We using DSE with Cassandra + Solr.
I'm not sure how it's spreading the data, let's say we have 6 nodes, replication factor of 3.
Our platform uses all the 6 nodes to query data, I query one node from the 6 there is a chance data will be missing?
Or I need to have the same replication factor as the number of the nodes I have if I want to use all the nodes from the platform.
How it's working?
In Cassandra each node stores some parts of the data. When you build the cluster each node will be responsible for specific part of the data. That is decided based on the token value that is assigned to that node. Now when you insert or select the data, each insert or select will have a partition key. Based on that partition key a hash value is calculated and the data will be sent to node which is responsible for that specific token value.
If there are 6 nodes and RF =3 then within cluster you have 3 copies of entire data. The primary copy is stored based on the above concept. The replicas will be stored based on the replication class that you specify while you create keyspace. If you take SimpleStrategy it stores replica on next node in clockwise i.e replica of node1 will be stored on node2 and node3 and replica of node2 will be stored on node3 and node4 and so on.
If you query from one node then based on the partition key the query will be sent to specific node which is responsible for that partition keys. To know to which node your query will be sent you can use nodetool utility.
nodetool getendpoints <keyspace> <table> <key>
This will give you the node Ip where the query will be sent to get the result
If you have 6 nodes and RF 3 it means 3 copies of data exist in your cassandra cluster. Data availability also depends on consistency level. if you are using ONE consistency and 2 node down then also will get data and no loss but if TWO, QOURAM, THREE or ALL consistency then scenario will be different.
Related
I have a 3 node Cassandra cluster with RF=3. Now when I do nodetool status I get the owns for each node in the cluster as 100%.
But when I have 5 nodes in the cluster wit RF=3. The owns is 60%(approx as shown in image below).
Now as per my understanding the partitioner will calculate the hash corresponding to first replica node and the data will also be replicated as per the RF on the other nodes.
Now we have a 5 node cluster and RF is 3.
Shouldn't 3 nodes be owning all the data evenly(100%) as partitioner will point to one node as per the partitoning strategy and then same data be replicated to remaining nodes which equals RF-1? It's like the data is getting evenly distributed among all the nodes(5) even though RF is 3.
Edit1:
As per my understanding the reason for 60%(approx) owns for each node is because the RF is 3. It means there will be 3 replicas for each row. It means there will be 300% data. Now there are 5 nodes in the cluster and partitioner will be using the default random hashing algorithm which will distribute the data evenly across all the nodes in the cluster.
But now the issue is that we checked all the nodes of our cluster and all the nodes contain all the data even though the RF is 3.
Edit2:
#Aaron I did as specified in the comment. I created a new cluster with 3 nodes.
I created a Keyspace "test" and set the class to simplestrategy and RF to 2.
Then I created a table "emp" having partition key (id,name).
Now I inserted a single row into the first node.
As per your explanation, It should only be in 2 nodes as RF=2.
But when I logged into all the 3 nodes, i could see the row replicated in all the nodes.
I think since the keyspace is getting replicated in all the nodes therefore, the data is also getting replicated.
Percent ownership is not affected (at all) by actual data being present. You could add a new node to a single node cluster (RF=1) and it would instantly say 50% on each.
Percent ownership is purely about the percentage of token ranges which a node is responsible for. When a node is added, the token ranges are recalculated, but data doesn't actually move until a streaming event happens. Likewise, data isn't actually removed from its original node until cleanup.
For example, if you have a 3 node cluster with a RF of 3, each node will be at 100%. Add one node (with RF=3), and percent ownership drops to about 75%. Add a 5th node (again, keep RF=3) and ownership for each node correctly drops to about 3/5, or 60%. Again, with a RF of 3 it's all about each node being responsible for a set of primary, secondary, and tertiary token ranges.
the default random hashing algorithm which will distribute the data evenly across all the nodes in the cluster.
Actually, the distributed hash with Murmur3 partitioner will evenly distribute the token ranges, not the data. That's an important distinction. If you wrote all of your data to a single partition, I guarantee that you would not get even distribution of data.
The data replicated to another nodes when you add them isn't cleared up automatically - you need to call nodetool cleanup on the "old" nodes after you add the new node into cluster. This will remove the ranges that were moved to other nodes.
We have a cassandra cluster running with 3 nodes and a replication factor of 2 -> maybe we should have selected 3 from the start, but this is not the case.
Our quorum is therefore = 2/2 + 1 = 2
Lets say we lose one node - so now only two cassandra nodes are online.
We still have the possibility to read from the cluster if we set our consistency level to "ONE" and then read -> so this is not a problem.
The thing I do not understand is the following.
We still have two nodes running, so why is it not possible to do a serial (lightweight transaction) insert into our keyspace? We have two nodes up, so shouldn't it be possible to get a quorum of 2 when trying to insert?
Is it because one of the row's is already put on the missing node?
When you are trying to insert a data, the data is stored based on the token values(based on the partitioner configured) and replicated in a circular way.
For e.g. If you are inserting a data X in a keyspace with replication factor of 2 in a 3 node cluster Node1 (owning token A), Node2 (owning token B) and Node3 (Owning token C). Say if the data X is computed to token B, then Cassandra starts inserting data from Node2 and Node3 (till it completes the replicas). Say if the data X is computed to token C, then Cassandra starts inserting data from Node3 and Node1.
So setting consistency level of 2 means the data must be written in 2 nodes.
In your case even though you have 2 nodes up Node1 (token A) and Node2 (token B) and one node down Node3 (token C), if the data is computed and selected as token B, then Cassandra tries to insert in Node2 and Node3 and you get consistency error as it cannot insert in Node3.
So to insert you must either increase replication to 3 or decrease the consistency to 1.
To know more on consistency see this docs https://docs.datastax.com/en/cassandra/2.1/cassandra/dml/dml_config_consistency_c.html
Lightweight transactions require a QUORUM consistency level, which cannot be reached in case the unavailable node is a replica of the affected key. What's relevant here is the number of available replicas, not the number of nodes in the cluster.
According to the documentation here http://orientdb.com/docs/2.0/orientdb.wiki/Distributed-Architecture.html under the heading Creation of records (documents, vertices and edges) it states that
In distributed mode the RID is assigned with cluster locality. If you have class Customer and 3 nodes (node1, node2, node3), you'll have these clusters:
customer with id=#15 (this is the default one, assigned to node1)
customer_node2 with id=#16
customer_node3 with id=#17
This would imply that you cannot rely on the clusterID in your code. For example, selecting a record like this
select from #15:1
would break in a distributed setup, because once node1 fails and node2 takes over, you'll have to select
select from #16:1
My question is, is it the responsibility of the programmer to handle which clusterID to use, or is this automatically handled by OrientDB, in which case
select from #15:1
will always work, not matter which node is up or down?
I have a distributed/federated database structured as follows:
The databases are spread across three geographic locations ("nodes")
Multiple databases are clustered at each node
The relational databases are a mix of PostgreSQL, MySQL, Oracle, and MS SQL Server; the non-relational databases are either MongoDB or Cassandra
Loose coupling within each node and across the node federation is achieved via RabbitMQ, with each node running a RabbitMQ broker
I am implementing a readonly inter-node aggregation job system for jobs that span the node federation (i.e. for jobs that are not local to a node). These jobs only perform "get" queries - they do not modify the databases. (If the results of the jobs are intended to go into one or more of the databases then this is accomplished by a separate job that is not part of the inter-node job system I am trying to optimize.) My objective is to minimize the network bandwidth required by these jobs (first to minimize the inter-node / WAN bandwidth, then to minimize the intra-node / LAN bandwidth); I assume a uniform cost for each WAN link, and another uniform cost for each LAN link. The jobs are not particularly time-sensitive. I perform some CPU load-balancing within a node but not between nodes.
The amount of data transported across the WAN/LAN for the aggregation jobs is small relative to the amount of database writes that are local to a cluster or to a specific database, so it would not be practical to fully distribute the databases across the federation.
The basic algorithm I use for minimizing network bandwidth is:
Given a job that runs on a set of data that is spread across the federation, the manager node sends a message to each the other nodes that contains the relevant database queries.
Each node runs its set of queries, compresses them with gzip, caches them, and sends their compressed sizes to the manager node.
The manager moves to the node containing the plurality of the data (specifically, to the machine within the cluster that has the most data and that has idle cores); it requests the rest of the data from the other two nodes and from the other machines within the cluster, then it runs the job.
When possible the jobs use a divide-and-conquer approach to minimize the amount of data co-location that is needed. For example, if the job needs to compute the sums of all Sales figures across the federation, then each node locally calculates its Sales sums which are then aggregated at the manager node (rather than copying all of the unprocessed Sales data to the manager node). However, sometimes (such as when performing a join between two tables that are located at different nodes) data co-location is needed.
The first thing I did to optimize this was aggregate the jobs, and to run the aggregated jobs at ten minute epochs (the machines are all running NTP, so I can be reasonably certain that "every ten minutes" means the same thing at each node). The goal is for two jobs to be able to share the same data, which reduces the overall cost of transporting the data.
Given two jobs that query the same table, I generate each job's resultset, and then I take the intersection of the two resultsets.
If both jobs are scheduled to run on the same node, then the network transfer cost is calculated as the sum of the two resultsets minus the intersection of the two resultsets.
The two resultsets are stored to PostgreSQL temporary tables (in the case of relational data) or else to temporary Cassandra columnfamilies / MongoDB collections (in the case of nosql data) at the node selected to run the jobs; the original queries are then performed against the combined resultsets and the data delivered is to the individual jobs. (This step is only performed on combined resultsets; individual resultset data is simply delivered to its job without first being stored on temporary tables/column families/collections.)
This results in an improvement to network bandwidth, but I'm wondering if there's a framework/library/algorithm that would improve on this. One option I considered is to cache the resultsets at a node and to account for these cached resultsets when determining network bandwidth (i.e. trying to reuse resultsets across jobs in addition to the current set of pre-scheduled co-located jobs, so that e.g. a job run in one 10-minute epoch can use a cached resultset from a previous 10-minute resultset), but unless the jobs use the exact same resultsets (i.e. unless they use identical where clauses) then I don't know of a general-purpose algorithm that would fill in the gaps in the resultset (for example, if the resultset used the clause "where N > 3" and a different job needs the resultset with the clause "where N > 0" then what algorithm could I use to determine that I need to take the union of the original resultset and with the resultset with the clause "where N > 0 AND N <= 3") - I could try to write my own algorithm to do this, but the result would be a buggy useless mess. I would also need to determine when the cached data is stale - the simplest way to do this is to compare the cached data's timestamp with the last-modified timestamp on the source table and replace all of the data if the timestamp has changed, but ideally I'd want to be able to update only the values that have changed with per-row or per-chunk timestamps.
I've started to implement my solution to the question.
In order to simplify the intra-node cache and also to simplify CPU load balancing, I'm using a Cassandra database at each database cluster ("Cassandra node") to run the aggregation jobs (previously I was aggregating the local database resultsets by hand) - I'm using the single Cassandra database for the relational, Cassandra, and MongoDB data (the downside is that some relational queries run slower on Cassandra, but this is made up for by the fact that the single unified aggregation database is easier to maintain than the separate relational and non-relational aggregation databases). I am also no longer aggregating jobs in ten minute epochs since the cache makes this algorithm unnecessary.
Each machine in a node refers to a Cassandra columnfamily called Cassandra_Cache_[MachineID] that is used to store the key_ids and column_ids that it has sent to the Cassandra node. The Cassandra_Cache columnfamily consists of a Table column, a Primary_Key column, a Column_ID column, a Last_Modified_Timestamp column, a Last_Used_Timestamp column, and a composite key consisting of the Table|Primary_Key|Column_ID. The Last_Modified_Timestamp column denotes the datum's last_modified timestamp from the source database, and the Last_Used_Timestamp column denotes the timestamp at which the datum was last used/read by an aggregation job. When the Cassandra node requests data from a machine, the machine calculates the resultset and then takes the set difference of the resultset and the table|key|columns that are in its Cassandra_Cache and that have the same Last_Modified_Timestamp as the rows in its Cassandra_Cache (if the timestamps don't match then the cached data is stale and is updated along with the new Last_Modified_Timestamp). The local machine then sends the set difference to the Cassandra node and updates its Cassandra_Cache with the set difference and updates the Last_Used_Timestamp on each cached datum that was used to compose the resultset. (A simpler alternative to maintaining a separate timestamp for each table|key|column is to maintain a timestamp for each table|key, but this is less precise and the table|key|column timestamp is not overly complex.) Keeping the Last_Used_Timestamps in sync between Cassandra_Caches only requires that the local machines and remote nodes send the Last_Used_Timestamp associated with each job, since all data within a job uses the same Last_Used_Timestamp.
The Cassandra node updates its resultset with the new data that it receives from within the node and also with the data that it receives from the other nodes. The Cassandra node also maintains a columnfamily that stores the same data that is in each machine's Cassandra_Cache (except for the Last_Modified_Timestamp, which is only needed on the local machine to determine when data is stale), along with a source id indicating if the data came from within the within the node or from another node - the id distinguishes between the different nodes, but does not distinguish between the different machines within the local node. (Another option is to use a unified Cassandra_Cache rather than using one Cassandra_Cache per machine plus another Cassandra_Cache for the node, but I decided that the added complexity was not worth the space savings.)
Each Cassandra node also maintains a Federated_Cassandra_Cache, which consists of the {Database, Table, Primary_Key, Column_ID, Last_Used_Timestamp} tuples that have been sent from the local node to one of the other two nodes.
When a job comes through the pipeline, each Cassandra node updates its intra-node cache with the local resultsets, and also completes the sub-jobs that can be performed locally (e.g. in a job to sum data between multiple nodes, each node sums its intra-node data in order to minimize the amount of data that needs to be co-located in the inter-node federation) - a sub-job can be performed locally if it only uses intra-node data. The manager node then determines on which node to perform the rest of the job: each Cassandra node can locally compute the cost of sending its resultset to another node by taking the set difference of its resultset and the subset of the resultset that has been cached according to its Federated_Cassandra_Cache, and the manager node minimizes the cost equation ["cost to transport resultset from NodeX" + "cost to transport resultset from NodeY"]. For example, it costs Node1 {3, 5} to transport its resultset to {Node2, Node3}, it costs Node2 {2, 2} to transport its resultset to {Node1, Node3}, and it costs Node3 {4, 3} to transport its resultset to {Node1, Node2}, therefore the job is run on Node1 with a cost of "6".
I'm using an LRU eviction policy for each Cassandra node; I was originally using an oldest-first eviction policy because it is simpler to implement and requires fewer writes to the Last_Used_Timestamp column (once per datum update instead of once per datum read), but the implementation of an LRU policy turned out not to be overly complex and the Last_Used_Timestamp writes did not create a bottleneck. When a Cassandra node reaches 20% free space it evicts data until it reaches 30% free space, hence each eviction is approximately the size of 10% of the total space available. The node maintains two timestamps: the timestamp of the last-evicted intra-node data, and the timestamp of the last-evicted inter-node / federated data; due to the increased latency of inter-node communication relative to that of intra-node communication, the goal of the eviction policy to have 75% of the cached data be inter-node data and 25% of the cached data be intra-node data, which can be quickly approximated by having 25% of each eviction be inter-node data and 75% of each eviction be intra-node data. Eviction works as follows:
while(evicted_local_data_size < 7.5% of total space available) {
evict local data with Last_Modified_Timestamp <
(last_evicted_local_timestamp += 1 hour)
update evicted_local_data_size with evicted data
}
while(evicted_federated_data_size < 2.5% of total space available) {
evict federated data with Last_Modified_Timestamp <
(last_evicted_federated_timestamp += 1 hour)
update evicted_federated_data_size with evicted data
}
Evicted data is not permanently deleted until eviction acknowledgments have been received from the machines within the node and from the other nodes.
The Cassandra node then sends a notification to the machines within its node indicating what the new last_evicted_local_timestamp is. The local machines update their Cassandra_Caches to reflect the new timestamp, and send a notification to the Cassandra node when this is complete; when the Cassandra node has received notifications from all local machines then it permanently deletes the evicted local data. The Cassandra node also sends a notification to the remote nodes with the new last_evicted_federated_timestamp; the other nodes update their Federated_Cassandra_Caches to reflect the new timestamp, and the Cassandra node permanently deletes the evicted federated data when it receives notifications from each node (the Cassandra node keeps track of which node a piece of data came from, so after receiving an eviction acknowledgment from NodeX the node can permanently delete the evicted NodeX data before receiving an eviction acknowledgment from NodeY). Until all machines/nodes have sent their notifications, the Cassandra node uses the cached evicted data in its queries if it receives a resultset from a machine/node that has not evicted its old data. For example, the Cassandra node has a local Table|Primary_Key|Column_ID datum that it has evicted, and meanwhile a local machine (which has not processed the eviction request) has not included the Table|Primary_Key|Column_ID datum in its resultset because it thinks that the Cassandra node already has the datum in its cache; the Cassandra node receives the resultset from the local machine, and because the local machine has not acknowledged the eviction request the Cassandra node includes the cached evicted datum in its own resultset.
I have two nodes running mnesia. I created schema and some tables on node 1, and used mnesia:add_table_copy on node 2 to copy the tables from node 1 to node 2.
Everything works well until I call q() on node 1 and then q() on node 2. I found that when I start node 1 again, mnesia:wait_for_tables([sometable], infinity) won't return. It will only return when I start node 2 again.
Is there a way to fix this? This is a problem because I won't be able to start node 1 again if node 2 is down.
In this discussion a situation similar to the one you're facing is presented.
Reading from that source:
At startup Mnesia tries to connect
with the other nodes and if that
suceeds it loads its tables from
them. If the other nodes are down, it
looks for mnesia_down marks in its
local transaction log in order to
determine if it has a consistent
replica or not of its tables. The node
that was shutdown last has
mnesia_down's from all the other
nodes. This means that it safely can
load its tables. If some of the other
nodes where started first (as in your
case) Mnesia will wait indefinitely
for another node to connect in order
to load its tables
You're shutting down node 1 first, so it doesn't have the mnesia_down from the other node. What happens if you reverse the shutting down order?
Also, it should be possible to force the table loading via the force_load_table/1 function:
force_load_table(Tab) -> yes | ErrorDescription
The Mnesia algorithm for table load
might lead to a situation where a
table cannot be loaded. This situation
occurs when a node is started and
Mnesia concludes, or suspects, that
another copy of the table was active
after this local copy became inactive
due to a system crash.
If this situation is not acceptable,
this function can be used to override
the strategy of the Mnesia table load
algorithm. This could lead to a
situation where some transaction
effects are lost with a inconsistent
database as result, but for some
applications high availability is more
important than consistent data.