0202 - Modeling - Best Practice.pdf

7/21/2019 0202 - Modeling - Best Practice.pdf

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SAP HANA 1.0Modeling Best Practice Notes



© 2012 SAP AG. All rights reserved. 2This presentation and SAP's strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is

provided without a warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement .

Disclaimer

This presentation outlines our general product direction and should not be relied on in

making a purchase decision. This presentation is not subject to your license

agreement or any other agreement with SAP. SAP has no obligation to pursue any

course of business outlined in this presentation or to develop or release any

functionality mentioned in this presentation. This presentation and SAP's strategy and

possible future developments are subject to change and may be changed by SAP at

any time for any reason without notice. This document is provided without a warranty

of any kind, either express or implied, including but not limited to, the implied

warranties of merchantability, fitness for a particular purpose, or non-infringement.

SAP assumes no responsibility for errors or omissions in this document, except if

such damages were caused by SAP intentionally or grossly negligent.





Agenda

General data movement in SAP HANA

Olap Engine Vs Calculation Engine Queries

Column Engine Vs Row Engine Queries

Analytical View WHERE clauses and filters

Calculation Views WHERE clauses and filters

Calculated Attributes Calculated Measures & Counters

Joining Views & Tables

Script based Calculation views Vs. Graphical Calculation views

Explorer Information Space

Aggregation Node

Multi Dimensional Reporting flag

Constraint filters





Column

Engine

Row

Engine

Olap

Engine

Join

Engine

Calculation Engine

Calculation Views

Client / Application

Row

Store

Column

Store

Attribute

Views

Analytical

Views





SPART KWMENG

ABC 60

SELECT

SPART

SUM(KWMENGA)

SUM(KWMENGB)

SUM(KWMENGC)

SUM(TOTAL)FROM CALC VIEW

GROUP BY SPART

SPART KWMENG

ABC 180

SPART KWMENG

EFG 60

SPART KWMENGA KWMENGB KWMENGC

ABC 60 0 0

ABC 0 180 0

EFG 0 0 60

SPART PLANT A PLANT B PLANT C TOTAL

ABC 60 180 0 240

EFG 0 0 60 60

SELECT SPART, SUM(KWMENG)

FROM AV GROUP BY SPART

AV C AV A AV B

1 – Analytical View static filters

• Dimensions and measures used in the queries will be pushed down and used in

subsequent queries where possible

• Static design time filters defined directly on views will get applied before the querystarts to execute

C A L C E N G I N E

O L A P E N G I N

E

Warning!

Refrain f rom

moving large

datasets

between engines





SPART MATNR KWMENG

ABC DPC1017 60

SELECT

SUM(KWMENG_A)

SUM(KWMENG_B)

SUM(KWMENG_C)

SUM(TOTAL)

FROM CV

WHERE MATNR = DPC1017

GROUP BY SPART

SPART MATNR KWMENG

ABC DPC1017 60

SPART MATNR KWMENG

- - -

SPART MATNR KWMENGA KWMENGB KWMENGC

ABC DPC1017 60 0 0

ABC DOC1017 0 60 0

PLANT A PLANT B PLANT C TOTAL

60 60 0 120

WHERE clause is

pushed down

SELECT SPART, MATNR, SUM(KWMENG)

FROM AV WHERE MATNR = ? …

AV C AV A AV B

2 – Analytical View static filters with WHERE clause

• WHERE clauses are generally pushed down to Analytical Views through Unions

although some restrictions apply.

• Even through SPART is not selected as a dimension in the query it is included in the Analytical View query due to the GROUP BY clause





SPART WERKS KWMENG

ABC 100 60

SPART WERKS KWMENG

ABC 1000 180

SPART WERKS KWMENG

EFG 1200 60

SPART KWMENGA KWMENGB KWMENGC

ABC 60 0 0

ABC 0 180 0

EFG 0 0 60

FILTERS pushed

down

SELECT SPART, WERKS, SUM(KWMENG)

FROM AV WHERE WERKS = ?

SELECT

SPART

SUM(KWMENGA)

SUM(KWMENGB)

SUM(KWMENGC)

SUM(TOTAL)FROM CV

GROUP BY SPART

AV A AV A AV A


ABC 60 180 0 240

EFG 0 0 60 60

3 – Calculation View static filters

• Static filters defined on Projection nodes will get pushed down automatically





SPART MATNR WERKS KWMENG

ABC DPC1017 100 60

FILTERS pusheddown

SELECT SPART, MATNR, WERKS,

SUM(KWMENG) FROM AV WHERE

WERKS = ? AND MATNR = ? …

SELECT SPART

SUM(KWMENGA)

SUM(KWMENGB)

SUM(KWMENGC)

SUM(TOTAL)

FROM CV

WHERE MATNR = DPC1017GROUP BY SPART

AV A AV A AV A


ABC 60 60 0 120

SPART MATNR KWMENGA KWMENGB KWMENGC

ABC DPC1017 60 0 0

ABC DOC1017 0 60 0


ABC DPC1017 1000 60


- - - -

4.1 – Calculation View filters with WHERE clause

• Static filters defined on Projection nodes will get pushed down automatically

• Dimensions and measures used in queries will get pushed down where possible





Warning!

Both WERKS and

MATNR FILTERS are

not pushed down

SELECT WERKS, MATNR,

SUM(KWMENG) FROM AV A

SELECT WERKS

SUM(KWMENGA)

SUM(KWMENGB)

SUM(TOTAL)

FROM CV

WHERE MATNR = DPC1018GROUP BY WERKS

AV A

WERKS PLANT A PLANT B TOTAL

1000 1400 0 1400

4.2 – Calculation View filters with WHERE clause

WERKS MATNR KWMENG

100 DPC1017 700

1000 DPC1017 700

1000 DPC1018 1400

1200 DPC1019 700

1500 DPC1023 200

WERKS MATNR KWMENG

100 DPC1017 700

1000 DPC1017 700

1000 DPC1018 1400

1200 DPC1019 700

1500 DPC1023 200

WERKS MATNR KWMENG

1000 DPC1018 1400

Hint!

To push down WHERE clause

add a second copy of the

Analy tical View





SELECT YEAR

SUM(A_GROSSREVENUE)

SUM(A_SALESDEDUCTION)

SUM(A_PRODUCTVARIANCE)

FROM AV AWHERE MATNR = DPC1017

GROUP BY YEAR

SELECT YEAR, MATNR, SUM(A_GROSSREVENUE),

SUM(A_SALESDEDUCTION),SUM(A_PRODUCTVARIANCE)

FROM AV/olap WHERE MATNR = DPC1017

GROUP BY YEAR, MATNR

5 – Calculated Attributes

Calculated Attributes

• A_CURRENT_YEAR_CA: midstr(string(now()),1,4)

• A_PREVIOUS_YEAR_CA: string(double(“A_CURRENT_YEAR_CA")-1)

• A_FILTER_CA: if(“A_CURRENT_YEAR_CA"="YEAR",1,if(“A_PREVIOUS_YEAR_CA"="YEAR",2,-1),-1)

YEAR CROSS REV SALES DEDUCT PROD VAR

2011 500 200 300

2012 300 100 200

Note!

WHERE clause can

be pushed down

Warning!

Refrain f rom

moving large

datasets

• The existence of Calc. Attributes results in the creation of a wrapper Calc. runtime object

• The Calc. View wrapper will always get executed regardless if any Calc. Attributes are selected

and then query the Analytical View via the (/olap) runtime object

Warning!

Use with caution





SELECT

YEAR,

A_FILTER_CA

SUM(A_GROSSREVENUE)


SUM(A_PRODUCTVARIANCE)

FROM AV AWHERE MATNR = DPC1017

GROUP BY YEAR,

A_FILTER_CA

SELECT YEAR, MATNR,

SUM(A_GROSSREVENUE),

SUM(A_SALESDEDUCTION),SUM(A_PRODUCTVARIANCE)

FROM AV A/olap WHERE MATNR = DPC1017GROUP BY YEAR, MATNR

6 – Calculated Attributes

YEAR FILTER CROSS REV SALES DEDUCT PROD VAR

2011 2 500 200 300

2012 1 300 100 200

Note!

WHERE clause can

be pushed down

• Dimensions and measures used in queries will get pushed down where possible

• Calc. Attributes (i.e. A_FILTER_CA) are executed in the Calc. View wrapper and thus

will not be pushed down into Analytical views

• The Calc. Attributes will execute only after the results from the /olap view query aretransferred to the Calc. View





SELECT

YEAR,

SUM(A_GROSSREVENUE)


SUM(A_PRODUCTVARIANCE)FROM AV A


AND A_FILTER_CA = 1

GROUP BY YEAR,

A_FILTER_CA

SELECT YEAR, MATNR, SUM(A_GROSSREVENUE),SUM(A_SALESDEDUCTION),SUM(A_PRODUCTVARIANCE)

FROM AV A/olap GROUP BY YEAR, MATNR

7 – Calculated Attributes USED in WHERE clause

YEAR CROSS REV SALES DEDUCT PROD VAR

2012 300 100 200

Warning!

WHERE clause i s

not pushed downdue to the existence

of a Calc. Attribute

filter

• Since the Calc attribute A_FILTER_CA is used in the SQL WHERE clause the MATNR

clause is not pushed down





SELECT

QUARTER

SUM(NETWR_PREVIOUS)

SUM(KWMENG_PREVIOUS)

SUM(KWMENG_CURRENT)SUM(NETWR_CURRENT)

FROM CALC VIEW

GROUP BY QUARTER

SELECT QUARTER,YEAR, SUM(KWMENG),

SUM(NETWR) FROM

AV/olap GROUP BY

QUARTER, YEAR

AV A AV A

8 – Dynamic Calculated Attribute Filter + Modeled Calculation View

FILTER 2 FILTER 1





SELECT

QUARTER

SUM(NETWR_PREVIOUS)



FROM CALC VIEW


GROUP BY QUARTER

SELECT

QUARTER, MATNR, YEAR,SUM(KWMENG),

SUM(NETW) FROM AV/olap

GROUP BY QUARTER,

MATNR, YEAR

AV A AV A

Warning!

WHERE clause no t pushed down. Similar

as using Calc Attrib . WHERE clause

FILTER 2 FILTER 1

9 – Dynamic Calculated Attribute Filter + WHERE clause





SELECT

QUARTER

SUM(NETWR_PREVIOUS)



FROM CALC VIEW


GROUP BY QUARTER

SELECT QUARTER, MATNR,YEAR, SUM(KWMENG),

SUM(NETW) FROM VIEW

GROUP BY QUARTER,

MATNR, YEAR

OLAP A OLAP A

10 – Dynamic Calculated Columns Filter + WHERE clause

• Dynamic filter expressions defined on Projection nodes cannot pass down WHEREclauses causing potential large amounts of data to be transferred

FILTER 1FILTER 2

Warning!

WHERE clause not pushed down





SELECT

CA, SUM(NETWR)

SUM(CM2), SUM(RM)

FROM OLAP VIEW

GROUP BY CA

SELECT WERKS (ca), MATNR (cm), MATKL

(cm2), NETWR, RM (spart) FROM /olap

GROUP BY WERKS, MATNR, MATKL

11 – Calculated Measures + Dimensions

WERKS MATNR MATKL NETWR RM

100 DPC1017 012 600 600

1000 DPC1017 012 600 600

1000 DPC1018 012 1200 1200

1200 DPC1019 013 600 0

NETRW CM2 RM WERKS MATNR MATKL CM CA

600 1 600 100 DPC1017 012 1 100

600 1 600 1000 DPC1017 012 1 1000

1200 1 1200 1000 DPC1018 012 1 1000

600 1 0 1200 DPC1019 013 1 1200

CA NETRW CM2 RM

100 600 1 600

1000 1800 2 1800

1200 600 2 1800

CM2 references CM

• Dimensions referenced within expressions will be used in the /olap query

• Even though WERKS, MATNR, MATKL is not explicitly selected they are used

in expressions and implicitly included in the SQL statement• WHERE clauses will be pushed down into Calculation view wrappers

WHERE clause wi ll be

pushed down if used





SELECT

SUM(MATNR_COUNT)

SUM(WERKS_COUNT)

SUM(SPART_COUNT)

FROM ANALYTICAL VIEW

SELECT MATNR, WERKS, SPART FROM Analytical

View GROUP BY MATNR, WERKS, SPART

12 – Counters

WHERE clause wi ll be

pushed down if used

MATNR WERKS SPART

DPC1017 1000 ABC

DPC1018 1000 ABC

MATNR_COUNT WERKS_COUNT SPART_COUNT

3 3 2

OLAP AOLAP A

MATNR WERKS SPART

DPC1017 100 ABC

MATNR WERKS SPART

DPC1019 1200 EFG

MATNR WERKS SPART

DPC1017 100 ABC

DPC1017 1000 ABC

DPC1018 1000 ABC

DPC1019 1200 EFG

• Counters are available in graphical Calculation view / output node

• Preferred and optimized modeling technique for COUNT DISTINCT calculations

• Dimensions used in Counters will be pushed down into Analytical views

• Where possible use WHERE clauses in combination with Counters

OLAP A





SELECT

PRODUCT_NUMBER

SUM(NETRW_A)

SUM(NETRW_P)

FROM VIEW WHERE

SALES_ORG = 3000 AND

YEAR = 2012

13 – JoinsPRODUCT_NUMBER NETRW_A NETRW_P

011 100000 3000000

012 100000000 1000

014 10000 100000000

SELECT PRODUCT_NUMBER, YEAR,

SUM(NETRW) FROM AV GROUPPRODUCT_NUMBER WHERE YEAR = ?

Warning!

Where clause can be

pushed down into RAW

tables but not Attribute

Views. Could result i nmassive data movement an

jo in degradationSALES ORG

ATTRIBUTE VIEW

Note!Where Clauses will be pushed down i f

supplied. Prior revisions did not push downWHERE clauses underneath JOINS





SELECT

VKORG, SUM (NETWR)

FROM CALCULATION

VIEW GROUP BY

VKORG

14 – Joining Analytical Views

ROWNUM VKORG NETRW

1 0001 1000

… … …

55 0003 4939

JOIN

Even through VBELN and POSNR is not used in the query the

JOIN will force the materialization of both fields

Millions of rows

could pot entially be

involved in this JOIN

Warning!

Joining of Analytical Views

should not be done! This

approach may have severe

performance implications

Note!

Where Clauses will

be pushed down if

supplied





SELECT

C.CUSTOMER, P.PRODUCT, S.YEAR, S.SALES

FROM CUSTOMER_AT C

INNER JOIN SALES_AT S ON

C.NUMBER= S.CUSTOMER

INNER JOIN PRODUCT_AT P ONS.PRODUCT_ID = P.PRODUCT_ID

WHERE

C.CUSTOMER = Nelson

15 – Joining Attribute views using SQL

CUSTOMER PRODUCT YEAR SALES

Nelson HANA 2010 100000

Nelson ECC 2010 100000000

Nelson Business Objects Explorer 2010 200000000

Nelson Marketing Slides 2010 10000

SELECT PRODUCT,

PRODUCT_ID FROMPRODUCT_AT

SELECT CUSTOMER,

PRODUCT_ID, YEAR, SALESFROM SALES_AT

SELECT CUSTOMER, NUMBER

FROM CUSTOMER_ATWHERE CUSTOMER = ?

Warning!

Refrain from Joining Att ribute views. No WHERE clauses defined fo r these

At tr ibute Views therefo re potent ial ly large materiali zation and excess iverows. Do not join A ttribu te views without using WHERE clauses

JOIN will occur after the materialization o f all Att ribute views

WHERE clause i spushed down only to

this Attribute View





SELECT CUSTOMER,

PRODUCT, YEAR , SALES

FROM CALC VIEW

WHERE

CUSTOMER = Nelson

16 – Joining Attribute views within Calculation Views

SELECT PRODUCT,

PRODUCT_ID FROM

PRODUCT_AT

SELECT NUMBER,

PRODUCT_ID, YEAR, SALES

FROM SALES_AT

SELECT CUSTOMER,

NUMBER FROM

CUSTOMER_AT

Warning!

Refrain from joining Attribute

views. Where clause is not pushed

down when joining attribute views

CUSTOMER NUMB ER

001 Nelson

002 CPG Industries

003 Pharma and Co

004 Fremont Suppliers

005 Electronic Media



Nelson ECC 2010 100000000

Nelson Business Objects Explorer 2010 200000000

Nelson Marketing Slides 2010 10000

Warning!

Excessive data due to noWhere clauses





SELECT CUSTOMER,


FROM CALC VIEW

WHERE

CUSTOMER = Nelson AND PRODUCT = HANA AND YEAR = 2010

17 – Join (Text) Tables within Calculation Views

SELECT PRODUCT,

PRODUCT_ID, SPRAS

FROM PRODUCT_AT

WHERE SPRAS = ?

SELECT CUSTOMER,

PRODUCT_ID, YEAR, SALES

FROM ACTUAL_SALES WHERE

YEAR = ?

SELECT CUSTOMER,

NUMBER, SPRAS FROM

CUSTOMER_AT WHERE

SPRAS = ?

Warning!

Where clause is not pushed

down in to tables that are

jo ined v ia Text Joins



No intermediate result set





SELECT CUSTOMER,


FROM CALC VIEW

WHERE

CUSTOMER = Nelson AND PRODUCT = HANA

18 - Join (Inner, Left) Tables within Graphical Calculation View

SELECT PRODUCT,

PRODUCT_ID FROM

PRODUCT WHERE

PRODUCT = ?

SELECT CUSTOMER,NUMBER FROM

CUSTOMER T WHERE

CUSTOMER = ?



Note!

Where clause pushed down and

jo ins occur without requi ring

intermediate result sets





19 – Ineffective data model with poor query performance

AN MATERIAL PLANT STOCK

WERKS MATNR

AT PLANT

WERKS BWKEY

JOIN

WERKS

AN MATERIAL STORAGE LOC STOCK

WERKS MATNR LGORT

AT STORAGE LOCATION

WERKS LGORT

JOIN

WERKS LGORT

AT MATERIAL

MATNR

AT MATERIAL VALUATION

VALUE MATNR BWKEY

AT MATERIAL VALUATED STOCK

WERKS MATNR LGORT

SELECT

WERKS,

VALUE

FROM

CA_STOCK_OVERVIEW

AT PLANT

WERKS BWKEY

JOIN

MATNR

JOIN

MATNR BWKEY

JOIN

MATNR WERKS LGORT

LGORT = 0

JOIN

WERKS

4 Million135 135 300 Million

200 267

3 Million

6 Million

260 Million

Warning!

Refrain from Joining views, WHERE

clauses can only be pushed down

into Analytical views and NOT

At tr ibute views.

Massive data sets has to be

materialized in order for the joins to

execute

Where clause (if supplied) will be pushed

down into Analytical views where possible





SELECT WERKS

SUM(QTY)FROM CALC VIEW

GROUP BY

WERKS

20 – Script based Calculation Views

Note!

Only WERKS is retrieved from the Database

Warning!

Refrain from u sing SQL in Calculation Views or

mixing SQL & CE functions! This approach may

have severe performance implications . Field

pruning is not possib le and as a result all fields

defined will be retrieved from the database(including underlying jo ins may get executed)

Both NAME1 & BWKEY will be

retrieved from the database even

though WERKS is the only field

requested





SELECT QUARTER,

SUM(PREVIOUS_NETWR)

SUM(CURRENT_NETWR)

SUM(ORDER_COUNT)


GROUP BY GROUPING SETS

LIMIT 26 STRUCTURED RESULT

WITH OVERVIEW PREFIX '#EXPL_TEMP'

((("QUARTER") ORDER BY

sum("PREVIOUS_NETWR")

DESC, "QUARTER" ASC))

21 – Explorer Information Space

Refrain from Grouping by KEYs without using

WHERE clauses. Using VBELN without any

WHERE clauses against the Analytical view

executes in 15 seconds and may process tensof millions of records

Q 2011 2012 COUNT

1 6101092 8621687 305

2 6317192 12762306 191

3 7304860 0 119

4 6771443 0 123

ROWNUM QUARTER YEAR VBELN NETWR

1 1 2011 81293934 122

… …. … … …

10 000 000 3 2015 34342334 233

Above query executes in 20 seconds when

Order Count is included and executes in ½second when the Order Count is excluded

SELECT QUARTER, YEAR, VBELN FROM Analytical

View GROUP BY QUARTER, YEAR, VBELN

Consider implementing a model that supports

push down of WHERE clauses into the

Analyti cal Views or using a front end too l that

supports WHERE clauses

Warning!

Referencing KEYs in

an Information Space

could severely impactperformance





SELECT

SUM(NETWR)

SUM(KURSK)

FROM CALC VIEW

22 – Multiple fact tables

NETWR KURSK

33 23

ROWNUM NETWR KURSK

1 20 10

2 13 13

SELECT SUM(NETWR),KURSK

FROM VIEWGROUP BY KURSK

Fact table

Use a Calculation View to

turn the Attribut e KHUSK

back into a Measure

Measure used as an

At tr ibute

Engine OLAP Query!from the Calculation

View

VBELN NETWR

1 10

2 10

3 13

Total 33

VBELN KURSK

1 10

2 10

3 13

Total 33

Warning!

Modeling measures as

attributes only works

in certain cases

VBRPVBAP

Incorrect Total!!!

Due to the measures having the same value. Aggregat ion removes dupli cate lines





SELECT

WERKS

SUM(NETWR)

FROM OLAP VIEW

GROUP BYWERKS

23 – Filters

Fact table

Note!

Filters are executed

independent of the Join

type and even though NO

fields are selected fromVBPA

67 Million 408 Million

JOIN FILTERS ResultINNER OFF 10 seconds

REFERENTIAL OFF .5 second

LEFT OUTER OFF .5 seconds

JOIN FILTERS ResultINNER ON 4 seconds

REFERENTIAL ON 4 seconds

LEFT OUTER ON 15 seconds

Suggested alternative modeling approach!

Create a Attribute View, add VBPA includ ing filters. Join fact tableVBAP with Attribute View. Left Outer Jo in executes in .5 seconds





SELECT MATNR,

SUM(NETWR_AGG,

SUM(NETWR_PROJ)

FROM CALC VIEW GROUP BY MATNR

24 – Aggregation Node

Note!

Difference in the amount of

data transferred when RAW

tables are used

Note!

No difference in data

transferred due to inherit

OLAP Aggregation





SELECT FLAG,

CUSTOMER,

YEAR, NETWRFROM CALC VIEW

25 – Multi Dimensional Disabled (Calculation View)

Note!

No intermediate results fo r

Union

CUSTOMER YEAR NETWR

1 2010 200

2 2010 300

SELECT CUSTOMER, YEAR,

SUM(NETWR) FROM AV

FLAG CUSTOMER YEAR NETWR

ACTUAL 1 2010 200

ACTUAL 2 2010 300

PLANNED 1 2010 210

PLANNED 2 2010 290

CUSTOMER YEAR NETWR

1 2010 210

2 2010 290

Note!

No measures, instead

measures defined as

attributes

Note!

Output node is not deployed

as a aggregation node but

instead a projection node





26 – Column Engine vs Row Engine

SELECTTO_DATE(DATE_SQL,'YYYY-MM-DD'),

SUM(NETWR)


GROUP BY DATE_SQL

Warning!

Query is processed by 2 engines. Rowengine will process DATE_SQL function

first, then transfer data to the Column

Engine. Depending on the amount of data

transferred it could severely impact

performance

When using Attribute, Analytical or Graphical Calculation Views the Row engine is not used;except if the SQL from the outside against these views contains elements that are not

natively supported by the Column engine.

The Explain Plan will indicate in what engine SQL queries executes.

Refer to the SAP HANA Database – Development Guide > Features of the Column Storage

Engine for supported Column store SQL elements.

SELECT

TO_DATE(DATE_SQL),SUM(NETWR)


GROUP BY DATE_SQL

Note!

Query is processed by Column engine

alone since the TO_DATE function without

parameters are natively supported



Thank You!



APPENDIX





Turn on the Python Trace file

• Tracing information writ ten to server trace file servertrace_ld9506_30003_000.py

• indexserver > pythontrace > trace > on

• indexserver > calcengine > trace_intermediate_results > yes

• indexserver > tracemodels > true





Diagnosis File location

• Tracing information writ ten to server trace file servertrace_ld9506_30003_000.py

• If the file is to large, make a copy and delete the file. Stop & Start the Index server

• Index server does not need to be restarted when changing the log file name





Example: WHERE clauses pushed down

Calculation View

Analytical View

WHERE clause

pushed down to

OLAP Engine

WHERE clause …





Example: WHERE clauses NOT pushed down

Analytical View

Excessive data

transferred from

OLAP to CALC

Engine



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0202 - Modeling - Best Practice.pdf

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