Click here to close now.


Microservices Expo Authors: Ian Khan, Jason Bloomberg, AppDynamics Blog, Elizabeth White, Liz McMillan

Related Topics: @CloudExpo, Java IoT, Microservices Expo, Containers Expo Blog, Agile Computing, @BigDataExpo, SDN Journal

@CloudExpo: Article

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.

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

Srinivasan is passionate about ownership and driving things on his own, with his breadth and depth on Enterprise Technology he could run any aspect of IT Industry and make it a success.

He is a seasoned Enterprise IT Expert, mainly in the areas of Solution, Integration and Architecture, across Structured, Unstructured data sources, especially in manufacturing domain.

He currently works as Technology Head For GAVS Technologies.

@MicroservicesExpo Stories
One of the most important tenets of digital transformation is that it’s customer-driven. In fact, the only reason technology is involved at all is because today’s customers demand technology-based interactions with the companies they do business with. It’s no surprise, therefore, that we at Intellyx agree with Patrick Maes, CTO, ANZ Bank, when he said, “the fundamental element in digital transformation is extreme customer centricity.” So true – but note the insightful twist that Maes adde...
Just over a week ago I received a long and loud sustained applause for a presentation I delivered at this year’s Cloud Expo in Santa Clara. I was extremely pleased with the turnout and had some very good conversations with many of the attendees. Over the next few days I had many more meaningful conversations and was not only happy with the results but also learned a few new things. Here is everything I learned in those three days distilled into three short points.
Using any programming framework to the fullest extent possible first requires an understanding of advanced software architecture concepts. While writing a little client-side JavaScript does not necessarily require as much consideration when designing a scalable software architecture, the evolution of tools like Node.js means that you could be facing large code bases that must be easy to maintain.
As organizations realize the scope of the Internet of Things, gaining key insights from Big Data, through the use of advanced analytics, becomes crucial. However, IoT also creates the need for petabyte scale storage of data from millions of devices. A new type of Storage is required which seamlessly integrates robust data analytics with massive scale. These storage systems will act as “smart systems” provide in-place analytics that speed discovery and enable businesses to quickly derive meaningf...
DevOps is about increasing efficiency, but nothing is more inefficient than building the same application twice. However, this is a routine occurrence with enterprise applications that need both a rich desktop web interface and strong mobile support. With recent technological advances from Isomorphic Software and others, rich desktop and tuned mobile experiences can now be created with a single codebase – without compromising functionality, performance or usability. In his session at DevOps Su...
You may have heard about the pets vs. cattle discussion – a reference to the way application servers are deployed in the cloud native world. If an application server goes down it can simply be dropped from the mix and a new server added in its place. The practice so far has mostly been applied to application deployments. Management software on the other hand is treated in a very special manner. Dedicated resources are set aside to run the management software components and several alerting syst...
In his General Session at DevOps Summit, Asaf Yigal, Co-Founder & VP of Product at, explored the value of Kibana 4 for log analysis and provided a hands-on tutorial on how to set up Kibana 4 and get the most out of Apache log files. He examined three use cases: IT operations, business intelligence, and security and compliance. Asaf Yigal is co-founder and VP of Product at log analytics software company In the past, he was co-founder of social-trading platform Currensee, which...
It's been a busy time for tech's ongoing infatuation with containers. Amazon just announced EC2 Container Registry to simply container management. The new Azure container service taps into Microsoft's partnership with Docker and Mesosphere. You know when there's a standard for containers on the table there's money on the table, too. Everyone is talking containers because they reduce a ton of development-related challenges and make it much easier to move across production and testing environm...
Continuous processes around the development and deployment of applications are both impacted by -- and a benefit to -- the Internet of Things trend. To help better understand the relationship between DevOps and a plethora of new end-devices and data please welcome Gary Gruver, consultant, author and a former IT executive who has led many large-scale IT transformation projects, and John Jeremiah, Technology Evangelist at Hewlett Packard Enterprise (HPE), on Twitter at @j_jeremiah. The discussion...
People want to get going with DevOps or Continuous Delivery, but need a place to start. Others are already on their way, but need some validation of their choices. A few months ago, I published the first volume of DevOps and Continuous Delivery reference architectures which has now been viewed over 50,000 times on SlideShare (it's free to registration required). Three things helped people in the deck: (1) the reference architectures, (2) links to the sources for each architectur...
Hiring the wrong candidate can cost a company hundreds of thousands of dollars, and result in lost profit and productivity during the search for a replacement. In fact, the Harvard Business Review has found that as much as 80 percent of turnover is caused by bad hiring decisions. But when your organization has implemented DevOps, the job is about more than just technical chops. It’s also about core behaviors: how they work with others, how they make decisions, and how those decisions translate t...
The Internet of Things is clearly many things: data collection and analytics, wearables, Smart Grids and Smart Cities, the Industrial Internet, and more. Cool platforms like Arduino, Raspberry Pi, Intel's Galileo and Edison, and a diverse world of sensors are making the IoT a great toy box for developers in all these areas. In this Power Panel at @ThingsExpo, moderated by Conference Chair Roger Strukhoff, panelists discussed what things are the most important, which will have the most profound...
In today's enterprise, digital transformation represents organizational change even more so than technology change, as customer preferences and behavior drive end-to-end transformation across lines of business as well as IT. To capitalize on the ubiquitous disruption driving this transformation, companies must be able to innovate at an increasingly rapid pace. Traditional approaches for driving innovation are now woefully inadequate for keeping up with the breadth of disruption and change facin...
PubNub has announced the release of BLOCKS, a set of customizable microservices that give developers a simple way to add code and deploy features for realtime apps.PubNub BLOCKS executes business logic directly on the data streaming through PubNub’s network without splitting it off to an intermediary server controlled by the customer. This revolutionary approach streamlines app development, reduces endpoint-to-endpoint latency, and allows apps to better leverage the enormous scalability of PubNu...
I recently attended and was a speaker at the 4th International Internet of @ThingsExpo at the Santa Clara Convention Center. I also had the opportunity to attend this event last year and I wrote a blog from that show talking about how the “Enterprise Impact of IoT” was a key theme of last year’s show. I was curious to see if the same theme would still resonate 365 days later and what, if any, changes I would see in the content presented.
Microservices are a very exciting architectural approach that many organizations are looking to as a way to accelerate innovation. Microservices promise to allow teams to move away from monolithic "ball of mud" systems, but the reality is that, in the vast majority of organizations, different projects and technologies will continue to be developed at different speeds. How to handle the dependencies between these disparate systems with different iteration cycles? Consider the "canoncial problem"...
Culture is the most important ingredient of DevOps. The challenge for most organizations is defining and communicating a vision of beneficial DevOps culture for their organizations, and then facilitating the changes needed to achieve that. Often this comes down to an ability to provide true leadership. As a CIO, are your direct reports IT managers or are they IT leaders? The hard truth is that many IT managers have risen through the ranks based on their technical skills, not their leadership ab...
Discussions of cloud computing have evolved in recent years from a focus on specific types of cloud, to a world of hybrid cloud, and to a world dominated by the APIs that make today's multi-cloud environments and hybrid clouds possible. In this Power Panel at 17th Cloud Expo, moderated by Conference Chair Roger Strukhoff, panelists addressed the importance of customers being able to use the specific technologies they need, through environments and ecosystems that expose their APIs to make true ...
There are over 120 breakout sessions in all, with Keynotes, General Sessions, and Power Panels adding to three days of incredibly rich presentations and content. Join @ThingsExpo conference chair Roger Strukhoff (@IoT2040), June 7-9, 2016 in New York City, for three days of intense 'Internet of Things' discussion and focus, including Big Data's indespensable role in IoT, Smart Grids and Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) IoT's use in Vertical Markets.
The Internet of Things (IoT) is growing rapidly by extending current technologies, products and networks. By 2020, Cisco estimates there will be 50 billion connected devices. Gartner has forecast revenues of over $300 billion, just to IoT suppliers. Now is the time to figure out how you’ll make money – not just create innovative products. With hundreds of new products and companies jumping into the IoT fray every month, there’s no shortage of innovation. Despite this, McKinsey/VisionMobile data...