Forgive but I’m at work and had to skim the info quickly.
When in doubt, simplify. So 1 rack per DC. Distributed systems get rapidly harder to reason about the more complicated you make them. There’s more than enough to learn about C* without jumping into the complexity too soon.
To deal with the unbalancing issue, pay attention to Jon Haddad’s advice on vnode count and how to fairly distribute tokens with a small vnode count. I’d rather point you to his information, as I haven’t dug into vnode counts and token distribution in detail; he’s got a lot more time in C* than I do. I come at this more as a traditional RDBMS and Java guy who has slowly gotten up to speed on C* over the last few years, and dealt with DynamoDB a lot so have lived with a lot of similarity in data modelling concerns. Detailed internals I only know in cases where I had reason to dig into C* source.
There are so many knobs to turn in C* that it can be very easy to overthink things. Simplify where you can. Remove GC pressure wherever you can. Negotiate with your consumers to have data models that make sense for C*. If you have those three criteria foremost in mind, you’ll likely be fine for quite some time. And in the times where something isn’t going well, simpler is easier to investigate.
Message from External Sender
Thank you very much for clearing these concepts for me.
https://community.datastax.com/comments/1133/view.html I posted this question on the datastax forum regarding our cluster that it is unbalanced and the reply was related that the number of racks should be a multiplier of the replication factor in order to be balanced or 1. I thought then if I have 3 availability zones I should have 3 racks for each datacenter and not 2 (us-east-1b, us-east-1a) as I have right now or in the easiest way, I should have a rack for each datacenter.
1. Datacenter: live
-- Address Load Tokens Owns Host ID Rack
UN 10.1.20.49 289.75 GiB 256 ? be5a0193-56e7-4d42-8cc8-5d2141ab4872 us-east-1a
UN 10.1.30.112 103.03 GiB 256 ? e5108a8e-cc2f-4914-a86e-fccf770e3f0f us-east-1b
UN 10.1.19.163 129.61 GiB 256 ? 3c2efdda-8dd4-4f08-b991-9aff062a5388 us-east-1a
UN 10.1.26.181 145.28 GiB 256 ? 0a8f07ba-a129-42b0-b73a-df649bd076ef us-east-1b
UN 10.1.17.213 149.04 GiB 256 ? 71563e86-b2ae-4d2c-91c5-49aa08386f67 us-east-1a
DN 10.1.19.198 52.41 GiB 256 ? 613b43c0-0688-4b86-994c-dc772b6fb8d2 us-east-1b
UN 10.1.31.60 195.17 GiB 256 ? 3647fcca-688a-4851-ab15-df36819910f4 us-east-1b
UN 10.1.25.206 100.67 GiB 256 ? f43532ad-7d2e-4480-a9ce-2529b47f823d us-east-1b
So each rack label right now matches the availability zone and we have 3 Datacenters and 2 Availability Zone with 2 racks per DC but the above is clearly unbalanced
If I have a keyspace with a replication factor = 3 and I want to minimize the number of nodes to scale up and down the cluster and keep it balanced should I consider an approach like OPTION A)
2. Node DC RACK AZ 1 read ONE us-east-1a 2 read ONE us-east-1a
3. 3 read ONE us-east-1a
4. 4 write ONE us-east-1b 5 write ONE us-east-1b
5. 6 write ONE us-east-1b
6. OPTION B)
7. Node DC RACK AZ 1 read ONE us-east-1a 2 read ONE us-east-1a
8. 3 read ONE us-east-1a
9. 4 write TWO us-east-1b 5 write TWO us-east-1b
10.6 write TWO us-east-1b
11.7 read ONE us-east-1c 8 write TWO us-east-1c
12.9 read ONE us-east-1c Option B looks to be unbalanced and I would exclude it OPTION C)
13.Node DC RACK AZ 1 read ONE us-east-1a 2 read ONE us-east-1b
14.3 read ONE us-east-1c
15.4 write TWO us-east-1a 5 write TWO us-east-1b
16.6 write TWO us-east-1c
so I am thinking of A if I have the restriction of 2 AZ but I guess that option C would be the best. If I have to add another DC for reads because we want to assign a new DC for each new microservice it would look like:
OPTION EXTRA DC For Reads
1. Node DC RACK AZ 1 read ONE us-east-1a 2 read ONE us-east-1b
2. 3 read ONE us-east-1c
3. 4 write TWO us-east-1a 5 write TWO us-east-1b
4. 6 write TWO us-east-1c 7 extra-read THREE us-east-1a
5. 8 extra-read THREE us-east-1b
1. 9 extra-read THREE us-east-1c
The DC for write will replicate the data in the other datacenters. My scope is to keep the read machines dedicated to serve reads and write machines to serve writes. Cassandra will handle the replication for me. Is there any other option that is I missing or wrong assumption? I am thinking that I will write a blog post about all my learnings so far, thank you very much for the replies Best, Sergio
Il giorno mer 23 ott 2019 alle ore 10:57 Reid Pinchback <email@example.com> ha scritto:
No, that’s not correct. The point of racks is to help you distribute the replicas, not further-replicate the replicas. Data centers are what do the latter. So for example, if you wanted to be able to ensure that you always had quorum if an AZ went down, then you could have two DCs where one was in each AZ, and use one rack in each DC. In your situation I think I’d be more tempted to consider that. Then if an AZ went away, you could fail over your traffic to the remaining DC and still be perfectly fine.
For background on replicas vs racks, I believe the information you want is under the heading ‘NetworkTopologyStrategy’ at:
That should help you better understand how replicas distribute.
As mentioned before, while you can choose to do the reads in one DC, except for concerns about contention related to network traffic and connection handling, you can’t isolate reads from writes. You can _mostly_ insulate the write DC from the activity within the read DC, and even that isn’t an absolute because of repairs. However, your mileage may vary, so do what makes sense for your usage pattern.
Message from External Sender
Thanks for your reply. I really appreciate your explanation.
We are in AWS and we are using right now 2 Availability Zone and not 3. We found our cluster really unbalanced because the keyspace has a replication factor = 3 and the number of racks is 2 with 2 datacenters.
We want the writes spread across all the nodes but we wanted the reads isolated from the writes to keep the load on that node low and to be able to identify problems in the consumers (reads) or producers (writes) applications.
It looks like that each rack contains an entire copy of the data so this would lead to replicate for each rack and then for each node the information. If I am correct if we have a keyspace with 100GB and Replication Factor = 3 and RACKS = 3 => 100 * 3 * 3 = 900GB
If I had only one rack across 2 or even 3 availability zone I would save in space and I would have 300GB only. Please correct me if I am wrong.
Il giorno mer 23 ott 2019 alle ore 09:21 Reid Pinchback <firstname.lastname@example.org> ha scritto:
Datacenters and racks are different concepts. While they don't have to be associated with their historical meanings, the historical meanings probably provide a helpful model for understanding what you want from them.
When companies own their own physical servers and have them housed somewhere, the questions arise on where you want to locate any particular server. It's a balancing act on things like network speed of related servers being able to talk to each other, versus fault-tolerance of having many servers not all exposed to the same risks.
"Same rack" in that physical world tended to mean something like "all behind the same network switch and all sharing the same power bus". The morning after an electrical glitch fries a power bus and thus everything in that rack, you realize you wished you didn't have so many of the same type of server together. Well, they were servers. Now they are door stops. Badness and sadness.
That's kind of the mindset to have in mind with racks in Cassandra. It's an artifact for you to separate servers into pools so that the disparate pools have hopefully somewhat independent infrastructure risks. However, all those servers are still doing the same kind of work, are the same version, etc.
Datacenters are amalgams of those racks, and how similar or different they are from each other depends on what you want to do with them. What is true is that if you have N datacenters, each one of them must have enough disk storage to house all the data. The actual physical footprint of that data in each DC depends on the replication factors in play.
Note that you sorta can't have "one datacenter for writes" because the writes will replicate across the data centers. You could definitely choose to have only one that takes read queries, but best to think of writing as being universal. One scenario you can have is where the DC not taking live traffic read queries is the one you use for maintenance or performance testing or version upgrades.
One rack makes your life easier if you don't have a reason for multiple racks. It depends on the environment you deploy into and your fault tolerance goals. If you were in AWS and wanting to spread risk across availability zones, then you would likely have as many racks as AZs you choose to be in, because that's really the point of using multiple AZs.
On 10/23/19, 4:06 AM, "Sergio Bilello" <email@example.com> wrote:
Message from External Sender
I was reading about https://urldefense.proofpoint.com/v2/url?u=https-3A__cassandra.apache.org_doc_latest_architecture_dynamo.html-23networktopologystrategy&d=DwIBaQ&c=9Hv6XPedRSA-5PSECC38X80c1h60_XWA4z1k_R1pROA&r=OIgB3poYhzp3_A7WgD7iBCnsJaYmspOa2okNpf6uqWc&m=xmgs1uQTlmvCtIoGJKHbByZZ6aDFzS5hDQzChDPCfFA&s=9ZDWAK6pstkCQfdbwLNsB-ZGsK64RwXSXfAkOWtmkq4&e=
I would like to understand a concept related to the node load balancing.
I know that Jon recommends Vnodes = 4 but right now I found a cluster with vnodes = 256 replication factor = 3 and 2 racks. This is unbalanced because the racks are not a multiplier of the replication factor.
However, my plan is to move all the nodes in a single rack to eventually scale up and down the node in the cluster once at the time.
If I had 3 racks and I would like to keep the things balanced I should scale up 3 nodes at the time one for each rack.
If I would have 3 racks, should I have also 3 different datacenters so one datacenter for each rack?
Can I have 2 datacenters and 3 racks? If this is possible one datacenter would have more nodes than the others? Could it be a problem?
I am thinking to split my cluster in one datacenter for reads and one for writes and keep all the nodes in the same rack so I can scale up once node at the time.
Please correct me if I am wrong
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