This document contains a workload repository report for a database named DB11G. Key details include:
- The database ran on a Linux server with 1 CPU and 1.96GB of memory.
- Between two snapshots taken an hour apart, the average wait time per session was 4.8-5.1 seconds.
- The top foreground wait event was log file sync, taking up 9.15% of database time.
This document summarizes an ArgoCD presentation about implementing GitOps. Some key points:
- ArgoCD was used because it has a great UI, self-healing capabilities, and supports SSO and RBAC.
- GitOps aims to synchronize infrastructure as code in a Git repository with a cluster. ArgoCD helps achieve this through continuous delivery triggered by Git changes.
- ArgoCD's architecture has three main components: the API server, repository server, and application controller. The application controller monitors for Git changes and syncs applications.
- An example Application CR was shown to define the Git repository, target revision, destination cluster and namespace to deploy the application to.
How to Use?EXAchk?Effectively to Manage Exadata EnvironmentsSandesh Rao
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This document discusses using the Autonomous Health Framework (AHF) to manage Exadata environments. AHF includes EXAchk for compliance checking and fault detection on Exadata. EXAchk can be run automatically or on-demand to check for compliance issues and potential problems. It integrates with tools like Enterprise Manager, MOS, and TFA to provide centralized reporting and issue resolution. The document provides instructions for installing and configuring AHF and EXAchk for optimal use.
Cutting-edge Hadoop clusters are bound to need custom (add-on) services that are not available in the Hadoop distribution of their choice. Agility is crucial for companies to integrate any service into existing large-scale Hadoop clusters with ease.
Apache Ambari manages the Hadoop cluster and solves this problem by extending the stack with add-on services, which can be a new Apache project, different Hadoop file system, or internal tool. This talk covers how to create a service definition in Ambari to manage lifecycle commands and configs, plus advanced topics like packaging, installing from multiple repositories, recommending and validating configs using Service Advisor, running custom commands, defining dependencies on configs and other services, and more. We will also cover how to create custom metrics and dashboards using Ambari Metric System and Grafana, generating alerts, and enabling security by authenticating with Kerberos.
Further, we will discuss the future of service definitions and how Ambari 3.0 will support custom services through Management Packs to enable Hadoop vendors to release software faster.
Speaker
Jayush Luniya, Principal Software Engineer, Hortonworks
企業間の連携においてもSaaS活用シフトが進む一方で、インターネット経由というイメージからセキュリティーに不安を感じて踏みとどまるユーザーは多くいます。こうした懸念を払しょくするAWS PrivateLinkを活用した企業間のプライベート接続や閉域網との構成例、SaaS事業者様からなるPrivateLinkパートナーコミュニティ形成の取り組みをご紹介します。
2021年12月9日に開催された「SaaS on AWS Day 2022」での講演内容です。
This presentation was presented at Percona Live UK.
Although a DBMS hides the internal mechanics of indexing. But to be able to create efficient indexes, you need to know how they work. This talk will help you understand the mechanics of the data structure used to store indexes and as to how it applies to InnoDB. At the end of the talk you will be able to learn how to use cost-analysis to pick and choose correct index definitions and will learn how to create indexes that will work efficiently with InnoDB.
Apache BookKeeper: A High Performance and Low Latency Storage ServiceSijie Guo
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Apache BookKeeper is a high-performance distributed log service that provides durability and ordering guarantees. It addresses challenges in distributed systems like failures, inconsistencies, and split-brain issues. It provides an immutable data abstraction of ledgers composed of segments and blocks. Projects like DistributedLog, Pulsar, and Salesforce Distributed Store use BookKeeper as a building block. DistributedLog scales to handle 1.5 trillion records per day at Twitter. Pulsar provides messaging at Yahoo at over 100 billion messages per day. BookKeeper provides durability and ordering which these systems leverage for use cases like logs, queues, and streams.
At Instagram, our mission is to capture and share the world's moments. Our app is used by over 400M people monthly; this creates a lot of challenging data needs. We use Cassandra heavily, as a general key-value storage. In this presentation, I will talk about how we use Cassandra to serve our critical use cases; the improvements/patches we made to make sure Cassandra can meet our low latency, high scalability requirements; and some pain points we have.
About the Speaker
Dikang Gu Software Engineer, Facebook
I'm a software engineer at Instagram core infra team, working on scaling Instagram infrastructure, especially on building a generic key-value store based on Cassandra. Prior to this, I worked on the development of HDFS in Facebook. I got the master degree of Computer Science in Shanghai Jiao Tong university in China.
Oracle applications r12.2.0 installation on linuxRavi Kumar Lanke
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This document provides steps for installing Oracle E-Business Suite R12.2 on Oracle Enterprise Linux 5.7 using a virtual machine. It outlines the virtual machine configuration, required software, and installation steps which include creating the virtual machine, installing the operating system, configuring OS prerequisites like kernel parameters and users, installing required RPMs, and verifying the installation. The document is authored by Ravi Kumar Lanke and provides a detailed guide over 8 pages for a single node Oracle EBS R12.2 installation on Linux.
Global Data Science Platform : Platform for AI DemocratizationRakuten Group, Inc.
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The document discusses personas and pain points in data science ecosystems, including that data scientists spend too much time on environment setup and data preparation instead of actual data science work. It then introduces a Machine Learning Workbench platform that can reduce preparation time by 90% by providing a centralized environment for data preparation, discovery, training, evaluation, versioning and deployment. Several examples are given of teams within the company using the Workbench platform for various machine learning projects and workflows.
Performance Update: When Apache ORC Met Apache SparkDataWorks Summit
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Apache Spark 1.4 introduced support for Apache ORC. However, initially it did not take advantage of the full power of ORC. For instance, it was slow because ORC vectorization was not used and push-down predicate wa s also not supported on DATE types. Recently the Apache Spark community has started to use the latest Apache ORC which include new enhancements to address these limitations. In this talk, we show the result of integrating the latest Apache ORC and Apache Spark. We will also review the latest enhancements and roadmap.
Speakers:
Owen O'Malley, Co-founder & Technical Fellow, Hortonworks
Dongjoon Hyun, Staff Software Engineer, Hortonworks
Oracle rac cachefusion - High Availability Day 2015aioughydchapter
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RAC Cache Fusion allows Oracle Real Application Clusters instances to share cached data in memory to avoid disk I/O and improve performance. Key aspects of Cache Fusion include global cache services coordinating cached data across instances, maintaining data consistency through modes and roles for cached blocks, and keeping past images of dirty blocks for recovery purposes. Cache blocks can be accessed locally or globally depending on their assigned role and mode.
This presentation was presented at Percona Live UK.
Although a DBMS hides the internal mechanics of indexing. But to be able to create efficient indexes, you need to know how they work. This talk will help you understand the mechanics of the data structure used to store indexes and as to how it applies to InnoDB. At the end of the talk you will be able to learn how to use cost-analysis to pick and choose correct index definitions and will learn how to create indexes that will work efficiently with InnoDB.
Apache BookKeeper: A High Performance and Low Latency Storage ServiceSijie Guo
?
Apache BookKeeper is a high-performance distributed log service that provides durability and ordering guarantees. It addresses challenges in distributed systems like failures, inconsistencies, and split-brain issues. It provides an immutable data abstraction of ledgers composed of segments and blocks. Projects like DistributedLog, Pulsar, and Salesforce Distributed Store use BookKeeper as a building block. DistributedLog scales to handle 1.5 trillion records per day at Twitter. Pulsar provides messaging at Yahoo at over 100 billion messages per day. BookKeeper provides durability and ordering which these systems leverage for use cases like logs, queues, and streams.
At Instagram, our mission is to capture and share the world's moments. Our app is used by over 400M people monthly; this creates a lot of challenging data needs. We use Cassandra heavily, as a general key-value storage. In this presentation, I will talk about how we use Cassandra to serve our critical use cases; the improvements/patches we made to make sure Cassandra can meet our low latency, high scalability requirements; and some pain points we have.
About the Speaker
Dikang Gu Software Engineer, Facebook
I'm a software engineer at Instagram core infra team, working on scaling Instagram infrastructure, especially on building a generic key-value store based on Cassandra. Prior to this, I worked on the development of HDFS in Facebook. I got the master degree of Computer Science in Shanghai Jiao Tong university in China.
Oracle applications r12.2.0 installation on linuxRavi Kumar Lanke
?
This document provides steps for installing Oracle E-Business Suite R12.2 on Oracle Enterprise Linux 5.7 using a virtual machine. It outlines the virtual machine configuration, required software, and installation steps which include creating the virtual machine, installing the operating system, configuring OS prerequisites like kernel parameters and users, installing required RPMs, and verifying the installation. The document is authored by Ravi Kumar Lanke and provides a detailed guide over 8 pages for a single node Oracle EBS R12.2 installation on Linux.
Global Data Science Platform : Platform for AI DemocratizationRakuten Group, Inc.
?
The document discusses personas and pain points in data science ecosystems, including that data scientists spend too much time on environment setup and data preparation instead of actual data science work. It then introduces a Machine Learning Workbench platform that can reduce preparation time by 90% by providing a centralized environment for data preparation, discovery, training, evaluation, versioning and deployment. Several examples are given of teams within the company using the Workbench platform for various machine learning projects and workflows.
Performance Update: When Apache ORC Met Apache SparkDataWorks Summit
?
Apache Spark 1.4 introduced support for Apache ORC. However, initially it did not take advantage of the full power of ORC. For instance, it was slow because ORC vectorization was not used and push-down predicate wa s also not supported on DATE types. Recently the Apache Spark community has started to use the latest Apache ORC which include new enhancements to address these limitations. In this talk, we show the result of integrating the latest Apache ORC and Apache Spark. We will also review the latest enhancements and roadmap.
Speakers:
Owen O'Malley, Co-founder & Technical Fellow, Hortonworks
Dongjoon Hyun, Staff Software Engineer, Hortonworks
Oracle rac cachefusion - High Availability Day 2015aioughydchapter
?
RAC Cache Fusion allows Oracle Real Application Clusters instances to share cached data in memory to avoid disk I/O and improve performance. Key aspects of Cache Fusion include global cache services coordinating cached data across instances, maintaining data consistency through modes and roles for cached blocks, and keeping past images of dirty blocks for recovery purposes. Cache blocks can be accessed locally or globally depending on their assigned role and mode.