CS4432-Project 2- Simplified Version of DBMS Solved

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 Project 2

Section 1: Overview

In this project, you will create a simple index structure to speed up the performance of the lookup queries. A lookup query is a query that involves a predicate (condition) on a certain column.

Ways to answer a lookup query: As covered in class, there are two main approaches as summarized below.

  • Full Table Scan: In this approach, the database system needs to read the entire table (all its blocks) one by one. Then, for each block, the records are scanned one by one and checked against the query predicate.
    • This approach is used ONLY if there is no index available.
  • Index Lookup: In this approach, the database system will first check the index, and figure out whether the value exists or not. If the value exists (or potentially exits), then the index will specify which data block to read. The DBMS will then read this data block only without scanning all other blocks.
    • This approach is used if there is an index available on the column involved in the condition

Dataset Format:

  • You are given a dataset that you can directly use.
  • The directory containing the data is named “Project2Dataset”. You can hardcode this name in your project. It is the name that TAs will use when doing the testing.
  • Put this directory “Project2Dataset” under the working directory of the Java program (the directory from which the program will run). This is the location from which the data is read.
  • The dataset directory contains 99 files, each file contains 100 records, and each record is 40 bytes. This very similar to Project 1 dataset with slight differences as follows. For this project the record format for a record #j in file #i is (for i use two digits like 01, and for j use three digits like 001):

Fi-Recj, Namej, addressj, RandomV

where RandomV is a four digit random number between 0001 and 5000. Since the number of records in the entire dataset is around 10,000, then each value within the range of 0001 and 5000 is expected to appear in the dataset (on average) twice, but it can be more. Also, each record ends with three dots “…” to complete to 40 bytes.

  • The RandomV column is the column on which we will do the search and build the index.
  • As in Project 1, all records are of the same length (40 bytes), they are concatenated after each other, and there are no “new line” characters. The record boundaries are computed based on the 40-byte length.
  • Section 2: Building Hash-Based Index Structure

In this part, you need to write code that builds a hash-based index on the the RandomV  column. The code will do the following functionalities:

  • Read all files in the dataset directory, one by one, and for each file, read record by record.
  • For each record, extract the RandomV value, and put it in a hash table. A hash table entry should have two components (key k, value v), where k = RandomV value, and v = the record locations (file number and the offset at which the record begins within this file).
    • It is up to you to design the appropriate data type (or structure for v) to keep multiple locations associated with a certain key.
    • Hint: You may concatenate multiple locations in a single string.
  • The hash table should be kept in memory, and this is your hash-based index.
  • Section 3: Building Array-based Index Structure

In this part, you need to write code that builds an array-based index on the RandomV column. The code will do the following functionalities:

  • Allocate an array of size 5,000, each entry should store record locations (file number and the offset at which the record begins within this file).
    • Keep in mind that for a single value, there can be multiple records with that value o It is up to you to design the appropriate structure
  • Read all files in the dataset directory, one by one, and for each file, read record by record.
  • For each record, extract the RandomV value, say the value = i . go to the ith slot in the array and add the record location information.
  • The array should be kept in memory, and this is your array-based index.

To build the hash and array-based indexes, your program should support this command

“CREATE INDEX ON RandomV”

  • This command should build both indexes
  • The data files should be read ONCE to build both indexes concurrently
  • Once the indexes are built, print out message “The hash-based and array-based indexes are built”

 Section 4: Equality-Based Query Lookup

To receive the query, your program should support this command

“SELECT * FROM Project2Dataset WHERE RandomV = v

  • It is up to you to decide which index (if any) to use to efficiently answer this query.
  • If you will use an index, make sure to leverage the record location information (both the fileId and byte offset) to minimize the I/O and CPU.
  • “v” is any constant number.
  • The output that you should generate is: o Print out the record(s) matching the query o Indicate which index did you use (if any).
    • Report the time taken to answer the query (in milli sec)
    • Indicate how many data file(s) (which are equivalent to disk blocks) did you need to read
    • Section 5: Range-Based Query Lookup

To receive the query, your program should support this command

“SELECT * FROM Project2Dataset WHERE RandomV > v1 AND RandomV < v2”

  • It is up to you to decide which index (if any) to use to efficiently answer this query.
  • If you will use an index, make sure to leverage the record location information (both the fileId and byte offset) to minimize the I/O and CPU.
  • v1 & v2 are any constant numbers.
  • The output that you should generate is:
    • Print out the record(s) matching the query
    • Indicate which index did you use (if any)
    • Report the time taken to answer the query (in milli sec)
    • Indicate how many data file(s) (which are equivalent to disk blocks) did you need to read
    • Section 6: Inequality-Based Query Lookup

To receive the query, your program should support this command

“SELECT * FROM Project2Dataset WHERE RandomV != v”

  • It is up to you to decide which index (if any) to use to efficiently answer this query.
  • If you will use an index, make sure to leverage the record location information (both the fileId and byte offset) to minimize the I/O and CPU.
  • v is any constant numbers.
  • The output that you should generate is: o Print out the record(s) matching the query o Indicate which index did you use (if any)

o Report the time taken to answer the query (in milli sec) o Indicate how many data file(s) (which are equivalent to disk blocks) did you need to read

 

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