LambdaFlow: Scala Functional Message Processing

Scala Functional Message
Processing: LambdaFlow
John Nestor 47 Degrees
www.47deg.com
https://github.com/47deg/LambdaFlow
May 9, 2017
147deg.com

47deg.com © Copyright 2017 47 Degrees
Outline
• Introduction
• Examples
• Simple
• Windowing
• Indexing and Query
• Graph
• Temperature
• Page Rank
• Questions
2

Inspiration
• Wanted to build a reactive system using Akka clustering
• Don’t like Spark micro-batching, want message at a time
• Want a Scala functional DSL, like the Spark Scala DSL
4

LambdaFlow
• Near-real-time message processing
• Message at a time
• Both set (create or update) and delete
• Written in Scala and Akka
• Two parts:
• Functional DSL
• Akka Cluster reactive implementation
5

LambdaFlow Use Cases
• * Near real time streaming with inﬁnite stream
• Finite streams
• Batch processing
• Input and outputs via
• ﬁles
• API
• Kafka
• …
6

Possible Use Case
7
LambdaFlow
...
Kafka In 1
Kafka In 2
Kafka In M Kafka Out N
Kafka Out 2
Kafka Out 1
...

Functional DSL
• Directed graph
• Strongly typed
• Graph nodes are actions
• Graph arcs are paths for message passing
• Graph can contain cycles
8

Key-Value Model
• (most) Actions contain an in-memory Key-Value
HashMap[K,V]
• Two messages
• Set/Update Key:k1 to Value:v1
• Delete Key-Value pair for Key:k1
9

Combining Actions
• Most actions have
• Input: used to set or delete keys in KV store
• Output: used to set or delete keys on next action
• Operation: maps KV of this action to KV of next action
10

Functional Eventual Consistency
• Two Actions A1[K1,V1] and A2[K2,V2]
• Each have a KV store: A1.kv[K1,V1] and A2.kv[K2,V2]
• the output of A1 is the input to A2
• A1 has an operation A1.f that maps  
[K1,V1] => Set([K2,V2])
• If all changes to A1 are stopped, eventually
• A2.kv = A1.kv.ﬂatMap(A1.f)
11

Incremental Operation
• Suppose an action has k1->v1 and we now set k1->v2
• To update the next action
• Undo the effect of k1->v1
• Do the effect of k1->v2
12

Incremental Operation Example
• Suppose action KV type is String=>String
• If we change “A”->”B” to “A”->”C”
• Operation example 1 (map): (k,v)=>(k+v,v+v)
• Delete “AB”->”BB”
• Set “AC”=> “CC”
• Operation example 2(mapvalue): (k,v) => (k,v+v)
• Delete “A” -> “BB” (not needed here)
• Set “A” -> “CC”
13

Implementations
• Class based
• Uses method calls rather than actor messages
• Local Akka Actor based
• Akka Cluster Based
• Multinode
• Reactive: scalable, responsive, fault-tolerant
14

Simple Example
• Input data:
• 8328 Tweets
• Look at input data (capture.json)
• Count number of tweets in each language
• Use method calls between actions
16

Simple Example
17
(Lang,UID) => ()
Src
Sink
Reduce Sum Per Lang
Lang => Count
Map
UID => Lang

Simple Example Code
val src = Source[String, String]("src") 
val counts = Sink[String, Long]("counts") 
 
val f1 = src 
.map("map",(uid,lang)=>((uid,lang),())) 
.reduce("red", _._2, (k, v) => 1L, LongSum) 
.sink(counts) 
 
run(f1)
18

Source and Sink Actions
• Source[K,V]
• gets input from outside the graph (e.g. Kafka)
• outputs to other actions
• Sink[K,V]
• gets it input from other actions
• outputs to outside the graph (e.g. Kafka)
19

Map Operation
• Action input type K,V
• Action output type KOUT,VOUT
• Action function type: (K,V)=>(KOUT,VOUT)
20

Reduce Operation
• Grouping function type: K=>KOUT (many to one)
• Value function type: (K,V)=>VOUT
• Reduce operation type (VOUT,VOUT)=>VOUT
• commutative, associative and inverse (abelian group)
21

Simple Example
• Run it (demo.simple)
• Look at code (Simple.scala)
22

Windowing Example
• Streaming Data
• 1000 Tweets
• sent in at ~20 per second
• each tweet expires after 20 seconds
• Count languages and ﬁnd 20 most common words
• Uses local actor implementation
• Uses Vaadin based visualization tool
24

Windowing Example
25
Lang
Count
Reduce Txt
Total Percent Words
Join
Map
FlatMap
MapReduce
Reduce
First
Map

Windowing Example Code
val f1 = src 
.reduce("red", _._1, (k, v) => 1L, LongSum) 
.to(counts1, counts2) 
.sink(counts) 
 
val f2 = counts1 
.reduce("red1", _ => List.empty[String], (k, v) => v, LongSum) 
.map[String, Long]("map1", (k, v) => ("total", v)) 
.to(total1) 
.sink(total) 
 
val f3 = counts2 
.join[String, Long, Unit, String, String]("product", total1, (s:String)=>(), (s:String)=>()) { 
case (lang, count, t, total) => Set((lang, f"${count * 100 / total}%02d$PERCENT")) 
} 
.sink(percent) 
 
val f4 = text 
.flatMap("GetWords", (k, v) => { 
val (text, uuid) = k 
getWords(text) 
.zipWithIndex 
.map { 
case (word, i) => ((word, uuid, i), ()) 
} 
.toSet 
}) 
.map("CountOrder", (word, count) => ((-count, word), ())) 
.first("first", 20) 
.map("CountOrder1", (k, v) => (k._2, f"${-k._1}%4d")) 
.sink(words)
run(f1, f2, f3, f4)
26

Labels,To and From
• Label[K,V]: used to connect an action output to another
action input
• .to[K,V]: send one input to multiple outputs (broadcast)
• .from[K,V]: combine multiple inputs to a single output
(union)
27

FlatMap Action
• Action function type: (K,V)=>Set[(KOUT,VOUT)]
28

First Action
• Action output type K,V
• Count: a small integer
• Output at most count key-values
• those with the lowest keys
• requires type K be ordered
29

Join Action
• Two inputs: K1,V1 and K2,V2
• Two key value stores
• Output types KOUT and VOUT
• Two mapping functions: K1=>K K2=>K (many to one)
• An operation
• Applied to each member of the product of KV pairs
from the two key stores with the same K value
• (K1,V1,K2,V2) => Set[(KOUT,VOUT)]
30

Viewer
31
LambdaFlow
Remote Actor
FlowView
Vaadin
Web BrowserTweets

Windowing Example
• Start up Vaadin viewer and web browser
• Run it (demo.TwitterFlow)
• Look at code (TwitterFlow.scala)
32

Index and Query Example
• Input data: Breweries
• look at breweries.data
• Build indexes
• set of breweries at a location (State/Country)
• set of breweries with a key word (in its description)
• use jline3 command hander
34

Index Example
35
Breweries
Reduce
Names Places Words
Map FlatMap
Reduce

Query
• words:Sink[String,Set[Int]]
• words.get(word) => Future[Option[Set[Id]]]
• breweries:Source[Int,String] : id->JsonString
• def name(json:String)=>name (extracts the brewery
name)
• breweries.get(id,name) => Future[Option[String]]
36

Input Data:A Directed Graph
38
A
B
C
D
E
F

Graph Example
• Note: there are two graphs
• The input data
• The graph of actions
• Output
• Number of distinct paths between two nodes
• Set of all cycles
• Loop in action graph for transitive closure
• Like recursive function
• Functional semantics is ﬁxed point of loop
39

Graph Transitive Closure
• Given
• route C -> E -> F
• link A -> C
• Form product (Common key = C)
• route C -> E -> F (routes starting at C)
• ﬂipped link C -> A (links ending at C)
• Result
• route A -> C -> E -> F (goes back around loop!)
40

Graph Example
41
Edges
Map
toPath
CyclesRoutes
Join
Link
Map
Route
Map
Invert
Filter
Cyles
Reduce
Count

Graph Example
• Start Vaadin viewer and browser window
• Run example (demo.GraphCycle)
• showCycles
• Look at code
42

Modiﬁed Graph
43
A
B
C
D
E
F

Modify Graph
• set F A
• look at browser
• showCycles
• delete F A
• look at browser
• showCycles
44

Temperature Grid
46
T1 T2 T3
T4 0,0 1,0 2,0 T7
T5 0,1 1,1 2,1 T8
T6 0,2 1,2 2,2 T9
T10 T11 T12

Computation
47
U
L (U+D+L+R) / 4 R
D

Temp Example
48
Edges
Temps
Map
FlatMap
1 => 4
Map
Reduce
4 => 1
Map

Temperature Result
49
30 20 10
50 35 27 27 50
40 34 27 21 10
30 33 27 20 10
40 30 20

Temperature Change
50
30 20 10
50/10 35/24 27/25 27/28 50
40 34/32 27/29 21/27 10
30 33/34 27/33 20/40 10/80
40 30 20

Page Rank Data
52
A B
C
D
E
F

Page Rank Example
53
Edges
Reduce
Rank
Join
Invert
Map
Initial Val
FlatMap
Map
Bias
Reduce
Join
Div by Cnt
Reduce
Sum
Map
Reduce
Select

Page Rank Result
54
1.4 .60
.88
.28
.75
.47

LambdaFlow and Kafka Common Properties
• JVM based
• Integrate with Kafka topics
• Key-value based
• One message at a time
• Operations: map, reduce, …
• Scalable and fault tolerant
• Open source
56

LambdaFlow versus Kafka
57
LambdaFlow Kafka
Akka Cluster Zookeeper
Scala Java
Set/Delete Set
Functional Imperative

LambdaFlow: Scala Functional Message Processing

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