I am trying to work out if Aurora Postgres cluster endpoint does automatic query read write splitting between the reader and writer/s? Or to I need to use something like pgpool2 for the splitting?
I have attempted pushing read/writes at it, but it looks like only the reader is being hit?
but it looks like only the reader is being hit?
Only the writer should be hit via the cluster endpoint.
The cluster endpoint connects you to the primary instance for the DB cluster. You can perform both read and write operations using the cluster endpoint.
https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Aurora.Connecting.html
Aurora does not do read/write splitting. It provides you with a cluster endpoint, which automatically points to the cluster's current writer, and a read-only endpoint, which connects you to any one of the readers.
If the cluster only has one instance (Aurora doesn't technically require you to have any readers, but recovery in the event of a failure would require much more time, since the one instance would have to be rebuilt by the system if it failed -- but no data would be lost) then both endpoints take you to the writer.
Related
I am trying to write a synthetic monitoring for my on-prem postgresql service, using airflow. The monitoring should return if a cluster is available for creating tables, writing and reading data, and deleting tables.
The clusters on my service are using SSL certificates for authentication, which means a client is required to provide a suitable client certificate in order to connect to the cluster.
Currently, I have implemented my monitoring by creating a global user which will have a certificate with permissions to all the cluster. The user will have permissions to create, write and read only on one schema, dedicated to this monitoring. Using airflow, I will connect with this user each of my postgresql clusters and try to create a table, write to it, read, and then delete it. If one of the actions fails - the DAG will write a log describing the reason for failure.
My main problem with this solution it not being able to limit such a powerful user with accessibility to all of my clusters. In case an intruder will get the user's client certificate, he would be able to explode the DB storage by writing huge amount of data or overload queries and fail the cluster.
I am looking for some ideas for limiting this user so it will be able to act only for it's purpose- the simple actions required for this monitoring, and could not be exploit by an attacker. Alternatively, I would appreciate any suggestions for different implementation for this monitoring.
I searched for build in postgresql configurations that will allow me to limit the dedicated monitoring schema / limiting the amount of queries performed by the user.
I am having a replica lag issue with documentDB. Where I am trying to write some data from a collection and read the same at the same time. But because I am using a distributed system, I am not able to read the already written data from the replica sets.
Here's the cluster design.
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So, is it possible to read from the primary instance in nodejs or is it possible to read from a specific instance?
How big is the replication lag? It might be worth investigating the cause for the lag, maybe bigger instances are needed or queries have to be optimized.
If your application can't tolerate eventual consistency or read after write consistency is required, then use readPreference: primaryPreferred to instruct the driver to read from the Primary instance when available. However, in this case, the replicas will not be used to scale horizontally the read traffic.
Amazon DocumentDB has other endpoints too:
reader endpoint - points to replica instances, it's found in the configuration section of the cluster (console or aws cli describe-db-clusters command)
instance endpoint - each instance has its own endpoint, it's found in the instances section (console or aws cli describe-db-instances command)
The best practice is to connect as replica set, using the readPreference parameter to adjust the preference. Instance endpoints can be useful when, for example, there's a need for large analytics queries and a bigger instance is deployed, temporarily, to run them.
The idea here is, I have mongo cluster deployed in managed cloud service atlas. I have enabled Continuous Backup.
Now what I want to do is :
1) I want to use existing backup.
2) Using this existing backup I want to create similar cluster
(having same data form backup)
3) Automate this process so that every day my new cluster gets upto date from original cluster.
Note: The idea here for cloning cluster is, The original cluster is production data. I want to create a db which has similar data on which I can plug and play using any analytic tools and perform diffrent operations without affecting production data and load.
So far what I have found is to use mongorestore and mongodump.But here mongodump is putting load on production db even though my backup is enabled. I want to use same backup to clone this to another db cluster.
Deployed on Atlas, your server must have replica set.
Here are 2 solutions :
You need only reading data : connect your tools to a secondary server (ideally dedicated with priority 0 for becoming primary)
You need to read/write data : on the same server than above, play your mongodump command with --oplog option. By this way, you're dumping your data from a read-only server, preventing slowing performances of your main servers.
In this last case, what you need will find its solution in backup strategies, take a look at the doc to know more.
There's an offering for this purpose in ATLAS called analytic node.Link.
Analytic node is read replica of your database. Plus it will not interfere with your production traffic which makes it safer.
Also, you can connect BI connectors to this node and create your analytic platform.
We used redash.
I've read all the docs on the Google Cloud SQL site, and I now understand how to created and manage Read Replicas, but I have not seen any information about how to use them,
Does Google automatically load-balance connections between all instances?
Do I have to manually connect to a specific Read Replica to avoid hitting the Master? If so, do I have to manage reconnecting on replica failure myself?
Does Google automatically load-balance connections between all instances?
No, it doesn't. Each instance is independent. You can connect to replicas and use them to read while using the master to read/write, but you need to design that logic into your application
Do I have to manually connect to a specific Read Replica to avoid hitting the Master? If so, do I have to manage reconnecting on replica failure myself?
Yes, you have to connect to a specific read replica. Right now you can't even save and reuse the instance IP like you can do with compute engine instances (sigh, I hope they fix this soon....).
There is now a failover replica option that you can use so you don't need to connect to the read replica yourself, but it only activates on failure, it is not a load balancer.
Read replica can be used by setting up ProxySQL. You can configure ProxySQL to distribute the database queries. Here is a community tutorial providing more details on architecture and configuration example.
How do I use Read Replicas?
Use them for disaster recovery or to migrate your database to
another region by promoting a read replica to become a primary
database.
https://cloud.google.com/sql/docs/postgres/replication/cross-region-replicas
Use them for separating read workloads from production workloads. This blog post covers using Read Replicas for analytics workloads:
Use Cloud SQL Read Replicas to separate your analytics and production workloads
Cloud SQL does not provide load balancing between replicas1
ref:https://cloud.google.com/sql/docs/sqlserver/replication
I'm trying to implement an architecture that's similar to the coreos's production architecture (shown below)
Should I run the database as a central service or one or more of the workers?
I figured the database needs some kind of replication, which makes me think that putting it in the worker cluster makes more sense, but I'm just not sure.
This should be run as a worker. The central services are the basic things that come with CoreOS (mainly etcd). The workers host your applications, the database being one of them. You do have a persistence issue because your database will have state to remember between restarts. So, there is a bigger issue of how do you make that persistence? One was to do it is use a host file and give the database an affinity to that host and mount the host file. Another thing you might consider is running more than one database (if your db technology supports that) and replicate that database so you have two (or more) copies in different workers. (non-affinity). If your database creates transaction logs that can be applied to a backup, you can manage those transaction logs in a worker.
Another thing to consider is not using a container for your database. The database is a weird animal, its care and feeding is not like the rest of the applications. So it is reasonable (in my opinion) to have your database managed and maintained outside the scope of your cluster (but still reachable by the cluster).