I am trying to evaluate couchbase`s performance on multiple nodes. I have a Client that generates data for me based on some schema(for 1 node currently, local). But I want to know how I can horizontally scale Couchbase and how it works. Like If I have multiple machines or AWS instances or Windows Azure how can I configure Couchbase to shard the data and than I can evaluate its performance for multiple nodes. Any suggestions and details as to how I can do this?
I am not (yet) familiar with Azure but you can find a very good white paper about Couchbase on AWS:
Running Couchbase on AWS
Let's talk about the cluster itself, you just need to
install Couchbase on multiple nodes
create a "cluster" on one of then
then you simply have to add other nodes to the cluster and rebalance.
I have created an Ansible script that use exactly the steps to create a cluster from command line, see
Create a Couchbase cluster with Ansible
Once you have done that your application will leverage all the nodes automatically, and you can add/remove nodes as you need.
Finally if you want to learn more about Couchbase architecture, how sharding, failover, data consistency, indexing work, I am inviting your to look at this white paper:
Couchbase Server: An Architectural Overview
Related
Between processing realtime data using Spark cluster on EC2 machines and using Elastic map reduce, some of the differences are:
In Elastic Map Reduce, one would not have to manage the infrastructure and cluster as compared to Spark cluster on EC2 machines where one has to create the cluster and manage it.
In case of Spark cluster on EC2, one has more control over the cluster as compared to Elastic Map Reduce which is a PAAS component.
I went through the below related link:
Hadoop on EC2 vs Elastic Map Reduce
I understand that going with Elastic Map reduce would give the advantage of not having to manage the infrastructure and cluster. What I want to know is that when should one prefer the other option, that is to create Spark cluster on EC2 machines instead of using Elastic Map Reduce? Thanks.
You and the answer you shared have have summed pretty much the advantages and disadvantages for both. But i would like to mention few things
Someone mentioned in comment on the answer you share (and there is infact impression in people) that EMR adds some cost on top of ec2 nodes (which is underlying master/compute nodes of spark) and provides just the cluster, which isnt the case.
But what elastic map reduce is focused on is elastic and scalability part , meaning to provide scalability for your jobs, where scalability is not just number of node in cluster but different parameters like
Dynamically resizing the cluster with running jobs
Reduces and optimizes spin time , provides efficient resubmitting steps and option like automatic termination on step completion
Configuration, management and updation time. Just as an small you have things like release version that automatically handles spark/hadoop/other-application versions providing you way to easy update the version which you have to do manually with ec2.
the ecosystem availability. EMR ecosystem is growing,it doesnt reflect when you start but for example when your requirements grow, for example when you start to integrate other systems stream processing with flink for example) then it is more easier to just select at time of launching flink, pig , hive and moany more etc if you need to use other things in future.
There are already implementing libraries with AWS SDK like boto3 in python that help you to submit steps, poll for completion etc, which are very helpful when you need to scale. Also, you have integration of emr with orchestration frameworks like airflow where can can sense the state, resubmit, one command spin the cluster within the pipeline.
Expanding on previous point, EMR notebook for example provide you the quick and interactive way to submit spark jobs from Jupiter notebook and see the result, progress of jobs immediately which can boost your productivity.
This point is most important from my experience, Sometimes, scaling up the jobs with more nodes save you more money then long running jobs with low number of nodes. Because the adding node cost sometime cost you low than the normalized hours you will be spending with ec2 or small emr cluster. Just to share my experience, we had a job that used to run for 3 days, we satrted to run it with bigger EMR cluster that reduced it to 6-8 hours and it still was in the same cost and was infact a bit less.
Building a Thingsboard cluster
I need help setting up a Thingsboard cluster, the documentation online is very limited.
The cluster will contain 2 Zookeeper nodes and 4 Thingsboard nodes with Cassandra DB.
Should Zookeeper be installed separately?
A step-by-step guide would be much appreciated!
I cannot provide you detailed step-by-step instructions to setup a ThingsBoard cluster. I can point you into the right direction by sharing the different documents you need to do so.
Bottom line, the following tasks must be completed:
Install and configure a ZooKeeper ensemble.
Check the ZooKeeper documentation for further installation details. Keep in mind that you need at least three different ZK-nodes in a clustered environment and that you always need an odd number of ZK nodes (3,5,7,...). It is a very very very bad idea to build a cluster consisting out of two ZK-nodes, check split brain condition that might appear under these circumstances! Basically you setup the number of individual nodes you wish to use and change the configuration file to enable the different nodes as an ensemble. This is documented quite well in the ZK-docs.
Install and configure a Cassandra cluster.
Again you will setup the number of individual nodes you need for your Cassandra cluster and modify the individual configuration files to convert them into a Cassandra cluster. Check Cassandra documentation for details. Be sure to check proper configuration using the nodetool status command as described at the end of the document. All your nodes should be up and running.
Install and configure a ThingsBoard cluster.
Use the instructions provided with ThingsBoard single node setup.
Install Java
Skip External database installation
ThingsBoard service installation
Configure ThingsBoard to use the external database - Cassandra
Go to Cluster setup and apply the configuration steps depicted (ZK, Cassandra and RPC). Keep in mind to point to ALL members of your ZK, Cassandra cluster. You can also use IP-addresses instead of host names.
Return to single node setup and run the installation script at ONE NODE only!
Start ThingsBoard service
If everything went well, you should be able to access your ThingsBoard nodes directly using the URL http://[NODE_IP]:8080. You can verify proper cluster operation by creating a tenant on one node and check its presence on another node.
I don't know if using an even number of ThingsBoard nodes is a good idea. The documentation does not mention anything about this.
One final remark, you could/should consider putting a proxy in front of your ThingsBoard cluster to provide load balancing to your web clients and improve user experience. This way you shouldn't share the individual host addresses with your users and you will prevent node overloading due to the fact that everybody is using the same web-address to access your dashboard(s). You could also proxy your MQTT broker to provide load balancing as well.
Good luck in setting up your cluster!
Zookeeper needs at least 3 nodes to run in a cluster mode. Each node voting and the valid replica count to gain the QUORUM is 3.
I've been using Couchbase for my database solution and so far it looks very good.
I'm confused however with connecting to a Cluster. A Cluster is just a group of nodes so when you use the API to connect to a Cluster what do you use as the IP? Do you just use one of the nodes in the Cluster? Does it matter which one?
I'm personally using the Node.js API.
Technically all you need is just one node in the list. As soon as it connects to that one, it will get the cluster map of the entire cluster and know all of the rest of the nodes. No it does not matter which node.
That being said, best practice is to have at least 3 nodes of the cluster listed in the connection string or better yet if the SDK you are using supports it, use a DNS SRV record with at least 3 nodes in there. With three nodes in the list if for some reason (e.g. server failure or maintenance) one of the nodes is unavailable, you can still bootstrap an application server to get that cluster map with one of the other nodes in the list.
I asked this question a few months ago on couchbase forums and the author of the node.js module answered that you should use "some" of them
like :
cluster.openBucket("couchbase://server1,server2,server3", function(err) {});
if you have server4 and 5 are added , they will be automatically added to the cluster as soon as they are available in the cluster.
Check here for details : https://forums.couchbase.com/t/couchnode-connection-to-cluster/6281
I am new to Cassandra and I want to install it. So far I've read a small article on it.
But there one thing that I do not understand and it is the meaning of 'node'.
Can anyone tell me what a 'node' is, what it is for, and how many nodes we can have in one cluster ?
A node is the storage layer within a server.
Newer versions of Cassandra use virtual nodes, or vnodes. There are 256 vnodes per server by default.
A vnode is essentially the storage layer.
machine: a physical server, EC2 instance, etc.
server: an installation of Cassandra. Each machine has one installation of Cassandra. The Cassandra server runs core processes such as the snitch, the partitioner, etc.
vnode: The storage layer in a Cassandra server. There are 256 vnodes per server by default.
Helpful tip:
Where you will get confused is that Cassandra terminology (in older blog posts, YouTube videos, and so on) had been used inconsistently. In older versions of Cassandra, each machine had one Cassandra server installed, and each server contained one node. Due to the 1-to-1-to-1 relationship between machine-server-node in old versions of Cassandra people previously used the terms machine, server and node interchangeably.
Cassandra is a distributed database management system designed to handle large amounts of data across many commodity servers. Like all other distributed database systems, it provides high availability with no single point of failure.
You may got some ideas from the description of above paragraph. Generally, when we talk Cassandra, we mean a Cassandra cluster, not a single PC. A node in a cluster is just a fully functional machine that is connected with other nodes in the cluster through high internal network. All nodes work together to make sure that even if one of them failed due to unexpected error, they as a whole cluster can provide service.
All nodes in a Cassandra cluster are same. There is no concept of Master node or slave nodes. There are multiple reason to design like this, and you can Google it for more details if you want.
Theoretically, you can have as many nodes as you want in a Cassandra cluster. For example, Apple used 75,000 nodes served Cassandra summit in 2014.
Of course you can try Cassandra with one machine. It still work while just one node in this cluster.
What is meant by a node in cassandra?
Cassandra Node is a place where data is stored.
Data center is a collection of related nodes.
A cluster is a component which contains one or more data centers.
In other words collection of multiple Cassandra nodes which communicates with each other to perform set of operation.
In Cassandra, each node is independent and at the same time interconnected to other nodes.
All the nodes in a cluster play the same role.
Every node in a cluster can accept read and write requests, regardless of where the data is actually located in the cluster.
In the case of failure of one node, Read/Write requests can be served from other nodes in the network.
If you're looking to understand Cassandra terminology, then the following post is a good reference:
http://exponential.io/blog/2015/01/08/cassandra-terminology/
I'm using the Cassandra CQL/JDBC driver I got from google code but it doesn't seem to let me provide a cluster name - is there a way?
I'm using cluster names to ensure I don't run commands against a live system, it has a different cluster name to my dev systems.
Edit: Just to clarify, I have two totally separate Cassandra clusters, one live and one for test. They have different cluster names to ensure that I don't accidentally run test code meant for the test cluster on the live cluster. Therefore any client I need to use must let me set a cluster name. Hector does this.
There is no inbuilt protection for checking cluster names for Cassandra clients. It is built to ensure nodes from different clusters don't try and join together but not to ensure clients connect to the right cluster. It would be possible to add this checking to a client though (since the cluster name is exposed to the client) but I'm not aware of any clients doing this.
I'd strongly recommend firewalling off your different environments to avoid this kind of mistake. If that isn't possible, you should choose different ports to avoid confusion. Change this with the 'rpc_port' setting in cassandra.yaml.
You'd have to mirror the data on two different clusters. You cant access the same cluster with different names.
To rename your cluster (from the default 'Test Cluster') you edit the cassandra configuration file found in location/of/cassandra/conf/cassandra.yaml. Its the top line, if you need more details look at the datastax configuration documentation and explanation.