I would like to discuss what are the best practices/approaches engineers do while upgrading elasticsearch clusters. I believe this post may serve as a good example of strategies and steps to perform, guaranteeing no data loss, minimum downtime, scalability and availability of the elasticsearch services.
To start the initiative, we can break the upgrade into two subsections:
1) Performing upgrade on master nodes:
Since master nodes do not contain any data and are responsible for controlling the cluster I believe we can safely do terraform apply to add all the upgraded master node VMs and then remove the old ones.
2) Performing upgrade on data nodes:
As many people already know, there is certain limitation on the ability to update data nodes. We cannot afford to completely deallocate the VM and replace it with another. A good practice in my opinion is to:
a) Stop the index allocation to the old VM
b) Then performing terraform apply to create the new upgraded version of the data node VM(and manually modifying the terraform state in order the old VM not to be destroyed)
c) Allowing traffic(index creation) to the new VM and using the elasticsearch APIs to transfer the data from the old to the new VM
d) Manually changing the terraform state allowing it to delete the old VM.
These are just idealistic steps, I would like to see your opinion and strategies to perform safe elasticsearch upgrades via Terraform.
The reference manual has guidelines regarding removing master-eligible nodes that you must respect in versions 7 and later. It's much trickier to get this right in earlier versions because of the discovery.zen.minimum_master_nodes setting.
Your strategy for data nodes sounds slow and expensive given that you might be moving many terabytes of data around for each node. It's normally better to restart/upgrade larger data nodes "in place", detaching and reattaching the underlying storage if needed.
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Is there a way to extract data from a Scylla database for use in data analysis without directly querying from the production DB?
I want to do large queries against the data but don't want to take down production
The typical way folks accomplish this, is to build an “analytics DC” in the same physical DC.
So if (one of) your prod DCs is named “dc-west” you would create a new one named “dc-west-analytics” or something like that. Once the new DCs nodes are out there, change the keyspace to replicate to it. Run a repair on the new DC, and it should be ready for use.
On the app side or wherever the queries are running from, make sure it uses the LOCAL consistency levels and points to “dc-west-analytics” as its “local” DC.
In ScyllaDB Enterprise, a feature called Workload Prioritization allows you to assign CPU and I/O shares to your analytics and production workloads, isolating them from each other.
How do folks migrate data between Pulsar environments either for disaster recovery or blue-green deployment are they copying data to a new AWS region or K8S namespace?
One of the easiest approaches is to rely on geo-replication to replicate the data across different clusters.
This PIP was just created to address this exact scenario of blue-green deployments and to address issues with a cluster becoming non-functioning or corrupted in case an upgrade or update (perhaps of experimental setting changes) goes wrong: https://github.com/pkumar-singh/pulsar/wiki/PIP-95:-Live-migration-of-producer-consumer-or-reader-from-one-Pulsar-cluster-to-another
Until then, aside from using geo-replication, another potential way around this is having an active-active setup (with two live clusters) with a DNS endpoint that producers and consumers use that can be cut over from the old cluster to the new cluster just before taking the old cluster goes down. If it's not acceptable to have the blip in latency this approach would cause, you could replicate just the ingest topics from the old cluster to the new cluster, cut over your consumers, and then cut over your producers. Depending on how your flows are designed, you may need to replicate additional topics to make that happen. Keep in mind that on the cloud there can be different cost implications depending on how you do this. (Network traffic across regions is typically much more expensive than traffic within a single zone.)
If you want to have the clusters completely separate (meaning avoiding using Pulsar geo-replication), you can splice a Pulsar replicator function into each of your flows to manually produce your messages to the new cluster until the traffic is cut over. If you do that, just be mindful that the function must have its own subscription on each topic you deploy it to ensure that you're not losing messages.
Trying to deploy a project on t3 large server with auto scaling.
I have my elastic search service deployed on same system as node and react projects.(Not using AWS elastic search)
Will it be facing issues in future and i need to segregate elastic search service to some other server?
It's always nice to have a separate dedicated server for running the Elasticsearch server but as you are using AWS some of the things which you can do to minimize the issues:
Elasticsearch is a stateful application contrast to your node and react app unless you are storing the state there as well which is not a good idea and due to stateless nature of the applications, autoscaling is very useful as you can on-demand based on the CPU, memory or other metrics scale up or down the instances.
But in case of Elasticsearch or other stateful applications, it becomes tricky as when you scale up or down the instance, shards get relocated if they are not reachable within a threshold which can lead to unbalanced Elasticsearech cluster.
Now in order to minimize these issues:
Make sure you can storing Elasticsearch indices on the network-attached disk so that there is no data loss when autoscaling brings a new instance and new instance again should use earlier network attaches EBS(where your data is stored).
Make sure you don't create a new Elasticsearch process when you scale up or down the instances according to your autoscaling policy and the Elasticsearch process should be fixed and scale up/down with some manual intervention.
If you have to scale up the Elasticsearch cluster then make sure you disable shard allocation to avoid the issues mentioned earlier.
These are some known issues which you might face and there could be even more based on your configuration and while writing the answer itself I felt, it so easy to just have a dedicated instance for Elasticsearch to avoid these weird issues.
I would add to other answers following:
Elasticsearch performs best if it has enough RAM to keep indexes in entirety in RAM. If the Elasticsearch is competing with Node/Application for RAM it will affect it's performance.
From maintenance/performance perspective you should consider having at least 3-node cluster. Even if that means you have smaller machines. If AWS is upgrading infrastructure and you have 1 machine, when than 0.05% unavailability hits your search is down. If you need to do maintenance on the node or do upgrades having multiple machines will help with availability.
Depending on your use of Elasticsearch and how often you update/delete items in the indexes, and how fast your indexes will grow, adding more machines/nodes to the cluster will help with growth.
There are probably many more things to consider, but that totally depends on your application, budget, SLAs etc.
I have an Asp.Net core 2.0 Wen API that has a relatively simple logic (simple select on a SQL Azure DB, return about 1000-2000 records. No joins, aggregates, functions etc.). I have only 1 GET API. which is called from an angular SPA. Both are deployed in service fabric as as stateless services, hosted in Kestrel as self hosting exes.
considering the number of users and how often they refresh, I've determined there will be around 15000 requests per minute. in other words 250 req/sec.
I'm trying to understand the different settings when creating my Service Fabric cluster.
I want to know:
How many Node Types? (I've determined as Front-End, and Back-End)
How many nodes per node type?
What is the VM size I need to select?
I have ready the azure documentation on cluster capacity planning. while I understand the concepts, I don't have a frame of reference to determine the actual values i need to provide to the above questions.
In most places where you read about the planning of a cluster they will suggest that this subject is part science and part art, because there is no easy answer to this question. It's hard to answer it because it depends a lot on the complexity of your application, without knowing the internals on how it works we can only guess a solution.
Based on your questions the best guidance I can give you is, Measure first, Measure again, Measure... Plan later. Your application might be memory intensive, network intensive, CPU, Disk and son on, the only way to find the best configuration is when you understand it.
To understand your application before you make any decision on SF structure, you could simply deploy a simple cluster with multiple node types containing one node of each VM size and measure your application behavior on each of them, and then you would add more nodes and span multiple instances of your service on these nodes and see which configuration is a best fit for each service.
1.How many Node Types?
I like to map node types as 1:1 to roles on your application, but is not a law, it will depend how much resource each service will consume, if the service consume enough resource to make a single VM(node) busy (Memory, CPU, Disk, IO), this is a good candidate to have it's own node type, in other cases there are services that are light-weight that would be a waste of resources provisioning an entire VM(node) just for it, an example is scheduled jobs, backups, and so on, In this case you could provision a set of machines that could be shared for these services, one important thing you have to keep in mind when you share a node-type with multiple service is that they will compete for resources(memory, CPU, network, disk) and the performance measures you took for each service in isolation might not be the same anymore, so they would require more resources, the option is test them together.
Another point is the number of replicas, having a single instance of your service is not reliable, so you would have to create replicas of it(the right number I describe on next answer), in this case you end up with a service load split in to multiple nodes, making this node-type under utilized, is where you would consider joining services on same node-type.
2.How many nodes per node type?
As stated before, it will depend on your service resource consumption, but a very basic rule is a minimum of 3 per node type.
Why 3?
Because 3 is the lowest number where you could have a rolling update and guarantee a quorum of 51% of nodes\service\instances running.
1 Node: If you have a service running 1 instance in a node-type of 1 node, when you deploy a new version of your service, you would have to bring down this instance before the new comes up, so you would not have any instance to serve the load while upgrading.
2 Nodes: Similar to 1 node, but in this case you keep only 1 node running, in case of failure, you wouldn't have a failover to handle the load until the new instance come up, it will worse if you are running a stateful service, because you will have only one copy of your data during the upgrade and in case of failure you might loose data.
3 Nodes: During a update you still have 2 nodes available, when the one being updated get back, the next one is put down and you still have 2 nodes running, in case of failure of one node, the other node can support the load until a new node is deployed.
3 nodes does not mean the your cluster will be highly reliable, it means the chances of failure and data loss will be lower, you might be unlucky a loose 2 nodes at same time. As suggested in the docs, in production is better to always keep the number of nodes as 5 or more, and plan to have a quorum of 51% nodes\services available. In this case I would recommend 5, 7 or 9 nodes in cases you really need higher uptime 99.9999...%
3.What is the VM size I need to select?
As said before, only measurements will give this answer.
Observations:
These recommendations does not take into account the planning for primary node types, it is recommended to have at least 5 nodes on primary Node Types, it is where SF system services are placed, they are responsible to manage the
cluster, so they must be highly reliable, otherwise you risk losing control of your cluster. If you plan to share these nodes with your application services, keep in mind that your services might impact them, so you have to always monitor them to check for any impact it might cause.
I'm investigating two alternatives for using a Hadoop cluster, the first one is using HDInsight (with either Blob or HDFS storage) and the second alternative is deploying a powerful Windows Server on Microsoft Azure and run HDP (Hortonwork Data Processing) on it (using virtualization). The second alternative gives me more flexibility, however what I'm interested in is investigating the overhead of each alternative. Any ideas on that? Particularly how is the effect of Blob storage in the efficiency?
This is a pretty broad question, so an answer of "it depends," is appropriate here. When I talk with customers, this is how I see them making the tradeoff. It's a spectrum of control at one end, and convenience on the other. Do you have specific requirements on which Linux distro or Hadoop distro you deploy? Then you will want to go with IaaS and simply deploy there. That's great, you get a lot of control, but patching and operations are still your responsibility.
We refer to HDInsight as a managed service, and what we mean by that is that we take care of running it for you (eg, there is an SLA we provide on the cluster itself, and the apps running on it, not just "can I ping the vm"). We operate that cluster, patch the OS, patch Hadoop, etc. So, lots of convenience there, but, we don't let you choose which Linux distro or allow you to have an arbitrary set of Hadoop bits there.
From a perf perspective, HDInsight can deploy on any Azure node size, similar to IaaS VM's (this is a new feature launched this week). On the question of Blob efficiency, you should try both out and see what you think. The nice part about Blob store is you get more economic flexibility, you can deploy a small cluster on a massive volume of data if that cluster only needs to run on a small chunk of data (as compared to putting it all in HDFS, where you need all of the nodes running all of the time to fit all of your data).