Amazon Redshift powers analytical workloads for Fortune 500 companies, startups, and everything in between. With Amazon Redshift, you can query petabytes of structured and semi-structured data across your data warehouse, operational database, and your data lake using standard SQL.
When you use Vertica, you have to install and upgrade Vertica database software and manage the cluster OS and hardware. Amazon Redshift is a fully managed cloud solution; you don’t have to install and upgrade database software and manage the OS and the hardware. In this post, we discuss the best practices for migrating from a self-managed Vertica cluster to the fully managed Amazon Redshift solution. We discuss how to plan for the migration, including sizing your Amazon Redshift cluster and strategies for data placement. We look at the tools for schema conversion and see how to choose the right keys for distributing and sorting your data. We also see how to speed up the data migration to Amazon Redshift based on your data size and network connectivity. Finally, we cover how cluster management on Amazon Redshift differs from Vertica.
When planning your migration, start with where you want to place the data. Your business use case drives what data gets loaded to Amazon Redshift and what data remains on the data lake. In this section, we discuss how to size the Amazon Redshift cluster based on the size of the Vertica dataset that you’re moving to Amazon Redshift. We also look at the Vertica schema and decide the best data distribution and sorting strategies to use for Amazon Redshift, if you choose to do it manually.
Amazon Redshift powers the lake house architecture, which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights not possible otherwise. In a Vertica data warehouse, you plan the capacity for all your data, whereas with Amazon Redshift, you can plan your data warehouse capacity much more efficiently. If you have a huge historical dataset being shared by multiple compute platforms, then it’s a good candidate to keep on Amazon Simple Storage Service (Amazon S3) and utilize Amazon Redshift Spectrum. Also, streaming data coming from Kafka and Amazon Kinesis Data Streams can add new files to an existing external table by writing to Amazon S3 with no resource impact to Amazon Redshift. This has a positive impact on concurrency. Amazon Redshift Spectrum is good for heavy scan and aggregate work. For tables that are frequently accessed from a business intelligence (BI) reporting or dashboarding interface and for tables frequently joined with other Amazon Redshift tables, it’s optimal to have tables loaded in Amazon Redshift.
Vertica has Flex tables to handle JSON data. You don’t need to load the JSON data to Amazon Redshift. You can use external tables to query JSON data stored on Amazon S3 directly from Amazon Redshift. You create external tables in Amazon Redshift within an external schema.
Vertica users typically create a projection on a Vertica table to optimize for a particular query. If necessary, use materialized views in Amazon Redshift. Vertica also has aggregate projection, which acts like a synchronized materialized view. With materialized views in Amazon Redshift, you can store the pre-computed results of queries and efficiently maintain them by incrementally processing the latest changes made to the source tables. Subsequent queries referencing the materialized views use the pre-computed results to run much faster. You can create materialized views based on one or more source tables using filters, inner joins, aggregations, grouping, functions, and other SQL constructs.
When you create a cluster on the Amazon Redshift console, you can get a recommendation of your cluster configuration based on the size of your data and query characteristics (see the following screenshot).
Amazon Redshift offers different node types to accommodate your workloads. We recommend using RA3 nodes so you can size compute and storage independently to achieve improved price and performance. Amazon Redshift takes advantage of optimizations such as data block temperature, data block age, and workload patterns to optimize performance and manage automatic data placement across tiers of storage in the RA3 clusters.
ETL pipelines and BI reports typically use temporary tables that are only valid for a session. Vertica has local and global temporary tables. If you’re using Vertica local temporary tables, no change is required during migration. Vertica local tables and Amazon Redshift temporary tables have similar behavior. They’re visible only to the session and get dropped when the session ends. Vertica global tables persist across sessions until they are explicitly dropped. If you use them now, you have to change them to permanent tables in Amazon Redshift and drop them when they’re no longer needed.
Data distribution, sorting, and compression
Amazon Redshift optimizes for performance by distributing the data across compute nodes and sorting the data. Make sure to set the sort key, distribution style, and compression encoding of the tables to take full advantage of the massively parallel processing (MPP) capabilities. The choice of distribution style and sort keys vary based on data model and access patterns. Use the data distribution and column order of the Vertica tables to help choose the distribution keys and sort keys on Amazon Redshift.
Choose a column with high cardinality of evenly spread out values as the distribution key. Profile the data for the columns used for distribution keys. Vertica has segmentation that specifies how to distribute data for superprojections of a table, where the data to be hashed consists of one or more column values. The columns used in segmentation are most likely good candidates for distribution keys on Amazon Redshift. If you have multiple columns in segmentation, pick the column that provides the highest cardinality to reduce the possibility of high data skew.
Besides supporting data distribution by key, Amazon Redshift also supports other distribution styles: ALL, EVEN, and AUTO. Use ALL distribution for small dimension tables and EVEN distribution for larger tables, or use AUTO distribution, where Amazon Redshift changes the distribution style from ALL to EVEN as the table size reaches a threshold.
Amazon Redshift stores your data on disk in sorted order using the sort key. The Amazon Redshift query optimizer uses the sort order for optimal query plans. Review if one of raw columns used in the Vertica table’s Order By clause is the best column to use as the
sort key in the Amazon Redshift table.
The order by fields in Vertica superprojections are good candidates for a sort key in Amazon Redshift, but the design criteria of sort order in Amazon Redshift is different from what you use in Vertica. In Vertica projections
Order By clause, you use the low-cardinality columns with high probability of having RLE encoding before the high-cardinality columns. In Amazon Redshift, you can set the SORTKEY to AUTO, or choose a column as SORTKEY or define a compound sort key. You define compound sort keys using multiple columns, starting with the most frequently used column first. All the columns in the compound sort key are used, in the order in which they are listed, to sort the data. You can use a compound sort key when query predicates use a subset of the sort key columns in order. Amazon Redshift stores the table rows on disk in sorted order and uses metadata to track the minimum and maximum values for each 1 MB block, called a zone map. Amazon Redshift uses the zone map and the sort key for filtering the block, thereby reducing the scanning cost to efficiently handle range-restricted predicates.
Profile the data for the columns used for sort keys. Make sure the first column of the sort key is not encoded. Choose timestamp columns or columns used in frequent range filtering, equality filtering, or joins as sort keys in Amazon Redshift.
You don’t always have to select compression encodings; Amazon Redshift automatically assigns RAW compression for columns that are defined as sort keys, AZ64 compression for the numeric and timestamp columns, and LZO compression for the VARCHAR columns. When you select compression encodings manually, choose AZ64 for numeric and date/time data stored in Amazon Redshift. AZ64 encoding has consistently better performance and compression than LZO. It has comparable compression with ZSTD but greatly better performance.
After we decide the data placement, cluster size, partition keys, and sort keys, the next step is to look at the tooling for schema conversion and data migration.
You can use AWS Schema Conversion Tool (AWS SCT) to convert your schema, which can automate about 80% of the conversion, including the conversion of DISTKEY and SORTKEY, or you can choose to convert the Vertica DDLs to Amazon Redshift manually.
To efficiently migrate your data, you want to choose the right tools depending on the data size. If you have a dataset that is smaller than a couple of terabytes, you can migrate your data using AWS Data Migration Service (AWS DMS) or AWS SCT data extraction agents. When you have more than a few terabytes of data, your tool choice depends on your network connectivity. When there is no dedicated network connection, you can run the AWS SCT data extraction agents to copy the data to AWS Snowball Edge and ship the device back to AWS to complete the data export to Amazon S3. If you have a dedicated network connection to AWS, you can run the S3EXPORT or S3EXPORT_PARTITION commands available in Vertica 9.x directly from the Vertica nodes to copy the data in parallel to the S3 bucket.
The following diagram visualizes the migration process.
AWS SCT uses extension pack schema to implement system functions of the source database that are required when writing your converted schema to your target database instance. Review the database migration assessment report for compatibility. AWS SCT can use source metadata and statistical information to determine the distribution key and sort key. AWS SCT adds a sort key in the Amazon Redshift table for the raw column used in the Vertica table’s
Order By clause.
The following code is an example of Vertica CREATE TABLE and CREATE PROJECTION statements:
The following code is the corresponding Amazon Redshift CREATE TABLE statement:
To significantly reduce the data migration time from large Vertica clusters (if you have a dedicated network connection from your premises to AWS with good bandwidth), run the S3EXPORT or S3EXPORT_PARTITION function in Vertica 9.x, which exports the data in parallel from the Vertica nodes directly to Amazon S3.
The Parquet files generated by S3EXPORT don’t have any partition key on them, because partitioning consumes time and resources on the database where the S3EXPORT runs, which is typically the Vertica production database. The following code is one command you can use:
The following code is another command option:
In this section, we look at best practices for ETL performance while copying the data from Amazon S3 to Amazon Redshift. We also discuss how to handle Vertica partition swapping and partition dropping scenarios in Amazon Redshift.
Copying using an Amazon S3 prefix
Make sure the ETL process is running from Amazon Elastic Compute Cloud (Amazon EC2) servers or other managed services within AWS. Exporting your data from Vertica as multiple files to Amazon S3 gives you the option to load your data in parallel to Amazon Redshift. While converting the Vertica ETL scripts, use the COPY command with an Amazon S3 object prefix to load an Amazon Redshift table in parallel from data files stored under that prefix on Amazon S3. See the following code:
Loading data using Amazon Redshift Spectrum queries
When you want to transform the exported Vertica data before loading to Amazon Redshift, or when you want to load only a subset of data into Amazon Redshift, use an Amazon Redshift Spectrum query. Create an external table in Amazon Redshift pointing to the exported Vertica data stored in Amazon S3 within an external schema. Put your transformation logic in a SELECT query, and ingest the result into Amazon Redshift using a CREATE TABLE or SELECT INTO statement:
Handling Vertica partitions
Vertica has partitions, and the data loads use partition swapping and partition dropping. In Amazon Redshift, we can use the sort key, staging table, and
alter table append to achieve similar results. First, the Amazon Redshift ETL job should use the sort key as filter conditions to insert the incremental data into a staging table or a temporary table in Amazon Redshift, for example the date from the
MyTimeStamp column between yesterday and today. The ETL job should then delete data from the primary table that matches the filter conditions. The delete operation is very efficient in Amazon Redshift because of the sort key on the source partition column. The Amazon Redshift ETL jobs can then use
alter table append to move the new data to the primary table. See the following code:
When a Vertica node fails, Vertica remains queryable but the performance is degraded until all the data is restored to the recovered node. When an Amazon Redshift node fails, Amazon Redshift automatically detects and replaces a failed node in your data warehouse cluster and replays the
ReadOnly queries. Amazon Redshift makes your replacement node available immediately and loads your most frequently accessed data from the S3 bucket first to allow you to resume querying your data as quickly as possible.
Vertica cluster resize, similar to Amazon Redshift classic resize, takes a few hours depending on data volume to rebalance the data when nodes are added or removed. With Amazon Redshift elastic resize, the cluster resize completes within minutes. We recommend elastic resize for most use cases to shorten the cluster downtime and schedule resizes to handle seasonal spikes in your workload.
This post shared some best practices for migrating your data warehouse from Vertica to Amazon Redshift. It also pointed out the differences between Amazon Redshift and Vertica in handling queries, data management, cluster management, and temporary tables. Create your cluster on the Amazon Redshift console and convert your schema using AWS SCT to start your migration to Amazon Redshift. If you have any questions or comments, please share your thoughts in the comments section.
About the Authors
Seetha Sarma is a Senior Database Solutions Architect with Amazon Web Services.
Veerendra Nayak is a Senior Database Solutions Architect with Amazon Web Services.
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