I need a worker to subscribe to new data entries in a column family.
I have to invoke the services consuming data on the producer side, or poll the column family for new data, which is a waste of resources and also leads to some extended latency.
I want some external service to be invoked when new data is written to column family. Is it possible to invoke an external service, such as an REST endpoint upon new data arrival?
There are two features, triggers and CDC (change data capture) that may work. You can create a trigger to receive updates and execute the http request, or you can use CDC to get a per replica copy of the mutations as a log to walk through.
CDC is better for consistency, since a trigger fires before mutations applied, your API endpoint may be notified but then have the mutation fail to apply so your at an inconsistent state. But triggers are easier since you dont need to worry about deduplication since its only 1 per query vs 1 per replica. Or you can use both, triggers that update a cached state and then CDC with a map reduce job to fix any inconsistencies.
Related
I'm working on an event-driven micro-services architecture and I'm using eventhubs to send a lot of data (around 20-30k events per minute) to multiple consumer groups and I'm using Azure Functions EventHubTrigger to process these events.
The data I'm passing around has a unique identifier and my other consumers can guarantee idempotency since I'm storing them on their data stores as well upon processing - so if the unique event identifier already exists, I can skip processing for that specific event.
I do however have one service that only does data-aggregation for reporting to a relational database - doing counts, sums, and what-not. Pretty much upserts so that I can do some queries against it to produce reports - and I did see quite a bit of events that have been processed multiple times.
So an idea that I had was to just have some sort of event store. Redis with TTL, or Azure Table Storage, or even a table on my relational database that only contains a single field with a unique constraint so I can do a transaction on the whole event processing.
Is there a better way to do this?
I have an API which allows other microservices to call on to check whether a particular product exists in the inventory. The API takes in only one parameter which is the ID of the product.
The API is served through API Gateway in Lambda and it simply queries against a Postgres RDS to check for the product ID. If it finds the product, it returns the information about the product in the response. If it doesn't, it just returns an empty response. The SQL is basically this:
SELECT * FROM inventory where expired = false and product_id = request.productId;
However, the problem is that many services are calling this particular API very heavily to check the existence of products. Not only that, the calls often come in bursts. I assume those services loop through a list of product IDs and check for their existence individually, hence the burst.
The number of concurrent calls on the API has resulted in it making many queries to the database. The rate can burst beyond 30 queries per sec and there can be a few hundred thousands of requests to fulfil. The queries are mostly the same, except for the product ID in the where clause. The column has been indexed and it takes an average of only 5-8ms to complete. Still, the connection to the database occasionally time out when the rate gets too high.
I'm using Sequelize as my ORM and the error I get when it time out is SequelizeConnectionAcquireTimeoutError. There is a good chance that the burst rate was too high and it max'ed out the pool too.
Some options I have considered:
Using a cache layer. But I have noticed that, most
of the time, 90% of the product IDs in the requests are not repeated.
This would mean that 90% of the time, it would be a cache miss and it
will still query against the database.
Auto scale up the database. But because the calls are bursty and I don't
know when they may come, the autoscaling won't complete in time to
avoid the time out. Moreover, the query is a very simple select statement and the CPU of the RDS instance hardly crosses 80% during the bursts. So I doubt scaling it would do much too.
What other techniques can I do to avoid the database from being hit hard when the API is getting burst calls which are mostly unique and difficult to cache?
Use cache in the boot time
You can load all necessary columns into an in-memory data storage (redis). Every update in database (cron job) will affect cached data.
Problems: memory overhead of updating cache
Limit db calls
Create a buffer for ids. Store n ids and then make one query for all of them. Or empty the buffer every m seconds!
Problems: client response time extra process for query result
Change your database
Use NoSql database for these data. According to this article and this one, I think choosing NoSql database is a better idea.
Problems: multiple data stores
Start with a covering index to handle your query. You might create an index like this for your table:
CREATE INDEX inv_lkup ON inventory (product_id, expired) INCLUDE (col, col, col);
Mention all the columns in your SELECT in the index, either in the main list of indexed columns or in the INCLUDE clause. Then the DBMS can satisfy your query completely from the index. It's faster.
You could start using AWS lambda throttling to handle this problem. But, for that to work the consumers of your API will need to retry when they get 429 responses. That might be super-inconvenient.
Sorry to say, you may need to stop using lambda. Ordinary web servers have good stuff in them to manage burst workload.
They have an incoming connection (TCP/IP listen) queue. Each new request coming in lands in that queue, where it waits until the server software accept the connection. When the server is busy requests wait in that queue. When there's a high load the requests wait for a bit longer in that queue. In nodejs's case, if you use clustering there's just one of these incoming connection queues, and all the processes in the cluster use it.
The server software you run (to handle your API) has a pool of connections to your DBMS. That pool has a maximum number of connections it it. As your server software handles each request, it awaits a connection from the pool. If no connection is immediately available the request-handling pauses until one is available, then handles it. This too smooths out the requests to the DBMS. (Be aware that each process in a nodejs cluster has its own pool.)
Paradoxically, a smaller DBMS connection pool can improve overall performance, by avoiding too many concurrent SELECTs (or other queries) on the DBMS.
This kind of server configuration can be scaled out: a load balancer will do. So will a server with more cores and more nodejs cluster processes. An elastic load balancer can also add new server VMs when necessary.
I want to send the event to Microsoft Event-hub with Db transaction:
Explanation:
User hit a endpoint of order creation.
OrderService accept the order and put that order into the db.
Now Order service want to send that orderId as event to another services using the Event-hub.
How can I achieve transactional behaviour for step 2 and 3?
I know these solutions:
Outbox pattern: Where I put message in another table with order creation transaction. And there is one cron/scheduler, that takes the message from table and mark them delivered. and next time cron will take only not delivered messages.
Use Database audit log and library that taken of this things. Library will bind the database table to Event-hub. Then on every update library will send that change to Event-hub.
I wanted to know is there any in-built transactional feature in Event-hub?
Or
Is there any better way to handle this thing?
There is no concept of transactions within Event Hubs at present. I'm not sure, given the limited context that was shared, that Event Hubs is the best fit for your scenario. Azure Service Bus has transaction support and may be a more natural fit for your intended flow.
In this kind of distributed scenario, regardless of which message broker you decide on, I would advise embracing eventual consistency and considering a pattern similar to:
Your order creation endpoint receives a request
The order creation endpoint assigns a unique identifier for the request and emits the event to Event Hubs; if the send was successful it returns a 202 (Accepted) to the caller and a Retry-After header to indicate to the caller that they should wait for that period of time before checking the status of that order's creation.
Some process is responsible for reading events from the Event Hub and creating that order within the database. Depending on your ecosystem's tolerance, this may be a dedicated process or could be something like an Azure Function with an Event Hubs trigger.
Other event consumers interested in orders will also see the creation request and will call into your order service or database for the details using the unique identifier that as assigned by the order creation endpoint; this may or may not be the official order number within the system.
I'm working on a project where DynamoDB is being used as database and every use case of the application is triggered by a message published after an item has been created/updated in DB. Currently the code follows this approach:
repository.save(entity);
messagePublisher.publish(event);
Udi Dahan has a video called Reliable Messaging Without Distributed Transactions where he talks about a solution to situations where a system can fail right after saving to DB but before publishing the message as messages are not part of a transaction. But in his solution I think he assumes using a SQL database as the process involves saving, as part of the transaction, the correlationId of the message being processed, the entity modification and the messages that are to be published. Using a NoSQL DB I cannot think of a clean way to store the information about the messages.
A solution would be using DynamoDB streams and subscribe to the events published either using a Lambda or another service to transformed them into domain-specific events. My problem with this is that I wouldn't be able to send the messages from the domain logic, the logic would be spread across the service processing the message and the Lambda/service reacting over changes and the solution would be platform-specific.
Is there any other way to handle this?
I can't say a specific solution based on DynamoDB since I've not used this engine ever. But I've built an event driven system on top of MongoDB so I can share my learnings you might find useful for your case.
You can have different approaches:
1) Based on an event sourcing approach you can just save the events/messages your use case produce within a transaction. In Mongo when you are just inserting/appending new items to the same collection you can ensure atomicity. Anyway, if the engine does not provide that capability the query operation is so centralized that you are reducing the possibility of an error at minimum.
Once all the events are stored, you can then consume them and project them to a given state and then persist the updated state in another transaction.
Here you have to deal with eventual consistency as data will be stale in your read model until you have projected the events.
2) Another approach is applying the UnitOfWork pattern where you cache all the query operations (insert/update/delete) to save both events and the state. Once your use case finishes, you execute all the cached queries against the database (flush). This way although the operations are not atomic you are again centralizing them quite enough to minimize errors.
Of course the best is to use an ACID database if you require that capability and any other approach will be a workaround to get close to it.
About publishing the events I don't know if you mean they are published to a messaging transportation mechanism such as rabbitmq, Kafka, etc. But that must be a background process where you fetch the events from the DB and publishes them in order to break the 2 phase commit within the same transaction.
I'm trying to implement logging mechanism in a Service-Workflow-hybrid application. The requirements for logging is that instead for independent log action, each log must be considered as a detail operation and placed against a parent/master operation. So, it's a parent-child and goes to database table(s). This is the primary reason, NLog failed.
To help understand better, I'm diving in a generic detail. This is how the application flow goes:
Now, the Main entry point of the application (normally called Program.cs) is Platform. It initializes an engine that is capable of listening incoming calls from ISDN lines, VoIP, or web services. The interface is generic, so any call that reaches the Platform triggers OnConnecting(). OnConnecting() is a thread-safe event and can be triggered as many times as system requires.
Within OnConnecting(), a new instance of our custom Workflow manager is launched and the context is a custom object called ProcessingInfo:
new WorkflowManager<ZeProcessingInfo>();
Where, ZeProcessingInfo:
var ZeProcessingInfo = new ProcessingInfo(this, new LogMaster());
As you can see, the ProcessingInfo is composed of Platform itself and a new instance of LogMaster. LogMaster is defined in an independent assembly.
Now this LogMaster is available throughout the WorkflowManager, all the Workflows it launches, all the activities within any running Workflow, and passed on to external code called from within any Activity. Now, when a new LogMaster is initialized, a Master Operation entry is created in the database and this LogMaster object now lives until this call is ended after a series of very serious roller coaster rides through different workflows. Upon every call of OnConnecting(), a new Master Operation is created and maintained.
The LogMaster allows for calling a AddDetail() method that adds new child detail under the internally stored Master Operation (distinguished through a Guid Primary Key). The LogMaster is built upon Entity Framework.
And, I'm able to log under the same Master Operation as many times as I require. But the application requirements are changing and there is a need to log from other assemblies now. There is a Platform Server assembly witch is a Windows Service that acts as a server listening to web service based calls and once a client calls a method, OnConnecting in Platform is triggered.
I need a mechanism to somehow retrieve the related LogMaster object so that I can add detail to the same Master Operation. But Platform Server is the once triggering the OnConnecting() on the Platform and thus, instantiating LogMaster. This creates a redundancy loop.
Also, failure scenarios are being considered as well. If LogMaster fails, need to revert to Event Logging from Database Logging. If Event Logging is failed (or not allowed through unified configuration), need to revert to file-based (XML) logging.
I hope I have given a rough idea. I don't expect code but I need some strategy for a very seamless plug-able configurable logging mechanism that supports Master-Child operations.
Thanks for reading. Any help would be much appreciated.
I've read this question a number of times and it was pretty hard to figure out what was going on. I don't think your diagram helps at all. If your question is about trying to retrieve the master log record when writing child log records then I would forget about trying to create normalised data in the log tables. You will just slow down the transactional system in trying to do so. You want the log/audit records to write as fast as possible and you can later aggregate them when you want to read them.
Create a de-normalised table for the logs entries and use a single Guid in that table to track the session/parent log master. Yes this will be a big table but it will write fast.
As for guaranteed delivery of log messages to a destination, I would try not to create multiple destinations as combining them later will be a nightmare but rather use something like MSMQ to emit the audit logs as fast as possible and have another service pick them up and process them in a guaranteed delivery manner. ETW (Event Logging) is not guaranteed under load and you will not know that it has failed.