A previous (OUTDATED) overview of resource management in Impala, relevant through Impala 2.2/CDH 5.4.
See the Cloudera documentation for the newest information: https://www.cloudera.com/documentation/enterprise/latest/topics/impala_howto_rm.html#impala_resource_management_example
This document discusses admission control in Impala to prevent oversubscription of resources from too many concurrent queries. It describes the problem of all queries taking longer when too many run at once. It then outlines Impala's solution of adding admission control by throttling incoming requests, queuing requests when workload increases, and executing queued requests when resources become available. The document provides details on how Impala implements admission control in a decentralized manner without requiring Yarn/Llama to handle throttling and queuing locally on each Impalad daemon.
Resource Management in Impala - StampedeCon 2016StampedeCon
Want to run queries in Impala as fast as possible without choking other workloads and services? If you are a Hadoop cluster administrator or a big data application developer, this course will help you understand how Impala Admission Control can help you make good use of available resources, avoid bad performance issues, and provide better user experiences in a multi-tenancy environment.
The document outlines topics covered in "The Impala Cookbook" published by Cloudera. It discusses physical and schema design best practices for Impala, including recommendations for data types, partition design, file formats, and block size. It also covers estimating and managing Impala's memory usage, and how to identify the cause when queries exceed memory limits.
This document discusses building applications on Hadoop and introduces the Kite SDK. It provides an overview of Hadoop and its components like HDFS and MapReduce. It then discusses that while Hadoop is powerful and flexible, it can be complex and low-level, making application development challenging. The Kite SDK aims to address this by providing higher-level APIs and abstractions to simplify common use cases and allow developers to focus on business logic rather than infrastructure details. It includes modules for data, ETL processing with Morphlines, and tools for working with datasets and jobs. The SDK is open source and supports modular adoption.
NYC HUG - Application Architectures with Apache Hadoopmarkgrover
This document summarizes Mark Grover's presentation on application architectures with Apache Hadoop. It discusses processing clickstream data from web logs using techniques like deduplication, filtering, and sessionization in Hadoop. Specifically, it describes how to implement sessionization in MapReduce by using the user's IP address and timestamp to group log lines into sessions in the reducer.
Apache Impala is a complex engine and requires a thorough technical understanding to utilize it fully. Without proper configuration or usage, Impala’s performance becomes unpredictable, and end-user experience suffers. However, for many users and administrators, the right configuration of Impala is still a mystery.
Drawing on work with some of the largest clusters in the world, Manish Maheshwari shares ingestion best practices to keep an Impala deployment scalable and details admission control configuration to provide a consistent experience to end users. Manish also takes a high-level look at Impala’s query profile, which is used as a first step in any performance troubleshooting, and discusses common mistakes users and BI tools make when interacting with Impala. Manish concludes by detailing an ideal setup to show all of this in practice.
This document discusses admission control in Impala to prevent oversubscription of resources from too many concurrent queries. It describes the problem of all queries taking longer when too many run at once. It then outlines Impala's solution of adding admission control by throttling incoming requests, queuing requests when workload increases, and executing queued requests when resources become available. The document provides details on how Impala implements admission control in a decentralized manner without requiring Yarn/Llama to handle throttling and queuing locally on each Impalad daemon.
Resource Management in Impala - StampedeCon 2016StampedeCon
Want to run queries in Impala as fast as possible without choking other workloads and services? If you are a Hadoop cluster administrator or a big data application developer, this course will help you understand how Impala Admission Control can help you make good use of available resources, avoid bad performance issues, and provide better user experiences in a multi-tenancy environment.
The document outlines topics covered in "The Impala Cookbook" published by Cloudera. It discusses physical and schema design best practices for Impala, including recommendations for data types, partition design, file formats, and block size. It also covers estimating and managing Impala's memory usage, and how to identify the cause when queries exceed memory limits.
This document discusses building applications on Hadoop and introduces the Kite SDK. It provides an overview of Hadoop and its components like HDFS and MapReduce. It then discusses that while Hadoop is powerful and flexible, it can be complex and low-level, making application development challenging. The Kite SDK aims to address this by providing higher-level APIs and abstractions to simplify common use cases and allow developers to focus on business logic rather than infrastructure details. It includes modules for data, ETL processing with Morphlines, and tools for working with datasets and jobs. The SDK is open source and supports modular adoption.
NYC HUG - Application Architectures with Apache Hadoopmarkgrover
This document summarizes Mark Grover's presentation on application architectures with Apache Hadoop. It discusses processing clickstream data from web logs using techniques like deduplication, filtering, and sessionization in Hadoop. Specifically, it describes how to implement sessionization in MapReduce by using the user's IP address and timestamp to group log lines into sessions in the reducer.
Apache Impala is a complex engine and requires a thorough technical understanding to utilize it fully. Without proper configuration or usage, Impala’s performance becomes unpredictable, and end-user experience suffers. However, for many users and administrators, the right configuration of Impala is still a mystery.
Drawing on work with some of the largest clusters in the world, Manish Maheshwari shares ingestion best practices to keep an Impala deployment scalable and details admission control configuration to provide a consistent experience to end users. Manish also takes a high-level look at Impala’s query profile, which is used as a first step in any performance troubleshooting, and discusses common mistakes users and BI tools make when interacting with Impala. Manish concludes by detailing an ideal setup to show all of this in practice.
The document summarizes strengths and weaknesses of Cloudera Impala. Key strengths include excellent performance for analytical queries over large datasets, SQL compliance, and integration with Hadoop ecosystem. Weaknesses are slow random access, lack of fault tolerance, tedious data updating process, and memory intensive queries. The conclusion is that Impala is well-suited for analytics on immutable data but not for workloads with frequent updates.
How to use Impala query plan and profile to fix performance issuesCloudera, Inc.
Apache Impala is an exceptional, best-of-breed massively parallel processing SQL query engine that is a fundamental component of the big data software stack. Juan Yu demystifies the cost model Impala Planner uses and how Impala optimizes queries and explains how to identify performance bottleneck through query plan and profile and how to drive Impala to its full potential.
The document discusses architectural considerations for implementing clickstream analytics using Hadoop. It covers choices for data storage layers like HDFS vs HBase, data modeling including file formats and partitioning, data ingestion methods like Flume and Sqoop, available processing engines like MapReduce, Hive, Spark and Impala, and the need to sessionize clickstream data to analyze metrics like bounce rates and attribution.
Real-time Big Data Analytics Engine using ImpalaJason Shih
Cloudera Impala is an open-source under Apache Licence enable real-time, interactive analytical SQL queries of the data stored in HBase or HDFS. The work was inspired by Google Dremel paper which is also the basis for Google BigQuery. It provide access same unified storage platform base on it's own distributed query engine but does not use mapreduce. In addition, it use also the same metadata, SQL syntax (HiveQL-like) ODBC driver and user interface (Hue Beeswax) as Hive. Besides the traditional Hadoop approach, aim to provide low-cost solution for resiliency and batch-oriented distributed data processing, we found more and more effort in the Big Data world pursuing the right solution for ad-hoc, fast queries and realtime data processing for large datasets. In this presentation, we'll explore how to run interactive queries inside Impala, advantages of the approach, architecture and understand how it optimizes data systems including also practical performance analysis.
Big Data Day LA 2016/ Big Data Track - How To Use Impala and Kudu To Optimize...Data Con LA
This session describes how Impala integrates with Kudu for analytic SQL queries on Hadoop and how this integration, taking full advantage of the distinct properties of Kudu, has significant performance benefits.
Impala 2.0 - The Best Analytic Database for HadoopCloudera, Inc.
A look at why SQL access in Hadoop is critical and the benefits of a native Hadoop analytic database, what’s new with Impala 2.0 and some of the recent performance benchmarks, some common Impala use cases and production customer stories, and insight into what’s next for Impala.
Performance evaluation of cloudera impala (with Comparison to Hive)Yukinori Suda
This document evaluates the performance of Cloudera Impala, an open-source SQL query engine for Apache Hadoop, and compares it to Apache Hive. It describes Impala's architecture and how the benchmark was conducted. The benchmark found Impala to be over 10 times faster than Hive for the modified TPC-H query, with the fastest Impala version taking 14.337 seconds compared to 164.161 seconds for Hive. The document concludes that future versions of Impala integrated with CDH5 may provide even better performance by supporting additional file formats.
Learn how Cloudera Impala empowers you to:
- Perform interactive, real-time analysis directly on source data stored in Hadoop
- Interact with data in HDFS and HBase at the “speed of thought”
- Reduce data movement between systems & eliminate double storage
The document discusses Impala, an SQL query engine for Hadoop. It provides an overview of Impala, details improvements in versions 1.4 and 2.0, and describes new features like subqueries, analytic functions, and data types. Performance optimizations like HDFS caching and partition pruning are also covered.
A brave new world in mutable big data relational storage (Strata NYC 2017)Todd Lipcon
The ever-increasing interest in running fast analytic scans on constantly updating data is stretching the capabilities of HDFS and NoSQL storage. Users want the fast online updates and serving of real-time data that NoSQL offers, as well as the fast scans, analytics, and processing of HDFS. Additionally, users are demanding that big data storage systems integrate natively with their existing BI and analytic technology investments, which typically use SQL as the standard query language of choice. This demand has led big data back to a familiar friend: relationally structured data storage systems.
Todd Lipcon explores the advantages of relational storage and reviews new developments, including Google Cloud Spanner and Apache Kudu, which provide a scalable relational solution for users who have too much data for a legacy high-performance analytic system. Todd explains how to address use cases that fall between HDFS and NoSQL with technologies like Apache Kudu or Google Cloud Spanner and how the combination of relational data models, SQL query support, and native API-based access enables the next generation of big data applications. Along the way, he also covers suggested architectures, the performance characteristics of Kudu and Spanner, and the deployment flexibility each option provides.
Impala's low-latency SQL queries for HDFS files motivated improvements to HDFS to better support Impala's needs. These included exposing block replica disk locations, allowing co-located block replicas, in-memory caching of hot files, and reduced data copying during reads. The changes helped Impala achieve significantly faster performance than Hive for queries, especially complex queries, by optimizing I/O and data locality.
Building Effective Near-Real-Time Analytics with Spark Streaming and KuduJeremy Beard
This document discusses building near-real-time analytics pipelines using Apache Spark Streaming and Apache Kudu on the Cloudera platform. It defines near-real-time analytics, describes the relevant components of the Cloudera stack (Kafka, Spark, Kudu, Impala), and how they can work together. The document then outlines the typical stages involved in implementing a Spark Streaming to Kudu pipeline, including sourcing from a queue, translating data, deriving storage records, planning mutations, and storing the data. It provides performance considerations and introduces Envelope, a Spark Streaming application on Cloudera Labs that implements these stages through configurable pipelines.
The document discusses Impala, a SQL query engine for Hadoop. It was created to enable low-latency queries on Hadoop data by using a new execution engine instead of MapReduce. Impala aims to provide high performance SQL queries on HDFS, HBase and other Hadoop data. It runs as a distributed service and queries are distributed to nodes and executed in parallel. The document covers Impala's architecture, query execution process, and its planner which partitions queries for efficient execution.
HBaseCon 2012 | HBase Filtering - Lars George, ClouderaCloudera, Inc.
This talk will run through the list of filters that are shipped with HBase and show how they are used from a client application. Filters expose varying feature sets, but also exhibit an equally varying impact on read performance – but neither are directly intuitive. A skilled HBase practitioner should know how to select the proper filter for a given use-case, or how to combine sets of filters to achieve what is needed. The talk will conclude with an example for a custom filter and explain how to deploy it on a cluster.
Intro to Hadoop Presentation at Carnegie Mellon - Silicon Valleymarkgrover
The document provides an introduction to Apache Hadoop and its ecosystem. It discusses how Hadoop addresses the need for scalable data storage and processing to handle large volumes, velocities and varieties of data. Hadoop's two main components are the Hadoop Distributed File System (HDFS) for reliable data storage across commodity hardware, and MapReduce for distributed processing of large datasets in parallel. The document also compares Hadoop to other distributed systems and outlines some of Hadoop's fundamental design principles around data locality, reliability, and throughput over latency.
Application architectures with Hadoop – Big Data TechCon 2014hadooparchbook
Building applications using Apache Hadoop with a use-case of clickstream analysis. Presented by Mark Grover and Jonathan Seidman at Big Data TechCon, Boston in April 2014
From: DataWorks Summit 2017 - Munich - 20170406
HBase hast established itself as the backend for many operational and interactive use-cases, powering well-known services that support millions of users and thousands of concurrent requests. In terms of features HBase has come a long way, overing advanced options such as multi-level caching on- and off-heap, pluggable request handling, fast recovery options such as region replicas, table snapshots for data governance, tuneable write-ahead logging and so on. This talk is based on the research for the an upcoming second release of the speakers HBase book, correlated with the practical experience in medium to large HBase projects around the world. You will learn how to plan for HBase, starting with the selection of the matching use-cases, to determining the number of servers needed, leading into performance tuning options. There is no reason to be afraid of using HBase, but knowing its basic premises and technical choices will make using it much more successful. You will also learn about many of the new features of HBase up to version 1.3, and where they are applicable.
Apache Impala is a complex engine and requires a thorough technical understanding to utilize it fully. Without proper configuration or usage, Impala’s performance becomes unpredictable, and end-user experience suffers. However, for many users and administrators, the right configuration of Impala is still a mystery.
Drawing on work with some of the largest clusters in the world, Manish Maheshwari shares ingestion best practices to keep an Impala deployment scalable and details admission control configuration to provide a consistent experience to end users. Manish also takes a high-level look at Impala’s query profile, which is used as a first step in any performance troubleshooting, and discusses common mistakes users and BI tools make when interacting with Impala. Manish concludes by detailing an ideal setup to show all of this in practice.
This document summarizes a presentation about migrating from MapReduce v1 to MapReduce v2 (MRv2) on YARN. Some key points:
- MRv2 (on YARN) provides improved scalability, availability, utilization, and multi-tenancy compared to MRv1.
- Migrating from MRv1 to MRv2 involves mapping configurations and understanding differences in functionality between the architectures.
- Potential pitfalls in upgrading to YARN include ensuring proper log configuration and addressing issues like containers being killed for exceeding memory limits.
- YARN allows various applications beyond MapReduce like Spark, Slider, and Llama to share cluster resources. Configuration is needed to
The document summarizes strengths and weaknesses of Cloudera Impala. Key strengths include excellent performance for analytical queries over large datasets, SQL compliance, and integration with Hadoop ecosystem. Weaknesses are slow random access, lack of fault tolerance, tedious data updating process, and memory intensive queries. The conclusion is that Impala is well-suited for analytics on immutable data but not for workloads with frequent updates.
How to use Impala query plan and profile to fix performance issuesCloudera, Inc.
Apache Impala is an exceptional, best-of-breed massively parallel processing SQL query engine that is a fundamental component of the big data software stack. Juan Yu demystifies the cost model Impala Planner uses and how Impala optimizes queries and explains how to identify performance bottleneck through query plan and profile and how to drive Impala to its full potential.
The document discusses architectural considerations for implementing clickstream analytics using Hadoop. It covers choices for data storage layers like HDFS vs HBase, data modeling including file formats and partitioning, data ingestion methods like Flume and Sqoop, available processing engines like MapReduce, Hive, Spark and Impala, and the need to sessionize clickstream data to analyze metrics like bounce rates and attribution.
Real-time Big Data Analytics Engine using ImpalaJason Shih
Cloudera Impala is an open-source under Apache Licence enable real-time, interactive analytical SQL queries of the data stored in HBase or HDFS. The work was inspired by Google Dremel paper which is also the basis for Google BigQuery. It provide access same unified storage platform base on it's own distributed query engine but does not use mapreduce. In addition, it use also the same metadata, SQL syntax (HiveQL-like) ODBC driver and user interface (Hue Beeswax) as Hive. Besides the traditional Hadoop approach, aim to provide low-cost solution for resiliency and batch-oriented distributed data processing, we found more and more effort in the Big Data world pursuing the right solution for ad-hoc, fast queries and realtime data processing for large datasets. In this presentation, we'll explore how to run interactive queries inside Impala, advantages of the approach, architecture and understand how it optimizes data systems including also practical performance analysis.
Big Data Day LA 2016/ Big Data Track - How To Use Impala and Kudu To Optimize...Data Con LA
This session describes how Impala integrates with Kudu for analytic SQL queries on Hadoop and how this integration, taking full advantage of the distinct properties of Kudu, has significant performance benefits.
Impala 2.0 - The Best Analytic Database for HadoopCloudera, Inc.
A look at why SQL access in Hadoop is critical and the benefits of a native Hadoop analytic database, what’s new with Impala 2.0 and some of the recent performance benchmarks, some common Impala use cases and production customer stories, and insight into what’s next for Impala.
Performance evaluation of cloudera impala (with Comparison to Hive)Yukinori Suda
This document evaluates the performance of Cloudera Impala, an open-source SQL query engine for Apache Hadoop, and compares it to Apache Hive. It describes Impala's architecture and how the benchmark was conducted. The benchmark found Impala to be over 10 times faster than Hive for the modified TPC-H query, with the fastest Impala version taking 14.337 seconds compared to 164.161 seconds for Hive. The document concludes that future versions of Impala integrated with CDH5 may provide even better performance by supporting additional file formats.
Learn how Cloudera Impala empowers you to:
- Perform interactive, real-time analysis directly on source data stored in Hadoop
- Interact with data in HDFS and HBase at the “speed of thought”
- Reduce data movement between systems & eliminate double storage
The document discusses Impala, an SQL query engine for Hadoop. It provides an overview of Impala, details improvements in versions 1.4 and 2.0, and describes new features like subqueries, analytic functions, and data types. Performance optimizations like HDFS caching and partition pruning are also covered.
A brave new world in mutable big data relational storage (Strata NYC 2017)Todd Lipcon
The ever-increasing interest in running fast analytic scans on constantly updating data is stretching the capabilities of HDFS and NoSQL storage. Users want the fast online updates and serving of real-time data that NoSQL offers, as well as the fast scans, analytics, and processing of HDFS. Additionally, users are demanding that big data storage systems integrate natively with their existing BI and analytic technology investments, which typically use SQL as the standard query language of choice. This demand has led big data back to a familiar friend: relationally structured data storage systems.
Todd Lipcon explores the advantages of relational storage and reviews new developments, including Google Cloud Spanner and Apache Kudu, which provide a scalable relational solution for users who have too much data for a legacy high-performance analytic system. Todd explains how to address use cases that fall between HDFS and NoSQL with technologies like Apache Kudu or Google Cloud Spanner and how the combination of relational data models, SQL query support, and native API-based access enables the next generation of big data applications. Along the way, he also covers suggested architectures, the performance characteristics of Kudu and Spanner, and the deployment flexibility each option provides.
Impala's low-latency SQL queries for HDFS files motivated improvements to HDFS to better support Impala's needs. These included exposing block replica disk locations, allowing co-located block replicas, in-memory caching of hot files, and reduced data copying during reads. The changes helped Impala achieve significantly faster performance than Hive for queries, especially complex queries, by optimizing I/O and data locality.
Building Effective Near-Real-Time Analytics with Spark Streaming and KuduJeremy Beard
This document discusses building near-real-time analytics pipelines using Apache Spark Streaming and Apache Kudu on the Cloudera platform. It defines near-real-time analytics, describes the relevant components of the Cloudera stack (Kafka, Spark, Kudu, Impala), and how they can work together. The document then outlines the typical stages involved in implementing a Spark Streaming to Kudu pipeline, including sourcing from a queue, translating data, deriving storage records, planning mutations, and storing the data. It provides performance considerations and introduces Envelope, a Spark Streaming application on Cloudera Labs that implements these stages through configurable pipelines.
The document discusses Impala, a SQL query engine for Hadoop. It was created to enable low-latency queries on Hadoop data by using a new execution engine instead of MapReduce. Impala aims to provide high performance SQL queries on HDFS, HBase and other Hadoop data. It runs as a distributed service and queries are distributed to nodes and executed in parallel. The document covers Impala's architecture, query execution process, and its planner which partitions queries for efficient execution.
HBaseCon 2012 | HBase Filtering - Lars George, ClouderaCloudera, Inc.
This talk will run through the list of filters that are shipped with HBase and show how they are used from a client application. Filters expose varying feature sets, but also exhibit an equally varying impact on read performance – but neither are directly intuitive. A skilled HBase practitioner should know how to select the proper filter for a given use-case, or how to combine sets of filters to achieve what is needed. The talk will conclude with an example for a custom filter and explain how to deploy it on a cluster.
Intro to Hadoop Presentation at Carnegie Mellon - Silicon Valleymarkgrover
The document provides an introduction to Apache Hadoop and its ecosystem. It discusses how Hadoop addresses the need for scalable data storage and processing to handle large volumes, velocities and varieties of data. Hadoop's two main components are the Hadoop Distributed File System (HDFS) for reliable data storage across commodity hardware, and MapReduce for distributed processing of large datasets in parallel. The document also compares Hadoop to other distributed systems and outlines some of Hadoop's fundamental design principles around data locality, reliability, and throughput over latency.
Application architectures with Hadoop – Big Data TechCon 2014hadooparchbook
Building applications using Apache Hadoop with a use-case of clickstream analysis. Presented by Mark Grover and Jonathan Seidman at Big Data TechCon, Boston in April 2014
From: DataWorks Summit 2017 - Munich - 20170406
HBase hast established itself as the backend for many operational and interactive use-cases, powering well-known services that support millions of users and thousands of concurrent requests. In terms of features HBase has come a long way, overing advanced options such as multi-level caching on- and off-heap, pluggable request handling, fast recovery options such as region replicas, table snapshots for data governance, tuneable write-ahead logging and so on. This talk is based on the research for the an upcoming second release of the speakers HBase book, correlated with the practical experience in medium to large HBase projects around the world. You will learn how to plan for HBase, starting with the selection of the matching use-cases, to determining the number of servers needed, leading into performance tuning options. There is no reason to be afraid of using HBase, but knowing its basic premises and technical choices will make using it much more successful. You will also learn about many of the new features of HBase up to version 1.3, and where they are applicable.
Apache Impala is a complex engine and requires a thorough technical understanding to utilize it fully. Without proper configuration or usage, Impala’s performance becomes unpredictable, and end-user experience suffers. However, for many users and administrators, the right configuration of Impala is still a mystery.
Drawing on work with some of the largest clusters in the world, Manish Maheshwari shares ingestion best practices to keep an Impala deployment scalable and details admission control configuration to provide a consistent experience to end users. Manish also takes a high-level look at Impala’s query profile, which is used as a first step in any performance troubleshooting, and discusses common mistakes users and BI tools make when interacting with Impala. Manish concludes by detailing an ideal setup to show all of this in practice.
This document summarizes a presentation about migrating from MapReduce v1 to MapReduce v2 (MRv2) on YARN. Some key points:
- MRv2 (on YARN) provides improved scalability, availability, utilization, and multi-tenancy compared to MRv1.
- Migrating from MRv1 to MRv2 involves mapping configurations and understanding differences in functionality between the architectures.
- Potential pitfalls in upgrading to YARN include ensuring proper log configuration and addressing issues like containers being killed for exceeding memory limits.
- YARN allows various applications beyond MapReduce like Spark, Slider, and Llama to share cluster resources. Configuration is needed to
A brief introduction to YARN: how and why it came into existence and how it fits together with this thing called Hadoop.
Focus given to architecture, availability, resource management and scheduling, migration from MR1 to MR2, job history and logging, interfaces, and applications.
Eventual Consistency @WalmartLabs with Kafka, Avro, SolrCloud and HadoopAyon Sinha
This document discusses Walmart Labs' use of eventual consistency with Kafka, SolrCloud, and Hadoop to power their large-scale ecommerce operations. It describes some of the challenges they faced, including slow query times, garbage collection pauses, and Zookeeper configuration issues. The key aspects of their solution involved using Kafka to handle asynchronous data ingestion into SolrCloud and Hadoop, batching updates for improved performance, dedicating hardware resources, and monitoring metrics to identify issues. This architecture has helped Walmart Labs scale to support their customers' high volumes of online shopping.
Presentación sobre la futura base de datos 18c, en la cual se incorpora todo lo mejor de las tecnologías Oracle, perfilando así una base de datos autónoma.
Building Efficient Pipelines in Apache SparkJeremy Beard
This document provides an overview of techniques for optimizing Apache Spark pipelines. It discusses fundamentals of Spark execution including jobs, stages and tasks. It then provides recommendations for tuning aspects like sizing executors, using DataFrames/Datasets over RDDs, caching frequently used data, joining techniques to avoid shuffling large datasets, and addressing skew. The document aims to help debug and optimize Spark applications.
The document discusses tuning Java for large data workloads. It covers symptoms of memory issues like jobs getting stuck or failing. It then discusses various Java and Hadoop configuration settings to optimize memory usage like mapreduce.child.java.opts and mapreduce.map.memory.mb. Finally, it provides an overview of different garbage collectors in Java and factors like generation sizes and concurrent marking that impact performance.
In this session we review the design of the newly released off heap storage feature in Apache Geode, and discuss use cases and potential direction for additional capabilities of this feature.
Motivation and goals for off-heap storage
Off-heap features and usage
Implementation overview
Preliminary benchmarks: off-heap vs. heap
Tips and best practices
The document discusses the Enterprise Manager 12c Automatic Workload Repository (AWR) Warehouse, which allows for long-term storage and analysis of AWR data across multiple databases. It provides an overview of the architecture, extraction, loading, and transformation (ETL) process, interface features in Enterprise Manager, and advanced usage examples.
Collaborate16 and first version ever of "Oracle Database In-Memory (DBIM) meets Oracle Real Application Clusters (RAC)" presented by Andy Rivenes and Markus Michalewicz
Mark Thomas presented on optimizing and tuning Apache Tomcat performance. He discussed:
1) Tuning options like logging configuration, connectors, content caching, and JVM settings to improve performance.
2) Following a process of understanding bottlenecks, setting targets, measuring, identifying causes, and repeating.
3) Scaling Tomcat through load balancing multiple instances and clustering for failover and session replication.
London JBUG April 2015 - Performance Tuning Apps with WildFly Application ServerJBUG London
This document discusses performance tuning for Wildfly8 applications. It outlines reasons for tuning like contractual obligations and user experience. It describes benchmarking methodology like defining test objectives and harnessing test tools. Common bottlenecks like the web tier, EJB tier, and JMS/JDBC connections are discussed. Wildfly tuning controls like thread pools, bean instance counts, and pool sizes are covered. Ideal request flow and queuing with timeouts are addressed. Specific thread pool types like unbounded, bounded, and blocking-bounded are explained. The presentation ends with questions.
The venerable Servlet Container still has some performance tricks up its sleeve - this talk will demonstrate Apache Tomcat's stability under high load, describe some do's (and some don'ts!), explain how to performance test a Servlet-based application, troubleshoot and tune the container and your application and compare the performance characteristics of the different Tomcat connectors. The presenters will share their combined experience supporting real Tomcat applications for over 20 years and show how a few small changes can make a big, big difference.
The document discusses key maintenance activities for an AEM implementation including backup, compaction, purging, cloning, and other approaches. It provides details on planning and executing online and offline backups, online and offline compaction, version purging, workflow purging, audit log purging, and cloning publish instances. The document emphasizes the importance of backups, compaction, and purging to optimize storage usage, improve performance, and maintain an optimal AEM instance.
Performance tuning Grails Applications GR8Conf US 2014Lari Hotari
The document discusses performance tuning for Grails applications. It covers optimizing for latency, throughput, and quality of operations. Key aspects discussed include Amdahl's law, Little's law, profiling tools, common pitfalls, and recommendations for improving performance like eliminating blocking and focusing on feedback cycles. Specific techniques mentioned include optimizing SQL queries, reducing regular expressions, improving caching, and using thread dumps to diagnose production issues.
Grails has great performance characteristics but as with all full stack frameworks, attention must be paid to optimize performance. In this talk Lari will discuss common missteps that can easily be avoided and share tips and tricks which help profile and tune Grails applications.
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Safelyio automates the delivery and documentation of safety talks, ensuring consistency and accessibility. The microlearning approach breaks down complex safety protocols into manageable, bite-sized pieces, making it easier for employees to absorb and retain information.
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Enhanced Screen Flows UI/UX using SLDS with Tom KittPeter Caitens
Join us for an engaging session led by Flow Champion, Tom Kitt. This session will dive into a technique of enhancing the user interfaces and user experiences within Screen Flows using the Salesforce Lightning Design System (SLDS). This technique uses Native functionality, with No Apex Code, No Custom Components and No Managed Packages required.
Why Apache Kafka Clusters Are Like Galaxies (And Other Cosmic Kafka Quandarie...Paul Brebner
Closing talk for the Performance Engineering track at Community Over Code EU (Bratislava, Slovakia, June 5 2024) https://eu.communityovercode.org/sessions/2024/why-apache-kafka-clusters-are-like-galaxies-and-other-cosmic-kafka-quandaries-explored/ Instaclustr (now part of NetApp) manages 100s of Apache Kafka clusters of many different sizes, for a variety of use cases and customers. For the last 7 years I’ve been focused outwardly on exploring Kafka application development challenges, but recently I decided to look inward and see what I could discover about the performance, scalability and resource characteristics of the Kafka clusters themselves. Using a suite of Performance Engineering techniques, I will reveal some surprising discoveries about cosmic Kafka mysteries in our data centres, related to: cluster sizes and distribution (using Zipf’s Law), horizontal vs. vertical scalability, and predicting Kafka performance using metrics, modelling and regression techniques. These insights are relevant to Kafka developers and operators.
A Comprehensive Guide on Implementing Real-World Mobile Testing Strategies fo...kalichargn70th171
In today's fiercely competitive mobile app market, the role of the QA team is pivotal for continuous improvement and sustained success. Effective testing strategies are essential to navigate the challenges confidently and precisely. Ensuring the perfection of mobile apps before they reach end-users requires thoughtful decisions in the testing plan.
The Rising Future of CPaaS in the Middle East 2024Yara Milbes
Explore "The Rising Future of CPaaS in the Middle East in 2024" with this comprehensive PPT presentation. Discover how Communication Platforms as a Service (CPaaS) is transforming communication across various sectors in the Middle East.
Ensuring Efficiency and Speed with Practical Solutions for Clinical OperationsOnePlan Solutions
Clinical operations professionals encounter unique challenges. Balancing regulatory requirements, tight timelines, and the need for cross-functional collaboration can create significant internal pressures. Our upcoming webinar will introduce key strategies and tools to streamline and enhance clinical development processes, helping you overcome these challenges.
Superpower Your Apache Kafka Applications Development with Complementary Open...Paul Brebner
Kafka Summit talk (Bangalore, India, May 2, 2024, https://events.bizzabo.com/573863/agenda/session/1300469 )
Many Apache Kafka use cases take advantage of Kafka’s ability to integrate multiple heterogeneous systems for stream processing and real-time machine learning scenarios. But Kafka also exists in a rich ecosystem of related but complementary stream processing technologies and tools, particularly from the open-source community. In this talk, we’ll take you on a tour of a selection of complementary tools that can make Kafka even more powerful. We’ll focus on tools for stream processing and querying, streaming machine learning, stream visibility and observation, stream meta-data, stream visualisation, stream development including testing and the use of Generative AI and LLMs, and stream performance and scalability. By the end you will have a good idea of the types of Kafka “superhero” tools that exist, which are my favourites (and what superpowers they have), and how they combine to save your Kafka applications development universe from swamploads of data stagnation monsters!
Secure-by-Design Using Hardware and Software Protection for FDA ComplianceICS
This webinar explores the “secure-by-design” approach to medical device software development. During this important session, we will outline which security measures should be considered for compliance, identify technical solutions available on various hardware platforms, summarize hardware protection methods you should consider when building in security and review security software such as Trusted Execution Environments for secure storage of keys and data, and Intrusion Detection Protection Systems to monitor for threats.
The Comprehensive Guide to Validating Audio-Visual Performances.pdfkalichargn70th171
Ensuring the optimal performance of your audio-visual (AV) equipment is crucial for delivering exceptional experiences. AV performance validation is a critical process that verifies the quality and functionality of your AV setup. Whether you're a content creator, a business conducting webinars, or a homeowner creating a home theater, validating your AV performance is essential.
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In this infographic, we have explored cost-effective strategies for iOS app development, focusing on building high-quality apps within a budget. Key points covered include prioritizing essential features, leveraging existing tools and libraries, adopting cross-platform development approaches, optimizing for a Minimum Viable Product (MVP), and integrating with cloud services and third-party APIs. By implementing these strategies, businesses and developers can create functional and engaging iOS apps while minimizing development costs and time-to-market.
Baha Majid WCA4Z IBM Z Customer Council Boston June 2024.pdfBaha Majid
IBM watsonx Code Assistant for Z, our latest Generative AI-assisted mainframe application modernization solution. Mainframe (IBM Z) application modernization is a topic that every mainframe client is addressing to various degrees today, driven largely from digital transformation. With generative AI comes the opportunity to reimagine the mainframe application modernization experience. Infusing generative AI will enable speed and trust, help de-risk, and lower total costs associated with heavy-lifting application modernization initiatives. This document provides an overview of the IBM watsonx Code Assistant for Z which uses the power of generative AI to make it easier for developers to selectively modernize COBOL business services while maintaining mainframe qualities of service.
Boost Your Savings with These Money Management AppsJhone kinadey
A money management app can transform your financial life by tracking expenses, creating budgets, and setting financial goals. These apps offer features like real-time expense tracking, bill reminders, and personalized insights to help you save and manage money effectively. With a user-friendly interface, they simplify financial planning, making it easier to stay on top of your finances and achieve long-term financial stability.