When a new Cassandra node joins the cluster for the first time it is assigned a random token. But it can match with an already existing token in the ring. What is the token conflict detection and resolution mechanism of Cassandra?
The token ranges really aren’t assigned randomly. When a new node joins the cluster, the token ranges of existing nodes are bisected and reassigned to the new node. Therefore, a token “collision” really isn’t a possibility.
I should note that if you are running with multiple nodes and a replication factor > 1 (which you should be), that token ranges are replicated to neighboring nodes as a secondary range. This helps to enforce high availability of your data in the event of a hardware failure. But that still wouldn’t result in a collision.
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We have a 1 DC cluster running Cassandra 3.11. The DC has 8 nodes total with 16 tokens per node and 3 seed nodes. We use Murmur3Partitioner.
In order to ensure better data distribution for the upcoming cluster in another DC, we want to use the tokens allocation approach where you manually specify initial_token for seed nodes and use allocate_tokens_for_keyspace for non seed nodes.
The problem is that our current datacenter cluster is not well balanced, since we built the cluster without a tokens allocation approach. So currently this means that the tokens are not well distributed. I can't figure out how to calculate initial_token for the new seed nodes in the new Datacenter. I probably cannot consider the token range of the new cluster as independent and calculate the initial token range as I would for a fresh cluster. At this point I am very unsure how to proceed. Any help will be appreciated, thanks.
Currently, I am trying to make a concept of migration and have come to the part where I do not know what to do and the documentation is not helpful.
There are scripts available to calculate the initial_token value, for example, you could use the one here to quickly calculate these values:
https://www.geroba.com/cassandra/cassandra-token-calculator/
You do have the ability to set allocate_tokens_for_keyspace and point it to a keyspace with a replication factor you plan to use for user-created keyspaces in the cluster, if you're adding a new DC, then you probably already have such a keyspace, and this should help you get better distribution. Remember to set this before bootstrapping nodes to the new DC.
Another option would be to avoid using vnodes entirely and go with single token architecture by setting num_tokens to 1. This gives you the ability to bootstrap nodes to the new DC, load/stream data and then monitor the distribution and make changes as needed using 'nodetool move':
https://cassandra.apache.org/doc/3.11/cassandra/tools/nodetool/move.html
This method would require you to monitor the distribution and make changes to the token assignments as needed, and you'd want to follow-up the move command with 'nodetool repair' and 'nodetool cleanup' on all nodes, but it gives you the ability to rectify uneven distribution quickly without bootstrapping new nodes. You would still want to use the same method for calculating the initial_token values with single-token architecture and set that before bootstrap.
I suspect either method could work well for you, but wanted to give you a second option.
Setting up the context:
Cassandra currently implements vnodes. 256 by default which is tweakable in the cassandra.yaml file
Vnodes as I understand are token-ranges/hash-ranges. Eg. (x...y], where y is the token number of the vnode. Each physical node in Cassandra is assigned random 256 tokens, and each of those tokens are the boundary value of a hash/token range. The tokens assigned are within the range of 2^-63 to 2^63-1 (the range of hash numbers which murmur3 has partitioner may generate). So far so good.
Question:
1. Is it that a token range(vnode) is a fixed range. Once set, this token range will be copied to other Cassandra nodes to satisfy the replication factor like a token range(vnode) being a fundamental chunk of data(tokens) which goes around together. Only in case of bootstrap of a new node in the cluster, this token range(vnode) might break apart to be assigned to other node.
Riding on the last proposition, (say the last proposition is true).
Then a vnode must only contain tokens which belong a given keyspace.
Because each keyspace(container of column family/tables) has a defined replication strategy and replication factor. And it is highly likely that replication factor of keyspaces in a Cassandra cluster will vary.
Consider an example. "system_schema" keyspace has a RF of 1 whereas I created a keyspace "test_ks" with RF 3. If a row of system_schema keyspace has a token number 2(say) and a row of my test_ks has token number 5(say).
these 2 tokens can't be placed in the same token range(vnode). If a vnode is consistent chunk of token ranges, say token 2 and 5 belong to vnode with token number 10. so vnode 10 has to be placed on 3 different physical nodes to satisfy the RF =3 for test_ks, but we are unnecessary placing token 2 on 3 different nodes whose RF is supposed to be 1.
Is this proposition correct that, a vnode is only dedicated to a given keyspace?
which boils down to out of 256 tokens on a physical node... 20(say) vnodes currently belong to "system" keyspace, 80 vnodes(say) belong to test_ks.
Again riding on the above proposition, this means that each node should have the info of keyspace-wise vnodes currently available in the cluster.
That way when a new write comes in for a Keyspace the co-ordinator node would locate all vnodes in the cluster for that keyspace and assign the new row a token number which falls within the token range of those keyspaces. That being the case can I know how many vnodes currently belong to a keyspace in the entire cluster/ or on a given node.
Please do correct me if I'm wrong.
I have been following the below blogs and videos to get an understanding of this concept:
https://www.scribd.com/document/253239514/Virtual-Nodes-Strategies-for-Apache-Cassandra
https://www.youtube.com/watch?v=GddZ3pXiDys&t=11s
Thanks in advance
There is no fixed token-range, the tokens are just generated randomly. This is one of the reasons that vnodes were implemented - the idea being that if there are more tokens it is more likely that the resulting token-ranges will be more evenly distributed across nodes.
Token generation was recently improved in 3.0, allowing Cassandra to place new tokens a little more intelligently (see CASSANDRA-7032). You can also manually configure tokens (see initial_token), although it can become tricky to keep things balanced when it comes time to expand the cluster unless you plan on doubling the number of nodes.
The total number of tokens in a cluster is the number of nodes in the cluster multiplied by the number of vnodes per node.
In regards to placement of replicas, the first copy of a partition is placed in the node that owns that partition's token. The additional n copies are placed sequentially on the next n nodes in the ring that are in the same data centre. There is no relationship between tokens and keyspaces.
When a new write comes into a coordinator node, the coordinator node determines which node owns the partition by hashing the partition key. Note that for better performance this can actually be done by the driver instead if you use TokenAwarePolicy. The coordinator sends the write to the node that owns the partition, and if the data needs to be replicated the coordinator node also writes the replicas to the next two nodes sequentially in the token-space.
For example, suppose that we have 3 nodes which each have one token: node1: 10, node2: 20 & node3: 30. If we write a record whose partition key hashes to 22, to a keyspace with RF3, then the first copy goes to node2, the second goes to node3 and the third goes to node1. Note that each replica is equally valid - there is nothing special about the "first" replica other than that it happens to be stored on the "first" replica node.
Vnodes do not change this process, they just split up each node's token ranges by allowing each node to have more than one token. For example, if our cluster now has 2 vnodes for each node, it might instead look like this: node1: 10, 25, node2: 20, 3 & node3: 30, 21. Now our write that hashed to 22 goes to node3 (because it owns the range from 21-24), and the copies go to node1 and node2.
This details how to replicate data to a new cluster:
https://docs.datastax.com/en/cassandra/2.1/cassandra/operations/ops_snapshot_restore_new_cluster.html
Can't a similar scheme be used to rapidly scale out a cluster with existing data? Say take a snapshot of all the nodes, copy them all to new nodes, set the tokens in the yaml, set the peers to point to the old instances, and then join them up?
Won't they be treated like nodes that once were part of the cluster and were rejoined?
That won't work, because snapshots are specific to the node which they are taken on. Once you add (or remove) a node, the token ranges on all nodes are recalculated, and you immediately invalidate any existing snapshots. Restoring the snapshots to another node would appear to work, but it would only serve the data which happened to match its token ranges.
Plus, it would try to serve data which matches its token ranges whether or not the snapshot you restored from had that data or not. Not a good scenario.
I've read quite a few articles and a lot of question/answers on SO about Cassandra but I still can't figure out how Cassandra decides which node(s) to go to when it's reading the data.
First, some assumptions about an imaginary cluster:
Replication Strategy = simple
Using Random Partitioner
Cluster of 10 nodes
Replication Factor of 5
Here's my understanding of how writes work based on various Datastax articles and other blog posts I've read:
Client sends the data to a random node
The "random" node is decided based on the MD5 hash of the primary key.
Data is written to the commit_log and memtable and then propagated 4 times (with RF = 5).
The 4 next nodes in the ring are then selected and data is persisted in them.
So far, so good.
Now the question is, when the client sends a read request (say with CL = 3) to the cluster, how does Cassandra know which nodes (5 out of 10 as the worst case scenario) it needs to contact to get this data? Surely it's not going to all 10 nodes as that would be inefficient.
Am I correct in assuming that Cassandra will again, do an MD5 hash of the primary key (of the request) and choose the node according to that and then walks the ring?
Also, how does the network topology case work? if I have multiple data centers, how does Cassandra know which nodes in each DC/Rack contain the data? From what I understand, only the first node is obvious (since the hash of the primary key has resulted in that node explicitly).
Sorry if the question is not very clear and please add a comment if you need more details about my question.
Many thanks,
Client sends the data to a random node
It might seem that way, but there is actually a non-random way that your driver picks a node to talk to. This node is called a "coordinator node" and is typically chosen based-on having the least (closest) "network distance." Client requests can really be sent to any node, and at first they will be sent to the nodes which your driver knows about. But once it connects and understands the topology of your cluster, it may change to a "closer" coordinator.
The nodes in your cluster exchange topology information with each other using the Gossip Protocol. The gossiper runs every second, and ensures that all nodes are kept current with data from whichever Snitch you have configured. The snitch keeps track of which data centers and racks each node belongs to.
In this way, the coordinator node also has data about which nodes are responsible for each token range. You can see this information by running a nodetool ring from the command line. Although if you are using vnodes, that will be trickier to ascertain, as data on all 256 (default) virtual nodes will quickly flash by on the screen.
So let's say that I have a table that I'm using to keep track of ship crew members by their first name, and let's assume that I want to look-up Malcolm Reynolds. Running this query:
SELECT token(firstname),firstname, id, lastname
FROM usersbyfirstname WHERE firstname='Mal';
...returns this row:
token(firstname) | firstname | id | lastname
----------------------+-----------+----+-----------
4016264465811926804 | Mal | 2 | Reynolds
By running a nodetool ring I can see which node is responsible for this token:
192.168.1.22 rack1 Up Normal 348.31 KB 3976595151390728557
192.168.1.22 rack1 Up Normal 348.31 KB 4142666302960897745
Or even easier, I can use nodetool getendpoints to see this data:
$ nodetool getendpoints stackoverflow usersbyfirstname Mal
Picked up JAVA_TOOL_OPTIONS: -javaagent:/usr/share/java/jayatanaag.jar
192.168.1.22
For more information, check out some of the items linked above, or try running nodetool gossipinfo.
Cassandra uses consistent hashing to map each partition key to a token value. Each node owns ranges of token values as its primary range, so that every possible hash value will map to one node. Extra replicas are then kept in a systematic way (such as the next node in the ring) and stored in the nodes as their secondary range.
Every node in the cluster knows the topology of the entire cluster, such as which nodes are in which data center, where they are in the ring, and which token ranges each nodes owns. The nodes get and maintain this information using the gossip protocol.
When a read request comes in, the node contacted becomes the coordinator for the read. It will calculate which nodes have replicas for the requested partition, and then pick the required number of nodes to meet the consistency level. It will then send requests to those nodes and wait for their responses and merge the results based on the column timestamps before sending the result back to the client.
Cassandra will locate any data based on a partition key that is mapped to a token value by the partitioner. Tokens are part of a finite token ring value range where each part of the ring is owned by a node in the cluster. The node owning the range of a certain token is said to be the primary for that token. Replicas will be selected by the data replication strategy. Basically this works by going clockwise in the token ring, starting from the primary, and stopping depending on the number of required replicas.
What's important to realize is that each node in the cluster is able to identify the nodes responsible for a certain key based on the logic described above. Whenever a value is written to the cluster, the node accepting the request (the coordinator node) will know right away the nodes that need to execute the write.
In case of multiple data-centers, all keys will be mapped across all DCs to the exact same token determined by the partitioner. Cassandra will try to write to each DC and each DC's replicas.
I'm new cassandra user. I know that there is initial token configuration and how to generate it.
The question is if I have an existen cluster with x nodes and I want to add additional node (one or more) should I reconfigure all the nodes to the new tokens (according to new generated values)?
Or is there more efficient way to manage this?
If you're looking for what the best practices are for handling such tasks, take a look at this section of the Cassandra 1.0 docs dedicated to token strategy.
Shortened version of your options, from the documentation:
Add capacity by doubling the cluster size -- [..] nodes can keep their existing token assignments, and new nodes are assigned tokens that bisect (or trisect) the existing token ranges.
Recalculate new tokens for all nodes and move nodes -- [..] you will have to recalculate tokens for the entire cluster. Existing nodes will have to have their new tokens assigned using nodetool move.
Add one node at a time and leave initial_token empty -- [..] splits the token range of the heaviest loaded node and places the new node into the ring at that position. [..] not result in a perfectly balanced ring, but it will alleviate hot spots.
link
If you were seeking a management solution Priam (from Netflix) might be worth looking at. It's open source and Apache-licensed, but requires some amount of configuration and is probably only worth investing [time] in for larger clusters.