I have a chain of synchronous events that take place.
a custom control calls an action
action creates a couple of records
action then triggers a plugin which tries to retrieve records that were created in step 2, but the query returns nothing
I suspect this is happening because all the events are in the same transaction and therefore the records they create are not yet committed to the database. Is this correct?
Is there an easy way to retrieve records that were created earlier in the pipeline or am I stuck having to stuff OutputParameter object into SharedVariables?
Related
When I create WorkFlow using Elsa Dashboard, the WorkFlow's information(Name, Description, Data, IsPublished, Persistance behaviour, version, finished at.....) is automatically inserted in These 3 Tables
WorkflowDefinitions
WorkflowInstances
WorkflowExecutionLogRecords
But When I Create WorkFlow using Builder API, Workflow's information is not inserted. Nothing is inserted in any table
I want to insert & Store WorkFlow's info in Those 3 tables.
So is there any way to insert WorkFlow's Info in tables
Or do I have to Manually Write code to Insert data in those 3 tables in Builder API?
As the name of these tables infer the WorkflowInstances are the instantiated workflows and the WorkflowExecutionLogRecords is the log for a flow execution. what you define by Builder API is the workflow blueprint or as it's called workflow definition so it's the only table that could be affected when saving a blueprint.
But you usually don't need to persist your workflow defined by Builder API in the DB, cause if you have it in your code it's somehow persisted and is part of your code.
I guess getting to know workflow providers can put you in the right direction, you can find out about them in this article.
I have a ReportGeneration lambda that takes request from client and adds following entries to a DDB table.
Customer ID <hash key>
ReportGenerationRequestID(UUID) <sort key>
ExecutionStartTime
ReportExecutionStatus < workflow status>
I have enabled DDB stream trigger on this table and a create entry in this table triggers the report generation workflow. This is a multi-step workflow that takes a while to complete.
Where ReportExecutionStatus is the status of the report processing workflow.
I am supposed to maintain the history of all report generation requests that a customer has initiated.
Now What I am trying to do is avoid concurrent processing requests by the same customer, so if a report for a customer is already getting generated don’t create another record in DDB ?
Option Considered :
query ddb for the customerID(consistent read) :
- From the list see if any entry is either InProgress or Scheduled
If not then create a new one (consistent write)
Otherwise return already existing
Issue: If customer clicks in a split second to generate report, two lambdas can be triggered, causing 2 entires in DDB and two parallel workflows can be initiated something that I don’t want.
Can someone recommend what will be the best approach to ensure that there are no concurrent executions (2 worklflows) for the same Report from same customer.
In short when one execution is in progress another one should not start.
You can use ConditionExpression to only create the entry if it doesn't already exist - if you need to check different items, than you can use DynamoDB Transactions to check if another item already exists and if not, create your item.
Those would be the ways to do it with DynamoDB, getting a higher consistency.
Another option would be to use SQS FIFO queues. You can group them by the customer ID, then you wouldn't have concurrent processing of messages for the same customer. Additionally with this SQS solution you get all the advantages of using SQS - like automated retry mechanisms or a dead letter queue.
Limiting the number of concurrent Lambda executions is not possible as far as I know. That is the whole point of AWS Lambda, to easily scale and run multiple Lambdas concurrently.
That said, there is probably a better solution for your problem using a DynamoDB feature called "Strongly Consistent Reads"
By default reads to DynamoDB (if you use the AWS SDK) are eventually consistent, causing the behaviour you observed: Two writes to the same table are made but your Lambda only was able to notice one of those writes.
If you use Strongly consistent reads, the documentation states:
When you request a strongly consistent read, DynamoDB returns a response with the most up-to-date data, reflecting the updates from all prior write operations that were successful.
So your Lambda needs to do a strongly consistent read to your table to check if the customer already has a job running. If there is already a job running the Lambda does not create a new job.
I'm planning to create a simple microservice app (set and get appointments) with CQRS and Event Sourcing but I'm not sure if I'm getting everything correctly. Here's the plan:
docker container: public delivery app with REST endpoints for getting and settings appointments. The endpoints for settings data are triggering a RabbitMQ event (async), the endpoint for getting data are calling the command service (sync).
docker container: for the command service with connection to a SQL database for setting (and editing) appointments. It's listening to the RabbidMQ event of the main app. A change doesn't overwrite the data but creates a new entry with a new version. When data has changed it also fires an event to sync the new data to the query service.
docker container: the SQL database for the command service.
docker container: the query service with connection to a MongoDB. It's listening for changes in the command service to update its database. It's possible for the main app to call for data but not with REST but with ??
docker container: an event sourcing service to listen to all commands and storing them in a MongoDB.
docker container: the event MongoDB.
Here are a couple of questions I don't get:
let's say there is one appointment in the command database and it already got synced to the query service. Now there is a call for changing the title of this appointment. So the command service is not performing an UPDATE but an INSERT with the same id but a new version number. What is it doing afterwards? Reading the new data from the SQL and triggering an event with it? The query service is listening and storing the same data in its MongoDB? Is it overwriting the old data or also creating a new entry with a version? That seems to be quite redundant? Do I in fact really need the SQL database here?
how can the main app call for data from the query service if one don't want to uses REST?
Because it stores all commands in the event DB (6. docker container) it is possible to restore every state by running all commands again in order. Is that "event sourcing"? Or is it "event sourcing" to not change the data in the SQL but creating a new version for each change? I'm confused what exactely event sourcing is and where to apply it. Do I really need the 5. (and 6.) docker container for event sourcing?
When a client wants to change something but afterwards also show the changed data the only way I see is to trigger the change and than wait (let's say with polling) for the query service to have that data. What's a good way to achieve that? Maybe checking for the existing of the future version number?
Is this whole structure a reasonable architecture or am I completely missing something?
Sorry, a lot of questions but thanks for any help!
Let’s take this one first.
Is this whole structure a reasonable architecture or am I completely
missing something?
Nice architecture plan! I know it feels like there are a lot of moving pieces, but having lots of small pieces instead of one big one is what makes this my favorite pattern.
What is it doing afterwards? Reading the new data from the SQL and
triggering an event with it? The query service is listening and
storing the same data in its MongoDB? Is it overwriting the old data
or also creating a new entry with a version? That seems to be quite
redundant? Do I in fact really need the SQL database here?
There are 2 logical databases (which can be in the same physical database but for scaling reasons it's best if they are not) in CQRS – the domain model and the read model. These are very different structures. The domain model is stored as in any CRUD app with third normal form, etc. The read model is meant to make data reads blazing fast by custom designing tables that match the data a view needs. There will be a lot of data duplication in these tables. The idea is that it’s more responsive to have a table for each view and update that table in when the domain model changes because there’s nobody sitting at a keyboard waiting for the view to render so it’s OK for the view model data generation to take a little longer. This results in some wasted CPU cycles because you could update the view model several times before anyone asked for that view, but that’s OK since we were really using up idle time anyway.
When a command updates an aggregate and persists it to the DB, it generates a message for the view side of CQRS to update the view. There are 2 ways to do this. The first is to send a message saying “aggregate 83483 needs to be updated” and the view model requeries everything it needs from the domain model and updates the view model. The other approach is to send a message saying “aggregate 83483 was updated to have the following values: …” and the read side can update its tables without having to query. The first approach requires fewer message types but more querying, while the second is the opposite. You can mix and match these two approaches in the same system.
Since the read side has very different table structures, you need both databases. On the read side, unless you want the user to be able to see old versions of the appointments, you only have to store the current state of the view so just update existing data. On the command side, keeping historical state using a version number is a good idea, but can make db size grow.
how can the main app call for data from the query service if one don't
want to uses REST?
How the request gets to the query side is unimportant, so you can use REST, postback, GraphQL or whatever.
Is that "event sourcing"?
Event Sourcing is when you persist all changes made to all entities. If the entities are small enough you can persist all properties, but in general events only have changes. Then to get current state you add up all those changes to see what your entities look like at a certain point in time. It has nothing to do with the read model – that’s CQRS. Note that events are not the request from the user to make a change, that’s a message which then is used to create a command. An event is a record of all fields that changed as a result of the command. That’s an important distinction because you don’t want to re-run all that business logic when rehydrating an entity or aggregate.
When a client wants to change something but afterwards also show the
changed data the only way I see is to trigger the change and than wait
(let's say with polling) for the query service to have that data.
What's a good way to achieve that? Maybe checking for the existing of
the future version number?
Showing historical data is a bit sticky. I would push back on this requirement if you can, but sometimes it’s necessary. If you must do it, take the standard read model approach and save all changes to a view model table. If the circumstances are right you can cheat and read historical data directly from the domain model tables, but that’s breaking a CQRS rule. This is important because one of the advantages of CQRS is its scalability. You can scale the read side as much as you want if each read instance maintains its own read database, but having to read from the domain model will ruin this. This is situation dependent so you’ll have to decide on your own, but the best course of action is to try to get that requirement removed.
In terms of timing, CQRS is all about eventual consistency. The data changes may not show up on the read side for a while (typically fractions of a second but that's enough to cause problems). If you must show new and old data, you can poll and wait for the proper version number to appear, which is ugly. There are other alternatives involving result queues in Rabbit, but they are even uglier.
I'm not sure whether I need a way to lock records returned by the context or simply need a new approach.
Here's the story. We currently have a small number of apps that integrate with our CRM. Some of them open a XrmServiceContext and return a few thousand record to perform updates. These scripts are calling SaveChanges along the way but there will still be accounts near the end that will be saved a couple of minutes after the context return them. If a user updates the record during this time, their changes are overwritten by the script.
Is there a way of locking the records until the context has saved the update back or is there a better approach I should be taking?
Kit
In my opinion, this type of database transaction issue is what CRM is currently lacking the most. There is no way to ensure that someone else doesn't monkey with your data, it's always a last-one-in-wins world in CRM.
With that being said, my suggestion would be to only update the attributes you care about. If you're returning all columns for an entity, when you update that entity, you're possibly going to update all the attributes of the entity, even if you only updated one of them.
If you're dealing with a system were you can't tolerate the last-one-in-wins mentality, then you're probably better off not using CRM.
Update 1
CRM 2015 SP1 and above supports Optimistic Updates. Which allows the use of a version number to ensure that no one has updated the record since you retrieved it.
You have a several options here, it just depends on what you want to do. First of all though, if you can move some of these automated processes to off-time hours, then that's the best option.
Another option would be to retrieve each record 1 by 1 instead of by 1000+.
If you are only updating a percentage of the records retrieved, then you would be better off to check before saving if an update occurred (comparing the modified date). If the modified date changed, then you need to do a single retrieve and then save.
At first thought, I would create a field or status that indicates a pending operation and then use JScript in the form OnLoad event to warn/lock the form. When you process completes, it could clear the flag.
I'm trying to initialize my data in my Azure Data Tables but I only want this to happen once on the server at startup (i.e. via the WebRole Role Entry OnStart routine). The problem is if I have multiple instances starting up at the same time then potentially either one of those instances can add records to the same table at the same time hence duplicating the data at runtime.
Is there there like an overarching routine for all instances? An application object in which I can shove a value into and check it in each of the instances to see if the tables have been created or not? A singleton of some sort that azure exposes?
Cheers
Rob
No, but you could use a Blob lease as a mutex. You could also use a table lock in SQL Azure, if you're using that.
You could also use a Queue, and drop a message in there and then just one role would pick up the message and process it.
You could create a new single instance role that does this job on role start.
To be really paranoid about this and address the event of failure in the middle of writing the data, you can do something even more complex.
A queue message is a great way to ensure transactional capabilities as long as the work you are doing can be idempotent.
Each instance adds a message to a queue.
Each instance polls the queue and on receiving a message
Reads the locking row from the table.
If the ‘create data state’ value is ‘unclaimed’
Attempts to update the row with a ‘in process’ value and a timeout expiration timestamp based on the amount of time needed to create the data.
if the update is successful, the instance owns the task of creating the data
So create the data
update the ‘create data state’ to ‘committed’
delete the message
else if the update is unsuccessful the instance does not own the task
so just delete the message.
Else if the ‘create data’ value is ‘in process’, check if the current time is past the expiration timestamp.
That would imply that the ‘in process’ failed
So try all over again to set the state to ‘in process’, delete the incomplete written rows
And try recreating the data, updating the state and deleting the message
Else if the ‘create data’ value is ‘committed’
Just delete the queue message, since the work has been done