a massive data warehouse using Amazon S3, and your database can grow exponentially over time. While Redshift can resize to cover the amount of data that you need relying on massively parallel processing (MPP), query execution for that data can become problematic.
Increasing the amount of columnar data in your data lake means that your queries may have to run against more sort keys to find what you are looking for. Your searches take longer to process.
Compression Minimizes That Slowdown
Applying compression to your Amazon Redshift data and compute nodes can minimize that slowdown is another Amazon Redshift performance tip. Compression essentially reduces code to its smallest possible memory usage for data storage and processing.
For example, code written with a lot of erroneous characters decreases throughput. Simple compression methods, such as removing spaces, line breaks, and other unnecessary syntax or fixing sort keys reduce latency when sorting through that data.
Advanced Compression Techniques Make Queries Much Faster
Compression can be much more complex than that, especially on the server-side of data warehouses. The larger your data lake is, the more effective compression can be at helping you make it usable in real-time.
Compression Tools Are Available
Compressing Amazon Redshift data can be done using two formats: LZ4 and Snappy. Snappy is an order of magnitude faster than LZ4 but requires more CPU power to compress and decompress data, so it’s advisable to only use Snappy when necessary.
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Optimize Memory Management
Optimizing memory management is important for running a successful Amazon Redshift cluster. The amount of memory that your Amazon Redshift Cluster uses is directly connected to the performance of your relational database. However, size is not as important as how you manage the memory that you use.
How Memory Management Helps
How you use that memory determines how effectively you can use your data, making big data management the single most important part of working with relational databases.
Redshift’s Workload Management tool has been refined to help you manage memory for your Amazon Redshift cluster more effectively.
Amazon Web Services automated much of the WLM tool’s processes so that it is easy to use SQL in a hyper-efficient way. It can minimize memory usage while running SQL queries, as well as optimize data management across your data warehousing setup.
Setting WLM priorities is an effective way to manage your WLM queue. WLM keeps its actions in a queue so that it can run SQL queries efficiently. By changing priorities, you can choose which things WLM focuses on first.
This can be an effective schema to tailor how it handles data. For example, if you have processes that you need to run in a specific order for your workflow schema, WLM can prioritize those actions in a specific order. That way, it can handle the workflow automatically, saving you time and effort.
Combine Processes in a Transaction
Amazon Redshift is a big data warehouse that is designed to provide high-performance analytical processing of petabyte-scale datasets. It can do this because it takes full advantage of its ability to combine all processes in a transaction. This can be especially helpful if you need to run many reports or want to perform advanced analyses.
Automation Makes Processing Parallel
More than that, it is efficient at combining operations so that they can run in parallel. This can drastically reduce the time needed to complete analytical queries. At scale, parallel processing can save a lot of time and resources.
Capitalize on Concurrency Processes
Concurrency processing covers more than just combined transactions. Redshift can run large segments of processes concurrently, even if it takes multiple transactions. Much of how concurrency processing is managed by Redshift happens automatically.
UNLOAD Data into the Lake
One way to take advantage of the concurrency processing features in Amazon Redshift is to Unload data into a data lake. When you run queries, you can have that data pooled in a data lake so that you can run multiple queries that all empty into it at the same time. This is an effective way to get a lot of data into your system quickly.
Make Improvements With Amazon Redshift Advisor
Amazon developed a system to help you improve your Redshift experience without any outside help. It is called Amazon Redshift Advisor. Using it is one of our favorite Amazon Redshift performance tips.
Optimize Based on Redshift Advisor’s Advice
Amazon Redshift Advisor is a machine learning-based system that can audit your Redshift implementation and identify ways that you can optimize query performance. If Amazon Redshift Advisor is suggesting something, that idea came from an extremely detailed analysis of how other Redshift implementations improved their query performance under similar situations.
Get Familiar With Redshift’s Data Types
Queries can have issues finding data if you store it as the wrong type. In these cases, it may miss that data entirely. In other cases, it may retrieve data that needs to be converted to a different type before you can use it.
Avoid such problems by ensuring that all of the data that you store is transformed into the right type of data.
Repair Tables To Reduce Fragmentation
Database tables can fragment over time, making it harder to find information. By repairing tables, you can bring all their contents back together and make it easier for queries to find the right data set quickly. This can save a lot of time when running many queries across the database.
Amazon Redshift has built-in systems to help you restructure data and bring tables back together. To prevent interruptions to service, schedule maintenance during downtimes or only do it at necessary intervals.
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Streamline the ETL Process With Integrate.io
Looking for more Amazon Redshift performance tips? Use Redshift to power your business intelligence. Integrate.io is a cloud-based ETL solution that can help you set up data pipelines, automate workflows and visualize the inner workings of your data management systems like Amazon Redshift and Amazon S3.
Learn more about how Integrate.io can help your company take full control of its ETL process using Amazon Redshift by scheduling a seven-day demo or working with one of our consultants to see what an impact on your business integrate.io can have.