Amazon Redshift is a fully-managed, cloud data warehouse that you can draw useful insights from. Clients rely on Amazon Redshift to analyze data and to execute complex analytical queries.
You can receive real-time insights and predictive analytics on your operational databases, data lake, data warehouse, and third-party datasets.
This article will talk about Redshift Managed Storage and Columnar Data Storage in Redshift Data Storage.
What is Amazon Redshift Managed Storage?
Managed storage
Redshift Managed Storage (RMS) is a separate storage tier for data warehouse data. RMS provides the capacity to scale your Amazon S3 storage up to petabytes.
RMS also enables you to scale and pay for computing and storage independently, allowing you to size your cluster based solely on computing requirements. As a tier-1 cache, it automatically employs high-performance SSD-based local storage.
It also takes advantage of optimizations, such as data block temperature, data blockage, and workload patterns, to deliver high performance while automatically scaling storage to Amazon S3 when required.
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How to Use Amazon Redshift’s Managed Storage?
Suppose you are currently utilizing Amazon Redshift Dense Storage or Dense Compute nodes. In that case, you can use Elastic Resize to upgrade your existing clusters to the new compute instance RA3 Amazon Redshift Serverless.
For clusters utilizing the RA3 instance use Redshift-managed storage to store data automatically. This capability can be utilized without additional steps beyond Amazon Redshift Serverless or RA3 instances.
Columnar Data Storage
Columnar storage for database tables is crucial for optimizing the performance of analytic queries because it drastically reduces the overall disc I/O requirements. It reduces the quantity of data that must be loaded from the disc.
The following images illustrate how columnar data storage implements efficiencies and how this translates to memory retrieval efficiencies.
This first illustration demonstrates how rows of database table records are typically stored in disc blocks.
illustration 1
- A typical relational database table row contains the values for a single record’s fields.
- In row-wise database storage, data blocks store sequential values for each column comprising the entire row.
- If the block size is smaller than the record size, it may take more than one block to store an entire record.
- If the block size is greater than the record size, a record may be stored in less than one block, resulting in inefficient use of disc space.
- The majority of transactions in online transaction processing (OLTP) applications involve reading and writing the values for entire records, typically one record or a small number of records at a time. Row-wise storage is, therefore, optimal for OLTP databases.
The following illustration demonstrates how columnar storage stores the values for each column sequentially in disc blocks.
Illustration 2
- Amazon Redshift makes use of columnar storage. This means that data blocks can store values for numerous rows in a single column, which helps to achieve much better storage efficiency. For the same count of column values read, this approach requires one-third fewer I/O operations compared to the row-based storage and is therefore obviously beneficial for large datasets.
- Since each block is of the same type of data, specialized compression schemes per column data type compress disk space and I/O even more. Queries only typically access a few columns most of the time, so memory can be saved by reading in only the blocks required; in contrast, row-wise storage would have to read in all columns.
- For example, in tables that have several columns, 100, a query that utilizes only five columns reads about 5% of the data, which subsequently minimizes unneeded retrieval of data. The other thing that Amazon Redshift offers is 1 MB block size; it increases efficiency and reduces I/O requests during both database loading and query operations.
Backup and Restoration of Data
- Amazon Redshift RA3 clusters and Amazon Redshift Serverless utilize Redshift Managed Storage, which always has the most recent data copy available. The DS2 and DC2 clusters mirror the cluster’s data to ensure that the most recent copy is accessible in the event of a failure.
- On all Redshift cluster types, backups are created automatically and kept for 24 hours, and on serverless environments, recovery points are available for the past 24 hours.
- Additionally, you can create your own backups that can be kept indefinitely. The Amazon Redshift automated backups or Amazon Redshift Serverless recovery points can be converted into user backups for longer retention.
- For disaster recovery, Amazon Redshift can also asynchronously replicate your snapshots or recovery points to Amazon S3 in a different Region.
- On a DS2 or DC2 cluster, free backup storage is limited to the total storage capacity of the data warehouse cluster’s nodes and is only applicable to active data warehouse clusters.
- For instance, if your data warehouse has a total storage capacity of 8 TB, we will provide up to 8 TB of backup storage at no additional cost. Using the AWS Management Console or Amazon Redshift APIs, you can extend your backup retention period beyond a single day.
- Amazon Redshift only backs up changed data, so most snapshots consume a negligible amount of your free backup storage. When you need to restore a backup, you can access all automated backups created during your backup retention period. Once you select a backup to restore from, they will create a new data warehouse cluster and restore your data to it.
What happens to the backups if you delete your data warehouse cluster?
When deleting a data warehouse cluster, you can specify whether a final snapshot should be created. This enables a later restoration of the deleted data warehouse cluster.
Unless you delete them, all previously created manual snapshots of your data warehouse cluster will be retained and billed at standard Amazon S3 rates.
Best Practices
Following are a few best practices for designing tables and loading tables with data in Redshift Data Storage:
- Amazon Redshift stores your data on a disc according to the sort key in sorted order. When determining optimal query plans, Amazon Redshift’s query optimizer employs sort order.
- Specify AUTO for the sort key to have Amazon Redshift determine the optimal sort order.
- If the most frequently requested data is recent, specify the timestamp column as the leading column for the sort key.
- Avoid the habit of using the maximum column size for convenience. During the processing of complex queries, it may be necessary to store intermediate query results in temporary tables.
- Since temporary tables are not compressed, unnecessarily large columns consume an excessive amount of memory and temporary disc space, which can negatively impact the performance of queries.
Learn more about Structure of Data in Amazon Redshift Warehouse.
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Conclusion
In this article about Redshift Data Storage, you learned about Amazon Redshift’s managed storage and columnar data storage. You also learned the best practices for designing tables and loading data. You also read about some of the limitations of Amazon Redshift. Understand how RA3 nodes optimize performance and cost. More at Redshift RA3 Overview.
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Frequently Asked Questions
1. How much data can Redshift hold?
It can support petabytes of data and scale all the way to exabytes with features such as Redshift Spectrum, where you can run queries on data stored directly in S3. It also provides flexibility for scaling your storage and performance needs.
2. Is AWS Redshift overkill?
It can be a bit of an overkill for small datasets or simple queries designed for large-scale data warehouses that may handle complex analytics. If one were going for lighter workloads, then perhaps more cost-effective options would be RDS or Aurora services for simpler jobs.
3. Does Redshift spectrum store data?
No. Redshift Spectrum does not store data; it queries data that is stored in Amazon S3. It allows you to query large datasets in S3 without having to load the data into Redshift.
Sharon is a data science enthusiast with a hands-on approach to data integration and infrastructure. She leverages her technical background in computer science and her experience as a Marketing Content Analyst at Hevo Data to create informative content that bridges the gap between technical concepts and practical applications. Sharon's passion lies in using data to solve real-world problems and empower others with data literacy.
