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 running a microservice in a openshift environment using hazelcast 4.1.1 and the 2.2.1 kubernetes discovery plugin. I have configured hazelcast in embedded mode and I'm running 4 instances of that service. When I scale down the application from 4 to 3 pods the whole migration does not finish and my application throws exception due to WrongTargetException all the time (after one minute).
I analyzed the diagnostic file and I believe that the error comes from the index calculation. If I disable all my indices on my maps, everthing works like a charm. I think this might be related to https://github.com/hazelcast/hazelcast/issues/18079
It seems that the deserialization of my objects are called for each index separatly. Since we have configured a custom (de-)serializer which also applies some compression (LZ4) the migration takes ages.
Can somebody confirm my assumtions? Or are there any other known issues with index calculation and migration?
I'm operating project using spring-boot, spring-data-cassandra.
When I setup that project, I set cassandra properties by ip and port.
(referred by https://www.baeldung.com/spring-data-cassandra-tutorial)
When set it up like this, If I had 3 cassandra nodes and 1 cassandra node died, I think project should fail to connect with cassandra at a 33% probability.
But my project was fine even though 1 cassandra node was dead. (just have some error on one's deathbed)
Do It happen to have A function in spring-data-cassandra like client-side-loadbalancing?
If they have that function, Where can I see that code??
I tried to find that code but failed.
Please give me a little clue.
Spring Data Cassandra relies on the functionality of the DataStax Java driver that is responsible for making everything works. This includes:
establishing the initial connection to the cluster. This is where the contact points play their role. After driver is connected to any of points, it reads information about the whole cluster and establishes connections to all nodes (by default)
establishing the control connection that is used to receive notifications about changes in the cluster - nodes going up & down, changes in schema, etc. If node goes down or up, this information is used to modify the list of the active nodes
providing the load balancing of requests based on the replication, and nodes availability - if the node is down, it's excluded from list of candidates, so we don't send queries to node that is known to be down
We recently deployed micro-services into our production and these micro-service communicates with Cassandra nodes for reads/writes.
After deployment, we started noticing sudden drop in CPU to 0 on all cassandra nodes in primary DC. This is happening at least once per day. when this happens each time, we see randomly 2 nodes (in SAME DC) are not able to reachable to each other ("nodetool describecluster") and when we check "nodetool tpstats", these 2 nodes has higher number of ACTIVE Native-Transport-Requests b/w 100-200. Also these 2 nodes are storing HINTS for each other but when i do longer "pings" b/w them i don't see any packet loss. when we restart those 2 cassandra nodes, issue will be fixed at that moment. This is happening since 2 weeks.
We use Apache Cassandra 2.2.8.
Also microservices logs are having reads/writes timeouts before sudden drop in CPU on all cassandra nodes.
You might be using token aware load balancing policy on client, and updating a single partition or range heavily. In which case all the coordination load will be focused on the single replica set. Can change your application to use RoundRobin (or dc aware round robin) LoadBalancingPolicy and it will likely resolve. If it does you have a hotspot in your application and you might want to give attention to your data model.
It does look like a datamodel problem (hot partitions causing issues in specific replicas).
But in any case you might want to add the following to your cassandra-env.sh to see if it helps:
JVM_OPTS="$JVM_OPTS -Dcassandra.max_queued_native_transport_requests=1024"
More information about this here: https://issues.apache.org/jira/browse/CASSANDRA-11363
To all Cassandra experts,
I am trying to understand cassandra failure detection and recovery. I am a little bit confused on how this exactly works.
From Datastax Doc:
Configuring the phi_convict_threshold property adjusts the sensitivity of the failure detector. Lower values increase the likelihood that an unresponsive node will be marked as down, while higher values decrease the likelihood that transient failures causing node failure. In unstable network environments (such as EC2 at times), raising the value to 10 or 12 helps prevent false failures.
From http://ljungblad.nu/post/44006928392/cassandra-and-its-accrual-failure-detector
Phi represents the likelihood that Node A is wrong about Node B’s state.The higher the Phi, the bigger the confidence that Node B has failed.
Can someone explain me in details C* failure detection mechanism and how C* recovers it in different scenarios.
Thanks in advance
Chaity
I don't consider myself a Cassandra expert, but here is my take on Cassandra's node failure detection :
Once per second, each node contacts 1-3 other nodes asking about the node state and location. These time-stamped messages are past of the Gossip protocol.
The Snitch informs the partitioner of a node's rack and data center topology. A dynamic snitch can detect if nodes are functioning at poor performance (read and write) levels and not perform read or write operations until it is functioning properly.
Hinted Handoff is a recovery mechanism for partition writes targeting offline nodes. The Coordinator stores whether or not each node on the write path acknowledges the write operation and stores the hint in the system.hints table. The write is re-attempted if the target node comes back online.
All of these communication methods work together when nodes go offline or are performing poorly, and can be configured. As far as I know, Cassandra will not bring nodes back to life after failure; this requires human intervention to bring the node back online and run nodetool to repair the data on the failed node.
Depending on your organization's failure tolerance for read and write operations, you can always configure the consistency level.
Some resources for managing node failure:
(Check your C* version first) DataStax Failure detection and recovery
C* High Availability from Planet Cassandra
Configuring Consistency Level