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The fallacies of distributed computing are a set of assertions describing false assumptions made about distributed systems.

Peter Deutsch drafted the first 7 fallacies inwith the 8 th added by James Gosling in Whereas the second fallacy relates to the time taken to transport data over a network, the seventh fallacy relates to the resources required to do so. There is a resource and resultant latency cost in marshalling data between the application layer Layer 7 on the OSI Reference Model and transport layer Layer 4 in order to send it over the network.

While this may be an abstract concern where a system is deployed on own hardware, in a cloud computing context where resource utilisation equals money, it can be a more concrete concern. The infrastructure used to set up and run the system will incur initial and ongoing costs — for on-premises systems these may include the cost of physical hardware such as servers, switches, and routers to the salaries of the network operators who will hopefully manage everything.

The Eight Fallacies of Distributed Computing

For cloud computing, while physical hardware costs will of course not be a factor of concern, you will pay a premium for the resources you utilise. While the cost of infrastructure is mostly outside your control, optimising its use is within your domain.

Reducing the time spent marshalling data between Layers 7 and 4 may be significant, depending on the functionality provided by the system. For example, the cost of transport for systems providing audio and video streaming, telephony, or realtime collaboration features can be brought closer to zero by leveraging an optimised data format e.

Transport cost is zero 29th June 2 min read Fallacies of Distributed Computing The fallacies of distributed computing are a set of assertions describing false assumptions made about distributed systems.

7 fallacies of distributed computing

Transport cost is zero The problem Whereas the second fallacy relates to the time taken to transport data over a network, the seventh fallacy relates to the resources required to do so.

Marshalling There is a resource and resultant latency cost in marshalling data between the application layer Layer 7 on the OSI Reference Model and transport layer Layer 4 in order to send it over the network.

Lesson 18 - The Fallacies of Distributed Computing

Computing time costs money. Infrastructure The infrastructure used to set up and run the system will incur initial and ongoing costs — for on-premises systems these may include the cost of physical hardware such as servers, switches, and routers to the salaries of the network operators who will hopefully manage everything.

Solutions While the cost of infrastructure is mostly outside your control, optimising its use is within your domain.Are you working on a distributed system?

Microservices, Web APIs, SOA, web server, application server, database server, cache server, load balancer - if these describe components in your system's design, then the answer is yes. Distributed systems are comprised of many computers that coordinate to achieve a common goal. More than 20 years ago Peter Deutsch and James Gosling defined the 8 fallacies of distributed computing.

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These are false assumptions that many developers make about distributed systems. These are usually proven wrong in the long run, leading to hard to fix bugs. Most of the systems today make calls to other systems. Are you integrating with 3rd party systems payment gateways, accounting systems, CRMs?

Are you doing web service calls? What happens if a call fails? If you're querying data, a simple retry will do. But what happens if you're sending a command? Let's take a simple example:. What happens if we receive an HTTP timeout exception? If the server did not process the request, then we can retry. But, if it did process the request, we need to make sure we are not double charging the customer.

You can do this by making the server idempotent. This means that if you call it 10 times with the same charge request, the customer will be charged only once. If you're not properly handling these errors, you're system is nondeterministic.

Handling all these cases can get quite complex really fast. So, if calls over a network can fail, what can we do? Well, we could automatically retry. Queuing systems are very good at this. They usually use a pattern called store and forward. They store a message locally, before forwarding it to the recipient.

If the recipient is offline, the queuing system will retry sending the message. MSMQ is an example of such a queuing system. But this change will have a big impact on the design of your system. Since you are not waiting for a response anymore, you need to change the user journeys through your system. You cannot just replace each web service call with a queue send. You might say that networks are more reliable these days - and they are.

But stuff happens. Hardware and software can fail - power supplies, routers, failed updates or patches, weak wireless signals, network congestion, rodents or sharks.Inthe U. These are both early examples of distributed systems: a collection of independent computers that appear to users as a single coherent system. This is often the result of assumptions architects and designers of distribution systems are likely to make.

InPeter Deutsch, who worked at Sun Microsystems, wrote about these assumptions to explore what can go wrong in distributed systems. InJames Gosling added to this list to create what is commonly known as the eight fallacies of distributed computing. Traditional approaches, which use time-based replication to architect and build distributed systems, suffer from many of these fallacies and result in systems that are inefficient, insecure and costly to maintain.

Modern approaches, using complex mathematics such as the Paxos algorithm, overcome many of these significant hurdles. The network is reliable The first fallacy is an easy way to set yourself up for failure, as Murphy made sure there will always be things that go wrong with the network—whether it is power failure or a cut cable. However, Active Transactional Data Replication ensures that should a single server or an entire data center go offline, the information you need will still be available, as each data note is continuously synchronised without geographical constraints.

Latency is zero Latency is how much time it takes for data to move from one place to another versus bandwidth, which is how much data can be transferred during that time.

In the past, latency has deteriorated quickly when you move to WAN or internet scenarios. As more enterprises look to migrate operations to the cloud or move to a hybrid cloud structure, network latency can lead to an inability to back up data and restore it at speed in any given situation, including disaster recovery. Active Transactional Data Replication technology can now ensure transactional data can be moved to the cloud at petabyte scale with no downtime and no data loss, making it possible for on-premise and cloud environments to operate as one.

Bandwidth is infinite Bandwidth is the capacity of a network to transfer data. Even though network bandwidth capacity has been improving the amount of information we want to transfer, it is also increasing exponentially.

But there are ways to increase bandwidth. This gives administrators the power to prioritize network traffic on the basis of source and target data centers and the ability to assign higher priority to specific files and directories during replication between data centers. The network is secure The only completely secure system is one that is not connected to any network.

When applications are deployed into an organization, network topology can quickly become out of control with laptops coming and going, wireless ad hoc networks and new mobile devices. In fact, one of the biggest benefits for organizations moving to the cloud is the ability to change topology at will. As data can now be replicated across different environments with guaranteed consistency, issues around topology are no longer as relevant as they used to be.

There is one administrator While there is never just one administrator, given your applications are likely interacting with systems outside your administrative control, the impact of this is no longer as critical as it once was. The fact is you care about administrators only when things go awry and you need to pinpoint a problem and solve it. Even then with the same data available across multiple locations, organizations can now carry on with their work with no risk of downtime.

Transport cost is zero While nothing in life is free, the costs of setting and running a WAN network are considerably cheaper than they used to be. Cloud computing means you can purchase what you need per transaction, gigabyte or an hour.

Active-active architecture means all servers and clusters are fully readable and writeable, always in sync and recover automatically from each other after planned or unplanned downtime. This means there are no passive read-only backup servers and clusters, so organizations can now use percent of their hardware without wasting budget on idle servers.

The network is homogenous This fallacy was added to the original seven by Gosling, the creator of Java, in With the advent of mobile, no network today is homogenous, but an increasing number of tools are being built that do let you try out things across different networks and environments. The key point here is to ensure you have no vendor lock-in. Technology has continued to evolve since then, rendering these fallacies increasingly irrelevant.

Google Spanner, for example, overcomes many of the fallacies by utilizing Paxos-based replication, along with plenty of dark fibre, to overcome latency and hardware-assisted time synchronization issues from using dedicated satellites, GPS clocks and atomic clocks.

The Paxos algorithm can also be extended to ensure global consistency through Active Transactional Data Replication with no additional hardware and no additional bandwidth requirements.Designers bring with them set of assumptions, prejudices and fallacies to their design efforts that if left unchallenged corrupt their design artefacts.

In turn, this gives rise to short and long term issues with the realisation of their designs. The fallacies of distributed computing attempts to challenge and disrupt Design and to cause Designers to think more carefully and undertake appropriate self-examination. We have settled upon 12 key fallacies, 81 of which are long standing, widely accepted and still contemporary and an additional 4 that reflect more recent situations.

InPeter Deutsch, a Sun Fellow at the time, drafted seven assumptions architects and designers of distributed systems were likely to make, which prove wrong in the long run and so resulted in all sorts of troubles and pains for the solution and architects who made those assumptions. InJames Gosling added another fallacy.

Unsurprisingly, this paper by Peter Deutsch did not make him all that popular at Sun as it was orthogonal to their entire marketing direction. I added a further four whilst I was reviewing the list concerning its current applicability.

Assertion : We hold the following commonly held assumptions to be invariably false:. An Application is a software entity deployed as a single executable it may have dependencies on child Assemblies as well. It usually runs in the Security Context of a single user, that being the User who started and is interacting with the Application.

A System is made up of multiple executables deployed and running as several instances on several machines. Whilst certain of the patterns it contains can be useful when designing Systems, most are not.

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It is also the case that most Software Engineers do not appreciate this and so attempt to inappropriately apply Design Patterns on Systems. Semantic Interoperability is the ability of two systems to understand the meaning of the data they exchange. The issues associated with achieving semantic interoperability are many and often very difficult to identify and to solve.

So this is all going to turn out well as these systems need to speak with each other about Patients, yes? The Pharmacy system requires the date of birth in order to meet with its compliance requirements ensuring that drugs are not prescribed inappropriately. The consequences of an inappropriate value is a safety critical, regulatory and legislative concern for all parties.

All Networks are unreliable. To think anything else is blind optimism.They are:. I have personal experience of major projects where these strange assumptions were made by senior management and the result was expensive disaster.

Many application designers have a touching faith in the network they use, and reality tends to come as a shock. One might think that, because networks are now faster and cheaper than they were in those far-off days where we first caught on to the idea of distributed systems, these assumptions are no longer fallacies, since technology has turned them into reality.

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However, despite the marketing spin, the reality of networks continue to trip up the unwary who design distributed systems on the basis of the eight fallacies.

Why not? Read on. When within a domain, networks probably seem rock-solid. After all, how often does a network component fail nowadays? Even if a single component should fail, there is plenty of redundancy, surely? Well, as networks get more complex, so network administration can become more prone to error, mostly mistakes in configuration. In some cases, up to a third of network changes lead to errors that affect the reliability of the network. Both software and hardware can fail, especially routers, which account for around a quarter of all failures.

Networks are subject to disaster both natural and unnatural, and it takes skill to design a network that is resilient to this sort of thing. The wide-area links are outside your control and can easily go wrong. The incidents on Azure alone, in recent months, make painful reading, and this rate of failure is typical of the major cloud-services providers.

For mobile applications, all sorts of things can go wrong: network requests will fail at unpredictable intervals, the destination will be unavailable, data will reach the destination but it fails to send back confirmation, data will be corrupted in transmission or arrive incomplete. Mobile applications must be resilient at the scary end of the reliability spectrum of networks, but all distributed applications must cope with all these eventualities, and network nodes must be able to cope with server failures.

7 fallacies of distributed computing

Latency is not the same as bandwidth. Latency is the time spent waiting for a response. The cause? As well as the obvious processing delay, there will be network latency, consisting of propagation delay, node delay and congestion delay. Propagation delay increases with distance: it is around 30 ms. The number of nodes in the path determines the node delay. Often, developers build distributed systems within an intranet that has insignificant latency and so there is almost no penalty to making frequent fine-grained network calls.

This design mistake only becomes apparent when put into a live system.The Eight Fallacies of Distributed Computing are a set of assumptions that developers make while designing Distributed Systems which might backfire and cause additional work and system redesign in the long run. The network is reliable. Service calls made over the network might fail.

There can be congestion in network or power failure impacting your systems. The request might reach the destination service but it might fail to send the response back to the primary service. The data might get corrupted or lost during transmission over the wire. While architecting distributed cloud applications, you should assume that these type of network failures will happen and design your applications for resiliency.

To handle this scenario, you should implement automatic retries in your code when such a network error occurs. Say one of your services is not able to establish a connection because of a network issue, you can implement retry logic to automatically re-establish the connection. Implementing message queues can also help in decoupling dependencies. Based on your Business use case you can implement queues to make your services more resilient. Latency is zero.

Service calls made over the network is not instant. There can be scenarios where you are spending time waiting for the service response.

Making remote calls over the internet has much higher latency as compared to making in-memory calls. To add to this pain, if you have services communicating with each other which are hosted in different data centers, there will be a latency associated depending upon the network connectivity, firewalls, available bandwidth, and the data transfer volume. There are a number of things which you can do to handle such latencies —.

Bandwidth is infinite. Bandwidth is the capacity of a network to transfer data over a period of time. For an enterprise, as the traffic flowing between the various data centers increases, the requirement for bandwidth increases. It is important to be aware of how much data you need to transfer over the network to suffice your Business use case and the associated dependencies.

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Depending upon the capacity say 1G vs 10G — the associated cost will change. Things get trickier when you have services communicating with each other and are hosted in different data centers since there is a latency associated with it.

The Fallacies of Distributed Computing

Some potential solutions and approaches to handle such scenario are —. The network is secure. There is a fun quote which states that the only network that is secure is the one that is not connected to any network. How many times have we heard in news about security breaches? In a distributed system, there can be multiple loopholes in securing your application. Hackers can break through your unsecured network and can cause huge information breach.

There can also be a security vulnerability associated with data transmitting over the network. Hence all untrustworthy networks need to be identified. Building secure applications and having a security mindset is critical to have while developing your applications. Security needs to be built at multiple levels — application, infrastructure, and network.

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With the rise in Containers and Microservicesenvironments are no more static. Traditionally the on-premise infrastructure was treated as Pets — they were long-lived and stable.

Understanding the 8 fallacies of Distributed Systems

With the advent of Docker containers and cloud technologies, the infrastructure is virtual where service instances are created dynamically based on load. If there is high load, we automatically spin up additional service instances and once the load decreases the instances are terminated.

7 fallacies of distributed computing

There is one administrator.Are you working on a distributed system? Distributed systems are comprised of many computers that coordinate to achieve a common goal. More than 20 years ago Peter Deutsch and James Gosling defined the 8 fallacies of distributed computing. These are false assumptions that many developers make about distributed systems. These are usually proven wrong in the long run, leading to hard to fix bugs.

Most of the systems today make calls to other systems. Are you integrating with 3rd party systems payment gateways, accounting systems, CRMs? Are you doing web service calls? What happens if a call fails? What happens if we receive an HTTP timeout exception? If the server did not process the request, then we can retry.

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But, if it did process the request, we need to make sure we are not double charging the customer. You can do this by making the server idempotent.

This means that if you call it 10 times with the same charge request, the customer will be charged only once. Handling all these cases can get quite complex really fast. So, if calls over a network can fail, what can we do? Well, we could automatically retry. Queuing systems are very good at this. They usually use a pattern called store and forward.

They store a message locally, before forwarding it to the recipient. If the recipient is offline, the queuing system will retry sending the message. MSMQ is an example of such a queuing system.

But this change will have a big impact on the design of your system. Since you are not waiting for a response anymore, you need to change the user journeys through your system. You cannot just replace each web service call with a queue send. You might say that networks are more reliable these days — and they are. But stuff happens. Hardware and software can fail — power supplies, routers, failed updates or patches, weak wireless signals, network congestion, rodents or sharks.

People can start DDOS attacks or they can sabotage physical equipment. Does this mean that you need to drop your current technology stack and use a messaging system? Probably not!

You need to weigh the risk of failure with the investment that you need to make. You can minimize the chance of failure by investing in infrastructure and software.