Building Performant Dossiers: Tips and Best Practices - #analytics19 - MicroStrategy
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Building Performant Dossiers:
Tips and Best Practices
Alejandro Olvera
Technology
#analytics19
Safe Harbor Notice
This presentation may include statements that constitute “forward-looking statements” for purposes of the safe harbor provisions
under the Private Securities Litigation Reform Act of 1995, including descriptions of technology and product features that are
under development and estimates of future business prospects. Forward-looking statements inherently involve risks and
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differ materially from the forward-looking statements.
Factors that could contribute to such differences include: the Company’s ability to meet product development goals while
aligning costs with anticipated revenues; the Company’s ability to develop, market, and deliver on a timely and cost-effective
basis new or enhanced offerings that respond to technological change or new customer requirements; the extent and timing of
market acceptance of the Company’s new offerings; continued acceptance of the Company’s other products in the marketplace;
the timing of significant orders; competitive factors; general economic conditions; and other risks detailed in the Company’s
Form 10-Q for the three months ended September 30, 2018 and other periodic reports filed with the Securities and Exchange
Commission. By making these forward-looking statements, the Company undertakes no obligation to update these statements
for revisions or changes after the date of this presentation.
Copyright © 2019 MicroStrategy Incorporated. All Rights Reserved.
Agenda • Who’s responsible for performance?
• Learning the Dossier Execution
Workflow
• Potential bottlenecks
• Strategies to achieve good
performance
| Analytics and Mobility M ic ro S tra te g y C o n fid e n tia l. C o p y rig h t © 2 0 1 9 M icro S tra te g y In c o rp o ra te d . A ll R ig h ts R e se rve d .
USERS Executives Management Sales Reps Workforce Vendors Influencers VIP Clientele Customers Citizens
TECHNOLOGY REGULATORY
OPPORTUNITIES FUNCTIONS Sales Marketing Customer Service Manufacturing Field Service Finance HR IT Facilities Vendors Customers REQUIREMENTS
DEVICES Wall TV Desktop Web Tablet Phone Watch Voice
C lo u d Mobile Identity, D a ta P riv a cy
C o m p u tin g Enterprise Enterprise Embedded Enterprise Mobile Mobile Security, and Mobile Telemetry a n d S e cu rity
APPLICATIONS Analytics Reporting Big Data Data Discovery Analytics Mobility Analytics Productivity External Apps
Communications and IoT
Google
TOOLS Email Search Excel PowerPoint Power BI Qlik Tableau Alteryx Paxata Trifacta Datawatch Jupyter Matlab R Studio SAS SPSS Eclipse Python IDE Visual Studio xCode
M a c h in e A d v e rtis in g
L e a rn in g a n d M a rk e tin g
EXECUTIVES BUSINESS USERS ANALYSTS DATA SCIENTISTS DEVELOPERS
ROLES T h e B u s in e s s E x e c u tive s e ts th e a n a ly tic s a n d m o b ility stra te g y fo r th e
fu n c tio n . T h e y e s ta b lis h th e fu n ctio n ’s p rio ritie s , p ro g ra m s , b u d g e t, a n d p la n ,
E x p lo re a n d in te ra ct w ith p u b lis h e d a n a ly tic s . E n h a n c e a p p lic a tio n s u sin g
s e lf-s e rv ic e d a ta d is co ve ry to cre a te cu sto m g ro u p s , d e rive d m e trics, a n d
C re a te , s h a re , a n d m a in ta in in te llig e n c e a p p lic a tio n s fo r th e
d e p a rtm e n t u sin g e n te rp rise se c u rity , d a ta , a n d a p p lica tio n o b je cts
B u ild a n d p u b lis h a d va n c e d s ta tis tics , p re d ictiv e m o d e ls , a n d m a c h in e
le a rn in g a lg o rith m s u s in g lib ra rie s su ch a s T e n so rF lo w , R , P yth o n , a n d
In je ct, e xte n d , a n d e m b e d in te llig e n ce in to cu sto m a n d th ird -p a rty a p p lica tio n s u sin g
p ro g ra m m in g la n g u a g e s su c h a s Ja v a S c rip t, Ja v a , P H P , P y th o n , S W IF T , O b je ctiv e -C , C # ,
w h ils t m a in ta in in g ju stifica tio n fo r th e p la tfo rm a s s e ts a n d re s o u rc e s b y d y n a m ic filte rs. F o ste r a d o p tio n th ro u g h co lla b o ra tio n a n d sh a rin g . to h e lp e n s u re a sin g le ve rsio n o f th e tru th . M A T L A B , th a t a re le ve ra g e d b y A n a lys ts a n d D e ve lo p e rs . .N e t, a n d o th e r co m m o n la n g u a g e s.
tra c k in g a n d p u b lis h in g a d o p tio n , im p a ct, a n d u ltim a te ly re tu rn -o n -in ve s tm e n t.
M o b ile
C o m p u tin g C o rp o ra te
G o v e rn a n c e
INTELLIGENCE CENTER
PROGRAM S A R C H IT E C T U R E PERSONAS
B lo ck c h a in O n - p r e m is e s , c lo u d , o r h y b r id
E n v ir o n m e n ts d e p lo y e d in m in u te s
E m p lo y m e n t
DEVELOPMENT Intelligence Director and Labor
V e r tic a l a n d h o r iz o n ta l s c a lin g C re a te In te llig e n c e e n viro n m e n ts b y d e p lo y in g th e In te llig e n ce
Applications Mobility Intelligence A rch ite ctu re , su p e rvis in g th e In te llig e n c e C e n te r a n d ru n n in g
P u b lis h a n a p p lic a tio n fra m e w o rk a n d b e s t p ra c tic e s th a t E s ta b lis h a fra m e w o rk , d is c ip lin e , a n d a rc h ite ctu re s o A n a lys ts In je ct a rtific ia l in te llig e n ce , m a ch in e le a rn in g , d e e p le a rn in g , M u lti- n o d e c lu s te r in g In te llig e n ce P ro g ra m s to s u p p o rt e n te rp ris e a n d d e p a rtm e n ta l
B ig e n a b le a ll fu n c tio n s to b u ild c o n s is te n tly im p a c tfu l a p p lic a tio n s . a n d D e v e lo p e rs c a n b u ild a n d d e p lo y m o b ile a p p lica tio n s. a n d p re d ictive a n a lytic s a lg o rith m s in to e n te rp rise a p p lica tio n s
D e p a rtm e n ta l
a n a lytic s a n d m o b ility a p p lica tio n s fo r a ll c o n stitu e n ts.
M u lti- r e g io n s u p p o r t
D a ta E s ta b lis h a fo u n d a tio n o f s h a re d c o m p o n e n ts to s p e e d
d e p a rtm e n ta l a p p lic a tio n d e v e lo p m e n t.
E s ta b lis h p ro c e s s e s , p ro to c o ls , a n d p ro g ra m s s o th e In te llig e n t
E n te rp ris e c a n c o n s u m e a p p s o n m o b ile d e vice s .
a n d fe d e ra te d d a ta s e ts . P u b lis h a n d c u ra te a lib ra ry o f m o d e ls
fo r A n a ly s ts a n d d e p a rtm e n ta l D a ta S c ie n tis ts.
H o t, w a r m , a n d c o ld fa ilo v e r F in a n cia l
Services Departmental Enterprise D e v e lo p m e n t U s e r A cc e p ta n ce T e s tin g
R e lia b le d r a g - a n d - d r o p p r o m o tio n
C o n v e rt d a ta s e ts a n d a p p lic a tio n c o m p o n e n ts in to p u b lis h e d E m p o w e r d e p a rtm e n ts to ra p id ly b u ild a p p lica tio n s o n
s e rv ic e s fo r D e v e lo p e rs to in je c t in te llig e n c e in to th e ir c u s to m fe d e ra te d tru s te d d a ta u s in g M ic ro S tra te g y o r o th e r to o ls (i.e . (U A T ) In te g r ity te s tin g o f a p p s a n d u p g r a d e s
H a rn e s s th e p o w e r o f y o u r o th e r B I in v e s tm e n ts a n d
a p p lic a tio n s . P u b lis h s a m p le s a n d d o c u m e n ta tio n a n d T a b le a u , P o w e r B I, E x c e l). O rch e s tra te c o lla b o ra tio n w ith th e
3 6 0 d e g r e e m o n ito r in g b u ilt in
In te rn e t e m p o w e r D e v e lo p e rs to u s e th e ir p re fe rre d p ro g ra m m in g to o ls in te llig e n c e c e n te r s o d a ta s e ts a re c o n tin u a lly a p p ra is e d , e x te n d th e v a lu e to a ll c o n s titu e n ts o n v a rio u s d e vice s.
M ig ra te le g a cy S A P B u sin e s sO b je cts a n d IB M C o g n o s E n te rp ris e
a n d la n g u a g e s . o p tim iz e d , a n d u p d a te d .
o f T h in g s a p p lic a tio n s o n to a m o d e rn p la tfo rm .
Application Architect Analytics Architect Mobile Architect Identity Architect E n v iro n m e n ta l
C re a te , sh a re , a n d m a in ta in in te llig e n ce C re a te , p u b lish , a n d o p tim ize a fe d e ra te d d a ta B u ild , co m p ile , d e p lo y, a n d m a in ta in m o b ile B u ild , co m p ile , d e p lo y, a n d m a in ta in d ig ita l
FOUNDATION DEPLOYM ENT a p p lica tio n s fo r th e e n te rp ris e . la y e r a s th e e n te rp rise ’s sin g le ve rsio n o f th e e n v iro n m e n ts a n d a p p lica tio n s. O p tim ize th e id e n tity a p p lica tio n s, in te g ra te d w ith
P u b lis h sta n d a rd ize d a p p lica tio n o b je c ts, tru th . B u ild a n d m a in ta in th e sc h e m a o b je cts a n d u s e r e xp e rie n ce w h e n a cce ssin g a p p lic a tio n s via e n te rp ris e d ire cto rie s. D ig ita lly se cu re a ll
a n d p ro m o te d e p a rtm e n ta l a p p lica tio n s fro m a b s tra ctio n la ye r o n to p o f v a rio u s , ch a n g in g m o b ile d e vice s. In te g ra te w ith p re fe rre d V P N , e x istin g a n d n e w lo g ica l a n d p h ysica l a sse ts.
Platforms Analytics Administration se lf-se rvice in to th e e n te rp rise e n viro n m e n t. e n te rp ris e a s se ts . S S O , a n d E M M p ro to co ls. In te g ra te a u th e n tic a tio n , co m m u n ica tio n , a n d
D ig ita l A rc h ite c t, in s ta ll, c o n fig u re , a n d d e p lo y th e In te llig e n t
E n te rp ris e a rc h ite c tu re . D e s ig n th e o p tim u m
D e s ig n th e o p tim a l fe d e ra te d e n te rp ris e d a ta la ye r a n d
p u b lis h it to A n a ly s ts , D a ta S c ie n tis ts , D e v e lo p e rs , a n d
M o n ito r, s u p p o rt, a n d m a in ta in th e In te llig e n t E n te rp rise
a rch ite ctu re to fa c ilita te o n g o in g s e c u rity , s ta b ility, a n d
Amazon Web Services On-Premises Microsoft Azure te le m e try in to o th e r a p p lica tio n s .
Id e n tity a rc h ite c tu re to d e liv e r s e c u rity , s ta b ility , s c a la b ility , A rc h ite c ts . W o rk w ith D e p a rtm e n ts to e vo lv e th e d a ta e c o n o m y . M o n ito r s ys te m u s e , a u to m a te ta s k s, a n d im p le m e n t
a n d e c o n o m y b y c o m b in in g p la tfo rm c a p a b ilitie s w ith a rc h ite c tu re to m e e t c h a n g in g b u s in e s s n e e d s . u p g ra d e s to h e lp e n su re a n o p tim a l, re lia b le , a n d m o d e rn u s e r
H e a lth
o n -p re m is e s , c lo u d , a n d /o r h y b rid s e rv ic e s . e x p e rie n c e .
a n d S a fe ty
Identity Database Systems Alibaba Cloud CenturyLink Google Cloud IBM Cloud Oracle Cloud Rackspace
D e s ig n a n d p u b lis h a d ig ita l id e n tity a rc h ite c tu re th a t C o n fig u re th e in te llig e n c e p la tfo rm to o p tim iz e In te g ra te d a ta fro m e n te rp rise s ys te m s (i.e . E R P , C R M ,
e n a b le s e n te rp ris e -w id e m u lti-fa c to r g e o -s p e c ific p e rfo rm a n c e a g a in s t v a rio u s d a ta b a s e te c h n o lo g ie s (i.e . M R P , H R ) a n d b le n d w ith o th e r d a ta s o u rce s to b u ild Services Architect Database Architect Platform Administrator System Administrator
d ig ita l a u th e n tic a tio n fo r in te rn a l a n d e x te rn a l u s e rs . O ra c le , S Q L , S n o w fla k e , H D F S ), in c lu d in g re la tio n a l, c u s to m a n a ly tic s a n d m o b ility a p p lic a tio n s . D e sig n , Docker Kubernetes vmware In je ct, e xte n d , a n d e m b e d a n a ly tics in to D e sig n a n d m a in ta in d a ta b a se e n te rp rise a sse ts . In s ta ll a n d c o n fig u re th e In te llig e n ce A rc h ite ctu re S e t u p , m a in ta in , m o n ito r, a n d c o n tin u o u s ly
Im p le m e n t th e d ig ita l id e n tity a rc h ite c tu re a n d O L A P , b ig d a ta , u n s tru c tu re d , v e c to r, a n d stre a m in g im p le m e n t, a n d o p tim iz e a n in te g ra te d a rc h ite ctu re to p o rta ls, th ird -p a rty, m o b ile , a n d w h ite -la b e le d O p tim iz e d a ta b a s e p e rfo rm a n ce a n d u tiliza tio n o n -p re m ise s a n d /o r in th e c lo u d . M a in ta in th e su p p o rt th e in fra stru ctu re e n viro n m e n t
g a te w a y s o n to p o f a ll lo g ic a l a n d p h y s ic a l a s s e ts . p la tfo rm s . T ra c k th ro u g h p u t a n d p e rfo rm a n c e , a n d o v e rco m e th e re p o rtin g lim ita tio n s a n d e x te n d th e a p p lica tio n s . P u b lis h w e b se rvic e s a n d d a ta b a s e d o n q u e ry ty p e , u s a g e p a tte rn s, a n d se cu rity la y e r, m o n ito r syste m u sa g e , a n d th ro u g h d e p lo ym e n t o n A W S , W in d o w s, o r
p ro v id e a rc h ite c tu re d e s ig n a n d o p tim iz a tio n c a p a b ilitie s o f e n te rp ris e s y ste m s o f re c o rd . se rvice s fo r u se b y D e ve lo p e rs in b u ild in g a p p lica tio n d e sig n re q u ire m e n ts . o p tim iz e a rc h ite c tu re in o rd e r to re d u ce e rro rs, L in u x, a ll w h ile o p tim izin g p e rfo rm a n ce a n d
re c o m m e n d a tio n s to th e d a ta b a s e a d m in istra to r. Linux Windows
MARKET
d e p a rtm e n ta l a p p lica tio n s. m a xim ize u p tim e , a n d b o o s t p e rfo rm a n ce . co n tro llin g c o sts .
COMPETITIVE
DISRUPTORS FORCES
IN T E L L IG E N C E CLIENT PRODUCTS
C lie n ts th a t e n a b le in tu itive , fa st, a n d e n jo y a b le a n a lytic s a n d m o b ility Architect Desktop Web x Reporter Mobile Communicator Badge Hyper Application H IE R A R C H Y O F N E E D S
PLATFORM e x p e rie n c e s a c ro ss w e b , d e s k to p , a n d m o b ile in te rfa ce s.
Functionality
A p p le An Intelligence Platform helps E n a b le B u s in e s s U s e r s , A n a ly s ts , a n d D e v e lo p e r s to p e r fo r m a ll ta s k s ( b u ild ,
make every device, application, SECURITY d e p lo y , a n a ly z e , s h a r e ) a c r o s s v a r io u s d e v ic e s ( d e s k to p , w e b , m o b ile , v o ic e )
and person more intelligent.
C a p a b ilitie s th a t e n a b le th e d e v e lo p m e n t o f p e rs o n a lize d a n d s e c u re d Library Users Groups Badges Privileges Permissions Roles Security Filters w ith o u t r e s tr ic tio n o r lim ita tio n .
a p p lic a tio n s in c o rp o ra tin g m u lti-fa cto r, m u lti-la ye r a u th e n tic a tio n .
N ew
It creates an enterprise semantic E n tra n ts
graph that connects, indexes, Popularity
Am azon abstracts, and federates your APPLICATION S u r fa c e in te g r a te d c o lla b o r a tio n a n d s h a r in g c a p a b ilitie s w ith in a p p lic a tio n s th a t
organization’s data, telemetry, and C o n ta in e rs o f a ctio n a b le in te llig e n c e , p a c ka g e d a n d p u b lish e d Dossiers Dashboards Documents Distributions Custom Apps Web Services Data Services Cards p r o m o te v ir a l a d o p tio n a n d u s a g e b y d e s ig n .
to th e e n te rp rise a n d d e p a rtm e n ts .
usage. Users can rapidly build
contextual applications and
deploy them anywhere, anytime, Economy
and on any standard device— M a n a g e m u ltip le e n te r p r is e e n v ir o n m e n ts u s in g a s in g le p o w e r fu l w o r k s ta tio n to o l S u p p lie r
delivering trusted answers to DATA fo r o n e - ta p d e p lo y m e n t, ta s k a u to m a tio n , a r c h ite c tu r e m a n a g e m e n t, B a rg a in in g
G o o g le constituents based on who they
F e d e ra te d , c e rtifie d c o lle c tio n s o f d a ta p u b lish e d to e n te rp ris e
Dictionaries Lineage Views Cubes Marts a n d s y s te m m o n ito r in g . Pow er
a rc h ite c ts a n d d e p a rtm e n ta l a n a lys ts , d a ta s cie n tis ts , a n d d e ve lo p e rs .
are, where they are, and what
they need.
Simplicity
D e liv e r u s e r e x p e r ie n c e s , lik e v o ic e , c h a tb o ts , a n d n a tu r a l la n g u a g e , th a t fe e l
SCHEMA fa m ilia r a n d e ffo r tle s s e v e n u p o n fir s t u s e , s u p p o r te d w ith to o ls th a t r e q u ir e
A s e m a n tic g ra p h c o n ta in in g re u s a b le o b je cts, ca p tu re d in b u sin e ss te rm s, th a t
Models Attributes Metrics Templates Filters Sets Visualizations Prompts Forms z e r o tr a in in g .
m a p to e n te rp ris e a ss e ts, a n d a b s tra ct co m p le x itie s o f th e u n d e rly in g d a ta .
Facebook Buyer
M ETADATA B a rg a in in g
Scalability Pow er
APPLICATION SERVICES S h a r e s o p h is tic a te d p e r s o n a liz e d a p p lic a tio n s , b u ilt o n b illio n s o f r o w s o f d a ta ,
A s u ite o f e n te rp rise -c a lib e r a d d -o n s e rv ic e s a va ila b le fo r Intelligence Analytics Transaction Distribution Telemetry Identity Collaboration Geospatial Language w ith 1 0 0 ,0 0 0 s u s e r s w h ile m a in ta in in g s u b - s e c o n d r e s p o n s e s .
a rc h ite c ts to q u ic kly a n d e a s ily in te g ra te in to a n y a p p lic a tio n .
A lib a b a
Stability
D e p lo y a p p lic a tio n s o n a r o b u s t a r c h ite c tu r e w ith h o t, w a r m , a n d c o ld fa ilo v e r
PLATFORM SERVICES Optimized T h re a t o f
C ritic a l c a p a b ilitie s to d e p lo y a n a lytic s a n d m o b ility a p p lic a tio n s w ith h ig h Multi-Source Parallel Multi-node Multi-Level Dynamic Platform Multi-Tenancy Usage Compute s tr a te g ie s , c lu s te r in g , a n d r e lia b le g o v e r n o r s c o m b in in g to a v e r t s y s te m c r a s h e s
S u b s titu te
p e rfo rm a n c e a n d s ca la b ility o n to p o f e n te rp rise a ss e ts. Connectivity Processing Server Cluster Caching Sourcing Analytics Telemetry Elasticity u n d e r p e a k u s a g e a n d d a ta lo a d s .
P ro d u cts
T w itte r
S E R V IC E S Security
F a c ilita te s e a m le s s m a n a g e d a c c e s s to e n te r p r is e a s s e ts , c o n tr o lle d v ia a
DRIVERS AND GATEWAYS
O u t-o f-th e -b o x g a te w a y s a n d d rive rs th a t m a k e it e a sy to c o n n e ct Relational OLAP Big Data EMM PACS Logical Application g r a n u la r s e c u r ity m o d e l, v ia c o n v e n ie n t m u lti- fa c to r d ig ita l c r e d e n tia ls .
to a lm o s t a n y e n te rp ris e in fo rm a tio n re so u rce .
In te n s ity o f
M icro s o ft R iv a lry a m o n g
C o m p e tito rs
ENTERPRISE ASSETS
(1 5 0 + D R IV E R S + G A T E W A Y S )
SQL MDX HADOOP NOSQL APPLICATIONS
The Intelligence Center
Building performant dossiers
Aim at a 5s response time, keeping a maximum of 10s (end-to-end)
Every role of the Intelligent Enterprise matters
• Systems Administrator
• Platform Administrator
• Analytics Architect
• Applications Architect
• Developers, Analysts, Business Users
Developing a mindset is the key!
• Thinking of performance implications with every dossier design decision
• Intelligence Center working together with users to improve the overall system
6
Systems and Platform Administrators
Enable performant (network) access to enterprise datasets
Ensure enough Server resources to leverage In-Memory capabilities, including Caching
Manage environments for enterprise and departmental applications
• Enterprise environment requiring 99% uptime and meeting all ü Use job prioritization mechanisms already in
company performance Service Level Agreements (SLAs) place to isolate users, including User Fencing
• Departmental environment with own governance allowing ad-hoc and Workload fencing on clustered
reporting & self-service environments
7
Applications Architect
Enterprise Applications standards and
guidelines
Performance tuning and troubleshooting
• Criteria for application datasets
• Application caching strategy
Work with business users, analysts,
developers, data scientists
8
Understanding the dossier execution
Datasets Execution
• Reports and View Reports
• In Memory Cubes or Cached Reports
• Live Connection
Dossier Schema
• Joins and Global Lookup Tables
Populate Filters • Calculate Derived Attributes and Groups
Visualizations
• Get Visualization data
• Visualization level calculations (e.g. DM’s)
• Rendering
9
Running Datasets
Datasets that require time to query data
• Reports run SQL against the Warehouse (unless already cached).
• View Reports run CSI against a Cube (unless cached – new!)
Datasets that do not consume time at this stage
• In Memory Cubes (already loaded / published)
• Reports that were pre-cached
• Live Connection datasets
10Dossier Schema
The layer that organizes the data available to the dossier globally
• Deals with Attribute linking (data blending across datasets)
• Attribute relationships
• Creation of Lookup Tables
• Derived Attributes
• Groups
11Attribute Element List Filters
Avoiding heavy attributes on Filter styles that get populated with a list of elements
12Running visualizations
What goes into executing each visualization?
Getting the data
• In-Memory datasets: Data is subset from the
Cube (CSI Engine)
• Live datasets: Data is queried directly from the
external source (SQL Engine)
Performing In-Memory calculations
• Calculating Derived Metrics
• With Live datasets, Derived Metrics are
pushed to the external data source. Some
Derived Attributes calculations too.
JSON Generation + Transfer through Network
13In-Memory vs Live Connection
What goes into executing each visualization?
14Rendering visualizations
Rendering time depends on number of data points and your machine resources
• Grids have incremental fetching, so only the first 100 rows render
• However, other visualizations may attempt to render every data point
15Strategies to achieve good performance Dataset Caching & Dossier Caching (Cache Subscription) Narrowing down the data for the dossier Blending your Data the right way Using In-Memory calculations wisely and remove unused objects Limiting the amount of individual data points rendered in each visualization
Dataset Caching Dataset Caching for Reports and View Reports (new!)
Dossier Caching Caching all the dossier data and definition
Caching in Library Configurable from Workstation (highly personalized!)
Caching Subscriptions Schedule a recurrent cache update subscription to speed up first response time
Narrowing down the data Dataset-level filters vs dossier-level filters Using Prompts If using Reports, having a dataset-level Report Filter limits the data volume from the start Filtering data at the individual visualization level
Narrowing down the data
Dataset-level filters
• Report Filters
• Filters in View Report
Dossier-level filters
• Filter Panel
• Advanced Filter
• Filters at the Metric level (or Conditional Metrics)Narrowing down the data with Prompts Prompting Reports to limit the data they must fetch from the source
Using Data Blending the right way Splitting your data in several datasets vs single consolidated dataset Data Blending and Global Lookup tables. How does this matter? Multiple joins across datasets and their performance implications Controlling join behavior across datasets
Multiple Datasets vs a Single Consolidated Dataset Consider implications of blending data from multiple datasets
OLAP vs Multi-Table Data Import Cubes
Two types of Cubes:
• OLAP: Governed project schema, typically created by BI department. Data
is denormalized.
• MTDI Cubes: Sometimes created by business users. Mini-schema in-
memory. Should be certified by Admin so they are governed (that way this
data can be federated to others). Data can be normalized.Using Data Blending the right way One OLAP Cube (denormalized data) vs many smaller datasets
Using Data Blending the right way Blending through a Multi-Table Data Import Cube (aka Super Cube)
Data blending between multiple datasets
Data Combination settings
• Dossier-level lookup tables for linked attributes:
this allows populating full sets of distinct elements
present in all datasets.
Load Chapters on Demand
• Run visualizations only of the currently viewed
Chapter. Switching Chapters may be slower.OLAP vs MTDI OLAP Cubes vs Multi-Table Data Import Cubes
Multi-Table Data Import Cubes
What can impact performance? (Hint: joins)
• One-to-many relationships. Correctly set relationships to avoiding more expensive joins.
• Number of tables joined in query. Retrieving metric data from many tables in the MTDI cube.
• Security filters. CSI/SQL engine may “auto-join” table with user-requested data with necessary
tables that contain secured attributes.
• Complex filters that will result in multiple passes and joins; especially if based on multiple
attributes not present in the same table.
• Many-to-Many relationships usually unnecessary, so avoid defining them in the MTDI cube.Cube Partitioning Leverage parallel aggregation on multiple CPU’s by enabling partitioning
Data Blending on multiple datasets Join Settings controlled via each dataset context menu
In-Memory calculations vs pre-calculated Columns
Derived Attributes and Groups are useful for ad-hoc reporting and self-service, but how do
they scale?
Performance considerations of Derived Attributes vs Data Blending (when they get calculated
against a Global Lookup)
How to make a dossier more scalable and better performing by moving these calculations to
an In-Memory dataset, so they don’t have to calculate at the dossier execution time.
Using data wrangling instead of Derived Attributes, and why this may perform better.
Considerations with Derived Metrics (mention of “Use Lookup for Attributes” setting)In-Memory calculations vs pre-calculated Columns Using Data Wrangling instead of Derived Attributes
Visualization rendering time
Incremental fetch on Grid, but not in other visualizations
Performance considerations of Bar Charts, Scatter plot, Heatmap and other visualizations. Use
filters to limit the volume of data points that the visualization will render.
Performance considerations with
Maps. Using clustering.
Using Image (png or jpg) thresholdsIn a nutshell
Watch for joins! Especially between unrelated attributes. Prefer inner joins.
Prefer In Memory cubes or cached Reports as datasets. Consider cache subscriptions.
One big dataset generally better than multiple smaller ones.
Use Derived Attributes, Groups and Derived Metrics only if necessary. Prefer having these
calculations built on the dataset (pushed to DB, or done when publishing the Cube).
Try to avoid complex functions. Watch for “Smart Metrics”.
The more tables have to be accessed, the longer it takes to run.
Clean up your dossier. Remove unused objects and datasets.Tips for Troubleshooting
Simple “binary search” on on Chapters, Pages, Visualizations, Derived Calculations, Filters
and Datasets
Using Chrome’s Developer Tools to break down the response time (time on Server side vs
time on client/browser used for rendering)
Turning on Performance Statistics on MicroStrategy Web
Intelligent Server logs that can be used to further break down the response time.
• Datasets execution time
• Time spent on calculations of Derived Attributes, Groups
• Time spent on each individual visualization, including Derived Metrics calculations and other.
• MCE Trace for Data Blending
Capacity Planning Tool (Administrators)Platform Analytics and other Tips for Troubleshooting Platform Analytics Captures telemetry from the MicroStrategy Platform in real-time and makes it available to administrators, developers and analysts to help them optimize performance
MicroStrategy Consulting
Application Performance Tuning Advisory
Best practice guidance to ensure your application performs seamlessly.
MicroStrategy.com/Services
44Enterprise Support Program
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Reinvesting in you.
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services custom-built to help you become a more Intelligent
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