SAP HANA Troubleshooting and Performance Analysis Guide Company

Total Page:16

File Type:pdf, Size:1020Kb

SAP HANA Troubleshooting and Performance Analysis Guide Company PUBLIC SAP HANA Platform 2.0 SPS 04 Document Version: 1.1 – 2019-10-31 SAP HANA Troubleshooting and Performance Analysis Guide company. All rights reserved. All rights company. affiliate THE BEST RUN 2019 SAP SE or an SAP SE or an SAP SAP 2019 © Content 1 SAP HANA Troubleshooting and Performance Analysis Guide.......................... 6 2 Analyzing Generic Symptoms...................................................9 2.1 Performance and High Resource Utilization...........................................9 2.2 Common Symptoms and Troubleshooting...........................................11 Slow System-wide Performance............................................... 12 Slow Individual SQL Statements............................................... 15 Frequent Out of Memory (OOM)............................................... 18 3 Root Causes and Solutions....................................................22 3.1 Memory Problems........................................................... 22 Memory Information in SAP HANA Cockpit....................................... 23 Memory Information from Logs and Traces....................................... 23 Memory Information from SQL Commands.......................................25 Memory Information from Other Tools...........................................29 Root Causes of Memory Problems............................................. 30 Transparent Huge Pages on Linux..............................................38 3.2 CPU Related Root Causes and Solutions............................................39 Indicators of CPU Related Issues...............................................39 Analysis of CPU Related Issues................................................39 Resolving CPU Related Issues.................................................41 Retrospective Analysis of CPU Related Issues......................................41 Controlling Parallel Execution of SQL Statements ...................................42 Controlling CPU Consumption................................................ 44 3.3 Disk Related Root Causes and Solutions............................................48 Reclaiming Disk Space......................................................52 Analyze and Resolve Internal Disk-Full Event (Alert 30)...............................53 3.4 I/O Related Root Causes and Solutions.............................................55 Analyzing I/O Throughput and Latency..........................................58 Savepoint Performance.....................................................59 3.5 Configuration Parameter Issues..................................................61 Issues with Configuration Parameter log_mode (Alert 32 and 33)........................63 3.6 Backup And Recovery.........................................................64 3.7 Delta Merge................................................................68 Inactive Delta Merge....................................................... 68 Indicator for Large Delta Storage of Column Store Tables..............................70 Failed Delta Merge.........................................................73 SAP HANA Troubleshooting and Performance Analysis Guide 2 PUBLIC Content Delta Storage Optimization...................................................74 3.8 SAP Web IDE............................................................... 75 Post-Installation Problems with Web IDE......................................... 75 Web IDE Common Issues with Modeling..........................................77 3.9 Troubleshooting BW on HANA...................................................79 3.10 Troubleshooting Multi-Dimensional Services Queries...................................90 3.11 Troubleshooting Tips for the Calculation Engine.......................................94 Native HANA Models.......................................................97 3.12 License Issues..............................................................97 System Locked Due to Missing, Expired, or Invalid License.............................98 License Problem Identification and Analysis.......................................98 Resolution of License Issues..................................................99 3.13 Security-Related Issues.......................................................100 Troubleshooting Authorization Problems........................................ 101 Troubleshooting Problems with User Name/Password Authentication....................106 Troubleshooting Problems with User Authentication and SSO......................... 108 3.14 Transactional Problems........................................................111 Blocked Transactions ......................................................111 Troubleshooting Blocked Transaction Issues that Occurred in the Past....................116 Multiversion Concurrency Control (MVCC) Issues..................................116 Version Garbage Collection Issues.............................................119 3.15 Statement Performance Analysis................................................ 121 SQL Statement Optimization.................................................122 Analysis of Critical SQL Statements............................................125 Optimization of Critical SQL Statements.........................................131 3.16 Application Performance Analysis................................................139 SQL Trace Analysis........................................................139 Statement Measurement................................................... 140 Data Analysis............................................................141 Source Analysis..........................................................142 Technical Analysis........................................................ 143 3.17 System Hanging Situations.................................................... 144 Transparent Huge Pages....................................................145 CPU Power Saving........................................................146 3.18 Troubleshoot System Replication................................................ 147 Replication Performance Problems............................................ 148 Setup and Initial Configuration Problems........................................152 Intermittent Connectivity Problems............................................157 LogReplay: Managing the Size of the Log File......................................158 3.19 Network Performance and Connectivity Problems.................................... 161 Network Performance Analysis on Transactional Level...............................161 SAP HANA Troubleshooting and Performance Analysis Guide Content PUBLIC 3 Stress Test with NIPING.................................................... 163 Application and Database Connectivity Analysis...................................164 SAP HANA System Replication Communication Problems............................166 SAP HANA Inter-Node Communication Problems..................................168 3.20 SAP HANA Dynamic Tiering....................................................170 Tools and Tracing.........................................................170 Query Plan Analysis.......................................................170 Data Loading Performance.................................................. 172 4 Tools and Tracing.......................................................... 174 4.1 System Performance Analysis...................................................174 Thread Monitoring........................................................ 174 Blocked Transaction Monitoring...............................................177 Session Monitoring........................................................178 Job Progress Monitoring....................................................179 Load Monitoring..........................................................180 4.2 SQL Statement Analysis...................................................... 180 Analyzing SQL Traces......................................................181 Analyzing Expensive Statements Traces.........................................185 Analyzing SQL Execution with the SQL Plan Cache................................. 189 4.3 Query Plan Analysis..........................................................190 Analyzing SQL Execution with the Plan Explanation.................................191 Analyzing SQL Execution with the Plan Visualizer.................................. 195 4.4 Result Cache.............................................................. 210 Static Result Cache........................................................211 Dynamic Result Cache.....................................................216 4.5 Tracing for Calculation View Queries..............................................223 4.6 Advanced Analysis..........................................................224 Analyzing Column Searches (qo trace)..........................................224 Analyzing Table Joins......................................................226 SQL Plan Stability........................................................ 227 4.7 Additional Analysis Tools for Support............................................. 231 Performance Trace........................................................231 Kernel Profiler...........................................................233 Diagnosis Information..................................................... 234 Analysis Tools in SAP HANA Web-based Developer Workbench........................ 235 5 SAP HANA Database
Recommended publications
  • (DDL) Reference Manual
    Data Definition Language (DDL) Reference Manual Abstract This publication describes the DDL language syntax and the DDL dictionary database. The audience includes application programmers and database administrators. Product Version DDL D40 DDL H01 Supported Release Version Updates (RVUs) This publication supports J06.03 and all subsequent J-series RVUs, H06.03 and all subsequent H-series RVUs, and G06.26 and all subsequent G-series RVUs, until otherwise indicated by its replacement publications. Part Number Published 529431-003 May 2010 Document History Part Number Product Version Published 529431-002 DDL D40, DDL H01 July 2005 529431-003 DDL D40, DDL H01 May 2010 Legal Notices Copyright 2010 Hewlett-Packard Development Company L.P. Confidential computer software. Valid license from HP required for possession, use or copying. Consistent with FAR 12.211 and 12.212, Commercial Computer Software, Computer Software Documentation, and Technical Data for Commercial Items are licensed to the U.S. Government under vendor's standard commercial license. The information contained herein is subject to change without notice. The only warranties for HP products and services are set forth in the express warranty statements accompanying such products and services. Nothing herein should be construed as constituting an additional warranty. HP shall not be liable for technical or editorial errors or omissions contained herein. Export of the information contained in this publication may require authorization from the U.S. Department of Commerce. Microsoft, Windows, and Windows NT are U.S. registered trademarks of Microsoft Corporation. Intel, Itanium, Pentium, and Celeron are trademarks or registered trademarks of Intel Corporation or its subsidiaries in the United States and other countries.
    [Show full text]
  • SAQE: Practical Privacy-Preserving Approximate Query Processing for Data Federations
    SAQE: Practical Privacy-Preserving Approximate Query Processing for Data Federations Johes Bater Yongjoo Park Xi He Northwestern University University of Illinois (UIUC) University of Waterloo [email protected] [email protected] [email protected] Xiao Wang Jennie Rogers Northwestern University Northwestern University [email protected] [email protected] ABSTRACT 1. INTRODUCTION A private data federation enables clients to query the union of data Querying the union of multiple private data stores is challeng- from multiple data providers without revealing any extra private ing due to the need to compute over the combined datasets without information to the client or any other data providers. Unfortu- data providers disclosing their secret query inputs to anyone. Here, nately, this strong end-to-end privacy guarantee requires crypto- a client issues a query against the union of these private records graphic protocols that incur a significant performance overhead as and he or she receives the output of their query over this shared high as 1,000× compared to executing the same query in the clear. data. Presently, systems of this kind use a trusted third party to se- As a result, private data federations are impractical for common curely query the union of multiple private datastores. For example, database workloads. This gap reveals the following key challenge some large hospitals in the Chicago area offer services for a cer- in a private data federation: offering significantly fast and accurate tain percentage of the residents; if we can query the union of these query answers without compromising strong end-to-end privacy. databases, it may serve as invaluable resources for accurate diag- To address this challenge, we propose SAQE, the Secure Ap- nosis, informed immunization, timely epidemic control, and so on.
    [Show full text]
  • Database Schema Migration Tools Open Source
    Database Schema Migration Tools Open Source Validating Darian sometimes tranquillize his barony afterwards and cast so stubbornly! Vilhelm rocket his flirt bludge round-arm or best after Worthy smuts and formulise conspiratorially, quinoidal and declaratory. Implied Ernest rinsings: he built his Kathy lexically and amorally. Does this coupon code that is ideal state can replicate for speaking with their database tools and handled it ensures data, a granular control Review the tool for migrating to? If necessary continue browsing the site, will agree specify the rush of cookies on this website. Iteratively make both necessary changes to applications. 1 Database Version Control DBMS Tools. It moves to schema migration database tools source database migration is a few clicks configuration as well as someone to. GDPR: floating video: is from consent? Openmysql rootwelcometcp1270013306migrationtest if err nil fmt. Database health Suite itself and Schema Sync across. The Top 33 Database Migrations Open Source Projects. The community edition of PDI is useful enough they perform our mystery here. Migration Supports schema migration for MySQL SQLite and PostgreSQL Reverse Engineering For existing database structures we to reverse enginering. Most schema migration tools aim to minimize the footprint of schema changes on any existing data in tally database. Contains errors, warnings, and informational messages relating to migration operations. To schema and tools with a tool allows you take years of the tooling uses the type of. But migrating data services ownership, and integrity checks will be able to other objects to use open source tools now part of. Making database schema while capturing any databases, open source endpoint to migrate to get started with constraints between data sources in an altered outside the.
    [Show full text]
  • Veritas: Shared Verifiable Databases and Tables in the Cloud
    Veritas: Shared Verifiable Databases and Tables in the Cloud Lindsey Alleny, Panagiotis Antonopoulosy, Arvind Arasuy, Johannes Gehrkey, Joachim Hammery, James Huntery, Raghav Kaushiky, Donald Kossmanny, Jonathan Leey, Ravi Ramamurthyy, Srinath Settyy, Jakub Szymaszeky, Alexander van Renenz, Ramarathnam Venkatesany yMicrosoft Corporation zTechnische Universität München ABSTRACT A recent class of new technology under the umbrella term In this paper we introduce shared, verifiable database tables, a new "blockchain" [11, 13, 22, 28, 33] promises to solve this conundrum. abstraction for trusted data sharing in the cloud. For example, companies A and B could write the state of these shared tables into a blockchain infrastructure (such as Ethereum [33]). The KEYWORDS blockchain then gives them transaction properties, such as atomic- ity of updates, durability, and isolation, and the blockchain allows Blockchain, data management a third party to go through its history and verify the transactions. Both companies A and B would have to write their updates into 1 INTRODUCTION the blockchain, wait until they are committed, and also read any Our economy depends on interactions – buyers and sellers, suppli- state changes from the blockchain as a means of sharing data across ers and manufacturers, professors and students, etc. Consider the A and B. To implement permissioning, A and B can use a permis- scenario where there are two companies A and B, where B supplies sioned blockchain variant that allows transactions only from a set widgets to A. A and B are exchanging data about the latest price for of well-defined participants; e.g., Ethereum [33]. widgets and dates of when widgets were shipped.
    [Show full text]
  • Dbartisan Reviewers Guide
    DBArtisan® XE Product Review Guide May 2010 Americas Headquarters EMEA Headquarters Asia-Pacific Headquarters 100 California Street, 12th Floor York House L7. 313 La Trobe Street San Francisco, California 94111 18 York Road Melbourne VIC 3000 Maidenhead, Berkshire Australia SL6 1SF, United Kingdom The High Performance DBA CONTENTS Contents ..................................................................................................................................................................... - 1 - Overview ......................................................................................................................................................................... - 2 - Introduction ............................................................................................................................................................... - 2 - Product Description .................................................................................................................................................. - 2 - Contact Information .................................................................................................................................................. - 2 - DBArtisan XE Highlights ................................................................................................................................................ - 3 - New and Interesting Features of DBArtisan XE ..................................................................................................... - 3 - Key Benefits
    [Show full text]
  • SQL Database Management Portal
    APPENDIX A SQL Database Management Portal This appendix introduces you to the online SQL Database Management Portal built by Microsoft. It is a web-based application that allows you to design, manage, and monitor your SQL Database instances. Although the current version of the portal does not support all the functions of SQL Server Management Studio, the portal offers some interesting capabilities unique to SQL Database. Launching the Management Portal You can launch the SQL Database Management Portal (SDMP) from the Windows Azure Management Portal. Open a browser and navigate to https://manage.windowsazure.com, and then login with your Live ID. Once logged in, click SQL Databases on the left and click on your database from the list of available databases. This brings up the database dashboard, as seen in Figure A-1. To launch the management portal, click the Manage icon at the bottom. Figure A-1. SQL Database dashboard in Windows Azure Management Portal 257 APPENDIX A N SQL DATABASE MANAGEMENT PORTAL N Note You can also access the SDMP directly from a browser by typing https://sqldatabasename.database. windows.net, where sqldatabasename is the server name of your SQL Database. A new web page opens up that prompts you to log in to your SQL Database server. If you clicked through the Windows Azure Management Portal, the database name will be automatically filled and read-only. If you typed the URL directly in a browser, you will need to enter the database name manually. Enter a user name and password; then click Log on (Figure A-2).
    [Show full text]
  • Opengis Catalog Services Specification
    OGC 02-087r3 Open GIS Consortium Inc. Date: 2002-12-13 Reference number of this OpenGIS® project document: OGC 02-087r3 Version: 1.1.1 Category: OpenGIS® Implementation Specification Editor: Douglas Nebert OpenGIS® Catalog Services Specification Copyright notice This OGC document is copyright-protected by OGC. While the reproduction of drafts in any form for use by participants in the OGC standards development process is permitted without prior permission from OGC, neither this document nor any extract from it may be reproduced, stored or transmitted in any form for any other purpose without prior written permission from OGC. Document type: OpenGIS® Publicly Available Standard Document subtype: Implementation Specification Document stage: Adopted Document language: English OGC 02-087r3 Contents 1 Scope........................................................................................................................1 2 Conformance ..........................................................................................................1 3 Normative references.............................................................................................1 4 Terms and definitions............................................................................................1 5 Conventions ............................................................................................................3 5.1 Symbols (and abbreviated terms).........................................................................3 5.2 UML notation.........................................................................................................4
    [Show full text]
  • Exploring the Visualization of Schemas for Aggregate-Oriented Nosql Databases?
    Exploring the Visualization of Schemas for Aggregate-Oriented NoSQL Databases? Alberto Hernández Chillón, Severino Feliciano Morales, Diego Sevilla Ruiz, and Jesús García Molina Faculty of Computer Science, University of Murcia Campus Espinardo, Murcia, Spain {alberto.hernandez1,severino.feliciano,dsevilla,jmolina}@um.es Abstract. The lack of an explicit data schema (schemaless) is one of the most attractive NoSQL database features for developers. Being schema- less, these databases provide a greater flexibility, as data with different structure can be stored for the same entity type, which in turn eases data evolution. This flexibility, however, should not be obtained at the expense of losing the benefits provided by having schemas: When writ- ing code that deals with NoSQL databases, developers need to keep in mind at any moment some kind of schema. Also, database tools usu- ally require the knowledge of a schema to implement their functionality. Several approaches to infer an implicit schema from NoSQL data have been proposed recently, and some utilities that take advantage of inferred schemas are emerging. In this article we focus on the requisites for the vi- sualization of schemas for aggregate-oriented NoSQL Databases. Schema diagrams have proven useful in designing and understanding databases. Plenty of tools are available to visualize relational schemas, but the vi- sualization of NoSQL schemas (and the variation that they allow) is still in an immature state, and a great R&D effort is required to achieve tools with the desired capabilities. Here, we study the main challenges to be addressed, and propose some visual representations. Moreover, we outline the desired features to be supported by visualization tools.
    [Show full text]
  • SQL: Triggers, Views, Indexes Introduction to Databases Compsci 316 Fall 2014 2 Announcements (Tue., Sep
    SQL: Triggers, Views, Indexes Introduction to Databases CompSci 316 Fall 2014 2 Announcements (Tue., Sep. 23) • Homework #1 sample solution posted on Sakai • Homework #2 due next Thursday • Midterm on the following Thursday • Project mixer this Thursday • See my email about format • Email me your “elevator pitch” by Wednesday midnight • Project Milestone #1 due Thursday, Oct. 16 • See project description on what to accomplish by then 3 Announcements (Tue., Sep. 30) • Homework #2 due date extended to Oct. 7 • Midterm in class next Thursday (Oct. 9) • Open-book, open-notes • Same format as sample midterm (from last year) • Already posted on Sakai • Solution to be posted later this week 4 “Active” data • Constraint enforcement: When an operation violates a constraint, abort the operation or try to “fix” data • Example: enforcing referential integrity constraints • Generalize to arbitrary constraints? • Data monitoring: When something happens to the data, automatically execute some action • Example: When price rises above $20 per share, sell • Example: When enrollment is at the limit and more students try to register, email the instructor 5 Triggers • A trigger is an event-condition-action (ECA ) rule • When event occurs, test condition ; if condition is satisfied, execute action • Example: • Event : some user’s popularity is updated • Condition : the user is a member of “Jessica’s Circle,” and pop drops below 0.5 • Action : kick that user out of Jessica’s Circle http://pt.simpsons.wikia.com/wiki/Arquivo:Jessica_lovejoy.jpg 6 Trigger example
    [Show full text]
  • Oracle Nosql Database EE Data Sheet
    Oracle NoSQL Database 21.1 Enterprise Edition (EE) Oracle NoSQL Database is a multi-model, multi-region, multi-cloud, active-active KEY BUSINESS BENEFITS database, designed to provide a highly-available, scalable, performant, flexible, High throughput and reliable data management solution to meet today’s most demanding Bounded latency workloads. It can be deployed in on-premise data centers and cloud. It is well- Linear scalability suited for high volume and velocity workloads, like Internet of Things, 360- High availability degree customer view, online contextual advertising, fraud detection, mobile Fast and easy deployment application, user personalization, and online gaming. Developers can use a single Smart topology management application interface to quickly build applications that run in on-premise and Online elastic configuration cloud environments. Multi-region data replication Enterprise grade software Applications send network requests against an Oracle NoSQL data store to and support perform database operations. With multi-region tables, data can be globally distributed and automatically replicated in real-time across different regions. Data can be modeled as fixed-schema tables, documents, key-value pairs, and large objects. Different data models interoperate with each other through a single programming interface. Oracle NoSQL Database is a sharded, shared-nothing system which distributes data uniformly across multiple shards in a NoSQL database cluster, based on the hashed value of the primary keys. An Oracle NoSQL Database data store is a collection of storage nodes, each of which hosts one or more replication nodes. Data is automatically populated across these replication nodes by internal replication mechanisms to ensure high availability and rapid failover in the event of a storage node failure.
    [Show full text]
  • KB SQL Database Administrator Guide a Guide for the Database Administrator of KB SQL
    KB_SQL Database Administrator Guide A Guide for the Database Administrator of KB_SQL © 1988-2019 by Knowledge Based Systems, Inc. All rights reserved. Printed in the United States of America. No part of this manual may be reproduced in any form or by any means (including electronic storage and retrieval or translation into a foreign language) without prior agreement and written consent from KB Systems, Inc., as governed by United States and international copyright laws. The information contained in this document is subject to change without notice. KB Systems, Inc., does not warrant that this document is free of errors. If you find any problems in the documentation, please report them to us in writing. Knowledge Based Systems, Inc. 43053 Midvale Court Ashburn, Virginia 20147 KB_SQL is a registered trademark of Knowledge Based Systems, Inc. MUMPS is a registered trademark of the Massachusetts General Hospital. All other trademarks or registered trademarks are properties of their respective companies. Table of Contents Preface ................................................. vii Purpose ............................................. vii Audience ............................................ vii Conventions Used in this Manual ...................................................................... viii The Organization of this Manual ......................... ... x Additional Documentation .............................. xii Chapter 1: An Overview of the KB_SQL User Groups and Menus ............................................................................................................
    [Show full text]
  • SUGI 23: an Investigation of the Efficiency of SQL DML Operations Performed on an Oracle DBMS Using SAS/Accessr Software
    Posters An Investigation of the Efficiency of SQL DML Operations Performed on an ORACLE DBMS using SAS/ACCESS Software Annie Guo, Ischemia Research and Education Foundation, San Francisco, California Abstract Table 1.1: Before Modification Of Final Records Id Entry MedCode Period1 Period2 Indication AG1001 Entry1 AN312 Postop Day1 Routine AG1001 Entry2 AN312 Postop Day1 Routine In an international epidemiological study of 2000 cardiac AG1001 Final AN312 Postop Day1 Non-routine ← To be updated surgery patients, the data of 7000 variables are entered AG1001 Final HC527 Intraop PostCPB Maintenance ← To be deleted AG1002 Entry1 PV946 Intraop PreCPB Non-routine ← To be inserted through a Visual Basic data entry system and stored in 57 AG1002 Entry2 PV946 Intraop PreCPB Non-routine as ‘Final’ large ORACLE tables. A SAS application is developed to Table 1.2: After Modification Of Final Records automatically convert the ORACLE tables to SAS data sets, Id Entry MedCode Period1 Period2 Indication AG1001 Entry1 AN312 Postop Day1 Routine perform a series of intensive data processing, and based AG1001 Entry2 AN312 Postop Day1 Routine AG1001 Final AN312 Postop Day1 Routine ← Updated upon the result of the data processing, dynamically pass AG1002 Entry1 PV946 Intraop PreCPB Non-routine AG1002 Entry2 PV946 Intraop PreCPB Non-routine ORACLE SQL Data Manipulation Language (DML) AG1002 Final PV946 Intraop PreCPB Non-routine ← Inserted commands such as UPDATE, DELETE and INSERT to ORACLE database and modify the data in the 57 tables. A Visual Basic module making use of ORACLE Views, Functions and PL/SQL Procedures has been developed to The modification of ORACLE data using SAS software can access the ORACLE data through ODBC driver, compare be resource-intensive, especially in dealing with large tables the 7000 variables between the 2 entries, and modify the and involving sorting in ORACLE data.
    [Show full text]