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Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
Scalability: Rdbms Vs Other Data Stores
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Scalability: Rdbms Vs Other Data Stores

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  • May have to discuss Queuing Systems, Idempotency and so on
  • Transcript

    • 1. RDBMS vs. Other Data Stores forScalability
      ramki.g@directi.com
      TechTalk 2009, IIIT Hyderabad
    • 2. Scalability
      Increase Resources  Increase Performance (Linearly)
      Performance?
      Latency, Capacity, Throughput
      Vertical Scalability (Scaling Up)
      Divide the functionality
      Horizontal Scalability (Scaling Out)
      Divide the data
    • 3. Relational Database
      Table, Row, Column
      Set, Item, Property
    • 4. Relational Theory
      Selection: SELECT
      Filter: WHERE
      Join: JOIN, LEFT JOIN,RIGHT JOIN
      Correlation: SELECT a FROM A WHERE A.b IN (SELECT b FROM B WHERE b.a > a)
    • 5. Relational Theory
      Aggregation
      Set Operators
      Union, Intersection, Minus
      Group By
      MAX, MIN, SUM, AVG
    • 6. Transactions: Atomicity
      Transaction Level
      Entire Logical operations is a transaction
      Multiple statements
      Statement level
      Each statement is either successful or not, no partial success
      Multiple records
      Record Level
      All modifications to a record are successful or not
    • 7. Transactions: Consistency
      Integrity Constraints
      Referential Integrity
    • 8. Transactions: Isolation Levels
      Serializable
      A definite order of mutations/transactions is possible to arrive to state B from state A
      Repeatable Read
      Any data read by a transaction will remain so till transaction is complete
      Non Repeatable Read aka Read Committed
      Two reads within a transaction may give different results
      Dirty Read
      A transaction might read data which might then be rolledback
    • 9. RDBMS Luxuries
      Multiple Indexes
      Auto Increments/Sequences
      Triggers
    • 10. Scalability in RDBMS
      Replication
      Read Replication (Master-Slave)
      Read Write Replication (Master-Master)
      Cluster
      Distributed Transaction
      Two-phase commits
    • 11. Scalability Impediments
      Performance
      Sub-Queries/Correlation, Joins, Aggregates,
      Referential Integrity constraints
      Basic Guarantee
      Consistency
      Availability
    • 12. CAP?
      Conjecture: Distributed systems cannot ensure all three of the following properties at once
      Consistency The client perceives that a set of operations has occurred all at once.
      Availability Every operation must terminate in an intended response.
      Partition tolerance Operations will complete, even if individual components are unavailable.
    • 13. ACID to BASE
      Basically Available - system seems to work all the time
      Soft State - it doesn't have to be consistent all the time
      Eventually Consistent - becomes consistent at some later time
    • 14. BASE: An Example
      BEGIN Transaction
      INSERT INTO ORDER( oid, timestamp, customer)
      FOREACH item IN itemList
      INSERT INTO ORDER_ITEM ( oid, item.id, item.quantity, item.unitprice)
      //UPDATE INVENTORY SET quantity=quantity- item.quantityWHERE item = item.id
      COMMIT
      END Transaction
      Assume Each statement is queued for execution
      You will get COMMIT success
    • 15. Alternate Implementations
      BigTable – Google – CP
      Hbase – Apache – CP
      HyperTable – Community - CP
      Dynamo – Amazon – AP
      SimpleDB– Amazon - AP
      Voldemort – LinkedIn – AP
      Cassandra – Facebook– AP
      MemcacheDB - community – CP/AP
    • 16. Data Models
      Key/Value Pairs
      Dynamo, MemcacheDB, Voldemort
      Row-Column
      BigTable, Casandra, SimpleDB, Hypertable, Hbase
    • 17. Programming Models
      // Open the table
      Table *T = OpenOrDie("/bigtable/web/webtable");
      // Write a new anchor and delete an old anchor
      RowMutation r1(T, "com.cnn.www");
      r1.Set("anchor:www.c-span.org", "CNN");
      r1.Delete("anchor:www.abc.com");
      Operation op;
      Apply(&op, &r1);
    • 18. BigTable: Consistent yet Infinitely Scalable
      Single Master
      B+ tree based data distribution
    • 19. BigTable: Transactions
      Enities and Entity Groups
      Invoice
      Invoice Item
      Delivery Note
    • 20. Dynamo: Highly available and Infinitely Scalable
      Consistent Hashing
      Peer to Peer Distributed
      Gossip based member discovery
    • 21. RDBMS or Other?
      Nature of Business
      Maturity of the Product
      Cost of Adoption
      Maturity of the alternative Datastores
    • 22. Q&A

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