Amazon Redshift: Ten years of continuous reinvention



Ippokratis: As we highlight in the paper, the service has evolved at a rapid pace in response to customers’ needs. We focused on four main areas: 1) customers’ demand for high-performance execution of increasingly complex analytical queries; 2) our customers’ need to process more data and significantly increase the number of users who need to derive insights from that data; 3) customers’ need for us to make the system easier to use; and 4) our customers’ desire to integrate Redshift with other AWS services, and the AWS ecosystem. That’s a lot, so we’ll provide some examples across each dimension.

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Enterprise companies use spatial data for decision optimization and gain new insights regarding the locality of their business and services. Industries rely on efficiently combining spatial and business data from different sources, such as data warehouses, geospatial information systems, transactional systems, and data lakes, where spatial data can be found in structured or unstructured form. In this demonstration

Offering the leading price performance has been our primary focus since Rahul first began working on what would become Redshift. From the beginning, the team has focused on making core query execution latency as low as possible so customers can run more workloads, issue more jobs into the system, and run their daily analysis. To do this, Redshift generates C++ code that is highly optimized and then sends it to the distributor in the parallel database and executes this highly optimized code. This makes Redshift unique in the way it executes queries, and it has always been the core of the service.

We have never stopped innovating here to deliver our customers the best possible performance. Another thing that’s been interesting to me is that in the traditional business intelligence (BI) world, you optimize your system for very long-running jobs. But as we observe the behavior of our customers in aggregate, what’s surprising is that 90 percent of our queries among the billions we run daily in our service execute in less than one second. That’s not what people had traditionally expected from a data warehouse, and that has changed the areas of the code that we optimize.

Rahul: As Ippokratis mentioned, the second area we focused on in the paper was customers’ need to process more data and to use that data to drive value throughout the organization. Analytics has always been super important, but eight or ten years ago it wasn’t necessarily mission critical for customers in the same way transactional databases were. That has definitely shifted. Today, core business processes rely on Redshift being highly available and performant. The biggest architectural change in the past decade in support of this goal was the introduction of Redshift Managed Storage, which allowed us to separate compute and storage, and focus a lot of innovation in each area.

The Redshift managed storage layer (RMS) is designed for a durability of 99.999999999% and 99.99% availability over a given year, across multiple availability zones. RMS manages both user data as well as transaction metadata.

Another big trend has been the desire of customers to query across and integrate disparate datasets. Redshift was the first data warehouse in the cloud to query Amazon S3 data, that was with Redshift Spectrum in 2017. Then we demonstrated the ability to run a query that scanned an exabyte of data in S3 as well as data in the cluster. That was a game changer.

Customers like NASDAQ have used this extensively to query data that’s on local disk for the highest performance, but also take advantage of Redshift’s ability to integrate with the data lake and query their entire history of data with high performance. In addition to querying the data lake, integrated querying of transactional data stores like Aurora and RDS has been another big innovation, so customers can really have a high-performance analytics system that’s capable of transparently querying all of the data that matters to them without having to manage these complex integration processes that other systems require.

This diagram from the research paper illustrates how a query flows through Redshift. The sequence is described in detail on pages 2 and 3 of the paper.

Ippokratis: The third area we focused on in the paper was ease of use. One change that stands out for me is that on-premises data warehousing required IT departments to have a DBA (data base administrator) who would be responsible for maintaining the environment. Over the past decade, the expectation from customers has evolved. Now, if you are offering data warehousing as a service, the systems must be capable of auto tuning, auto healing, and auto optimizing. This has become a big area of focus for us where we incorporate machine learning and automation into the system to make it easier to use, and to reduce the amount of involvement required of administrators.

Rahul: In terms of ease of use, three innovations come to mind. One is concurrency scaling. Similar to workload management, customers would previously have to manually tweak concurrency or reset clusters of the manually split workloads. Now, the system automatically provisions new resources and scales up and down without customers having to take any action. This is a great example of how Redshift has gotten much more dynamic and elastic.

The second ease of use innovation is automated table optimization. This is another place where the system is able to observe workloads and data layouts and automatically suggest how data should be sorted and distributed across nodes in the cluster. This is great because it’s a continuously learning system so workloads are never static in time.

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How should we split data among the nodes of a distributed data warehouse in order to boost performance for a forecasted workload? In this paper, we study the effect of different data partitioning schemes on the overall network cost of pairwise joins. We describe a generally-applicable data distribution framework initially designed for Amazon Redshift, a fully-managed petabyte-scale data warehouse in the

Customers are always adding more datasets, and adding more users, so what was optimal yesterday might not be optimal tomorrow. Redshift observes this and modifies what’s happening under the covers to balance that. This was the focus of a really interesting graph optimization paper that we wrote a few years ago about how to analyze for optimal distribution keys for how data is laid out within a multi-node parallel-processing system. We’ve coupled this with automated optimization and then table encoding. In an analytics system, how you compress data has a big impact because the less data you scan, the faster your queries go. Customers had to reason about this in the past. Now Redshift can automatically determine how to encode data correctly to deliver the best possible performance for the data and the workload.

The third innovation I want to highlight here is Amazon Redshift Serverless, which we launched in public preview at re:Invent last fall. Redshift Serverless removes all of the management of instances and clusters, so customers can focus on getting to insights from data faster and not spend time managing infrastructure. With Redshift Serverless, customers can simply provision an endpoint and begin to interact with their data, and Redshift Serverless will auto scale and automatically manage the system to essentially remove all of that complexity from customers.

Customers can just focus on their data, set limits to manage their budgets, and we deliver optimal performance between those limits. This is another massive step forward in terms of ease of use because it eliminates any operations for customers. The early response to the preview has been tremendous. Thousands of customers have been excited to put Amazon Redshift Serverless through its paces over the past few months, and we’re excited about making it generally available in the near future.

The Amazon Redshift architecture as presented in the research paper.

Ippokratis: A fourth area of focus in the paper is on integration with other AWS services, and the AWS ecosystem. Integration is another area where customer behavior has evolved from traditional BI use cases. Today, cloud data warehouses are a central hub with tight integration with a broader set of AWS services. We provided the ability for customers to join data from the warehouse with the data lake. Then customers said they needed access to high-velocity business data in operational databases like Aurora and RDS, so we provided access to these operational data stores. Then we added support for streams, as well as integration with SageMaker and Lambda so customers can run machine learning training and inference without moving their data, and do generic compute. As a result, we’ve converted the traditional BI system into a well-integrated set of AWS services.

Rahul: One big area of integration has been with our machine-learning ecosystem. With Redshift ML we have enabled anyone who knows SQL to take advantage of all of our machine-learning innovation. We built the ability to create a model from the SQL prompt, which gets the data into Amazon S3 and calls Amazon SageMaker, to use automated machine learning to build the most appropriate model to provide predictions on the data.

This model is compiled efficiently and brought back into the data warehouse for customers to run very high-performance parallel inferences with no additional compute or no extra cost. The beauty of this integration is that every innovation we make within SageMaker means that Redshift ML gets better as well. This is just another means by which customers benefit from us connecting our services together.

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Another big area for integration has been data sharing. Once we separated storage and compute layers with RA3 instances, we could enable data sharing, giving customers the ability to share data with clusters in the same account, and other accounts, or across regions. This allows us to separate consumers from producers of data, which enables things like modern data mesh architectures. Customers can share data without data copying, so they are transactionally consistent across accounts.

For example, users within a data-science organization can securely work from the shared data, as can users within the reporting or marketing organization. We’ve also integrated data sharing with AWS Data Exchange, so now customers can search for — and subscribe to — third-party datasets that are live, up to date, and can be queried immediately in Redshift. This has been another game changer from the perspective of setting data free, enabling data monetization for third-party providers, and secure and live data access and licensing for subscribers for high-performance analytics within and across organizations. The fact that Redshift is part of an incredibly rich data ecosystem is a huge win for customers, and in keeping with customers’ desire to make data more pervasively available across the company.

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