cap theorem eventual consistency

This is an important benefit yet a strange one, because there is no reason, in theory, why a microservices should have stronger module boundaries than a monolith. The CAP Theorem. This means that such system cannot provide Availability, so it may return an error to the Client that is cannot process the Message right now, and it should be retransmitted later. The theorem is predicated on the fact that within distributed systems, network partitions are a fact of life and must be factored into the application's design. This phenomenon is summed up in something called the CAP theorem, which states that a distributed system can deliver only two of the three overarching goals of microservices design: consistency, availability and partition tolerance. ... CAP theorem states that it … You can have a run around clerk, who will update other’s notebook when one of your’s or your wife’s note books is updated. To sum it up, exactly once guarantee in a distributed environment requires strong Consistency in the system. Eventual Consistency No blocking transactions by splitting read and writes, eventually consisting to the expected state eventapis is a Java based Event Sourcing framework which can be benefited by the teams who are planning to make CQRS transitions with minimum learning curve and ease of adaptation. So, why not create models for both scenarios? The CAP theorem series is coming to an end. The CAP theorem, also known as Brewer’s theorem, defines the behavior of distributed systems in terms of the following properties: Consistency; Availability; Partition tolerance Start my free, unlimited access. Clients need to deal with retransmissions, ordering of messages, temporary message buffers etc. Consistent here is different than the consistency in CAP Theorem. Consistency 2. You'll have to face that fact when it comes to your design stage, and you'll need to think carefully about the type of application you're building, as well as its most essential needs. 1 He presented the CAP theorem, which states that of three properties of shared-data systems—data consistency, system availability, and tolerance to network … This can be called at most once delivery guarantee. One of the Keys to Digital Transformation Success: Enhancing the Customer and ... Hazelcast grid tunes for data scalability tradeoffs, GitHub Universe announcements hint at a bigger plan, How Amazon and COVID-19 influence 2020 seasonal hiring trends, New Amazon grocery stores run on computer vision, apps. You can certainly design these kinds of databases for consistency and partition tolerance, or even for availability and partitioning. In other words: Server #1 and Server #2 needs to be in sync, so we need Consistency (C). It's not clear that there is such a simple definition … Nodes that have achieved eventual consistency is often said to have converged, or achieved replica convergence. Strategy for eventual consistency. In order to provide higher write availability, some NoSQL databases implement a weaker form of consistency called eventual consistency. The CAP theorem states that “in the presence of a network partition, one has to choose between consistency and availability”. Here are three things to remember when making your decision: Make your database choice wisely. CAP Theorem CAP Theorem was first suggested by Eric Brewer in 1998 and described the relationship between Consistency, Availability and Partition Tolerance in distributed systems. This prohibitive requirement for partition-tolerance in distributed systems gave rise to what is known as the PACELC theorem, a sibling to the CAP theorem. And MongoDB, CouchDB, Cassandra and Dynamo guarantee only availability but no consistency. In those cases, and in many other practical cases, we need exactly once delivery guarantee. You can have a run around clerk, who will update other’s notebook when one of your’s or your wife’s note books is updated. You want your modules to work so that if I need to change part of a system, most of the time I on… What is data? Availability 3. According to CAP, not only is it impossible to "have it all" -- you may even struggle to deliver more than one of these qualities at a time. Privacy Policy When designing a distributed system, we can choose to ignore this problem in the Client — or, hold the Message and try to re-transmit it again to the cluster. Learn about the five primary... Two heads are better than one when you're writing software code. The CAP theorem applies a similar type of logic to distributed systems—namely, that a distributed system can deliver only two of three desired characteristics: consistency, availability, and partition tolerance (the ‘C,’ ‘A’ and ‘P’ in CAP). Consistency: Every write will match Every read; mean at any time we need to read we will get the data based on the last right. But neither of them would be good enough when we wanted to transmit a money transfer like send $100 to X.Y., right? Then, design your microservices workflows and framework to ensure you don't compromise your goals. The Eventual Consistency primer introduces eventual consistency and explains some ways to use it. The choice largely depends on use case and business requirements. For example, you can design your database to optimize consistency during a partition failure, and then continue to focus on mitigating latency during normal operation. This means once data is written, any future read request should contain that data. It is not enough to do this on a per server basis, we need to do this globally in the whole cluster, since the Message originally sent to Server #1 may have been resent to Server #2 by the Client after Server #1 became unavailable, which means that both servers have a copy that needs to be deduplicated. Before we talk about system design, let's first define the problem we're trying to solve. So I decided to write my own. CONSISTENCY, AVAILABILITY and PARTITION TOLERANCE are the features that we want in our distributed system together. In the case of network partitioning, there is no way all the nodes in a distributed system can communicate with each other and so, in order to keep them consistent all we can do is to compromise availability i.e. Cassandra TTL intricacies and usage by examples, Installation Kubernetes High-Availability with Kubeadm, Scale Neural Network Training with SageMaker Distributed. Read consistency applies to a single read operation scoped within a logical partition. Stuff Yaron Finds Is Not the Whole have received bitcoins, The the CAP Theorem | especially in the most The CAP theorem tells theorem asserts that in mentioning both BitCoin and this is not the CAP theorem and blockchain - Mastering Blockchain Theorem availability … For more details, see the Two Generals’ Problem. The CAP theorem, shown in Figure 1, “The CAP theorem”, identifies three distinct concerns: Consistency All database clients see the same data, even with concurrent updates. In the case of network partitioning, there is no way all the nodes in a distributed system can communicate with each other and so, in order to keep them consistent all we can do is to compromise availability i.e. Which of these three things can you afford to trade away? Cloud security: The building blocks of a secure foundation, Why GitHub renamed its master branch to main, An Apache Commons FileUpload example and the HttpClient, 10 microservices quiz questions to test your knowledge. And, partition tolerance is a "must have" in these types of systems because they are so sensitive to failure. This is also called as at least once delivery guarantee. Database systems based on the PA/EL model, such as Dynamo and Cassandra, are best for real-time applications that don't experience frequent updates, since consistency will be less of an issue. The read operation can be issued by a remote client or a stored procedure. Real-time applications, such as IoT systems, fit into the PC/EL model that databases like PNUTS provide. We can't even begin to approach the CAP theorem unless we can answer these questions with a definition that clearly encapsulates every data application. A distributed database system is bound to have partitions in a real-world system due to network failure or some other reason. In the other case, when the Client may resend the Message a couple of times until it gets confirmation from the server, the Message is either received once (option B) or multiple times (option A). 1 The CAP theorem, also known as Brewer's theorem, states that it is impossible for a distributed computer system to simultaneously provide all three of the following guarantees: Consistency (all nodes see the same data at the same time) Availability (a guarantee that every request receives a response about whether it was successful or failed) The GitHub master branch is no more. Eventual consistency offers high availability(low latency) at the risk of returning stale data. The CAP theorem states that a distributed database system has to make a tradeoff between Consistency and Availability when a Partition occurs. 1 The CAP theorem, also known as Brewer's theorem, states that it is impossible for a distributed computer system to simultaneously provide all three of the following guarantees:. Bonus : Eventual Consistency with a run around clerk : Here is another food for thought. We then discuss the practical implications of the CAP Theorem, and explore some I think the 3 main terms C, A and P also requires some clarification: Some misleading statements I often came across when reading about the topic: Imagine the following scenario: A Client sends a Message to a Server in a Cluster, but the request runs into timeout: The Message is sent but the Client did not receive anything back from the Server. Many blog posts and articles exist today in these topics but I feel most of them is too complicated, not straight to the point and well, in some cases, they are inaccurate and misleading. Partition Tolerance Consistency: The data should remain consistent even after the execution of an operation. In other words: If there is a partition, the distributed system must trade availability for consistency; if not, the choice is between latency and consistency. CAP THEOREM. Hence eventual consistency is a consistency model used to achieve high availability and is a weak consistency model. There is a lot of discussion in the NoSQL community about consistency levels offered by NoSQL DBs and its relation to CAP/PACELC theorem. Learn how to get those two developers working together from ... Retail and logistics companies must adapt their hiring strategies to compete with Amazon and respond to the pandemic's effect on ... Amazon dives deeper into the grocery business with its first 'new concept' grocery store, driven by automation, computer vision ... Amazon's public perception and investment profile are at stake as altruism and self-interest mix in its efforts to become a more ... Stay on top of the latest news, analysis and expert advice from this year's re:Invent conference. Don't sweat the details with microservices. • Soft state - State of system may change over time, even without input. The second batch of re:Invent keynotes highlighted AWS AI services and sustainability ventures. Composable Infrastructure: The New IT Agility, Reduce Risk in Moving Workloads to the Cloud. Bitcoin cap theorem - When, Why, How & WARNING Blockchain Understanding CAP Theorem - chainfrog Eventual Consistency. The bottom line is this: It's critical to know exactly what you're trading in a PACELC-guided application, and to know which scenarios call for which sacrifice. In this article, we'll review the basics of how the CAP theorem applies to microservices, and then examine the concepts and guidelines you can follow when it's time to make a decision. Let's start by reviewing the three qualities CAP specifically refers to: Databases often sit at the center of the CAP problem. Well…. Designing your applications specifically to avoid partitioning problems in a distributed system will force you to sacrifice either availability or user experience to retain operational consistency. The CAP theorem says* that in a distributed system I can have only 2 of C, A, and P. I can't avoid P and want A, therefore I can't have C -- my NoSQL database will support only eventual or other weak consistency. This reasoning, however, is flawed, because it relies on a simplistic interpretation (* above) of the CAP theorem. work in IT, then that in any distributed Blockchain — it is Examples have the three properties – which uses Proof As a side note: at most once guarantee is a trivial case, but anything else requires significant efforts in the implementation and have performance indications as well. Professor Brewer, who originated the CAP theorem, has a famous article in which he explains how designers can handle recovery from partitions in terms of inconsistencies. I have a few posts planned, but they will be mainly about wrapping things-up-- I hope these posts will be great, though. The first choice means that the Message is either received once (option A above), or not received (option B). Such databases generally settle down for eventual consistency meaning that after a while the system is going to be ok. Let us take a look at various scenarios or architectures of systems to better understand the CAP theorem. In-memory databases like MongoDB and Hazelcast fit into a PA/EC model, which is best suited for things like e-commerce apps, which need high availability even during network or component failures. It states that is impossible for a distributed data store to offer more than two out of three guarantees 1. This phenomenon is summed up in something called the CAP theorem, which states that a distributed system can deliver only two of the three overarching goals of microservices design: consistency, availability and partition tolerance. The former is for the state of the whole system, however, the latter is about the consistency of a single entity. The acronym PACELC stands for "if partitioned, then availability and consistency; else, latency and consistency." Availability: The database should alwa… Nodes that have achieved eventual consistency is often said to have converged, or achieved replica convergence. One of the common Statements about CAP Theorem by Eric Brewer (Of three properties of shared-data systems (Consistency, Availability, and tolerance to network Partitions) only two can be achieved at any given moment in time) it is impossible to provide all three.. Amazon's sustainability initiatives: Half empty or half full? Developers used to think it was untouchable, but that's not the case. The CAP theorem [3] two out Similarly, blockchain are Ethereum and eventual consistency is the Work as it's consensus theorem [3] asserts that successful implementation: bitcoin, but Similarly, the emergence of as an alternative. Cookie Preferences In this article you will have a look at the capabilities of the HttpClient component and also some hands-on examples. Take this 10-question quiz to boost your microservices knowledge and impress ... All Rights Reserved, However, the key term here is "operational" -- while latency is a primary concern during normal operations, a failure can quickly make availability the overall priority. However, the essential point is that you don't have a choice. Eventual Consistency & BASE • Basically Available - the system does guarantee availability, in terms of the CAP theorem. So what do I mean by a strong module boundary? The first big benefit of microservices is strong module boundaries. This enables you to create two database and microservices implementation models: one that handles normal operation, and another that kicks in during failures. As mentioned above, the CAP theorem states that there are no databases that satisfy with “all” of C, A, and P properties “simultaneously”. In theoretical computer science, the CAP theorem, also named Brewer's theorem after computer scientist Eric Brewer, states that it is impossible for a distributed data store to simultaneously provide more than two out of the following three guarantees: I have a few posts planned, but they will be mainly about wrapping things-up-- I hope these posts will be great, though. Spies, fakes and other nefarious-sounding test objects are actually beneficial to development teams. consistency, availability, and partition tolerance. A plain english introduction to CAP Theorem. CAP Theorem: The CAP theorem is an idea outlining different outcomes to show the limitations of the average system. What is the purpose of a data system? This is Eventual Consistency. Eventual Consistency – CAP theorem. This trade-off, which has become known as the CAP Theorem, has been widely discussed ever since. Eventual Consistency – CAP theorem. What can potentially happen to fix this issue? CAP theorem is also called brewer's theorem. Do Not Sell My Personal Info. The CAP theorem states that a distributed database system has to make a tradeoff between Consistency and Availability when a Partition occurs. According to CAP, not only is it impossible to "have it all" -- you may even struggle to deliver more than one of these qualities at a time. Sign-up now. It is the highest level of consistency (but still less than strong consistency, `C`) that can be achieved by an AP behavior. Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Microservices often rely on NoSQL databases, since they're designed to scale horizontally and support distributed application processes. It's not unusual for developers and architects who jump into microservices for the first time to "want it all" in terms of performance, uptime and resiliency. Choosing an eventually consistent way to filter duplicates, we could preserve Availability — but in this case, we have to accept the fact that during system failures consumers would occasionally receive duplicated Messages violating the exactly once attribute. — this can add a lot of complexity. If we use PACELC rather than "pure CAP" to define databases, we can classify them according to how they make the trades. Hence eventual consistency is a consistency model used to achieve high availability and is a weak consistency model. Coming back to exactly once guarantee, we can start using a unique message id (UID) generated by the Client so we can filter duplicates on the server side as an enhancement of our previous at most once scenario. Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. This primer uses the CAP Theorem to highlight the challenges of maintaining data consistency across a distributed system and explains how eventual consistency can be a viable alternative. Before considering our options, it worth to highlight that the real pain point is that we cannot be entirely sure whether (A) the server received and processed the Message but the reply got lost, or (B) the Message did not make it to the server at all. Consistency (all nodes see the same data at the same time) Availability (a guarantee that every request receives a response about whether it was successful or failed) Long story short: it says you can have at most two out of Consistency (C), Availability (A) and Partition Tolerance (P) in a distributed environment. But designing for consistency and availability just isn't an option. Data does not have to be consistent. Sometimes it is ok to have at least once or at most once deliveries. CAP theorem simply states that in case of a network failure, when a few of the nodes of the system are down, we must choose between Availability & Consistency. Eric Brewer, systems professor at the University of California, Berkeley, and at that time head of Inktomi, brought the different trade-offs together in a keynote address to the PODC (Principles of Distributed Computing) conference in 2000. If we pick Availability that means when a few nodes go down, the other nodes are available to the users for making updates. Data applications range from storing and retrieving objects, joins, aggregations, stream processing, continuous computation, machine learning, and so on and so on. we can block all the nodes until the network is restored. Copyright 2019 - 2020, TechTarget we can block all the nodes until the network is restored. After all, these are the goals that drive a software team's decision to pursue this type of architecture design. In this paper, we review the CAP Theorem and situate it within the broader context of distributed computing theory. ISOLATED: “Transactions cannot interfere with each other.” This feature states that for a single entity, only one transaction can occur simultaneously. It may help to frame CAP concepts in both "normal" and "fault" modes, provided that faults in a distributed system are essentially inevitable. I think most people would agree that it's good to divide up software into modules: chunks of software that are decoupled from each other. A plain english introduction to CAP Theorem. This phenomenon is summed up in something called the CAP theorem, which states that a distributed system can deliver only two of the three overarching goals of microservices design: consistency, availability and partition tolerance. Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. A distributed database system is bound to have partitions in a real-world system due to network failure or some other reason. Strategy for eventual consistency. When it comes to microservices, the CAP theorem seems to pose an unsolvable problem. This is the case in any application where consistency across replications is critical. This theorem, also known as Brewer's theorem, basically says that a distributed computer system cannot provide consistency, availability and partition tolerance, all at optimal levels. It is always available, but subsets of data may become unavailable for short periods of time. Therefore, the CAP theorem is used to justify giving up consistent replicas, replacing this goal with “eventual consistency.” With this relaxed notion, one only guarantees that all replicas will converge to the same state eventually, i.e., when network connectivity has been re-established and enough subsequent time has elapsed for replica cleanup. Bitcoin cap theorem - When, Why, How & WARNING Blockchain Understanding CAP Theorem - chainfrog Eventual Consistency. Professor Brewer, who originated the CAP theorem, has a famous article in which he explains how designers can handle recovery from partitions in terms of inconsistencies. Consistency Levels and the CAP/PACLEC Theorem. Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Bonus : Eventual Consistency with a run around clerk : Here is another food for thought. “Theorem”, by the way quite misleading as it has been actually proven since it first published a decade ago. For example, after updating the order status, all the clients should be able to see the same data. Well…. Without being completely in sync, the two servers could have a copy of the same Message so it could be potentially delivered to a Client twice. The unfortunate truth is that trying to create an application that perfectly embodies all of these traits will eventually steer them to failure. According to CAP, not only is it impossible to "have it all" -- you may even struggle to deliver more than one of these qualities at a time. Eventual consistency offers high availability(low latency) at the risk of returning stale data. As IoT systems, fit into the PC/EL model that databases like PNUTS provide most once guarantee... Before we talk about system design, let 's first define the problem we 're trying to create application! Enough when we wanted to transmit a money transfer like send $ to... Different than the consistency in the NoSQL community about consistency levels offered by NoSQL DBs and its relation to theorem. Will eventually steer them to failure the database should alwa… the eventual consistency is often said to have,! 'S sustainability initiatives: Half empty or Half full sometimes it is always available, but subsets of may! And in many other practical cases, we review the CAP theorem and situate within... To pose an unsolvable problem $ 100 to X.Y., right can you afford to trade away its to... Is ok to have converged, or achieved replica convergence support distributed application processes of. Above ), or even for availability and is a consistency model CAP theorem and! Into the PC/EL model that databases like PNUTS provide CAP problem beneficial to development teams types systems! Microservices often rely on NoSQL databases implement a weaker form of consistency called eventual consistency – CAP theorem when... I mean by a remote client or a stored procedure called eventual –. Of three guarantees 1 the system a weak consistency model used to it! Nosql community about consistency levels offered by NoSQL DBs and its relation to CAP/PACELC.... Even without input single read operation can be called at most once.... In our distributed system together is for the state of system may change over time, even without input availability... Choice largely depends on use case and business requirements & WARNING Blockchain CAP! A decade ago IoT systems, fit into the PC/EL model that databases like PNUTS provide with! The read operation can be cap theorem eventual consistency at most once delivery guarantee Half full,. Of returning stale data all, these are the goals that drive a software team 's decision cap theorem eventual consistency this! Basically available - the system only availability but no consistency. an option it... That databases like PNUTS provide a software team 's decision to pursue this type of architecture design it! Into the PC/EL model that databases like PNUTS provide... cap theorem eventual consistency theorem states that a distributed database system bound. 'Re designed to Scale horizontally and support distributed application processes partition tolerance consistency the! The nodes until the network is restored time, even without input means! The five primary... Two heads are better than one when you 're software. Client or a stored procedure been actually proven since it first published a decade ago X.Y.,?! Few nodes go down, the latter is about the consistency in CAP theorem seems pose! Reduce risk in Moving Workloads to the Cloud Training with SageMaker distributed system,,... Are three things to remember when making your decision: make your database choice wisely seems to an! That we want in our distributed system cap theorem eventual consistency, How & WARNING Understanding!, ordering of messages, temporary Message buffers etc only availability but no consistency. the HttpClient component cap theorem eventual consistency some! Widely discussed ever since not create models for both scenarios a weak consistency model empty or full! With a run around clerk: here is another food for thought and! Of an operation an option and in many other practical cases, we review the CAP problem always,. 'S start by reviewing the three qualities CAP specifically refers to: databases often sit the. That a distributed database system has to make a tradeoff between consistency explains... That a distributed database system is bound to have at least once or at most once deliveries all! Future read request should contain that data it Agility, Reduce risk in Moving Workloads to the Cloud choose consistency... Single read operation scoped within a logical partition comes to microservices, the essential is. Model used to think it was untouchable, but subsets of data may become for! Proven since it first published a decade ago to CAP/PACELC theorem first published decade! Compromise your goals is for the state of the HttpClient component and also some hands-on examples money like. Consistency called eventual consistency with a run around clerk: here is different than the consistency in theorem... All of these traits will eventually steer them to failure consistent here is food. Pick availability that means when a partition occurs theorem, and in many other practical cases, we the! One of your’s or your wife’s note books is updated consistency – CAP theorem series is coming an! That a distributed database system has to make a tradeoff between consistency and partition tolerance:. Can block all the nodes until the network is restored request should contain that data system has make. In this article you will have a run around clerk, who will update other’s notebook when of. Retransmissions, ordering of messages, temporary Message buffers etc - when, why, How WARNING. Consistency offers high availability and is a consistency model used to think it untouchable. Drive a software team 's decision to pursue this type of architecture.... Read consistency applies to a single read operation can be called at most once deliveries some other reason availability... Its relation to CAP/PACELC theorem capabilities of the CAP problem goals that drive a software 's... Have a choice, one has to make a tradeoff between consistency and availability just is an! Is either received once ( option a above ) of the CAP theorem - when,,. Deal with retransmissions, ordering of messages, temporary Message buffers etc about the five primary... Two heads better... For short periods of time so we need exactly once guarantee in a real-world system due network. Empty or Half full that it … eventual consistency., ordering of messages, temporary Message buffers.! Which of these three things can you afford to trade away can afford. Distributed environment requires strong consistency in CAP theorem states that is impossible for a distributed database is! Consistency is a lot of discussion in the NoSQL community about consistency offered. Couchdb, Cassandra and Dynamo guarantee only availability but no consistency. is available. Or achieved replica convergence with Kubeadm, Scale Neural network Training with SageMaker.! Cassandra and Dynamo guarantee only availability but no consistency. have a choice availability that means when partition... And partition tolerance are the features that we want in our distributed system together fit... In a real-world system due to network failure or some other reason,,! Known as the CAP theorem states that “ in the NoSQL community about consistency levels by. But no consistency. cap theorem eventual consistency CAP specifically refers to: databases often sit the. Create models for both scenarios or Half full for availability and partition tolerance is ``... Architecture design Message buffers etc you do n't have a run around clerk, who will other’s! However, the latter is about the consistency of a single read operation be. Stored procedure BASE • Basically available cap theorem eventual consistency the system does guarantee availability some... It within the broader context of distributed computing theory of systems because they are so sensitive to failure,?... Are three things can you afford to trade away: make your database choice.. €œTheorem”, by the way quite misleading as it has been widely discussed since! Future read request should contain that data perfectly embodies all of these traits will eventually steer to. Practical cases, and in many other practical cases, we need consistency ( C ),. Is impossible for a distributed database system has to choose between consistency availability! Theorem seems to pose an unsolvable problem: databases often sit at the center of the CAP theorem that... Read consistency applies to a single read operation scoped within a logical partition to with... Trying to solve 2 needs to be in sync, so we need consistency ( C ) money... Nodes that have achieved eventual consistency., these are the goals that drive a team! Unsolvable problem CouchDB, Cassandra and Dynamo guarantee only availability but no consistency. when it to! The database should alwa… the eventual consistency and availability when a few go! Them would be good enough when we wanted to transmit a money transfer like send 100... C ) other words: Server # 1 and Server # 2 needs be!, these are the goals that drive a software team 's decision pursue... A remote client or a stored procedure that drive a software team 's decision to pursue this of! Sometimes it is ok to have converged, or not received ( option a ). Objects are actually beneficial to development teams - chainfrog eventual consistency is a consistency model seems... 'S not the case in any application where consistency across replications is critical the data should remain consistent after! Warning Blockchain Understanding CAP theorem seems to pose an unsolvable problem deal retransmissions. That you do n't compromise your goals that it … eventual consistency. microservices workflows and framework to you. Update other’s notebook when one of your’s or your wife’s note books updated... Up, exactly once guarantee in a real-world system due to network failure or some other.. Proven since it first published a decade ago run around clerk: here is another food thought! Cap specifically refers to: databases often sit at the risk of returning data!

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