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Introduction to Cassandra

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Recent talk I gave at the Wellington Rails User Group.

Recent talk I gave at the Wellington Rails User Group.

I tried to build up the model of how and why Cassandra does things.

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Introduction to Cassandra Introduction to Cassandra Presentation Transcript

  • Introduction to CassandraWellington Ruby on Rails User Group Aaron Morton @aaronmorton 24/11/2010
  • Disclaimer.This is an introduction not a reference.
  • I may, from time to timeand for the best possible reasons, bullshit you.
  • What do you already know about Cassandra?
  • Get ready.
  • The next slide has a lot on it.
  • Cassandra is a distributed, fault tolerant, scalable, column oriented data store.
  • A word about “column oriented”.
  • Relax.
  • It’s different to a roworiented DB like MySQL. So...
  • For now, think about keys and values. Where each value is a hash / dict.
  • Cassandra’s data model and on disk storage are based on the Google Bigtable paper from 2006.
  • The distributed cluster design is based on theAmazon Dynamo paper from 2007.
  • {‘foo’ => {‘bar’ => ‘baz’,},} {key => {col_name => col_value,},}
  • Easy.Lets store ‘foo’ somewhere.
  • foo
  • But I want to be able toread it back if one machine fails.
  • Lets distribute it on 3 of the 5 nodes I have.
  • This is the Replication Factor. Called RF or N.
  • Each node has a token thatidentifies the upper value of the key range it is responsible for.
  • #1 <= E #5 #2<= Z <= J #4 #3<= T <= O
  • Client connects to arandom node and asks it tocoordinate storing the ‘foo’ key.
  • Each node knows about allother nodes in the cluster, including their tokens.
  • This is achieved using a Gossip protocol. Every second each node sharesit’s full view of the cluster with 1 to 3 other nodes.
  • Our coordinator is node 5. It knows node 2 is responsible for the ‘foo’ key.
  • #1Client <= E #5 #2<= Z foo #4 #3<= T <= O
  • But there is a problem...
  • What if we have lots ofvalues between F and J?
  • We end up with a “hot” section in our ring of nodes.
  • That’s bad mmmkay?
  • You shouldnt have a hot section in your ring. mmmkay?
  • A Partitioner is used to apply a transform to thekey. The transformed values are also used to define a nodes’ range.
  • The Random Partitioner applies a MD5 transform. The range of all possiblekeys values is changed to a 128 bit number.
  • There are other Partitioners, such as theOrder Preserving Partition.But start with the Random Partitioner.
  • Let’s pretend all keys are now transformed to aninteger between 0 and 9.
  • Our 5 node cluster now looks like.
  • #1 <= 2 #5 #2<= 0 <= 4 #4 #3<= 8 <= 6
  • Pretend our ‘foo’ key transforms to 3.
  • #1Client <= 2 #5 #2<= 0 "3" #4 #3<= 8 <= 6
  • Good start.
  • But where are the replicas? We want to replicate the ‘foo’ key 3 times.
  • A Replication Strategy is used to determine whichnodes should store replicas.
  • It’s also used to work outwhich nodes should have a value when reading.
  • Simple Strategy orders the nodes by their token andplaces the replicas around the ring.
  • Network Topology Strategy is aware of the racks andData Centres your servers are in. Can split replicas between DC’s.
  • Simple Strategy will do in most cases.
  • Our coordinator will sendthe write to all 3 nodes at once.
  • #1Client <= 2 #5 #2<= 0 "3" #4 #3 "3" "3"
  • Once the 3 replicas tell the coordinator they havefinished, it will tell the client the write completed.
  • Done.Let’s go home.
  • Hang on.What about fault tolerant?What if node #4 is down?
  • #1Client <= 2 #5 #2<= 0 "3" #4 #3 "3" "3"
  • The client must specify aConsistency Level for each operation.
  • Consistency Level specifies how many nodes mustagree before the operation is a success.
  • For reads is known as R.For writes is known as W.
  • Here are the simple ones(there are a few more)...
  • One.The coordinator will only wait for one node to acknowledge the write.
  • Quorum.N/2 + 1
  • All.
  • The cluster will work toeventually make all copies of the data consistent.
  • To get consistent behaviourmake sure that R + W > N. You can do this by...
  • Always using Quorum for read and writes. Or...
  • Use All for writes and One for reads. Or...
  • Use All for reads and One for writes.
  • Try our write again, usingQuorum consistency level.
  • Coordinator will wait for 2 nodes to complete the write before telling the client has completed.
  • #1Client <= 2 #5 #2<= 0 "3" #4 #3 "3" "3"
  • What about when node 4 comes online?
  • It will not have our “foo” key.
  • Won’t somebody pleasethink of the “foo” key!?
  • During our write the coordinator will send aHinted Handoff to one of the online replicas.
  • Hinted Handoff tells the node that one of the replicas was down andneeds to be updated later.
  • #1Client <= 2 #5 #2<= 0 "3" #4 #3 "3" "3" send "3" to #4
  • When node 4 comes backup, node 3 will eventually process the Hinted Handoffs and send the “foo” key to it.
  • #1Client <= 2 #5 #2<= 0 "3" #4 #3 "3" "3"
  • What if the “foo” key isread before the Hinted Handoff is processed?
  • #1Client <= 2 #5 #2<= 0 "3" #4 #3 "" "3" send "3" to #4
  • At our Quorum CL the coordinator asks all nodesthat should have replicas to perform the read.
  • Once CL nodes havereturned, their values are compared.
  • If the do not match a ReadRepair process is kicked off.
  • A timestamp provided bythe client during the write is used to determine the “latest” value.
  • The “foo” key is written to node 4, and consistency achieved, before thecoordinator returns to the client.
  • At lower CL the ReadRepair happens in the background and is probabilistic.
  • We can force Cassandra torepair everything using the Anti Entropy feature.
  • Anti Entropy is the main feature for achievingconsistency. RR and HH are optimisations.
  • Anti Entropy startedmanually via command line or Java JMX.
  • Great so far.
  • But ratemylolcats.com is going to be huge.How do I store 100 Million pictures of cats?
  • Add more nodes.
  • More disk capacity, disk IO,memory, CPU, network IO. More everything.
  • Linear scaling.
  • Clusters of 100+ TB.
  • And now for the data model.
  • From the outside in.
  • A Keyspace is the container for everything in your application.
  • Keyspaces can be thought of as Databases.
  • A Column Family is acontainer for ordered and indexed Columns.
  • Columns have a name, value, and timestampprovided by the client.
  • The CF indexes the columns by name andsupports get operations by name.
  • CF’s do not define whichcolumns can be stored in them.
  • Column Families have alarge memory overhead.
  • You typically have few (<10) CF’s in your Keyspace. But there is no limit.
  • We have Rows.Rows have a key.
  • Rows store columns in oneor more Column Families.
  • Different rows can storedifferent columns in the same Column Family.
  • User CF username => fredkey => fred d_o_b => 04/03 username => bobkey => bob city => wellington
  • A key can store different columns in different Column Families.
  • User CF username => fredkey => fred d_o_b => 04/03 Timeline CF 09:01 => tweet_60key => fred 09:02 => tweet_70
  • Here comes the SuperColumn Family to ruin it all.
  • Arrgggghhhhh.
  • A Super Column Family is acontainer for ordered and indexes Super Columns.
  • A Super Column has aname and an ordered and indexed list of Columns.
  • So the Super ColumnFamily just gives another level to our hash.
  • Social Super CFkey => fred following => { bob => 01/01/2010, tom => 01/02/2010} followers => { bob => 01/01/2010}
  • How about some code?