1. What Does Ab Initio Mean?
Ab Initio is a Latin phrase that means:
Of, relating to, or occurring at the beginning; first
From first principles, in scientific circles
From the beginning, in legal circles

2. About Ab Initio
Ab Initio is a general purpose data processing platform for enterprise class, mission critical applications such as data warehousing, clickstream processing, data movement, data transformation and analytics.
Supports integration of arbitrary data sources and programs, and provides complete metadata management across the enterprise.
Proven best of breed ETL solution.
Applications of Ab Initio:
ETL for data warehouses, data marts and operational data sources.
Parallel data cleansing and validation.
Parallel data transformation and filtering.
High performance analytics
Real time, parallel data capture.

3. Ab initio Platforms
No problem is too big or too small for Ab Initio. Ab Initio runs on a few processors or few hundred processors. Ab Initio runs on virtually every kind of hardware
SMP (Symmetric Multiprocessor) systems
MPP (Massively Parallel Processor) systems
Clusters
PCs

4. Ab Initio runs on many operating systems
Compaq Tru64 UNIX
Digital unix
Hewlett-Packard HP-UX
Ibm aix
NCR MP-RAS
Red Hat Linux
IBM/Sequent DYNIX/ptx
Siemens Pyramid Reliant UNIX
Slicon Graphics IRIX
Sun Solaris
Windows NT and Windows 2000

5. Ab Initio base software consists of three main pieces:
Ab Initio Co>Operating System and core components
Graphical Development environment(GDE)
Enterprise Metadata Environment(EME)

6. Ab Initio Architecture
Applications
Ab Initio
Metadata
Repository
Application Development Environments
Graphical C Shell
Component
Library
User-defined
Components
Third Party
Components
Ab Initio Co>Operating System
Native Operating System
UNIX Windows NT

7. Graph when deployed generate .ksh
Ab Initio Overview
Store all variables in a repository / is also used for control / also collects all metadata about graph developed in GDE
Run all your graphs
Create all your graphs
User
EME
GDE
Co>Operating
system
User
DTM
Graph when deployed generate .ksh
User
Used to schedule graphs developed in GDE. It also has capability to maintain dependencies between graphs

8. Co>Operating System
The Co>Operating System is core software that unites a network of computing resources-CPUs, storage disks, programs, datasets-into a production-quality data processing system with scalable performance and mainframe reliability.
The Co>Operating System is layered on top of the native operating systems of a collection of computers. It provides a distributed model for process execution, file management, process monitoring, check-pointing, and debugging.

9. Graphical Development Environment (GDE)
GDE lets create applications by dragging and dropping components onto a canvas configuring them with familiar, intuitive point and click operations, and connecting them into executable flowcharts.
These diagrams are architectural documents that developers and managers alike can understand and use. the co>operating system executes these flowcharts directly. This means that there is a seamless and solid connection between the abstract picture of the application and the concrete reality of its execution.

10. Graphical Development Environment (GDE)
The Graphical Development Environment (GDE) provides a graphical user interface into the services of the Co>Operating System.
Unlimited scalability : Data parallelism results in speedups proportional to the hardware resources provided, double the number of CPUs and execution time is halved.
Flexibility : Provides a powerful and efficient data transformation engine and an open component model for extending and customizing Ab Initio’s functionality.
Portability : Runs heterogeneously across a huge variety of operating system and hardware platforms.

11. Graphical Method for Building Business Applications
A Graph is a picture that represents the various processing stages of task and the streams of data as they move from one stage to another.
One Picture is worth a thousand words, is one graph worth a thousand lines of code? Ab Initio application graphs often represent in a diagram or two what might have taken hundreds to thousands of lines of code. This can dramatically reduce the time it takes to develop, test, and maintain application

12. What is Graph Programming
Ab Initio has based the GDE on the Data Flow Model
Data flow diagrams allow you to think in terms of meaningful processing steps, not microscopic lines of code
Data flow diagrams capture the movement of information through the application.
Ab Initio calls this development method Graph Programming

13. Graph Programming?
The process of constructing Ab Initio applications is called Graph Programming.
In Ab Initio’s Graphical Development Environment, you build an application by manipulating components, the building blocks of the graph.
Ab Initio Graphs are based on the Data Flow Model. Even the symbols are similar. The basic parts of Ab Initio graphs are shown below.

14. Symbols Boxes for processing and Data Transforms
Arrows for Data Flows between
processes
Cylinders for serial I/O files
Divided cylinders for parallel I/O files
Grid boxes for database tables

15. Graph Programming
Working with the GDE on your desktop is easier than drawing a data flow diagram on a white board. You simply drag and drop functional modules called Components and link them with a swipe of the mouse. When it’s time to run the application, Ab Initio Co>Operating System turns the diagram into a collection of process running on servers
The Ab Initio term for running data flow diagram is a Graph. The inputs and outputs are dataset components; the processing steps are program components; and the data conduits are flows.

16. Anatomy of a Running Job
What happens when you push the “Run” button?
Your graph is translated into a script that can be executed in the Shell Development Environment.
This script and any metadata files stored on the GDE client machine are shipped (via FTP) to the server.
The script is invoked (via REXEC or TELNET) on the server.
The script creates and runs a job that may run across many nodes.
Monitoring information is sent back to the GDE client.

17. Anatomy of a Running Job
Host Process Creation
Pushing “Run” button generates script.
Script is transmitted to Host node.
Script is invoked, creating Host process.
Host
GDE
Client
Host
Processing nodes

18. Anatomy of a Running Job
Agent Process Creation
Host process spawns Agent processes.
Host
GDE
Agent
Agent
Client
Host
Processing nodes

19. Anatomy of a Running Job
Component Process Creation
Agent processes create Component processes on each processing node.
Host
GDE
Agent
Agent
Client
Host
Processing nodes

20. Anatomy of a Running Job
Component Execution
Component processes do their jobs.
Component processes communicate directly with datasets and each other to move data around.
Host
GDE
Agent
Agent
Client
Host
Processing nodes

21. Anatomy of a Running Job
Successful Component Termination
As each Component process finishes with its data, it exits with success status.
Host
GDE
Agent
Agent
Client
Host
Processing nodes

22. Anatomy of a Running Job
Agent Termination
When all of an Agent’s Component processes exit, the Agent informs the Host process that those components are finished.
The Agent process then exits.
Host
GDE
Client
Host
Processing nodes

23. Anatomy of a Running Job
Host Termination
When all Agents have exited, the Host process informs the GDE that the job is complete.
The Host process then exits.
Host
GDE
Client
Host
Processing nodes

24. Ab Initio S/w Versions & File Extensions
Software Versions
Co>Operating System Version =>
GDE Version =>
File Extensions
.mp Stored Ab Initio graph or graph component
.mpc Program or custom component
.mdc Dataset or custom dataset component
.dml Data Manipulation Language file or record type definition
.xfr Transform function file
.dat Data file (either serial file or multifile)

25. Versions
To find the GDE version Select Help >> About Ab Initio from the GDE window.
To find the Co>Operating System version Select Run >> Settings from the GDE window. Look for the Detected base System Version.

26. Connecting to Co>op Server from GDE

27. Host Profile Setting Choose settings from the run menu
Check the use host profile setting checkbox.
Click Edit button to open the Host profile dialog.
If running Ab Initio on your local NT system, check Local Execution (NT) checkbox and go to step 6.
If running Ab Initio on a Remote UNIX system, fill in the path to the Host and Host Login and Password.
Type the full path of Host directory.
Select the Shell Type from pull down menu.
Test Login and if necessary make changes.

28. Enter Host, Login, Password & Host directory
Host Profile
Enter Host, Login, Password & Host directory
Select the Shell Type

29. Ab Initio Components
Ab Initio provided components. Datasets, Partition, Transform, Sort, Database are frequently used.

30. Specify the Input .dat file
Creating Graph
Type the Label
Specify the Input .dat file

31. Create Graph - Dml
Propagate from Neighbors: Copy record formats from connected flow.
Same As: Copy record format’s from a specific component’s port.
Path: Store record formats in a Local file, Host File, or in the Ab Initio repository.
Embedded: Type the record format directly in a string.
Specify the .dml file

32. Editing .dml file through Record Format Editor – Grid View
Creating Graph - dml
DML is Ab Initio’s Data Manipulation Language.
DML describes data in terms of
Record Formats that list the fields and format of input, output, and intermediate records.
Expressions that define simple computations, for example, selection.
Transform Functions that control reformatting, aggregation, and other data transformations.
Keys that specify groupings, ordering, and partitioning relationships between records.
Editing .dml file through Record Format Editor – Grid View

33. Creating Graph - Transform
A transform function is either a DML file or a DML string that describes how you manipulate your data.
Ab Initio transform functions mainly consist of a series of assignment statements. Each statement is called a business rule.
When Ab Initio evaluates a transform function, it performs following tasks:
Initializes local variables
Evaluates statements
Evaluates rules.
Transform function files have the xfr extension.
Specify the .xfr file

34. Creating Graph - xfr
Transform functions: A set of rules that compute output values from input values.
Business rule: Part of a transform function that describes how you manipulate one field of your output data.
Variable: Optional part of a transform function that provides storage for temporary values.
Statement: Optional part of a transform function that assigns values of variables in a specific order.

35. Sample Components Sort Dedup Join Replicate Rollup
Filter by Expression
Merge
Lookup
Reformat etc.

36. Creating Graph – Sort Component
Sort: The sort component reorders data. It comprises two parameters: Key and max-core.
Key: The Key is one of the parameters for Sort component which describes the collation order.
Max-core: The max-core parameter controls how often the sort component dumps data from memory to disk.
Specify Key for the Sort

37. Creating Graph – Dedup component
Dedup component removes duplicate records.
Dedup criteria will be either unique-only, First or Last.
Select Dedup criteria.

38. Creating Graph – Replicate Component
Replicate combines the data records from the inputs into one flow and writes a copy of that flow to each of its output ports.
Use Replicate to support component parallelism.

39. Creating Graph – Join Component
Specify the key for join
Specify Type of Join

40. Database Configuration (.dbc)
A file with a .dbc extension which provides the GDE with the information it needs to connect to a database. A configuration file contains the following information:
The name and version number of the database to which you want to connect.
The name of the computer on which the database instance or server to which you want to connect runs, or on which the database remote access software is installed.
The name of the database instance, server, or provider to which you want to connect.
You generate a configuration file by using the Properties dialog box for one of the Database components.

41. Creating Parallel Applications
Types of Parallel Processing
Component-level Parallelism: An application with multiple components running simultaneously on separate data uses component parallelism.
Pipeline parallelism: An application with multiple components running simultaneously on the same data uses pipeline parallelism.
Data Parallelism: An application with data divided into segments that operates on each segment simultaneously uses data parallelism.

42. Partition Components
Partition by Expression: Dividing data according to a DML expression.
Partition by Key: Grouping data by a key.
Partition with Load balance: Dynamic load balancing.
Partition by Percentage: Distributing data, so the output is proportional to fractions of 100.
Partition by Range: Dividing data evenly among nodes, based on a key and a set of partitioning ranges.
Partition by Round-robin: Distributing data evenly, in blocksize chunks, across the output partitions.

43. Departition Components
Concatenate: Concatenate component produces a single output flow that contains first all the records from the first input partition, then all the records from the second input partition and so on.
Gather: Gather component collects inputs from multiple partitions in an arbitrary manner, and produces a single output flow, does not maintain sort order.
Interleave: Interleave component collects records from many sources in round robin fashion.
Merge: Merge component collects inputs from multiple sorted partitions and maintains the sort order.

44. Multifile systems
A multifile system is a specially created set of directories, possibly on different machines, which have identical substructure.
Each directory is a partition of the multifile system. When a multifile is placed in a multifile system, its partitions are files within each of the partitions of the multifile system.
Multifile system leads to better performance than flat file systems because multifile systems can divide your data among multiple disks or CPUs.
Typically (SMP machine is exception) a multifile system is created with the control partition on one node and data partitions on other nodes to distribute the work and improve performance.
To do this use full internet URLs that specify file and directory names and locations on remote machines.

45. Multifile

46. SANDBOX
A sandbox is a collection of graphs and related files that are stored in a single directory tree, and treated as a group for purposes of version control, navigation, and migration.
A sandbox can be a file system copy of a datastore project.
In the graph, instead of specifying the entire path for any file location ,we specify only the sandbox parameter variable. For ex : $AI_IN_DATA/customer_info.dat. where $AI_IN_DATA contains the entire path with reference to the sandbox $AI_HOME variable.
The actual in_data dir is $AI_HOME/in_data in sandbox

47. SANDBOX
The sandbox provides an excellent mechanism to maintain uniqueness while moving from development to production environment by means switch parameters.
We can define parameters in sandbox those can be used across all the graphs pertaining to that sandbox.
The topmost variable $PROJECT_DIR contains the path of the home directory

48. SANDBOX

49. Deploying
Every graph after validation and testing has to be deployed as .ksh file into the run directory on UNIX.
This .ksh file is an executable file which is the backbone for the entire automation/wrapper process.
The wrapper automation consists of .run, .env, dependency list ,job list etc
For a detailed description on wrapper and different directories and files , Please refer the documentation on wrapper / UNIX presentation.

50. Symbols Boxes for processing and Data Transforms
Arrows for Data Flows between
process
Cylinders for serial I/O files
Divided cylinders for parallel I/O files
Grid boxes for database tables

51. Parallelism Component parallelism Pipeline parallelism
Data parallelism

52. Component Parallelism
Sorting Customers
Sorting Transactions

53. Component Parallelism
Comes “for free” with graph programming.
Limitation:
Scales to number of “branches” a graph.

54. Pipeline Parallelism
Processing Record: 100
Processing Record: 99

55. Pipeline Parallelism Comes “for free” with graph programming.
Limitations:
Scales to length of “branches” in a graph.
Some operations, like sorting, do not pipeline.

56. Data Parallelism
Partitions

57. Two Ways of Looking at Data Parallelism
Expanded View:
Global View:

58. Data Parallelism Scales with data. Requires data partitioning.
Different partitioning methods for different operations.

59. Data Partitioning
Expanded View:
Global View:

60. Data Partitioning: The Global View
Degree of Parallelism
Fan-out Flow

61. Session III
Partitioning

62. Partitioning Review For the various partitioning components:
Fan-out Flow
For the various partitioning components:
Is it Key-based? Does the problem require a key-based partition?
Performance: Are the partitions balanced or skewed?

63. Partitioning: Performance
Balanced:
Processors get neither
too much nor too little.
Skewed:
Some processors get
too much, others too little.

64. Sample Data to be Partitioned
Customers
42John
43Mark
44Bob
45Sue
46Rick
47Bill
48Mary
49Jane
record
decimal(2) id;
string(5) name;
decimal(5) zipcode;
decimal(3) amount;
string(1) newline;
end

65. Partition by Round-robin
Customers
42John
45Sue
48Mary
Customers
43Mark
46Rick
49Jane
Customers
44Bob
47Bill

66. Partition by Round-robin
Not key based.
Results in very well balanced data, especially with block-size of 1.
Useful for record-independent parallelism.

67. Partition by Key partition on zipcode: Customers 43Mark 02114 9
45Sue
47Bill
49Jane
Customers
42John
44Bob
46Rick
48Mary

68. Partition by Key often followed by a Sort
Sort on zipcode:
Customers
43Mark
47Bill
45Sue
49Jane
Customers
42John
44Bob
46Rick
48Mary
Rollup by zipcode:
Totals by Zipcode
Totals by Zipcode

69. Partition by Key Key-based. Usually results in well balanced data.
Useful for key-dependent parallelism.

70. Partition by Expression
Expression: amount/33
Customers
42John
43Mark
44Bob
46Rick
47Bill
49Jane
Customers
48Mary
Customers
45Sue

71. Partition by Expression
Key-based, depending on the expression.
Resulting balance very dependent on expression and on data.
Various application-dependent uses.

72. Partition by Range With splitter values of 9 and 23: Customers
43Mark
44Bob
49Jane
Customers
46Rick
47Bill
Customers
42John
45Sue
48Mary

73. Range+Sort: Global Ordering
Sort following a partition by range:
Customers
49Jane
44Bob
43Mark
Customers
47Bill
46Rick
Customers
42John
48Mary
45Sue

74. Partition by Range Key-based.
Resulting balance dependent on set of splitters chosen.
Useful for “binning” and global sorting.

75. Partition with Load Balance
if middle node highly loaded:
Customers
42John
43Mark
44Bob
49Jane
Customers
45Sue
Customers
46Rick
47Bill
48Mary

76. Partition by Load Balance
Not key-based.
Results in skewed data distribution to complement skewed load.
Useful for record-independent parallelism.

77. Partition with Percentage
With percentages: 4, 20
Customers
42John
43Mark
44Bob
45Sue
Customers
46Rick
47Bill
48Mary
49Jane
Customers
...
The next 16 records
would go here,
and the next 76 records would go here

78. Partition by Percentage
Not key-based
Results in usually skewed data distribution conforming to the provided percentages.
Useful for record-independent parallelism.

79. Broadcast (as a Partitioner)
Unlike all other partitioners which write a record to ONE output
flow, Broadcast writes each record to EVERY output flow.
Customers
42John
43Mark
44Bob
45Sue
46Rick
47Bill
48Mary
49Jane
Customers
42John
43Mark
44Bob
45Sue
46Rick
47Bill
48Mary
49Jane
Customers
42John
43Mark
44Bob
45Sue
46Rick
47Bill
48Mary
49Jane

80. Broadcast Not key-based Results in perfectly balanced partitions
Useful for record-independent parallelism.

81. Session IV
De-Partitioning

82. Departitioning Departitioning combines many flows of data to
produce one flow. It is the opposite of partitioning.
Each departition component combines flows in a
different manner.

83. Departitioning Expanded View: Global View: Score 1 Departition Score 2
Output File
Score 3
Global View:

84. Departitioning Fan-in Flow For the various departitioning components:
Key-based?
Result ordering?
Effect on parallelism?
Uses?

85. Concatenation Globally ordered, partitioned data: Sorted data:
49Jane
44Bob
43Mark
47Bill
46Rick
42John
48Mary
45Sue
Sorted data:
49Jane
44Bob
43Mark
47Bill
46Rick
42John
48Mary
45Sue

86. Concatenation Not key-based. Result ordering is by partition.
Serializes pipelined computation.
Useful for:
creating serial flow from partitioned data
appending headers and trailers
writing DML
Used infrequently

87. Merge Round-robin partitioned and sorted by amount:
42John
48Mary
45Sue
49Jane
43Mark
46Rick
44Bob
47Bill
Sorted data, following merge on amount:
49Jane
44Bob
43Mark
47Bill
46Rick
42John
48Mary
45Sue

88. Merge Key-based. Result ordering is sorted if each input is sorted.
Possibly synchronizes pipelined computation; may even serialize.
Useful for creating ordered data flows.
Used more than concatenate, but still infrequently

89. Interleave Round-robin partitioned and scored:
42John A
45Sue A
48Mary A
43Mark C
46Rick B
49Jane C
44Bob C
47Bill B
Scored dataset in original order, following interleave:
42John A
43Mark C
44Bob C
45Sue A
46Rick B
47Bill B
48Mary A
49Jane C

90. Interleave Not key-based. Result ordering is inverse of round-robin.
Synchronizes pipelined computation.
Useful for restoring original order following a record-independent parallel computation partitioned by round-robin.
Used in rare circumstances

91. Gather Round-robin partitioned and scored:
42John A
45Sue A
48Mary A
43Mark C
46Rick B
49Jane C
44Bob C
47Bill B
Scored dataset in random order, following gather:
43Mark C
46Rick B
42John A
45Sue A
48Mary A
44Bob C
47Bill B
49Jane C

92. Gather Not key-based. Result ordering is unpredictable.
Neither serializes nor synchronizes pipelined computation.
Useful for efficient collection of data from multiple partitions and for repartitioning.
Used most frequently

93. Layout Layout determines the location of a resource.
A layout is either serial or parallel.
A serial layout specifies one node and one directory.
A parallel layout specifies multiple nodes and multiple directories. It is permissible for the same node to be repeated.

94. Layout
The location of a Dataset is one or more places on one or more disks.
The location of a computing component is one or more directories on one or more nodes. By default, the node and directory is unknown.
Computing components propagate their layouts from neighbors, unless specifically given a layout by the user.

95. Session V
Join

96. Join Types
Inner join — sets the record-required parameters for all ports
to True.
Outer join — sets the record-required parameters for all ports
to False.
Explicit — allows you to set the record-required parameter
for each port individually.

97. Join Types .. Contd. Case 1: Inner Join join-type
Case 2: Full Outer Join join-type
Case 3: Explicit join-type:record-required0: false record-required1: true
Case 4: Explicit join-type:record-required0: true record-required1: false

98. Some key Join Parameters
Name(s) of the field(s) in the input records that must have matching values for Join to call the transform function.
driving
Number of the port to which you want to connect the driving input. The driving input is the largest input. All other inputs are read into memory.
The driving parameter is only available when the sorted-input parameter is set to In memory: Input need not be sorted.

99. Some key Join Parameters
dedupn
Set the dedupn parameter to true to remove duplicates from the corresponding inn port before joining. This allows you to choose only one record from a group with matching key values as the argument to the transform function.
Default is false, which does not remove duplicates
override-keyn
Alternative name(s) for the key field(s) for a particular in port.

100. References Ab Initio Tutorial Ab Initio Online Help
Website (abinitio.com)