How to separate read traffic from the rds database. Serverless doesn't have read replica. Already the production load is too high and it is not able to handle at the moment. I have to implement business reports. Can use clone? Any advice on this?
Since Amazon Aurora serverless option does not have separate replicas, you can manually scale up the cluster with more ACUs to get more resources for the workload.
Clone would have initially the same data, but as the data changes on the source cluster, clone would divert from the same and might not help with the purpose.
If the workload is predictable, and needs more replicas, I would recommend switching to an Aurora provisioned cluster where you can create upto 15 replicas.
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I m testing the Aurora RDS database, and are inserting data (1TB), and it seems to go OK.
However, I can't find how much disk storage it consumes ( for billing estimation ).
Any ideas ?
Thanks in advance.
Aurora does not have a notion of "disk storage". It uses a distributed volume to store your data, and what you are looking for is the Volume of that volume for your cluster. This is emitted as a Cloudwatch metrics, with the name VolumesBytesUsed.
https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.Monitoring.html
That is the same metric used in your Billing computation as well. While you are there, do check the VolumeIOPS metrics as well (read and write) as they add to your billing as well.
I want to create AWS RDS (MYSQL) setup , which can give me high Availability, Fail Over and Replication. I have tried AWS multi-AZ but it will not provide Read Replica. So can any one please help me out in creating topology through which i can achieve high Availability, Fail Over and Replication.
When I try to use clone/restore point in time from amazon console. It clones cluster as well as all the instances which belongs to that. But when I consume the same functionality using amazon API, it clones only cluster alone.
Is there any other API to clone cluster alone with their instances, security/parameter group and other settings?
Console adds a convenience layer where in it internally makes multiple API calls to make the experience better. Restoring from a snapshot or from point in time is done in 2 steps:
RestoreDBClusterFromSnapshot or RestoreDBClusterToPointInTime API - To create a new cluster, backed by a new distributed aurora volume. No DB instances are added when then API is issued.
CreateDBInstance API - To add instances to the cluster.
So in short, if you want to do it via CLI, you need to issue both these API calls. The same is true while creating a cluster with instances as well. Console would create a cluster and add instances in the same UX workflow, but behind the scenes, it is actually issuing a CreateDBCluster API followed by one or more CreateDBInstance API call(s).
Hope this helps.
I have a query on AZURE HDInsights. How do I need to design AZURE HDInsights Cluster according to my on-premises infrastructure ?
What are the major parameters which I need to consider before designing the cluster ?
(For Example) If I have 100 servers running on-premises, how many nodes I need to select in my Cloud Cluster like that. ?!! In AWS we have EMR sizing calculator and Cluster Planner/Advisor. Do we have anything similar planning mechanism in AZURE apart from Pricing Calculator ? Please clarify and provide your inputs. With Any example will be really great. Thanks.
Before deploying an HDInsight cluster, plan for the desired cluster capacity by determining the needed performance and scale. This planning helps optimize both usability and costs. Some cluster capacity decisions cannot be changed after deployment. If the performance parameters change, a cluster can be dismantled and re-created without losing stored data.
The key questions to ask for capacity planning are:
In which geographic region should you deploy your cluster?
How much storage do you need?
What cluster type should you deploy?
What size and type of virtual machine (VM) should your cluster nodes use?
How many worker nodes should your cluster have?
Each and every question is addressed here: "Capacity planning for HDInsight Clusters".
title describes pretty much what we are trying to accomplish in our organization.
We have a very database intensive application, and our single SQL Server machine is struggling.
We are reading articles about Azure, Docker and Kubernetes but we are afraid of trying these technologies.
Our problem is data replication.
How can we have scalability here? If we have three different SQL Server instances in three different containers, How does data get replicated across them? (meaning, user inserts a new product into a shared library, other user accessing a different node/container should be able to see that product).
Maybe we don't need containers at all and Azure provides another way to scale databases?
We really appreciate any help from you guys.
Regards, Cris.
I would advise against trying to run your databases on K8s. Kubernetes Containers should generally be stateless application, and were not designed for persistent data storage. Azure provides a Database as a Service, which will be able to scale appropriately with your needs(Azure Pricing for Cloud SQL
We once experimented with running our Postgres DB inside of a Kubernetes pod, but I was terrified to change anything. Not worth it, and not what the system was designed for.
If you are really really committed to this path, you can check out MySQL NDB ClusterMySQL for distributed environments. It should be adaptable to the Kubernetes paradigm.