Database systems project
Database 数据库作业代写 You will hand in your code using Reprozip inside a Docker Virtual Machine so that it is reproducible across platforms.
2.5 Project: A miniature relational database with order Database 数据库作业代写
This project is due on Monday Dec 2, 2019 at 4:30 PM.
Given ordered tables (array-tables) whose rows consist of strings and integers, you are to write a program which will
- Perform the basic operations of relational algebra: selection, projec-tion, join, group by, and count, sum and avg aggregates. The com-parators for select and join will be = <, >, ! =, ≥, ≤
- Because the array-tables are potentially ordered, you can sort an array-table by one or more columns, and running moving sums and average aggregates on a column of an array-table.
- Import a vertical bar delimited fifile into an array-table (in the same order), export from an array-table to a fifile preserving its order, and assign the result of a query to an array-table.
-
Each operation will be on a single line. Each time you execute a line,you should print the time it took to execute.
- You will support in memory B-trees and hash structures. You are welcome to take those implementations from wherever you can fifind them, but you must say where.
- Your program should be written in python or java. You will hand in clean and well structured source code in which each function has a header that says (i) what the function deos, (ii) what its inputs are and what they mean (iii) what the outputs are and mean (iv) any side effffects to globals.
- You must ensure that your software runs on the Courant Institute (cims) machine crunchy5.cims.nyu.edu
- You may NOT use any relational algebra or SQL library or system (e.g. no SQLite, no mySQL, no other relational database system, no Pandas ). Stick pretty much to the standard stuffff (e.g. in Python: numpy, core language features, string manipulation, random number generators, and data structure support for in memory B-trees and hash structures). You may not use anyone else’s code (other than for the data structure implementation). Doing so will constitute plagiarism.
We will run your programs on test cases of our choosing. For ease of parsing there will be one operation per line. Comments begin with // and go to the end of the line. For example, Database 数据库作业代写
R := inputfromfile(sales1) // import vertical bar delimited foo, first line
// has column headers.
// Suppose they are saleid|itemid|customerid|storeid|time|qty|pricerange
R1 := select(R, (time > 50) or (qty < 30))
// select * from R where time > 50 or qty < 30
R2 := project(R1, saleid, qty, pricerange) // select saleid, qty, pricerange
// from R1
R3 := avg(R1, qty) // select avg(qty) from R1
R4 := sumgroup(R1, time, qty) // select qty, sum(time) from R1 group by qty
R5 := sumgroup(R1, qty, time, pricerange) // select sum(qty), time,
// pricerange from R1 group by time, pricerange
R6 := avggroup(R1, qty, pricerange) // select avg(qty), pricerange
// from R1 group by by pricerange
S := inputfromfile(sales2) // suppose column headers are
// saleid|I|C|S|T|Q|P
T := join(R, S, R.customerid = S.C) // select * from R, S
// where R.customerid = S.C
T1 := join(R1, S, R1.qty > S.Q) // select * from R1, S where R1.qty > S.Q
T2 := sort(T1, S_C) // sort T1 by S_C
T2prime := sort(T1, R1_time, S_C) // sort T1 by R_itemid, S_C (in that order)
T3 := movavg(T2prime, R1_qty, 3) // perform the three item moving average of T2prime
// on column R_qty. This will be as long as R_qty with the three way
// moving average of 4 8 9 7 being 4 6 7 8 Database 数据库作业代写
T4 := movsum(T2prime, R1_qty, 5) // perform the five item moving sum of T2prime
// on column R_qty
Q1 := select(R, qty = 5) // select * from R where qty=5
Btree(R, qty) // create an index on R based on column qty
// Equality selections and joins on R should use the index.
Q2 := select(R, qty = 5) // this should use the index
Q3 := select(R, itemid = 7) // select * from R where itemid = 7
Hash(R,itemid)
Q4 := select(R, itemid = 7) // this should use the hash index
Q5 := concat(Q4, Q2) // concatenate the two tables (must have the same schema)
// Duplicate rows may result (though not with this example).
outputtofile(Q5, Q5) // This should output the table Q5 into a file
// with the same name and with vertical bar separators
Our tests may operate on difffferent fifiles with difffferent column headers.
Our queries may use difffferent paramter values (e.g. 14 way moving average).
Our joins may be on difffferent fifields.
Some constraints to make your life easier: Database 数据库作业代写
- There will be no syntax errors in our tests. However, white space may vary from the above examples.
- The only aggregates are count, sum, and avg and the corresponding countgroup, sumgroup, and avggroup.
- The only moving aggregates are movsum and movavg. There is no group by for moving sums and averages.
- All data is in main memory.
- The joins are on single columns
- The selects will be all ors or all ands.
Here is an example of the fifirst few lines of the sales1 fifile:
saleid|itemid|customerid|storeid|time|qty|pricerange
45|133|2|63|49|23|outrageous
658|75|2|89|46|43|outrageous
149|103|2|23|67|2|cheap
398|82|2|41|3|27|outrageous
147|81|2|4|92|11|outrageous
778|75|160|72|67|17|supercheap
829|112|2|70|63|43|supercheap
101|105|2|9|74|28|expensive
940|62|2|90|67|39|outrageous
864|119|12|38|67|49|outrageous
288|46|2|95|67|26|outrageous
875|83|59|56|59|20|outrageous
783|86|180|29|67|46|outrageous
289|16|2|95|92|2|cheap
6Full example fifiles can be found here:
http://cs.nyu.edu/cs/faculty/shasha/papers/sales1
http://cs.nyu.edu/cs/faculty/shasha/papers/sales2
You will hand in your code using Reprozip inside a Docker Virtual Machine so that it is reproducible across platforms.
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