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  • Volume is High: Aqua is about 6MB Simple query: get the names of pesticides for crop disease x Complex query: the amount of money got by selling the crops
  • Why not bit identifiers? Storage is byte addressable. Packing bit identifiers in bytes increases the storage management complexity.
  • Cpu intensive query optimization eg


  • 1. The DELite Project: Database Support for Embedded Lightweight Devices Prof. Krithi Ramamritham
  • 2. Outline of the talk
    • Need for small footprint DBMSs
    • New Issues in Implementation
    • Project Goals
    • Review of Existing Work
    • Current Implementation Status
  • 3. Small DBMSs, e.g., for Handhelds
    • Small, Convenient, Carry anywhere
    • Powerful
      • E.g. Simputer- 206MHz, 32MB SDRAM, 24 MB Flash memory, LCD display, Smart card
    • Applications
      • Personal Info Management
        • E-dairy
      • Enterprise Applications
        • Health-care, Micro-banking
  • 4. Need for Handheld DBMS
    • Handheld applications
      • Volume of data is high
      • Simple and Complex Queries
        • select, project, aggregate
      • ACID properties of transactions
      • Require Data Privacy
      • Need Synchronization
    • Database management techniques are needed to meet the above requirements
  • 5. New Issues in Implementation
    • Small DBMS vs. Disk DBMS
      • Handheld DB is Flash memory based
        • Disk read time is very small
      • Storage model should consider small memory and computation power
      • Transaction management and synchronization have to consider disconnections, mobility and communication cost
      • Handheld Operating System provides lesser facilities
        • E.g. no multi-threading support in PalmOS
      • Better security measures are required as handhelds are easily stolen, damaged and lost
  • 6. Project Goals
    • Existing work –
    • Investigations of
      • Storage models
      • Query processing & optimization
      • Executor
    • Proposed work
      • Compression in Storage
      • Transaction management
      • Synchronization
  • 7. Existing Work – Review
    • Storage Management
      • Aim at compactness in representation of data
      • Limited storage could preclude any additional index
        • Data model should try to incorporate some index information
    • Query Processing
      • Minimize writes to secondary storage
      • Efficient usage of limited main memory
  • 8. Storage Management
    • Existing storage models
      • Flat Storage
        • Tuples are stored sequentially. Duplicates not eliminated
      • Pointer-based Domain Storage
        • Values partitioned into domains which are sets of unique values
        • Tuples reference the attribute value by means of pointers
        • One domain shared among multiple attributes
  • 9. Storage Management (cont) Flat Storage Domain Storage
    • In Domain Storage, pointer of size p (typically 4 bytes) points to the domain value. Can we further reduce the storage cost?
    10 20 30 40 p q s r IT12 Flat Relation CSE11 CSE11 CSE11 CSE11 10 20 30 40 p q r s Domain Relation 4 bytes IT12
  • 10. ID Based Storage Relation R ID Values 0 1 2 1 n 0 n v0 v1 vn Domain Values Positional Indexing
  • 11. ID Based Storage
    • ID Storage
      • An identifier for each of the domain values
      • Store the smaller identifier instead of the pointer
      • Identifier is the positional value in the domain table. Use it as an offset into the domain table
      • D domain values can be distinguished by identifiers of length log 2 D /8 bytes.
  • 12. ID Storage (cont)
      • Extendable IDs are used. Length of the identifier grows and shrinks depending on the number of domain values
      • Starting with 1 byte identifiers, the length grows and shrinks.
      • To reduce reorganization of data, ID values are projected out from the rest of the relation and stored separately maintaining Positional Indexing.
  • 13. ID Storage (cont)
    • Ping Pong Effect
      • At the boundaries, there is reorganization of ID values
      • when the identifier length changes
      • Frequent insertions and deletions at the boundaries might
      • result in a lot of reorganization
      • Phenomena should be avoided
    • No deletion of Domain values
      • Domain structure means a future insertion might reference
      • the deleted value
      • Do not delete a domain value even it is not referenced
    • Setting a threshold for deletion for domain values
      • Delete only if number of deletions exceeds a threshold
      • Increase the threshold when boundaries are being crossed to reduce ping pong effect
  • 14. ID Storage (cont)
    • Primary Key-Foreign Key relationship
      • Primary key is a domain in itself
      • IDs for primary key values
      • Values present in child table are the corresponding primary key IDs
      • Projected foreign key column forms a Join Index
    Figure: Primary Key-Foreign Key Join Index 0 1 2 1 n 0 n v0 v1 vn Parent Table Relation R Child Table
  • 15. ID Storage (cont)
    • ID based Storage wins over Domain Storage when pointer size > log 2 D /8
    • Relations in a small device do not have a very high cardinality.
    • Above condition true for most of the data.
    • Advantages of ID storage
      • Considerable saving in storage cost.
      • Efficient join between parent table and child table
  • 16. Query Processing
    • Considerations
      • Minimize writes to secondary storage
      • Use Main memory as write buffer
    • Need for Left-deep Query Plan
      • Reduce materialization in flash memory. If absolutely necessary use main memory
      • Bushy trees use materialization
      • Left deep tree is most suited for pipelined evaluation
      • Right operand in a left-deep tree is always a stored relation
  • 17. Query Processing (cont)
    • Need for optimal memory allocation
      • Using nested loop algorithms for every operator ensures that minimum amount of memory used to execute the plan
      • Nested loop algorithms are inefficient
      • Different devices come with different memory sizes
      • Query plans should make efficient use of memory. Memory must be optimally allocated among all operators
    • Need to generate the best query execution plan depending on the available memory
  • 18. Query Processing (cont)
    • Operator evaluation schemes
      • Different schemes for an operator
      • Schemes conform to left-deep tree query plan
      • All have different memory usage and cost
      • Cost of a scheme is the computation time
  • 19. Query Processing (cont)
    • 2-Phase optimizer
      • Phase 1: Query is first optimized to get a query plan
      • Phase 2: Division of memory among the operators
      • Scheme for every operator is determined in phase 1 and remains unchanged after phase 2, memory allocation in phase 2 is on the basis of the cost functions of the schemes
      • Memory is assumed to be available for all the schemes, this may not be true for a resource constrained device
    • Traditional 2-phase optimization cannot be used
  • 20. Query Processing (cont)
    • 1-Phase optimizer
      • Query optimizer is made memory cognizant
      • Modified optimizer takes into account division of memory among operators while choosing between plans
      • Ideally, 1-phase optimization should be done but the optimizer becomes complex.
  • 21. Query Processing (cont)
    • Modified 2-phase optimizer
      • Optimal division of memory involves the decision of selecting the best scheme for every operator
      • Phase 1:
        • Determine the optimal left-deep join order using dynamic programming approach
      • Phase 2:
        • Divide memory among the operators
        • Choose the scheme for every operator depending on the memory allocated
  • 22. Query Processing (cont)
    • Memory allocation algorithms
      • Exact memory allocation
      • Heuristic memory allocation
    • Conclusions
      • Response times highest with minimum memory and least with maximum memory
      • Computing power of the handheld affects the response time in a big way
      • Heuristic memory allocation differed from exact algorithm in a few points only
  • 23. Compression in DB
    • Advantages
      • Saves space
      • Reduces read time and write time as less data is processed
      • Logging consumes less space and time
    • Disadvantages
      • CPU intensive
      • Competes with other CPU intensive DBMS tasks.
      • May slow down the DBMS
  • 24. Compression in Disk DB
    • Main assumption
      • The high disk read time compensates for the extra time required for compression and decompression
      • E.g. Let time taken to read 10 blocks of data from the disk be 10ms. Let the time taken for compression and decompression be 5ms. After compression 10 blocks occupy only 1 block.
      • Processing time with compression/decompression
        • = ( 1ms + 5ms) = 6ms
    • Handheld DB is Flash memory based
      • Read time is very less. Above assumption is no longer valid!!
  • 25. Transaction Management
    • Ensure ACID properties of local and global transactions
      • Local transaction - Update address book entry in Simputer
      • Global transaction - Transfer money from a bank account to an epurse in a smart card attached to a Simputer
    • Issues
      • Frequent disconnections, resource constraints, mobility, loss or damage to handheld
  • 26. Synchronization
    • Access data Anytime and Anywhere using the handheld
      • Mobile sales person, Wireless ware house
    • Problem – Not possible to remain connected always
    • Solution- Replicate data in the handheld
      • Download a copy of the data into the handheld from the remote server and process it offline. Periodically merge the changes with the server
  • 27. Synchronization -Issues
    • Data replication can lead to conflicts
      • Update-update, Update-delete, Unique key violation, Integrity constraint violation
    • Maintain global consistency between replicated copies
      • Strict consistency with Data partitioning
      • Strict consistency with Reservation protocols or Leases
        • Efficient when data is rarely shared
      • Weak consistency with Eventual consistency
        • leases restrictive when data is shared between many copies
        • Independently access and update data
        • only tentative commits possible
        • Actual commit when transaction is executed at the server
  • 28. Conclusions
    • Handheld DBMS techniques have to consider the resource constraints, mobility, frequent disconnections, and security aspects of the handheld
    • The techniques used for one component will influence the choice of the technique used in another component. There is a very strong interdependence between the components of the handheld DBMS
    • Techniques rejected for the disk environment may be explored in the handheld environment
  • 29. Future work
    • Sync tool
    • Transaction management component
    • Recovery management component
    • Concurrency control component
    • Performance analysis of existing compression techniques in handheld environment
  • 30. References
  • 31. References (cont)
  • 32. References (cont)
  • 33. References (cont)