In this lab, you will implement a query optimizer on top of SimpleDB. The main tasks include implementing a selectivity estimation framework and a cost-based optimizer. You have freedom as to exactly what you implement, but we recommend using something similar what was discussed in our lecture.
The remainder of this document describes what is involved in adding optimizer support and provides a basic outline of how you might add this support to your database.
As with the previous lab, we recommend that you start as early as possible.
We have provided you with extra test cases as well as source code files for this lab that are not in the original code distribution you received. We reiterate that the unit tests we provide are to help guide your implementation along, but they are not intended to be comprehensive or to establish correctness. You will need to add these new test cases to your release. The easiest way to do this is to untar the new code in the same directory as your top-level simpledb directory, as follows:
$ tar -cvzf CS6530-lab3-submitted.tar.gz CS6530-lab3
$ mv CS6530-lab3 CS6530-lab4
$ cd CS6530-lab4
$ wget http://www.cs.utah.edu/~lifeifei/cs6530/CS6530-lab4-supplement.tar.gz
tar -xvzf CS6530-lab4-supplement.tar.gz
Here's a rough outline of one way you might proceed with this lab. More details on these steps are given in Section 2 below.
The optimizer will be invoked from simpledb/Parser.java. Briefly, if you have a catalog file catalog.txt describing your tables, you can run the parser by typing:
java -jar dist/simpledb.jar parser catalog.txt
When the Parser is invoked, it will compute statistics over all of the tables (using statistics code you provide). When a query is issued, the parser will convert the query into a logical plan representation and then call your query optimizer to generate an optimal plan.
Before getting started with the implementation, you need to understand the overall structure of the SimpleDB optimizer.
The overall control flow of the SimpleDB modules of the parser and optimizer is shown in Figure 1.
Figure 1: Diagram illustrating classes, methods, and objects used in the parser and optimizer.
The key at the bottom explains the symbols; you will implement the components with double-borders. The classes and methods will be explained in more detail in the text that follows (you may wish to refer back to this diagram), but the basic operation is as follows:
In the exercises to come, you will implement the methods that help physicalPlan devise an optimal plan.
p=t1 join t2 join ... tn
, which signifies a left deep join where t1 is the left-most
join (deepest in the tree).
Given a plan like p
, its cost
can be expressed as:
scancost(t1) + scancost(t2) + joincost(t1 join t2) + scancost(t3) + joincost((t1 join t2) join t3) + ...Here,
scancost(t1)
is the I/O cost of scanning table t1,
joincost(t1,t2)
is the CPU cost to join t1 to t2.
To make I/O and CPU cost comparable, typically a constant scaling factor
is used, e.g.:
cost(predicate application) = 1 cost(pageScan) = SCALING_FACTOR x cost(predicate application)For this lab, you can ignore the effects of caching (e.g., assume that every access to a table incurs the full cost of a scan). Therefore,
scancost(t1)
is simply the
number of pages in t1 x SCALING_FACTOR
.
joincost(t1 join t2) = scancost(t1) + ntups(t1) x scancost(t2) //IO cost + ntups(t1) x ntups(t2) //CPU costHere,
ntups(t1)
is the number of tuples in table t1.
ntups
can be directly computed for a base table by
scanning that table. Estimating ntups
for a table with
one or more selection predicates over it can be trickier --
this is the filter/selection selectivity estimation problem. Here's one
approach that you might use, based on computing a histogram over the
values in the table:
Figure 2: Diagram illustrating the histograms you will implement in Lab 4.
In the next two exercises, you will code to perform selectivity estimation of joins and filters.
You will need to implement some way to record table statistics for selectivity estimation. We have provided a skeleton class, IntHistogram that will do this. Our intent is that you calculate histograms using the bucket-based method described above, but you are free to use some other method so long as it provides reasonable selectivity estimates.
We have provided a class StringHistogram that uses IntHistogram to compute selecitivites for String predicates. You may modify StringHistogram if you want to implement a better estimator, though you should not need to in order to complete this lab.
After completing this exercise, you should be able to pass the IntHistogramTest unit test.
.
The class TableStats contains methods that compute the number of tuples and pages in a table and that estimate the selectivity of predicates over the fields of that table. The query parser we have created creates one instance of TableStats per table, and passes these structures into your query optimizer (which you will need in later exercises).
You should fill in the following methods and classes in TableStats:
After completing these tasks you should be able to pass the unit tests in TableStatsTest.
While implementing, your simple solution, you should keep in mind the following:
The class JoinOptimizer.java includes all of the methods for ordering and computing costs of joins. In this exercise, you will write the methods for estimating the selectivity and cost of a join, specifically:
Now that you have implemented methods for estimating costs, you will implement the query optimizer. For these methods, joins are expressed as a list of join nodes (e.g., predicates over two tables) as opposed to a list of relations to join as described in class.
Translating the algorithm from our class to the join node list form mentioned above, an outline in pseudocode would be:
1. j = set of join nodes 2. for (i in 1...|j|): 3. for s in {all length i subsets of j} 4. bestPlan = {} 5. for s' in {all length d-1 subsets of s} 6. subplan = optjoin(s') 7. plan = best way to join (s-s') to subplan 8. if (cost(plan) < cost(bestPlan)) 9. bestPlan = plan 10. optjoin(s) = bestPlan 11. return optjoin(j)To help you implement this algorithm, we have provided several classes and methods to assist you. First, the method enumerateSubsets(Vector v, int size) in JoinOptimizer.java will return a set of all of the subsets of v of size size. This method is not particularly efficient; you can try to implement a more efficient enumerator, but not required.
Second, we have provided the method:
private CostCard computeCostAndCardOfSubplan(HashMap<String, TableStats> stats, HashMap<String, Double> filterSelectivities, LogicalJoinNode joinToRemove, Set<LogicalJoinNode> joinSet, double bestCostSoFar, PlanCache pc)Given a subset of joins (joinSet), and a join to remove from this set (joinToRemove), this method computes the best way to join joinToRemove to joinSet - {joinToRemove}. It returns this best method in a CostCard object, which includes the cost, cardinality, and best join ordering (as a vector). computeCostAndCardOfSubplan may return null, if no plan can be found (because, for example, there is no left-deep join that is possible), or if the cost of all plans is greater than the bestCostSoFar argument. The method uses a cache of previous joins called pc (optjoin in the psuedocode above) to quickly lookup the fastest way to join joinSet - {joinToRemove}. The other arguments (stats and filterSelectivities) are passed into the orderJoins method that you must implement as a part of Exercise 4, and are explained below. This method essentially performs lines 6--8 of the psuedocode described earlier.
Third, we have provided the method:
private void printJoins(Vector<LogicalJoinNode> js, PlanCache pc, HashMap<String, TableStats> stats, HashMap<String, Double> selectivities)This method can be used to display a graphical representation of a join plan (when the "explain" flag is set via the "-explain" option to the optimizer, for example).
Fourth, we have provided a class PlanCache that can be used to cache the best way to join a subset of the joins considered so far in your implementation of Selinger (an instance of this class is needed to use computeCostAndCardOfSubplan).
.
In JoinOptimizer.java, implement the method:
VectorThis method should operate on the joins class member, returning a new Vector that specifies the order in which joins should be done. Item 0 of this vector indicates the left-most, bottom-most join in a left-deep plan. Adjacent joins in the returned vector should share at least one field to ensure the plan is left-deep. Here stats is an object that lets you find the TableStats for a given table name that appears in the FROM list of the query. filterSelectivities allows you to find the selectivity of any predicates over a table; it is guaranteed to have one entry per table name in the FROM list. Finally, explain specifies that you should output a representation of the join order for informational purposes.orderJoins(HashMap<String, TableStats> stats, HashMap<String, Double> filterSelectivities, boolean explain)
You may wish to use the helper methods and classes described above to assist in your implementation. Roughly, your implementation should follow the psuedocode above, looping through subset sizes, subsets, and sub-plans of subsets, calling computeCostAndCardOfSubplan and building a PlanCache object that stores the minimal-cost way to perform each subset join.
After implementing this method, you should be able to pass the test OrderJoinsTest. You should also pass the system test QueryTest.
Please follow the same submission procedure as before. If applicable, please indicate your partner in your writeup. Please submit your writeup as a PDF or plain text file only (no .rtf/.doc/.docx/.odf formats please).
SimpleDB is a relatively complex piece of code. It is very possible you are going to find bugs, inconsistencies, and bad, outdated, or incorrect documentation, etc.
We ask you, therefore, to do this lab with an adventurous mindset. Don't get mad if something is not clear, or even wrong; rather, try to figure it out yourself or send us a friendly email. Please submit (friendly!) bug reports to either TAs or the instructor. When you do, please try to include:
test/simpledb
directory, compile, and run.
HeapFileEncoder
.
80% of your grade will be based on whether or not your code passes the test suite we will run over it. These tests will be a superset of the tests we have provided. Before handing in your code, you should make sure it produces no errors (passes all of the tests) from both ant test and ant systemtest.
Important: before testing, we will replace your build.xml, HeapFileEncoder.java, and the entire contents of the test/ directory with our version of these files! This means you cannot change the format of .dat files! You should therefore be careful changing our APIs. This also means you need to test whether your code compiles with our test programs. In other words, we will untar your tarball, replace the files mentioned above, compile it, and then grade it. It will look roughly like this:
[replace build.xml, HeapFileEncoder.java, and test] $ ant test $ ant systemtest [additional tests]If any of these commands fail, we'll be unhappy, and, therefore, so will your grade.
An additional 20% of your grade will be based on the quality of your writeup and our subjective evaluation of your code.
We've had a lot of fun designing this assignment, and we hope you enjoy hacking on it!