PC8_Performance_Tuning_MidAtlantic

8/7/2019 PC8_Performance_Tuning_MidAtlantic

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Performance Tuning

Version 8.6

Ben LeeInformatica Professional Services

Senior ConsultantMid-Atlantic IUG October 8, 2009


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Agenda

Memory optimization

Performance tuning methodology Tuning source, target, & mapping bottlenecks

Pipeline partitioning Server Grid

Q & A Course evaluation


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Anatomy of a Session

Integration Service

Data Transformation Manager(DTM)

READER WRITER

DTM Buffer

Transformation caches

TRANSFORMER

Source

data

Target

data

Target

data


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DTM Buffer

Temporary storage area for data

Buffer is divided into blocks Buffer size and block size are tunable

Default setting for each is Auto


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Reader Bottleneck

Transformer & writer threads wait for data

DTM Buffer waiting

WRITER READER Slow reader

waiting waiting

TRANSFORMER


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Transformer Bottleneck

Reader waits for free blocks; writer waits for data

DTM Buffer waitingwaiting

WRITER READER

TRANSFORMER Slow transformer


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Writer Bottleneck

Reader & transformer wait for free blocks

DTM Buffer waiting

WRITER READER Slow writer

waiting waiting

TRANSFORMER


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Large Commit Interval

DTM Buffer

waiting

WRITER READER

TRANSFORMER

Target rows remain in the buffers until the DTM reaches thecommit point


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Tuning the DTM Buffer

Temporary slowdowns in reading, transformingor writing may cause large fluctuations inthroughput

A slow thread typically provides data in

spurts Extra memory blocks can act as a cushion,

keeping other threads busy in case of abottleneck


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Transformation Caches

Temporary storage area for certain transformations

Except for Sorter, each is divided into a Data & Index Cache

The size of each transformation cache is tunable

If runtime cache requirement > setting, overflowwritten to disk

The default setting for each cache is Auto


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Max Memory for Transformation Caches

Only applies to transformation caches set to Auto


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Max Memory for Transformation Caches

Two settings: fixed number & percentage System uses the smaller of the two If either setting is 0, DTM assigns a default size to each

transformation cache thats set to Auto

Recommendation: use fixed limit if this is theonly session running; otherwise, use percentage

Use percentage if running in grid or HAenvironment


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Tuning the Transformation Caches

If a cache setting is too small, DTM writesoverflow to disk

Determine if transformation caches areoverflowing: Watch the cache directory on the file system while the

session runs Use the session performance counters

Options to tune: Increase the maximum memory allowed for Auto transformation cache sizes

Set the cache sizes for individual transformations manually


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Performance Counters


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Tuning the Transformation Caches

Non-0 counts for readfromdisk and writetodisk indicate sub-optimal settings for transformation

index or data caches

This may indicate the need to tune transformation

caches manually Any manual setting allocates memory outside of

previously set maximum

Cache Calculators provide guidance in manualtuning of transformation caches


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Aggregator Caches

Unsorted Input Must read all input before releasing any output rows

Index cache contains group keys Data cache contains non-group-by ports

Sorted Input Releases output row as each input group is processed Does not require data or index cache

(both =0) May run much faster than unsorted BUT

must consider the expense of sorting


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Joiner Caches: Unsorted Input

MASTER

DETAIL

Staging algorithm:

All master data loadedinto cache

Specify smaller dataset as master

Index cache contains join keys

Data cache contains non-key connected outputs


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Joiner Caches: Sorted Input

Index cache contains up to 100 keys

Data cache contains non-key connected outputsassociated with the 100 keys

MASTER

DETAIL

Both inputs must besorted on join keys

Selected master dataloaded into cache

Specify data set withfewest records under asingle key as master

Streaming algorithm:


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Lookup Caches

To cache or not to cache? Large number of invocations cache

Large lookup table dont cache Flat file lookup is always cached


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Rank Caches

Index cache contains group keys

Data cache contains non-group-by ports Cache sizes related to the number of groups &

the number of ranks


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Sorter Cache

Sorter Transformation May be faster than a DB sort or 3 rd party sorter

Index read from RDB = pre-sorted data SQL SELECT DISTINCT may reduce the volume of data

across the network versus sorter with Distinct property set

Single cache(no separation of index & data)


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64 bit vs. 32 bit OS

Take advantage of large memory support in 64-bit

Cache based transformations like Sorter,Lookup, Aggregator, Joiner, and XML Target can

address larger blocks of memory


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Performance Tuning Methodology

It is an iterative process Establish benchmark

Optimize memory Isolate bottleneck Tune bottleneck Take advantage of under-utilized CPU & memory


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The Production Environment

Multi-vendor, multi-system environment withmany components: Operating systems, databases, networks and I/O Usually need to monitor performance in several places Usually need to monitor outside Informatica as well

Disk

PowerCenter

DiskDisk

Disk Disk

Disk Disk

OSDBMSLAN /

WAN

Disk

DiskDisk

Disk Disk

Disk Disk


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Preliminary Steps

Eliminate transformation errors & data rejects

first make it work, then make it faster Source row logging requires reader to hold onto buffers

until data is written to target, EVEN IF THERE ARE NOERRORS; can significantly increase DTM buffer

requirement You may want to set stop on errors to 1


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Preliminary Steps

Override tracing level to terse or normal Override at session level to avoid having to examine each

transformation in the mapping Only use verbose tracing during development & only with

very small data sets

If you expect row errors that you will not need to correct,avoid logging them by overriding the tracing level to terse

(not recommended as a long-term error handling solution)


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Benchmarking

Hardware (CPU bandwidth, RAM, disk space,etc.) should be similar to production

Database configuration should be similar toproduction

Data volume should be similar to production Challenge: production data is constantly

changing Optimal tuning may be data dependent Estimate average behavior Estimate worst case behavior


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Identifying Bottlenecks

The first challenge is to identify the bottleneck Target

Source Transformations Mapping/Session

Tuning the most severe bottleneck may revealanother one

This is an iterative process


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Thread Statistics

The DTM spawns multiple threads

Each thread has busy time & idle time Goal maximize the busy time & minimize the

idle time


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Thread Statistics - Terminology

A pipeline consists of: A source qualifier

The sources that feed that source qualifier All transformations and targets that receive data from that

source qualifier


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Thread Statistics - Terminology

M A S T E R

PIPELINE 1 A pipeline on the master

input of a joiner terminatesat the joiner

DETAIL

PIPELINE 2


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Using Thread Statistics

By default PowerCenter assigns a partition point ( ) at each Source Qualifier, Target, Aggregator

and Rank.

Reader Thread Transformation Thread Transform Writer ThreadThread

(First Stage) (Second Stage) (Third Stage) (Fourth Stage)

Partition Points


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Target Bottleneck

The Aggregator transformation stage is waitingfor target buffers



Busy% Busy% Busy%=15 Busy%=95


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Transformation Bottleneck

Both the reader & writer are waiting for buffers



Busy%=15 Busy%=60 Busy%=95 Busy%=10


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Integration Service Monitor in WFMonitor


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Other Methods of Bottleneck Isolation

Write to flat fileIf significantly faster than relational target Target

Bottleneck Place FALSE Filter right after Source Qualifier

If significantly faster Transformation Bottleneck

If target & transformation bottlenecks are ruledout Source Bottleneck


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Session Statistics in WFMonitor


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Target Optimization

Target Optimization often involves non-Informatica components

Drop Indexes and Constraints Use pre/post SQL to drop and rebuild

Use pre/post-load stored procedures Use constraint-based loading only when

necessary

T O i i i


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Target Optimization

Use Bulk Loading Informatica bypasses the database log

Target cannot perform rollback Weigh importance of performance over recovery

Use External Loader Similar to bulk loader, but the DB reads from a flat file

T O i i i


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Target Optimization

update else insert session property Works well if you rarely insert

Index required for update key but slows down insert PowerCenter must wait for database to return error before

inserting

Alternative lookup followed by update strategy

S B ttl k


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Source Bottlenecks

Source optimization often involves non-Informatica components

Generated SQL available in session log Execute directly against DB

Update statistics on DB Used tuned SELECT as SQL override

Set the Line Sequential Buffer Length sessionproperty to correspond with the record size

S B ttl k


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Source Bottlenecks

Avoid transferring more than once from remotemachine

Avoid reading same data more than once

Filter at source if possible (reduce data set)

Minimize connected outputs from the sourcequalifier Only connect what you need The DTM only includes connected outputs when it

generates the SQL SELECT statement

Reduce Data Set


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Reduce Data Set

Remove Unnecessary Ports Not all ports are needed

Fewer ports = better performance & lower memory req.

Reduce Rows in Pipeline Place Filter Transformation as far upstream as possible Filter before aggregator, rank, or sorter if possible

Filter in source qualifier if possible

Expressions Language Tips


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Functions are more expensive than operators Use || instead of CONCAT()

Use variable ports to factor out common logic



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Simplify nested functions when possible

instead of:IIF(condition1,result1,IIF(condition2,result2,IIF ))))))))))))

try:DECODE (TRUE,

condition1, result1,:conditionn, resultn)

General Guidelines


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General Guidelines

High precision (session property) is expensivebut only applies to decimal data type

UNICODE requires 2 bytes per character; ASCIIrequires 1 byte per character Performance difference depends on number of string ports

only.

Transformation Specific


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Transformation Specific

Reusable Sequence Generator Number of Cached Values Property

Purpose: enables different sessions to share thesame sequence without generating the samenumbers

>0: allocates the specified number of values &updates the current value in the repository at theend of each block(each session gets a different block of numbers)

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Other Transformations


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Other Transformations

Normalizer This transformation INCREASES the number of rows

Place as far downstream as possible

XML Reader/ Mid Stream XML Parser Remove groups that are not projected We do not allocate memory for these groups, but still need

to maintain PK/FK relationships Dont leave port size lengths as infinite. Use appropriate

length.

Iterative Process


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Iterative Process

After tuning your bottlenecks, revisit memoryoptimization

Tuning often REDUCES memory requirements(you might even be able to change some settings back to Auto )

Change one thing at a time & record your results

Partitioning


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Partitioning

Apply after optimizing source, target, &transformation bottlenecks

Apply after optimizing memory usage

Exploit under-utilized CPU & memory

To customize partitioning settings, you need thepartitioning license

Rules for Adding Partition Points


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Rules for Adding Partition Points

You cannot add a partition point to a SequenceGenerator

You cannot add a partition point to an unconnectedtransformation

You cannot add a partition point on a sourcedefinition

If a pipeline is split and then concatenated, youcannot add a partition point on any transformationbetween the split and the concatenation

Adding or removing partition points requires thepartitioning license

Guidelines for Adding Partition Points


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Guidelines for Adding Partition Points

Make sure you have ample CPU bandwidth

Make sure you have gone through other optimizationtechniques

Add on complex transformations that could benefitfrom additional threads

If you have >1 partitions, add where data needs to bere-distributed Aggregator, Rank, or Sorter, where data must be grouped Where data is distributed unevenly On partitioned sources and targets

Partition Points & Partitions


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Partition Points & Partitions

Partitions subdivide the data

Each partition represents a thread within a stage

Each partition point distributes the data among the partitions

3 Reader Threads 3 Transformation Threads 3 more trans threads 3 Writer Threads


Threads - partition 1

Threads partition 2

Threads partition 3

Rules for Adding Partitions


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g

The master input of a joiner can only have 1 partitionunless you add a partition point at the joiner

A pipeline with an XML target can only have 1partition

If the pipeline has a relational source or target andyou define n partitions, each database must supportn parallel connections

A pipeline containing a custom or externalprocedure transformation can only have 1 partitionunless those transformations are configured to allowmultiple partitions

Rules for Adding Partitions


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The number of partitions is constant on a givenpipeline

If you have a partition point on a Joiner, the number ofpartitions on both inputs will be the same

At each partition point, you can specify how youwant the data distributed among the partitions(this is known as the partition type )

Cache Partitioning


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g

DTM may create separate caches for eachpartition for each cached transformation; this iscalled cache partitioning

DTM treats cache size settings as per partitionfor example, if you configure an aggregator with:

2 MB for the index cache,3 MB for the data cache,& you create 2 partitions

DTM will allocate up to 4 MB & 6 MB total

DTM does not partition lookup or joiner cachesunless the lookup or joiner itself is a partitionpoint

Monitoring Partitions


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g

The Workflow Monitor provides runtime detailsfor each partition

Per partition, you can determine the following: Number of rows processed Memory usage CPU usage

If one partition is doing more work than theothers, you may want to redistribute the data

Dynamic Partitioning


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y g

Integration Service can automatically set thenumber of partitions at runtime.

Useful when the data volume increases or thenumber of CPUs available changes.

Basis for the number of partitions is specified asa session property

Concurrent Workflow Execution (8.5)


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Prior to 8.5

Only one instance of Workflow can run

Users duplicate workflows maintenance issues

Concurrent sessions required duplicate of session

Concurrent Workflow Execution


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Allow workflow instances to be runconcurrently

Override parameters/ variables across runinstances

Same scheduler across multiple instances Supports independent recovery/ failover

semantics

Workflow on Grid (WonG)


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Integration Service is deployed on a Grid an ISservice process (pmserver) runs on each node in

the grid Allows tasks of a workflow to be distributed

across a grid no user configuration necessaryif all nodes homogenous

Load Balancer Modes


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Round Robin Honors Max Number of Processes per Node

Metric-based Evaluates nodes in round-robin Honors resource provision thresholds Uses stats from last 3 runs - if no statistics is collected yet,

defaults used (40 MB memory, 15% CPU)


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Configuring Session on Grid


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Enable Session on Grid attribute in session configurationtab

Assign workflow to be executed by an integration servicethat has been assigned to a grid

SonG Partitioning Guidelines


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Set # of partitions = # of nodes to get an evendistribution

Tip: use dynamic partitioning feature to ease expansion ofgrid

In addition, continue to create partition-points to

achieve parallelism

SonG Partitioning Guidelines


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To minimize data traffic across nodes: Use pass-through partition type which will try to keep

transformations on the same node Use resource-map to dispatch the source and target

transformations to the node where source or target arelocated

Keep the target files unmerged whenever possible (e.g. ifbeing used for staging)

Resource requirement should be specified at the

lowest granularity e.g. transformation instead ofsession (as far as possible) This will ensure better distribution in SonG

File Placement Best Practices


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Files that should be placed on a high-bandwidth sharedfile system (CFS / NAS)

Source files

Lookup source files [sequential file access] Target files [sequential file access] Persistent cache files for lookup or incremental aggregation [random file

access]

Files that should be placed on a shared file system butbandwidth requirement is low (NFS)

Parameter files

Other configuration files Indirect source or target files Log files.

File Placement Best Practices


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Files that should be put on local storage Non-persistent cache files (i.e. sorter temporary files)

Intermediate target files for sequential merge Other temporary files created during a session execution

$PmTempFileDir should point to a local file system

For best performance, ensure sufficientbandwidth for shared file system and localstorage (possibly by using additional disk i/ocontrollers)

Data Integration Certification Path


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Level Certification Title Recommended Training Required Exams

Architecture & Administration;Advanced Administration

Informatica CertifiedAdministrator

Informatica CertifiedDeveloper

Informatica CertifiedConsultant

PowerCenter QuickStart (eLearning) PowerCenter 8.5+ Administrator (4 days)

PowerCenter QuickStart (eLearning) PowerCenter 8.5+ Administrator (4 days) PowerCenter Developer 8.x Level I (4 days) PowerCenter Developer 8 Level II (4 days)

Architecture & Administration;Mapping DesignAdvanced Mapping Design

PowerCenter QuickStart (eLearning) PowerCenter 8.5+ Administrator (4 days) PowerCenter Developer 8.x Level I (4 days) PowerCenter Developer 8 Level II (4 days)

PowerCenter 8 Data Migration (4 days) PowerCenter 8 High Availability (1 day)

Architecture & Administration;Advanced AdministrationMapping DesignAdvanced Mapping Design

Enablement Technologies

Additional Training: PowerCenter 8.5 New Features PowerCenter 8.6 New Features

PowerCenter 8 Upgrade

PowerCenter 8 Team-Based Development PowerCenter 8.5 Unified Security `

Architecture & Administration;Advanced Administration
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Q & A

Thomas BennettInformatica Professional Services

Senior Consultant


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AppendixInformatica Services by Solution

B2B Data ExchangeRecommended Services

B2B


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Professional Services Education ServicesRecommended Courses Informatica B2B Data

Transformation (D) Informatica B2B Data Exchange

(D)

Strategy Engagements B2B Data Transformation

Architectural Review

Baseline Engagements B2B Data Transformation

Baseline ArchitectureImplement Engagements B2B Full Project Lifecycle Transaction/Customer/

Payment Hub

Target Audience for CoursesD = DeveloperA = Administrator

M = Project Manager

Data GovernanceRecommended Services


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Professional Services Education ServicesRecommended Courses PowerCenter Level I Developer (D) Informatica Data Explorer (D) Informatica Data Quality (D)

Related Courses PowerCenter Administrator (A) Metadata Manager (D)

Certifications: PowerCenter Data Quality


M = Project Manager

Strategy Engagements Informatica Environment

Assessment Service Metadata Strategy and Enablement Data Quality Audit

Baseline Engagements

Data Governance Implementation Metadata Manager Quick Start Informatica Data Quality Baseline

Deployment

Implement Engagements Metadata Manager Customization Data Quality Management

Implementation

Data MigrationRecommended Services

Data Migration


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Professional Services Education ServicesRecommended Courses Data Migration (M) Informatica Data Explorer (D) Informatica Data Quality (D) PowerCenter Level I Developer (D)

Related Courses PowerExchange Basics (D) PowerCenter Administrator (A)

Certifications

PowerCenter Data Quality

Strategy Engagements Data Migration Readiness

Assessment Informatica Data Quality Audit

Baseline Engagements PowerCenter Baseline Deployment Informatica Data Quality (IDQ),

and/or Informatica Data Explorer(IDE) Baseline Deployment

Implement Engagements

Data Migration Jumpstart Data Migration End-to-End

Implementation


M = Project Manager

Data QualityRecommended Services

Data Quality


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Professional Services Education ServicesRecommended Courses Informatica Data Explorer (D) Informatica Data Quality (D)

Related Courses Informatica Identity Resolution (D) PowerCenter Level I Developer (D)

Certifications Data Quality

Strategy Engagements Data Quality Management Strategy

Informatica Data Quality AuditBaseline Engagements Informatica Data Quality (IDQ),

and/or Informatica Data Explorer

(IDE) Baseline Deployment Informatica Data Quality Web

Services Quick Start


Data Quality ManagementImplementation


M = Project Manager

Data SynchronizationRecommended Services

DataS h i ti


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Synchronization

Strategy Engagements Project Definition and Assessment

Baseline Engagements PowerExchange Baseline

Architecture Deployment PowerCenter Baseline Architecture

DeploymentImplement Engagements Data Synchronization

Implementation

Professional Services Education ServicesRecommended Courses PowerCenter Level I Developer (D) PowerCenter Level II Developer (D) PowerCenter Administrator (A)

Related Courses PowerExchange Basics Oracle Real-

Time CDC (D) PowerExchange SQL RT (D) PowerExchange for MVS DB2 (D)

Certifications PowerCenter


M = Project Manager

Enterprise Data WarehousingRecommended Services

Data Warehouse


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Strategy Engagements Enterprise Data Warehousing (EDW)

Strategy Informatica Environment

Assessment Service Metadata Strategy & Enablement

Baseline Engagements PowerCenter Baseline Architecture

Deployment


EDW Implementation

Professional Services Education ServicesRecommended Courses PowerCenter Level I Developer (D)

PowerCenter Level II Developer (D) PowerCenter Metadata Manager (D)

Related Courses Informatica Data Quality (D) Data Warehouse Development (D)



M = Project Manager

Integration Competency CentersRecommended Services

ICC


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Professional Services Education ServicesRecommended Courses ICC Overview (M)

PowerCenter Level I Developer (D) PowerCenter Administrator (A)

Related Courses

Metadata Manager (D) Informatica Data Explorer (D) Informatica Data Quality (D)

Certifications

PowerCenter Data Quality

Strategy Engagements ICC Assessment

Baseline Engagements ICC Master Class Series ICC Director

Implement Engagements ICC Launch ICC Implementation Informatica Production Support


M = Project Manager

Master Data ManagementRecommended Services

Master DataManagement


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Management

Professional Services Education ServicesRecommended Courses Informatica Data Explorer (D)

Informatica Data Quality (D) PowerCenter Level I Developer (D)

Related Courses Metadata Manager (D) Informatica Identity Resolution (D)


Data Quality


M = Project Manager

Strategy Engagements Master Data Management (MDM)

Strategy Informatica Data Quality Audit

Baseline Engagements Informatica Data Explorer (IDE)

Baseline Deployment Informatica Data Quality (IDQ)

Baseline Deployment PowerCenter Baseline Architecture

DeploymentImplementation MDM Implementation

Services Oriented ArchitectureRecommended Services

Data Services


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Professional Services Education ServicesRecommended Courses PowerCenter Level I Developer (D)

Informatica Data Quality (D)

Certifications PowerCenter Data Quality

Strategy Engagements Data Services (SOA) Strategy

Baseline Engagements Informatica Web Services Quick

Start Informatica Data Quality Web

Services Quick Start

Implement Engagements Data Services (SOA) Implementation


M = Project Manager


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Deliver Your

Project Rightthe First Timewith

InformaticaProfessionalServices


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Informatica Contact Information


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Informatica Corporation Headquarters100 Cardinal WayRedwood City, CA 94063Tel: 650-385-5000Toll-free: 800-653-3871Toll-free Sales: 888-635-0899Fax: 650-385-5500

Informatica EMEA HeadquartersInformatica Nederland B.V.Edisonbaan 14a3439 MN NieuwegeinPostbus 1163430 AC NieuwegeinTel: +31 (0) 30-608-6700Fax: +31 (0) 30-608-6777

Informatica Asia/Pacific HeadquartersInformatica Australia Pty LtdLevel 5, 255 George StreetSydneyN.S.W. 2000AustraliaTel: +612-8907-4400Fax: +612-8907-4499

Global Customer [email protected] at my.informatica.com to open a new service requestor to check on the status of an existing SR.

http://www.informatica.com

Title: Times Bold 32 pt.; Title Case
mailto:[email protected]://www.informatica.com/http://www.informatica.com/mailto:[email protected]


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Subtitle: Garamond bold 28 pt.; Sentence style

Body: Arial Bold 26 pt.; Bullet: Normal text;Sentence case Sub-bullets: Arial Bold 22 pt.; Bullet: Normal text;

Second sub-bullet

Third sub-bullet

Bullet Format


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Use smart quotes

Fiscal years should be formatted as FY 01 Highlight in this color for overheads

Hyperlink: www.informatica.com

Correct dashes used: -, ,

Chart Example

h
http://www.informatica.com/http://www.informatica.com/


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Source: Placeholder for notes, etc. 14 pt..

0

10

20

30

40

50

60

70

80

1st Qtr 2nd Qtr 3rd Qtr 4th Qtr

One Two Three Four

Pie Chart Example


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35%

20%

20%

25%

1st Qtr

2nd Qtr

3rd Qtr

4th Qtr

Source: Placeholder for notes, etc. 14 pt..


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Text with Graphic on Right


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Body: Arial Bold 26 pt.;Bullet: Normal text;Sentence case Sub-bullets: Arial 22 pt.; Bullet:

Normal text;

Second sub-bullet Third sub-bullet

Large Label

Small Label Small Label

Sample Quote


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When a billion wireless phones wake up inthe next few years, a distributed architecturewill be the only way people will be able to

receive the information they need.

Anonymous

TitleCompany


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How To Apply Different Colors To Objects

U thi if d t l t t f th G hi El t lid


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Use this way if you dont see a color you want to use from the Graphics Elements slide

Step 1 Step 2 Step 3

Click on the Colorpop-up and select

Fill Effects

Final Result

SelectMore Colors

Step 4

Select the newcolor you want and

click OK

Right mouse click onthe object you want

to change and selectFormat AutoShape

from the pop-up

Color Palette


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HighlightsPrimary colors

Gradient/effects

whitetext

whitetext

Use this style for PPT objectsTo apply this style to an object:

Click on object with desired style, click the format painter tool in the toolbar

Then click on your new object to apply the style

whitetext

whitetext

whitetext

whitetext

Solid whitetextwhitetext whitetext whitetext whitetext whitetext

0-85-149 190-192-194 92-135-39 236-137-29 128-130-133 191-49-26


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2008 PowerPoint Icon Library

For an up to date version of our icon library


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For an up to date version of our icon library,please download the latest file from ourMarketing website on INFAnet:

http://infanet-prod/SiteDirectory/Departments/marketing/

Sample icons from our library:

Informatica Logo
http://infanet-prod/SiteDirectory/Departments/marketing/http://infanet-prod/SiteDirectory/Departments/marketing/

Documents

PC8_Performance_Tuning_MidAtlantic