Ab Initio Is is a Latin word , Meaning From the beginning.
Ab Initio software helps you build large-scale data processing applications and run them in parallel environments. Ab Initio software consists of two main programs:
· Co>Operating System, which your system administrator installs on a host UNIX or Windows NT Server, as well as on processing nodes. (The host is also referred to as the control node.)
· Graphical Development Environment (GDE), which you install on your PC (client node) and configure to communicate with the host (control node).
PICTURE
BASIC TERMS of In Ab Initio
What is Dataset ?
In Simple terms dataset is a file. A file can be main frame file or any fixed or delimited files. There are various types of datasets
FIDXED EBCDIC
FIXED ASCII
DELIMITED ASCII
SAS dataset etc..
You can also think of dataset as a table in database world.
What is Component?
Component is Ab Initio Program
There are various components like SELECT, FILTER, SORT, JOIN, MERGE, DEDUP, ROLLUP, SCAN, USER DEFINED etc…
What is Port?
A port is a connection point for the input or output to a component.
What is Flow?
A flow carries a stream of data between components in a graph. Flows connect components via ports. Ab Initio supplies four kinds of flows with different patterns: straight, fan-in, fan-out, and all-to-all. We will discuss various kinds of flows as we go through this training.
What is Graph ?
A graph is a diagram that defines the various processing stages of a task and the streams of data as they move from one stage to another. Visually, stages are represented by components and streams are represented by flows. The collection of components and flows comprise an Ab Initio graph.
What is Field ?
A filed is Equivalent to column of a table in Database world. Filed is also called variable which holds value.
What is Key ?
Key is used many places in Ab Initio Development , We use key to sort the data , Join two files , Rollup data etc..
See the Below Graph Which explained Basic Terms.
AB Initio DML’s
DML is an acronym for Data Manipulation Language in Ab Initio World. It is the Ab Initio programming language you use to define record formats. DML is equivalent to DDL in traditional databases. DML tells Ab Initio how to interpret your data.
Following list are various types of DML’s
Delimited.dml
record
decimal('') cust_id;
string(‘') last_name;
string(‘') first_name;
string(‘') street_addr;
string(‘') state;
decimal(‘') zip;
string(‘') gender;
decimal('\n') income;
end
Example of data :
297457AlexNeil Steven149 Inkwell St.KY40541M0073900
901288AndrusTom165 Eboli St.WY60313M0492500
662197BannonJeffrey C21 Compuserve St.CO70307M0140200
denorm.dml
record
decimal(5) custid;
decimal(3) num_trans;
record
date("YYYYMMDD") dt;
decimal(6.2) amount;
end transactions[num_trans];
string(1) newline;
end
Example Of Data: -
12345 219970204 5.9519970209125.05
14521 119970205 15.50
12341 0
12341 319970202 9.9019970206 12.2319970210 62.75
ebcdic.dml
record
ebcdic decimal(6) cust_id;
ebcdic string(18) last_name;
ebcdic string(16) first_name;
ebcdic string(26) street_addr;
ebcdic string(2) state;
ebcdic decimal(5) zip;
ebcdic string(1) gender;
ebcdic decimal(7) income;
ebcdic string(1) newline;
end
// Rather than using the "ebcdic" keyword in every text field,
// this example uses the "ebcdic" keyword in front of the "record"
// keyword, affecting all text fields.
ebcdic record
decimal(6) cust_id;
string(18) last_name;
string(16) first_name;
string(26) street_addr;
string(2) state;
decimal(5) zip;
string(1) gender;
decimal(7) income;
string(1) newline;
end
Example Of data
òù÷ôõ÷Á“…§@@@@@@@@@@@@@@Õ…‰“@⣅¥…•@@@@@ñôù@É•’¦…““@â£K@@@@@@@@@@@ÒèôðõôñÔðð÷óùðð%ùðñòøøÁ•„™¤¢@@@@@@@@@@@@ã–”@@@@@@@@@@@@@ñöõ@Å‚–“‰@
fixed.dml
record
decimal(6) cust_id; // Customer ID
string(18) last_name; // Last name
string(16) first_name; // First name
string(26) street_addr; // Street address
string(2) state; // State
decimal(5) zip; // Zipcode
string(1) gender; // Gender (M = male; F = female)
decimal(7) income; // Income (in dollars)
string(1) newline;
end
Example Of data
297457Alex Neil Steven 149 Inkwell St. KY40541M0073900
901288Andrus Tom 165 Eboli St. WY60313M0492500
662197Bannon Jeffrey C 21 Compuserve St. CO70307M0140200
unix-text.ml
string("\n")
Example Of data
This is text as you might
find it on a computer running a
win-text.dml
string("\r\n")
Refrer to the Graph Types.mp to define dml’s and View the data
FILTER BY EXPRESSION
Following Graph is our First Ab Initio Graph. This graph processes a file to produce customers whose income is greater than $5000.
INPUT DML: -
record
decimal(6) cust_id; // Customer ID
string(18) last_name; // Last name
string(16) first_name; // First name
string(26) street_addr; // Street address
string(2) state; // State
decimal(5) zip; // Zipcode
string(1) gender; // Gender (M = male; F = female)
decimal(7) income; // Income (in dollars)
string(1) newline;
end
See the Attached Graph.
Code: -
This Graph contains no code.
Performance / Interview Question?
Don’t use filter by Expression. Most of the components has embedded filter by expression called select expression use embedded select instead of Filter by expression if possible to improve performance.
Exercise: Use Filter By Express to split the above customer information into two files one with Males and Females.
Transformation with Reformat (XFR)
What is XFR?
You write your code (logic) in XFR. Your code file extension is ..xfr. All the Transform components use XFR to run.
REFORMAT COMPONENT: -
As name suggests reformat means changing the format of input file and produce the desired output file. Example if you have 10 fields in input file you want out put file with 5 out of those 10 input fields then you will use reformat component.
With reformat you can derive new fields. See the examples below.
Example 1: Customer info Dml
record
decimal(6) cust_id; // Customer ID
string(18) last_name; // Last name
string(16) first_name; // First name
string(26) street_addr; // Street address
string(2) state; // State
decimal(5) zip; // Zipcode
string(1) gender; // Gender (M = male; F = female)
decimal(7) income; // Income (in dollars)
string(1) newline;
end
INPUT DATA
297457AlexNeil Steven149 Inkwell St.KY40541M0073900
901288AndrusTom165 Eboli St.WY60313M0492500
662197BannonJeffrey C21 Compuserve St.CO70307M0140200
Reformat above DML like this
record
decimal(6) cust_id; // Customer ID
string(18) last_name; // Last name
string(16) first_name; // First name
string(1) gender; // Gender (M = male; F = female)
decimal(7) income; // Income (in dollars)
string(1) newline;
end
YOUR OUTPUT DATA LOOK’s LIKE this
297457AlexNeil StevenM0073900
901288AndrusTomM0492500662197BannonJeffrey CM0140200
XFR Code For Above Example: -
/*Reformat operation*/
out::reformat(in) =
begin
out.cust_id :: in.cust_id;
out.last_name :: in.last_name;
out.first_name :: in.first_name;
out.gender :: in.gender;
out.income :: in.income;
out.newline :: in.newline;
end;
Reformat Example With Derived Field :-
Reformat customer DML like this , We are deriving new field called full_address which is concatenation Street_address , state, zip into one line
record
decimal(6) cust_id; // Customer ID
string(18) last_name; // Last name
string(16) first_name; // First name
String(33) Full_address ---------DERIVED FILED,
string(1) gender; // Gender (M = male; F = female)
decimal(7) income; // Income (in dollars)
string(1) newline;
end
XFR Code For Above Example: -
/*Reformat operation*/
out::reformat(in) =
begin
out.cust_id :: in.cust_id;
out.last_name :: in.last_name;
out.first_name :: in.first_name;
out.Full_address :1: string_concat ( in.street_addr,in.state,in.zip);
out.Full_address :2: “NO Address Found”;
out.gender :: in.gender;
out.income :: in.income;
out.newline :: in.newline;
end;
In the above code string_concat is Ab Initio built-in function, Read help for all built in function. They are similar to C-Programming functions. Also note Priority Assignments 1 and 2 are like Case statements in SQL, If 1 is success take it else use 2.
Caution: - AB INITIO DML names are case sensitive.
Generate Records: -
Generate Records generates a specified number of data records with fields of specified lengths and types.
You can let Generate Records generate random values within the specified length and type for each field, or you can control various aspects of the generated values using command line option of Generate Records component. Typically, the output of Generate Records is used for testing a graph.
Example: -
Input DML:- record decimal(6) cust_id; string(18) last_name; string(16) first_name; string(26) street_addr; decimal(2) state_code; decimal(5) zip; string(1) gender="M"; decimal(7) income; date("MM/DD/YYYY") dob; string(1) newline="\n"; end Set num_records option to 10000 Set command Line option as follows:- -sequential cust_id 350000 -minimum state_code 1 -maximum state_code 50 -minimum income 100 –maximum income 100000 -default gender -default newline
Above Command line telling generate records component to generate 10,000 records , generate cust_id’s sequentially starting from 350,000 , set state_code between 1 to 50 and income between 100 to 100000 and keep default values for gender and newline.
Exercises for reformat: - Generate 50000 records with above DML in /data/abwork/your_dir/in_file1.dat use following command line and DML -sequential cust_id 350000 -minimum state_code 1 -maximum state_code 50 -minimum income 100 –maximum income 100000 -default gender -default newline record decimal(6) cust_id; string(18) last_name; string(16) first_name; string(26) street_addr; decimal(2) state_code; decimal(5) zip; string(1) gender="M"; decimal(7) income; date("MM/DD/YYYY") dob; string(1) newline="\n"; end Generate 100000 records with above DML in /data/abwork/your_dir/in_file2.datuse following command line and DML -sequential cust_id 350000 -minimum state_code 1 -maximum state_code 50 -minimum income 100 –maximum income 100000 -default gender -default newline record decimal(6) cust_id; string(18) last_name; string(16) first_name; string(26) street_addr; decimal(2) state_code; decimal(5) zip; string(1) gender="F"; decimal(7) income; date("MM/DD/YYYY") dob; string(1) newline="\n"; end Develop Following Graphs Exercise 1 ( GRAPH 1) : - Use Unix cat command to make above generated data into one file like this Cd /data/abwork/your_dirCat in_file1.dat in_file2.dat >> in_file.dat Use following input DML to map in_file.dat input DML :- record decimal(6) cust_id; string(18) last_name; string(16) first_name; string(26) street_addr; decimal(2) state_code; decimal(5) zip; string(1) gender; decimal(7) income; date("MM/DD/YYYY") dob; string(1) newline="\n"; endDefine Output file with following DML Set your output file to file:/data/abwork/your_dir/reform_ex1.out OUT PUT DML :- record decimal(6) cust_id; string(18) last_name; string(16) first_name; string(26) street_addr; decimal(2) state_code; decimal(5) zip; string(1) gender; decimal(7) income; date("MM/DD/YYYY") dob; decimal(3) age; string(1) minor_flag; string(1) newline="\n"; end 1) Derive a field called Age using his dob use following expression to get age ((date("MM/DD/YYYY"))"03/17/2003" - (date("MM/DD/YYYY"))in0.dob); 2) Derive a field called minor_flag , Set this flag to "Y" if age is less than 18 or set it to "N" isage is >= 18 Exercise 2 (GRAPH 2): - INPUT DML ( same as Above Example) record decimal(6) cust_id; string(18) last_name; string(16) first_name; string(26) street_addr; decimal(2) state_code; decimal(5) zip; string(1) gender; decimal(7) income; date("MM/DD/YYYY") dob; string(1) newline="\n"; end OUTPUT DML record decimal(6) cust_id; string(18) last_name; string(16) first_name; string(26) street_addr; decimal(2) state_code; decimal(5) zip; string(1) gender; decimal(7) income; date("MM/DD/YYYY") dob; decimal(5) score; string(1) newline="\n"; end Based on his gender derive a filed called score, Business logic to derive score is if (in.gender == "M") score = income / 2000;if (in.gender == "M") score = income / 2000;if (in.gender == "F") score = income / 2000 + 500; Exercise 3 (GRAPH 2): - INPUT DML ( same as Above Example) record decimal(6) cust_id; string(18) last_name; string(16) first_name; string(26) street_addr; decimal(2) state_code; decimal(5) zip; string(1) gender; decimal(7) income; date("MM/DD/YYYY") dob; string(1) newline="\n"; end OUTPUT DML :- record decimal(6) cust_id; string(18) last_name; string(16) first_name; string(26) street_addr; decimal(2) state_code; decimal(5) zip; string(1) gender; decimal(7) income; date("MM/DD/YYYY") dob; decimal(2) dayb; decimal(2) monthb; decimal(2) yearb; string(1) newline="\n"; end Use data functions to find day born and month born and year born of above customers into derived fields dayb, monthb, yearb respectively.
ROLLUP
What is rollup?
Rollup summarize groups of data records. It is like Group by operation in SQL. The Best Way to understand rollup is using an example.
Let us say you have input dataset with following DML
The input dataset has records of this format:
record
string(" ") cust_name;
decimal(" ") purchase;
decimal(" ") age;
string("\n") coupon;
end;
A group of records like this:
Cust_name purchase age coupon
----- -------- --- ------
Steve 100 13 Y
Steve 200 34 N
Kathy 200 38 N
Kathy 400 70 N
We would like to rollup these records by the key field to produce Records of this format:
record
string(" ") cust_name;
decimal(" ") total_purchases;
string("\n") ever_used_coupon;
end;
We want to see the output like this
Cust_name total_purchases ever_used_coupon
Steve 300 Y
Kathy 600 N
The total purchases field will contain the sum of all of the purchase field values for all records with the same key. The ever_used_coupon field will be "Y" if the customer uses a coupon in any transaction, or "N" otherwise.
See the Graph below:-
In this Graph we are using Sort Component, which is required before rollup. Sort requires a key to sort on, We set the key in this example as cust_name . We connect a flow from sort to rollup component. Rollup component requires a key to group the records, In this example we set the key to rollup component as cust_name and we write a transformation code as follows
// While we are doing the rollup for each unique key,
// we keep the following information around:
type temporary_type =
record
decimal("\266") total_purchases;
string("\266") ever_used_coupon;
end;
// The initialize function sets up the initial temporary record.
temp :: initialize(in) =
begin
temp.total_purchases :: 0;
temp.ever_used_coupon :: "N";
end;
// The rollup function does the work for each record in the group
// with the same key.
out :: rollup(temp, in) =
begin
temp.total_purchases :: temp.total_purchases + in.purchase;
temp.ever_used_coupon :1: if ( temp.ever_used_coupon == "Y") "Y";
temp.ever_used_coupon :2: in.ever_used_coupon;
end;
// The finalize function produces the output record from the temporary
// record and the last input record in the group.
out :: finalize(temp, in) =
begin
out.cust_name :: in.cust_name;
out.ever_used_coupon :: in.ever_used_coupon;
out.total_purchases :: in.total_purchases;
end
Rollup component reads one record at a time from sort and compare current cust_name to next cust_name , if they are same then Rollup function in above XFR does the work for each record in the group. The important thing to understand here is rollup operates on each group. Every record in the group loops through rollup function in above XFR.
JOIN
Join performs inner, outer joins with multiple input datasets.
There 3 types in Ab Initio
Inner Join, which is default
Explicit Join, Which is again divided into to left outer join and right outer join
Full outer Join.
All the joins require a Key, if you are joining two tables then the name of the Joining key in DML must be same, If not you have to use over ride key option in side the join.
If there is no key then it is called cartition join, you can do this by setting the key value to {}.
You can use unused ports to achieve A MINUS B and B MINUS A on any two files.
Joins can be done in memory by setting In-Memory option of Join. When you do In-Memory option you have to set the driving table. Join loads all the tables into memory except the driving table and performs the join in memory.
See The Example of Graph below and notice various components
MFS AND Parallelism
Parallelism:-
There Are 3 types
Component
Pipeline
Data
Component
A graph running simultaneously on separate data using different components like Filter, Rollup, Join etc in same phase is called Component Parallelism.
Pipeline
Each component in the pipeline continuously reads from upstream components, processes data, and writes to downstream components. Since a downstream component can process records previously written by an upstream component, both components can operate in parallel.
Component and Pipeline Parallelisms are default in Ab Initio, Programmer don’t have any control on these Parallelisms.
Data
Data Parallelism is achieved using Multi File System (MFS). A multifile is a parallel file that is composed of individual files on different disks and/or nodes. The individual files are partitions of the multifile. Each multifile contains one control partition and one or more data partitions. Control partition will have pointers to data partition.
If there 4 data partition then MFS is called 4 Way MFS
If you have 8 data partition then that MFS is called 8 Way MFS and so on.
Multi File System (MFS) is created using a command called m_mkfs , And deleted using m_rmfs
Following command outlines how to create a MFS
m_mkfs //razzle/data/abwork/traing/b1/my_4way \
//razzle/data/abwork/traing/b1/d1 \
//razzle/data/abwork/traing/b1/d2 \
//razzle/data/abwork/traing/b1/d3 \
//razzle/data/abwork/traing/b1/d4 \
cd /data/abwork/traing/b1/
chmod 777 my_4way
chmod 777 my_4way /.WORK
m_touch my_4way/x.txt
The First line MFS is called control partition, you write all you files in control partition by specify your output file name in output file compoent.
To send a single file to Multifile we have to use partitioning components. There are various partitioning components.
Partition by Key :- distributes data records to its output flow partitions according to key values.
Partition by Expression :- distributes data records to its output flow partitions according to a specified DML expression.
Partition by Percentage:- distributes a specified percentage of the total number of input data records to each output flow.
Partition by Range :- distributes data records to its output flow partitions according to the ranges of key values specified for each partition.
Partition by Round-robin :- distributes data records evenly to each output flow in round-robin fashion.
Partition with Load Balance:- distributes data records to its output flow partitions, writing more records to the flow partitions that consume records faster.
Broadcast: - Broadcast can act like replicate but it does more than replicate. Boadcast can be used to send single file into MFS with out splitting. I.e if you broadcast small file with 10 records in to 4 way, Broadcast send 1 copy of 10 records to all 4 data partitions.
To convert Multifile into single file we have to use Departitioning components. There are various Departition components.
Concatenate : appends multiple flow partitions of data records one after another.
Gather: combines data records from multiple flow partitions arbitrarily.
Interleave : combines blocks of data records from multiple flow partitions in round-robin fashion.
Merge : combines data records from multiple flow partitions that have been sorted according to the same key specifier and maintains the sort order.
