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REAL TIME BOM EXPLOSIONS
WITH APACHE SOLR AND SPARK
Andreas	Zitzelsberger
BILLS OF MATERIAL (BOMS) EXPLAINED
BOMS ARE NEEDED FOR…
Production	Planning Forecasting	Demand Scenario-Based	PlanningRunning	Simulations
Demand	Resolver
THE BIG PICTURE
Parts and 

abstract demands
Orders /
Independent
Demands
Production	
Planning
Analytics
BOMs /
Dependent
demands
ORDERS / INDEPENDENT DEMANDS
Order	Data Car	Configuration
Id,	Order	Time,	… TYPE VARIANT UPHOLSTERY PAINT OPTION_1,	OPTION_2,	…
… …
… …
Customer‘s	
orders
Predicted	
Orders
Stock	cars
Image:	Energy	Solutions	International	Inc
PARTS AND ABSTRACT DEMANDS
Id Description Attributes Terms
TR_1 Automatic	transmission,	supplier	A … TYPE_1	OR	
VAR_2	AND	NOT	OPT_2	
…
TR_2 Automatic	transmission,	supplier	B … TYPE_2	AND	VAR_1	OR	
OPT_5	OR	OPT_6
DT_1 150hp,	automatic	transmission	drive	train …
LI_1 Xenon	lights	 … OPT_3	OR	VAR_9	OR	…
BT_1 Small	Battery … 8	<=	OPT_3*200	+	VAR_9*5	+	
OPT_4*2	…	<=	12
The	constructive	approach	is	impossible	due	to	combinatory	explosion.	

The	rules	need	to	be	evaluated	during	demand	resolution.
Demand	Resolver
WE NEED TO OVERCOME THREE CORE PROBLEMS…
Parts and 

abstract demands
Orders /
Independent
Demands
Production	
Planning
Analytics
BOMs /
Dependent
demands
Demand	Resolver
1. HOW TO RESOLVE DEMANDS AS FAST AS POSSIBLE?
Parts and 

abstract demands
Orders /
Independent
Demands
BOMs /
Dependent
demands
2. HOW TO JOIN LARGE DATA SETS?
Parts and 

abstract demands
Analytics
BOMs /
Dependent
demands
Orders /
Independent
Demands
3. ANALYTICS ON LARGE DATA SETS?
Parts and 

abstract demands
Analytics
BOMs /
Dependent
demands
Orders /
Independent
Demands
Demand	Resolver
1. HOW TO RESOLVE DEMANDS AS FAST AS POSSIBLE?
Parts and 

abstract demands
Orders /
Independent
Demands
On	to	problem	#1…
BOMs /
Dependent
demands
THE SIMPLE SOLUTION…
A BETTER SOLUTION…
2	million	parts	
15	million	abstract	
demands
OR: ONLY CALCULATE WHAT IS STRICTLY NECESSARY
Step	2:	REFINE	
Refine	the	superset	by	
evaluating	the	usage	terms	in	
O(n).
Actual	demands
Post-
Filter
Pre-
Filter
Abstract	
Demands
Step	1:	FILTER		
Pre-filter	demands	with	a	fast	
index	in	O(log	n).	
Yields	a	superset	of	the	
required	parts.
~	6000	possible	
demands
~	3000	actual	demands
PRE-FILTER USING CANDIDATE AND A PROSCRIPTION SETS
■ Consider	((OPT_1)	OR	(OPT_2))	AND	NOT(OPT_3	OR	OPT_4)	
■ Candidate	set:		 C(t)	=	{OPT_1,	OPT_2}	
■ Proscription	set:	 P(t)	=	{OPT_3,	OPT_4}	
■ Preselect	by	the	criterium:	
■ At	least	one	condition	from	C(t)	is	satisfied	
■ None	of	the	conditions	from	P(t)	are	satisfied
EXAMPLE CANDIDATE AND PROSCRIPTION SETS
Candidate	set Proscription	set
A	part	can	only	be	used	together	with		
“white”	or	“blue”	paint.	
Entry:	PART_1,	C-PAINT:[white,	blue]	
Query:	C-PAINT=blue
A	part	cannot	be	used	with	“yellow”	
paint.					
Entry:	PART_2,	P-PAINT:[yellow]	
Query:	NOT	P-PAINT=yellow
Solr	Server
SOLR IMPLEMENTATION
PART_ID:	“…”	
//	Part	attributes	
//	….	
TERMS:	[…]	
TYPE_C:	[…]	
TYPE_P	:	[…]	
VARIANT_C	:	[…]	
VARIANT_P	:	[…]	
OPTIONS_C	:	[…]	
OPTIONS_P	:	[…]
Part
If	evaluate(TERMS)	
	 accept	
Else	
	 drop
Post	filter
Bulk	Solr	query	
for	N	orders
Bulk	export	of	
the	search	
results
Dependent	
Demands	
(Solr)
Demand	Resolver
WHY APACHE SOLR
■ Really	fast	
■ Powerful	faceting	
■ Solr	cloud	
■ Extensible	
■ Most	importantly,	it	just	works
BOMs /
Dependent
demands
Demand	Resolver
1. HOW TO RESOLVE DEMANDS AS FAST AS POSSIBLE?
Parts and 

abstract demands
Orders /
Independent
Demands
2. HOW TO JOIN TWO LARGE DATA SETS?
Parts and 

abstract demands
Analytics
BOMs /
Dependend
demands
Orders /
Independent
Demands
let‘s	continue	with	
problem	#2…
FORWARD AND BACKWARD JOINS ON PARTS
Orders	/		
Independent	Demands
Part	Usages	/

Dependend	Demands
Parts	/		
Abstract	Demands
Given:			 Order	criteria	(TYPE=	XY12)	

	 	 	 and	parts	criteria	(PART_ID	=	4711	OR	4712)	
Task:	 	 Find	all	orders	that	satisfy	the	order	criteria

	 	 	 AND	contain	at	least	one	part,	that	satisifies	the	part	criteria	
	 	 	 Return	all	orders	and	the	parts	for	each	order	that	satisfy	the	part	criteria.
Id … Id …Order	Id Part	Id
7.000.000
2.000.000
21.000.000.000
■ Nobody	wants	to	join	7	million	x	21	billion	x	2	million	records.
THIS IS ACTUALLY HARD
■ Nobody	wants	to	store	21	billion	records.
PARTIAL DENORMALIZATION TO THE RESCUE
Orders
<10	Mio.	records	instead	of	21	billion
Ids Attributes Part	Ids
1 … 1,	3,	786,…
2 … 2,	6,	786,…
3 .. 3,	75,	95,	..
Ids Attributes Order	Ids
1 … 54,	23321,	..
2 … 1,	4,	8652,	..
3 … 864,	23452,	…
Parts
SIMPLE, BUT NOT EASY: CUSTOM JOIN LOGIC IN SOLR
Step	1	
Pre-filter	the	parts	
using	an	index	search
Step	2	
Pre-filter	the	orders	
using	an	index	
search
Step	3	
Compute	the	
intersection	of	both	
supersets
All	parts
Searched	parts	with	
attribute	A=	XY
All	orders
Orders	to	be	built	in	Q4	with	
attribute	A
Intersect	records	
based	on	technical	idsOrders	to	be	built	in	
Q4
Final		
Result
• Custom	Search	Components	
• Solr	Search	Components
WE ONLY EXECUTE THE JOIN IF WE MUST
No	filter Filter	by	order	attributes Filter	by	part	attributes	 Filter	by	order	and	part	
attributes
Facets	on	parts Index JOIN Index JOIN
Facects	on	orders	 Index Index JOIN JOIN
List	parts JOIN JOIN JOIN JOIN
List	orders Index Index JOIN JOIN
Result	sets	may	be	unwieldingly	large.		
Early	termination	for	interactive	queries		
if	the	result	grows	too	large.
Orders /
Independent
Demands
BOMs /
Dependend
demands
2. HOW TO JOIN TWO LARGE DATA SETS?
Parts and 

abstract demands
Analytics
3. ANALYTICS ON VERY LARGE DATA SETS?
Parts and 

abstract demands
Analytics
BOMs /
Dependend
demands
Orders /
Independent
Demands
What	about	problem	
#3?
WHY APACHE SPARK
■ Fast	
■ High-Level	functional	API	
■ Well	integrated	
■ Batch	semantics	
■ Works	well	with	Solr	(If	you	use	
your	own	connector…)
TECHNICAL ARCHITECTURE
Apache	Solr
Apache	Spark
Demand	Resolver
API	Facade
Loader
Source	Systems
Hardware	Node	1
Solr	Instance
Shard	20
Hardware	Node	2
Solr	Instance
Shard	30
Spark	Worker Spark	Worker
Shard	21Shard	11
Spark	Master
ZooKeeper
.	.	. .	.	.
Spark	Master
ZooKeeper	(Leader)
API	FacadeAPI	Facade
Load-Balancer
Jenkins	+	Loader	(Leader)Jenkins	+	Loader	(Backup)
Hardware	Node	n
Solr	Instance	
Shard	n
Shard	n
.	.	.
Spark	Worker
Add	nodes	as	required
OPERATIONS ARCHITECTURE
API	Facade
BULK EXPORT WITH A SHARED OFF-HEAP CACHE
Lucene	Segment
Lucene	Segment
Lucene	Segment
Shared Off-Heap
Cache
1
DocId	Order	
DocId	Part
Web
interface
2
3
Business	Facade	Object
data
4
http://chronicle.software/products/chronicle-map/
Orders /
Independent
Demands
BOMs /
Dependend
demands
3. ANALYTICS ON VERY LARGE DATA SETS?
Parts and 

abstract demands
Analytics
LESSONS LEARNED
https://en.wikipedia.org/wiki/Melk_Abbey
OPTIMIZE LATE, BUT DO OPTIMIZE
©	2015	Max	Siedentopf
SOME OF OUR OPTIMIZATIONS
Bulk RequestHandler
Binary DocValue support
Boolean interpreter as postfilter
Mass data binary response format
Search components with 

custom JOIN algorithm
Solving thousands of
orders with one request
Be able to store data
effective using our own
JOIN implementation.
Speed up the access to
persisted data dramatically
using binary doc values.
0111 0111
Use the standard Solr cinary
codec with an optimized data-
model that reduce the amount 

of data by a factor of 8.
Computing
BOM
explosions
Enable Solr with custom post filters
to filter documents using stored bool
expessions .
LOW LEVEL OPTIMIZATIONS CAN YIELD GREAT RETURNS
October 14 January 15 May 15 October 15
4,9 ms
0,28 ms
24 ms
TimetocalculatetheBoMforoneorder
0,08 ms
Scoring (-8%)
Solr–Query Parser (-25%)
Stat-Cache (-8%)
Using String DocValues (-28%)
Development of the processing time Demand Calulation Service PoC Profiling result and the some improvements to reduce the query time.
DON’T TRUST PAPER, DO POCS
IN CONCLUSION
THANK YOU
@andreasz82
andreas.zitzelsberger@qaware.de

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