I wrote about it as well and found some similar performance characteristics. Tweaking some memory settings seems to help but does not eliminate the problem entirely. Thanks for the feedback! “By believing passionately in something that still does not exist, we create it. Since I’m using the pre-aggregated measurements (the provided min, max, avg, etc) I will be more explicit about which tests were which. First I started with trying to get the table of numbers out: Then to sum up those numbers column wise (so across reclen, AKA block size), we can feed this data into datamash (-W broadens whitespace handling, and we get min/max/mean/median for the 3rd column of values): And just to confirm that I’m not using datamash wrong, the minimum write column value is clearly 203992, the max value is clearly 222408, and the mean is ~214404. Ceph underneath) can’t operate over just a folder on disk – of course there are the obvious control issues (linux filesystems can’t really limit folder size easily) to contend with but the only other alternative seems to be creating some loopback virtual drives, but if I don’t want to have to do that provisioning step.

For each new customer, we created a different bucket so they can have individual data and not have to worry about data loss.

CPU utilization/system load often gets very high when handling a moderate amount of data with Longhorn. OpenEBS allows you to treat your persistent workload containers, such as DBs on containers, just like other containers. 2 cores, 4gb RAM, w/ an openebs volume), it ran through various block sizes for the file size I gave it (). But so far so good. What are some alternatives to Amazon S3, OpenEBS, and Rook? Decisions about Amazon S3, OpenEBS, and Rook, - No public GitHub repository available -, Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web. Someone commented on the post suggesting that I try it, so I'm reading the docs right now. In this post I’m going to run through some quick tests that will hopefully make it somewhat clearer the cost of the robustness that OpenEBS provides. Amazing read. I would prefer the ability to back up snapshots instead of file-level backups, but I can live with that if I can have performant and reliable storage. There’s a generous Community Edition which allows up to 10 TB of storage and 3 nodes. Since in my case I really only care about disk IO performance, I think dd, iozone and sysbench are more than enough. A streaming platform has three key capabilities: Kafka is generally used for two broad classes of applications: To learn more about Apache Kafka, please refer to the official Introduction to Apache Kafka and related resources in Awesome Kafka Resources. The sysbench results will be used mostly for latency measurements – at this point I’m getting lazy (this post has taken a while to write), so I’m going to just take the min,avg,max metrics as they’re provided in each test that is run and compare those, I think we’ve got a good enough idea of thoughput with the dd and iozone tests. I am wondering how mature Linstor is for Kubernetes but the actual storage layer lives outside Kubernetes and it seems this software has been around for a while already, so it should be battle tested. One thing I didn’t really properly explore were the failure modes of OpenEBS – There are differences between the Jiva and CStor file system options, as well as how live systems shift when nodes go down. Are you using Robin in a replicated setup? A commit log is basically a data structure that only appends. Hey Docker run EKS, with Auto Fleet Spotting Support, please! We’re Josh and Alisha, the creators of Terradrift. One thing I really want to do is get a test with OpenEBS vs Rook vs vanilla Longhorn (as I mentioned, OpenEBS JIVA is actually longhorn), but from your testing it looks like Ceph via Rook is the best of the open source solutions (which would make sense, it's been around the longest and Ceph is a rock solid project). This seems like a pretty drastic change, but I guess if I find that all 3 methods have the exact same performance profile it will be pretty easy to discount the results, as I’m expecting OpenEBS to impose some performance penalty at least. The 0.8.1 release has some pretty big fixes and improvements which are pretty amazing, but we’ll leave testing that release until the release actually happens. Overall, I like OpenEBS a lot and I really wish the performance was better.

The simple answer to this question is not “why not”, you should!, in case you’d like to enjoy the resiliency nature and scalability capabilities of a cluster and container orchestrator such as Kubernetes. The reason? I have updated the post with a few comments on Linstore, which I have added to the list.

RKE1 uses ROOK-CEPH and RKE2 has OpenEBS beneath. I also had to enable some additional config for the kube-controller in my Rancher cluster which I haven't needed before. This is one of the things I like about Robin; it wasn’t clear to me does PortWorx have a similar capability? It was pretty interesting to dip in to the world of hard-drive testing and to get a chance to compare OpenEBS to the easiest (but mosy unsafe/fragile) way of provisioning volumes on k8s. Portworx.

To start load testing, you shall switch to the loadtesting namespace, run the start_test.sh script and provide the pepper_box.jmx for testing. suggested I try Linstor, so I have tried it and added a section about it. Storage! Before that I hadn’t seen any mentions of it, perhaps because I was focussing my search on open source solutions, while Robin is a paid product. Create the rook storage class on RKE1 cluster: Define the rook storage class as default: Verify if the rook-ceph-operator is properly deployt: Verify if the rook-ceph cluster is properly deployt: You can use ceph commands to verify the status of your rook-ceph cluster. Linstor has snapshots, but not off site backups, so also in this case I would have to use Velero with Restic to back up volumes. The 1GB setting was to get an idea of the performance with a benchmark that wouldn’t take too long to run. by Press J to jump to the feed. OpenEBS platform contains three core building blocks: An orchestration platform, Maya, that works with kubernetes and manages the thousands of volumes with ease. OpenEBS builds on Kubernetes to enable Stateful applications to easily access Dynamic Local PVs or Replicated PVs. OpenEBS is very easy to install and use, but I have to admit that I am very disappointed with performance after doing more tests with real data, under load. As far as performance it’s just a world apart IMO.

It doesn’t have off site backups though, so you need to use something with Velero/Restic which does file-level backups instead of backing up point-in-time snapshots. In trying to figure out a reasonable usage I needed to consult the github documentation as well as calling --help on the command line a few times. Unfortunately, performance is very poor compared to that of the other options, so because of that I had … Kubernetes is about Resiliency and Scale, Kafka too! I used the severalnines/sysbench docker image, and spent some time in a container trying to figure out how to properly use sysbench. In our scenario for kafka1 cluster, you need to deploy the zoo-entrance with: And call http://127.0.0.1:8080, you should get something similar to this: Strimzi provides a CRD for mirror maker which you can use to setup mirroring between the source / consumer (that is the bootstrap server where the clients connect and the messages come in) and the target / producer (in our case kafka2 cluster, where the messages are replicated). Installation isn’t particularly “difficult”, but it’s not as straightforward as with the others. Have I found a solution that will make me change my mind about giving up on Kubernetes? Clean up the rook-ceph cluster and the operator, if something doesn’t work or you don’t need it anymore: And on the storage / worker nodes clean up the mounted flex volumes (RKE Ubuntu specific): For testing purposes we create 3 namespaces, strimzi, kafka1 and kafka2 on the first cluster and deploy the Strimzi Cluster Operator in the strimzi namespace as following: Or simply use kn, if you have kubectx installed.

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