CON7643 Transform JD Edwards Applications

CON7643 Transform JD Edwards Applications
with Oracle Database In-Memory Keith Sholes Director, Product Management Oracle’s JD Edwards AJ Schifano Principal Product Manager September 30, 2014 Copyright © 2014, Oracle and/or its affiliates. All rights reserved. |

Program Agenda 1 Oracle Database In-Memory: What’s the big deal? What’s the big deal for JD Edwards customers? Q & A 2 3

Oracle Database In-Memory Option Powering the Real-Time Enterprise
Available in Release Now

Oracle Database In-Memory Goals
Trivial to Implement Real Time Analytics Accelerate Mixed Workload OLTP No Changes to Applications Oracle Database In-Memory transparently accelerates analytic queries by orders of magnitude, enabling real-time business decisions. Using Database In-Memory, businesses can instantaneously run analytics and reports that previously took hours or days. Businesses benefit from better decisions made in real-time, resulting in lower costs, improved productivity, and increased competitiveness But what do we mean by real-time analytics? Imagine being able to know the total sales you have made in the state of California as of right now. Not last week, or even last night but right now and have that query return in sub-second time. But isn’t it possible to run analytic on operational data today? It is, but the analytics are often limited to a small set of known or canned reports that have specific indexes or materialized view create for them to ensure they have a reasonable response time and they do not impact the operational workload. The number of reports Is often limited as each additional index will slow down the operational workload. Oracle Database In-Memory removes these limitations and allows businesses ask any analytic queries they wish. It will also speed up operational transactions in a mixed workload environment, as it removes the necessity to create And maintain so many summary objects or indexes. Embedding the in-memory capabilities into the existing Oracle Database software ensures that it is fully compatible with ALL existing features, and requires no changes in the application layer. Companies striving to become real-time enterprises can more easily achieve their goals, regardless of what applications they are running when they take advantage of Oracle Database In-Memory 100x 2x 7

Row Format Databases vs. Column Format Databases
SALES Query a single sales order in row format One contiguous row accessed = FAST Query Row format is optimized for OLTP workloads. OLTP operations tend to access only a few rows but touch all of the columns. A row format allows quick access to all of the columns in a record since all the data for a given record are kept together in-memory and on-storage. Since all data for a row is kept together, much of the row data will be brought into the CPU with a single memory reference. Row format is also much more efficient for row updates and inserts. Analytical workloads access few columns but scan the entire data set. They also typically require some sort of aggregation. A columnar format allows for much faster data retrieval when only a few columns in a table are selected because all the data for a column is kept together in-memory and a single memory access will load many column values into the CPU. It also lends itself to faster filtering and aggregation, making it the most optimzed format for analytics. Up until now you have been force to pick just one format and suffer the tradeoff of either sub-optimal OLTP or sub-optimal Analytics. Column Stores SALES Query a sales order in Column Format Many column accessed = SLOW Query Query Query Query Until Now Must Choose One Format and Suffer Tradeoffs

Breakthrough: Dual Format Database
Normal Buffer Cache New In-Memory Format SALES Row Format Column BOTH row and column formats for same table Simultaneously active and transactionally consistent Analytics & reporting use new in-memory Column format OLTP uses proven row format SALES

Complex OLTP is Slowed by Analytic Indexes
1 – 3 OLTP Indexes 10 – 20 Analytic Indexes Most Indexes in complex OLTP (e.g. ERP) databases are only used for analytic queries Inserting one row into a table requires updating analytic indexes: Slow! Indexes only speed up predictable queries & reports Table Up until now, the only way to run analytic queries with an acceptable response on an OLTP environment was to create specific indexes for these queries. The good thing about indexes is that they are extremely scalable. They work well in-memory and also are extremely efficient on-disk since they minimize disk IO needed to find the requested data. Each reporting index is an additional stored structure that must be maintained and logged on every change.

Oracle In-Memory: Simple to Implement
Configure Memory Capacity inmemory_size = XXX GB Configure tables or partitions to be in memory alter table | partition … inmemory; Later drop analytic indexes to speed up OLTP Implementing the In-Memory column store is as simple as flipping a switch. All you have to do is specify how much memory the column store can use, Then decide which objects (tables, partitions, and columns) you want to be loaded into the column store and your done using Oracle’s advisor. Oracle will take care of the rest. The Optimizer will simply direct any analytic queries to the In-Memory Column store, allowing you to do drop any indexes that were specifically created to enable analytic query performance.

Oracle In-Memory Requires Zero Application Changes
Full Functionality - ZERO restrictions on SQL Easy to Implement - No migration of data Fully Compatible - All existing applications run unchanged Fully Multitenant - Oracle In-Memory is Cloud Ready Because the In-Memory Column Store is embedded in the Oracle Database it is fully compatible with ALL existing features, And requires absolutely no changes in the application layer. This means you can start taking full advantage of it on day one, regardless of what applications you run in your environment. Any application that runs against the Oracle database will transparently benefit from the in-memory column store. Uniquely Achieves All In-Memory Benefits With No Application Changes

Trivial to Implement Real Time Analytics Accelerate Mixed Workload OLTP No Changes to Applications 100x 2x 14

Oracle Database In-Memory Goals No Changes to Applications
Certified with JD Edwards EnterpriseOne Tools Linux and Solaris (other platforms planned) Install Oracle Database Configure the Database In-Memory Option inmemory_size = XXX GB alter table | partition … inmemory; ALTER TABLE CRPDTA.F0006 INMEMORY MEMCOMPRESS FOR QUERY DUPLICATE; ALTER TABLE CRPDTA.F0911 INMEMORY MEMCOMPRESS FOR QUERY DUPLICATE; ALTER TABLE CRPDTA.F4211 INMEMORY MEMCOMPRESS FOR QUERY DUPLICATE; … Trivial to Implement Real Time Analytics No Changes to Applications  15

JD Edwards EnterpriseOne “Day- in-the-Life” Benchmark kit run with Applications 9.1 / Tools with ZERO application changes. Install JD Edwards EnterpriseOne Applications 9.1+ Update to Tools Trivial to Implement Real Time Analytics Accelerate Mixed Workload OLTP No Changes to Applications 17

Accelerate Mixed Workload OLTP 2x 19

Planned Oracle Database In-Memory Goals Accelerate Mixed Workload OLTP 2x Node 1 Node 2 JDE E1 Apps 9.1.2 JDE E1 IM-PA JDE E1 IM-SA JDE E1 IM-PPA HTML Avail Logic Exadata X3-2 ¼ Rack JD Edwards EnterpriseOne Logic and Web Tiers Exalogic X3-2 ¼ Rack Oracle Database with Database In-Memory JDE E1 Data RAC Test Configuration JDE E1 Tools Prerelease JDE E1 Prerelease 20

Ad-Hoc Queries How do I find answers to unanticipated questions
What sales orders are assigned to a carrier that just went on strike? What sales orders contained items from a recalled lot? What sales orders involved an unscrupulous business partner? 1000’s of use cases across all functional areas Time-outs, batch jobs, exports, etc. The JD Edwards system contains a vast amount of data to enable reporting and analysis on the sales order processes. Traditionally to locate the sales data you want to evaluate, you run a batch reports, export the data, or use third-party systems. When evaluating hundreds or millions of sales order lines, these traditional processes are time consuming and do not provide timely results. Another option is to query on data interactively, however if the data you want is not indexed the application can take a long time and typically times out. You could create new indexes to mitigate this issue, but that can impact performance and require IT department resources each time you have a new inquiry. Real-Time Operational Analysis empowers sales executives to run queries on non-indexed fields to answer unanticipated questions. Here are just a few examples of unanticipated queries on sales order information: A transportation carrier goes on strike, I need to know what sales orders were assigned to that carrier so I can schedule new carriers to ensure my customer’s orders are on time. I need to recall a manufactured batch (lot) of a product, I must determine which sales orders to which that lot was assigned in order to stop shipments before they get to my customer. There is a rumor that a business partner has unethical business practices and I want to evaluate all the sales order they were involved in to limit my exposure. Tests using the in-memory db were run over 104 million sales order lines and the results went from days of work to milliseconds. Using the new Real-Time Operational Analysis was more than 1000x faster than traditional methods of evaluation.

Operational Analysis ‘Ad-Hoc Query’ with Oracle Database In-Memory
From Batch to Real-Time Query non-indexed columns in real- time Find immediate answers to unanticipated questions Eliminate batch jobs, data exports, third-party systems Requires no change to JD Edwards applications Real Time Analytics No Changes to Applications The JD Edwards system contains a vast amount of data to enable reporting and analysis on the sales order processes. Traditionally to locate the sales data you want to evaluate, you run a batch reports, export the data, or use third-party systems. When evaluating hundreds or millions of sales order lines, these traditional processes are time consuming and do not provide timely results. Another option is to query on data interactively, however if the data you want is not indexed the application can take a long time and typically times out. You could create new indexes to mitigate this issue, but that can impact performance and require IT department resources each time you have a new inquiry. Real-Time Operational Analysis empowers sales executives to run queries on non-indexed fields to answer unanticipated questions. Here are just a few examples of unanticipated queries on sales order information: A transportation carrier goes on strike, I need to know what sales orders were assigned to that carrier so I can schedule new carriers to ensure my customer’s orders are on time. I need to recall a manufactured batch (lot) of a product, I must determine which sales orders to which that lot was assigned in order to stop shipments before they get to my customer. There is a rumor that a business partner has unethical business practices and I want to evaluate all the sales order they were involved in to limit my exposure. Tests using the in-memory db were run over 104 million sales order lines and the results went from days of work to milliseconds. Using the new Real-Time Operational Analysis was more than 1000x faster than traditional methods of evaluation. 1762x 104 million sales order lines From 22.5 Min to Sub-second

So many things to do…so little time!
Financial Close Limited batch window results in false choices Manual processes increase errors and decrease efficiency Long running batch jobs make it very time consuming to validate changes Increased pressure to close the books faster So many things to do…so little time! The JD Edwards system contains a vast amount of data to enable reporting and analysis on the sales order processes. Traditionally to locate the sales data you want to evaluate, you run a batch reports, export the data, or use third-party systems. When evaluating hundreds or millions of sales order lines, these traditional processes are time consuming and do not provide timely results. Another option is to query on data interactively, however if the data you want is not indexed the application can take a long time and typically times out. You could create new indexes to mitigate this issue, but that can impact performance and require IT department resources each time you have a new inquiry. Real-Time Operational Analysis empowers sales executives to run queries on non-indexed fields to answer unanticipated questions. Here are just a few examples of unanticipated queries on sales order information: A transportation carrier goes on strike, I need to know what sales orders were assigned to that carrier so I can schedule new carriers to ensure my customer’s orders are on time. I need to recall a manufactured batch (lot) of a product, I must determine which sales orders to which that lot was assigned in order to stop shipments before they get to my customer. There is a rumor that a business partner has unethical business practices and I want to evaluate all the sales order they were involved in to limit my exposure. Tests using the in-memory db were run over 104 million sales order lines and the results went from days of work to milliseconds. Using the new Real-Time Operational Analysis was more than 1000x faster than traditional methods of evaluation.

Real Time Financial Reconciliation with Oracle Database In-Memory
From Batch to Real-Time Batch financial integrities re-imagined as interactive applications Watch lists provide real time visibility No more PDF! Reduce time to resolve and increase quality by working exceptions interactively Why wait till month end..reconcile daily! Faster re-organizations Significantly reduce your time to close Real Time Analytics 122x 120 Million Ledger Lines From 12 hours to 6 minutes Planned

Real-Time Financial Reconciliation
Planned Real-Time Financial Reconciliation Watch lists for notification Interactive UI lists exceptions Interactively work exceptions

Real-Time Financial Reconciliation
Planned Real-Time Financial Reconciliation Performance Old New X Times Faster Accounts to Business Units 8.1 minutes 5.8 seconds 83x Transactions to Account Master 2.7 hours 51 seconds 190x Account Balance w/out Account Master 1.3 hours 17.2 seconds 272x Transactions to Batch Header 2.9 hours 99 seconds 104x Companies by Batch out of Balance 2 hours 63 seconds 117x Batches out of Balance 69 seconds 105x Companies out of Balance 32.1 minutes 38 seconds 50x Cumulative 11.6 hours 5.7 minutes 122x This is a sample Table slide, ideal for comparing data by year, product, revenues, etc. To Edit this Table, follow the steps below (this applies to all slides in this template that contain tables): To Change Text in Table: Select text in table. Begin typing desired text. To move from one cell to another, press Tab. To Change Font Color/Size: Select text, right-click and adjust the font setting on the Mini toolbar. Select desired attributes to change: font, size, boldness, color, etc. Note: many of the same commands can also be accessed from the Font group of the Home tab. To Insert or Delete Rows in Table: To delete a row place cursor in a cell of the row, right-click for a pop up menu, select Delete Row. To Insert a row, place cursor in the row you want to place a row before, right-click for a pop up menu, select Insert and make a selection from the submenu. You can also use the Insert and Delete Rows and Columns tools on the Table Tools Layout tab to perform these actions. To Insert or Delete Columns in Table: To delete a column, place cursor above the column (you will see a solid downward pointing arrow) click to select the column, right-click on the selected column, chose Delete Column from the pop up menu. To insert a column, place cursor above the column you wish to insert the new column before (you will see a solid downward pointing arrow) click to select the column, right-click for a pop up menu, select Insert and make a selection from the submenu. You can also use the Insert and Delete Rows and Columns tools on the Table Tools Layout tab to perform these actions. To Highlight a Cell, Column or Row (for emphasis): Click to select an individual cell or click and drag through the desired column(s) or row(s). From the Table Tools Design tab, select the Shading button and select a desired fill color. (Note: It is best to use white text over dark backgrounds and black or dark gray text over light backgrounds.) While a table is selected, other formatting options are available on the Table Tools Design and Layout tabs. 120M Transactions… 10M Account Balances… 10M Accounts

Go-To-End You need quick access to totals
Total supplier open amount Posted and un-posted G/L totals Forecast totals by forecast type Total quantity shipped by item Spend by Supplier Many apps show totals after the last grid row…go-to-end Millions of rows to process….slow Time-outs, multiple queries, batch jobs The JD Edwards system contains a vast amount of data to enable reporting and analysis on the sales order processes. Traditionally to locate the sales data you want to evaluate, you run a batch reports, export the data, or use third-party systems. When evaluating hundreds or millions of sales order lines, these traditional processes are time consuming and do not provide timely results. Another option is to query on data interactively, however if the data you want is not indexed the application can take a long time and typically times out. You could create new indexes to mitigate this issue, but that can impact performance and require IT department resources each time you have a new inquiry. Real-Time Operational Analysis empowers sales executives to run queries on non-indexed fields to answer unanticipated questions. Here are just a few examples of unanticipated queries on sales order information: A transportation carrier goes on strike, I need to know what sales orders were assigned to that carrier so I can schedule new carriers to ensure my customer’s orders are on time. I need to recall a manufactured batch (lot) of a product, I must determine which sales orders to which that lot was assigned in order to stop shipments before they get to my customer. There is a rumor that a business partner has unethical business practices and I want to evaluate all the sales order they were involved in to limit my exposure. Tests using the in-memory db were run over 104 million sales order lines and the results went from days of work to milliseconds. Using the new Real-Time Operational Analysis was more than 1000x faster than traditional methods of evaluation.

Real-Time Summarization with Oracle Database In-Memory
Planned Real-Time Summarization with Oracle Database In-Memory From Batch to Real-Time Click on summation icon for totals No need for go-to-end! Incredible response time Balances by customer, line of business, and currency over 10 million invoice lines in 4 seconds Eliminate multiple queries, batch jobs, data exports Real Time Analytics JDE Real Time Summarization allows customers to summarize amounts such as open or Gross balances within JD Edwards applications in real time. Within the area of A/R users can view real time balances by Customer, line of business and currency or normalized across currency in seconds allowing A/R specialists to respond live to customer inquiries Cash managers can make real-time decisions on currency exposures Credit managers will make immediate decisions on extending credit Performance testing of customer balances across 40 parent companies, the old process would take a total of 244 minutes (over 4 hours). With in-memory db, the user can do a query of this same information with over 10 million invoice lines, and get those results in 4 seconds. Similar use cases can be found across JD Edwards. Customer Ledger Inquiry Standard Voucher Entry Account Ledger Inquiry Account Ledger by Object Account Ledger by Category Code Forecast Revisions (Mfg) Purchase Order Inquiry 3500x 10 million invoice lines From 244 Min to 4 Secs

Real-Time Summarization
Planned Real-Time Summarization Real-Time Summarization Enabled in key applications Customer Ledger Inquiry Account Ledger Inquiry Account Ledger by Category Code Account Ledger by Object Supplier Ledger Inquiry Forecast Inquiry Customer Service Inquiry Purchase Order Inquiry The JD Edwards system contains a vast amount of data to enable reporting and analysis on the sales order processes. Traditionally to locate the sales data you want to evaluate, you run a batch reports, export the data, or use third-party systems. When evaluating hundreds or millions of sales order lines, these traditional processes are time consuming and do not provide timely results. Another option is to query on data interactively, however if the data you want is not indexed the application can take a long time and typically times out. You could create new indexes to mitigate this issue, but that can impact performance and require IT department resources each time you have a new inquiry. Real-Time Operational Analysis empowers sales executives to run queries on non-indexed fields to answer unanticipated questions. Here are just a few examples of unanticipated queries on sales order information: A transportation carrier goes on strike, I need to know what sales orders were assigned to that carrier so I can schedule new carriers to ensure my customer’s orders are on time. I need to recall a manufactured batch (lot) of a product, I must determine which sales orders to which that lot was assigned in order to stop shipments before they get to my customer. There is a rumor that a business partner has unethical business practices and I want to evaluate all the sales order they were involved in to limit my exposure. Tests using the in-memory db were run over 104 million sales order lines and the results went from days of work to milliseconds. Using the new Real-Time Operational Analysis was more than 1000x faster than traditional methods of evaluation.

    Oracle Database In-Memory Goals ˄ for JD Edwards EnterpriseOne
Trivial to Implement Real Time Analytics Accelerate Mixed Workload OLTP No Changes to Applications 100x 2x     32

CON7643 Transform JD Edwards Applications

Similar presentations

Presentation on theme: "CON7643 Transform JD Edwards Applications"— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

CON7643 Transform JD Edwards Applications

Similar presentations

Presentation on theme: "CON7643 Transform JD Edwards Applications"— Presentation transcript:

Similar presentations

About project

Feedback