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Cassandra Structured Storage System

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Jeff Hammerbacher
Jeff Hammerbacher

SIGMOD presentation by the Cassandra team

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Cassandra Structured Storage System over a P2P Network Avinash Lakshman, Prashant Malik, Karthik Ranganathan
Why Cassandra? ,[object Object],[object Object],[object Object],[object Object]
Design Goals ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cassandra Architecture Messaging Layer Cluster Membership Failure Detector Storage Layer Partitioner Replicator Cassandra API Tools
Data Model KEY ColumnFamily1 Name : MailList Type : Simple Sort : Name Name : tid1 Value : <Binary> TimeStamp : t1 Name : tid2 Value : <Binary> TimeStamp : t2 Name : tid3 Value : <Binary> TimeStamp : t3 Name : tid4 Value : <Binary> TimeStamp : t4 ColumnFamily2 Name : WordList Type : Super Sort : Time Name : aloha ColumnFamily3 Name : System Type : Super Sort : Name Name : hint1 <Column List> Name : hint2 <Column List> Name : hint3 <Column List> Name : hint4 <Column List> Name : dude C2 V2 T2 C6 V6 T6 Column Families are declared upfront Columns are added and modified dynamically SuperColumns are added and modified dynamically Columns are added and modified dynamically C1 V1 T1 C2 V2 T2 C3 V3 T3 C4 V4 T4
Write Operations ,[object Object],[object Object],[object Object],[object Object]
Write cont’d Key (CF1 , CF2 , CF3) Commit Log Binary serialized Key ( CF1 , CF2 , CF3 ) Memtable ( CF1) Memtable ( CF2) Memtable ( CF2) FLUSH ,[object Object],[object Object],[object Object],Dedicated Disk <Key name><Size of key Data><Index of columns/supercolumns>< Serialized column family> --- --- --- --- <Key name><Size of key Data><Index of columns/supercolumns>< Serialized column family> BLOCK Index <Key Name> Offset, <Key Name> Offset K 128 Offset K 256 Offset K 384 Offset Bloom Filter (Index in memory) Data file on disk
Compactions K1 < Serialized data > K2 < Serialized data > K3 < Serialized data > -- -- -- Sorted K2 < Serialized data > K10 < Serialized data > K30 < Serialized data > -- -- -- Sorted K4 < Serialized data > K5 < Serialized data > K10 < Serialized data > -- -- -- Sorted MERGE SORT K1 < Serialized data > K2 < Serialized data > K3 < Serialized data > K4 < Serialized data > K5 < Serialized data > K10 < Serialized data > K30 < Serialized data > Sorted K1 Offset K5 Offset K30 Offset Bloom Filter Loaded in memory Index File Data File D E L E T E D
Write Properties ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Read Query Closest replica Cassandra Cluster Replica A Result Replica B Replica C Digest Query Digest Response Digest Response Result Client Read repair if digests differ
Partitioning N=3 h(key2) And Replication 0 1 1/2 F E D C B A h(key1)
Cluster Membership and Failure Detection ,[object Object],[object Object],[object Object],[object Object],[object Object]
Accrual Failure Detector ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Properties of the Failure Detector ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Implementation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Information Flow in the Implementation
Performance Benchmark ,[object Object],[object Object],Search Interactions Term Search Min 7.69 ms 7.78 ms Median 15.69 ms 18.27 ms Average 26.13 ms 44.41 ms
Lessons Learnt ,[object Object],[object Object],[object Object],[object Object]
Future work ,[object Object],[object Object],[object Object],[object Object]
Questions?

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Cassandra Structured Storage System

  • 1. Cassandra Structured Storage System over a P2P Network Avinash Lakshman, Prashant Malik, Karthik Ranganathan
  • 2.
  • 3.
  • 4. Cassandra Architecture Messaging Layer Cluster Membership Failure Detector Storage Layer Partitioner Replicator Cassandra API Tools
  • 5. Data Model KEY ColumnFamily1 Name : MailList Type : Simple Sort : Name Name : tid1 Value : <Binary> TimeStamp : t1 Name : tid2 Value : <Binary> TimeStamp : t2 Name : tid3 Value : <Binary> TimeStamp : t3 Name : tid4 Value : <Binary> TimeStamp : t4 ColumnFamily2 Name : WordList Type : Super Sort : Time Name : aloha ColumnFamily3 Name : System Type : Super Sort : Name Name : hint1 <Column List> Name : hint2 <Column List> Name : hint3 <Column List> Name : hint4 <Column List> Name : dude C2 V2 T2 C6 V6 T6 Column Families are declared upfront Columns are added and modified dynamically SuperColumns are added and modified dynamically Columns are added and modified dynamically C1 V1 T1 C2 V2 T2 C3 V3 T3 C4 V4 T4
  • 6.
  • 7.
  • 8. Compactions K1 < Serialized data > K2 < Serialized data > K3 < Serialized data > -- -- -- Sorted K2 < Serialized data > K10 < Serialized data > K30 < Serialized data > -- -- -- Sorted K4 < Serialized data > K5 < Serialized data > K10 < Serialized data > -- -- -- Sorted MERGE SORT K1 < Serialized data > K2 < Serialized data > K3 < Serialized data > K4 < Serialized data > K5 < Serialized data > K10 < Serialized data > K30 < Serialized data > Sorted K1 Offset K5 Offset K30 Offset Bloom Filter Loaded in memory Index File Data File D E L E T E D
  • 9.
  • 10. Read Query Closest replica Cassandra Cluster Replica A Result Replica B Replica C Digest Query Digest Response Digest Response Result Client Read repair if digests differ
  • 11. Partitioning N=3 h(key2) And Replication 0 1 1/2 F E D C B A h(key1)
  • 12.
  • 13.
  • 14.
  • 15.
  • 16. Information Flow in the Implementation
  • 17.
  • 18.
  • 19.