State of HBase: Meet the Release Managers

Digital Library Collection Management using HBase

Apache HDFS, the file system on which HBase is most commonly deployed, was originally designed for high-latency high-throughput batch analytic systems like MapReduce. Over the past two to three years, the rising popularity of HBase has driven many enhancements in HDFS to improve its suitability for real-time systems, including durability support for write-ahead logs, high availability, and improved low-latency performance. This talk will give a brief history of some of the enhancements from Hadoop 0.20.2 through 0.23.0, discuss some of the most exciting work currently under way, and explore some of the future enhancements we expect to develop in the coming years. We will include both high-level overviews of the new features as well as practical tips and benchmark results from real deployments.

HBaseCon 2012 | HBase and HDFS: Past, Present, Future - Todd Lipcon, Cloudera

Shuaifeng Zhou When we do real-time data loading to HBase, we use put/putlist interface. After receiving put request, regionserver will write WAL, write data into memory store, flush memory store to disk-store, then compact files again and again. That precedure occupies too much resource and causing read/write performance decrease. To solve the problem, we provide a kind of near-line loading method and architecture, greatly increase the loading bandwidth, and decrease the influence to read operations. hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#

hbaseconasia2017: Large scale data near-line loading method and architecture

Ashish Singhi HBase Disaster recovery solution aims to maintain high availability of HBase service in case of disaster of one HBase cluster with very minimal user intervention. This session will introduce the HBase disaster recovery use cases and the various solutions adopted at Huawei like. a) Cluster Read-Write mode b) DDL operations synchronization with standby cluster c) Mutation and bulk loaded data replication d) Further challenges and pending work hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#

hbaseconasia2017: HBase Disaster Recovery Solution at Huawei

Solbase is an exciting new open-source, real-time search engine being developed at Photobucket to service the over 30 million daily search requests Photobucket handles. Solbase replaces Lucene’s file system-based index with HBase. This allows the system to update in real-time and linearly scale to serve millions of daily search requests on a large dataset. This session will explore the architecture of Solbase as well as some of Lucene/Solr’s inherent issues we overcame. Finally, we’ll go over performance metrics of Solbase against production traffic.

HBaseCon 2012 | Solbase - Kyungseog Oh, Photobucket

DataWorks Summit/Hadoop Summit

HBase Accelerated: In-Memory Flush and Compaction

HBaseCon 2015: HBase Operations at Xiaomi

HBase 2.0 is the next stable major release for Apache HBase scheduled for early 2017. It is the biggest and most exciting milestone release from the Apache community after 1.0. HBase-2.0 contains a large number of features that is long time in the development, some of which include rewritten region assignment, perf improvements (RPC, rewritten write pipeline, etc), async clients, C++ client, offheaping memstore and other buffers, Spark integration, shading of dependencies as well as a lot of other fixes and stability improvements. We will go into technical details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Existing users of HBase/Phoenix as well as operators managing HBase clusters will benefit the most where they can learn about the new release and the long list of features. We will also briefly cover earlier 1.x release lines and compatibility and upgrade paths for existing users and conclude by giving an outlook on the next level of initiatives for the project.

Meet hbase 2.0

Meet HBase 1.0

HBaseCon 2015: HBase Performance Tuning @ Salesforce

Nitin Verma, Pravin Mittal, and Maxim Lukiyanov (Microsoft) This session presents our success story of enabling a big internal customer on Microsoft Azure’s HBase service along with the methodology and tools used to meet high-throughput goals. We will also present how new features in HBase (like BucketCache and MultiWAL) are helping our customers in the medium-latency/high-bandwidth cloud-storage scenario.

HBase Advanced - Lars George

JAX London

Optimizing Apache HBase for Cloud Storage in Microsoft Azure HDInsight

The strength of an open source project resides entirely in its developer community; a strong democratic culture of participation and hacking makes for a better piece of software. The key requirement is having developers who are not only willing to contribute, but also knowledgeable about the project’s internal structure and architecture. This session will introduce developers to the core internal architectural concepts of HBase, not just “what” it does from the outside, but “how” it works internally, and “why” it does things a certain way. We’ll walk through key sections of code and discuss key concepts like the MVCC implementation and memstore organization. The goal is to convert serious “HBase Users” into HBase Developer Users”, and give voice to some of the deep knowledge locked in the committers’ heads.

HBaseCon 2012 | Learning HBase Internals - Lars Hofhansl, Salesforce

Qianxi Zhang 1. Hulu是美国最受欢迎的在线视频网站之一，Hulu Beijing是Hulu第二大研发中心。北京大数据基础架构团队负责整个公司的大数据基础架构的研发和运维。 2. HBase在Hulu的概况 3. HBase在Hulu的使用 4. 用户画像系统，存放所有用户的基本信息，用户行为，第三方DMP数据和机器学习结果标签(几十万个Qualifier)，Spark和Spark Streaming读写HBase数据，运行各种机器学习模型，为公司的视频推荐，精准广告和Marketing团队服务 5. HBase在Hulu的优化 hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#

HBase Applications - Atlanta HUG - May 2014

larsgeorge

Multi-tenant, Multi-cluster and Multi-container Apache HBase Deployments

DataWorks Summit

hbaseconasia2017: HBase在Hulu的使用和实践

Optimizing dynamic websites like www.thetimes.co.uk and www.thesundaytimes.co.uk isn't an easy task! Speeding up a site requires a "war plan" and having a clear vision, dedicated team, appropriate tools and most importantly speed comparison data with similar sites. Mehdi Ali, Optimisation Manager for the Times websites, will show us how this strategy was applied for The Times and Sunday Times sites with great results.

Rigorous and Multi-tenant HBase Performance Measurement

DataWorks Summit

Tendances (18)

Operating and supporting HBase Clusters

Digital Library Collection Management using HBase

HBaseCon 2012 | HBase and HDFS: Past, Present, Future - Todd Lipcon, Cloudera

hbaseconasia2017: Large scale data near-line loading method and architecture

hbaseconasia2017: HBase Disaster Recovery Solution at Huawei

HBaseCon 2012 | Solbase - Kyungseog Oh, Photobucket

HBase Accelerated: In-Memory Flush and Compaction

HBaseCon 2015: HBase Operations at Xiaomi

Meet hbase 2.0

Meet HBase 1.0

HBaseCon 2015: HBase Performance Tuning @ Salesforce

HBase Advanced - Lars George

Optimizing Apache HBase for Cloud Storage in Microsoft Azure HDInsight

HBaseCon 2012 | Learning HBase Internals - Lars Hofhansl, Salesforce

HBase Applications - Atlanta HUG - May 2014

Multi-tenant, Multi-cluster and Multi-container Apache HBase Deployments

hbaseconasia2017: HBase在Hulu的使用和实践

Rigorous and Multi-tenant HBase Performance Measurement

En vedette

Configuration Management - The Operations Managers View

Stephen Thair

Mori 2004 Iew

FNian

Web Performance Optimisation at times.co.uk

Stephen Thair

Continuous Delivery Maturity Model

IBM UrbanCode Products

Watch the recorded version of this Webinar here: Curious about Continuous Integration? Tune in! Continuous Integration (CI), which is a big part of continuous delivery, is the concept of continuously building and testing software using an automated process. We have learned that utilizing CI could help us catch bugs earlier, enable better visibility, reduce repetitive processes, enable the development team to produce deployable products at a moment's notice, and reduce risk overall. These slides will identify the various levels of continuous integration and delivery with regards to a release maturity of the development team or parent organization.

Continuous Integration & the Release Maturity Model

Keynote: The Phoenix Project: Lessons Learned - PuppetConf 2014

Puppet

This session is a case study of how we used our already existing HBase cluster as content addressable storage for BLOBs. We will discuss how we wrote a CAS implementation using HBase as the backend, Scala and Finagle as the application and using caching reverse proxies (i.e. Varnish in our case) for serving BLOBs at scale. The talk will dicuss why content addressable storage is the right pattern for many web use cases, how to foster an already existing HBase cluster for better usage of possibly underutilized resources, and operational gotchas to store and serve BLOBs from HBase at scale.

HBaseCon 2012 | Content Addressable Storages for Fun and Profit - Berk Demir,...

Determining the number of unique users that have interacted with a web page, game, or application is a very common use case. HBase is becoming an increasingly accepted tool for calculating sets or counts of unique individuals who meet some criteria. Computing these statistics can range in difficulty from very simple to very difficult. This session will explore how different approaches have worked or not worked at scale for counting uniques on HBase with Hadoop.

HBaseCon 2012 | Unique Sets on HBase and Hadoop - Elliot Clark, StumbleUpon

Scaling geospatial data is hard. State of the art GIS technologies available to the general public are locked in the realm of relational databases with PostGIS as the prominent leader. Though a number of location-based startups have walked this path before, few have marked their trail along the way. Act one proveds a survey of the landscape, defining terms, and highlighting pitfalls. Act two explores the world of open source, horizontally scalable GIS and outlines the problems they solve. Act three explores implementations backed by HBase. No previous GIS knowledge is required.

HBaseCon 2012 | Scaling GIS In Three Acts

HBase application developers face a number of challenges: schema management is performed at the application level, decoupled components of a system can break one another in unexpected ways, less-technical users cannot easily access data, and evolving data collection and analysis needs are difficult to plan for. In this talk, we describe a schema management methodology based on Apache Avro that enables users and applications to share data in HBase in a scalable, evolvable fashion. By adopting these practices, engineers independently using the same data have guarantees on how their applications interact. As data collection needs change, applications are resilient to drift in the underlying data representation. This methodology results in a data dictionary that allows less-technical users to understand what data is available to them for analysis and inspect data using general-purpose tools (for example, export it via Sqoop to an RDBMS). And because of Avro’s cross-language capabilities, HBase’s power can reach new domains, like web apps built in Ruby.

HBaseCon 2012 | Living Data: Applying Adaptable Schemas to HBase - Aaron Kimb...

WorldCat is the world’s largest network of library content and services. Over 25,000 libraries in 170 countries have cooperated for 40 years to build WorldCat. OCLC is currently in the process of transitioning Worldcat from Oracle to Apache HBase. This session will discuss our data design for representing the constantly changing ownership information for thousands of libraries (billions of data points, millions of daily updates) and our plans for how we’re managing HBase in an environment that is equal parts end user facing and batch.

HBaseCon 2012 | HBase for the Worlds Libraries - OCLC

In this session you will learn the common mistakes made when deploying a high write environment when building an analytics database in HBase, as well as tips on how to diagnose and debug performance bottlenecks, and an overview of an open source monitoring utility developed at Urban Airship for finding HBase hotspots. This session will also present a case study on how Urban Airship replaced a tag system running on a highly sharded PostgreSQL cluster to HBase, the options explored to create a high throughput Boolean tag system and how it was ultimately built on HBase.

HBaseCon 2012 | Building Mobile Infrastructure with HBase

HBaseCon 2013: Being Smarter Than the Smart Meter

HBaseCon 2012 | Leveraging HBase for the World’s Largest Curated Genomic Data...

For Map/Reduce programmers used to HDFS, the mutability of HBase tables poses new challenges: Data can change over the duration of a job, multiple jobs can write concurrently, writes are effective immediately, and it is not trivial to clean up partial writes. Revision Manager introduces atomic commits and point-in-time consistent snapshots over a table, guaranteeing repeatable reads and protection from partial writes. Revision Manager is optimized for a relatively small number of concurrent write jobs, which is typical within Hadoop clusters. This session will discuss the implementation of Revision Manager using ZooKeeper and coprocessors, and paying extra care to ensure security in multi-tenant clusters. Revision Manager is available as part of the HBase storage handler in HCatalog, but can easily be used stand-alone with little coding effort.

HBaseCon 2012 | Relaxed Transactions for HBase - Francis Liu, Yahoo!

HBaseCon 2013: Apache HBase on Flash

HBaseCon 2015: DeathStar - Easy, Dynamic, Multi-tenant HBase via YARN

HBaseCon 2013: Rebuilding for Scale on Apache HBase

Speakers: Enis Soztutar and Devaraj Das (Hortonworks) HBase has ACID semantics within a row that make it a perfect candidate for a lot of real-time serving workloads. However, single homing a region to a server implies some periods of unavailability for the regions after a server crash. Although the mean time to recovery has improved a lot recently, for some use cases, it is still preferable to do possibly stale reads while the region is recovering. In this talk, you will get an overview of our design and implementation of region replicas in HBase, which provide timeline-consistent reads even when the primary region is unavailable or busy.

HBase Read High Availability Using Timeline-Consistent Region Replicas

HBaseCon 2013: Project Valta - A Resource Management Layer over Apache HBase

At Tuenti, we do two code pushes per week, sometimes modifying thousands of files and running thousands of automated tests and build operations before, to ensure not only that the code works but also that proper localization is applied, bundles are generated and files get deployed to hundreds of servers as fast and reliable as possible. We use opensource tools like Mercurial, MySQL, Jenkins, Selenium, PHPUnit and Rsync among our own in-house ones, and have different development, testing, staging and production environments. We had to fight with problems like statics bundling and versioning, syntax errors and of course the fact that we have +100 engineers working on the codebase, sometimes merging and releasing more than a dozen branches the same day. We also switched from Subversion to Mercurial to obtain more flexibility and faster branching operations. With this talk we will explain the process of how code changes in ourcode repository end up in live code, detailing some practices and tips that we apply.

En vedette (20)

Configuration Management - The Operations Managers View

Mori 2004 Iew

Web Performance Optimisation at times.co.uk

Continuous Delivery Maturity Model

Continuous Integration & the Release Maturity Model

Keynote: The Phoenix Project: Lessons Learned - PuppetConf 2014

HBaseCon 2012 | Content Addressable Storages for Fun and Profit - Berk Demir,...

HBaseCon 2012 | Unique Sets on HBase and Hadoop - Elliot Clark, StumbleUpon

HBaseCon 2012 | Scaling GIS In Three Acts

HBaseCon 2012 | Living Data: Applying Adaptable Schemas to HBase - Aaron Kimb...

HBaseCon 2012 | HBase for the Worlds Libraries - OCLC

HBaseCon 2012 | Building Mobile Infrastructure with HBase

HBaseCon 2013: Being Smarter Than the Smart Meter

HBaseCon 2012 | Leveraging HBase for the World’s Largest Curated Genomic Data...

HBaseCon 2012 | Relaxed Transactions for HBase - Francis Liu, Yahoo!

HBaseCon 2013: Apache HBase on Flash

HBaseCon 2015: DeathStar - Easy, Dynamic, Multi-tenant HBase via YARN

HBaseCon 2013: Rebuilding for Scale on Apache HBase

HBase Read High Availability Using Timeline-Consistent Region Replicas

HBaseCon 2013: Project Valta - A Resource Management Layer over Apache HBase

Similaire à State of HBase: Meet the Release Managers

Hadoop Versioning

Hanborq Inc.

Using LuaJIT in mid-load web-projects

Alexander Gladysh

How static analysis supports quality over 50 million lines of C++ code

cppfrug

Into The Box 2020 Keynote Day 1

Ortus Solutions, Corp

Tuenti Release Workflow

Tuenti

HBase state of the union

DataWorks Summit/Hadoop Summit

Apache HBase: State of the Union

At Tuenti, we do 3 code pushes per week, sometimes modifying thousands of files and running thousands of automated tests and build operations before, to ensure not only that the code works but also that proper localization is applied, bundles are generated and files get deployed to hundreds of servers as fast and reliable as possible. We use opensource tools like Mercurial, MySQL, Jenkins, Selenium, PHPUnit and Rsync among our own in-house ones, and have different development, testing, staging and production environments. We had to fight with problems like statics bundling and versioning, syntax errors and of course the fact that we have +100 engineers working on the codebase, merging and releasing more than a 15 branches the same day. We also switched from Subversion to Mercurial to obtain more flexibility and faster branching operations. With this talk we will explain the process of how code changes in ourcode repository end up in live code, detailing some practices and tips that we apply, problems we had and how we solved them.

Tuenti Release Workflow v1.1

Tuenti

HBaseCon 2015 General Session: State of HBase

InfluxData builds a time series platform primarily deployed for DevOps and IoT monitoring. This talk presents several lessons learned while scaling the platform across a large number of deployments—from single server open source instances to highly available high-throughput clusters. This talk presents a number of failure conditions that informed subsequent design choices. Ryan Betts (Director of Engineering at InfluxData) will discuss designing backpressure in an AP system with tens of thousands of resource-limited writers; trade-offs between monolithic and service-oriented database implementations; and lessons learned implementing multiple query processing systems.

InfluxData Internals by Ryan Betts

InfluxData

What's up with HTTP?

Mark Nottingham

Get the Facts: Oracle's Unbreakable Enterprise Kernel

Terry Wang

Having spent more than the last decade being the main point of contact for distributions shipping MySQL, then MariaDB Server, it's clear that working with distributions have many challenges. Licensing changes (when MySQL moved the client libraries from LGPL to GPL with a FOSS Exception), ABI changes, speed (or lack thereof) of distribution releases/freezes, supporting the software throughout the lifespan of the distribution, specific bugs due to platforms, and a lot more will be discussed in this talk. Let's not forget the politics. How do we decide "tiers" of importance for distributions? As a bonus, there will be a focus on how much effort it took to "replace" MySQL with MariaDB. Benefits: if you're making a distribution, this is the point of view of the upstream package makers. Why are distribution statistics important to us? Do we monitor your bugs system or do you have a better escalation to us? How do we test to make sure things are going well before release. This and more will be spoken about. As an upstream project (package), we love nothing more than being available everywhere. But time and energy goes into making this is so as there are quirks in every distribution.

Distributions from the view a package

Colin Charles

Hadoop Robot from eBay at China Hadoop Summit 2015

polo li

Chef for OpenStack: Grizzly Roadmap

Matt Ray

Storm worker redesign

Roshan Naik

In this talk, Tim Bird will discuss the recent status of the Linux with regard to embedded systems. This will include a review of the last year's worth of mainline kernel releases, as well as topic areas specifically related to embedded, such as boot-up time, security, system size, etc. Tim will also present recent and planned work by the Core Embedded Linux Project of the Linux Foundation, and discuss the current status of Linux in various markets and fields. Tim will go over current areas of work, and discuss remaining challenges faced by Linux in embedded projects.

Status of Embedded Linux

LinuxCon ContainerCon CloudOpen China

The MySQL Server ecosystem in 2016

sys army

OpenStack - JobShop @Iași, 2016

Alexandru Coman

HBase 2.0 is the next stable major release for Apache HBase scheduled for early 2017. It is the biggest and most exciting milestone release from the Apache community after 1.0. HBase-2.0 contains a large number of features that is long time in the development, some of which include rewritten region assignment, perf improvements (RPC, rewritten write pipeline, etc), async clients, C++ client, offheaping memstore and other buffers, Spark integration, shading of dependencies as well as a lot of other fixes and stability improvements. We will go into technical details on some of the most important improvements in the release, as well as what are the implications for the users in terms of API and upgrade paths. Speaker Ankit Singhal, Member of Technical Staff, Hortonworks

Meet Apache HBase - 2.0

DataWorks Summit

Similaire à State of HBase: Meet the Release Managers (20)

Hadoop Versioning

Using LuaJIT in mid-load web-projects

How static analysis supports quality over 50 million lines of C++ code

Into The Box 2020 Keynote Day 1

Tuenti Release Workflow

HBase state of the union

Apache HBase: State of the Union

Tuenti Release Workflow v1.1

HBaseCon 2015 General Session: State of HBase

InfluxData Internals by Ryan Betts

What's up with HTTP?

Get the Facts: Oracle's Unbreakable Enterprise Kernel

Distributions from the view a package

Hadoop Robot from eBay at China Hadoop Summit 2015

Chef for OpenStack: Grizzly Roadmap

Storm worker redesign

Status of Embedded Linux

The MySQL Server ecosystem in 2016

OpenStack - JobShop @Iași, 2016

Meet Apache HBase - 2.0

Plus de HBaseCon

Zhiyong Bai As a high performance and scalable key value database, Zhihu use HBase to provide online data store system along with Mysql and Redis. Zhihu’s platform team had accumulated some experience in technology of container, and this time, based on Kubernetes, we build flexible platform of online HBase system, create multiple logic isolated HBase clusters on the shared physical cluster with fast rapid，and provide customized service for different business needs. Combined with Consul and DNS server, we implement high available access of HBase using client mainly written with Python. This presentation is mainly shared the architecture of online HBase platform in Zhihu and some practical experience in production environment. hbaseconasia2017 hbasecon hbase

hbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes

Jingcheng Du Apache Beam is an open source and unified programming model for defining batch and streaming jobs that run on many execution engines, HBase on Beam is a connector that allows Beam to use HBase as a bounded data source and target data store for both batch and streaming data sets. With this connector HBase can work with many batch and streaming engines directly, for example Spark, Flink, Google Cloud Dataflow, etc. In this session, I will introduce Apache Beam, and the current implementation of HBase on Beam and the future plan on this. hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#

hbaseconasia2017: HBase on Beam

Tianying Chang HBase is used to serve online facing traffic in Pinterest. It means no downtime is allowed. However, we were on HBase 94. To upgrade to latest version, we need to figure out a way to live upgrade while keeping Pinterest site live. Recently, we successfully upgrade 94 HBase cluster to 1.2 with no downtime. We made change to both Asynchbase and HBase server side. We will talk about what we did and how we did it. We will also talk about the finding in config and performance tuning we did to achieve low latency. hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#

hbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinterest

Mon-Fong Mike Jiang, Kuan-Yu Hubert Fan-Chiang and Tienyu Rebecca Lin 自2011年起，我們就開始使用HBase作為結構化大數據的儲存工具，主要是做為半導體製造設備參數的分析。為了有效進行數據查詢，我們開發Standard Query Language(SQL)的整合介面，最早的方式是(1)自行開發GUI操作介面及(2)透過自行定義SQL語法的方式進行，但是這樣會衍生出很多額外的工作，特別是SQL Parser與對應的HBase API的連結。為了解決此問題，我們解析了Hive QL Parser作為主要的核心，將此部分的原始碼整合進HareDB HBase Client之中，另外，也整合了HBase Coprocessor，可以加速查詢的進行，這個架構我們實際使用在數個半導體製造廠的大數據系統中，也展現了高查詢效率。除此之外，透過整合Kafka來處理串流數據的匯入，同時對於數據分析的呈現也加上Cube建立工具，這些都是實際開發大數據系統時陸續面對的問題與解決方法，我們將分享這一連串的系統開發過程。 hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#

hbaseconasia2017: HareQL：快速HBase查詢工具的發展過程

Xinxin Fan and Hongxiang Jiang First, we will give a brief introduction about the HBase service at Netease，include the basic cluster info and the key HBase service. And then we will talk same tips about the tuning practices for HBase. Last, we will introduce some improvements at the internal HBase version. hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#

hbaseconasia2017: Apache HBase at Netease

Xinyu Zhang, Xueliang Chen and Zheng Fan 基于HBase的大数据平台已经成为中国人寿新一代综合业务处理系统中非常重要的基础性数据平台。目前基于该平台已经整合了上百TB的数据，并将几亿客户的客户、业务、接触数据整合到一个统一的数据模型中，并基于此形成了上千个客户标签。同时，基于该平台为客户、营销员和内部管理人员提供了销售支持、客户服务、运营支持等多类应用。通过APP、网页等形式提供了多种信息的检索和查询，并通过深度学习模型提供了反欺诈等方面的数据应用。 hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#

hbaseconasia2017: 基于HBase的企业级大数据平台

Xingbo Peng, Nan Zhang and Bang Wen 1.规模现状 HBase在京东CTO体系中经历了数年的发展，集群规模已经达到3000+台，支持了京东600+业务系统，京东CTO体系的HBase集群，已经经历了多次618和双11的考验。京东CTO体系是HBase的重要用户。 2.应用的业务场景介绍HBase在京东的典型应用的业务，包括监控、风控、推荐、广告等 3.高可用改进介绍我们在HBase集群高可用方面做的一些工作，包括跨机房容灾、多租户-资源分组、集群安全等 4.运维实践主要介绍我们在HBase集群运维上的一些实践，包括：HBase集群监控系统Mummut、报警系统、HBase集群与大数据平台结合、业务运营及数据迁移等 5.未来展望介绍我们正在基于HBase做的及未来要做的一些工作，包括：kylin、phoenix和容器化部署等 hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#

hbaseconasia2017: HBase at JD.com

Jieshan Bi and Yanhui Zhong 1. CTBase: A light-weight HBase client for structured data. 1). Schematized table, more friendly for structured data storage. 2). Global secondary index for HBase. 3). HBase Query DSL. JSON based light-weight API. 4) Cluster table. Pre-joining with keys, a better solution for cross-table join queries from HBase. 2. Tagram: Distributed bitmap index implementation with HBase. 1). Distributed bitmap index for accelerating AD-HOC queries with low cardinality columns. 2). Powerful and flexible query API. 3). Tagram offers millisecond-level query latency. 3. CloudTable Service Introduction: HBase on Huawei cloud. hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#

hbaseconasia2017: Ecosystems with HBase and CloudTable service at Huawei

hbaseconasia2017: HBase Practice At XiaoMi

hbaseconasia2017: hbase-2.0.0

As HBase and Hadoop continue to become routine across enterprises, these enterprises inevitably shift priorities from effective deployments to cost-efficient operations. Consolidation of infrastructure, the sum of hardware, software, and system-administrator effort, is the most common strategy to reduce costs. As a company grows, the number of business organizations, development teams, and individuals accessing HBase grows commensurately, creating a not-so-simple requirement: HBase must effectively service many users, each with a variety of use-cases. This is problem is known as multi-tenancy. While multi-tenancy isn’t a new problem, it also isn’t a solved one, in HBase or otherwise. This talk will present a high-level view of the common issues organizations face when multiple users and teams share a single HBase instance and how certain HBase features were designed specifically to mitigate the issues created by the sharing of finite resources.

HBaseCon2017 Democratizing HBase

HBase is used to serve online facing traffic in Pinterest. It means no downtime is allowed. However, we were on HBase 94. To upgrade to latest version, we need to figure out a way to live upgrade while keeping Pinterest site live. Recently, we successfully upgrade 94 HBase cluster to 1.2 with no downtime. We made change to both Asynchbase and HBase server side. We will talk about what we did and how we did it. We will also talk about the finding in config and performance tuning we did to achieve low latency.

HBaseCon2017 Removable singularity: a story of HBase upgrade in Pinterest

Hundreds of millions of people use Quora to find accurate, informative, and trustworthy answers to their questions. As it so happens, counting things at scale is both an important and a difficult problem to solve. In this talk, we will be talking about Quanta, Quora's counting system built on top of HBase that powers our high-volume near-realtime analytics that serves many applications like ads, content views, and many dashboards. In addition to regular counting, Quanta supports count propagation along the edges of an arbitrary DAG. HBase is the underlying data store for both the counting data and the graph data. We will describe the high-level architecture of Quanta and share our design goals, constraints, and choices that enabled us to build Quanta very quickly on top of our existing infrastructure systems.

HBaseCon2017 Quanta: Quora's hierarchical counting system on HBase

In the age of NoSQL, big data storage engines such as HBase have given up ACID semantics of traditional relational databases, in exchange for high scalability and availability. However, it turns out that in practice, many applications require consistency guarantees to protect data from concurrent modification in a massively parallel environment. In the past few years, several transaction engines have been proposed as add-ons to HBase; three different engines, namely Omid, Tephra, and Trafodion were open-sourced in Apache alone. In this talk, we will introduce and compare the different approaches from various perspectives including scalability, efficiency, operability and portability, and make recommendations pertaining to different use cases.

HBaseCon2017 Transactions in HBase

HBaseCon2017 Highly-Available HBase

In DiDi Chuxing Company, which is China’s most popular ride-sharing company. we use HBase to serve when we have a bigdata problem. We run three clusters which serve different business needs. We backported the Region Grouping feature back to our internal HBase version so we could isolate the different use cases. We built the Didi HBase Service platform which is popular amongst engineers at our company. It includes a workflow and project management function as well as a user monitoring view. Internally we recommend users use Phoenix to simplify access.even more,we used row timestamp;multidimensional table schema to slove muti dimension query problems C++, Go, Python, and PHP clients get to HBase via thrift2 proxies and QueryServer. We run many important buisness applications out of our HBase cluster such as ETA/GPS/History Order/API metrics monitoring/ and Traffic in the Cloud. If you are interested in any aspects listed above, please come to our talk. We would like to share our experiences with you.

HBaseCon2017 Apache HBase at Didi

HBaseCon2017 gohbase: Pure Go HBase Client

Infrastructure failures are a given in the cloud, but in a multi-tenant environment separating those failures from usage can be a challenge. I'll be presenting data gathered from over a hundred region server failures at HubSpot along with what we've done to improve our MTTR and what we're contributing back to the community. Covered topics will include separating usage-related failures from infrastructure and hardware failures, as well as steps we've taken to improve MTTR in both scenarios.

HBaseCon2017 Improving HBase availability in a multi tenant environment

Both Spark and HBase are widely used, but how to use them together with high performance and simplicity is a very hard topic. Spark HBase Connector(SHC) provides feature rich and efficient access to HBase through Spark SQL. It bridges the gap between the simple HBase key value store and complex relational SQL queries and enables users to perform complex data analytics on top of HBase using Spark. SHC implements the standard Spark data source APIs, and leverages the Spark catalyst engine for query optimization. To achieve high performance, SHC constructs the RDD from scratch instead of using the standard HadoopRDD. With the customized RDD, all critical techniques can be applied and fully implemented, such as partition pruning, column pruning, predicate pushdown and data locality. The design makes the maintenance very easy, while achieving a good tradeoff between performance and simplicity. Also, SHC has supported Phoenix data as input to HBase in addition to Avro data. Defaulting to a simple native binary encoding seems susceptible to future changes and is a risk for users who write data from SHC into HBase. For example, with SHC going forward, backwards compatibility needs to be properly handled. So the default, SHC needs to support a more standard and well tested format like Phoenix. In this talk, we will demo how SHC works, how to use SHC in secure/non-secure clusters, how SHC works with multi-HBase clusters, etc. This talk will also benefit people who use Spark and other data sources (besides HBase) as it inspires them with ideas of how to support high performance data source access at the Spark DataFrame level.

HBaseCon2017 Spark HBase Connector: Feature Rich and Efficient Access to HBas...

In this talk we introduce Apache Beam, a unified model to create efficient and portable data processing pipelines. Beam uses a single set of abstractions to implement both batch and streaming computations that can be executed in different environments, e.g. Apache Spark, Apache Flink and Google Dataflow. Beam not only does data processing, but can be used as a tool to ingest/extract data to/from different data stores including HBase. We will present interaction scenarios between HBase and Beam and explore Beam's Input/Output (IO) model and how we leverage it to provide support for HBase.

HBaseCon2017 Efficient and portable data processing with Apache Beam and HBase

Investing in AI transformation today The modern business advantage: Uncovering deep insights with AI Organizations around the world have come to recognize AI as the transformative technology that enables them to gain real business advantage. AI’s ability to organize vast quantities of data allows those who implement it to uncover deep business insights, augment human expertise, drive operational efficiency, transform their products, and better serve their customers

Plus de HBaseCon (20)

hbaseconasia2017: Building online HBase cluster of Zhihu based on Kubernetes

hbaseconasia2017: HBase on Beam

hbaseconasia2017: Removable singularity: a story of HBase upgrade in Pinterest

hbaseconasia2017: HareQL：快速HBase查詢工具的發展過程

hbaseconasia2017: Apache HBase at Netease

hbaseconasia2017: 基于HBase的企业级大数据平台

hbaseconasia2017: HBase at JD.com

hbaseconasia2017: Ecosystems with HBase and CloudTable service at Huawei

hbaseconasia2017: HBase Practice At XiaoMi

hbaseconasia2017: hbase-2.0.0

HBaseCon2017 Democratizing HBase

HBaseCon2017 Removable singularity: a story of HBase upgrade in Pinterest

HBaseCon2017 Quanta: Quora's hierarchical counting system on HBase

HBaseCon2017 Transactions in HBase

HBaseCon2017 Highly-Available HBase

HBaseCon2017 Apache HBase at Didi

HBaseCon2017 gohbase: Pure Go HBase Client

HBaseCon2017 Improving HBase availability in a multi tenant environment

HBaseCon2017 Spark HBase Connector: Feature Rich and Efficient Access to HBas...

HBaseCon2017 Efficient and portable data processing with Apache Beam and HBase

Dernier

Microsoft AI Transformation Partner Playbook.pdf

Willy Marroquin (WillyDevNET)

ADR, or Architecture Decision Record, is a valuable tool in software development for several reasons. It provides a centralized location for documenting and tracking architectural decisions, aiding both current and future team members. ADRs enhance communication among team members by documenting the rationale behind architectural decisions, especially beneficial during onboarding of new team members or when revisiting decisions. They serve as a knowledge base, enabling teams to learn from past decisions and refine their decision-making process. Additionally, ADRs contribute to transparency by helping stakeholders understand the reasons behind specific architectural choices. As with any other tool or process, introducing them into an organization can face several obstacles, and overcoming these challenges is crucial for successful implementation. In this talk I go through some common problems and our way of solving them.

Architecture decision records - How not to get lost in the past

Papp Krisztián

Foundation models are machine learning models which are easily capable of performing variable tasks on large and huge datasets. FMs have managed to get a lot of attention due to this feature of handling large datasets. It can do text generation, video editing to protein folding and robotics. In case we believe that FMs can help the hospitals and patients in any way, we need to perform some important evaluations, tests to test these assumptions. In this review, we take a walk through Fms and their evaluation regimes assumed clinical value. To clarify on this topic, we reviewed no less than 80 clinical FMs built from the EMR data. We added all the models trained on structured and unstructured data. We are referring to this combination of structured and unstructured EMR data or clinical data.

Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...

harshavardhanraghave

Test automation is a cornerstone of software development and quality assurance in today's rapidly evolving digital landscape. Its significance cannot be overstated. Businesses can enhance efficiency, productivity, and accelerate software delivery to market through automation, streamlining testing processes effectively. This comprehensive guide addresses the best practices for test automation in 2024. It offers a detailed checklist to empower you to optimize your automation efforts and maintain a competitive edge.

The Ultimate Test Automation Guide_ Best Practices and Tips.pdf

kalichargn70th171

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Craft an AI & Machine Learning Pitch with our Editable Professional PowerPoint Template. Ignite your AI & Machine Learning pitch with our cutting-edge PowerPoint template tailored for the industry. Perfect for AI conferences, investor presentations, sales pitches to tech-focused companies, training sessions, and educational programs. - 20+ editable slides: Get a variety of options to choose from for your presentation. - Time-saving solution: Download, replace text/images with a few clicks. - User-friendly customization: Easy to use and personalize. - Modern and attractive design: Captivating visuals, sleek layout. - Tailored to your requirements: Fully alterable for customization. - Well-organized slides: Complete control over content. - Thematic specificity: Reflects healthcare industry with relevant graphics. - Showcase your business idea: Communicate value proposition effectively.

AI & Machine Learning Presentation Template

Presentation.STUDIO

Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.

Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...

Steffen Staab

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Generic or specific? Making sensible software design decisions

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