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Management of transactions within the microservice architecture

As easy as it sounds however, there are many kinds of issues that arise from distributed transac-tions. One of the issues is that contemporary technologies, like NoSQL databases like MongoDB and Cassandra do not support distributed transactions. Additionally, distributed transactions aren’t supported in modern message brokers like RabbitMQ or Apache Kafka. In the end when you decide to using distributed transaction, it’s not possible to utilize a number of mod-ern technology.

Top Sell email lists

Top Sell email lists

Another issue of distributed transactions lies in the fact they’re a type of synchro-nous IPC that reduces the availability. To allow distributed transactions to commit the transaction, all of the participants have to be in use. In chapter 3, availabil-ity is determined by being available for all those involved of the transaction. If a dis-tributed transaction includes two service providers that have 99.5 percent accessible, the total availability is 99% which is considerably less. Each additional service that is involved in a dis-tributed transaction reduces availability. There’s even Eric Brewer’s CAP theo-remthat states that a system should contain three of the following properties:

consistency, availability, and partition tolerance ( _theorem). Nowadays, architects would rather be able to access a system that’s accessible as opposed to one that’s reliable. Top Sell email lists.

At first glance, distributed transactions appear attractive. From a developer’s perspective they follow the same model of programming like local ones. However, because of the limitations previously mentioned distributed transactions aren’t an option for the modern-day applications. Chapter 3 explained the method of sending messages in an online database transaction without distributed transactions. 

To tackle the more difficult issue of maintaining consistency of data within a microservices architecture, the application has to employ a different method which is based on the concept of asynchronous, loosely coupled services. Here is the place where stories are able to help. Top Sell email lists.

Making use of using the Saga pattern to ensure the consistency of data

Sagas are mechanisms for maintaining consistency of data in microservice architectures without having to utilize distributed transactions. You can define a saga for each system that requires to update data across multiple services. A saga is an order that includes local transaction. Each local transaction update the data in one service, making use of the standard ACID frameworks and libraries for transactions that were mentioned earlier.

Pattern: Saga

Keep data consistency across various services with a sequence of local transactions coordinated with asynchronous messages. Top Sell email lists.

The system operation begins the initial step in the story. The successful completion of an local transaction triggers the execution of the following local transaction. In the next section, 4.2 where you’ll discover how the coordination of these actions is achieved by using asynchronous messaging. A major benefit of asynchronous messaging is that it makes sure that the entire sequence of a saga are carried out even when any of the participants is temporarily not available.

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Sagas vary in comparison to ACID transactions in several important ways. As I explain in depth in section 4.3 They do not possess the isolation characteristic that is characteristic of ACID transactions.

Sell email lists online

Sell email lists online

Furthermore, since every local transaction makes its changes permanent and a saga has to be reversed using compensat-ing transactions. I’ll discuss more about compensating transactions in this section. Let’s review an example of a saga.


The saga that is used in this chapter is called the Create Order Saga, which is illustrated in Figure 4.2. It is the Order Service implements the createOrder() operation by using this saga. The first local transaction of the saga is initiated through an outside request for an order. The remaining five local transactions are caused by the successful conclusion of the first.

The saga is comprised of these local transaction types:

Order Service–Create an order with an approval_pending state. Sell email lists online.

Consumer Service – Verify that the customer can make an order.

Kitchen Service–Validate order information and create a Ticket within the”CREATE_Pending.

Accounting Service – Authorize the credit card of the consumer.

Kitchen Service – Change the status of the ticket to Acceptance to.

Order Service–Change the status of the order to APPROVED. Sell email lists online.

In the next section, section 4.2 I’ll explain how the various service that are part of a story can communicate using asynchronous messaging. A service releases the message after an action on the local level is completed. The message starts the next phase of the story. Not only does the use of messaging ensure that the characters in the story are not tightly bound, but it also ensures that the sequence of events is complete. It’s because, if the person who is the recipient of a message becomes temporarily not available the message broker will buffer the message until it’s ready to be sent.

At first glance, sagas appear straightforward, however there are many issues with making use of them. One issue has to do with the insufficient separation between the sagas. Section 4.3 discusses how to deal with this issue. Another issue is changing the settings when an error happens. Let’s examine how to do this. Sell email lists online.


One of the advantages in traditional ACID transactions is the business logic is able to quickly roll back a transaction in the event that it discovers a infraction of a law. The system executes a ROLL-BACK command which causes the database to undo all changes made so far. However, sagas aren’t immediately rolled back because every step commits the changes into the locally-based database. 

This means, for instance that, should the authorization for the credit card fails during 4th step in the Create Order Saga The FTGO application has to explicitly reverse any changes made during the previous three steps. It is necessary to write what are commonly referred to as compensating transactions. Sell email lists online.

Imagine you’re able to prove that you are in the situation that (n + 1)th operation of a saga is unsuccessful. The consequences of the prior n transactions have to be erased. In theory, each of these actions, Ti, has a equivalent compensating transaction, Ci, which undoes the effects of Ti. To undo the effect of those initial step the saga must perform every Ci in reverse sequence. It is in the order the following: T1 … Tn Cn … C1 as illustrated in the figure 4.3. In this case Tn+1 fails, and necessitates actions T1 … Tn to be reversed.
The saga will execute all compensations in reverse of forward transactions Cn … C1. The mechanisms of sequencing the Cis aren’t different from sequence the Tis. The successful completion of Ci must cause the Ci-1 to execute.

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Think about, for example for instance, the Create Order Saga. The saga could fail due to many reasons:

The customer’s information is not valid or the consumer isn’t able to place orders.

Sell email address lists

Sell email address lists

The restaurant’s information is incorrect or the restaurant is not able to take orders.

The authorization for the consumer’s credit card is denied.

If an order is not successful locally the saga’s coordination system will execute compensating transactions to refuse the Order as well as the ticket. Table 4.1 illustrates the compensating actions for each step in the Create Order Saga. It is important to remember that not all steps require compensating transactions. Read-only steps, such as verify-ConsumerDetails(), don’t need compensating transactions. Nor do steps such as authorizeCreditCard() that are followed by steps that always succeed. Buy Sell email address lists online.

Section 4.3 examines how the initial three phases of Create Order Saga are termed compensatable because they’re accompanied by steps that could be unsuccessful, the reason this fourth stage is referred to as the pivotal step of the saga because it’s accompanied by steps that are guaranteed to fail, and why the final two steps are referred to as retriable because they’re guaranteed to succeed.
To understand how compensating transactions work Imagine a scenario in which the authorization for the credit card of the customer does not work. The saga performs one of the local transaction types:

Order Service–Create an ad with an approval_pending state. Buy Sell email address lists online.

Consumer Service – Verify that the customer is able to place an order.

Kitchen Service–Validate order information and create a ticket in the CREATE _PENDING status.

Accounting Service – Authorize the consumer’s credit card in the event that it is not able to be used.

Kitchen Service–Change the status of the ticket to CREATE_REJECTED. Buy Sell email address lists online.

Order Service–Change the status of the order to REJECTED.

The sixth and fifth actions are compensating transactions which cancel the updates performed through Kitchen Service and Order Service in the respective steps. A saga’s coordination algorithm is responsible for the sequencing of the process of compensating and forward transactions. Let’s take a look at how that operates.

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Sagas of coordination

The saga’s implementation is comprised in a logic system that coordinates all the steps in the story. When a saga begins via system commands the coordination logic has to choose and instruct the initial participant in the saga to perform an local transaction. After the transaction is completed the saga’s sequencing coordination decides to invoke the next participant in the saga. The process is repeated until the saga has completed each step. If the local transaction fails, the saga has to execute compensation transactions reversed.

Sell contact lists database

Sell contact lists database

There are two different ways to organize the coordina-tion logic of a saga:

Choreography–Distribute the decision making and sequencing among the saga participants. They mostly communicate through sharing events.

Orchestration–Centralize a saga’s coordination logic in a saga orchestrator class. The orchestrator for sagas communicates commands to the participants in the saga informing them of the actions they should carry out. Sell contact lists database.

Choreography-based stories

One way to create the saga by using choreography. With choreogra-phy there is no central coordinator to tell the participants in the saga what to do. Instead, the saga members are able to follow each other’s activities and then respond in a manner that is appropriate. To illustrate how sagas that are based on choreography are constructed, I’ll first provide an example. Following that, I’ll address some design concerns which you should be aware of. In the next section, I’ll talk about the advantages and drawbacks to using choreography.


The path to happiness in the story can be described as:

Order Service creates an Order in the APPROVAL_PENDING status and then publishes the OrderCreated event. Sell contact lists database.

Consumer Service consumes the OrderCreated event, confirms that the consumer is able to make the purchase and also publishes a consumer verified event.

Kitchen Service consumes the OrderCreated event it authenticates the Order and creates the Ticket that is in the CREATE_PENDING status and releases an event titled TicketCreated event.

Accounting Service consumes the OrderCreated event and creates a Credit-CardAuthorization in a PENDING state. Sell contact lists database.

Accounting Service consumes the TicketCreated and ConsumerVerified events, charges the consumer’s credit card, and publishes the CreditCard-Authorized event.

Kitchen Service consumes the CreditCardAuthorized event and alters the status of the ticket to AWAITING_ACCEPTANCE.

Order Service receives CreditCardAuthorized events, updates the status that an Order to APPROVED, then announces the OrderApproved event.

It is important to note that the Create Order Saga also has to deal with the situation in which a saga player refuses to accept the Order and then publishes a type of failure incident.  Sell contact lists database.

For instance the authorization for the credit card used by the customer could be denied. The saga needs to perform compensating actions to reverse the damage that was already done. Figure 4.5 illustrates the sequence of events that occur when the AccountingService cannot authorize the customer’s credit card.

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This sequence runs as the following:

Order Service creates an Order in the APPROVAL_PENDING status and then publishes the OrderCreated event.

Sell contact lists database online

Sell contact lists database online

Consumer Service consumes the OrderCreated event, checks that the person who placed the order is the one who can actually make the purchase and then publishes a ConsumerVerified event.

Kitchen Service consumes the OrderCreated event It checks the Order it creates the Ticket with a CREATE_PENDING status and then releases an event titled TicketCreated event.

Accounting Service consumes the OrderCreated event and creates a Credit-CardAuthorization in a PENDING state. Sell contact lists database online.

Accounting Service consumes the TicketCreated and ConsumerVerified events, then charges the credit card of the user and creates the Credit Card Authorization Failed event.

Kitchen Service consumes the Credit Card Authorization Failure event and alters the status of the ticket from REJECTED to REJECTED.

Order Service consumes the Credit Card Authorization Failed event and changes the status that the Order to REJECTED.

It is evident that characters in choreography-based sagas communicate with each other using the publish/subscribe. Let’s take a closer look at some issues you’ll need to consider when imple-menting publish/subscribe-based communication for your sagas. Sell contact lists database online.


There are a couple of interservice communication-related issues that you must con-sider when implementing choreography-based sagas. One of them is making sure that a participant in a saga updates their database and publishes an event in the database transaction. Every step in an saga based on choreography publishes the database and announces an event. For instance, in the Create Order Saga Kitchen Service receives a Consumer Verified event, then creates the Ticket and then publishes an Ticket Created Event. It is crucial for the updating of the database as well as the publication of the event occur in a timely manner. Sell contact lists database online.

The second thing to think about is whether the saga participant is capable of mapping each event it encounters to its own personal data. For example, if Order Service receives a Credit Card Authorized event the system must be able look up the order that corresponds to it. The best solution can be found by allowing a member to post events that contain the correlation id. This allows others to complete the mapping.

For instance, participants in Create Order Saga, for instance Create Order Saga may make use of the orderId as an identification number that can be passed from one participant the next. Accounting Service pub-lishes a Credit Card Authorized event that contains the orderId that was generated from the created ticket. In the event that Order Service receives a Credit Card Authorized event using the orderId, it is able to find the appropriate Order. Similar to that, Kitchen Service uses the orderId associated with that event to locate the appropriate ticket.

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Benefits and Drawbacks of CHOREOGRAPHY-BASED SAGAS

The benefits of sagas with a choreography are numerous:

Simplicity: Services publish events whenever they create, update or remove business objects.

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Sell email address lists

Loose coupling: The participants sign up to events, but do not know anything about the other.

However, there are some disadvantages:

A bit more difficult to grasp–unlike orchestration, there’s no single line of code that determines the story. Instead, choreography spreads the execution of the saga between the different services. This makes it difficult for developers to understand how a specific story works. Sell email address lists.

Cyclic dependencies between the various services — The characters in the saga subscribe to events of each other, which can result in dependent cyclic relationships. For instance, if you take a close look at the diagram 4.4 and you’ll find how there’s cyclic dependencies like Order Service Accounting Service Order Service. While this isn’t necessarily a issue, cyclic dependencies are thought of as a design smell.

The risk of tight coupling is that each Saga participant must subscribe to all events that impact their lives. For instance, Accounting Service must subscribe to any event that causes the credit card of the customer to be charged or reimbursed. In the end it is possible that the system will need to be updated in line with the order lifecycle that is implemented in the Order Service. Order Service.

Choreography can be effective for sagas that are simple, however because of the drawbacks, it is generally better for more complex stories to use orchestration. Let’s examine how orches-tration is done. Sell email address lists.

Sagas with orchestration

Orchestration is a different method to make sagas work. With orchestration, you create an orchestrator class that’s sole duty is to inform the participants in the saga the right way to proceed. The orchestrator of saga communicates with participants via command or async-style reply. In order to execute a saga action is to send an instruction message to a participant informing the operation it will perform. Once the participant in the saga completed the task and completed the task, it will send a response note to an orchestrator. The orchestrator analyzes the message to determine which saga stage to follow the next. Sell email address lists.
To demonstrate how orchestration-based sagas function I’ll first present an illustration. I’ll then explain how to conceptualize orchestration-based sagas as state machines. I’ll explain how to use transactions to ensure an efficient communication between the orchestrator and the participants in the saga. I’ll also discuss the advantages and drawbacks of orchestration-based stories.

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Figure 4.6 illustrates the layout of the orchestration-based version the Create Order Saga. The saga is orchestrated using the CreateOrderSaga class, which involves the saga players through an asynchronous request/response. This class monitors the pro-cess, and also sends commands to the saga’s participants, like Kitchen Service and Consumer Service.

Sell email id lists

Sell email id lists

The CreateOrderSaga class is able to read the replies from its response channel and then decides what the next step is to take, if there is one, within the saga.

Order Service first creates an Order as well as an Create order orchestrator. Then, the sequence to a happy ending is following:

The orchestrator for the saga transmits an Verify Consumer command to Consumer Service.

Consumer Service replies with a Consumer Verified message. Sell email id lists.

The orchestrator of the saga issues an Create Ticket request for Kitchen Service.

Kitchen Service replies with a Ticket created message.

The orchestrator for the saga transmits the Authorize Card message to Accounting Service.

Accounting Service replies with a Card Authorized message.

The orchestrator for the saga sends an Approve ticket command for Kitchen Service.

The orchestrator of the saga transmits an Approve Order request the Order Service. Sell email id lists.

It is important to note that in the last step the orchestrator of the saga sends a command signal for Order Service, even though it’s an element that is part of Order Service. In theory it is possible that the Create Order Saga could accept the order by modifying it directly. To be legally compliant, the saga considers Order Service as just another participant.

Diagrams like the figure 4.6 each show a specific scenario from a saga however, a saga is likely to include multiple scenarios. For instance in the Create Order Saga contains four scenarios. Apart from the happy route the saga could be unable to complete due to a malfunction in one of the following areas: Consumer Service, Kitchen Service and Accounting Service. It’s beneficial, therefore, to think of a saga in terms of a state machinesince it can be used to describe all scenarios. Sell email id lists.


A great way to conceptualize the saga orchestrator is to model it as the state machine. A state machine is an array of states as well as the transitions between states caused by the events. Every transition has an action, and for an saga is the calling of a participant in a saga. The state transitions are initiated by the conclusion of a local operation performed by a participant in a saga. The current state as well as the precise result from the transaction local to it determine state transition and the action is required to take. There are also efficient methods for testing state machines. This is why the use of a model for state machines helps in the design, implementation and testing sagas much easier. Sell email id lists.

The state machine is also able to define many state transitions. For instance the state machine can transition from the creating Ticket state , to either Authorizing Card or Rejected Order condition. It changes into the Authorizing Card state when it receives a positive response in response to the Create Ticket command. In the alternative, if Kitchen Service couldn’t create the ticket, the machine shifts back to state of Rejected Order status.

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The initial step of the state machine is to transmit an VerifyConsumer request for Consumer Service. Consumer Service’s response Consumer Service triggers the next state trans-sition. If the consumer has been successful in proving their identity then the saga will create the Ticket and then transitions back to that state. Creating Ticket state. If the verification of the consumer was unsuccessful, the saga denies the Order and moves into that Rejecting Order state. The machine’s state machine experiences a number of other state transformations, triggered by the responses of the members of the saga until it is in a final state, which is or order Approved or Rejected Order.

Sell email accounts list

Sell email accounts list


Every step in an orchestration-based saga is an update to databases and sending out messages. For instance, Order Service persists an order and then creates a Create Orchestrator for Orders, and transmits a message to the first participant in the saga. A Saga participant, like Kitchen Service, handles a command by updating its database and then sending an answer message. 

Order Service processes the partici-pant’s reply message by updating the status of the orchestrator in the saga, and then sending a command to the next participant in the saga. As explained in chapter 3.3, a service should utilize transactional messaging to update the database in a synchronized fashion and then publish messages. In section 4.4 I’ll go over how to implement the Create Order Saga orchestrator in greater detail, including how it makes use of transactional messaging. Sell email id lists.

Let’s look at the advantages and disadvantages of the use of Saga Orchestration.


Sagas that are orchestrated have many advantages:

Orchestration has simpler dependencies. One benefit has to do with the fact that it won’t cause dependents that are cyclic. The orchestrator of saga invokes players in the saga, however, participants do not invoke the orchestrator. This means that the orchestrator relies on the participants , but not vice versa, consequently, there are no cycles of dependencies.

There is less coupling. Each service implements an API which is invoked by the orches-trator. Therefore, it does not have to be aware of the events reported by the saga’s members. Sell email id lists.

Improves separation of concern and reduces the complexity of business logic. coordina-tion logic for sagas is located within the orchestrator for sagas. Domain objects are simpler and don’t have any understanding of the sagas they are part of. For instance, when it comes to orchestration the Order class is not aware of any saga and therefore has a simplified State Machine model. When the Create Order Saga is being executed Create Order Saga it goes directly from the APPROVAL_PENDING to the APPROVED status. The Order class does not contain any intermediate states that correspond to the sequence of events in the Saga. This means that the business process is much easier.

Orchestration has its own drawbacks it is the risk from centralizing too many of the business logic within the orchestrator. This can lead to a situation which the orchestrator’s smartness gives the dumb services instructions on which operations to perform. Fortunately, you can get around this issue by creating orchestrators that are only responsible for executing the sequence and don’t include additional business-related logic. Sell email id lists.

I suggest orchestration in all but the most basic epics. The implementation of the co-ordination logic in your sagas is only an example of one design challenges you’ll need to resolve. Another, and perhaps the most challenging issue you’ll encounter when you use stories, is coping with the issue of isolation. Let’s look at the issue and ways to deal with it.

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In a bid to manage the isolation issue

The letter I in ACID is for isolation. The property of isolation in ACID transactions guarantees that the result of running simultaneous transactions is the same as when it were done in sequential order. The database gives the impression that every ACID transaction has access to the database. It is a lot simpler to write business logic which runs simultaneously.

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Sell email database list

The issue with sagas is that they don’t have the ability to isolate ACID transactions. This is because the changes generated by each of a local transaction in a saga are instantly visible to all other sagas when the transaction has committed. This can lead to two issues. Second, other sagas may modify the data that is that the saga is able to access while it’s running. Additionally, other sagas could access its data prior to the saga has finished its updates, which means that the saga may be exposed to unreliable data. In reality, you can be able to consider a saga be an ACD:

Atomicity–The Saga implementation makes sure that all transactions are carried out or any changes are undone. Sell email database lists.

Consistency–Referential integrity within a service is handled by local databases. Integrity of references across services is managed by the services.

Durability–Managed through local database systems.

The lack of separation creates what the database literature refers to as anomalies. An anomaly occurs when transactions read or write data in a manner that it would not be if transactions were executed one by one. When an anomaly happens and the result of running simultaneous sagas is different than the case if they were executed sequentially.

On on the surface, the lack of isolation may sound unworkable. In reality developers are accustomed to agree to lower isolation to get better performance. A RDBMS allows you to define the level of isolation you want for every transaction. Sell email database lists.

.com/doc/refman/5.7/en/innodb-transaction-isolation-levels.html). The default iso-lation setting is typically an isolation level that is weaker than complete isolation, also called serializable transactions. The real-world database transactions are usually different from the definitions in textbooks for ACID transactions.

The following section will discuss the strategy for designing sagas to tackle the issue of isolation. These strategies are referred to as countermeasures. Certain countermeasures create isolation at the level of the application. Other countermeasures can reduce the risk to business from the absence of isolation. Through countermeasures, it is possible to create a saga-based business logic that is working properly. Sell email database lists.

The section will begin by describing the anomalies which result from the absence of iso-lation. Following that, I’ll discuss about ways to eliminate the anomalies or lessen their risk for business.

Aspects of the anomalies

Inadequate isolation could cause three different abnormalities:

One saga has overwritten without any reading changes being made by another saga.

A transaction or story reads the updates created by a saga, but hasn’t yet completed the updates. Sell email database lists.

Fuzzy or nonrepeatable reads: Two different steps of a saga are able to read identical data, but get different results because a different story has been updated.

The three types of anomalies are all possible in a variety of ways, but these two anomalies are most frequent and difficult. Let’s examine these two kinds of anomalies beginning with the lost updates.

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LOST Updates

An anomaly of lost updates occurs when one saga is able to overwrite an update created by a different saga. For instance, consider this scenario

The first step in the Create Order Saga is to create an Order.

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While the saga continues to unfold as it should, it is also happening that the Cancel Order Saga cancels the order.

The final stage of the Create Order Saga, which is the approval of the Order.

In this case in this scenario, in this scenario, the Create Order Saga will not take into account the change made by Cancel Order Saga and overwrites it. This means that it is likely that the FTGO application will send an order which the customer has cancelled. In the next section, I’ll explain how to avoid losing updates. Sell email ids lists.


A dirty read happens when one saga is reading data that’s currently being updated by another. For instance, consider a variant of that FTGO application store in which customers have an amount of credit. In this case the saga of cancelling an order is comprised of the following actions:

Consumer Service — Increase the amount of credit available.

Order Service – Change the status of the Order to cancel. Sell email ids lists.

Delivery Service–Cancel delivery.

Let’s think of a scenario in which is a cross-section in the Cancel Order and Create Order Sagas, and then the Cancel Order Saga is rolled back due to the fact that it’s too late in order to provide a refund. It is probable that the chain of the transactions that trigger Consumer Service Consumer Service is as follows:

Cancel Order Saga — Increase the available credit.

Create Order Saga – Reduce the amount of credit.

Cancel Order Saga – A compensating transaction which reduces credit available.

In this situation this episode of the Create Order Saga provides an unclean analysis of the available credit which permits the customer to make an order that is greater than the credit limit. It’s possible that this is an unacceptably risk for the company.

Let’s take a look at ways to avoid this, and other abnormalities from impacting your application. Sell email ids lists.
Countermeasures to handle the absence of isolation

The saga model for transactions is ACD and its inability to isolate could result in issues that cause programs to behave in a strange manner. It is the responsibility of the programmers to create the sagas in a way which either eliminates the anomalies or reduces the impact they have on the business. It may seem like a daunting job however, you’ve seen the example of a plan which prevents the occurrence of anomalies. 

The use of states *_PENDING like APPROVAL _PENDING can be a good example of this kind of strategy. Sagas that update Orders like those in the Create Order Saga start by setting the status of an order to *_PENDING. The *_PENDING state informs any other transaction that the Order has been updated through the saga and will respond accordingly. Sell email ids lists.

An Order’s use of *_PENDING states is an example of what the 1998 paper “Seman-tic ACID properties in multidatabases using remote procedure calls and update prop-agations” by Lars Frank and Torben U. Zahle calls a semantic lock countermeasure. The paper discusses ways to overcome the issue of isolation of transactions in multi-database structures that do not utilize dis-tributed transactions. A lot of the ideas in it can be useful in the design of stories. The paper outlines a series of countermeasures to deal with irregularities caused by a insufficient isolation, which are either able to stop or reduce some or all anomalies or reduce their impact on business. The countermeasures described in this paper are:

Semantic lock — An application-level lock.

Updates that are commutative – Design updates to be executed at any time.

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A pessimistic perspective: Reorder the steps of the saga to limit the risk to your business.

Reread value–Reduce the chance of dirty writing by reading data several times to ensure that it’s not changed before you write it.

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Version file–Record updates to an entry in order to make them changed in order.

By value–Use the business risk to choose the concurrency method.

In the next section, I’ll go over the various countermeasures I have described, however, I’d like to provide some terms for explaining how a story is constructed which can be helpful in discussing countermeasures.

The structure of a SAGA

The countermeasures paper discussed in the previous section provides an effective model to understand the structure of the story. This model is as shown in the figure 4.8, a saga comprises of three kinds of transactions:

Compensatable transactions–Transactions that can potentially be rolled back using a compensating transaction. Sell email directory lists.

Pivot transaction – The point of no-go or go in the story. In the event that the pivot transaction is commingled the story, it will continue until the end. A pivot transaction may be a transaction that’s not compensatable or recoverable. In addition, it could be the last compensatable transaction, or the first to be retriable.


If you use the countermeasure against semantic locks, the compensatable operation of saga creates an indicator in every record it creates or updates. The flag signifies that the record isn’t in the process of being committed and may alter. The flag could be a lock that blocks any other transaction from touching the record , or an alert that warns that other transactions must treat the record with caution. The record is cleared through either an retriable transaction – saga is running successfully or a compensating transaction or a compensating transaction: the saga is rolling back. Sell email directory lists.

Its Order.state field is an excellent example of an semantic lock. The *_PENDING states, like REVISION_PENDING or APPROVAL_PENDING are both examples the concept of a semantic lock. They inform other sagas that have access to an Order that a saga has been currently making changes to the Order. For example, the initial stage of the Create Order Saga that is a compensatable transaction, creates an Order that is in an APPROVAL_PENDING condition. The last step within the Create Order Saga that is a retriable operation transforms the field into APPROVED. A compens-sating transaction alters the field’s status to REJECTED.

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The management of the lock is only part of the issue. You must also decide on a case-by-case basis what you will handle the record that is locked. Take, for instance, using the cancellationOrder() option in the system. A user could use this command to end an Order that is in the APPROVAL_PENDING status.

There are several methods to address this issue. One option is to allow cancel-Order() program to be unable, and then tell the client to try after. The major benefit of this strategy is that it’s easy to implement. The downside is that it creates a more complex client due to the need to implement the retry logic.

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Another option is to cancellationOrder() to stop till the lock has been removed. The advantage from using semantic locks is they replicate the isolation that is provided through ACID transactions. These sagas which update the identical record are serialized, which drastically reduces programmers’ effort. Another advantage is that they eliminate the need for retries from the user. However, the drawback is that the program must handle locks. It should also incorporate an algorithm for deadlock detection that uses a rollback tale in order to crack a deadlock before being able to repeat the process.


An easy solution is to design update processes to be commutative. Operation are commutative when they can be carried out in any sequence. The debit() and credit() operations are computable (if you don’t pay attention to overdraft checks). This countermeasure is helpful since it prevents updates from being lost.

Think about, for example an instance where the saga has to be restored when a compensatable transaction has deducted (or credit) accounts. The compensating transaction can debit (or debit) the account in order to reverse the modification. It is not possible to overwrite the updates of other dramas. Buy Sell b2b email lists online.


Another approach to combat the absence of separation is to use the pessimistic countermeasure. It is a way to reorder the steps of a story to lessen the risk to business due to an unclean read. Take, for instance, the scenario used earlier to explain the dirty read anomaly. In this scenario it was the case that Create Order Saga took an unclean read of the credit available and created an order that was over the limit for consumer credit. To lessen the chance of this being the case, this countermeasure would restore an order from the Cancel Order Saga:

Order Service – Change the status of the Order to cancel. Buy Sell b2b email lists online.

Delivery Service–Cancel delivery.

Customer Service — Increase the amount of credit available.

In this reordered rendition of the story, the amount of credit is increased through the form of a retriable transaction. This makes it impossible to get the card being soiled. Buy Sell b2b email lists online.


The countermeasure to prevent reread values can stop losing updates. A saga using this countermeasure reads a record prior to updating it, checks to see if the record is not changed, then changes the record. If the record is been altered, the story ceases and could even restart. This countermeasure is a form of the Optimistic Offline Lock pattern.

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In the Create Order Saga might employ this method to deal with the possibility that the Order is rejected while going through the process of getting accepted.

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The transaction which approves of the Order confirms that the Order is in the same form as when it was first created in the story. If it’s not the transaction will approve the Order. If the Order was deleted, then the transaction ends the story, which causes its compensat-ing transaction to be carried out.


The countermeasure against version files is named so because it records the actions which are carried out on a document to allow it to reorder the records. It’s a method of turning noncommutative actions into commutative ones. To understand the way this countermeasure operates think about a scenario where you have a Create Order Saga executes concurrently with the Cancel Order Saga. Buy Sell b2b email database lists.

If the sagas don’t employ the countermeasure known as semantic lock and both the Cancel Order Saga cancels the authorization for the consumer’s credit card prior to that the Create Order Saga has the ability to authorize the card.

One option to assist an Accounting Service to handle these out-of-order orders is to keep track of the transactions as they occur and perform them in the right order. In this case the Accounting Service would first document that Cancel Authorization request. After that, when Accounting Service receives the request, Accounting Service receives the subsequent Authorize Card request, it will notice that it has previously received the Cancel Authorization request and skip authorizing the credit card.


The final countermeasure is a through value countermeasure. It’s a way to select the right concurrency mechanism based on business risk. A program that employs this countermeasure takes into account the properties of every request to determine whether it is better to use distributed transactions and sagas. It can execute low-risk transactions with sagas, possibly using the countermeasures mentioned in the previous section. However, it also executes high-risk demands which involve, for instance huge amounts of cash through distributed transactions. Buy Sell b2b email database lists.

This technique allows an application to automatically make trade-offs between business risk capacity, availability, and risk.

You’re likely to need to employ one or one or more of these countermeasures while you implement sagas into your application. Let’s take a look at the in-depth outline and implementation of Create Order Saga, which employs an anti-semantic lock measure.

The style and the design Order Service and the Create Order Saga

After we’ve examined various saga design as well as implementation problems, let’s look at an illustration. Buy Sell b2b email database lists.

The business logic of the service is comprised of business logic that is traditional that include Order Service as well as the Order entity. There are also saga orchestrator class which include the CreateOrderSaga class, which orchestrates the Create Order Saga. Furthermore, since Order Service participates in its own sagas, there is the OrderCommandHandlers adapter class that manages command messages by calling OrderService.

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Certain elements in Order Service should look familiar. Similar to a typical application, the heart of business logic is executed through the OrderService, Order as well as Order-Repository class. The next chapter in this series will provide a brief description of these classes. I’ll explain them in greater depth in chapter 5.

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What’s not as well-known with Order Service are the saga-related classes. The service can be described as orchestrator of sagas as well as an active participant in the saga. Order Service has several saga orchestrators, including CreateOrderSaga. The orchestrators in saga transmit commands to a saga participant by using a saga proxy class, for example KitchenServiceProxy as well as OrderServiceProxy. A saga participant proxy defines a saga participant’s messaging API. Order Service also has an OrderCommandHandlers class that handles the com-mand messages that are sent by Sagas for Order Service. Buy Sell b2b email address lists.

The class OrderService

The class OrderService is an API service that is accessed by the API layer of the service. It’s responsible for generating as well as managing the order. Figure 4.10 illustrates OrderService as well as some of its collaborators. OrderService makes and edits Orders, calls the OrderRepository to keep Orders in place, and even creates sagas, like the CreateOrderSaga with the help of SagaManager. SagaManager is a class that SagaManager class belongs to the classes that are provided through the Eventuate Tram Saga framework, that is a framework used to write saga orchestrators and participants. It is described in a bit later in this article.

CreateOrder() method generates an Order using the Factory Method to create an Order using the factory method .createOrder(). The Order is then stored through the OrderRepository which is an JPA-based repository. It creates the CreateOrderSaga by calling SagaManager.create(), passing a CreateOrderSagaState containing the ID of the newly saved Order and the OrderDetails. The SagaManager creates the orchestrator for the saga which triggers it to transmit an instruction message to the first participant. It also stores the saga orchestrator’s information within the database. Buy Sell b2b email address lists.

CreateOrderSaga: A singleton class which defines the state machine used by the saga. It uses the CreateOrderSagaState to generate command messages and distributes them to players via the channels defined by the saga’s participants proxy classes, for example KitchenServiceProxy.

CreateOrderSagaState–A saga’s persistent state, which creates command messages.

Saga participant proxy classes, such as KitchenServiceProxy–Each proxy class defines a saga participant’s messaging API, which consists of the command channel, the command message types, and the reply types.

The classes are written in Eventuate Tram Saga, the Eventuate Tram Saga framework. Buy Sell b2b email address lists.

The Eventuate Tram Saga framework provides the domain-specific language (DSL) to define the state machine for a saga. It runs the saga’s state machine, and then exchanges mes-sages between the saga’s participants through the Eventuate Tram framework. The framework also stores the state of the saga on the database.

Let’s look more closely at the process of implementation for Create Order Saga. We’ll begin by introducing an introduction to the CreateOrderSaga class. Buy Sell b2b email address lists.


CreateOrderSaga is a class that CreateOrderSaga class is a state machine implementation that was shown previously in Figure 4.7. The class implements SimpleSaga which is a basic interface for stories. The basis of the Create Order Saga class is the saga’s definition as shown in the following table. It makes use of the DSL that is provided through the Eventuate Tram Saga framework to define the steps involved in creating the Create order saga.

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The saga orchestration application is the most complicated part of the framework. It offers SimpleSaga an interface base for sagas, as well as an SagaManager class, which generates and manages Saga instances.

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The SagaManager manages the persistence of a sagaby sending commands that it generates, subscribing replies, and calling the saga to handle responses. Figure 4.13 illustrates what happens occurring when OrderService creates an story. Its sequence can be described as follows:

OrderService creates the CreateOrderSagaState.

It starts the impression of an story by activating the SagaManager.

The SagaManager is the one who handles the first part of the saga’s definition.

CreateOrderSagaState CreateOrderSagaState will be used to create an order message. Buy Sell b2b email accounts lists.

The class OrderCommandHandlers

Order Service participates in its own stories. For instance, CreateOrderSaga invokes Order Service to approve or deny an order. The OrderCommandHandlers class, which is shown in Figure 4.15 describes the methods to handle command messages that are sent out by these stories.

Each handler method invokes the OrderService to update an order and sends a reply. The SagaCommandDispatcher class routes the command messages to the appropriate handler method and sends the reply. Buy Sell b2b email accounts lists.

The foundation of any enterprise application is its business logic, which implements business rules. Making complicated business logic is always an obstacle. This FTGO software’s logic for business is some extremely complex business logic, especially for order as well as delivery administration. Mary has urged her team to use the principles of object-oriented design, as she had found that this was the best method to build complex business logic. Certain business logic was implemented using procedures like the Transcription scripting pattern. However, the majority of FTGO application’s logic is implemented as an object-oriented domain model which is connected to the database via JPA. Buy Sell b2b email accounts lists.

Making complex business logic can be much more difficult when you are working with a microservice model in which the business logic is spread across several services. There are two main issues. The first is that the traditional domain model becomes a complex web of interconnected classes. While this isn’t an issue for the case of a monolithic application, when you’re using micro-service architectures where classes are distributed across multiple services, it is necessary to remove object references that could otherwise cross the boundaries of a service. Another challenge is constructing business logic that operates within the limitations of transaction management of microservice architecture. Business logic can make use of ACID transactions within the services, however, as discussed in chapter 4it must employ the Saga pattern to ensure the consistency of data across services.

Fortunately, we can tackle these problems employing the Aggregate pattern

The pattern of Aggregate structures the business logic of a service by forming a set of aggregates. Aggregates are a group of objects that is used as a whole. There are two main reasons aggregates can be useful in the development of business logic for micro-services architecture:

Aggregates eliminate the potential for object references to span boundaries of services, since an inter-aggregate reference is the primary important value, not the term “object reference. Buy Sell b2b email accounts lists.

Since a transaction is able to create or update one aggregate, the term aggregates is a good fit for the requirements of the microservices model of transaction.

Therefore it is guaranteed that an ACID transaction can be guaranteed to occur within one service.

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This chapter begins by explaining the various ways of organizing business logic. The Transcription script pattern as well as the Domain model pattern. In the next chapter, I will introduce the idea of the DDD aggregate and discuss why it is a useful foundation to a service’s business logic.

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Following that, I will describe the Domain events that are pattern-based and provide the reason for a service’s publication of events. The chapter ends by providing a few examples for business logic such as Kitchen Service and Order Service.

Let’s look at some patterns of business logic.

Business logic patterns of organization
The business logic is at the heart of a hexagonal design. It is supported by the outbound and inbound adapters. Inbound adapters handle the requests of cli-ents, and also triggers its business logic. A outbound adapter, that is invoked by the business logic, calls various applications and services. Buy Sell b2b email and contact lists database.

This service comprises the business logic and the adapters:

Inbound adapter that implements REST API that invokes the business logic

OrderCommandHandlers–An inbound adapter that consumes command mes-sages from a message channel and invokes the business logic
Database Adapter–An adapter for outbound connections that is called by the business logic to connect to the database

Domain Event Publishing Adapter – An outbound adapter that can publish events to messages to a message broker.

The business logic is often the most complicated part in the process. When you are developing business logic it is important to arrange your business logic in a manner that is most suitable for your particular application. In all likelihood, you’ve had the experience of maintaining an unstructured code written by someone else. A majority of enterprise software is developed using an object-oriented programming language like Java therefore they comprise of methods and classes. Buy Sell b2b email and contact lists database.

However, having an object-oriented programming language doesn’t mean that the logic of your business has an object-oriented layout. The most important decision you have to make when creating business logic is to make use of an object-oriented method or a more procedural approach. There are two major patterns to organize business logic that you can use: the process-oriented Transaction script pattern or the pattern that is oriented towards objects. Domain model.

The design of business logic by using Transaction script pattern Transaction script pattern

While I’m a firm advocate of the object-oriented model however, there are situations in which it’s too much for instance, when you’re creating simple business logic. In this case it is better to write code in a procedural manner and apply what The publication Patterns of Enterprise Application Architecture by Martin Fowler (Addison-Wesley Professional, 2002) refers to as”the Transaction scripting pattern. Instead of creating any other object-oriented designs then you create a process known as a transaction script. It is designed to manage each request coming from the presentation layer. As diagram 5.2 illustrates, a key aspect of this method is the fact that classes that execute behaviour are different from the classes that store information. Buy Sell b2b email and contact lists database.

If you’re employing this Transaction script pattern The scripts are typically placed in service classes, that is in this instance, an OrderService class. A service class contains a method for each operation or request. The method is the one that implements the business logic that is required for the particular request. It connects to the database by using Data Access Objects (DAOs) like the OrderDao. These data objects in this case are the Order class are pure data that has minimal or no interaction.

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Pattern Transaction script

Organise the business logic in a set of procedural transactions scripts that are one for every type of request.

This design style is extremely procedural and relies on a few capabilities that are available in object-oriented programming (OOP) language. It’s what you’d create in the event that you wrote the program in C or any other non-OOP language.

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But, don’t be embarrassed to employ the procedural approach when suitable. This method is great for basic business logic. The downside is that it is not an ideal method to implement sophisticated business logic.

Business logic design using Domain model pattern Domain model pattern

The ease of the procedural method can be very attractive. It is possible to write code without having to consider carefully how to organize your classes. But the business logic you write becomes complex, you may create code that is an absolute nightmare to maintain. In the same way as the monolithic software tends to keep expanding, transaction scripts face the same issue. Therefore, unless you’re creating an extremely basic application, it is best to avoid writing procedural code. Instead, you should follow to the Domain model pattern and create objects-oriented designs. Buy Sell b2b email and contact lists database online.

Model Domain model

The business logic can be organized as an object model that is composed of classes with state and behaviour.

In an object-oriented design the business logic is comprised of objects, which is a collection of classes that are relatively small. The classes usually correspond directly to concepts of the domain of concern. In such a case, classes only have behavior or state, however they often have both, which is the hallmark of a well-designed and well-designed class. Figure 5.3 illustrates an example that illustrates this Domain Model Pattern.
Similar to in the Transaction script pattern the OrderService class provides an approach for every request or system operation. When using the Domain model pattern, the methods used by the service tend to be straightforward. It is because a method of service typically relies on persistent domain objects that include the majority all the logic for business. A service method could be able to, for instance, load domain objects from the database and call some of it’s methods. In this case the Order class contains both behavior and state. Additionally its state is confidential and accessible only by indirect means through its methods. Buy Sell b2b email and contact lists database online.

The use of an object-oriented design offers numerous advantages. The first is that the design is simple to comprehend and maintain. Instead of having one class that is responsible for everything, the design is composed of a variety of smaller classes each with a limited amount of responsibility. Additionally classes such as BankingTransaction, Account and OverdraftPolicy closely replicate reality, making their purpose in the design much easier to comprehend.

Furthermore, an object-oriented approach makes it easier to test because Each class is able to be tested separately. Furthermore, an object-oriented model can be extended more easily because it is able to use familiar design patterns, like the Strategy pattern as well as template method patterns which define methods of expanding components without changing the code. Buy Sell b2b email and contact lists database online.

It is true that the Domain model pattern works great however, there are some issues with this method particularly when it comes to the microservices architecture. To resolve these issues it is necessary to implement an improvement of OOD that is known as DDD.

About Domain-driven design

DDD that is explained within the text Domain-Driven Design by Eric Evans (Addison-Wesley Professional 2003) is a further enhancement of OOD and is a technique to develop complicated business logic. In chapter 2, I discussed DDD during chapter 2 as I discussed the benefits of DDD subdomains for breaking down an application into service. 

With DDD each service has its own domain model that eliminates the need for having a single, all-encompassing domain model. Subdomains and the notion of Bounded Context are two of the key DDD patterns. They also have strategic patterns that form the elements of domain models. Buy Sell b2b email and contact lists database online.

Each pattern represents a role that a class has in a domain model , and determines the characteristics for the particular class. The basic building blocks that are commonly used by developers are the following:

Entity–An object with an identity that is persistent. Two entities whose attributes share identical values are distinct objects. In the case of a Java EE application, classes that are stored by using JPA @Entity are generally DDD entities.

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Value object is a collection of value. Two value objects with attributes that are identical are able to be utilized in conjunction. A good illustration of an object of value would be Money class, made up of a currency as well as an amount.

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Factory is an object or method which implements logic for creating objects that is too complicated to be handled in a direct way by the constructor. It also conceals the class that is created. The factory can be used in the form of a static implementation for the class.

Repository is an object that gives an access point to entities that persist as well as provides the mechanism to access the database.

Service is an object which implements business logic that’s not to an entity or value object. Buy Sell b2c email database lists online.

The building blocks can be utilized by a wide range of developers. Certain have been supported by frames, such for JPA as well as the framework Spring. Another component that is mostly left out (myself too!) aside from DDD experts aggregates. In the end, aggregates can be a very useful concept to develop microser-vices. Let’s begin by looking at some of the subtle issues with the classic OOD that can be solved using aggregates.

In the old-fashioned object-oriented design the domain model is an array of classes and connections between classes. Classes are typically separated into packages. As an example the figure 5.4 shows a part of a domain model used by an application called the FTGO application. It’s a typical domain model made up of an interconnected web of classes.

The issue is that there are no clear boundaries. Buy Sell b2c email database lists online.

Imagine, for instance you need to carry out an operation, like a delete or load using an order object. What exactly is that? What’s the scope of the operation? It is likely that you will either delete or load your Order object. In reality, it’s much more in an Order that just an Order object. There are the orders line items, pay-ment information and so on. Figure 5.4 is a way to leave all the domain objects’ boundaries up to the developer’s own intuition.

Alongside a conceptual fuzziness the absence of clear boundaries can cause problems when it comes to updating an object in business. The typical business object is characterized by certain rules, which have to be adhered to continuously. An order is subject to an amount that is a minimum for orders like. The FTGO application should make sure the attempt made to alter an order isn’t in violation of an invariant like the minimum amount of order. The issue is that to ensure that invariants are enforced it is necessary to develop your business logic with care. Buy Sell b2c email database lists online.

Let’s take a consider ways to ensure the minimum order amount is met when several consumers are working together to create an order. Two people, Sam and Mary, work together on an order , and simultaneously realize that the purchase is over their budget. Sam reduces the amount of samosas. Mary reduces the amount of Naan bread. From the standpoint of the application both consumers access the order as well as it’s line items directly from the database. Both customers then modify an item in the line to lower the price of the order. From the perspective of each customer, the minimum purchase amount remains. This is the order of transactions in the database.

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Each time a consumer makes a change to the line item by two transactions. First, the transaction load’s entire order and the line item. The UI checks to ensure that the minimum requirement for the order is met prior to executing the second one. The second transaction update the line item’s quantity by with an optimistic offline locking test which ensures that the line item is unchanged from the time it was loaded by the initial transaction.

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In this situation, Sam reduces the order total by $X while Mary cuts it down by $Y. This means that the order no longer valid even though an application has verified that the order was still in compliance with the minimum requirements for an order following each update by the consumer. You can clearly see that simply updating a specific part of a company object can lead to the breach of business rules. DDD aggregates are designed to address this issue.

Aggregates are defined by their boundaries.

A aggregate can be described as a collection of domain objects inside an area that is considered as a unit. It is comprised of a root entity, as well as several additional entities and value objects. Many business objects are described as aggregates. In chapter 2, we developed an approximate domain model by studying the words employed in the requirements and the domain expert’s opinions. A large portion of these words like Order Consumer, Restau-rant, and Order are aggregates. Buy Sell b2c email address database lists online.

Pattern: Aggregate

Create a domain model by organizing it as an array of aggregates that are an object which can be viewed as one unit.

Figure 5.5 illustrates the Order aggregate as well as its boundaries. An Order aggregate is comprised of an entity called an Order that is comprised of the OrderLineItem Value object and any other value objects, like a delivery address and PaymentInformation.

Aggregates help to break down the domain model into pieces, which can be analyzed individually. They also define the scope of the operations like load, update and delete. These operations affect the entire aggregate instead of just on portions of it. The aggregate is usually loaded completely out of the database getting rid of any problems caused by slow loading. The deletion of an aggregate erases the entire object from the database. Buy Sell b2c email address database lists online.


The process of updating an aggregate as a whole instead of its components solves problems of consistency, like the earlier example. Update operations are performed on the root of the aggregate which ensures that invariants are maintained. Additionally, concurrency is managed via locking of the aggregate root with such a version number or database-level lock. As an example, instead changing the quantity of line items directly, the client has to use a method that is on an element of the aggregate Order, that enforces invariants, such as the minimum amount of order. It is important to note that this method doesn’t need the whole aggregate to be updated inside the database. A program could, for instance, update only the rows that correspond to an Order object, and the Updated OrderLineItem.

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The process of identifying aggregates is crucial.

In DDD the most important aspect of creating the domain model is to identify the aggregates and their borders and their root. The specifics of inside structure of aggregates are secondary. The advantage of aggregates however, extends far beyond creating a modular model of a domain. This is because aggregates have to follow certain rules.

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Aggregate rules demand that aggregates adhere to a set of guidelines. These rules make sure that aggregates are self-contained unit that is able to ensure its rules. Let’s take a look at the different rules.

The previous example has illustrated the risks of updating OrderLineItems in a direct manner. Best Sell email  lists online. The aim of the initial rules on aggregates is fix this issue. It demands an entity named the aggre-gate root is the sole part of an aggregate that could be referred to by classes that are not part that of. A client is able to update an aggregate by calling an action on the aggregate root.

A service, such as that uses a repository, for example, to pull an aggregate from a database and then obtain the reference to the aggregate root. The service updates an aggregate by calling an algorithm to the root of the aggregate. This is to ensure that the aggregate is able to enforce its invariant.

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