I have two computers each one being a Cassandra node and they communicate well with each other.
From what I understand Cassandra will replicate the data to each other but will always query certain portions from one of them.
I would like though to have the data being copied to each other so they have the same data but they only use data from the local node. Is that possible?
The background reason is that the application in each node keeps generating and downloading a lot of data and at the same time both are doing some CPU super intensive tasks. What happens is that one node saves the data and suddenly can't find it anymore because it has been saved in the other node which is busy enough to reply with that data.
Technically, you just need to change replication factor to a number of nodes, and set your application to always read from a local node using whitelist load balancing mode. But it may not help you because if your nodes are very busy, replication of data from another node may also not happen, so the query will fail as well. Or replication will add an additional overhead making the situation much worse.
You need to rethink your approach - typically, you need to separate application nodes from database nodes, so application processes doesn't affect database processes.
How the peer-to-peer Cassandra architecture really works ? I mean :
When the request hits the Cluster, it must hit some machine based on an IP, right ?
So which machine it will hit first ? : one of the nodes, or something in the Cluster who is responsible to balance and redirect the request to the right node ?
Could you describe what it is ? And how this differ from the Master/Folowers architecture ?
For the purposes of my answer, I will use the Java driver as an example since it is the most popular.
When you connect to a cluster using one of the driver, you need to configure it with details of your cluster including:
Contact points - the entry point to your cluster which is a comma-separated list of IPs/hostnames for some of the nodes in your cluster.
Login credentials - username and password if authentication is enabled on your cluster.
SSL/TLS certificate and credentials - if encryption is enabled on your cluster.
When your application starts, a control connection is established with the first available node in the list of contact points. The driver uses this control connection for admin tasks such as:
get topology information about the cluster including node IPs, rack placement, network/DC information, etc
get schema information such as keyspaces and tables
subscribe to metadata changes including topology and schema updates
When you configure the driver with a load-balancing policy (LBP), the policy will determine which node the driver will pick as the coordinator for each and every single query. By default, the Java driver uses a load balancing policy which picks nodes in the local datacenter. If you don't specify which DC is local to the app, the driver will set the local DC to the DC of the first contact point.
Each time a driver executes a query, it generates a query plan or a list of nodes to contact. This list of nodes has the following characteristics:
A query plan is different for each query to balance the load across nodes in the cluster.
A query plan only lists available nodes and does not include nodes which are down or temporarily unavailable.
Nodes in the local DC are listed first and if the load-balancing policy allows it, remote nodes are included last.
The driver tries to contact each node in the query plan in the order they are listed. If the first node is available then the driver uses it as the coordinator. If the first node does not respond (for whatever reason), the driver tries the next node in the query plan and so on.
Finally, all nodes are equal in Cassandra. There is no active-passive, no leader-follower, no primary-secondary and this makes Cassandra a truly high availability (HA) cluster with no single point-of-failure. Any node can do the work of any other node and the load is distributed equally to all nodes by design.
If you're new to Cassandra, I recommend having a look at datastax.com/dev which has lots of free hands-on interactive learning resources. In particular, the Cassandra Fundamentals learning series lets you learn the basic concepts quickly.
For what it's worth, you can also use the Stargate.io data platform. It allows you to connect to a Cassandra cluster using APIs you're already familiar with. It is fully open-source so it's free to use. Here are links to the Stargate tutorials on datastax.com/dev: REST API, Document API, GraphQL API, and more recently gRPC API. Cheers!
Working with Cassandra, we have to remember two very important things: data is partitioned (split into chunks) and data is replicated (each chunk is stored on a few different servers). Partitioning is needed for scalability purposes while Replication serves High Availability. Given that Cassandra is designed to handle petabytes of data under huge pressure (dozens of millions of queries per second), and there is no single server able to handle such the load, each cluster server is responsible only for a range of data, not for the whole dataset. A node storing data you need for a particular query is called a "replica node". Notice that the different queries there will have different replica nodes.
Together, it brings a few implications:
We have to reach multiple servers during a single query to assure the data is consistent (read) / write data to all responsible servers (write).
How do we know which node is right for that particular query? What happens if a query hits a "wrong" node? How do we configure the application so it sends queries to the replica nodes?
Funny enough, as a developer you have to do one and only one thing: understand partitions and partition keys, and then Cassandra will take care of all the potential issues. Simple as that. When you design a table, you have to declare partition keys and the data placement will be based on that - automagically. Next thing, you have to always specify partition keys while doing your queries. That's it, your job is done, get yourself some coffee!
Meanwhile, Cassandra starts her job. Cassandra nodes are smart, they know data placement, they know what servers are responsible for the data you are writing, and they know the partitions - in Cassandra language it's called token-aware. That does not matter which server will receive the query, as literally every server is able to answer it. Any node that got the request (it's called query coordinator because it coordinates the query operations) will find replica nodes based on the placement of the partitions. With that, the query coordinator will execute the query, making proper calls to the replicas - the coordinator knows which nodes to ask because you did your part of the job and specified partition key value in the query, which is used for the routing.
In short, you can ask any of your cluster nodes to write/read your data, Cassandra is decentralized and you'll get it done. But how do we make it better and get directly to the replica to avoid bothering nodes that don't store our data?
So which machine it will hit first ?
The travel of a request starts much earlier than we could think of - when your application starts, a Cassandra driver connects to a cluster and reads information about data placement: which partition is stored on which nodes, It means that driver knows which node has to be contacted for different queries. You got it right, a driver is token-aware too!
Token-aware drivers understand data placement and will route a query to a proper replica node. Answering the question: under normal circumstances, your query will first hit one of the replica nodes, this node will get answers or write data to the other replica nodes and that's it, we are good. In some rare situations, your query may hit a "wrong" non-replica server, but it doesn't really matter as it also will do the job, with just a minor delay - for example, if your Replication Factor = 3 (you have three replicas), and your query got to a "wrong" node, it will have to ask all three replicas while hitting the "right one" still require 2 network operations. It's not a big deal though as all the operations are done in parallel.
how this differ from the Master/Folowers architecture
With leader/follower architecture, you can read from any server but you can write only to a leader server, which gives two issues:
Your app needs to know who is the leader (or you need to have a special proxy)
Single Point of Failure (SPoF) - if the leader is down, you can't write to the DB at all
With Cassandra's peer-to-peer architecture you can write to any of the cluster nodes, even if there are thousands of them. Of course, there is no SPoF.
P.S. Cassandra is an extremely powerful technology, but great power comes with great responsibility, it's quite complex too. If you plan to work with it, you better invest some time into learning to use it properly. I do suggest taking a Developer Path on the academy.datastax.com (it's free!) or at least watch DataStax "Intro to Cassandra" workshop
It is based on the driver that you used to connect to the Cassandra™ cluster. Again, all nodes in the datacenter are one and same. It would connect to any of the nodes the localdatacenter that you have provided in driver configs based on the contact points configuration (i.e. datastax-java-driver.basic.contact-points in Java Driver).
For example, the Java driver (& most drivers logic will be the same) uses system.peers.rpc-address to connect to newly discovered nodes. For special network topologies, an address translation component can be plugged in.
advanced.address-translator in the configuration.
none by default. Also available: EC2-specific (for deployments that span multiple regions), or write your own.
Each node in the Cassandra cluster is uniquely identified by an IP address that the driver will use to establish connections.
for contact points, these are provided as part of configuring the CqlSession object;
for other nodes, addresses will be discovered dynamically, either by inspecting system.peers on already connected nodes, or via push notifications received on the control connection when new nodes are discovered by gossip.
More info can be found here.
It seems you are asking how specifically Cassandra selects which Node gets hit with data and which ones doesn't.
There are two sides to this: the client and the servers
On the client
When a CQL Connection is established the client (if implemented in the client library and configured) usually also retrieves the Topology from the Cluster. A topology is the information about the token ownership inside the ring as well as information about quorums etc..
So the client itself can already make a decision on the next request what Node to contact for a certain amount of information due to Consistent Hashing of the primary keys in Cassandra. The client is aware who would be the right choice of Node to contact.
But still the client can choose not to use this information and just send the information to any node of the ring - the nodes will then forward the requests to the appropriate token owners -> See the next section.
In the Cluster
The same applies to the nodes themselves. If a client sends a request to a node it will simply look up the owner nodes in it's topology table and forward the request to exactly the nodes that do own this token.
It will always forward it to all of them so the data is consistent across the cluster. Depending on the replication factor it will return a success response to the client if the required replication is acknowledged by the cluster (eg. LOCAL_QUORUM with RF=3 will return a success response when 2 nodes acknowledge the receipt while the 3rd node is still pending).
If a node is detected as down or can't be reached the Command that would have been sent to the node is saved in the local hints table - a buffer that keeps all the operations that haven't been successfully sent to other nodes.
You can read more on Hints in the Cassandra Docs
Compared to a Leader/Follower architecture the Cassandra model is actually simpler and depends mostly on all involved nodes seeing all the mutation commands happening to the data they "own" via the tokens.
I'm going build apache cassandra 3.11.X cluster with 44 nodes. Each application server will have one cluster node so that application do r/w locally.
I have couple of questions running in my mind kindly answer if possible.
1.How many server Ip's should mention in seednode parameter?
2.How HA works when all the mentioned seed node goes down?
3.What is the dis-advantage to mention all the serverIP's in seednode parameter?
4.How cassandra scales with respect to data other than(Primary key and Tunable consistency). As per my assumption replication factor can improve HA chances but not performances.
then how performance will increase by adding more nodes?
5.Is there any sharding mechanism in Cassandra.
Answers are in order:
It's recommended to point to at least to 2 nodes per DC
Seed/contact node is used only for initial bootstrap - when your program reaches any of listed nodes, it "learns" the topology of whole cluster, and then driver listens for nodes status change, and adjust a list of available hosts. So even if seed node(s) goes down after connection is already established, driver will able to reach other nodes
it's harder to maintain usually - you need to keep a configuration parameters for your driver & list of nodes in sync.
When you have RF > 1, Cassandra may read or write data from/to any replica. Consistency level regulates how many nodes should return answer for read or write operation. When you add the new node, the data is redistributed to new node, and if you have correctly selected partition key, then new node start to receive requests in parallel to old nodes
Partition key is responsible for selection of replica(s) that will hold data associated with it - you can see it as a shard. But you need to be careful with selection of partition key - it's easy to create too big partitions, or partitions that will be "hot" (receiving most of operations in cluster - for example, if you're using the date as partition key, and always writing reading data for today).
P.S. I would recommend to read DataStax Architecture guide - it contains a lot of information about Cassandra as well...
Is it possible to put a Cassandra cluster with single node DC with 2 remote DC which is also having a single node assuming the replication factor is required to be 3 in this case? The remote cluster is in the same geographical area but not same building for HA. Or is there any hard rules that for high availability and consistency for a need for a local quorum node to achieve that?
Our setup may be smaller compared to big data and usually used to store time series data with approximately 2000/3000 (on different key) sampling per second.
Is there other implications other than read/write may be slow due to the comms delay?
disclaimer: I am new to cassandra.
Turns out I want to deploy a similar setup: 3 nodes on aws, each in its own AZ (But all in the same region). from what I read, this setup is just a single DC, with 3 nodes.
You need to use Ec2Snitch to reduce the latency between your clients and the nodes.
Using RF=3 provides you with the HA that you need, since every node has all the data
Inter-AZ communication should be fairly fast. refer to this: http://highscalability.com/blog/2016/8/1/how-to-setup-a-highly-available-multi-az-cassandra-cluster-o.html
becuase you'll be running in a single DC, local-quorum == quorum. so as long as you'll be writing to QUROUM (which requires 2/3 nodes (AZs) to be up), you'll be strongly consistent and HA.
Let's imagine I have a Cassandra cluster with 3 nodes, each having 100GB of available hard disk space. Replication Factor for this cluster is set to 3 and R/W CLs are set to 2, meaning I can tolerate one of my nodes going down without sacrificing consistency or availability.
Now imagine my servers have started to fill up (80GB as an example) and I would like to add another 3 servers of the same specification to my cluster, maintaining the same CLs and RFs.
My question is: after I've added the new nodes to my cluster and run the node repair tool, is it fair to assume that each of my nodes should roughly (more or less a few GBs) contain 40GB of data each?
If not, how can I add new nodes without having the fear of running out of hard disk space?
A little background of why I'm asking this question: I am developing an app that connects to a server that runs Cassandra for its data storage. As this is only developed by me, and I have limited resources in terms of money to buy servers, I've decided that I would like to buy small, cheap "servers" instead of the more expensive rack options but I'm really worried about the nodes running out of space if the disk allocation is not (at least partially)
homogenous.
Many thanks for you help,
My question is: after I've added the new nodes to my cluster and run
the node repair tool, is it fair to assume that each of my nodes
should roughly (more or less a few GBs) 40GB of data each
After also running nodetool cleanup you should see roughly 40GB of data on each node. Cleanup removes data which the node is no longer responsible for. If you don't run this command the old data will remain on the machine.