From ee1b7eb600b88d437b8798745ac29d7f71164066 Mon Sep 17 00:00:00 2001 From: Oliver Date: Sun, 14 Feb 2021 22:37:07 -0500 Subject: [PATCH] Slides for the week --- src/teaching/cse-562/2021sp/checkpoint1.erb | 4 +- src/teaching/cse-562/2021sp/index.erb | 15 +- .../2021sp/slide/2021-02-16-Checkpoint1.erb | 785 ++ .../slide/2021-02-18-QueryAlgorithms.erb | 489 + .../slide/2021-02-25-PhysicalLayout.erb | 258 + .../slide/graphics/2021-02-16-RA-Tree.svg | 150 + .../slide/graphics/2021-02-18-Flow-Cross.svg | 423 + .../graphics/2021-02-18-Flow-Project.svg | 252 + .../slide/graphics/2021-02-18-Flow-Select.svg | 285 + .../slide/graphics/2021-02-18-Flow-Union.svg | 372 + .../graphics/2021-02-18-Join-1PassHash.svg | 430 + .../graphics/2021-02-18-Join-2PassHash.svg | 608 ++ .../slide/graphics/2021-02-18-Join-BNLJ.svg | 274 + .../slide/graphics/2021-02-18-Join-Grid.svg | 368 + .../slide/graphics/2021-02-18-Join-NLJ.svg | 263 + .../graphics/2021-02-18-Join-OrderGrid.svg | 353 + .../graphics/2021-02-18-Join-SortMerge.svg | 303 + .../graphics/2021-02-25-Buffer-Manager.svg | 577 ++ .../slide/graphics/2021-02-25-Heap-File-1.svg | 390 + .../slide/graphics/2021-02-25-Heap-File-2.svg | 269 + .../graphics/2021-02-25-Layout-ColumnWise.svg | 473 + .../graphics/2021-02-25-Layout-RowWise.svg | 416 + .../graphics/2021-02-25-Page-Layouts-1.svg | 729 ++ .../graphics/2021-02-25-Page-Layouts-2.svg | 385 + .../graphics/2021-02-25-mem_bulk_loading.svg | 9149 +++++++++++++++++ .../graphics/2021-02-25-mem_hierarchy.png | Bin 0 -> 234215 bytes .../graphics/2021-02-25-record-fixed.svg | 209 + .../graphics/2021-02-25-record-header.svg | 372 + .../graphics/2021-02-25-record-separator.svg | 288 + 29 files changed, 18882 insertions(+), 7 deletions(-) create mode 100644 src/teaching/cse-562/2021sp/slide/2021-02-16-Checkpoint1.erb create mode 100644 src/teaching/cse-562/2021sp/slide/2021-02-18-QueryAlgorithms.erb create mode 100644 src/teaching/cse-562/2021sp/slide/2021-02-25-PhysicalLayout.erb create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-16-RA-Tree.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-18-Flow-Cross.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-18-Flow-Project.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-18-Flow-Select.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-18-Flow-Union.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-18-Join-1PassHash.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-18-Join-2PassHash.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-18-Join-BNLJ.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-18-Join-Grid.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-18-Join-NLJ.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-18-Join-OrderGrid.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-18-Join-SortMerge.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-25-Buffer-Manager.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-25-Heap-File-1.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-25-Heap-File-2.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-25-Layout-ColumnWise.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-25-Layout-RowWise.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-25-Page-Layouts-1.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-25-Page-Layouts-2.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-25-mem_bulk_loading.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-25-mem_hierarchy.png create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-25-record-fixed.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-25-record-header.svg create mode 100644 src/teaching/cse-562/2021sp/slide/graphics/2021-02-25-record-separator.svg diff --git a/src/teaching/cse-562/2021sp/checkpoint1.erb b/src/teaching/cse-562/2021sp/checkpoint1.erb index 75f95ef2..788f1128 100644 --- a/src/teaching/cse-562/2021sp/checkpoint1.erb +++ b/src/teaching/cse-562/2021sp/checkpoint1.erb @@ -202,7 +202,7 @@ case class AttributeReference( That all being said, here are unresolved nodes you can expect to encounter:

-
LogicalPlan
+

LogicalPlan

 case class UnresolvedRelation(
     nameElements: Seq[String], 
@@ -224,7 +224,7 @@ case class ____(____)
 Note that AttributeSet is a subclass of Seq[Attribute].  In general, the  output field should be given as a sequence of AttributeReferences (see above).
 

 
-
Expression

+
Expression

 
 case class UnresolvedStar(target: Option[Seq[String]])
 

diff --git a/src/teaching/cse-562/2021sp/index.erb b/src/teaching/cse-562/2021sp/index.erb
index 9d453a19..8dd9d4b7 100644
--- a/src/teaching/cse-562/2021sp/index.erb
+++ b/src/teaching/cse-562/2021sp/index.erb
@@ -14,26 +14,31 @@ schedule:
   - date: "Feb. 9"
     topic: "Relational Algebra + Spark"
     materials: 
+      lecture: https://youtu.be/xnJNTTirgoY
       slides: slide/2021-02-09-RA-Basics-and-Spark.html
   - date: "Feb. 11"
     topic: "Relational Algebra Equivalence Rules"
     materials:
+      lecture: https://youtu.be/IJLLCB6tdCk
       slides: slide/2021-02-11-RA-Equivs.html
   - date: "Feb. 16"
     topic: "Algorithms, Checkpoint 1"
     due: "Checkpoint 0"
     materials: 
       checkpoint1: "checkpoint1.html"
+      slides: slide/2021-02-16-Checkpoint1.html
   - date: "Feb. 18"
-    topic: "Extended Relational Algebra"
+    topic: "Relational Algebra Algorithms"
+    materials: 
+      slides: slide/2021-02-18-QueryAlgorithms.html
   - date: "Feb. 23"
-    topic: "Physical Data Layout"
+    topic: "Extended Relational Algebra"
   - date: "Feb. 25"
-    topic: "Indexes: Tree-Based"
+    topic: "Physical Data Layout"
   - date: "Mar. 2"
-    topic: "Indexes: Hash, View-Based"
+    topic: "Indexes: Tree-Based, Hash"
   - date: "Mar. 4"
-    topic: "Indexes: Modern"
+    topic: "Indexes: View-Based, Modern"
   - date: "Mar. 9"
     topic: "Spark's Optimizer + Checkpoint 2"
     due: "Checkpoint 1"
diff --git a/src/teaching/cse-562/2021sp/slide/2021-02-16-Checkpoint1.erb b/src/teaching/cse-562/2021sp/slide/2021-02-16-Checkpoint1.erb
new file mode 100644
index 00000000..63fe996d
--- /dev/null
+++ b/src/teaching/cse-562/2021sp/slide/2021-02-16-Checkpoint1.erb
@@ -0,0 +1,785 @@
+---
+template: templates/cse4562_2021_slides.erb
+title: "Checkpoint 1"
+date: February 16, 2021
+textbook: "Ch. 16.1"
+---
+
+
+

+  

+    
Checkpoint 1

+
+    
+      
+      
+      
+        sif$
+        
+          scalac -cp build:Catalyzer.jar -jar submission.jar {all .java files}
+          sif$
+        
+      
+      
+        ls data/
+        R.data      S.data     T.data
+        sif$
+      
+      
+        head -n 2 data/R.data
+        1|3|5
+        2|9|1
+        sif$
+      
+      
+        scala -cp submission.jar:Catalyzer.jar microbase.Microbase
+      
+      
+        $>
+      
+      
+        CREATE TABLE R(A int, B int, C int);
+        $>
+      
+      
+        SELECT A, C FROM R WHERE B < 5;
+      
+      
+        1|5
+        ...
+        $>
+      
+    
+  

+  

+    
Checkpoint 1

+    
    
+      
  • Your code is compiled just the same as in Checkpoint 0.
    • 
+      
  • Print a prompt '$>' at the start and after each command.
    • 
+      
  • Read one command per line.
    • 
+      
  • CREATE TABLE statements tell you the schema of each table.
    • 
+      
  • Data lives in a '|'-separated file in 'data/[tablename].data'
    • 
+      
  • Print query results '|'-separated
    • 
+    

+  

+
+  

+    
Setup

+
+    Add this to your build.sbt (modify as appropriate for your IDE)
+
+    

+  resolvers += "MimirDB" at "https://maven.mimirdb.info/"
+  libraryDependencies += "edu.buffalo.cse.odin" %% "catalyzer" % "3.0"
+    

+
+    
Docs: https://doc.odin.cse.buffalo.edu/catalyzer/

+    
Code: https://gitlab.odin.cse.buffalo.edu/okennedy/catalyzer

+
+  

+

+

+  

+    
+      
+        
+        → SQL
+      
+    
+    
CREATE TABLE PLAYERS(
+  ID string, 
+  FIRSTNAME string, 
+  LASTNAME string, 
+  FIRSTSEASON int, 
+  LASTSEASON int, 
+  WEIGHT int, 
+  BIRTHDATE date
+);
+
+SELECT FIRSTNAME, LASTNAME, WEIGHT, BIRTHDATE 
+FROM PLAYERS WHERE WEIGHT>200;

+  

+
+  

+    
+      
+        
+        → SQL
+      
+    
+    

+import org.apache.spark.sql.execution.SparkSqlParser
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+
+...
+  def parseSql(sql: String): LogicalPlan = 
+    new SparkSqlParser().parsePlan(sql)
+...
+    

+  

+
+  

+    
+      
+        
+        → SQL
+        → ?
+      
+    
+    

+...
+  plan match {
+    case c:CreateTableStatement => 
+      /* do something with c.name, c.tableSchema */
+    case _ => 
+      /* Interpret plan like a query */
+  }
+...
+    

+  

+
+  

+    
+      
+        
+        → SQL
+        
+          →
+          CREATE TABLE
+        
+        
+          →
+          SELECT
+        
+      
+    
+    
CREATE TABLE PLAYERS(
+  ID string, 
+  FIRSTNAME string, 
+  LASTNAME string, 
+  FIRSTSEASON int, 
+  LASTSEASON int, 
+  WEIGHT int, 
+  BIRTHDATE date
+);

+↓
+    
There is a table named "PLAYERS"...
+      
    
+        
  • ... with 7 attributes
    • 
+        
  • ... who's attributes have the given types
    • 
+        
  • ... with data in the file "data/PLAYERS.data"
    • 
+      

+    

+  

+  

+    
+      
+        
+        → SQL
+        
+          →
+          CREATE TABLE
+          
+            →
+            
+            
+            Saved Schema
+          
+          
+            
+            .data
+          
+        
+        
+          →
+          SELECT
+        
+      
+    
+    
+ABDELAL01|Alaa|Abdelnaby|1990|1994|240|1968-06-24
+ABDULKA01|Kareem|Abdul-jabbar|1969|1988|225|1947-04-16
+ABDULMA01|Mahmo|Abdul-rauf|1990|2000|162|1969-03-09
+ABDULTA01|Tariq|Abdul-wahad|1997|2002|223|1974-11-03
+ABDURSH01|Shareef|Abdur-rahim|1996|2007|225|1976-12-11
+ABERNTO01|Tom|Abernethy|1976|1980|220|1954-05-06
+ABRAMJO01|John|Abramovic|1946|1947|195|1919-02-09
+ACKERAL01|Alex|Acker|2005|2008|185|1983-01-21
+ACKERDO01|Donald|Ackerman|1953|1953|183|1930-09-04
+ACRESMA01|Mark|Acres|1987|1992|220|1962-11-15
+ACTONCH01|Charles|Acton|1967|1967|210|1942-01-11
+...   

+  

+  

+    
+      
+        
+        → SQL
+        
+          →
+          CREATE TABLE
+          
+            →
+            
+            
+            Saved Schema
+          
+          
+            
+            .data
+            
+              
+            
+          
+        
+        
+          →
+          SELECT
+          
+            
+            
+              
+            
+            
+              Results
+            
+          
+        
+      
+    
+    
Example Queries

+    
    
+      
  1. SELECT A, B, ... FROM R (Project)
    2. 
+      
  2. SELECT A, B, ... FROM R WHERE ... (Project+Filter)
    3. 
+      
  3. SELECT A+B AS C, ... FROM R (Map)
    4. 
+      
  4. SELECT A+B AS C, ... FROM R WHERE ... (Map+Filter)
    5. 
+      
  5. SELECT SUM(A+B) AS C, ... FROM R (Aggregate)
    6. 
+      
  6. SELECT SUM(A+B) AS C, ... FROM R WHERE ... (Aggregate+Filter)
    7. 
+    

+  

+  

+    
+      
+        
+        → SQL
+        
+          →
+          CREATE TABLE
+          
+            →
+            
+            
+            Saved Schema
+          
+          
+            
+            .data
+            
+              
+            
+          
+        
+        
+          →
+          SELECT
+          
+            
+            
+              
+            
+            
+              Results
+            
+          
+        
+      
+    
+
+    
Spark's Workflow

+
+    
    
+      
  1. Analysis
    2. 
+      
  2. Optimization
    3. 
+      
  3. Physical Planning
    4. 
+      
  4. Code Generation
    5. 
+      
  5. Execution
    6. 
+    

+  

+
+  

+    
+      
+        
+        → SQL
+        
+          →
+          CREATE TABLE
+          
+            →
+            
+            
+            Saved Schema
+          
+          
+            
+            .data
+          
+        
+        
+          →
+          SELECT
+          
+            
+            
+              
+            
+            
+              LogicalPlan
+              
+                
+                
+                  
+                
+                
+                  
+                    
+                    
+                      
+                    
+                  
+                  Analyzed Plan
+                  
+                    
+                    
+                      
+                    
+                    
+                      Iterators
+                      
+                        
+                        
+                          
+                        
+                      
+                      
+                        
+                        
+                          
+                        
+                        
+                          Results
+                        
+                      
+                    
+                  
+                
+              
+            
+          
+        
+      
+    
+  

+

+
+

+  

+    
Analysis

+
+    

+      

+        
Resolution

+        

+          
    
+            
  • Replace placeholder values from parsing.
    • 
+            
  • "Wire up" attributes between operators.
    • 
+          

+        

+      

+      

+        
Validation

+        

+          
    
+            
  • Ensure all of the types line up.
    • 
+          

+        

+      

+    

+  

+
+  

+    
Placeholders

+
+    

+  case class UnresolvedRelation(
+    nameElements: Seq[String], 
+    options: CaseInsensitiveStringMap, 
+    isStreaming: Boolean
+  )
+    

+    
Separation of concerns: The parser doesn't know what tables have been defined.

+  

+
+  

+    
Try It

+  

+    println(
+      parser.parsePlan("SELECT * FROM R").treeString
+    )
+  

+      ↓
+  

+'Project [*]
++- 'UnresolvedRelation [R], [], false
+  

+  

+
+  

+    

+  Project(Seq(UnresolvedStar(None)), 
+    UnresolvedRelation(Seq("R"), CaseInsensitiveStringMap.empty, false)
+  )
+    

+
+    
The interesting thing here is the nameElements field
(Seq("R") above)

+    
+    
This is a sequence to handle multipart names
(e.g., source.table→ Seq("source", "table"))

+  

+
+  

+    
Replacing Placeholders

+    

+  import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
+  ...
+  plan.transform { 
+    case UnresolvedRelation(nameElements, _, _) => ???
+  }
+    

+
+    
By the way, ??? is valid Scala.
It means "I haven't implemented this yet".

+
+    
So what goes there?

+  

+
+  

+    
Suggested Approach

+    

+  import org.apache.spark.sql.catalyst.plans.logical.LeafNode
+  import org.apache.spark.sql.catalyst.expressions.AttributeSequence
+
+  class Table( /* parameters */ ) extends LeafNode
+  {
+    def output: AttributeSequence = ???
+  }
+    

+  

+
+  

+    
Expressions

+  

+
+  

+    
org.apache.spark.sql.catalyst.analysis.UnresolvedStar

+    
↓

+    
Seq(AttributeReference)

+
+    
org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute

+    
↓

+    
AttributeReference

+  

+
+  

+    

+  case class AttributeReference(
+      name: String,
+      dataType: DataType,
+      nullable: Boolean = true,
+      override val metadata: Metadata = Metadata.empty
+    )(
+      val exprId: ExprId = NamedExpression.newExprId,
+      val qualifier: Seq[String] = Seq.empty[String]
+    ) extends Attribute with Unevaluable { ... }
+    

+
+    
Simple Constructor:  AttributeReference(name, dt)()

+    
ExprId Constructor:  AttributeReference(name, dt)(id)

+  

+
+  

+    
exprId

+
+  

+    AttributeReference("a", IntType)().equals(
+      AttributeReference("a", IntType)())
+  

+
+  
returns false

+
+  
Spark uses exprId equivalence to check whether two attributes are the same.

+  

+
+  

+    
exprId

+
+  

+    val id = NamedExpression.newExprId
+    AttributeReference("a", IntType)(id).equals(
+      AttributeReference("a", IntType)(id))
+  

+
+  
returns true

+  

+
+  

+    
qualifiers

+
+    

+    AttributeReference("foo", IntType)(
+      qualifier = Seq("bar")
+    )
+    

+
+    
represents bar.foo

+
+    
You don't need to use this, but Spark already uses it, and it helps during analysis.

+  

+
+  

+    
Why Do Analysis?

+
+    
    
+      
  • Makes output work automagically on all existing LogicalPlan nodes.
    • 
+      
  • Makes dataType work automagically on all existing Expression nodes.
    • 
+      
  • Makes it easier to support eval on all existing Expression nodes.
    • 
+    

+  

+
+

+
+

+  

+    
Evaluating Expressions

+
+    

+  import org.apache.spark.sql.catalyst.InternalRow
+  ...
+  def eval(input: InternalRow): Any = ???  
+    

+
+    
Input:  An InternalRow

+    
Output:  The result of evaluating the expression

+
+    
eval is implemented for most existing Spark Expression nodes.

+  

+
+  

+    

+  case class AttributeReference(
+      name: String,
+      dataType: DataType,
+      nullable: Boolean = true,
+      override val metadata: Metadata = Metadata.empty
+    )(
+      val exprId: ExprId = NamedExpression.newExprId,
+      val qualifier: Seq[String] = Seq.empty[String]
+    ) extends Attribute with Unevaluable { ... }
+    

+
+    
Unevaluable, huh?

+  

+
+  

+    

+  abstract class InternalRow extends SpecializedGetters with Serializable {
+    boolean getBoolean(int ordinal);
+    byte getByte(int ordinal);
+    short getShort(int ordinal);
+    int getInt(int ordinal);
+    long getLong(int ordinal);
+    float getFloat(int ordinal);
+    double getDouble(int ordinal);
+    ...
+  }
+    

+    
InternalRow is basically just an Array[Any]

+    
AttributeReference doesn't know which position the attribute will be at.

+  

+
+  

+    
Suggested Approach

+
+    
Make your own RowLookup subclass of Expression

+
+    
Why not use this class instead of AttributeReference in the first place?

+
+    
Once we start optimizing, optimization rules (e.g., Projection Pushdown) can change an attribute's position.

+  

+

+
+

+  

+    
Evaluating LogicalPlan nodes

+
+    
Separation of concerns: Look at each LogicalPlan node individually.

+
+    
Naive approach: Compute the full result.

+  

+
+  

+    

+  def eval(plan: LogicalPlan): Seq[InternalRow] =
+    plan match { 
+      case Project(targets, child) => 
+        evalProject(targets, eval(child))
+      case Filter(condition, child) => 
+        evalFilter(targets, eval(child))
+      ...
+    }
+
+  def evalProject(targets: Seq[Expression], 
+                  table: Seq[InternalRow]): Seq[InternalRow] = ???
+  def evalFilter(condition: Expression, 
+                 table: Seq[InternalRow]): Seq[InternalRow] = ???
+  ...
+    

+  

+
+  

+    
Basic Mindset

+    
+    

+  r = readCSVFile("R")
+
+  s = readCSVFile("S")
+  
+  temp1 = evalCrossProduct(r, s)
+  
+  temp2 = evalFilter({R.B=S.B AND S.C=10}, 
+                     temp1)
+  
+  result = evalProject(Seq( {R.A} ), 
+                       temp2)
+    

+  

+
+  

+    
Select

+
+    

+      $$\sigma_{A \neq 3} R$$
+    

+    
+      
+      
+      
+      
+    
AB
12
34
56

+  

+
+  

+    
Select

+
+    

+  def evalFilter(condition: Expression, input: Seq[InternalRow]) =
+      input.filter { row =>
+        condition.eval(row).asInstanceOf[Boolean] 
+      }
+    

+    
(All-At-Once)

+  

+
+  

+    
Problem: A "table" can get very very big.

+  

+
+  

+    
Better Idea: Iterators

+    

+      
hasNext()

+      
Returns true if there are more rows to return

+      
next()

+      
Returns the next row

+      
reset()

+      
Resets the iterator back to the first row

+    

+    
All "functions" can be implemented as iterators that use constant space

+  

+  

+    
Select

+
+    

+      $$\sigma_{A \neq 3} R$$
+    

+    
+      
+      
+      
+      
+      
+      
+      
+      
+    
AB
getNext()for row in input:
12return row;
getNext()for row in input:
34X
56return row;
getNext()for row in input:
Nonereturn None;

+  

+
+  

+    
Hint: Scala makes working with iterators very easy

+  

+
+  

+    
Joins

+  

+
+  

+    
Example: Join (Naive)

+    

+      for r in R:
+        for s in S:
+          emit(merge(r, s))
+    

+
+    
Project challenge: Implement this as an iterator

+  

+  

+    
'|'-separated Value File Suggestions

+    
    
+      
  • Use Scala's scala.io.Source's lineIterator() method
    • 
+      
  • Use String's split() to separate fields.
    • 
+      
  • Parse everything upfront
    • 
+      
  • Iterate over InternalRow
    • 
+      
  • Use InternalRow.fromSeq to create rows.
    • 
+      
  • For Codd's sake, don't store entire tables in memory
    • 
+    

+  

+  

+    
+      
+      
+      
+      
+      
+      
+    
SQL TypeSpark typeScala Type
stringStringTypeUTF8String
intIntTypeInteger
floatFloatTypeFloat
decimalDoubleTypeDouble
dateDateTypejava.time.Date

+    
it's org.apache.spark.unsafe.types.UTF8String

+  

+

+
+

+  
Questions?

+

+
+

+  
Next time...

+
+  
Algorithms for Basic RA

+

+
diff --git a/src/teaching/cse-562/2021sp/slide/2021-02-18-QueryAlgorithms.erb b/src/teaching/cse-562/2021sp/slide/2021-02-18-QueryAlgorithms.erb
new file mode 100644
index 00000000..50fa456d
--- /dev/null
+++ b/src/teaching/cse-562/2021sp/slide/2021-02-18-QueryAlgorithms.erb
@@ -0,0 +1,489 @@
+---
+template: templates/cse4562_2021_slides.erb
+title: "Algorithms for Queries"
+date: February 18, 2021
+textbook: "Ch. 15.1-15.5, 16.7"
+---
+
+
+
+
+

+  

+    
Query Evaluation Styles

+
+    

+      
All-At-Once (Collections)

+      
Bottom-up, one operator at a time.

+
+      
Volcano-Style (Iterators)

+      
Operators "request" one tuple at a time from children.

+
+      
Push-Style (Buffers)

+      
Operators continuously produce/consume tuples.

+    

+  

+
+  

+    
Analyzing Volcano Operators

+
+    
    
+      
  • Memory Bounds
    • 
+      
  • Disk IO Used
    • 
+      
  • CPU Used
    • 
+    

+
+    
Databases are usually IO- or Memory-bound

+  

+
+  

+    
Memory Bounds

+
+    
    
+      
  • Constant
    • 
+      
  • Scales with output
    • 
+      
  • Scales with part of the input
    • 
+      
  • Worse
    • 
+    

+
+    
Core Question:  Do we have enough memory to use this operator?

+  

+
+  

+    
Disk IO

+
+    
IO measured in:

+      
    
+        
  • Number of Tuples
    • 
+        
  • Number of Data Pages (absolute size)
    • 
+      

+    

+    

+      
Accounting

+      
Figure out the cost of each individual operator.

+      
Only count the number of IOs added by each operator.

+    

+  

+

+
+

+  
Note

+
+  
We'll be discussing the "default" algorithm for each operator.
 
+
+  
Often, there are many algorithms, some of which cover multiple operators.

+
+  
This is why Spark has a PhysicalPlan

+  
+  
In the suggested Iterator-based approach the Iterators are your PhysicalPlan

+

+
+

+  

+    
Table Scan ($R$)

+    

+      
Memory Required?

+      
Constant!

+
+      
IOs added?

+      
$|R|$ tuples read

+    

+  

+
+  

+    
Select ($\sigma(R)$)

+    
+  

+  

+    
Select ($\sigma(R)$)

+    

+      
Memory Required?

+      
Constant!

+
+      
IOs added?

+      
None! (Can "inline" into cost of $R$)

+    

+  

+
+  

+    
Example

+
+    
Example, assume $R$ is 100 tuples.

+
+    
How many IOs do we need to compute $Q := R$

+    
How many IOs do we need to compute $Q := \sigma(R)$

+  

+
+  

+    
Project ($\pi(R)$)

+    
+  

+  

+    
Project ($\pi(R)$)

+    

+      
Memory Required?

+      
Constant!

+
+      
IOs added?

+      
None!

+    

+  

+
+  

+    
Example

+
+    
Example, assume $R$ is 100 tuples.

+
+    
How many IOs do we need to compute $Q := \pi(R)$

+    
How many IOs do we need to compute $Q := \pi(\sigma(R))$

+
+    
Projection and Selection do not add IO.

+  

+
+
+  

+    
Union ($R \cup S$)

+    
+  

+  

+    
Union ($R \cup S$)

+    

+      
Memory Required?

+      
Constant!

+
+      
IOs added?

+      
None!

+    

+  

+
+  

+    
Cross ($R \times S$)

+    
+  

+  

+    
Cross ($R \times S$)

+    

+      
Memory Required?

+      
It depends

+
+      
IOs added?

+      
It depends

+    

+  

+  

+    
Cross ($R \times S$)

+    
How do you "reset" $S$?

+
+    

+      
"Materialize" S into memory

+      
No extra IOs (but $O(|S|)$ memory)

+      
Rerun the entire iterator

+      
$(|R|-1) \cdot \texttt{cost}(S)$ extra tuples read

+      
"Materialize" S onto disk

+      
$|S|$ tuples written

+      
$(|R|-1) \cdot |S|$ extra tuples read

+    

+    
This can get very expensive

+  

+
+  

+    
Example

+
+    
Example, assume $R$ and $S$ are both 100 tuples.

+
+    
How many IOs do we need to compute $Q := R \cup S$?

+    
    
+      
  1. Getting an Iterator on $R$: 100 tuples
    2. 
+      
  2. Getting an Iterator on $S$: 100 tuples
    3. 
+      
  3. Getting an Iterator on $R \cup S$ using the above iterators: 0 extra tuples
    4. 
+    

+  

+
+  

+    
Example

+
+    
Example, assume $R$ is 20 tuples and $S$ is 100 tuples.

+
+    
How many IOs do we need to compute $Q := R \times S$?

+    
    
+      
  1. Getting an Iterator on $R$: 100 tuples
    2. 
+      
  2. Getting an Iterator on $S$: 20 tuples
    3. 
+      
  3. Getting an Iterator on $R \times S$ using the above iterators: 
    4. 
+    

+  

+
+  

+    
    
+      
  • Memory: 0 extra tuples
    • 
+      
  • Replay: $(|R|-1) \times \texttt{cost}(S) = 19 \times 100 = 1900$ extra tuples
    • 
+      
  • Cache: $|R| \times |S| = 20 \times 100 = 2000$ extra tuples
    • 
+    

+
+    
Best Total Cost $100 + 20 + 1900 = 2020$

+  

+
+  

+    
Example

+
+    
Example, assume $R$ is 20 tuples and $S$ is 100 tuples, 
and $c$ filters out 90% of tuples.

+
+    
How many IOs do we need to compute $Q := R \times \sigma_c(R \times S)$

+    
    
+      
  1. Getting an Iterator on $\sigma_c(R \times S)$: 2020 tuples
    2. 
+      
  2. Getting an Iterator on $R$: 20 tuples
    3. 
+      
  3. Getting an Iterator on $R \times \sigma_c(R \times S)$ using the above iterators: 
    4. 
+    

+  

+
+  

+    
    
+      
  • Memory: 0 extra tuples
    • 
+      
  • Replay: $(|R|-1) \times \texttt{cost}(\sigma_c(R \times S)) = 19 \times 2020 = 38380$ extra tuples
    • 
+      
  • Cache: $|R| \times |S| = 20 \times 200 = 4000$ extra tuples
    • 
+    

+
+    
Best Total Cost $2020 + 20 + 4000 = 6040$

+  

+
+  

+    
Is there a middle ground?

+  

+

+
+

+
+  

+    
Nested-Loop Join

+    
+  

+
+  

+    
Problem: We need to evaluate rhs iterator
 once per record in lhs

+  

+
+  

+    
Preloading Data

+
+    
Better Solution: Load both lhs and rhs records in blocks.

+
+    

+                    def apply_cross(lhs, rhs):
+                      result = []
+
+                      while r_block = lhs.take(100):
+                        while s_block = rhs.take(100):
+                          for r in r_block:
+                            for s in s_block: 
+                              result += [r + s]
+                        rhs.reset()
+
+                      return result
+    

+  

+
+  

+    
Block-Nested Loop Join

+    
+  

+
+  

+    
Block-Nested Loop ($R \times S$)

+
+    
(with $\mathcal B$ as the block size for $R$)

+    
(and with caching $S$ to disk)

+
+    

+      
Memory Required?

+      
$O(\mathcal B)$

+
+      
IOs added?

+      
$|S|$ tuples written.

+      
$(\frac{|R|}{\mathcal B} - 1) \cdot |S|$ tuples read.

+    

+    
In-memory caching is a special case of block-nested loop with $\mathcal B = |S|$

+    
Does the block size for $R$ matter?

+  

+
+  

+    
How big should the blocks be?

+
+    
+  

+

+
+

+  

+    
Cross product is expensive!
Can we do better?

+    
$\sigma_c(R\times S) \equiv R\bowtie_c S$

+  

+
+  

+    
Cross Product

+    
+  

+  

+    
Problem: Naively, any tuple matches any other

+  

+
+  

+    
Join Conditions

+    
+    
Solution: First organize the data

+  

+
+

+
+

+
+  

+    
Strategies for Implementing $R \bowtie_{R.A = S.A} S$

+
+    

+      
In-Memory Index Join (1-pass Hash; Hash Join)

+      
Build an in-memory index on one table, scan the other.

+
+      
Partition Join (2-pass Hash; External Hash Join)

+      
Partition both sides so that tuples don't join across partitions.

+
+      
Sort/Merge Join

+      
Sort all of the data upfront, then scan over both sides.

+    

+  

+
+  

+    
Hash Functions

+
+    
    
+      
  • A hash function is a function that maps a large data value to a small fixed-size value
      
+        
    • Typically is deterministic & pseudorandom
      • 
+      
    • 
+      
  • Used in Checksums, Hash Tables, Partitioning, Bloom Filters, Caching, Cryptography, Password Storage, …
    • 
+      
  • Examples: MD5, SHA1, SHA2
      
+        
    • MD5() part of OpenSSL (on most OSX / Linux / Unix)
      • 
+      
    • 
+      
  • Can map h(k) to range [0,N) with h(k) % N (modulus)
    • 
+    

+  

+
+  

+    
Hash Functions

+
+    

+      $$h(X) \mod N$$
+
+      
    
+        
  • Pseudorandom output between $[0, N)$
    • 
+        
  • Always the same output for a given $X$
    • 
+      

+    

+  

+
+  

+    
1-Pass Hash Join

+    
+  

+
+  

+    
1-Pass Hash Join

+    

+      
Limited Queries

+      
Only supports join conditions of the form $R.A = S.B$

+
+      
Moderate-High Memory

+      
Keeps 1 full relation in memory

+
+      
Low Added IO Cost

+      
Only requires 1 scan over each input.

+    
          
+  

+
+  

+    
Alternative:  Build an in-memory tree (e.g., B+Tree) instead of a hash table!

+
+    

+      
Limited Queries

+      
Also supports $R.A \geq S.B$, $R.A > S.B$

+
+      
Moderate-High Memory

+      
Keeps 1 full relation in memory

+
+      
Low Added IO Cost

+      
Only requires 1 scan over each input.

+    
          
+  

+
+  

+    
2-Pass Hash Join

+    
+  

+
+  

+    
2-Pass Hash Join

+    

+      
Limited Queries

+      
Only supports join conditions of the form $R.A = S.B$

+
+      
Low Memory

+      
Never need more than 1 pair of partitions in memory

+
+      
High IO Cost

+      
$|R| + |S|$ tuples written out

+      
$|R| + |S|$ tuples read in

+    
          
+  

+
+  

+    
Why is it important that the hash function is pseudorandom?

+  

+
+  

+    
What if the data is already organized (e.g., sorted) in a useful way?

+  

+
+
+  

+    
+    
Sort/Merge Join

+    
+  

+
+  

+    
Sort/Merge Join

+    

+      
Limited Queries

+      
Only supports join conditions of the form $R.A = S.B$

+
+      
Low Memory

+      
Only needs to keep ~2 rows in memory at a time (not counting sort).

+
+      
Low Added IO Cost

+      
No added IO! (not counting sort).

+    
          
+  

+

+
+

+  
Next time...

+
+  
Extended Relational Algebra

+

+
diff --git a/src/teaching/cse-562/2021sp/slide/2021-02-25-PhysicalLayout.erb b/src/teaching/cse-562/2021sp/slide/2021-02-25-PhysicalLayout.erb
new file mode 100644
index 00000000..a5c87452
--- /dev/null
+++ b/src/teaching/cse-562/2021sp/slide/2021-02-25-PhysicalLayout.erb
@@ -0,0 +1,258 @@
+---
+template: templates/cse4562_2021_slides.erb
+textbook: "Ch. 13.1-13.7, 15.7, 16.7"
+date: February 25, 2021
+title: "Physical Layout & Memory Management"
+---
+

+
+  

+    

+      
+        
+        
+        
+        
+        
+        
+      
CREATED_ATTREE_IDBLOCK_IDTHE_GEOMTREE_DBHSTUMP_DIAMCURB_LOCSTATUSHEALTHSPC_LATINSPC_COMMONSTEWARDGUARDSSIDEWALKUSER_TYPEPROBLEMSROOT_STONEROOT_GRATEROOT_OTHERTRNK_WIRETRNK_LIGHTTRNK_OTHERBRNCH_LIGHBRNCH_SHOEBRNCH_OTHEADDRESSZIPCODEZIP_CITYCB_NUMBOROCODEBORONAMECNCLDISTST_ASSEMST_SENATENTANTA_NAMEBORO_CTSTATELATITUDELONGITUDEX_SPY_SP
'08/27/2015'180683348711'POINT (-73.84421521958048 40.723091773924274)'30'OnCurb''Alive''Fair''Acer rubrum''red maple''None''None''NoDamage''TreesCount Staff''None''No''No''No''No''No''No''No''No''No''108-005 70 AVENUE''11375''Forest Hills'4064'Queens'292816'QN17''Forest Hills'4073900'New York'40.72309177-73.844215221027431.14821202756.768749
'09/03/2015'200540315986'POINT (-73.81867945834878 40.79411066708779)'210'OnCurb''Alive''Fair''Quercus palustris''pin oak''None''None''Damage''TreesCount Staff''Stones''Yes''No''No''No''No''No''No''No''No''147-074 7 AVENUE''11357''Whitestone'4074'Queens'192711'QN49''Whitestone'4097300'New York'40.79411067-73.818679461034455.70109228644.837379
'09/05/2015'204026218365'POINT (-73.93660770459083 40.717580740099116)'30'OnCurb''Alive''Good''Gleditsia triacanthos var. inermis''honeylocust''1or2''None''Damage''Volunteer''None''No''No''No''No''No''No''No''No''No''390 MORGAN AVENUE''11211''Brooklyn'3013'Brooklyn'345018'BK90''East Williamsburg'3044900'New York'40.71758074-73.93660771001822.83131200716.891267
'09/05/2015'204337217969'POINT (-73.93445615919741 40.713537494833226)'100'OnCurb''Alive''Good''Gleditsia triacanthos var. inermis''honeylocust''None''None''Damage''Volunteer''Stones''Yes''No''No''No''No''No''No''No''No''1027 GRAND STREET''11211''Brooklyn'3013'Brooklyn'345318'BK90''East Williamsburg'3044900'New York'40.71353749-73.934456161002420.35833199244.253136
'08/30/2015'189565223043'POINT (-73.97597938483258 40.66677775537875)'210'OnCurb''Alive''Good''Tilia americana''American linden''None''None''Damage''Volunteer''Stones''Yes''No''No''No''No''No''No''No''No''603 6 STREET''11215''Brooklyn'3063'Brooklyn'394421'BK37''Park Slope-Gowanus'3016500'New York'40.66677776-73.97597938990913.775046182202.425999

+    

+    
↓

+    
0101010010111010101010001010101001101001001010010001010101001...

+  

+

+
+
+

+  

+    
Record Layouts

+
+    
How is data stored?

+  

+
+  

+    
Problem 1: How should you encode one tuple?

+  

+
+  

+    
Record Layout 1: Fixed

+    
+  

+
+  

+    
Record Layout 2: Delimiters

+    
+  

+
+  

+    
Record Layout 3: Headers

+    
+  

+
+  

+    
Record Formats

+    

+      
Fixed

+      
Constant-size fields.  Field $i$ at byte $\sum_{j < i} |Field_j|$

+      
Delimited

+      
Special character or string (e.g., ,) between fields

+      
Header

+      
Fixed-size header points to start of each field

+      
 

+      
 

+    

+  

+
+  

+    
Problem 2: How should you encode a file of tuples?

+  

+
+  

+    
File Formats

+    

+      
Fixed

+      
Constant-size records.  Record $i$ at byte $|Record| \times i$

+      
Delimited

+      
Special character or string (e.g., \r\n) at record end

+      
Header

+      
Index in file points to start of each record

+      
Paged

+      
Align records to paging boundaries

+    

+  

+
+  

+    
+  

+
+  

+    
+    openclipart.org
+  

+

+
+

+  

+    

+      
File

+      
A collection of pages (or records)

+      
Page

+      
A fixed-size collection of records

+      
Page size is usually dictated by hardware.
Mem Page $\approx$ 4KB   Cache Line $\approx$ 64B

+      
Record

+      
One or more fields (for now)

+      
Field

+      
A primitive value (for now)

+    

+  

+
+  

+    
Problem 2.b: How should you store records in a page?

+  

+
+  

+    
Goal 1: Where is record $X$?

+    
Goal 2: Support updates/deletions

+  

+
+  

+    
Fixed size records

+  

+
+  

+    
+    
What about variable-size records?

+  

+
+  

+    
+    
Why store the key and records from opposite ends?

+  

+
+

+
+

+
+  

+    
Problem 3:  How should you organize pages in a file?

+    
Key question:  What happens when all records on a page are deleted?

+    
Idea:  Track empty pages.

+  

+  

+    
+  

+  

+    
+  

+

+
+

+  

+    
An Alternative Layout

+  

+
+  

+    
Row-Wise Layouts

+    
+  

+  

+    
Column-Wise Layouts

+    
+  

+
+  

+    
Each file stores 2-tuples $\left< RowID, Value\right >$.

+    
Values only for one attribute.

+  

+
+  

+    
Benefits

+    
    
+      
  • Only one attribute to sort per file.
    • 
+      
  • No IO cost for unused attributes ($\pi$-pushdown!)
    • 
+    

+    
Drawbacks

+    
    
+      
  • Result attributes must be stitched back together ($\bowtie$)
    • 
+    

+    
Great for wide, rarely-updated tables where only a few attributes are used per-query

+  

+
+  

+    
Example Column Stores

+
+    

+      Cassandra logo
+      Vertica pos blk rgb.svg
+      MonetDB logo
+    

+    By Apache Software Foundation - https://svn.apache.org/repos/asf/cassandra/logo/cassandra.svg, Apache License 2.0, Link

+    By Ariolica - Own work, CC BY-SA 4.0, Link

+    By Source (WP:NFCC#4), Fair use, Link
+  

+

+
+

+  

+    
Buffer Manager

+    
Abstract the messy details of File-IO

+  

+
+  

+    
+    openclipart.org
+  

+
+  

+    

+      
Frame

+      
A "slot" managed by the buffer manager that holds one page.

+
+      
Pinned Page

+      
A page currently in use by part of the database.  Must stay in its current frame until unpinned.  (A page may be pinned multiple times)

+
+      
Dirty Page

+      
A page that has been modified since it was last read in.

+    

+  

+
+  

+    
When a page is requested

+
+    
Is the page in the buffer pool?
+    
    
+      
  • Yes? Pin the page (again) and return the address.
    • 
+      
  • No?
      
+        
    • Pick a frame for replacement with your favorite algorithm (e.g., LRU)...
      • 
+        
    • If the frame is dirty, write it to disk
      • 
+        
    • Read requested page into chosen frame
      • 
+        
    • Pin the page and return its address
      • 
+      
    • 
+    

+  

+
+  

+    
Does this all sound familiar?

+    
Isn't this just Virtual Memory?

+  

+
+  

+    
Yes!

+    
(Many databases use memory-mapped files as a buffer manager)

+  

+
+  

+    
Why Re-implement VMem?

+    

+      
+      
Databases can predict the future!

+    

+    
SELECT * FROM R WHERE A > 500 AND A < 2000   →    Pages 10-12

+  

+
+  

+    
How do we decide which pages hold the query results?

+    
Answers next class!

+  

+
+

diff --git a/src/teaching/cse-562/2021sp/slide/graphics/2021-02-16-RA-Tree.svg b/src/teaching/cse-562/2021sp/slide/graphics/2021-02-16-RA-Tree.svg
new file mode 100644
index 00000000..910a0882
--- /dev/null
+++ b/src/teaching/cse-562/2021sp/slide/graphics/2021-02-16-RA-Tree.svg
@@ -0,0 +1,150 @@
+
+
+
+
+  
+  
+  
+    
+      
+        image/svg+xml
+        
+        
+      
+    
+  
+  
+    σS.C=10
+    πR.A
+    ⋈R.B=S.B
+    
+      R
+      S
+    
+    
+      
+      
+    
+    
+    
+  
+
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diff --git a/src/teaching/cse-562/2021sp/slide/graphics/2021-02-25-mem_hierarchy.png b/src/teaching/cse-562/2021sp/slide/graphics/2021-02-25-mem_hierarchy.png
new file mode 100644
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new file mode 100644
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