Interconnect session 3498: Deployment Topologies for Jazz Reporting Service
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The document provides an overview of the Jazz Reporting Service architecture and deployment topologies. It discusses the key components of JRS including the Data Collection Component, Data Warehouse, Lifecycle Query Engine, and Report Builder. It then describes example deployment topologies such as departmental, enterprise, and federated models. The document outlines two major phases in reporting - data collection and report execution. It discusses factors that affect the performance of each phase and provides strategies for handling large data volumes and high user loads.
Interconnect session 3498: Deployment Topologies for Jazz Reporting Service
- 1. PDD-3498: Deployment Topologies for Jazz Reporting Service Rosa Naranjo (rosy@us.ibm.com) Unleash the Labs – IBM rhnaranjo.wordpress.com, @rnjazz Ernest Mah (ernest@ca.ibm.com) Jazz Reporting Service Architect – IBM
- 2. Agenda • Jazz Reporting Service Architecture – Diagram – Components Explained • Deployment Topologies • Two Phases to Reporting – Factors Affecting Data Collection • Strategies for high data volume – Factors Affecting Report Execution • Strategies for high user load • Q & A 1
- 3. Jazz Reporting Service Architecture Diagram Components Explained
- 4. Jazz Reporting Service Architecture – 4Q2015 3 Report Builder (Query mgmt, OOTB Reports) LQE DOORS NG RQM RTC Other DWDCC TRS Reports Gadgets Spreadsheets Embedded Cognos Tools (BI Server, Data Manager, Framework Manager) OOTB/Custom Cognos Reports OOTB/Custom Cognos Reports ++ OOTB Birt Reports (RQM, RTC) CLM OfferingCLM Offering Insight 1.1.1.7 Jazz Reporting Service Built in Reports (e.g. Quick Planner)
- 5. Jazz Reporting Service – Components explained Report Builder • Guided, self service reporting authoring for mainstream reports • Report management and sharing • Visualization (bar, line, pie) created and sent to browser for rendering • Queries Data Warehouse or Lifecycle Query Engine for Data Warehouse Data Collection Component • Collects data from RTC, DNG, RQM and feeds into the Data Warehouse • Schedule ODS (Operational Data Store) and Metrics Collection Jobs • Parallel loading technology • Offloads the work of storing data into the Data Warehouse from each application (Java ETLs from CLM 5.0.x and prior) • ODS collection based on deltas 4
- 6. Jazz Reporting Service – Components explained Data Warehouse • Data source for Report Builder queries • Database instance supported by DB2, Oracle, … Lifecycle Query Engine • Collects data from RTC, DNG, RQM and feeds into a local disk based index • Data source for Report Builder • Managing collection intervals from the CLM applications. • Parallel loading technology • Collection based on deltas • Essentially like DCC + Data Warehouse 5
- 7. Jazz Reporting Service – Components explained LDX – Links Index • Not a reporting component • Used only if you are interested in configurations • Configuration enabled projects now store directed 1 way links • Service used by the tools to help locate inbound links from other tools 6
- 8. Jazz Reporting Service – Components explained Cognos BI Server • Advanced visualizations and report needs • Requires a Cognos BI expertise • Allows advanced CLM and other data source ETLs ALM Cognos Connector • Component of Jazz Reporting Service to allow Cognos Data Manager ETLs to collect from CLM • Installed into the Cognos BI Server 7
- 10. Example Deployment Topologies • High level overview – https://jazz.net/wiki/bin/view/Deployment/StandardTopologiesOverview • Example topologies with hardware and supporting software – https://jazz.net/wiki/bin/view/Deployment/RecommendedALMDeploymentTopologies6 9
- 11. Example Deployment Topologies - Departmental • Small team and grouped single-server deployments • Requires less hardware 10
- 12. Example Deployment Topologies – Enterprise • Production or medium-sized to large-sized teams and multiple server (or distributed) deployments • Flexible application per server deployment 11
- 13. Example Deployment Topologies - Federated • Very large enterprises who tend to deploy an ALM solution per product line or organizational division • Enterprise wide view with rollup reporting across solution required 12
- 14. Example Deployment Topologies - Federated 13
- 15. Two Phases of Reporting Data Collection and Reporting Strategies for Increasing Scale
- 16. Two Major Phases in Jazz Reporting Service • Data Collection – Data Warehouse • DCC requests changes from CLM apps • Changes are sent to the Relational DB – Lifecycle Query Engine • LQE requests changes from CLM apps • LQE stores and indexes the information locally • Reporting – Run queries against the relational database or LQE – Create final report result by combining data from queries and visualizing them into tables, line chart, pie chart, bar charts 15
- 17. Comparison of DCC/Data Warehouse and LQE DCC/Data Warehouse LQE Collection interval 15 minutes 1 minute Configuration management Not Supported Supported Support new data in future CLM now, DOOR9 future Yes Enterprise Scale Yes Improving Query Language SQL SPARQL Ready to use / Ready to copy reports Mature Initial set 16
- 18. Factors affecting data collection performance • Initial data population – Total number of artifacts across the applications you would like to report on • Ongoing data population – Frequency of change across all connected applications • Components involved – Data Warehouse - DCC, Relational DB – LQE - LQE 17
- 19. Factors affecting report execution • Number of users running reports • Number of reports running • Quantity of data returned in the reports • Components involved – Data Warehouse – Report Builder, Relational DB, Cognos BI – LQE – Report Builder, LQE 18
- 20. Strategies for Large Data Collection • Consider separating data collection into logical related project groupings for handling frequent reports at those levels – Data warehouse or LQE per grouping • Enterprise wide reporting still required? – Data warehouse or LQE across the enterprise – Minimize number of requests against this larger dataset – Use appropriate filters to grab data specifically to what is needed 19
- 21. Strategies for Large Number of Users • Separate Report Builder Servers to group related reports together • Increase cache timeout levels in Report Builder – Data less fresh, but can handle more users • LQE based data – Utilize LQE horizontal scaling support to handle more query requests – Increase cache timeout in LQE 20
- 23. LQE LQE Tracked Resource Set (TRS)IBM Rational Team Concert IBM Rational Quality Manager IBM Rational DOORS NG Horizontal Scaling - LQE Report Builder Existing Reverse Proxy Server
- 24. Reporting Components – Sample Specs** • For standalone deployment - JRS Report Builder or DCC – 64-bit RHEL – 2 core – 8 GB RAM • Report Builder + DCC Combined – 64-bit RHEL – 4 core – 16 GB RAM • Standalone LQE - https://jazz.net/wiki/bin/view/Deployment/LifecycleQueryEngineBestPractises – 64-bit RHEL (Version 7+) – 16 core – 64 GB RAM – SSD 23 **Note: Recommendations are a starting point, data volume and user activity greatly affect requirements
- 25. STG Deployment (IBM Internal deployment of JRS) 1. How many deployments of JRS / DCC do you have? We have only one JRS and one DCC deployment which retrieves data from 3 RQM instances, 12 JTS instances, 17 RTC instances and 4 DOORS instances. 2. How many registered users do you have per deployment? We currently have 8984 registered users in the deployment (although only a subset of them uses JRS directly). 3. What issues with respect to scale / performance do you know about with these deployments? We have encountered just one significant performance issue related to the traceability report in JRS. This issue was resolved by adding indices to the RIDW database. 4. DCC ODS Schedule: 10 minutes 24
- 26. STG Deployment: Reporting Server Specs 1 shared server for DCC and JRS 64-bit RHEL on an 8-way blade AIX server 2.9 GHz processors 64 GB RAM 25
- 27. SDAD Deployment (IBM Internal deployment of JRS) 1. How many deployments of JRS / DCC do you have? 1 CLM enterprise environment 2. How many registered users do you have per deployment? 3000 registered users (300 CLM practitioner floating license). Typically 180 users worldwide accessing the system across 20 projects. 3. What issues with respect to scale / performance do you know about with these deployments? Had issues with database performance. Moved from a single DB2 repo to separate instance for each application. (assuming DW is still consolidated). 4. DCC ODS Schedule every 120 minutes, Data Mart once per day at 7:30 EST 26
- 28. SDAD Deployment: Reporting Server Specs Separate server for DCC and JRS JRS Server Linux xSeries, 2 CPU(s), 8 GB Memory, 80 GB SAN Storage DCC Server Linux xSeries, 4 CPU(s), 16 GB Memory, 120 GB SAN Storage 27
- 29. Questions 28
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