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I am looking for a case study of companies that use Cloud message queueing.
What are the benefits of such a service over rabbitmq (if any)
I know there are several mature services like SQS of amazon, OnlineMQ and Linxter.
The advantage of Cloud Messaging service is the managing the MQ systems is not your head-ache. It is being pushed to the cloud service provider. Amazon SQS integrated very well with other Amazon AWS products like EC2,S3 so it is the choice if you are already on Amazon AWS infrastructure. But SQS performance way is low compared to RabbitMQ in terms of latency.
So if you want a Message Queue service which has customized needs, RabbitMQ (or any other MQ system) can be what you are looking for.
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I configured Redis and Redis-Sentinel in order to enable automatic failover.
Redis has one master and 2 slaves.
There are 3 sentinel nodes.
I configured redis with authentication, so in redis.conf file of the master I added this:
requirepass mypassword
and in the redis.conf of the slaves, this:
masterauth mypassword
When I stop the master, one of the slaves becomes the master, as expected.
However, when I connect to the new master using redis-cli I notice that no password is required.
I expect this is because the redis.conf file of the new master remains with the value of 'masterauth mypassword', instead of 'requirepass mypassword'.
Is this the expected behavior? Shouldn't Redis-Sentinel configure this? or should I set something else in the conf files for the new master to require authentication?
You should also configure slave node with password, i.e. requirepass mypassword.
Slave node sync data from master node, but it does not sync password. So you need to configure it manually.
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We are running a self managed EC2 instances in the EKS Cluster. While upgrading control plane is managed by AWS, the worker nodes are self managed. For running security updates and patches, we use the latest optimized AMI and role out the new instances. Some of these are manual effort, what is the best automated approach that you are following to update/patch those self managed EC2 worker nodes?
Steps that we follow:
Look for the latest optimized AMI version released by AWS
Update the Launch configuration with new AMI
Scale up nodes with New AMI
Seamlessly transfer pods from old to new nodes
scale down and delete the old nodes.
The issue here after we update with new optimized AMI, we still find out some security updates that are listed during scanning those instances.
Some of these are manual effort, what is the best automated approach
I suggest to have a look at Managed Node Groups since that is an automated approach for the lifecycle management that you are doing.
Nodes run using the latest Amazon EKS optimized AMIs in your AWS account while node updates and terminations gracefully drain nodes to ensure that your applications stay available.
All managed nodes are provisioned as part of an Amazon EC2 Auto Scaling group that is managed for you by Amazon EKS. All resources including the instances and Auto Scaling groups run within your AWS account. Each node group uses the Amazon EKS optimized Amazon Linux 2 AMI and can run across multiple Availability Zones that you define.
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Hi I looking for In memory data grid or similar one.
My use case.
Data griding in memory. scale out available.
backup node available.
persistent backup available.
(optional) free or opensource solution
I did googling and I found candidates below
- Apache Ignite
- Redis cluster
- Hazelcast(community)
I prefer Ignite to Hazelcast because, Ignite support use direct buffer.
But I don't know Redis cluster partitioning whether it is stable or not. and, I don't know if apache ignite performance better than redis cluster or not.
Apache Ignite comparable to redis cluster? or impropert comparison?
Thanks for your answer
But I don't know Redis cluster partitioning whether it is stable or not
Redis cluster feature is stable since 3.x version and used in production by many companies.
Apache Ignite comparable to redis cluster? or impropert comparison?
Comparison Apache Ignite vs Redis only is wrong, because these projects have different grade. Redis is positioned as a storage and not as a Data Grid like Apache Ignite. So for proper comparison Apache Ignite should be compared vs Redisson - Redis Java Client
with features of In-Memory Data Grid. It offers the same features as Apache Ignite.
Redisson supports fully managed Redis services like AWS Elasticache, Azure Redis Cache. You don't need to manage/deploy/maintain Redis cluster by yourself of hire devops to do this. Apache Ignite doesn't offer such feature and you should manage/deploy/maintain it by yourself.
I used Redis in production for one of the largest US mobile network operator (IoT department). It is stable from 2.8 (Master/Slave) but cluster stable is from 3.2. Used 2.8 for 3 years and 3.2 cluster for 2 years on production with about 50k TPS load with no restart for years and no issues (except BGSAVE and memory issues but that was due to RAM limitations).
If we compare Redis and Apache Ignite:
Performance. Redis is faster, single thread and 100% in memory.
Data structure. Redis is key-value storage (even that is not a limitation, you can imagine and map almost everything in key-value models). Ingrid is a data grid as it was mentioned above.
If you are looking for a memory data grid and performance is on second priority then Ingrid will be more appropriate for you.
Redis only provides a key-value storage, while Ignite is much more functional. Here is a good feature comparison provided by GridGain: https://www.gridgain.com/resources/product-comparisons/redis-comparison
Which one to use, depends on your requirements and expectations.
I want to implement High availability for Rabbitmq server.i have read the Rabbitmq provide document,on document is use DRBD,i don't wants to use DRBD for share storage,from my side i have did clustering with two node and prepare mirror queue.
rest needs to be implemented high availability help me.
Thanks
The HA documentation can be found on the RabbitMQ site at
http://www.rabbitmq.com/ha.html
with the main set up being described in
http://www.rabbitmq.com/clustering.html
This doesn't involve DRBD and is simply a guide on how to mirror queues across multiple servers.
I have implemented a HA rabbit cluster based on the instructions in the above so I can attest to their clarity.
If you have any specific questions regarding HA setup that isn't clear from the above then I'm happy to answer them.
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According to the EC2 documentation, its more cost-effective to use the internal address to communicate between EC2 instances. What is the optimal way to communicate between EC2 and S3? Is there a notion of an "internal address" for S3 and is it any faster/more cost effective than fetching from public address?
http://aws.amazon.com/ec2/:
There is no Data Transfer charge
between Amazon EC2 and other Amazon
Web Services within the same region
(i.e. between Amazon EC2 US West and
Amazon S3 in US West)
http://aws.amazon.com/s3/:
There is no Data Transfer charge for
data transferred between Amazon EC2
and Amazon S3 within the same Region
or for data transferred between the
Amazon EC2 Northern Virginia Region
and the Amazon S3 US Standard Region.
so there is no need for a separate internal address.