Microservices for Breadth, Libraries for Depth
April 10, 2018 | Posted By: Marty Abbott
The decomposition of monoliths into services, or alternatively the development of new products in a services-oriented fashion (oftentimes called microservices), is one of the greatest architectural movements of the last decade. The benefits of a services (alternatively microservices or micro-services) approach are clear:
- Independent deployment, decreasing time to market and decreasing time to value realization– especially when continuous delivery is employed.
- Team velocity and ownership (informed by Conway’s Law).
- Increased fault isolation – but only when properly deployed (see below).
- Individual scalability – and the decreasing cost of operations that entails when properly architected.
- Freedom of implementation and technology choices – choosing the best solution for each service rather than subjecting services to the lowest common denominator implementation.
Unfortunately, without proper architectural oversight and planning, improperly architected services can also result in:
- Lower overall availability, especially when those services are deployed in one of a handful of microservice anti-patterns like the mesh, services in depth (aka the Christmas Tree Light String) and the Fuse.
- Higher (longer) response times to end customers.
- Complicated fault isolation and troubleshooting that increases average recovery time for failures.
- Service bloat: Too many services to comprehend (see our service sizing post)
The following are patterns companies should avoid (anti-patterns) when developing services or microservices architectures:
Mesh architectures, where individual services both “fan out” and “share” subsequent services result in the lowest possible availability.
Services that are strung together in long (deep) call trees suffer from low availability and slow page response times as calculated from the product of each service offering availability.
The Fuse is a much smaller anti-pattern than “The Mesh”. In “The Fuse”, 2 distinct services (A and B) rely on service C. Should service C become slow or unavailable, both service A and B suffer.
Architecture Principle: Services – Broad, But Never Deep
These services anti-patterns protect against a lack of fault isolation, where slowness and failures propagate along a synchronous path. One service fails, and the others relying upon that service also suffer.
They also serve to guard against longer latency in call streams. While network calls tend to be minimal relative to total customer response times, many solutions (e.g. payment solutions) need to respond as quickly as possible and service calls slow that down.
Finally, these patterns help protect against difficult to diagnose failures. The Xmas Tree pattern name is chosen because of the difficulty in finding the “failed bulb” in old tree lights wired in series. Similarly, imagine attempting to find the fault in “The Mesh”. The time necessary to find faults negatively effects service restoration time and therefore availability.
As such, we suggest a principal that services should never be deep but instead should be deployed in breadth along product offering boundaries defined by nouns (resources like “customer” or “sales”) or verbs (services like “search” or “add to cart”). We often call this approach “slices instead of layers”.
How then do we accomplish the separation of software for team ownership, and time to market where a single service would otherwise be too large or unwieldy?
Old School – Libraries!
When you need service-like segmentation in a deep call tree but can’t suffer the availability impact and latency associated with multiple calls, look to libraries. Libraries will both eliminate the network associated latency of a service call. In the case of both The Fuse and The Mesh libraries eliminate the shared availability constraints. Unfortunately, we still have the multiplicative effect of failure of the Xmas Tree, but overall it is a faster pattern.
“But My Teams Can’t Release Separately!”
Sure they can – they just have to change how they think about releasing. If you need immediate effect from what you release and don’t want to release the calling services with libraries compiled or linked, consider performing releases with shared objects or dynamically loadable libraries. While these require restarts of the calling service, simple automation will help you keep from having an outage for the purpose of deploying software.
AKF Partners helps companies architecture highly available, highly scalable microservice architecture products. We apply our aggregate experience, proprietary models, patterns, and anti-patterns to help ensure your products can meet your company’s scale and availability goals. Contact us today - we can help!
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SaaS Migration Challenges
March 12, 2018 | Posted By: Dave Swenson
More and more companies are waking up from the 20th century, realizing that their on-premise, packaged, waterfall paradigms no longer play in today’s SaaS, agile world. SaaS (Software as a Service) has taken over, and for good reason. Companies (and investors) long for the higher valuation and increased margins that SaaS’ economies of scale provide. Many of these same companies realize that in order to fully benefit from a SaaS model, they need to release far more frequently, enhancing their products through frequent iterative cycles rather than massive upgrades occurring only 4 times a year. So, they not only perform a ‘lift and shift’ into the cloud, they also move to an Agile PDLC. Customers, tired of incurring on-premise IT costs and risks, are also pushing their software vendors towards SaaS.
But, what many of the companies migrating to SaaS don’t realize is that migrating to SaaS is not just a technology exercise. Successful SaaS migrations require a ‘reboot’ of the entire company. Certainly, the technology organization will be most affected, but almost every department in a company will need to change. Sales teams need to pitch the product differently, selling a leased service vs. a purchased product, and must learn to address customers’ typical concerns around security. The role of professional services teams in SaaS drastically changes, and in most cases, shrinks. Customer support personnel should have far greater insight into reported problems. Product management in a SaaS world requires small, nimble enhancements vs. massive, ‘big-bang’ upgrades. Your marketing organization will potentially need to target a different type of customer for your initial SaaS releases - leveraging the Technology Adoption Lifecycle to identify early adopters of your product in order to inform a small initial release (Minimum Viable Product).
It is important to recognize the risks that will shift from your customers to you. In an on-premise (“on-prem”) product, your customer carries the burden of capacity planning, security, availability, disaster recovery. SaaS companies sell a service (we like to say an outcome), not just a bundle of software. That service represents a shift of the risks once held by a customer to the company provisioning the service. In most cases, understanding and properly addressing these risks are new undertakings for the company in question and not something for which they have the proper mindset or skills to be successful.
This company-wide reboot can certainly be a daunting challenge, but if approached carefully and honestly, addressing key questions up front, communicating, educating, and transparently addressing likely organizational and personnel changes along the way, it is an accomplishment that transforms, even reignites, a company.
This is the first in a series of articles that captures AKF’s observations and first-hand experiences in guiding companies through this process.
Don’t treat this as a simple rewrite of your existing product - answer these questions first…
Any company about to launch into a SaaS migration should first take a long, hard look at their current product, determining what out of the legacy product is not worth carrying forward. Is all of that existing functionality really being used, and still relevant? Prior to any move towards SaaS, the following questions and issues need to be addressed:
Customization or Configuration?
SaaS efficiencies come from many angles, but certainly one of those is having a single codebase for all customers. If your product today is highly customized, where code has been written and is in use for specific customers, you’ve got a tough question to address. Most product variances can likely be handled through configuration, a data-driven mechanism that enables/disables or otherwise shapes functionality for each customer. No customer-specific code from the legacy product should be carried forward unless it is expected to be used by multiple clients. Note that this shift has implications on how a sales force promotes the product (they can no longer promise to build whatever a potential customer wants, but must sell the current, existing functionality) as well as professional services (no customizations means less work for them).
Many customers, even those who accept the improved security posture a cloud-hosted product provides over their own on-premise infrastructure, absolutely freak when they hear that their data will coexist with other customers’ data in a single multi-tenant instance, no matter what access management mechanisms exist. Multi-tenancy is another key to achieving economies of scale that bring greater SaaS efficiencies. Don’t let go of it easily, but if you must, price extra for it.
Who owns the data?
Many products focus only on the transactional set of functionality, leaving the analytics side to their customers. In an on-premise scenario, where the data resides in the customers’ facilities, ownership of the data is clear. Customers are free to slice & dice the data as they please. When that data is hosted, particularly in a multi-tenant scenario where multiple customers’ data lives in the same database, direct customer access presents significant challenges. Beyond the obvious related security issues is the need to keep your customers abreast of the more frequent updates that occur with SaaS product iterations. The decision is whether you replicate customer data into read-only instances, provide bulk export into their own hosted databases, or build analytics into your product?
All of these have costs - ensure you’re passing those on to your customers who need this functionality.
May I Upgrade Now?
Today, do your customers require permission for you to upgrade their installation? You’ll need to change that behavior to realize another SaaS efficiency - supporting of as few versions as possible. Ideally, you’ll typically only support a single version (other than during deployment). If your customers need to ‘bless’ a release before migrating on to it, you’re doing it wrong. Your releases should be small, incremental enhancements, potentially even reaching continuous deployment. Therefore, the changes should be far easier to accept and learn than the prior big-bang, huge upgrades of the past. If absolutely necessary, create a sandbox for customers to access new releases, but be prepared to deal with the potentially unwanted, non-representative feedback from the select few who try it out in that sandbox.
Wait? Who Are We Targeting?
All of the questions above lead to this fundamental issue: Are tomorrow’s SaaS customers the same as today’s? The answer? Not necessarily. First, in order to migrate existing customers on to your bright, shiny new SaaS platform, you’ll need to have functional parity with the legacy product. Reaching that parity will take significant effort and lead to a big-bang approach. Instead, pick a subset or an MVP of existing functionality, and find new customers who will be satisfied with that. Then, after proving out the SaaS architecture and related processes, gradually migrate more and more functionality, and once functional parity is close, move existing customers on to your SaaS platform.
To find those new customers interested in placing their bets on your initial SaaS MVP, you’ll need to shift your current focus on the right side of the Technology Adoption Lifecycle (TALC) to the left - from your current ‘Late Majority’ or ‘Laggards’ to ‘Early Adopters’ or ‘Early Majority’. Ideally, those customers on the left side of the TALC will be slightly more forgiving of the ‘learnings’ you’ll face along the way, as well as prove to be far more valuable partners with you as you further enhance your MVP.
The key is to think out of the existing box your customers are in, to reset your TALC targeting and to consider a new breed of customer, one that doesn’t need all that you’ve built, is willing to be an early adopter, and will be a cooperative partner throughout the process.
Our next article on SaaS migration will touch on organizational approaches, particularly during the build-out of the SaaS product, and the paradigm shifts your product and engineering teams need to embrace in order to be successful.
AKF has led many companies on their journey to SaaS, often getting called in as that journey has been derailed. We’ve seen the many potholes and pitfalls and have learned how to avoid them. Let us help you move your product into the 21st century.
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The Scale Cube
January 29, 2018 | Posted By: Robin McGlothin
The Scale Cube - Architecting for Scale
In every Industry, companies with similar strategies grow differently. Some grow with metronomic regularity to become leaders in their segment. Walmart, Dell, Amazon exemplify this trend. Others grow in fits and starts, often languishing, or at best, get acquired. While several attributes define successful companies, one is often overlooked – their ability to scale.
The Scale Cube is a model for building resilient architectures using patterns and practices that apply broadly to any solution. We developed the cube 11 years ago for our practice and included it in the first edition of “The Art of Scalability” (AKF Partners ).
The Scale Cube (sometimes known as the “AKF Scale Cube” or “AKF Cube”) is comprised of an X-axis, Y-axis, and Z-axis.
• Horizontal Duplication and Cloning (X-Axis )
• Functional Decomposition and Segmentation - Microservices (Y-Axis)
• Horizontal Data Partitioning - Shards (Z-Axis)
The Scale Cube helps teams keep critical dimensions of system scale in mind when solutions are designed. Scalability is all about the capability of a design to support ever growing client traffic without compromising performance. It is important to understand there are no “silver bullets” to designing scalable solutions. An architecture is scalable if each component is scalable. For example, a well-designed solution should be able to scale seamlessly as demand increases and decreases, and be resilient enough to withstand the loss of one or more compute resources.
Most internet enabled products start their life as a single application running on an appserver or appserver/webserver combination and likely communicate with a database. This monolithic design will work fine for relatively small applications that receive low levels of client traffic. However, this monolithic architecture becomes a kiss of death for complex applications.
A large monolithic application can be difficult for developers to understand and maintain. It is also an obstacle to frequent deployments. To deploy changes to one application component you need to build and deploy the entire monolith, which can be complex, risky, time consuming, require the coordination of many developers and result in long test cycles.
Consequently, you are often stuck with the technology choices that you made at the start of the project. In other words, the monolithic architecture doesn’t scale to support large, long-lived applications.
Scaling Solutions with the Scale Cube
The most commonly used approach of scaling an solution is by running multiple identical copies of the application behind a load balancer also known as X-axis scaling. That’s a great way of improving the capacity and the availability of an application.
When using X-axis scaling each server runs an identical copy of the service (if disaggregated) or monolith. One benefit of the X axis is that it is typically intellectually easy to implement and it scales well from a transaction perspective. Impediments to implementing the X axis include heavy session related information which is often difficult to distribute or requires persistence to servers – both of which can cause availability and scalability problems. Comparative drawbacks to the X axis is that while intellectually easy to implement, data sets have to be replicated in their entirety which increases operational costs. Further, caching tends to degrade at many levels as the size of data increases with transaction volumes. Finally, the X axis doesn’t engender higher levels of organizational scale.
Y-axis scaling (think services oriented architecture, micro services or functional decomposition of a monolith) focuses on separating services and data along noun or verb boundaries. These splits are “dissimilar” from each other. Examples in commerce solutions may be splitting search from browse, checkout from add-to-cart, login from account status, etc. In implementing splits, Y-axis scaling splits a monolithic application into a set of services. Each service implements a set of related functionalities such as order management, customer management, inventory, etc. Further, each service should have its own, non-shared data to ensure high availability and fault isolation. Y axis scaling shares the benefit of increasing transaction scalability with all the axes of the cube.
Further, because the Y axis allows segmentation of teams and ownership of code and data, organizational scalability is increased. Cache hit ratios should increase as data and the services are appropriately segmented and similarly sized memory spaces can be allocated to smaller data sets accessed by comparatively fewer transactions. Operational cost often is reduced as systems can be sized down to commodity servers or smaller IaaS instances can be employed.
Whereas the Y axis addresses the splitting of dissimilar things (often along noun or verb boundaries), the Z-axis addresses segmentation of “similar” things. Examples may include splitting customers along an unbiased modulus of customer_id, or along a somewhat biased (but beneficial for response time) geographic boundary. Product catalogs may be split by SKU, and content may be split by content_id. Z-axis scaling, like all of the axes, improves the solution’s transactional scalability and if fault isolated it’s availability. Because the software deployed to servers is essentially the same in each Z axis shard (but the data is distinct) there is no increase in organizational scalability. Cache hit rates often go up with smaller data sets, and operational costs generally go down as commodity servers or smaller IaaS instances can be used.
Like Goldilocks and the three bears, the goal of decomposition is not to have services that are too small, or services that are too large but to ensure that the system is “just right” along the dimensions of scale, cost, availability, time to market and response times.
AKF Partners has helped hundreds of companies, big and small, employ the AKF Scale Cube to scale their technology product solutions. Let us help you succeed and thrive!
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The Top 20 Technology Blunders
January 3, 2018 | Posted By: AKF
One of the most common questions we get is “What are the most common failures you see tech and product teams make?”. To answer that question we queried our database consisting of 11 years of anonymous client recommendations. Here are the top 20 most repeated failures and recommendations:
1) Failing to design for rollback
If you are developing a SaaS platform and you can only make one change to your current process make it so that you can always roll back any of your code changes. Yes, we know that it takes additional engineering work and additional testing to make nearly any change backwards compatible but in our experience that work has the greatest ROI of any work you can do. It only takes one really bad release in which your site performance is significantly degraded for several hours or even days while you attempt to “fix forward” for you to agree this is of the utmost importance. The one thing that is most likely to give you an opportunity to find other work (i.e. “get fired”) is to roll a product that destroys your business. In other words, if you are new to your job DO THIS BEFORE ANYTHING ELSE; if you have been in your job for awhile and have not done this DO THIS TOMORROW.
2) Confusing product release with product success
Do you have “release” parties? Stop it! You are sending your team the wrong message! A release has nothing to do with creating shareholder value and very often it is not even the end of your work with a specific product offering or set of features. Align your celebrations with achieving specific business objectives like a release increasing signups by 10%, or increasing checkouts by 15% or increasing the average sale price of a all checkouts by 12% or increasing click-through-rates by 22%. See #10 below on incenting a culture of excellence. Don’t celebrate the cessation of work – celebrate achieving the success that makes shareholder’s wealthy.
3) Insular product development/engineering
How often does one of your engineering teams complain about not “being in the loop” or “being surprised” by a change? Does your operations team get surprised about some new feature and its associated load on a database? Does engineering get surprised by some new firewall or routing infrastructure resulting in dropped connections? Do not let your teams design in a vacuum and “throw things over the wall” to another group. Organize around your outcomes and “what you produce” in cross functional teams rather than around activities and “how you work”.
4) Over engineering the solution
One of our favorite company mottos is “simple solutions to complex problems”. The simpler the solution, the lower the cost and the faster the time to market. If you get blank stares from peers or within your organization when you explain a design do not assume that you have a team of idiots – assume that you have made the solution overly complex and ask for assistance in resolving the complexity.
5) Allowing history to repeat itself
Organizations do not spend enough time looking at past failures. In the engineering world, a failure to look back into the past and find the most commonly repeated mistakes is a failure to maximize the value of the team. In the operations world, a failure to correlate past site incidents and find thematically related root causes is a guarantee to continue to fight the same fires over and over. The best and easiest way to improve our future performance is to track our past failures, group them into groups of causation and treat the root cause rather than the symptoms. Keep incident logs and review them monthly and quarterly for repeating issues and improve your performance. Perform post mortems of projects and site incidents and review them quarterly for themes.
6) Scaling through 3d parties
Every vendor has a quick fix for your scale issues. If you are a hyper growth SaaS site, however, you do not want to be locked into a vendor for your future business viability; rather you want to make sure that the scalability of your site is a core competency and that it is built into your architecture. This is not to say that after you design your system to scale horizontally that you will not rely upon some technology to help you; rather, once you define how you can horizontally scale you want to be able to use any of a number of different commodity systems to meet your needs. As an example, most popular databases (and NoSQL solutions) provide for multiple types of native replication to keep hosts in synch.
7) Relying on QA to find your mistakes
You cannot test quality into a system and it is mathematically impossible to test all possibilities within complex systems to guarantee the correctness of a platform or feature. QA is a risk mitigation function and it should be treated as such. Defects are an engineering problem and that is where the problem should be treated. If you are finding a large number of bugs in QA, do not reward QA – figure out how to fix the problem in engineering. Consider implementing test driven design as part of your PDLC. If you find problems in production, do not punish QA; figure out how you created them in engineering. All of this is not to say that QA should not be held responsible for helping to mitigate risk – they should – but your quality problems are an engineering issue and should be treated within engineering.
8) Revolutionary or “big bang” fixes
In our experiences, complete re-writes or re-architecture efforts end up somewhere on the spectrum of not returning the desired ROI to complete and disastrous failures. The best projects we have seen with the greatest returns have been evolutionary rather than revolutionary in design. That is not to say that your end vision should not be to end up in a place significantly different from where you are now, but rather that the path to get there should not include “and then we turn off version 1.0 and completely cutover to version 2.0”. Go ahead and paint that vivid description of the ideal future, but approach it as a series of small (but potentially rapid) steps to get to that future. And if you do not have architects who can help paint that roadmap from here to there, go find some new architects.
9) The Multiplicative Effect of Failure
Every time you have one service call another service in a synchronous fashion you are lowering your theoretical availability. If each of your services are designed to be 99.999% available, where a service is a database, application server, application, webserver, etc then the product of all of the service calls is your theoretical availability. 5 calls is (.99999)^5 or 99.995 availability. Eliminate synchronous calls wherever possible and create fault-isolative architectures to help you identify problems quickly.
10) Failing to create and incent a culture of excellence
Bring in the right people and hold them to high standards. You will never know what your team can do unless you find out how far they can go. Set aggressive yet achievable goals and motivate them with your vision. Understand that people make mistakes and that we will all ultimately fail somewhere, but expect that no failure will happen twice. If you do not expect excellence and lead by example, you will get less than excellence and you will fail in your mission of maximizing shareholder wealth.
11) Under-engineering for scale
The time to think about scale is when you are first developing your platform. If you did not do it then, the time to think about scaling for the future is right now. That is not to say that you have to implement everything on the day you launch, but that you should have thought about how it is that you are going to scale your application services and your database services. You should have made conscious decisions about tradeoffs between speed to market and scalability and you should have ensured that the code will not preclude any of the concepts we have discussed in our scalability postings. Hold quarterly scalability meetings where you discuss what you need to do to scale to 10x your current volume and create projects out of the action items. Approach your scale needs in evolutionary rather than revolutionary fashion as in #8 above.
12) “Not Built Here” Culture
We see this all the time. You may even have agreed with point (6) above because you have a “we are the smartest people in the world and we must build it ourselves” culture. The point on relying upon third parties to scale was not meant as an excuse to build everything yourselves. The real point to be made is that you have to focus on your core competencies and not dilute your engineering efforts with things that other companies or open source providers can do better than you. Unless you are building databases as a business, you are probably not the best database builder. And if you are not the best database builder, you have no business building your own databases for your SaaS platform. Focus on what you should be the best at: building functionality that maximizes your shareholder wealth and scaling your platform. Let other companies focus on the other things you need like routers, operating systems, application servers, databases, firewalls, load balancers and the like.
13) A new PDLC will fix my problems
Too often CTO’s see repeated problems in their product development life cycles such as missing dates or dissatisfied customers and blame the PDLC itself.
The real problem, regardless of the lifecycle you use, is likely one of commitment and measurement. For instance in most Agile lifecycles there needs to be consistent involvement from the business or product owner. A lack of involvement leads to misunderstandings and delayed products. Another very common problem is an incomplete understanding or training on the existing PDLC. Everyone in the organization should have a working knowledge of the entire process and how their roles fit within it. Most often, the biggest problem within a PDLC is the lack of progress measurement to help understand likely dates and the lack of an appropriate “product discovery” phase to meet customer needs.
14) We cannot hire great people quickly
Often when growing an engineering team quickly the engineering managers will push back on hiring plans and state that they cannot possibly find, interview, and hire engineers that meet their high standards. We agree that hiring great people takes time and hiring decisions are some of the most important decisions managers can make. A poor hiring decision takes a lot of energy and time to fix. However, there are lots of ways to streamline the hiring process in order to recruit, interview, and make offers very quickly. A useful idea that we have seen work well in the past are interview days, where potential candidates are all invited on the same day. This should be no more than 2 - 3 weeks out from the initial phone screen, so having an interview day per months is a great way to get most of your interviewing in a single day. Because you optimize the interview process people are much more efficient and it is much less disruptive to the daily work that needs to get done the rest of the month. Post interview discussions and hiring decisions should all be made that same day so that candidates get offers or letters of regret quickly; this will increase the likelihood of offers being accepted or make a professional impression on those not getting offers. The key is to start with the right answer that “there is a way to hire great people quickly” and the myriad of ways to make it happen will be generated by a motivated leadership team.
15) It is a SPOF (Single Point of Failure) but we can recover it onto another host quickly
A SPOF is a SPOF and even if the impact to the customer is low it still takes time away from other work to fix right away in the event of a failure. And there will be a failure…because that is what hardware and software does, it works for a long time and then eventually it fails! As you should know by now, it will fail at the most inconvenient time. It will fail when you have just repurposed the host that you were saving for it or it will fail while you are releasing code. Plan for the worst case and have it run on two hosts (we actually recommend to always deploy in pools of three or more hosts) so that when it does fail you can fix it when it is most convenient for you.
16) No Business Continuity plan
No one expects a disaster but they happen and if you cannot keep up normal operations of the business you will lose revenue and customers that you might never get back. Disasters can be huge like Hurricane Katrina, where it take weeks or months to relocate and start the business back up in a new location. Disasters can also be small like a winter snow storm that keeps everyone at home for two days or a HAZMAT spill near your office that keeps employees from coming to work. A solid business continuity plan is something that is thought through ahead of time, before you need it, and explains to everyone how they will operate in the event of an emergency. Perhaps your satellite office will pick up customer questions or your tech team will open up an IRC channel to centralize communication for everyone capable of working remotely. Do you have enough remote connections through your VPN server to allow for remote work? Spend the time now to think through what and how you will operate in the event of a major or minor disruption of your business operations and document the steps necessary for recovery.
17) No Disaster Recovery Plan
Even worse, in our opinion, than not having a BC plan is not having a disaster recovery plan. If your company is a SaaS based company, the site and services provided is the company’s sole source of revenue. Moreover, with a SaaS company, you hold all the data for your customers that allow them to operate. When you are down they are more than likely seriously impaired in attempting to conduct their own business. When your collocation facility has a power outage that takes you completely down, think 365 Main datacenter in San Francisco, how many customers of yours will leave and never return? Our preference is to provide your own disaster recovery through multiple collocation facilities but if that is not yet technically feasible nor in the budget, at a minimum you need your code, executables, configurations, loads, and data offsite and an agreement in place for both collocation services as well as hosts. Lots of vendors offer such packages and they should be thought of as necessary business insurance.
18) No Product Management team or person
In a similar vein to #13 above, there needs to be someone or a team of people in the organization who have responsibility for the product lines. They need to have authority to make decisions about what features get added, which get delayed, and which get deprecated (yes, we know, nothing ever gets deprecated but we can always hope!). Ideally these people have ownership of business goals (see #10) so they feel the pressure to make great business decisions.
19) It is okay to bring the site down to roll code
Just because you call it scheduled maintenance does not mean that it does not count against your uptime. While some of your customers might be willing to endure the frustration of having the site down when they want to access it in order to get some new features, most care much more about the site being available when they want it. They are on the site because the existing features serve some purpose for them; they are not there in the hopes that you will rollout a certain feature that they have been waiting on. They might want new features, but they rely on existing features. There are ways to roll code, even with database changes, without bringing the site down. It is important to put these techniques and processes in place so that you plan for 100% availability instead of planning for much less because of planned down time.
20) Firewalls, Firewalls, Everywhere!
We often see technology teams that have put all public facing services behind firewalls while many go so far as to put firewalls between every tier of the application. Security is important because there are always people trying to do malicious things to your site, whether through directed attacks or random scripts port scanning your site. However, security needs to be balanced with the increased cost as well as the degradation in performance. It has been our experience that too often tech teams throw up firewalls instead of doing the real analysis to determine how they can mitigate risk in other ways such as through the use of ACLs and LAN segmentation. You as the CTO ultimately have to make the decision about what are the best risks and benefits for your site.
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Whatever you do, don’t make the mistakes above! AKF Partners helps companies avoid costly product and technology mistakes - and we’ve seen most of them. Give us a call or shoot us an email. We’d love to help you achieve the success you desire.
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Hosting Lessons from Harvey and Irma
September 19, 2017 | Posted By: Greg Fennewald
Everyone was saddened to see the horrific destruction storms caused to Houston and Florida, including deaths and extensive property damage. It seems reasonable that the impact of these hurricanes was lessened by advanced notice and preparation – stockpiling supplies, evacuating the highest risk areas, and staging response resources to assist with recovery and rebuilding.
Data centers operate every day with a similar preparation mindset: diesel generators to provide power should the utility fail, batteries to keep servers running during a transition, potentially stored water or a well to replace municipal water service for cooling systems, and food and water for personnel unable to leave the location.
What happens when a “prepared” location such as a data center encounters a hurricane with strong winds, heavy rain, and extensive flooding? In some cases, the data center survives without impact, although there certainly will be outages and failures. Examples of data centers surviving Harvey in good shape can be seen here, while accounts of the service impacts caused by Hurricane Sandy can be seen here.
Data Center Points of Failure
Let’s examine what may enable a data center to survive without functional impact. Extensive risk investigation goes into site selection for data centers. Data centers are expensive to build with costs measured in the tens or even hundreds of millions of dollars. The potential business impact of a failure can be costly with liquidated damage clauses in hosting contracts. These factors lead to data centers being located outside of flood plains, away from hazardous material routes, and stoutly constructed to endure storm winds likely in the region.
Losing utility power is regarded as a “when” not an “if” in the data center industry (be that an outage or a planned maintenance activity), and diesel generators are a common solution, often with 24 hours or more of fuel on hand and multiple replenishment contracts. Data centers can survive for days/weeks without utility power, and in some cases for months. How could flooding impact power? The service entrance for a data center, where the utility power is routed, is often buried underground. Utility power is likely to be lost during flooding, either from damage due to flooding or intentional actions to prevent damage by shutting down the local grid. A data center would operate on generator if the data center itself is not flooded, although fuel replenishment is not likely. If there are two feet of water in the main electrical room(s), the data center is going dark.
Many large data centers rely on evaporating water to cool the servers it hosts. Evaporative cooling is generally more energy efficient than other options, but introduces an additional risk to operations – water supply. In many locations, municipal water pressure is lost during an extend power outage. Data centers can mitigate this risk by using water storage tanks or water wells onsite. Like diesel generators, the data centers can operate normally for hours or days without municipal water. A data center should be outside the flood plain, able to operate without utility power or municipal water for hours or days, is structurally strong enough to handle the winds of a major storm – is there any other risk to mitigate? Network connectivity and bandwidth.
Most data centers need to communicate with other data centers to fulfill their OLAP or OLTP purpose. Without connectivity, services are not available. Data should be fine, but it is becoming increasingly stale. Transactions and traffic are done. Like utility power, network connections are usually buried. With distance and geographic limitations involved, network pathways may get flooded as may the facilities that aggregate and transmit the data. Telecom facilities generally have generators and other availability measures, but can be forced into less advantageous locations and may have a shorter runtime standard than a data center.
Data centers that are serious about availability generally have carrier diversity and physical pathway diversity to mitigate carrier outages and “backhoe fades”. This may help in the event of widespread flooding as well. The reality is a data center without connectivity is generally useless. All the risk mitigation going into structural design, power and cooling redundancy, and fire protection is moot if connectivity fails.
Preparing for the Inevitable
The best way to mitigate these risks is to not rely on a single data center location. One is none and two is one. Owned, colo, managed hosting, or cloud – be able to survive the loss of a single location. The RTO and RPO of the business will guide the choice of active – active, hot – cold, or data backup with an elastic compute response plan. Hurricanes can cause regional impact, such as Irma disrupting most of Florida. In years past, many companies decided to have two data center within 20 miles of each other to support synchronous data base replication. A primary site in one borough of New York City, and the DR site in a different borough. Replication options and data base management techniques have advanced sufficiently to allow far greater dispersion today. Avoid a regionally impacting event by choosing data centers in diverse regions.
Operating from 3 locations can be cheaper than 2, and can also improve customer satisfaction with reduced response times produced by serving customers from the nearest location. See Rule 12 in Scalability Rules. The ability to operate from multiple locations also enables a choice to adjust the redundancy of those locations. A combination of Tier II and III locations may be a more economical choice than a pair of Tier IV locations.
Developing a hosting plan can be complicated and frustrating, particularly since the core competency of your business is likely not data centers. AKF Partners can help – not only with hosting strategy, but also the product architecture and operational processes needed to weld infrastructure, architecture, and process into a seamless vehicle that delivers services to your clients with availability the market demands.
Hurricanes aren’t the only disasters that can take down your data center. Solar flares, runaway SUVs, civil disruption, tornadoes and localized power outages have all caused data centers to fail. Natural disasters of all types trail equipment failures and human error as causes of service impacting events (source: 365DataCenters). According to FEMA, 40% of businesses that close due to a disaster don’t reopen, and of those that do only 29% are in business two years after the disaster (source: FEMA). Don’t be a statistic. AKF Partners can help you with the product architecture and data center planning necessary to survive nearly any disaster.
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How an AI bot beat the world's best gamers
September 5, 2017 | Posted By: Roger Andelin
Last month, a bot developed by OpenAI (co-founded by Elon Musk) beat the world’s best, pro Dota 2 players. This is another milestone accomplishment in the field of artificial intelligence and machine learning and more fuel for the fire of concerns surrounding the AI debate. However, before we jump into that debate, here is some background you should understand about the technology fueling this debate.
The Evolution of Traditional Programming
A lot of what computer programming is can be simplified into three steps. First step, read in some data. Second step, do something with that data. Third step, output some result.
For example, imagine you want to fly somewhere for the weekend. You may first go to your travel app and input some dates, times, number of people traveling, airports, etc. Second, the app uses that information to search its database of available flights. Third, it returns a list of available flights for you to see.
This approach to software design has been the norm since the earliest days of programming. Artificial intelligence, in particular machine learning, has changed that approach. The first step is still the same: Read in some data. The third step is the same: Output some result.
However, with artificial intelligence technologies like machine learning, the second step, doing something with the data, is very different. In the example of finding a flight, a programmer easily can read the software code to understand the sequence of steps the computer has been programmed to do to produce the output data. If the programmer wants to change or improve the program’s behavior she can do that by writing new code or by altering the existing code. For example, if you wanted to compare the prices for available flights near the dates you have selected, a programmer can easily change several lines of code in the program to do just that. The programming code identifies every step the computer takes to arrive at its output. Said another way, the program only does what it’s specifically told to do in the code, nothing more or nothing less.
By contrast, the output of today’s most common machine learning programs is not determined by instructions written in computer code. There is no code for a programmer to read or modify when a change is desired. The output is determined by the program’s neural network.
Neural Networks in Action
What is a neural network? At the core of a neural network is a neuron. Similar to a traditional computer program, a neuron takes some input data, does a mathematical calculation on that data and then outputs some data. A typical neuron in a neural network will receive as input hundreds to thousands of numbers, typically between 0 and 1. A neuron will then multiply each number by a weight and sum the result of all the numbers. Many neurons will then convert the result into a number between 0 and 1. That result is then sent to the next neuron in sequence until the final output neuron is reach.
Here is an example of the math a typical neuron will do: If “x1, x2, x3…” represents input data and “w1, w2, w3…” represent the weights stored in the neuron, the calculation done by the neuron in a neural network looks like this: x1*w1 + x2*w2 + x3*w3 and so forth.
You can think of the calculation inside the neuron in a different way: The neuron is reading in a bunch of numbers and the weights in the neuron determine the importance, “or weight” of that input in producing the output. If the input is not important the weight for the input will be near zero and the input is not passed along to the next neuron. Therefore, the weights in a neuron effectively decided what input is valuable and what input should be ignored.
In a neural network, neurons like the one I described above are connected in parallel and in series to create a matrix of neurons. The input data to a neural network will go into hundreds or thousands of neurons in parallel, all with different weights. The output of those neurons is then sent to another layer of neurons and so forth, usually multiple layers deep. This is called a deep neural network. Another way to look at this is the neurons are grouped into a matrix of rows and columns, all interconnected. The final layer of the neural network is the output layer. Therefore, the final output of a neural network is the result of millions of calculations done by the neurons of that network.
When a programmer creates a neural network in software, the weights for each neuron are initially just random numbers. In other words, the weights arbitrarily decide to either diminish, increase or leave the input data alone, and output from the network is random. However, through a process called training, the weights move from randomly assigned values to values that can produce very useful outputs.
Training is both a time consuming and complicated mathematical process. However, it is much like training you and I would do to get better at something. For example, let’s say I wanted to learn how to shoot and arrow with a bow. I might pick up the bow and arrow, point it at the target, pull back the string and release. In my case, I know the arrow would miss the target. Therefore, I would try again and again making corrections to my aim based on on how far and which direction I was off from the target.
During the training process for a neural network, the weights in each of the neurons are changed slightly to improve the output, or “aim.” The most common approach for making those changes is called backpropagation. Backpropagation is a mathematical approach for applying corrections to every weight in every neuron of the network. During training, input is fed into the network and output is generated. The output is compared to the desired target and the difference between the output and the target is the error. Using the error, backpropagation makes changes to the weights in each neuron to reduce the error. If all goes well during training and backpropagation, the output error diminishes until it reaches expert or better than expert level.
AI vs Humans
In the case of the OpenAI Dota bot that recently beat the world’s best Dota 2 player, the outputs, which were a sequence of steps, strategies and decisions, went from random moves to moves that were so good the bot was able to easily defeat the best pro players in the world. The critical information that enabled the bot to win its matches was stored in the weights of the neurons and the neural network architecture itself.
A good question at this point is to ask if a programmer looking at the Dota 2 bot’s neural network could understand the steps taken by the bot to beat the human player. The answer is no. A programmer can see areas of the neural network that influence an output but it is not possible to explain why the bot took specific steps to formulate its moves and strategies. All the programmer would see is a huge matrix of weights that would be quite overwhelming to interpret.
Another good question to ask is whether or not a program written traditionally by a programmer with step by step instructions could beat the best Dota 2 player. The answer is no. Step by step programs where the programmer specifically instructs the computer to do something would easily be defeated by a professional player. However, a neural network can learn from training things that a programmer would never have the knowledge to program, store that learning in its neurons and use that learning to do things like defeat a human pro.
What makes the Dota 2 bot special is that it learned to beat the best pro players by playing against itself whereas most machine learning programs learn from training on data given to it by a programmer. In machine learning, good training data is like gold. It’s scarce and valuable. (note: This is one reason why Google and other big tech companies want to collect so much data.) Data is used to train neural networks to do useful things like recognizing people and places in your pictures or recognizing your voice from others in your family. OpenAI built a bot that learned almost entirely by playing against itself with the exception of some coaching provided by the OpenAI team. OpenAI has shown clearly that learning can occur without having tons of training data. It’s a little like being able to make gold.
Does the development of the OpenAI dota bot mean bots can now decide to train against themselves and become super bots? No. But it does say that humans can now program two bots to train against each other to become superbots. The key enabler being us. It’s anyone’s guess what type of bot can be imagined and developed in this way, useful or harmful. Obviously to most, a gaming superbot seems pretty innocuous, except of course to the gamer who may unexpectedly run into one during a match. However, it’s not hard to imagine super bots that are not so harmless. Or, perhaps you can just imagine a time when someone trains a bot to play football against itself until the bot becomes better at calling plays and strategy than every coach in the NFL. What happens then? The answer is disruption. Are you ready for it?
AKF Partners recommends that boards and executives direct their teams to identify sources of innovation and patterns of disruption that AI techniques may represent within their respective markets Walmart is already working on facial recognition technology in their stores to determine whether or not shoppers are satisfied at checkout. Will this give them a potential advantage over Amazon? How can machine learning and AI help you prevent fraud in your payment systems or the use of your commerce system to launder money?
AKF is prepared to help answer that question and others you may be facing. We will help you craft your AI strategy, sort through the hype, help you find the opportunities, and identify the potential threats of AI technology to your business.
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When Should You Split Services?
April 3, 2017 | Posted By: AKF
The Y axis of the AKF Scale Cube (alternatively the Scale Cube or AKF Cube) indicates that growing companies should consider splitting their products along services (verb) or resources (noun) oriented boundaries. A common question we receive is “how granular should one make a services split?” A similar question to this is “how many swim lanes should our application be split into?” To help answer these questions, we’ve put together a list of considerations based on developer throughput, availability, scalability, and cost. By considering these, you can decide if your application should be grouped into a large, monolithic codebases or split up into smaller individual services and swim lanes. You must also keep in mind that splitting too aggressively can be overly costly and have little return for the effort involved. Companies with little to no growth will be better served focusing their resources on developing a marketable product than by fine tuning their service sizes using the considerations below.
Frequency of Change – Services with a high rate of change in a monolithic codebase cause competition for code resources and can create a number of time to market impacting conflicts between teams including product merge conflicts. Such high change services should be split off into small granular services and ideally placed in their own fault isolative swim lane such that the frequent updates don’t impact other services. Services with low rates of change can be grouped together as there is little value created from disaggregation and a lower level of risk of being impacted by updates.
The diagram below illustrates the relationship we recommend between functionality, frequency of updates, and relative percentage of the codebase. Your high risk, business critical services should reside in the upper right portion being frequently updated by small, dedicated teams. The lower risk functions that rarely change can be grouped together into larger, monolithic services as shown in the bottom left.
Degree of Reuse – If libraries or services have a high level of reuse throughout the product, consider separating and maintaining them apart from code that is specialized for individual features or services. A service in this regard may be something that is linked at compile time, deployed as a shared dynamically loadable library or operate as an independent runtime service.
Team Size – Small, dedicated teams can handle micro services with limited functionality and high rates of change, or large functionality (monolithic solutions) with low rates of change. This will give them a better sense of ownership, increase specialization, and allow them to work autonomously. Team size also has an impact on whether a service should be split. The larger the team, the higher the coordination overhead inherent to the team and the greater the need to consider splitting the team to reduce codebase conflict. In this scenario, we are splitting the product up primarily based on reducing the size of the team in order to reduce product conflicts. Ideally splits would be made based on evaluating the availability increases they allow, the scalability they enable or how they decrease the time to market of development.
Specialized Skills – Some services may need special skills in development that are distinct from the remainder of the team. You may for instance have the need to have some portion of your product run very fast. They in turn may require a compiled language and a great depth of knowledge in algorithms and asymptotic analysis. These engineers may have a completely different skillset than the remainder of your code base which may in turn be interpreted and mostly focused on user interaction and experience. In other cases, you may have code that requires deep domain experience in a very specific area like payments. Each of these are examples of considerations that may indicate a need to split into a service and which may inform the size of that service.
Availability and Fault Tolerance Considerations:
Desired Reliability – If other functions can afford to be impacted when the service fails, then you may be fine grouping them together into a larger service. Indeed, sometimes certain functions should NOT work if another function fails (e.g. one should not be able to trade in an equity trading platform if the solution that understands how many equities are available to trade is not available). However, if you require each function to be available independent of the others, then split them into individual services.
Criticality to the Business – Determine how important the service is to business value creation while also taking into account the service’s visibility. One way to view this is to measure the cost of one hour of downtime against a day’s total revenue. If the business can’t afford for the service to fail, split it up until the impact is more acceptable.
Risk of Failure – Determine the different failure modes for the service (e.g. a billing service charging the wrong amount), what the likelihood and severity of each failure mode occurring is, and how likely you are to detect the failure should it happen. The higher the risk, the greater the segmentation should be.
Scalability of Data – A service may be already be a small percentage of the codebase, but as the data that the service needs to operate scales up, it may make sense to split again.
Scalability of Services – What is the volume of usage relative to the rest of the services? For example, one service may need to support short bursts during peak hours while another has steady, gradual growth. If you separate them, you can address their needs independently without having to over engineer a solution to satisfy both.
Dependency on Other Service’s Data – If the dependency on another service’s data can’t be removed or handled with an asynchronous call, the benefits of disaggregating the service probably won’t outweigh the effort required to make the split.
Effort to Split the Code – If the services are so tightly bound that it will take months to split them, you’ll have to decide whether the value created is worth the time spent. You’ll also need to take into account the effort required to develop the deployment scripts for the new service.
Shared Persistent Storage Tier – If you split off the new service, but it still relies on a shared database, you may not fully realize the benefits of disaggregation. Placing a readonly DB replica in the new service’s swim lane will increase performance and availability, but it can also raise the effort and cost required.
Network Configuration – Does the service need its own subdomain? Will you need to make changes load balancer routing or firewall rules? Depending on the team’s expertise, some network changes require more effort than others. Ensure you consider these changes in the total cost of the split.
The illustration below can be used to quickly determine whether a service or function should be segmented into smaller microservices, be grouped together with similar or dependent services, or remain in a multifunctional, infrequently changing monolith.
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Splitting Databases for Scale
April 3, 2017 | Posted By: AKF
The most common point of congestion and therefore barrier to scale that we see in our practice is the database. Referring back to our earlier article “Splitting Applications or Services for Scale”, it is very common for engineers to create scalability along the X axis of our cube by persisting data in a single monolithic database and having multiple “cloned” applications servers retrieve and store data within that database. For young companies this is a very good approach as if done properly it will also eliminate the need for persistence or affinity to a given application server and as a result will increase customer perceived availability.
The problem, however, with this single monolithic data structure is threefold:
- Even with clustering technology (the existence of a second physical system or database that can take the load of the first in the event of failure), failures of the primary database will result in short service outages for 100% of the user community.
- This approach ultimately relies solely on technical improvements in cpu speed, memory access speed, memory access size, mass
storage access speeds and size, etc to insure the companies needs for scale.
- Relying upon (2) above in the extreme cases is not the most cost effective solutions as the newest and fastest technologies come at
a premium to older generations of technology and do not necessarily have the same processing power per dollar as older and/or
smaller (fewer cpus etc) systems.
As we have argued in the aforementioned post, a great engineering team will think about how to scale their platform well in advance of the need to rely solely upon partner technology advances. By making small modifications to our previously presented “Scale Cube”, the same concepts applied to the problem of splitting services for scale can be useful in addressing how to split a database for scale. As with the AKF Services Scale Cube, the AKF Database Scale Cube consists of an X, Y and Z axes – each addressing a different approach to scale transactions applied to a database. The lowest left point of the cube (coordinates X=0, Y=0 and Z=0) represents the worst case monolithic database – a case where all data is located in a single location and all accesses go to this single database.
The X Axis of the cube represents a means of spreading load across multiple instances of a replicated representation of the data. This is the first approach most companies use in scaling databases and is often both the easiest to implement and the least costly in both engineering time and hardware. Many third party and open source databases have native properties or functions that will allow the near real time replication of data to multiple “read databases”. The engineering cost of such an approach is low as typically database calls only need to be identified as a “read” or “write” and sent to the appropriate write database or bank of read databases. The “bank” of read databases should have reads evenly split across this if possible and many companies employ simple 3d party load balancers to perform this distribution.
Included in our Xaxis split are third party and open source caching solutions that allow reads to be split across “cache” hosts before actually reading from a database upon a cache miss. Caching is another simple way to reduce the load on the database but in our experience is not sufficient for hyper growth SaaS sites. If implemented properly, this Xaxis split also can increase availability as if replication is near real time, a read server can be promoted as the singular “write server” in the event of a “write server” failure. The combination of caching and read/write splits (our X axis) is sufficient for many companies but for companies with extreme hyper growth and massive data retention needs it is often not enough.
The Y Axis of our database cube represents a split by function, service or resource just as it did with the service cube. A service might represent a set of usecases and is most often easiest to envision through thinking of it as a verb or action like “login” and a resource oriented split is easiest to envision by thinking of splits as nouns like “account information”. These splits help handle not only the split of transactions across multiple systems as did the X axis, but can also be helpful in speeding up database calls by allowing more information specific to the request to be held in memory rather than needing to make a disk access. Just as with our approach in scaling services, our recommended approach to identify the order in which these splits should be accomplished is to determine which ones will give you the greatest “headroom” or capacity “runway” for the least amount of work. These splits often come at a higher cost to the engineering team as very often they will require that the application be split up as well. It is possible to take a monolithic application and perform physical splits by say URL/URI to different service or resource oriented pools. While this approach will help spread transaction processing across multiple systems similar to our X axis implementation it may not offer the added benefit of reducing the amount of system memory required by service / pool / resource / application. Another reason to consider this type of split in very large teams is to dedicate separate engineering teams to focus on specific services or resources in order to reduce your application learning curve, increase quality, decrease time to market (smaller code bases), etc. This type of split is often referred to as “swimlaning” an application and data set, especially when both the database and applications are split to represent a “failure domain” or fault isolative infrastructure.
The Z Axis represents ways to split transactions by performing a lookup, a modulus or other indiscriminate function (hash for instance). The most common way to view this is to consider splitting your resources by customer if your entity relationships allow that to happen. In the world of media, you might consider splitting it by article_id or media_id and in the world of commerce a split by product_id might be appropriate. In the case where you split customers from your products and perform splits within customers and products you would be implementing both a Y axis split (splitting by resource or call – customers and products) and a Z axis split (a
modulus of customers and products within their functional splits).
Z axis splits tend to be the most costly for an engineering team to perform as often many functions that might be performed within the database (joins for instance) now need to be performed within the application. That said, if done appropriately they represent the greatest potential for scale for most companies.
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Splitting Applications or Services for Scale
April 3, 2017 | Posted By: AKF
Splitting Applications or Services for Scale
Most internet enabled products start their life as a single application running on an appserver or appserver/webserver combination and potentially communicating with a database. Many if not all of the functions are likely to exist within a monolithic application code base making use of the same physical and virtual resources of the system upon which the functions operate: memory, cpu, disk, network interfaces, etc. Potentially the engineers have the forethought to make the system highly available by positioning a second application server in the mix to be used in the event that the first application server fails.
This monolithic design will likely work fine for many sites that receive low levels of traffic. However, if the product is very successful and receives wide and fast adoption user perceived response times are likely to significantly degrade to the point that the product is almost entirely unusable. At some point, the system will likely even fail under the load as the inbound request rate is significantly greater than the processing power of the system and the resulting departure rate of responses to requests.
A great engineering team will think about how to scale their platform well in advance of such a catastrophic failure. There are many ways to approach how to think about such scalability of a platform and we present several through a representation of a three dimensional cube addressing three approaches to scale that we call the AKF Scale Cube.
The AKF Scale Cube (aka Scale Cube and AKF Cube) consists of an X, Y and Z axes – each addressing a different approach to scale a service. The lowest left point of the cube (coordinates X=0, Y=0 and Z=0) represents the worst case monolithic service or product identified above: a product wherein all functions exist within a single code base on a single server making use of that server’s finite resources of memory, cpu speed, network ports, mass storage, etc.
The X Axis of the cube represents a means of spreading load across multiple instances of the same application and data set. This is the first approach most companies use to scale their services and it is effective in scaling from a request per second perspective. Oftentimes it is sufficient to handle the scale needs of a moderate sized business. The engineering cost of such an approach is low compared to many of the other options as no significant rearchitecting of the code base is required unless the engineering team needs to eliminate affinity to a specific server because the application maintains state. The approach is simple: clone the system and service and allow it to exist on N servers with each server handling 1/Nth the total requests. Ideally the method of distribution is a loadbalancer configured in a highly available manner with a passive peer that becomes active should the active peer fail as a result of hardware or software problems. We do not recommend leveraging roundrobin DNS as a method of load balancing. If the application does maintain state there are various ways of solving this including a centralized state service, redesigning for statelessness, or as a last resort using the load balancer to provide persistent connections. While the Xaxis approach is sufficient for many companies and distributes the processing of requests across several hosts it does not address other potential bottlenecks like memory constraints where memory is used to cache information or results.
The Y Axis of the cube represents a split by function, service or resource. A service might represent a set of usecases and is most often easiest to envision through thinking of it as a verb or action like “login” and a resource oriented split is easiest to envision by thinking of splits as nouns like “account information”. These splits help handle not only the split of transactions across multiple systems as did the X axis, but can also be helpful in reducing or distributing the amount of memory dedicated to any given application across several systems. A recommended approach to identify the order in which these splits should be accomplished is to determine which ones will give you the greatest “headroom” or capacity “runway” for the least amount of work. These splits often come at a higher cost to the engineering team as very often they will require that the application be split up as well. As a quick first step, a monolithic application can be placed on multiple servers and dedicate certain of those servers to specific “services” or URIs. While this approach will help spread transaction processing across multiple systems similar to our X axis implementation it may not offer the added benefit of reducing the amount of system memory required by service/pool/resource/application. Another reason to consider this type of split in very large teams is to dedicate separate engineering teams to focus on specific services or resources in order to reduce your application learning curve, increase quality, decrease time to market (smaller code bases), etc. This type of split is often referred to as
“swimlaning” an application.
The Z Axis represents ways to split transactions by performing a lookup, a modulus or other indiscriminate function (hash for instance). As with the Y axis split, this split aids not only fault isolation, but significantly reduces the amount of memory necessary
(caching, etc) for most transactions and also reduces the amount of stabile storage to which the device/service needs attach. In this case, you might try a modulus by content id (article), or listing id, or a hash from the received IP address, etc. The Z axis split is often the most costly of all splits and we only recommend it for clients that have hypergrowth or very high rates of transaction. It should only be used after a company has implemented a very granular split along the Y axis. That said, it also can offer the greatest degree of scalability as the number of “swimlanes within swimlanes” that it creates is virtually limitless. For instance, if a company implements a Z axis split as a modulus of some transaction id and the implementation is a configurable number “N”, then N can be 10, 100, 1000, etc and each order of magnitude increase in N creates nearly an order of magnitude of greater scale for the company.
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Fault Isolative Architectures or “Swimlanes” Using the Scale Cube
April 3, 2017 | Posted By: AKF
Two of our previous articles, Splitting Databases for Scale and Splitting Applications or Services for Scale have made references to a concept that we call “Swimlaning Architectures”. We sometimes also call “swimlanes” fault isolation zones or fault isolated architecture.
The basics of this concept are covered in our two previous posts, but we have not spent a lot of time discussing the reasons for such a split or approach in technology architecture.
In our definition, a “Swimlane” is a failure domain. A failure domain is a group of services within a boundary such that any failure within that boundary is contained within the boundary and the failure does not propagate or affect services outside of said boundary. The benefit of such a failure domain is twofold:
1) Fault Detection: Given a granular enough approach, the component of availability associated with the time to identify the failure is significantly reduced. This is because all effort to find the root cause or failed component is isolated to the section of the product or platform associated with the failure domain.
2) Fault Isolation: As stated previously, the failure does not propagate or cause a deterioration of other services within the platform. As such, and depending upon approach only a portion of users or a portion of functionality of the product is affected.
A “swimlaned” architecture is one in which each failure domain is completely isolated. In order to achieve this, ideally there are no calls between swimlanes or failure domains. Synchronous calls are absolutely forbidden in this type of architecture as any synchronous call between failure domains, even with appropriate timeout and detection mechanisms is very likely to cause a series of failures across other domains. Strictly speaking, you do not have a failure domain if that domain is connected via a call to any other service in another domain, to any service outside of the domain, or if the domain receives calls from other domains or services.
It is acceptable, but not advisable, to have asynchronous calls between domains. If such a communication is necessary it is very important to include failure detection and timeouts even with the asynchronous calls to ensure that retries do not call port overloads on any services. Here is an interesting blog post about runaway scripts and their impact on Apache, PHP, and MySQL.
As we have previously indicated, a swimlane should have all of its services located within the failure domain. For instance, if database accesses are necessary the database with all appropriate information for that swimlane should exist within the same failure domain as all of the application and webservers necessary to perform the function or functions of the swimlane. Furthermore, that database should not be used for other requests of service from other swimlanes. Our rule is one production database on one host.
As we have indicated with our Scale Cube in the past, there are many ways in which to think about swimlaned architectures. You can think about them in terms of a separation of services e.g. “login” and “shopping cart” (two separate swimlanes) each having the web and app servers as well as all data stores located within the swimlane and answering only to systems within that swimlane. Corresponding to the Scale Cube we have previously introduced this would be a “Y” axis swimlane.
Another approach would be to perform a separation of your customer base or a separation of your order numbers or product catalog.
Assuming an indiscriminate function to perform this separation (like a modulus of id), such a split would be a Z axis swimlane along customer, order number or product id lines.
Combining the concepts of service and database separation into several fault isolative failure domains creates both a scalable and highly available platform.
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