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Best Practices for Amazon Redshift

Data Warehouse Analytics as a Service

Data Warehouse as a Service
Recently Amazon announced the availability of Redshift Data warehouse as a Service as a beta offering. Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It's optimized for datasets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Architecture Behind Redshift
Any data warehouse service meant to serve data of petabyte scale should have a robust architecture as its backbone. The following are the salient features of Redshift service.

  • Shared Nothing Architecture: As indicated in one of my earlier articles, Cloud Database Scale Out Using Shared Nothing Architecture, the shared nothing architectural pattern is the most desired for databases of this scale and the same concept is adhered to in Redshift. The core component of Redshift is a cluster and each cluster consists of multiple compute nodes, each node has its dedicated storage following the shared nothing principle.
  • Massively Parallel Processing (MPP): Hand in hand with the shared nothing pattern MPP provides horizontal scale out capabilities for large data warehouses rather than scaling up the individual servers. Massively parallel processing (MPP) enables fast execution of the most complex queries operating on large amounts of data. Multiple compute nodes handle all query processing leading up to the final result aggregation, with each core of each node executing the same compiled query segments on portions of the entire data. With the concept of NodeSlices Redshift has taken the MPP to the next level to the cores of a compute node. A compute node is partitioned into slices; one slice for each core of the node's multi-core processor. Each slice is allocated a portion of the node's memory and disk space, where it processes a portion of the workload assigned to the node.

Refer to the following diagram from AWS Documentation, about Data warehouse system architecture

  • Columnar Data Storage: Storing database table information in a columnar fashion reduces the number of disk I/O requests and reduces the amount of data you need to load from disk. Columnar storage for database tables drastically reduces the overall disk I/O requirements and is an important factor in optimizing analytic query performance.
  • Leader Node: The leader node manages most communications with client programs and all communication with compute nodes. It parses and develops execution plans to carry out database operations, in particular, the series of steps necessary to obtain results for complex queries. Based on the execution plan, the leader node distributes compiled code to the compute nodes and assigns a portion of the data to each compute node.
  • High Speed Network Connect: The clusters are connected internally by a 10 Gigabit Ethernet network, providing very fast communication between the leader node and the compute clusters.

Best Practices in Application Design on Redshift
The enablement of Big Data analytics through Redshift has created lot of excitement among the community. The usage of these kinds of alternate approaches to traditional data warehousing will be best in conjunction with the best practices for utilizing the features. The following are some of the best practices that can be considered for the design of applications on Redshift.

1. Collocated Tables: It is good practice to try to avoid sending data between the nodes to satisfy JOIN queries. Colocation between two joined tables occurs when the matching rows of the two tables are stored in the same compute nodes, so that the data need not be sent between nodes.

When you add data to a table, Amazon Redshift distributes the rows in the table to the cluster slices using one of two methods:

  • Even distribution
  • Key distribution

Even distribution is the default distribution method. With even distribution, the leader node spreads data rows across the slices in a round-robin fashion, regardless of the values that exist in any particular column. This approach is a good choice when you don't have a clear option for a distribution key.

If you specify a distribution key when you create a table, the leader node distributes the data rows to the slices based on the values in the distribution key column. Matching values from the distribution key column are stored together.

Colocation is best achieved by choosing the appropriate distribution keys than using the even distribution.

If you frequently join a table, specify the join column as the distribution key. If a table joins with multiple other tables, distribute on the foreign key of the largest dimension that the table joins with. If the dimension tables are filtered as part of the joins, compare the size of the data after filtering when you choose the largest dimension. This ensures that the rows involved with your largest joins will generally be distributed to the same physical nodes. Because local joins avoid data movement, they will perform better than network joins.

2. De-Normalization: In the traditional RDBMS, database storage is optimized by applying the normalization principles such that a particular attribute (column) is associated with one and only entity (Table). However in shared nothing scalable databases like Redshift this technique will not yield the desired results, rather keeping the redundancy of certain columns in the form of de-normalization is very important.

For example, the following query is one of the examples of a high performance query in the Redshift documentation.

SELECT * FROM tab1, tab2

WHERE tab1.key = tab2.key

AND tab1.timestamp > ‘1/1/2013'

AND tab2.timestamp > ‘1/1/2013';

Even if a predicate is already being applied on a table in a join query but transitively applies to another table in the query, it's useful to re-specify the redundant predicate if that other table is also sorted on the column in the predicate. That way, when scanning the other table, Redshift can efficiently skip blocks from that table as well.

By carefully applying de-normalization to bring the required redundancy, Amazon Redshift can perform at its best.

3. Native Parallelism: One of the biggest advantages of a shared nothing MPP architecture is about parallelism. Parallelism is achieved in multiple ways.

  • Inter Node Parallelism: It refers the ability of the database system to break up a query into multiple parts across multiple instances across the cluster.
  • Intra Node Parallelism: Intra node parallelism refers to the ability to break up query into multiple parts within a single compute node.

Typically in MPP architectures, both Inter Node Parallelism and Intra Node Parallelism will be combined and used at the same time to provide dramatic performance gains.

Amazon Redshift provides lot of operations to utilize both Intra Node and Inter Node parallelism.

When you use a COPY command to load data from Amazon S3, first split your data into multiple files instead of loading all the data from a single large file.

The COPY command then loads the data in parallel from multiple files, dividing the workload among the nodes in your cluster. Split your data into files so that the number of files is a multiple of the number of slices in your cluster. That way Amazon Redshift can divide the data evenly among the slices. Name each file with a common prefix. For example, each XL compute node has two slices, and each 8XL compute node has 16 slices. If you have a cluster with two XL nodes, you might split your data into four files named customer_1, customer_2, customer_3, and customer_4. Amazon Redshift does not take file size into account when dividing the workload, so make sure the files are roughly the same size.

Pre-Processing Data: Over the years RDBMS engines take pride of Location Independence. The Codd's 12 rules of the RDBMS states the following:

Rule 8: Physical data independence:

Changes to the physical level (how the data is stored, whether in arrays or linked lists, etc.) must not require a change to an application based on the structure.

However, in the columnar database services like Redshift the physical ordering of data does make major impact to the performance.

Sorting data is a mechanism for optimizing query performance.

When you create a table, you can define one or more of its columns as the sort key. When data is loaded into the table, the values in the sort key column (or columns) are stored on disk in sorted order. Information about sort key columns is passed to the query planner, and the planner uses this information to construct plans that exploit the way that the data is sorted. For example, a merge join, which is often faster than a hash join, is feasible when the data is distributed and presorted on the joining columns.

The VACUUM command also makes sure that new data in tables is fully sorted on disk. Vacuum as often as you need to in order to maintain a consistent query performance.

Summary
Platform as a Service (PaaS) is one of the greatest benefits to the IT community due to the Cloud Delivery Model, and from the beginning of pure play programming models like Windows Azure and Elastic Beanstalk it has moved to high-end services like data warehouse Platform as a Service. As the industry analysts see good adoption of the above service due to the huge cost advantages when compared to the traditional data warehouse platform, the best practices mentioned above will help to achieve the desired level of performance. Detailed documentation is also available on the vendor site in the form of developer and administrator guides.

More Stories By Srinivasan Sundara Rajan

I am passionate about ownership and driving things on my own, with my breadth and depth on Enterprise Technology I could run any aspect of IT Industry and make it a success. I am a Seasoned Enterprise IT Expert, mainly in the areas of Solution,Integration and Architecture, across Structured, Unstructured data sources, especially in manufacturing domain. My recent work is on Natural Language Processing, Semantic Enrichment of Unstructured Data, Data Mining and Predictive Analytics. However I have a strong footing across all tiers of Enterprise IT spectrum. I am geared to handle the massive flow of data by Internet Of Things with appropriate platform, tools and processes.