Lianghong Xu
Track 3: Applications
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
HBaseConAsia2018 Track3-7: The application of HBase in New Energy Vehicle Mon...Michael Stack
This document discusses the use of HBase in a vehicle monitoring system. It describes challenges including handling huge amounts of vehicle data from 100k vehicles generating 2TB of data daily. It outlines decisions around using Java, Kafka, HBase, and microservices. The system architecture is shown storing vehicle data in HBase with data backup. Challenges with HBase like query speed are discussed. Prospects include rewriting components in Go, splitting to microservices, and data analysis.
Володимир Цап "Constraint driven infrastructure - scale or tune?"Fwdays
Volodymyr Tsap discusses how to save money on infrastructure through constraint driven design. He provides examples of hardware configurations on AWS, bare metal servers, and PaaS platforms to demonstrate how costs can be optimized. Tsap also outlines ways to reduce software costs through choices in operating system, virtualization, databases, and orchestration. Infrastructure support costs depend on the complexity of the environment, with basic setups costing $500-800 per month while more advanced architectures are $4,000-6,000 per month. The overall message is that money saved through optimization can be invested in people.
HBaseCon 2015: State of HBase Docs and How to ContributeHBaseCon
In this session, learn about the move to Asciidoc in HBase docs, some of the other notable changes lately, and things we've done to make it easier for you to contribute to the docs.
HBaseCon 2013: Near Real Time Indexing for eBay SearchCloudera, Inc.
Near Real Time Indexing for ebay Search
The document discusses eBay's near real time indexing pipeline to build indexes for search within minutes of data updates. It outlines the challenges of handling a large volume of updates at scale and describes optimizations made to HBase and the indexing process to reduce indexing time and improve stability. These include improved HBase configuration, major compaction scheduling, and standalone indexing to reduce overhead.
HBaseCon 2015: Optimizing HBase for the Cloud in Microsoft Azure HDInsightHBaseCon
Microsoft Azure's Hadoop cloud service, HDInsight, offers Hadoop, Storm, and HBase as fully managed clusters. In this talk, you'll explore the architecture of HBase clusters in Azure, which is optimized for the cloud, and a set of unique challenges and advantages that come with that architecture. We'll also talk about common patterns and use cases utilizing HBase on Azure.
HBaseConEast2016: Splice machine open source rdbmsMichael Stack
This document discusses Splice Machine, an open source RDBMS that runs queries using Apache Spark for analytics and Apache HBase for transactions. It provides concise summaries of how Splice Machine executes queries by parsing SQL, optimizing query plans, and generating byte code to run queries on either HBase or Spark. It also benchmarks Splice Machine's performance on loading and running TPCH queries compared to other systems like Phoenix and shows how it enables advanced Spark integration by creating RDDs directly from HFiles.
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.
Date-tiered Compaction Policy for Time-series DataHBaseCon
Clara Xiong (Flurry/Yahoo!)
With petabytes of data on thousands of nodes replicated across multiple data centers, growing at an accelerating rate, we have been running a workload at scale with a bottleneck of IO bandwidth. This talk covers a new compaction policy to improve efficiency for time-range scans of various look-back windows by structuring and maintaining a date-tiered store file layout for time-series data with infrequent updates and deletes.
This document summarizes a presentation about using the HBase database with Ruby on Rails applications. It discusses what HBase is, some of the tradeoffs it involves compared to relational databases, when it may be suitable versus not suitable for an application, and how to interface with it from Rails. Examples are provided of libraries that can be used to connect Rails and HBase, as well as demos of JRuby scripts and Rails code that access an HBase backend.
This document summarizes Netease's use of Apache HBase for big data. It discusses Netease operating 7 HBase clusters with 200+ RegionServers and hundreds of terabytes of data across more than 40 applications. It outlines key practices for Linux system configuration, HBase schema design, garbage collection, and request queueing at the table level. Ongoing work includes region server grouping, inverted indexes, and improving high availability of HBase.
Introduction to streaming and messaging flume,kafka,SQS,kinesis Omid Vahdaty
Big data makes you a bit Confused ? messaging? batch processing? data streaming? in flight analytics? Cloud? open source? Flume? kafka? flafka (both)? SQS? kinesis? firehose?
Chicago Data Summit: Geo-based Content Processing Using HBaseCloudera, Inc.
NAVTEQ uses Cloudera Distribution including Apache Hadoop (CDH) and HBase with Cloudera Enterprise support to process and store location content data. With HBase and its distributed and column-oriented architecture, NAVTEQ is able to process large amounts of data in a scalable and cost-effective way.
Protecting Your API with Redis by Jane Paek - Redis Day Seattle 2020Redis Labs
The document discusses rate limiting and metering using Redis. It begins by introducing rate limiting and metering and why Redis is well-suited for these tasks. It then covers different Redis data structures that can be used, such as lists, hashes, sorted sets and strings. Common Redis commands for counting, setting keys and checking time to live are also presented. Different rate limiting design patterns and anti-patterns are described, including fixed window, sliding window and token bucket approaches. Finally, resources for further information are provided.
This document discusses using HBase for online transaction processing (OLTP) workloads. It provides background on SQL-on-Hadoop and transaction processing with snapshot isolation. It then describes challenges in adding transactions directly to HBase, including using additional system tables to coordinate transactions. Examples are given for implementing transactions in HBase, along with issues like rollback handling. Finally, it discusses using SQL interfaces like Apache Phoenix or Drill on top of HBase, as well as open questions around the future of OLTP and OLAP processing on Hadoop versus traditional databases.
HBaseCon2017 Splice Machine as a Service: Multi-tenant HBase using DCOS (Meso...HBaseCon
Splice Machine is a hybrid relational database management system (RDBMS) that allows for both online transaction processing (OLTP) and online analytical processing (OLAP) without the need for separate systems. It provides ANSI SQL support and transactional consistency for massive amounts of data while offering 10x faster performance at 1/4 the cost of other systems. Splice Machine can be deployed on-premises or in the cloud as a fully managed database as a service using the DC/OS platform, which provides container orchestration using Mesos and Docker along with networking and storage integration using CNI and RexRay.
We’ll present details about Argus, a time-series monitoring and alerting platform developed at Salesforce to provide insight into the health of infrastructure as an alternative to systems such as Graphite and Seyren.
RedisConf17 - Home Depot - Turbo charging existing applications with RedisRedis Labs
The Home Depot is transforming its architecture to use microservices and polyglot persistence to handle increasing online order volumes of 250,000 lines per hour. Redis is being used to turbo charge existing monolithic applications by offloading pieces to new processes using patterns like caching, concurrency management, and powering algorithms. This improves performance by reducing database degradation and wait times by over 95%. Next steps include setting up Redis clusters on-premises and off-premises to further reduce database CPU usage and onboard more patterns.
How to ensure Presto scalability in multi use case Kai Sasaki
This document discusses how to ensure Presto scalability in multi-use case environments. It describes how Treasure Data uses Prestobase Proxy, a Finagle-based RPC proxy, to provide a scalable interface for BI tools. It also discusses Presto's node scheduler for distributing query stages across nodes and Treasure Data's use of resource groups to limit resource usage and isolate queries. The document advocates for approaches like dependency injection, VCR testing, and multi-dimensional resource scheduling to make Presto and its components reliable in distributed systems.
AWS re:Invent 2016: Streaming ETL for RDS and DynamoDB (DAT315)Amazon Web Services
During this session Greg Brandt and Liyin Tang, Data Infrastructure engineers from Airbnb, will discuss the design and architecture of Airbnb's streaming ETL infrastructure, which exports data from RDS for MySQL and DynamoDB into Airbnb's data warehouse, using a system called SpinalTap. We will also discuss how we leverage Spark Streaming to compute derived data from tracking topics and/or database tables, and HBase to provide immediate data access and generate cleanly time-partitioned Hive tables.
MyDBOPS Team has presented on Oracle MySQL user Camp ( 29-07-2016 ). This presentation is about Grafana and Prometheus for MySQL alerting and Dashboard setup.
Large-scale projects development (scaling LAMP)Alexey Rybak
This 8-hours tutorial was given at various conferences including Percona conference (London), DevConf (Moscow), Highload++ (Moscow).
ABSTRACT
During this tutorial we will cover various topics related to high scalability for the LAMP stack. This workshop is divided into three sections.
The first section covers basic principles of shared nothing architectures and horizontal scaling for the app//cache/database tiers.
Section two of this tutorial is devoted to MySQL sharding techniques, queues and a few performance-related tips and tricks.
In section three we will cover the practical approach for measuring site performance and quality, porviding a "lean" support philosophy, connecting buesiness and technology metrics.
In addition we will cover a very useful Pinba real-time statistical server, it's features and various use cases. All of the sections will be based on real-world examples built in Badoo, one of the biggest dating sites on the Internet.
This document provides an overview of Apache HBase, an open-source, non-relational database modeled after Google's Bigtable. It discusses how HBase stores and partitions key-value data across servers, with a master coordinating operations. Common uses of HBase involve storing either entity data about current state or event data as time-series points. The document examines how different types of workloads, such as real-time reads/writes versus batch processing, are most efficiently handled by HBase or HDFS. Finally, it outlines some common HBase application archetypes and use cases.
AWS Webcast - Build high-scale applications with Amazon DynamoDBAmazon Web Services
This document discusses Amazon DynamoDB and how it provides a fully managed NoSQL database service. Some key points:
- DynamoDB allows developers to offload operational tasks like provisioned throughput, automated scaling and patching to AWS. This simplifies development and reduces costs.
- The document outlines DynamoDB's data model including tables, items, attributes and indexes. It also discusses how DynamoDB partitions and distributes data automatically based on hash keys to enable massive scale.
- Various AWS services are shown that integrate with DynamoDB for different data workloads like search, analytics and caching. Best practices are also provided around data modeling, queries and system design.
Stateful Interaction In Serverless Architecture With Redis: Pyounguk ChoRedis Labs
This presentation discusses how to bring stateful behaviors to serverless architecture using Redis. It introduces the problem of enabling statefulness in serverless applications and proposes using Redis as a solution. Key considerations for the Redis-based architectural approach are discussed, including topology, high availability and scalability, and Redis configuration tuning. A demo is then presented to illustrate "Redis in Serverless" in action.
My talk at ScaleConf 2017 in Cape Town on some tips and tactics for scaling WordPress, with reference to WordPress.com and the container-based VIP Go platform.
Video of my talk is here: https://www.youtube.com/watch?v=cs0DcY80spw
Cloudera Impala: A Modern SQL Engine for HadoopCloudera, Inc.
Cloudera Impala is a modern SQL query engine for Apache Hadoop that provides high performance for both analytical and transactional workloads. It runs directly within Hadoop clusters, reading common Hadoop file formats and communicating with Hadoop storage systems. Impala uses a C++ implementation and runtime code generation for high performance compared to other Hadoop SQL query engines like Hive that use Java and MapReduce.
HBaseConAsia2018 Track2-3: Bringing MySQL Compatibility to HBase using Databa...Michael Stack
This document discusses AntsDB, an open source project that brings MySQL compatibility to HBase in order to address the need for relational database capabilities in NoSQL systems. It describes AntsDB's architecture, which uses caching and other techniques to provide low-latency transactions and joins on HBase. Performance tests show AntsDB can achieve high throughput for writes and OLTP workloads. AntsDB aims to be complementary to HBase by virtualizing MySQL atop HBase while simulating MySQL behaviors and allowing applications built for MySQL to run unchanged on HBase.
The Evolution of a Relational Database Layer over HBaseDataWorks Summit
Apache Phoenix is a SQL query layer over Apache HBase that allows users to interact with HBase through JDBC and SQL. It transforms SQL queries into native HBase API calls for efficient parallel execution on the cluster. Phoenix provides metadata storage, SQL support, and a JDBC driver. It is now a top-level Apache project after originally being developed at Salesforce. The speaker discussed Phoenix's capabilities like joins and subqueries, new features like HBase 1.0 support and functional indexes, and future plans like improved optimization through Calcite and transaction support.
AWS APAC Webinar Week - Big Data on AWS. RedShift, EMR, & IOTAmazon Web Services
The world is producing an ever-increasing volume, velocity, and variety of data including data from devices. As we step into the era of Internet of things (IOT), for many consumers, batch analytics is no longer enough; they need sub-second analysis on fast-moving data. AWS delivers many technologies for solving big data and IOT problems. But what services should you use, why, when, and how? In this webinar where we simplify big data processing as a pipeline comprising various stages: ingest, store, process, analyze & visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, and durability. Finally, we provide a reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems.
Maximizing performance via tuning and optimizationMariaDB plc
This document provides an overview of best practices for maximizing performance of MariaDB Server through tuning and optimization. It discusses general best practices like service level agreements and metrics collection. It also covers specific areas like server, storage, and network configuration, connection pooling, MariaDB configuration settings, query tuning using indexes and EXPLAIN, and monitoring tools like performance schema. The goal is to help users get the most out of their MariaDB deployment through performance analysis and tuning.
Learn how Aerospike's Hybrid Memory Architecture brings transactions and analytics together to power real-time Systems of Engagement ( SOEs) for companies across AdTech, financial services, telecommunications, and eCommerce. We take a deep dive into the architecture including use cases, topology, Smart Clients, XDR and more. Aerospike delivers predictable performance, high uptime and availability at the lowest total cost of ownership (TCO).
This document provides an overview of serverless computing without code (Lambda). It introduces CloudHesive as a professional services company and describes their services including assessment, strategy, migration, implementation, support, DevOps, security, and managed services. The agenda outlines topics on serverless, APIs, event buses, data stores, operational considerations, and adoption approaches. References are provided for the AWS Well Architected Framework and serverless application lens as well as examples for API Gateway, AppSync, Lambda, and Step Functions.
Serverless computing allows developers to build and run applications and services without having to manage infrastructure. It uses third party services to handle servers and allows developers to focus only on their application code. Serverless applications are built using event-driven compute services like AWS Lambda, Azure Functions, and Google Cloud Functions. These services allow code to be triggered by events and auto-scale as needed, without the need to provision or manage servers.
Building Distributed Systems With Riak and Riak CoreAndy Gross
Andy Gross from Basho discussed Riak Core, an open source distributed systems framework extracted from Riak. Riak Core provides abstractions like virtual nodes, preference lists, and event watchers to help developers build distributed applications. It is currently Erlang-only but will support other languages. Riak Core aims to allow developers to outsource complex distributed systems tasks and implement their own distributed systems more easily.
Impala is a SQL query engine for Apache Hadoop that allows real-time queries on large datasets. It is designed to provide high performance for both analytical and transactional workloads by running directly on Hadoop clusters and utilizing C++ code generation and in-memory processing. Impala uses the existing Hadoop ecosystem including metadata storage in Hive and data formats like Avro, but provides faster performance through its new query execution engine compared to traditional MapReduce-based systems like Hive. Future development of Impala will focus on improved support for features like HBase, additional SQL functionality, and query optimization.
This document discusses using the HBase database with Ruby on Rails applications. It provides an overview of HBase, including what it is, its core concepts like tables, columns, and column families. It also covers some of HBase's tradeoffs compared to relational databases, such as limitations on real-time queries and joins. The document discusses when HBase may be a good fit, such as for large datasets or highly distributed applications, and libraries for integrating HBase into Rails like hbase-stargate and MassiveRecord. It concludes with a demo of a URL shortener application built on Rails and HBase.
Similar to hbaseconasia2019 Recent work on HBase at Pinterest (20)
hbaseconasia2019 HBase Table Monitoring and Troubleshooting System on CloudMichael Stack
Long Chen
Track 3: Applications
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
hbaseconasia2019 Phoenix Practice in China Life Insurance Co., LtdMichael Stack
Yechao Chen
Track 3: Applications
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
TianHang Tang
Track 3: Applications
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
hbaseconasia2019 The Practice in trillion-level Video Storage and billion-lev...Michael Stack
Xu Ming
Track 3: Applications
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
hbaseconasia2019 Spatio temporal Data Management based on Ali-HBase Ganos and...Michael Stack
Fei Xiao of Alibaba
Track 2: Ecology and Solutions
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
hbaseconasia2019 Bridging the Gap between Big Data System Software Stack and ...Michael Stack
Huan-Ping Su (蘇桓平), Yi-Sheng Lien (連奕盛) National Cheng Kung University
Track 2: Ecology and Solutions
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
hbaseconasia2019 Pharos as a Pluggable Secondary Index ComponentMichael Stack
Lei Wang China Everbright Bank
Track 2: Ecology and Solutions
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
hbaseconasia2019 Phoenix Improvements and Practices on Cloud HBase at AlibabaMichael Stack
Yun Zhang
Track 2: Ecology and Solutions
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
Junhong Xu of Xiaomi
Track 2: Ecology and Solutions
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
hbaseconasia2019 BigData NoSQL System: ApsaraDB, HBase and SparkMichael Stack
Wei Li of Alibaba
Track 2: Ecology and Solutions
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
hbaseconasia2019 Test-suite for Automating Data-consistency checks on HBaseMichael Stack
Pradeep S, Mallikarjun V of Flipkart
Track 1: Internals
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
hbaseconasia2019 Distributed Bitmap Index SolutionMichael Stack
Xingjun Hao of Huawei
Track 1: Internals
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
hbaseconasia2019 HBase Bucket Cache on Persistent MemoryMichael Stack
Anoop Sam John, Ramkrishna S Vasudevan, and Xu Kai of Intel
Track 1: Internals
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
hbaseconasia2019 The Procedure v2 Implementation of WAL Splitting and ACLMichael Stack
Mei Yi of Xiaomi
Track 1: Internals
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
hbaseconasia2019 BDS: A data synchronization platform for HBaseMichael Stack
熊嘉男
Track 1: Internals
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
hbaseconasia2019 Further GC optimization for HBase 2.x: Reading HFileBlock in...Michael Stack
Anoop Sam John of Intel and Zheng Hu of Alibaba
Track 1: Internals
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
hbaseconasia2019 HBCK2: Concepts, trends, and recipes for fixing issues in HB...Michael Stack
The document discusses HBCK2, a tool for fixing issues in HBase 2. Some key points:
1. HBCK2 is simpler than HBCK1, with fewer fix commands and no diagnosis commands. It requires a deeper understanding of HBase internals.
2. HBCK2 commands are master-oriented and fix issues one at a time. Common issues include regions not online, stuck procedures, and tables in the wrong state.
3. Recipes are provided to fix specific issues like missing meta regions or regions in transition using HBCK2 commands like assigns and bypass.
4. HBCK2 is still a work in progress but contributions are welcome
Keynote given by Duo Zhang of Xiaomi and Chunhui Shen of Alibab
Track 1: Internals
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
HBaseConAsia2018 Track3-1: Serving billions of queries in millisecond latenciesMichael Stack
This document discusses how Bloomberg uses HBase to serve billions of queries with millisecond latency. It covers HBase principles like being an ordered key-value store and providing ACID transactions. It also discusses modeling data for HBase, including dealing with data and query skew. Implementation details covered include caching, block size tuning, column families, and compaction. The overall goal is to optimize HBase for Bloomberg's low-latency data storage and retrieval needs.
HBaseConAsia2018 Track1-3: HBase at XiaomiMichael Stack
This document summarizes Xiaomi's implementation and use of HBase for data storage. It discusses Xiaomi's HBase clusters across multiple public cloud providers and data centers. It also describes Xiaomi's approaches to multi-tenancy, quota and throttling, synchronous replication between clusters, and high availability in the case of node or cluster failures. Synchronous replication provides stronger consistency guarantees but with some performance overhead compared to asynchronous replication.
Choosing the right web hosting provider can be a daunting task, especially with the plethora of options available. To help you make an informed decision, we’ve compiled comprehensive reviews of some of the top web hosting providers for 2024, with a special focus on Hosting Mastery Hub. This guide will cover the features, pros, cons, and unique offerings of each provider. By the end, you’ll have a clearer understanding of which hosting service best suits your needs.
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Do it again anti Republican shirt,Do it again anti Republican t shirts,Do it again anti Republican sweatshirts Grabs yours today. tag and share who loves it.
How Can Microsoft Office 365 Improve Your Productivity?Digital Host
Microsoft Office 365 is a cloud-based subscription service offering essential productivity tools. It includes Word for documents, Excel for data analysis, PowerPoint for presentations, Outlook for email, OneDrive for cloud storage, and Teams for collaboration. Key benefits are accessibility from any device, advanced security, and regular updates. Office 365 enhances collaboration with real-time co-authoring and Teams, streamlines communication with Outlook and Teams Chat, and improves data management with OneDrive and SharePoint. For reliable office 365 hosting, Digital Host offers various subscription plans, setup support, and training resources. Visit https://www.digitalhost.com/email-office/office-365/
Java Training in Chandigarh.Mastering Java: From Fundamentals to Advanced App...aryan4bhardwaj37
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The Money Wave 2024 Review: Is It the Key to Financial Success?nirahealhty
What is The Money Wave?
The Money Wave is a wealth manifestation software designed to help individuals attract financial abundance through audio tracks. Created by James Rivers, this program uses scientifically-backed methods to improve cognitive functions and reduce stress, thereby enhancing one's ability to manifest wealth.
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The Cash Wave program works by utilizing the force of sound frequencies to overhaul your cerebrum. These audio tracks are designed to promote deep relaxation and improve cognitive functions. The underlying science suggests that specific sound waves can influence brain activity, leading to enhanced problem-solving abilities and reduced stress levels.
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Using The Money Wave program is straightforward:
Download the Audio Tracks: Once purchased, you can download the audio files from the official website.
Listen Daily: For best results, listen to the tracks daily. Consistency is key.
Relax and Visualize: Find a quiet place, relax, and visualize your financial goals as you listen.
Follow the Guide: The program includes a detailed guide to help you maximize the benefits.
The Money Wave 2024 Review_ Is It the Key to Financial Success.pdfnirahealhty
What is The Money Wave?
The Money Wave is a comprehensive financial program designed to equip individuals with the knowledge and tools necessary for achieving financial independence. It encompasses a range of resources, including educational materials, webinars, and community support, all aimed at helping users understand and leverage various financial opportunities.
➡️ Click here to get The Money Wave from the official website.
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Educational Resources: The Money Wave offers a wealth of educational materials that cover essential financial topics, including budgeting, investing, and wealth-building strategies. These resources are designed to empower users with the knowledge needed to make informed financial decisions.
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In today's digital world, digital marketers are indispensable. They play a crucial role in helping businesses connect with their audiences effectively through various online channels. Whether you're considering a career change or aiming to advance in the field, here’s a detailed guide to thriving as a digital marketer in 2024.
Why Choose Digital Marketing?
Digital marketing encompasses a wide array of strategies aimed at engaging and converting online audiences. From optimizing websites for search engines to crafting compelling social media campaigns and leveraging data analytics, digital marketers drive business growth and enhance brand visibility in the digital sphere.
Essential Skills for Success
To excel in digital marketing, mastering a diverse skill set is essential:
1. SEO (Search Engine Optimization)
Understanding Search Engine Optimization principles is vital for enhancing a website's visibility in search engine results. This includes keyword research, on-page optimization techniques, and building authoritative backlinks to boost organic traffic.
2. PPC (Pay-Per-Click) Advertising
PPC advertising involves placing targeted ads on search engines and social media platforms, paying only when users click. Proficiency in platforms like Google Ads and Facebook Ads, along with strategic bidding and ad copywriting skills, is crucial for maximizing campaign ROI.
3. Social Media Marketing
Social media platforms serve as powerful tools for engaging with audiences and building brand loyalty. Effective social media marketers understand platform nuances, create engaging content, and utilize analytics to refine strategies and drive meaningful engagement.
4. Content Marketing
Content marketing revolves around creating valuable, relevant content that attracts and retains target audiences. This includes blog posts, videos, infographics, and eBooks tailored to resonate with audience interests and needs.
5. Email Marketing
Email marketing remains an effective channel for nurturing leads and maintaining customer relationships. Skills in crafting personalized campaigns, segmenting audiences, and analyzing email performance metrics are essential for optimizing campaign effectiveness.
6. Analytics and Data Interpretation
Data-driven decision-making is pivotal in digital marketing success. Proficiency in tools like Google Analytics enables marketers to track website traffic, user behavior, and campaign performance, providing actionable insights to drive continuous improvement.
4. HBase at Pinterest
• Backend for many critical services
• Graph database (Zen)
• Generic KV store (UMS)
• Around 50 HBase clusters
• HBase 0.94 since 2013, HBase 1.2 since 2016
• Internal repo with ZSTD, CCSMAP, Bucket cache, etc.
10. Apache Omid at Pinterest
• Omid (Optimistically transaction Management In Datastores)
• Transaction framework on top of KV stores with HBase support
• Open-sourced by Yahoo! in 2016
• Powers next generation of Ads indexing at Pinterest
11. Apache Omid at Pinterest
• Omid (Optimistically transaction Management In Datastores)
• Transaction framework on top of KV stores with HBase support
• Open-sourced by Yahoo! in 2016
• Powers next generation of Ads indexing at Pinterest
• Pros: simple, reasonable performance, HA, pluggable backend with native HBase support
• Cons: No SQL interface, limited isolation levels, requires MVCC support
13. Omid internals
• Leverages Multi-version Concurrency Control (MVCC) support in HBase
• Transaction ID (begin timestamp) in version, commit timestamp in shadow cell
• OCC: lock-free implementation with central conflict detection mechanism
Omid data and commit table
22. Sparrow techniques
• Client-side commit
• Client writes to commit table when there is no conflicts
• Explicitly mark aborted txn in commit table (-1)
• Reader may back off and abort concurrent writer in case of client failure or network partition
• Avoid performance bottleneck on TM
• Parallel request processing
• Multi-threaded request processor with in-memory conflict map
• beginTx no longer needs to wait until whole commit batch is written to HBase
• Timestamp allocation still needs to be synchronized (with negligible overhead)
25. Argus: Motivation and Problem Statement
• Clients request a real-time notification feature similar to a database trigger
• Incremental processing based on database changes
• Notification cannot be missed - ”at least once”
• Notification events could have different priorities and object types
28. Kafka-based Notification Pipeline
Percolator
(Google)
Argus
• Special notification column
• Observer threads periodically scan for changes
• Heavy-weight distributed scan and locking
• Async notification by tailing HBase WAL
• Kafka for replayable DB change stream
• Support different priorities and types
• Lightweight, minimal impact on DB
30. Argus Observers
• Process notification events in parallel with user-defined handlers
• Event dispatching, filtering, collapse, etc.
• Notification Handlers can be chained
31. Argus Observers
• Process notification events in parallel with user-defined handlers
• Event dispatching, filtering, collapse, etc.
• Notification Handlers can be chained
Use case on Ads indexing:
Batch processing (15 mins) -> incremental indexing (several seconds)
33. Ixia: Motivation
• Clients ask for secondary indexing support in HBase
• Analytics queries on HBase columns (filtering, range, aggregation)
• Why not SQL?
• Index build could take a long time
• Lack of horizontal scalability and tuning expertise
34. Ixia: Near-realtime Indexing with HBase + Muse (In-house Search Engine)
• Inspired by Lily indexer (HBase + Solr)
• Secondary indexes in Muse (written in C++, fast in-memory inverted/forward index)
• Source-of-truth data in HBase
• Index built asynchronously with HBase WAL through Kafka
• Ixia query engine: Thrift-based query service with a SQL-like interface
36. Ixia: Pros and Cons
Pros Cons
Minimal impact on write path
Index and data stores scaled separately
Efficient indexing & retrieval
No strong index consistency
37. Ixia: status and future work
• Batch indexing in prod, reducing indexing time by ~15X
• Query engine serving full dark traffic, reducing query latency by up to 100X
• Future work:
• Realtime indexing into production
• SQL support
• Dynamic index backfilling