I was wondering if I could run multiple standalone instances of neo4j on a single machine. I understand that I could configure multiple instances as HA cluster (here), but that is not my intention, I only need two totally different and independent instances of neo4j on my machine (Which is a Mac OSX if that makes a difference). This is only for my dev testing and I tried having two separate directories with different data/ and setting two different ports for them, but only one runs properly.
I would appreciate any help coming my way. Thank you.
The most easy way is to unpack the neo4j installation into two different locations. In one of the locations you need to change the port settings in conf/neo4j-server.properties and, if neo4j-shell is enabled conf/neo4j.properties as well.
Also consider to set dbms.pagecache.memory to a reasonable value. By default each instance will eat up up to 75 % of RAM minus heap space - which is too much when running multiple instance on one box.
Based on #mepla's findings: the https port in neo4j-server.properties needs to be changed as well.
You can also run individual docker images which point to different data directories,
see: http://neo4j.com/developer/docker
You can use Ineo:
https://github.com/cohesivestack/ineo
A simple but useful Neo4j instance manager
this github repository (Multiple-Instances-Neo4j-Manager) provides neo4j manager to deal with multiple standalone instances on a single machine.
Related
I am new to launching ES for the production environment. I want to create production-ready ElasticSearch clusters having master nodes and data and backup nodes and etc. I read tutorials on the internet regarding this matter including the official document but I cannot get my head around the topic in the official document it's running multiple clusters under one machine what if that machine goes down for some reason? where are the master nodes playing in that scenario? where are the backup nodes? to protect against data loss?
I want to know if there are any straightforward solutions that I can use for deploying the ES on multiple machines serving the same purpose (for one project with specific data types) that can be easily distributed and fault-tolerant?
Running multiple containers on a single host makes sense if you have a lot of resources on a given host that you want to partition up and use efficiently. then you can have multiple hosts with multiple Elasticsearch containers forming a cluster
If you do that, look at using allocation awareness to make sure shards are adequately balanced so that the loss of a single host will mean you maintain your data
I recently finished a project where I created an App consisting of several docker containers. The purpose of the app was to collect some data and safe it to an databank and also allow user interactions over an simple web gui. The app was hosted on four different Raspberry Pi's and it was possible to collect data from all physicial maschines through an api. Further you could do some simple machine learning tasks like calculating anomalies in the sensor data of the Pi's.
Now I'm trying to take the next step and using kubernetes for some load balancing and remote updates. My main goal is to remote update all raspberries from my master node. Which, in theory, would be a very handy feature. Also I want to share the ressources of the Pi's within the cluster for calculations.
I read a lot about Kubernets, Minikube, K3's, Kind and all the different approaches to set up an Kubernetes cluster, but feel like I am missing "a last puzzle piece".
So from what I understood I need an approach which allows me to set up an local (because all machines are laying on my desk/ no cloud needed) multi node cluster. My master node would be (idealy) my laptop, running Ubuntu in a virtual machine. My rasberry's would be my slave/worker nodes. If I would want to update my cluster I can use the kubernetes remote update functionality.
So my question out of this would be: Does it makes sense to use several rasberries as nodes in a kubernetes cluster and to manage them from one master node (laptop) and do you have any suggestions about the way to achieve this setup.
I usally dont like those question not containing any specific code or questions by myself, but feel like an simple hint could accelerate my project noteable. If it's the wrong place please feel free to delete this question.
Best regards
You didn't mention which rpi models you are using, but I assume you are not using rpi zeros.
My main goal is to remote update all raspberries from my master node.
Assuming that by that you mean updating your applications running in kubernetes that is installed on rpi then keep reading. Otherwise ignore all I wrote, and what you probably need is ansible or other simmilar provisioning/configuration-management/application-deployment tool.
Now answering to your question:
Does it makes sense to use several rasberries as nodes in a kubernetes cluster
yes, this is why people created k3s, so such setup is possible using less resources.
and to manage them from one master node (laptop)
assuming you will be using it for learning purpouses then why not. It is possible, but just be aware that when master node goes down (e.g. when you turn off your laptop), all cluster goes down (or at least api-server communication so you wont be able to change cluster's state). Also make sure you are using bridge networking interface for your VM so it is visible in your local network as a standalone instance.
and do you have any suggestions about the way to achieve this setup.
installing k3s on all nodes would be the easiest in your case. There are plenty of resources on the internet explaining how to achieve it.
One last thing I would like to explain is the thing with updates.
Speaking of kubernetes updates you need to know that kubernetes doesn't update itself automatically. You need to explicitly update it. New k8s version is beeing released every 3 months that sometimes "breaks" things and backward compatibility is not possible (so always read changelog before updating stuff because rollbacks may not be possible unless you backed up an etcd cluster earlier).
Speaking of updating applications - To run your app all you do is send yaml files describing your application to k8s and it handles the rest. So if you want to update your app just update the tag on container image to newer version and k8s will handle the updates. Read here more about update strategies in k8s.
Our company is developing an application which runs in 3 seperate kubernetes-clusters in different versions (production, staging, testing).
We need to monitor our clusters and the applications over time (metrics and logs). We also need to run a mailserver.
So basically we have 3 different environments with different versions of our application. And we have some shared services that just need to run and we do not care much about them:
Monitoring: We need to install influxdb and grafana. In every cluster there's a pre-installed heapster, that needs to send data to our tools.
Logging: We didn't decide yet.
Mailserver (https://github.com/tomav/docker-mailserver)
independant services: Sentry, Gitlab
I am not sure where to run these external shared services. I found these options:
1. Inside each cluster
We need to install the tools 3 times for the 3 environments.
Con:
We don't have one central point to analyze our systems.
If the whole cluster is down, we cannot look at anything.
Installing the same tools multiple times does not feel right.
2. Create an additional cluster
We install the shared tools in an additional kubernetes-cluster.
Con:
Cost for an additional cluster
It's probably harder to send ongoing data to external cluster (networking, security, firewall etc.).
3) Use an additional root-server
We run docker-containers on an oldschool-root-server.
Con:
Feels contradictory to use root-server instead of cutting-edge-k8s.
Single point of failure.
We need to control the docker-containers manually (or attach the machine to rancher).
I tried to google for the problem but I cannot find anything about the topic. Can anyone give me a hint or some links on this topic?
Or is it just no relevant problem that a cluster might go down?
To me, the second option sound less evil but I cannot estimate yet if it's hard to transfer data from one cluster to another.
The important questions are:
Is it a problem to have monitoring-data in a cluster because one cannot see the monitoring-data if the cluster is offline?
Is it common practice to have an additional cluster for shared services that should not have an impact on other parts of the application?
Is it (easily) possible to send metrics and logs from one kubernetes-cluster to another (we are running kubernetes in OpenTelekomCloud which is basically OpenStack)?
Thanks for your hints,
Marius
That is a very complex and philosophic topic, but I will give you my view on it and some facts to support it.
I think the best way is the second one - Create an additional cluster, and that's why:
You need a point which should be accessible from any of your environments. With a separate cluster, you can set the same firewall rules, routes, etc. in all your environments and it doesn't affect your current workload.
Yes, you need to pay a bit more. However, you need resources to run your shared applications, and overhead for a Kubernetes infrastructure is not high in comparison with applications.
With a separate cluster, you can setup a real HA solution, which you might not need for staging and development clusters, so you will not pay for that multiple times.
Technically, it is also OK. You can use Heapster to collect data from multiple clusters; almost any logging solution can also work with multiple clusters. All other applications can be just run on the separate cluster, and that's all you need to do with them.
Now, about your questions:
Is it a problem to have monitoring-data in a cluster because one cannot see the monitoring-data if the cluster is offline?
No, it is not a problem with a separate cluster.
Is it common practice to have an additional cluster for shared services that should not have an impact on other parts of the application?
I think, yes. At least I did it several times, and I know some other projects with similar architecture.
Is it (easily) possible to send metrics and logs from one kubernetes-cluster to another (we are running kubernetes in OpenTelekomCloud which is basically OpenStack)?
Yes, nothing complex there. Usually, it does not depend on the platform.
I'm totally new to docker and started yesterday to do some tutorials. I want to build a small test application consisting of several different services (replicated and so on) that interact with each other and encountered a problem regarding 'service-discovery'. I started with the get-started tutorials on docker.com and at the moment i'm not really sure what's best practice in the world of docker to let the different containers in a network get to know each other...
As this is a rather vague 'problem description', i try to make this more precise. I want to use a few independent services (e.g. with stuff like postgre, mongodb, redis and rabbitmq...) together with a set of worker nodes to which work is assigned by a dedicated master node. Since it seems to be quite convenient, I wanted to use a docker-composer.yml file to define all my services and deploy them as a stack.
Moreover, I created a custom network and since it seems not to be possible to attach a stacked service to a bridge network, I created an attachable overlay network.
To finally get to the point: even though the services are deployed correctly, their actual container-name is random and without using somekind of service registry I'm not able to resolve their addresses.
A simple solution would be to use single containers with fixed container names - however this does not seem to be a best practice solution (even though it is actually just a docker-based DNS that is based on container names rather than domain names). Another problem are the randomly generated container names that contain underscores, and hence these names are not valid addresses that can be resolved...
best regards
Have you looked at something like Kubernetes? To quote from the home page:
It groups containers that make up an application into logical units for easy management and discovery.
How can I run multiple Neo4j databases simultaneously on a single server? I would like to have separate data directories and ports if this is possible.
Has anyone done this successfully and if so explain how to do this
I have tried something like:
bin\neo4j start
To set up Neo4j with multiple instances on a single server, you essentially configure a cluster, with each node having its own set of configuration properties. You then run the cluster in single-instance (non-HA) mode (otherwise you'll just end up with a replication cluster, which doesn't meet your requirement).
Full instructions are in the Neo4j docs online and in your local doc\manual folder.
Note: The folks at Neo Technology call this out for dev/test purposes. I can't offer guidance on running this in production, other than the fact you'd have multiple instances competing for the same resources (cpu, disk, memory, network).
It's possible to setup Rexster to serve up multiple neo4j database directories. This is great if you're using the Gremlin query language. Other access forms may not be available (beyond my knowledge). Check out this question/answer: possible to connect to multiple neo4j databases via bulbs/Rexster?