Walmart proves the obvious, devknob wonders why people don't understand why page speed matters. This has been true and known to be true since the beginning of the internet. Do you think people won't get distracted easily and bounce when they're surfing on 2g, 3g and even 4g connections? Page speed matters, devknob is probably the best page speed optimizer in the world so if you need conversion optimization, you may want to visit devknob online at devknob.com
Brian Brazil is an engineer passionate about reliable software operations. He worked at Google SRE for 7 years and is the founder of Prometheus, an open source time series database designed for monitoring system and service metrics. Prometheus supports metric labeling, unified alerting and graphing, and is efficient, decentralized, reliable, and opinionated in how it encourages good monitoring practices.
This document provides an overview of patterns for scalability, availability, and stability in distributed systems. It discusses general recommendations like immutability and referential transparency. It covers scalability trade-offs around performance vs scalability, latency vs throughput, and availability vs consistency. It then describes various patterns for scalability including managing state through partitioning, caching, sharding databases, and using distributed caching. It also covers patterns for managing behavior through event-driven architecture, compute grids, load balancing, and parallel computing. Availability patterns like fail-over, replication, and fault tolerance are discussed. The document provides examples of popular technologies that implement many of these patterns.
Jose portillo dev con presentation 1138Jose Portillo
This document discusses best practices for implementing Solr sharding in Alfresco. It defines what sharding is and explains that it involves splitting a single index into multiple parts or shards to improve search performance, distribute indexing load, and scale horizontally. The document outlines different types of sharding, considerations for the number of shards, high availability, backup procedures, and common configuration settings when using Solr sharding in Alfresco.
High Concurrency Architecture and Laravel Performance TuningAlbert Chen
This document summarizes techniques for improving performance and concurrency in Laravel applications. It discusses caching routes and configuration files, using caching beyond just the database, implementing asynchronous event handling with message queues, separating database reads and writes, enabling OPcache and preloading in PHP 7.4, and analyzing use cases like a news site, ticketing system, and chat service. The document provides benchmarks showing performance improvements from these techniques.
This document summarizes the process of transforming Medicare claims data into the OMOP CDM format and loading it into an Atlas database to make it accessible for analysis. Over 2 million synthetic patient records were extracted from the Medicare DE-SynPUF dataset and mapped to standardized vocabularies during the ETL process. The transformed data and vocabularies were loaded into an OMOP CDM v5 database and characterization analyses were run using Achilles. The results were loaded into Atlas to enable interactive exploration and cohort analysis of the open Medicare claims data using OHDSI tools.
Large Scale Feature Aggregation Using Apache Spark with Pulkit Bhanot and Ami...Databricks
Aggregation based features account for a quarter of the several 1000s features used by the ML-based decisioning system by the Risk team at Uber. We observed several repetitive, cumbersome steps needed for onboarding a feature, every single time. Therefore, to accelerate developer velocity, and to enable Feature Engineering at scale, we decided to develop a generic spark based infrastructure to simplify the process to no more than a simple spec file, containing a parameterized query, along with some metadata on where the feature should be aggregated and stored.
In the presentation, we will describe the architecture of the final solution, highlighting some of the advanced capabilities like backfill support and self-healing for correctness. We will showcase how, using data stored in Hive and using Spark, we developed a highly scalable solution to carry out feature aggregation in an incremental way. By dividing data aggregation responsibility across the realtime access layer, and the batch computation components, we ensured that only entities for which there is actual value changes are dispersed to our real-time access store (Cassandra). We will share how we did data modeling in Cassandra using its native capabilities such as counters, and how we worked around some of the limitations of Cassandra. We will also cover the details about the access service how we do different types of feature stitching together. How, based on our data model we were able to ensure that all the feature for an entity with the same aggregation window, were queried via a single query. Finally, we will cover some of the details on how these incremental aggregated features have enabled shorter turnaround times for the models using such features.
Brian Brazil is an engineer passionate about reliable software operations. He worked at Google SRE for 7 years and is the founder of Prometheus, an open source time series database designed for monitoring system and service metrics. Prometheus supports metric labeling, unified alerting and graphing, and is efficient, decentralized, reliable, and opinionated in how it encourages good monitoring practices.
This document provides an overview of patterns for scalability, availability, and stability in distributed systems. It discusses general recommendations like immutability and referential transparency. It covers scalability trade-offs around performance vs scalability, latency vs throughput, and availability vs consistency. It then describes various patterns for scalability including managing state through partitioning, caching, sharding databases, and using distributed caching. It also covers patterns for managing behavior through event-driven architecture, compute grids, load balancing, and parallel computing. Availability patterns like fail-over, replication, and fault tolerance are discussed. The document provides examples of popular technologies that implement many of these patterns.
Jose portillo dev con presentation 1138Jose Portillo
This document discusses best practices for implementing Solr sharding in Alfresco. It defines what sharding is and explains that it involves splitting a single index into multiple parts or shards to improve search performance, distribute indexing load, and scale horizontally. The document outlines different types of sharding, considerations for the number of shards, high availability, backup procedures, and common configuration settings when using Solr sharding in Alfresco.
High Concurrency Architecture and Laravel Performance TuningAlbert Chen
This document summarizes techniques for improving performance and concurrency in Laravel applications. It discusses caching routes and configuration files, using caching beyond just the database, implementing asynchronous event handling with message queues, separating database reads and writes, enabling OPcache and preloading in PHP 7.4, and analyzing use cases like a news site, ticketing system, and chat service. The document provides benchmarks showing performance improvements from these techniques.
This document summarizes the process of transforming Medicare claims data into the OMOP CDM format and loading it into an Atlas database to make it accessible for analysis. Over 2 million synthetic patient records were extracted from the Medicare DE-SynPUF dataset and mapped to standardized vocabularies during the ETL process. The transformed data and vocabularies were loaded into an OMOP CDM v5 database and characterization analyses were run using Achilles. The results were loaded into Atlas to enable interactive exploration and cohort analysis of the open Medicare claims data using OHDSI tools.
Large Scale Feature Aggregation Using Apache Spark with Pulkit Bhanot and Ami...Databricks
Aggregation based features account for a quarter of the several 1000s features used by the ML-based decisioning system by the Risk team at Uber. We observed several repetitive, cumbersome steps needed for onboarding a feature, every single time. Therefore, to accelerate developer velocity, and to enable Feature Engineering at scale, we decided to develop a generic spark based infrastructure to simplify the process to no more than a simple spec file, containing a parameterized query, along with some metadata on where the feature should be aggregated and stored.
In the presentation, we will describe the architecture of the final solution, highlighting some of the advanced capabilities like backfill support and self-healing for correctness. We will showcase how, using data stored in Hive and using Spark, we developed a highly scalable solution to carry out feature aggregation in an incremental way. By dividing data aggregation responsibility across the realtime access layer, and the batch computation components, we ensured that only entities for which there is actual value changes are dispersed to our real-time access store (Cassandra). We will share how we did data modeling in Cassandra using its native capabilities such as counters, and how we worked around some of the limitations of Cassandra. We will also cover the details about the access service how we do different types of feature stitching together. How, based on our data model we were able to ensure that all the feature for an entity with the same aggregation window, were queried via a single query. Finally, we will cover some of the details on how these incremental aggregated features have enabled shorter turnaround times for the models using such features.
Introduction to memcached, a caching service designed for optimizing performance and scaling in the web stack, seen from perspective of MySQL/PHP users. Given for 2nd year students of professional bachelor in ICT at Kaho St. Lieven, Gent.
1) Netflix uses Apache Cassandra as its main data store and has hundreds of Cassandra clusters across multiple regions containing terabytes of customer data for services like viewing history and payments.
2) Maintaining and monitoring Cassandra at Netflix's scale presents challenges around configuration, availability across regions and availability zones, and operating Cassandra in public clouds.
3) Netflix addresses these challenges through tools like Priam for automated bootstrapping and backup/restore, monitoring through services like Mantis and Atlas, and capacity planning with tools like NDBench and Unomia.
An overview of the Amazon ElastiCache managed service, with examples of how it can be used to increase performance, lower costs and augment other database services and databases to make things faster, easier and less expensive.
1일 수천대의 서버에서 발생하는 30~50억건의 Log와 Metric을 처리하는 Planet Mon 을 지탱하는 기술인 Collection(Collectd, NXlog), Transport(Kakfa, Logstash), Log Stream Analytics, Storage(Elasticsearch), Visualization을 구성하는 Architecture에 대해 설명드리고 제가 개발한 Log Stream Analytics 서버들의 구현 기술에 대해 좀더 상세히 설명합니다.
Grokking Techtalk #37: Software design and refactoringGrokking VN
Even though software engineering has been around for decades, there is still no clear ways to assess the strengths and weaknesses of software design.
This talk introduces a framework to assess the strength of any specific software design and steps to refactor and improve it. Both object-oriented and functional programming will be discussed as ways to improve the design.
In the talk, the speaker also proposes a software architecture that incorporates all the ideas presented as the conclusion.
About speaker:
Thành currently works at Holistics Software as Co-founder and Chief Engineer architecting the next generation DataOps driven BI platform.
Before joining Holistics as co-founder, Thanh had 8 years of experience as a software engineer and big-data consultant from multiple companies, notably Revolution Analytics which was acquired by Microsoft in 2015.
Thanh graduated from National University of Singapore in 2009 majoring in Computer Engineering with a minor in Technopreneurship.
High-speed Database Throughput Using Apache Arrow Flight SQLScyllaDB
Flight SQL is a revolutionary new open database protocol designed for modern architectures. Key features in Flight SQL include a columnar-oriented design and native support for parallel processing of data partitions. This talk will go over how these new features can push SQL query throughput beyond existing standards such as ODBC.
The document discusses Apache Kudu, an open source storage layer for Apache Hadoop that enables fast analytics on fast data. Kudu is designed to fill the gap between HDFS and HBase by providing fast analytics capabilities on fast-changing or frequently updated data. It achieves this through its scalable and fast tabular storage design that allows for both high insert/update throughput and fast scans/queries. The document provides an overview of Kudu's architecture and capabilities, examples of how to use its NoSQL and SQL APIs, and real-world use cases like enabling low-latency analytics pipelines for companies like Xiaomi.
Hadoop World 2011: Advanced HBase Schema Design - Lars George, ClouderaCloudera, Inc.
"While running a simple key/value based solution on HBase usually requires an equally simple schema, it is less trivial to operate a different application that has to insert thousands of records per second.
This talk will address the architectural challenges when designing for either read or write performance imposed by HBase. It will include examples of real world use-cases and how they can be implemented on top of HBase, using schemas that optimize for the given access patterns. "
1. The document discusses microservices architecture and how Netflix transitioned from a monolithic architecture to microservices. Key aspects discussed include breaking the monolith into many small, independent services that are loosely coupled.
2. Netflix's microservices architecture is composed of hundreds of microservices running on thousands of servers. Each service focuses on doing a small, well-defined piece of work. Services communicate through well-defined APIs and share no code or databases.
3. The document provides examples of how other companies like Samsung and Vingle have also adopted microservices architectures on AWS, breaking monolithic applications into independent, scalable services. This allows for independent deployments, rapid innovation, and improved resilience.
HBase and HDFS: Understanding FileSystem Usage in HBaseenissoz
This document discusses file system usage in HBase. It provides an overview of the three main file types in HBase: write-ahead logs (WALs), data files, and reference files. It describes durability semantics, IO fencing techniques for region server recovery, and how HBase leverages data locality through short circuit reads, checksums, and block placement hints. The document is intended help understand HBase's interactions with HDFS for tuning IO performance.
Ingesting Data at Blazing Speed Using Apache OrcDataWorks Summit
Big SQL is a SQL engine for Hadoop that excels at performance and scalability at high concurrency. Big SQL complements and integrates with Apache Hive for both data and metadata. An architecture that separates compute from storage allows Big SQL to support multiple open data formats natively. Until recently, Parquet provided a significant performance advantage over other data formats for SQL on Hadoop. The landscape changed when ORC became a top level Apache project independent from Hive. Gone were the days of reading ORC files using slow, single-row-at-a-time Hive Serdes. The new vectorized APIs in the Apache ORC libraries make it possible to ingest ORC data at blazing speed. This talk is about the journey leading to ORC taking the crown of best performing data format for Big SQL away from Parquet. We'll have a look under the hood at the architecture of Big SQL ORC readers, and how to tune them. We'll share lessons learned in walking the fine line between maximizing performance at scale and avoiding dreaded Java OOMs . You'll learn the techniques that SQL engines use for fast data ingestion, so that you can leverage the full potential of Apache ORC in any application.
Speaker:
Gustavo Arocena, Big Data Architect, IBM
This document discusses Apache Arrow, an open source cross-language development platform for in-memory analytics. It provides an overview of Arrow's goals of being cross-language compatible, optimized for modern CPUs, and enabling interoperability between systems. Key components include core C++/Java libraries, integrations with projects like Pandas and Spark, and common message patterns for sharing data. The document also describes how Arrow is implemented in practice in systems like Dremio's Sabot query engine.
Hive LLAP: A High Performance, Cost-effective Alternative to Traditional MPP ...DataWorks Summit
The document discusses Hive LLAP (Live Long and Process) as a high performance and cost-effective alternative to traditional Massively Parallel Processing (MPP) databases for querying large datasets on Hadoop. It describes Walmart's implementation of Hive LLAP on their data lake to improve query performance for business users. A proof-of-concept found Hive LLAP queries were up to 50% faster when using 15 nodes instead of 10, and it performed comparably or better than two MPP databases with similar or larger infrastructures. Walmart plans to further evaluate Hive LLAP on newer Hadoop distributions and technologies to improve availability and workload management.
Decomposing Applications for Scalability and Deployability (April 2012)Chris Richardson
Today, there are several trends that are forcing application architectures to evolve. Users expect a rich, interactive and dynamic user experience on a wide variety of clients including mobile devices. Applications must be highly scalable, highly available and run on cloud environments. Organizations often want to frequently roll out updates, even multiple times a day. Consequently, it’s no longer adequate to develop simple, monolithic web applications that serve up HTML to desktop browsers. In this talk we describe the limitations of a monolithic architecture. You will learn how to use the scale cube to decompose your application into a set of narrowly focused, independently deployable back-end services and an HTML 5 client. We will also discuss the role of technologies such as NodeJS and AMQP brokers. You will learn how a modern PaaS such as Cloud Foundry simplifies the development and deployment of this style of application.
Building Event Driven Architectures with Kafka and Cloud Events (Dan Rosanova...confluent
Apache Kafka is changing the way we build scalable and highly available software systems. Providing a simplified path to eventual consistency and event sourcing Kafka gives us the platform to make these patterns a reality for a much broader segment of applications and customers than was possible in the past. Cloud Events is an interoperable specification for eventing that is part of the CNCF. This session will combine open source and open standards to show you how you can build highly reliable application that scale linearly, provide interoperability and are easily extensible leveraging both push and pull semantics. Concrete real world examples will be shown of how Kafka makes event sourcing more approachable and how streams and events complement each other including the difference between business events and technical events.
This document discusses web performance optimization and provides tips to improve performance. It emphasizes that performance is important for user experience, search engine optimization, conversion rates, and costs. It outlines common causes of performance issues like round-trip times, payload sizes, browser rendering delays, and inefficient JavaScript. Specific recommendations are given to optimize images, stylesheets, scripts, and browser rendering through techniques like compression, caching, deferred loading, and efficient coding practices. A variety of tools for measuring and improving performance are also listed.
Mage uk-2013-1345-chris-wells-131030120920-phpapp01Karla Mae Tejon
Your site's performance directly correlates to order volume. A tuned Magneto install can instantly mean more sales and the converse is also true. This session is meant to give you an overview of the importance of performance for your e-commerce site as well as provide steps to make Magento perform as your business grows.
Introduction to memcached, a caching service designed for optimizing performance and scaling in the web stack, seen from perspective of MySQL/PHP users. Given for 2nd year students of professional bachelor in ICT at Kaho St. Lieven, Gent.
1) Netflix uses Apache Cassandra as its main data store and has hundreds of Cassandra clusters across multiple regions containing terabytes of customer data for services like viewing history and payments.
2) Maintaining and monitoring Cassandra at Netflix's scale presents challenges around configuration, availability across regions and availability zones, and operating Cassandra in public clouds.
3) Netflix addresses these challenges through tools like Priam for automated bootstrapping and backup/restore, monitoring through services like Mantis and Atlas, and capacity planning with tools like NDBench and Unomia.
An overview of the Amazon ElastiCache managed service, with examples of how it can be used to increase performance, lower costs and augment other database services and databases to make things faster, easier and less expensive.
1일 수천대의 서버에서 발생하는 30~50억건의 Log와 Metric을 처리하는 Planet Mon 을 지탱하는 기술인 Collection(Collectd, NXlog), Transport(Kakfa, Logstash), Log Stream Analytics, Storage(Elasticsearch), Visualization을 구성하는 Architecture에 대해 설명드리고 제가 개발한 Log Stream Analytics 서버들의 구현 기술에 대해 좀더 상세히 설명합니다.
Grokking Techtalk #37: Software design and refactoringGrokking VN
Even though software engineering has been around for decades, there is still no clear ways to assess the strengths and weaknesses of software design.
This talk introduces a framework to assess the strength of any specific software design and steps to refactor and improve it. Both object-oriented and functional programming will be discussed as ways to improve the design.
In the talk, the speaker also proposes a software architecture that incorporates all the ideas presented as the conclusion.
About speaker:
Thành currently works at Holistics Software as Co-founder and Chief Engineer architecting the next generation DataOps driven BI platform.
Before joining Holistics as co-founder, Thanh had 8 years of experience as a software engineer and big-data consultant from multiple companies, notably Revolution Analytics which was acquired by Microsoft in 2015.
Thanh graduated from National University of Singapore in 2009 majoring in Computer Engineering with a minor in Technopreneurship.
High-speed Database Throughput Using Apache Arrow Flight SQLScyllaDB
Flight SQL is a revolutionary new open database protocol designed for modern architectures. Key features in Flight SQL include a columnar-oriented design and native support for parallel processing of data partitions. This talk will go over how these new features can push SQL query throughput beyond existing standards such as ODBC.
The document discusses Apache Kudu, an open source storage layer for Apache Hadoop that enables fast analytics on fast data. Kudu is designed to fill the gap between HDFS and HBase by providing fast analytics capabilities on fast-changing or frequently updated data. It achieves this through its scalable and fast tabular storage design that allows for both high insert/update throughput and fast scans/queries. The document provides an overview of Kudu's architecture and capabilities, examples of how to use its NoSQL and SQL APIs, and real-world use cases like enabling low-latency analytics pipelines for companies like Xiaomi.
Hadoop World 2011: Advanced HBase Schema Design - Lars George, ClouderaCloudera, Inc.
"While running a simple key/value based solution on HBase usually requires an equally simple schema, it is less trivial to operate a different application that has to insert thousands of records per second.
This talk will address the architectural challenges when designing for either read or write performance imposed by HBase. It will include examples of real world use-cases and how they can be implemented on top of HBase, using schemas that optimize for the given access patterns. "
1. The document discusses microservices architecture and how Netflix transitioned from a monolithic architecture to microservices. Key aspects discussed include breaking the monolith into many small, independent services that are loosely coupled.
2. Netflix's microservices architecture is composed of hundreds of microservices running on thousands of servers. Each service focuses on doing a small, well-defined piece of work. Services communicate through well-defined APIs and share no code or databases.
3. The document provides examples of how other companies like Samsung and Vingle have also adopted microservices architectures on AWS, breaking monolithic applications into independent, scalable services. This allows for independent deployments, rapid innovation, and improved resilience.
HBase and HDFS: Understanding FileSystem Usage in HBaseenissoz
This document discusses file system usage in HBase. It provides an overview of the three main file types in HBase: write-ahead logs (WALs), data files, and reference files. It describes durability semantics, IO fencing techniques for region server recovery, and how HBase leverages data locality through short circuit reads, checksums, and block placement hints. The document is intended help understand HBase's interactions with HDFS for tuning IO performance.
Ingesting Data at Blazing Speed Using Apache OrcDataWorks Summit
Big SQL is a SQL engine for Hadoop that excels at performance and scalability at high concurrency. Big SQL complements and integrates with Apache Hive for both data and metadata. An architecture that separates compute from storage allows Big SQL to support multiple open data formats natively. Until recently, Parquet provided a significant performance advantage over other data formats for SQL on Hadoop. The landscape changed when ORC became a top level Apache project independent from Hive. Gone were the days of reading ORC files using slow, single-row-at-a-time Hive Serdes. The new vectorized APIs in the Apache ORC libraries make it possible to ingest ORC data at blazing speed. This talk is about the journey leading to ORC taking the crown of best performing data format for Big SQL away from Parquet. We'll have a look under the hood at the architecture of Big SQL ORC readers, and how to tune them. We'll share lessons learned in walking the fine line between maximizing performance at scale and avoiding dreaded Java OOMs . You'll learn the techniques that SQL engines use for fast data ingestion, so that you can leverage the full potential of Apache ORC in any application.
Speaker:
Gustavo Arocena, Big Data Architect, IBM
This document discusses Apache Arrow, an open source cross-language development platform for in-memory analytics. It provides an overview of Arrow's goals of being cross-language compatible, optimized for modern CPUs, and enabling interoperability between systems. Key components include core C++/Java libraries, integrations with projects like Pandas and Spark, and common message patterns for sharing data. The document also describes how Arrow is implemented in practice in systems like Dremio's Sabot query engine.
Hive LLAP: A High Performance, Cost-effective Alternative to Traditional MPP ...DataWorks Summit
The document discusses Hive LLAP (Live Long and Process) as a high performance and cost-effective alternative to traditional Massively Parallel Processing (MPP) databases for querying large datasets on Hadoop. It describes Walmart's implementation of Hive LLAP on their data lake to improve query performance for business users. A proof-of-concept found Hive LLAP queries were up to 50% faster when using 15 nodes instead of 10, and it performed comparably or better than two MPP databases with similar or larger infrastructures. Walmart plans to further evaluate Hive LLAP on newer Hadoop distributions and technologies to improve availability and workload management.
Decomposing Applications for Scalability and Deployability (April 2012)Chris Richardson
Today, there are several trends that are forcing application architectures to evolve. Users expect a rich, interactive and dynamic user experience on a wide variety of clients including mobile devices. Applications must be highly scalable, highly available and run on cloud environments. Organizations often want to frequently roll out updates, even multiple times a day. Consequently, it’s no longer adequate to develop simple, monolithic web applications that serve up HTML to desktop browsers. In this talk we describe the limitations of a monolithic architecture. You will learn how to use the scale cube to decompose your application into a set of narrowly focused, independently deployable back-end services and an HTML 5 client. We will also discuss the role of technologies such as NodeJS and AMQP brokers. You will learn how a modern PaaS such as Cloud Foundry simplifies the development and deployment of this style of application.
Building Event Driven Architectures with Kafka and Cloud Events (Dan Rosanova...confluent
Apache Kafka is changing the way we build scalable and highly available software systems. Providing a simplified path to eventual consistency and event sourcing Kafka gives us the platform to make these patterns a reality for a much broader segment of applications and customers than was possible in the past. Cloud Events is an interoperable specification for eventing that is part of the CNCF. This session will combine open source and open standards to show you how you can build highly reliable application that scale linearly, provide interoperability and are easily extensible leveraging both push and pull semantics. Concrete real world examples will be shown of how Kafka makes event sourcing more approachable and how streams and events complement each other including the difference between business events and technical events.
This document discusses web performance optimization and provides tips to improve performance. It emphasizes that performance is important for user experience, search engine optimization, conversion rates, and costs. It outlines common causes of performance issues like round-trip times, payload sizes, browser rendering delays, and inefficient JavaScript. Specific recommendations are given to optimize images, stylesheets, scripts, and browser rendering through techniques like compression, caching, deferred loading, and efficient coding practices. A variety of tools for measuring and improving performance are also listed.
Mage uk-2013-1345-chris-wells-131030120920-phpapp01Karla Mae Tejon
Your site's performance directly correlates to order volume. A tuned Magneto install can instantly mean more sales and the converse is also true. This session is meant to give you an overview of the importance of performance for your e-commerce site as well as provide steps to make Magento perform as your business grows.
The Importance of Site Performance and Simple Steps to Achieve ItNexcess.net LLC
Your site's performance directly correlates to order volume. A tuned Magneto install can instantly mean more sales and the converse is also true. This session is meant to give you an overview of the importance of performance for your e-commerce site as well as provide steps to make Magento perform as your business grows.
Performance budgets have been around for more than ten years. Over those years, we’ve learned a lot about what works, what doesn’t, and what we need to improve. In this session, I revisit old assumptions about performance budgets and offers some new practices. Topics include:
• Aligning budgets with user experience
• Pros and cons of Core Web Vitals
• Budgets for beginners
The LIMELIGHT project aimed to improve efficiency in Huawei's Project Management Office (PMO) space by automating report generation and distributing workload. It developed automated reports in Excel, assigned one resource to run reports instead of each manager, and established data governance. This reduced time spent on reports by 49% (156 hours/month) and other cost savings. Benefits included improved productivity, motivation, delivery numbers, and customer experience through decreased waiting times. The project was completed on October 7, 2016 and its processes may be expanded to other teams.
MeasureWorks - Why people hate to wait for your website to load (and how to f...MeasureWorks
My slides from DrupalJam 2014... About why users abandon your website and best practices to align content and speed to create a fast user experience, and continue to keep it aligned for every release
Lean Six Sigma Green Belt Certification 1Fred Zuercher
This document outlines a project to map and optimize the end-to-end total account process for a client globally. The project aims to identify opportunities to align with global best practices, standardize processes, and reduce turnaround times by 30-50%. Key deliverables include developing level 1 and 2 process maps, identifying optimization opportunities, and helping to validate process flows for a lift and shift project. The document describes the current state process which experiences high data mismatches and variations. Analysis identifies constraints like specialized roles and data issues. Recommendations include process documentation, error proofing, reducing handoffs, and using lean tools like 5S, visual management and standard work. The target state aims to improve cycle time, reduce defects, and
Metrics, metrics everywhere (but where the heck do you start?)Tammy Everts
You want a single, unicorn metric that magically sums up the user experience, business value, and numbers that DevOps cares about, but so far, you're just not getting it. So where do you start? In this talk at the 2015 Velocity conference in Santa Clara, Cliff Crocker and I walked through various metrics that answer performance questions from multiple perspectives -- from designer and DevOps to CRO and CEO.
Metrics, Metrics Everywhere (but where the heck do you start?)SOASTA
Not surprisingly, there’s no one-size-fits-all performance metric (though life would be simpler if there were). Different metrics will give you different critical insights into whether or not your pages are delivering the results you want — both from your end user’s perspective and ultimately from your organization’s perspective. Join Tammy Everts, and walk through various metrics that answer performance questions from multiple perspectives. You’ll walk away with a better understanding of your options, as well as a clear understanding of how to choose the right metric for the right audience.
Metrics, Metrics Everywhere (but where the heck do you start?)SOASTA
Not surprisingly, there’s no one-size-fits-all performance metric (though life would be simpler if there were). Different metrics will give you different critical insights into whether or not your pages are delivering the results you want — both from your end user’s perspective and ultimately from your organization’s perspective. Join Tammy Everts, and walk through various metrics that answer performance questions from multiple perspectives. You’ll walk away with a better understanding of your options, as well as a clear understanding of how to choose the right metric for the right audience.
improving the performance of Rails web ApplicationsJohn McCaffrey
This presentation is the first in a series on Improving Rails application performance. This session covers the basic motivations and goals for improving performance, the best way to approach a performance assessment, and a review of the tools and techniques that will yield the best results. Tools covered include: Firebug, yslow, page speed, speed tracer, dom monster, request log analyzer, oink, rack bug, new relic rpm, rails metrics, showslow.org, msfast, webpagetest.org and gtmetrix.org.
The upcoming sessions will focus on:
Improving sql queries, and active record use
Improving general rails/ruby code
Improving the front-end
And a final presentation will cover how to be a more efficient and effective developer!
This series will be compressed into a best of session for the 2010 http://windycityRails.org conference
Ruby on Rails Performance Tuning. Make it faster, make it better (WindyCityRa...John McCaffrey
(reposting with clearer title)
Performance tuning presentation from WindyCityRails 2010.
Why performance matters
The right way to approach it
Front end testing tools
Automated testing tools
Common problems and the ways to solve them in Rails
Rails specific tools
bullet
slim_scrooge
rack bug
request log analyzer
rails indexes
This document provides a summary of a mobile workforce management solution. Key points:
- The solution is cloud-based and integrates with various finance, scheduling, and estimating systems. It provides mobile apps for iOS, Android, and Windows.
- Features include real-time progress tracking, GPS tracking, photos, signatures, bar code scanning, and integration with ERP, CRM, and scheduling systems.
- It aims to revolutionize field execution and provide visibility into projects for various stakeholders through the mobile platform.
Case study migration from cm13 to cm14 - Oracle Primavera P6 Collaborate 14p6academy
This document summarizes Hill International's experience migrating from Primavera Contract Management version 13 (CM13) to version 14 (CM14). Key points include:
- Hill tested the new CM14 environment extensively before migrating live to identify issues. Testing revealed problems with logins, versioning, and report formatting differences between CM13 and CM14.
- The migration involved installing new CM14 and BI Publisher environments, upgrading databases from CM13 to CM14, and importing reports and configurations. Attachments also had to be migrated.
- Differences between CM13 and CM14 included the application server (Weblogic instead of JBoss), repository (SharePoint instead of Jackrabbit), and reporting
Ahead of the Curve: How 23andMe Improved UX with Performance EdgeOptimizely
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2. Presentation Schedule
• Cliff - 10-20m on RUM
• Aaron – 10-20m on Monitoring in real-time
• Balaji – 10-20m on Correlating RUM and Business Analytics
• Final Q&A
4. shhhh…..
• We are not the
fastest retail site
on the internet
today.
IE 8 – Web Page Test Comp
Index – Item Page Performance
11s – Fully Loaded time
4
5. What gets measured, gets done…
• Before we start to optimize – see where our pain is
and prepare to measure for success.
• You never go on a diet without first stepping on the
scale…
5
Source: Flickr
6. Synthetic Monitoring
Pros
– Technology is great
– Real Browsers (IE, Chrome,
FF)
– Built in Alerting
– Charting/Trending
– Waterfall analysis
– Screen shots & headers
– Object level detail
Cons
– False Positives
– Impossible/impractical to
measure everything
– Fixed number of browser/OS
combinations
– Simulated bandwidth
constraints at best
– Too few data points for
statistical relevance
6
8. • If only those users
could tell us about their
site experience…
8
Source: Flickr
9. Enter boomerang.js - https://github.com/yahoo/boomerang
boomerang.js with NavTiming (thanks Buddy & Phil)
• Doc Complete, Page Response, Page Processing, TTFB, DNS, Connect & more
• Cookie data (for parent domain)
• Location (geolookup on IP)
• Referrer
• User Agent
• Anything else you want to stuff into the beacon
+
9
Source: Flickr
14. What we found scared us…
Home Page Performance – Jan 2012
14
15. Set some goals and SLAs
• Focus on ‘Page Processing Time’ First
– see Golden Rule – 80% of time spent here (more like 90% for Walmart)
• Look at backend & network response time SLAs later
• Use 95th
Percentile
• Set Achievable SLAs
• Revisit Monthly
• Celebrate Wins!
15
16. Case Study: Item Page
• Problem:
– Page takes ~24s for slowest 5% of users
• Too many elements
• Slow third party modules
• Several other pagespeed ‘no-nos’
• Goal: Meet SLA for February
– 20s (95th
percentile)
• Approach:
– Scrum team dedicated to perf optimization for 1 sprint
– Team pools resources and ideas - focuses on biggest bang
16
23. Where’s the data?
The initial incarnation of RUM @ WMT leveraged Akamai at the edge.
• data reliability issues
• data availability issues
• data “freshness” issues
It works… for certain values of work
Source: Flickr
Source: Flickr
23
27. Pretty Pictures
RAW DATA FORMAT
uswmt.all.t_page.upper_95(9289.0),1329259510,1329260710,10|15904.0,9184.0,9125.0,12736.0,11735.0,16776.0,8484.0,
10839.0,14620.0,7579.0,8871.0,8240.0,12390.0,5211.0,10301.0,24784.0,9410.0,16554.0,9609.0,11871.0,12751.0,9797.0,
11003.0,15962.0,7953.0,7707.0,4181.0,11616.0,11746.0,12814.0,10566.0,24782.0,18303.0,20904.0,7718.0,8531.0,7312.0,
9614.0,8749.0,11671.0,5989.0,9832.0,10592.0,11611.0,16946.0,18858.0,14360.0,15927.0,10470.0,10140.0,11307.0,9739.0,
9772.0,9875.0,13641.0,11626.0,14758.0,6529.0,11727.0,10194.0,8003.0,10639.0,7297.0,9891.0,10312.0,12497.0,11557.0,
11406.0,12456.0,12939.0,11029.0,10813.0,11737.0,10618.0,14128.0,16879.0,15865.0,6255.0,14605.0,8861.0,27425.0,
10948.0,19666.0,7185.0,13266.0,13156.0,15111.0,13110.0,15151.0,8666.0,16775.0,10110.0,10387.0,17274.0,22183.0,
8937.0,13168.0,12267.0,11891.0,9635.0,10446.0,8129.0,9550.0,9229.0,8375.0,8657.0,11119.0,6799.0,9094.0,21952.0,
14989.0,16828.0,9001.0,13444.0,10332.0,13609.0,9266.0,13349.0,11546.0,9289.0
uswmt.all.t_page.median(1844.0),1329259510,1329260710,10|4165.0,2333.5,2073.5,2584.0,2547.0,2627.5,2401.0,1575.0,
2170.0,1169.0,1970.0,1838.0,2083.0,5211.0,2496.0,3242.5,1541.0,1437.5,1928.0,1971.0,1776.0,3108.0,2010.5,2044.0,2325.5,
2640.0,1733.0,3924.0,2629.0,1867.5,1782.0,2370.5,2921.0,4783.0,2260.0,1340.0,3256.0,2297.0,2565.0,1874.0,2000.0,2483.5,
2705.5,2432.0,1809.0,2826.0,2204.0,2695.0,1045.0,1615.5,2250.0,2387.0,1562.5,1998.0,2512.0,2139.0,1482.0,2138.5,2100.0,
2583.0,2652.0,3277.0,2549.0,1755.0,2196.5,2766.0,2989.5,3638.0,3034.0,3615.5,2650.5,5207.5,3023.0,1941.0,1918.5,1768.0,
3048.0,1522.5,2710.0,1392.0,2402.0,2005.0,3246.0,1383.0,1880.0,2398.0,1833.0,2579.0,2052.0,2622.0,2089.0,1102.0,1296.0,
3339.0,2132.5,2831.0,3466.0,2131.0,2026.0,2754.0,3228.5,1000.0,2075.0,2011.5,2428.0,4019.5,2788.0,1665.0,1968.0,2695.5,
2873.0,1752.0,2314.5,1766.0,2971.0,3091.5,2205.5,3033.0,2476.0,1844.0
27
28. The Work Tomorrow…
The Good
•Metric Throughput
•Commodity Storage
•Commodity Infrastructure
The Bad
•More Metric Throughput
•Calculation Complexity
•Web Sockets (pretty)
•Metric Fan-out
28
30. Is Page Performance a Factor of
Site Conversion? And how big is
it?
February, 2012
v
s
31. 31
Walmart.com - Fun Facts
• Reach
–Millions of Shoppers/week.
–Billions of page requests/year - Spikes up to 1500%
–Billions of internal product search volumes/year
• Scale
–Millions of active product SKUs + Market Place
–Millions of pages indexed in search engines
• Complexity
–1/4th
of page contents served by partners, affiliates and Marketplace
–Multiple departments, 10+ checkout paths
Page Performance & Site Conversion – Feb 2012
32. 32
So, how do you monitor?...
Page Performance & Site Conversion – Feb 2012
33. 33
Few Industry Benchmarks…
• Factoid 1: Large eCommerce site extensively A/B
tested page performance and published a study
showing 100 millisecond delay = 1% drop in revenue
• Factoid 2: Search Engines A/B tested performance
and found that a 500 millisecond delay caused a 20%
drop in traffic.
• Factoid 3: In an experiment across multiple retailers,
a 1 second delay caused a 7% decline in conversion
Page Performance & Site Conversion – Feb 2012
34. 34
So, how big is it for Walmart.com?
Page Performance & Site Conversion – Feb 2012
35. 35
Agenda
• Phase 1 – Baseline Measurement - Impact of Site
Performance on Conversion, Bounce rates &
Revenue
• Phase 2 - Targets for Page Performance
• Phase 3 – Optimization Results
• Key Highlights & Takeaways
Page Performance & Site Conversion – Feb 2012
36. 36
Agenda
• Phase 1 – Baseline Measurement - Impact of Site
Performance on Conversion, Bounce rates &
Revenue
• Phase 2 – Targets for Page Performance
• Phase 3 – Optimization Results
• Key Highlights & Takeaways
Page Performance & Site Conversion – Feb 2012
37. 37
Impact of site performance on overall site conversion rate….
Baseline – 1 in 2 site visits had response time > 4 seconds
* Sharp decline in conversion rate as average site load time increases from 1 to 4 seconds
* Overall average site load time is lower for the converted population (3.22 Seconds) than the non-
converted population (6.03 Seconds)
Note: Load Time here is the time taken from head of the page to page ready (T_Page)
Page Performance & Site Conversion – Feb 2012
38. 38
@ Page level….
Page load time is lower for Buyers compared to Non-Buyers
* The Page load time is highest for certain pages - 6.38 secs when there was a conversion and
8.06 where there was no conversion.
Note: Load Time here is the time taken from head of the page to page ready (T_Page)
Page Performance & Site Conversion – Feb 2012
39. 39
@ Department level….
Department load time is lower for Buyers compared to Non-Buyers
* Key Categories has 2-3 seconds difference b/w buyer Vs non-buyer
Note: Load Time here is the time taken from head of the page to page ready (T_Page)
Page Performance & Site Conversion – Feb 2012
40. 40
What about bounce?
Page Bounce Rate Vs Response Time
* Key pages have high bounce rates which correlates with high T_Page as well
* Significant difference ( up to 9secs) in T_Page between bounced and non-bounced for landing
pages.
Note: Load Time here is the time taken from head of the page to page ready (T_Page)
Page Performance & Site Conversion – Feb 2012
41. 41
Bounce rates @ department level….
Department Bounce Rate Vs Response Time
* High T_Page for key pages (up to 19.82s) and key department making Bounce rate significantly
higher
Note: Load Time here is the time taken from head of the page to page ready (T_Page)
Page Performance & Site Conversion – Feb 2012
42. 42
Agenda
• Phase 1 – Baseline Measurement - Impact of Site
Performance on Conversion, Bounce rates &
Revenue
• Phase 2 - Targets for Page Performance
• Phase 3 – Optimization Results
• Key Highlights & Takeaways
Page Performance & Site Conversion – Feb 2012
43. 43
Phase 2 – Targets for Page Performance……
Conversion & Bounce Rate Impacts Drives Prioritization
Note: Load Time here is the time taken from head of the page to page ready (T_Page)
Page Performance & Site Conversion – Feb 2012
44. 44
Agenda
• Phase 1 – Baseline Measurement - Impact of Site
Performance on Conversion, Bounce rates &
Revenue
• Phase 2 - Targets for Page Performance
• Phase 3 – Optimization Results
• Key Highlights & Takeaways
Page Performance & Site Conversion – Feb 2012
45. 45
Phase 3 – Success Story….
First Win….and yes conversion had positive improvements…
Page Performance & Site Conversion – Feb 2012
46. 46
• Page speed matters for site conversion!
• Monitor real user performance in a “Big Data” way!!
• Every 1s improvement = Up to 2% increase in CVR
• 100ms improvement = Up to 1% incremental revenue
• SEO benefits for entry pages and reduce bounces
• Test & Learn - Target segments and run A/B Tests focused on
improving page performance
Key Highlights
Page Performance & Site Conversion – Feb 2012
47. We’re Hiring for Everything!!!!
47
Source: Flickr
http://www.walmartlabs.com/open-positions/
https://walmartstores.com/careers/apply/?ba=eCom
@cliffcrocker or @GoFastWeb