Case 2 inspecting cause of service delay - a tire manufacturer
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onTune Case Study How customer use onTune in their complex environment. How they can take a benefit from onTune
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1. Install the dhcp rpm package and configure the dhcpd service to run on a specific network interface such as eth1.
2. Copy the sample dhcpd configuration file and modify it to suit your network settings such as IP ranges, DNS servers, and lease times. You can provide static IP addresses by specifying client hardware addresses or names.
3. Start the dhcpd service and test that DHCP is functioning properly by requesting IP addresses with client devices on the configured network interface. Restart dhcpd if the configuration file is modified.
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Outline to use if needed.
- Introduce the Challenge of Troubleshooting by Random Googling (1min)
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2. Copy the sample dhcpd configuration file and modify it to suit your network settings such as IP ranges, DNS servers, and lease times. You can provide static IP addresses by specifying client hardware addresses or names.
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Have you heard about the USE Method (Utilization - Saturation - Errors), RED (Rate - Errors - Duration), or Golden Signals (Latency - Traffic - Errors - Saturations)?
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We will use MySQL as an example but most of the talk will apply to other database technologies as well.
Outline to use if needed.
- Introduce the Challenge of Troubleshooting by Random Googling (1min)
- Introduce USE Method, how it applies to databases (5 min)
- Introduce RED Method, how it applies to databases (5 min)
- Introduce Golden Signals (4 min)
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eBay is highly using Spark as one of most significant data engines. In data warehouse domain, there are millions of batch queries running every day against 6000+ key DW tables, which contains over 22PB data (compressed) and still keeps booming every year. In machine learning domain, it is playing a more and more significant role. We have introduced our great achievement in migration work from MPP database to Apache Spark last year in Europe Summit. Furthermore, from the vision of the entire infrastructure, it is still a big challenge on managing workload and efficiency for all Spark jobs upon our data center. Our team is leading the whole infrastructure of big data platform and the management tools upon it, helping our customers -- not only DW engineers and data scientists, but also AI engineers -- to leverage on the same page. In this session, we will introduce how to benefit all of them within a self-service workload management portal/system. First, we will share the basic architecture of this system to illustrate how it collects metrics from multiple data centers and how it detects the abnormal workload real-time. We develop a component called Profiler which is to enhance the current Spark core to support customized metric collection. Next, we will demonstrate some real user stories in eBay to show how the self-service system reduces the efforts both in customer side and infra-team side. That's the highlight part about Spark job analysis and diagnosis. Finally, some incoming advanced features will be introduced to describe an automatic optimizing workflow rather than just alerting.
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Video: http://www.youtube.com/watch?v=mefOE9K7sLI&list=PLqcm6qE9lgKLoYaakl3YwIWP4hmGsHm5e&index=28
At Orange, Jean Armel has helped develop an open source tool for the migration of data to Cassandra; Jean and his team were in need of the NoSQL solution Apache Cassandra in order to sustain the growth of requests and volume of data required by their application PnS. In this session, Jean Armel will start out with an overview of the Orange application PnS and dive into why they chose Apache Cassandra how they did their data migration without any interruption of service. Jean Armel will also show how his application behaves after the migration
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Effectively Migrating to Cassandra from a Relational Database
Cassandra kicks butt. But, how do enjoy the benefits when refactoring an existing RDBMS based app? How can we migrate to Cassandra and test our data model, prepare our operations while simultaneously leaving our RDBMS online? What do we need to consider? How can we do it? Why should we do it?
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Latency problem was reported for VDisk CA-CL1-Disk04-N at 02/05/15 8:09,
The environment are two clusters connected with Metro Mirror. The first aim of this document is to show how we found the root cause of this problem in the link between the two clusters.
The second aim of this document is to describe how the root cause for this problem was found by using the BVQ structured performance problem analysis method. It demonstrates that successful analysis work needs a structured method and also a tool which supports this method and delivers the needed technical insight. We have the concept that everybody should be able to conduct a performance analysis. This is important because the level of service is lowered day by day and especially small customers are more and more reliant on their own skills or on the skills of their partners. This is a common problem occurring at all vendors!
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The document provides an overview of initiatives and findings related to improving backup and restore processes at Century Link. Key accomplishments include developing health check scripts for TSM servers to reduce downtime and improve backup success rates. New findings identified opportunities to streamline TSM tape storage utilization by removing expired nodes and eliminating duplicate data to save on tapes. Issues were also found with some TSM server storage pools not having offsite copies. Suggested changes to address issues included evaluating backup tools, server configurations, and separating test/dev environments. Help was requested to approve removing expired and duplicate data.
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Similar to Case 2 inspecting cause of service delay - a tire manufacturer
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eBay is highly using Spark as one of most significant data engines. In data warehouse domain, there are millions of batch queries running every day against 6000+ key DW tables, which contains over 22PB data (compressed) and still keeps booming every year. In machine learning domain, it is playing a more and more significant role. We have introduced our great achievement in migration work from MPP database to Apache Spark last year in Europe Summit. Furthermore, from the vision of the entire infrastructure, it is still a big challenge on managing workload and efficiency for all Spark jobs upon our data center. Our team is leading the whole infrastructure of big data platform and the management tools upon it, helping our customers -- not only DW engineers and data scientists, but also AI engineers -- to leverage on the same page. In this session, we will introduce how to benefit all of them within a self-service workload management portal/system. First, we will share the basic architecture of this system to illustrate how it collects metrics from multiple data centers and how it detects the abnormal workload real-time. We develop a component called Profiler which is to enhance the current Spark core to support customized metric collection. Next, we will demonstrate some real user stories in eBay to show how the self-service system reduces the efforts both in customer side and infra-team side. That's the highlight part about Spark job analysis and diagnosis. Finally, some incoming advanced features will be introduced to describe an automatic optimizing workflow rather than just alerting.
Speaker: Lantao Jin
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Describes the thinking behind MapR's architecture. MapR"s Hadoop achieves better reliability on commodity hardware compared to anything on the planet, including custom, proprietary hardware from other vendors. Apache HDFS and Cassandra replication is also discussed, as are SAN and NAS storage systems like Netapp and EMC.
QCon 2015 Broken Performance Tools
Talk for QConSF 2015: "Broken benchmarks, misleading metrics, and terrible tools. This talk will help you navigate the treacherous waters of system performance tools, touring common problems with system metrics, monitoring, statistics, visualizations, measurement overhead, and benchmarks. This will likely involve some unlearning, as you discover tools you have been using for years, are in fact, misleading, dangerous, or broken.
The speaker, Brendan Gregg, has given many popular talks on operating system performance tools. This is an anti-version of these talks, to focus on broken tools and metrics instead of the working ones. Metrics can be misleading, and counters can be counter-intuitive! This talk will include advice and methodologies for verifying new performance tools, understanding how they work, and using them successfully."
Scaling Apache Spark at Facebook
Spark started at Facebook as an experiment when the project was still in its early phases. Spark's appeal stemmed from its ease of use and an integrated environment to run SQL, MLlib, and custom applications. At that time the system was used by a handful of people to process small amounts of data. However, we've come a long way since then. Currently, Spark is one of the primary SQL engines at Facebook in addition to being the primary system for writing custom batch applications. This talk will cover the story of how we optimized, tuned and scaled Apache Spark at Facebook to run on 10s of thousands of machines, processing 100s of petabytes of data, and used by 1000s of data scientists, engineers and product analysts every day. In this talk, we'll focus on three areas: * *Scaling Compute*: How Facebook runs Spark efficiently and reliably on tens of thousands of heterogenous machines in disaggregated (shared-storage) clusters. * *Optimizing Core Engine*: How we continuously tune, optimize and add features to the core engine in order to maximize the useful work done per second. * *Scaling Users:* How we make Spark easy to use, and faster to debug to seamlessly onboard new users.
Speakers: Ankit Agarwal, Sameer Agarwal
Mysql8 advance tuning with resource group
I have a very noisy secondary application written by a very, very bad developer that accesses my servers, mostly with read queries, and occasionally with write updates. Reads and writes are obsessive and create an impact on the MAIN application. My task is to limit the impact of this secondary application without having the main one affected. To do that I will create two resource groups, one for WRITE and another for READ. The first group, Write_app2, will have no cpu affiliation, but will have lowest priority.
Hadoop Query Performance Smackdown
Are you using the fastest query tool for Hadoop? Provide and discuss the latest performance results of the industry standard TPC_H benchmarks executed across an assortment of open source query tools such as Hive (using MR, TEZ, LLAP, SPARK), SparkSQL, Presto, and Drill. Additionally, the performance tests will utilize a variety of data sizes and popular storage formats such as ORC, Parquet and Text and compression codecs.
SnapDiff performance issue
The document discusses troubleshooting a SnapDiff performance issue. Key factors that can affect SnapDiff throughput include hardware resources, Data ONTAP version, disk I/O latency, number of files in volumes, parallel SnapDiff sessions, and overlapping backup jobs. Symptoms of timeouts between ONTAP and the media agent during the indexing phase suggest the indexing is taking too long. The document provides workarounds like skipping cataloging and running it later, and using live browse for restores if needed before investigation is complete.
Critical Performance Metrics for DDR4 based Systems
Servers are critical to today's Cloud Computing and DDR memory is at the heart of all Cloud Computing Servers. Presented at DesignCon 2015 this presentation outlines new measurable performance metrics for DDR4 Memory Subsystems.
SVC / Storwize: cache partition analysis (BVQ howto)
This document provides a summary of cache partitioning and management for the SVC storage system. It discusses how the cache is divided into partitions that correspond to managed disk groups. Each partition is allocated a percentage of the total cache and thresholds are used to trigger destaging to avoid any single partition becoming overloaded. Examples are given showing partitions operating normally, a partition becoming overloaded when its backend controller cannot keep up with destaging, and the overall cache responding when usage crosses thresholds.
Getting Under the Hood of Kafka Streams: Optimizing Storage Engines to Tune U...
"With a few tweaks under the hood of your Kafka Streams implementation, you greatly improve performance. Sound too good to be true? Well, the secret lies in understanding storage engines.
You may already know that if you're using Kafka streams, you already have a storage engine in place, but do you know what options are available to tune it for optimal performance and scalability?
This presentation will discuss the importance of optimizing and choosing storage engines for Kafka streams applications.
Outline:
- What a storage engine is and how it relates to Kafka stateful streams
- The importance of understanding storage engines for optimal performance and scalability
- Evaluation of Storage Engines - Overview of popular storage engines, including Leveldb, Rocksdb, and Speedb open-source
- Review of the 5 most relevant configurable items and how they affect performance
- Practical ways to optimize and fine tune your storage engine
- Showcase - 2 minutes drop-in replacement demonstration"
Drill Down the most underestimate Oracle Feature - Database Resource Manager
Being a crucial feature on managing database load and with real world practice showing that Database
Resource Manager (DBRM) is not often used, this talk want to change this and demystify this feature by
explaining how it works in detail on different scenarios, the CPU math behind it, how to measure it in
real-time using Python and SQL and exploring more complex features to understand its behaviour.
Special attention will be made to understand its internals whenever possible.
Hptf 2240 Final
The document discusses using capacity planning and performance analysis to improve system performance. It describes two case studies: capacity planning for an Oracle RAC database and performance analysis of a SQL Server application on HP blades. For the Oracle case, different platform options were evaluated and optimized configurations identified. For SQL Server, enabling AWE resolved soft paging and reduced response times by improving memory usage. The lessons highlight challenges in using performance tools and the need for better fault detection and data presentation.
Slurm @ 2018 LabTech
This presentation will give a quick introduction on how to use slurm, the scheduler that runs programs (scripts) in HPC. Targeted for audience who are new to the Lawrencium or who may want to learn a few more things in troubleshooting their jobs.
From Zero to Data Flow in Hours with Apache NiFi
This document discusses Apache NiFi and how it was used to create a new composable data flow system for Schlumberger in just 10 man hours. The previous system was very complex, took over 100 man years to create, and was difficult to change. NiFi allows for easy visualization of the data flow, debugging of issues, and rapid creation of new processors. It also enables quick testing of data flows using curated test data sets and live data in Docker containers. Next steps discussed include further exploring use cases for rig data ingestion with NiFi to provide data provenance and understand the chain of custody of data as it moves through the system.
Similar to Case 2 inspecting cause of service delay - a tire manufacturer (20)
Managing Apache Spark Workload and Automatic Optimizing
Managing Apache Spark Workload and Automatic Optimizing
Architectural Overview of MapR's Apache Hadoop Distribution
Architectural Overview of MapR's Apache Hadoop Distribution
Critical Performance Metrics for DDR4 based Systems
Critical Performance Metrics for DDR4 based Systems
SVC / Storwize: cache partition analysis (BVQ howto)
SVC / Storwize: cache partition analysis (BVQ howto)
Getting Under the Hood of Kafka Streams: Optimizing Storage Engines to Tune U...
Getting Under the Hood of Kafka Streams: Optimizing Storage Engines to Tune U...
Drill Down the most underestimate Oracle Feature - Database Resource Manager
Drill Down the most underestimate Oracle Feature - Database Resource Manager
More from TeemStone Pty Ltd
onTune the differences
OnTune provides real-time system performance monitoring down to the second, allowing it to detect issues that other solutions miss. It collects a wide range of system data without customization and stores historical data to help analyze long-term trends and troubleshoot intermittent problems. Case studies demonstrate how OnTune's high-granularity data helped users identify specific processes causing performance issues and crashes.
Brief introduction to onTune(cio context)
- onTune is a system analysis and performance monitoring tool that provides real-time data collection and monitoring of key system metrics like CPU, memory, disk, network usage, and applications every 2 seconds with low overhead.
- It allows visibility into performance across on-premise and cloud environments from a single view and helps identify issues through deep diagnostic reports and root cause analysis.
- onTune's simple interface makes it fast and easy to implement and use to optimize system performance, reduce costs, and ensure service quality and SLAs.
Case 4 mdm system change report - a car maker
onTune Case Study
How customer use onTune in their complex environment.
How they can take a benefit from onTune
Case 1 general performance inspection - a telecomm company
onTune Case Study
How customer use onTune in their complex environment.
How they can take a benefit from onTune
Case 3 inspecting cause of failure - a car maker plm
Three Oracle Java processes on the PLM DB#2 server requested 3GB of memory at 13:26, causing high CPU I/O wait times and paging as the server struggled to allocate memory. This overwhelmed the server and caused it to temporarily hang. Inspecting the server's CPU usage, paging activity, and memory usage graphs during the hang showed abnormal peaks at 13:26 that identified the memory allocation request as the likely root cause of the problem. Stopping the EM monitoring agent on the troubled server reduced memory usage and prevented further hangs.
What is onTune for management
George, a system administrator, was having trouble completing a deployment that was taking all morning. He had contacted colleagues, experts, and developers for help but was still unable to resolve the issue. System administrators play a critical role in maintaining computer infrastructure but troubleshooting problems can be challenging and time-consuming. Expert performance analysis tools like onTune that collect real-time system data and monitor all processes can help administrators identify issues faster and save organizations time and money compared to general system management solutions.
Brief summary of onTune [teemstone]
This document discusses the System Analysis & Performance Instrument tool from TeemStone Corp. It outlines some of the key benefits of their onTune product over competitors like IBM Tivoli, HP OpenView, and BMC Patrol. Specifically, it notes that onTune allows for real-time monitoring at the second level, collects abundant performance data, and has intuitive functions for problem and performance analysis. Case studies and customer testimonials are also provided showing how onTune helped customers identify issues that other tools could not detect.
onTune Case Study sep 2012
An internet shopping mall was experiencing frequent system errors and crashes from a new interactive flash program. The system operator could not determine the root cause of the issues through standard Windows performance monitoring tools. After installing OnTune to monitor system and process resource usage, the operator discovered that the virtual memory usage of the flash program was steadily increasing until it caused the system to crash. This identified a memory leak in the flash program code. The developer was notified and fixed the problem, resolving the system crashes.
On Tune General Monitoring
onTune is a next generation system management solution that can quickly analyze, identify, and offer solutions to any performance problems for the system administrator in real-time. It can be installed through a simple installation and automatically monitors the system without additional configuration changes. onTune also creates reports like PowerPoint to analyze historical system performance and status.
On Tune Leaflet
onTune is a next generation system management solution from TeemStone that allows real-time monitoring and analysis of system performance. It collects system data without customization and allows administrators to monitor CPU, memory, I/O, and other essential elements. onTune supports monitoring in virtualization environments from vendors like IBM, HP, and Oracle. It is currently used by IBM for virtualization monitoring and supports various monitoring and analysis scenarios for ensuring system performance.
On Tune Performance Monitoring
onTune is a next generation performance monitoring and analysis solution that allows real-time monitoring of systems to maximize analytic capacity and understand the reasons for any errors. It collects performance data every 2 seconds to provide more detailed insight compared to traditional SMS tools with collection intervals of 1 minute or more. System administrators can monitor CPU, memory, I/O and other metrics for overall systems or individual processes.
On Tune Virtualisation Monitoring
onTune provides powerful virtualization monitoring and analysis capabilities. It can monitor virtual resources on servers from various hardware vendors that use technologies like VMware, Xen, and Oracle's LDOM. onTune collects structure information and monitors both logical virtualized resources and physical server resources. It presents this information visually to help administrators respond to issues. onTune also helps analyze the root causes of errors in virtualized systems through real-time performance monitoring and analysis.
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Case 1 general performance inspection - a telecomm company
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Case 2 inspecting cause of service delay - a tire manufacturer
- 1. © Copyright TeemStone Corporation 2014 Easiest way to manage your critical systems Case 2 – Inspecting Cause of Service Delay A Tire Manufacturer
- 2. 1 hqplvc1 – Memory Usage It seemed that the service responses of hqplvc1 in the evening on 19 Mar were very bad due to lack of memory. At that time, PagingSpace In/Out was very high, and RSS of Kernel process was very low. It seems that the business applications were restarted or memory of those applications were paged out and the services were delayed. It seems that 30% of memory is lacked.
- 3. 2 CPU usage of all servers on 24, Mar Server Average Max hqpap4 88.95 100 hqpap3 41.94 95.2 hqpap2 28.66 96.23 hqplvc1 26.77 92.67 hqpdb1 37.82 74 hqpap6 24.05 68.1 hqpscm0 13.52 39.57 hqpha1 12.24 37.5 hqpscm3 7.78 35.63 hqpscm2 6.48 27.43 hqpap5 0.09 0.7 Time Period : 08h ~ 12h on 24, Mar. hqpap4, hqpap3, hqpap2, hqplvc1 shows CPU usage peak. See details in following page. High CPU usage of all the servers except hqplvc1 were caused by process disp+work. On haplvc1 server, CPU time had mostly consumed by the kernel processes by sdb user.
- 4. 3 CPU usage of servers - hqpap4, hqpap3, hqpap2, hqplvc1
- 5. 4 hqpap3 – Memory usage It seemed that the service responses of hqpap3 in the morning on 24 Mar were very bad due to lack of memory. At that time, high PagingSpace In/Out occurred. The cause of service delay may be caused by lack of memory Considering max values of ActiveVM, the size of lack of memory is approximately 5 GB. Lack of Memory: Approx. 5GB
- 6. 5 hqplvc1 – Memory Usage It seemed that the service responses of hqplvc1 in the evening on 19 Mar were very bad due to lack of memory. At that time, PagingSpace In/Out was very high, and RSS of Kernel process was very low. It seems that the business applications were restarted or memory of those applications were paged out and the services were delayed. It seems that 30% of memory is lacked.