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.
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.
My application crawls user's mailbox and saves it to an RDBMS database. I started using Redis as a cache (simple key-value store) for RDBMS database. But gradually I started storing crawler states and other data in Redis that needs to be persistent. Loosing this data means a few hours of downtime. I must ensure airtight consistency for this data. The data should not be lost in node failures or split brain scenarios. Strong consistency is a must. Sharding is done by my application. One Redis process runs on each of ten EC2 m4.large instances. On each of these instances. I am doing up to 20K IOPS to Redis. I am doing more writes than reads, though I have not determined the actual percentage of both. All my data is completely in memory, not backed by disk.
My only problem is each of these instances are SPOF. I cannot use Redis cluster as it does not guarantee consistency. I have evaluated a few more tools like Aerospike, none gives 'No data loss guarantee'.
Cassandra looks promising as I can tune the consistency level I want. I plan to use Cassandra with a replication factor 2, and a write must be written to both the replicas before considered committed. This gives 'No data loss guarantee.
By launching enough cassandra nodes (ssd backed) can I replace my Redis key-value store and still get similar read/write IOPS and
latency? Will opensource cassandra suffice my use case? If not, will the Datastax enterprise In-Memory version solve it?
EDIT 1:
A bit of clarification:
I think I need to use Write consistency level 'ALL' and Read consistency level 'One'. I understand that with this consistency level my cluster will not tolerate any failure. That is OK for me. A few minutes of downtime occasionally is not a problem, as long as my data is consistent. In my present setup, one Redis instance failure causes a few hours of downtime.
I must ensure airtight consistency for this data.
Cassandra deals with failure better when there are more nodes. Assuming your case allows for having more nodes, this is my suggestion.
So, if you have 5 nodes, use CL of QUORUM for both READ and WRITE. What it means is that you always write to at least 3 nodes and read from 3 nodes.(for 5 nodes , QUORUM is 3).
This ensures a very high level consistency
Also ensures limited downtime. Even if a node is down your writes and reads won't break.
If you use CL ALL, then even if one node is down or overloaded, you will have to take a full downtime.
I hope it helps!
After a constant flow of read/write operations to a Cassandra cluster, I would like to know, for each node of the cluster, for how many read/write requests the node has been used as coordinator.
I would like to know if the read/write requests are distributed uniformly/randomly in the entire cluster or some particular nodes get chosen as coordinators more often than the others.
I have not found this kind of statistic on nodetool. Is there a way to obtain this data ?
There are JMX MBeans that track coordinator metrics. They are org.apache.cassandra.metrics.ClientRequest.Read and ...ClientRequest.Write. There's a Count attribute on each of them that will give you the number of reads and writes that the node has coordinated.
I don't believe the count is currently exposed through any tools, although latencies are (through nodetool proxyhistograms).
Is there a possibility to write to a particular node using datastax driver?
For example, I have three nodes in datacenter 1 and three nodes in datacenter 2.
Existing
If i build up the cluster with any one of them as seed, all the nodes will get detected by the datastax java driver. So, in this case, if i insert a data using driver, it will automatically choose one of the nodes and proceed with it as the co-ordinator(preferably local data center)
Requirement
I want a way to contact any node in datacenter 2 and hand over the co-ordinator job to one of the nodes in datacenter 2.
Why i need this
I am trying to use the trigger functionality from datacenter 2 alone. Since triggers are taken care by co-ordinator , i want a co-ordinator to be selected from datacenter 2 so that data center 1 doesnt have to do this operation.
You may be able to use the DCAwareRoundRobinPolicy load balancing policy to achieve this by creating the policy such that DC2 is considered the "local" DC.
Cluster.Builder builder = Cluster.builder().withLoadBalancingPolicy(new DCAwareRoundRobinPolicy("dc2"));
In the above example, remote (non-DC2) nodes will be ignored.
There is also a new WhiteListPolicy in driver version 2.0.2 that wraps another load balancing policy and restricts the nodes to a specific list you provide.
Cluster.Builder builder = Cluster.builder().withLoadBalancingPolicy(new WhiteListPolicy(new DCAwareRoundRobinPolicy("dc2"), whiteList));
For multi-DC scenarios Cassandra provides EACH and LOCAL consistency levels where EACH will acknowledge successful operation in each DC and LOCAL only in local one.
If I understood correctly, what you are trying to achieve is DC failover in your application. This is not a good practice. Let's assume your application is hosted in DC1 alongside with Cassandra. If DC1 goes down, your entire application is unavailable. If DC2 goes down, your application still can write with LOCAL CL and C* will replicate changes when DC2 is back.
If you want to achieve HA, you need to deploy application in each DC, use CL=LOCAL_X and finally do failover on DNS level (e.g. using AWS Route53).
See data consistency docs and this blog post for more info about consistency levels for multiple DCs.