In this talk we speak about ORC (Optimized Row Columnar) file format, features and performance optimizations that went in after its initial version (Hive 0.11 back in May 2013). We will also briefly talk about the latest and greatest features, and future enhancements that are planned for Hive 0.15.
Are you taking advantage of all of Hadoop’s features to operate a stable and effective cluster? Inspired by real-world support cases, this talk discusses best practices and new features to help improve incident response and daily operations. Chances are that you’ll walk away from this talk with some new ideas to implement in your own clusters.
Apache Hive is a data warehousing system for large volumes of data stored in Hadoop. However, the data is useless unless you can use it to add value to your company. Hive provides a SQL-based query language that dramatically simplifies the process of querying your large data sets. That is especially important while your data scientists are developing and refining their queries to improve their understanding of the data. In many companies, such as Facebook, Hive accounts for a large percentage of the total MapReduce queries that are run on the system. Although Hive makes writing large data queries easier for the user, there are many performance traps for the unwary. Many of them are artifacts of the way Hive has evolved over the years and the requirement that the default behavior must be safe for all users. This talk will present examples of how Hive users have made mistakes that made their queries run much much longer than necessary. It will also present guidelines for how to get better performance for your queries and how to look at the query plan to understand what Hive is doing.
Speed Up Your Queries with Hive LLAP Engine on Hadoop or in the Cloudgluent.
Hive was the first popular SQL layer built on Hadoop and has long been known as a heavyweight SQL engine suitable mainly for long-running batch jobs. This has greatly changed since Hive was announced to the world over 8 years ago. Hortonworks and the open source community have evolved Apache Hive into a fast, dynamic SQL on Hadoop engine capable of running highly concurrent query workloads over large datasets with sub-second response time.
The latest Hortonworks and Azure HDInsight platform versions fully support Hive with LLAP execution engine for production use. In this webinar, we will go through the architecture of Hive + LLAP engine and explain how it differs from previous Hive versions. We will then dive deeper and show how features like query vectorization and LLAP columnar caching bring further automatic performance improvements.
In the end, we will show how Gluent brings these new performance benefits to traditional enterprise database platforms via transparent data virtualization, allowing even your largest databases to benefit from all this without changing any application code. Join this webinar to learn about significant improvements in modern Hive architecture and how Gluent and Hive LLAP on Hortonworks or Azure HDInsight platforms can accelerate cloud migrations and greatly improve hybrid query performance!
Are you taking advantage of all of Hadoop’s features to operate a stable and effective cluster? Inspired by real-world support cases, this talk discusses best practices and new features to help improve incident response and daily operations. Chances are that you’ll walk away from this talk with some new ideas to implement in your own clusters.
Apache Hive is a data warehousing system for large volumes of data stored in Hadoop. However, the data is useless unless you can use it to add value to your company. Hive provides a SQL-based query language that dramatically simplifies the process of querying your large data sets. That is especially important while your data scientists are developing and refining their queries to improve their understanding of the data. In many companies, such as Facebook, Hive accounts for a large percentage of the total MapReduce queries that are run on the system. Although Hive makes writing large data queries easier for the user, there are many performance traps for the unwary. Many of them are artifacts of the way Hive has evolved over the years and the requirement that the default behavior must be safe for all users. This talk will present examples of how Hive users have made mistakes that made their queries run much much longer than necessary. It will also present guidelines for how to get better performance for your queries and how to look at the query plan to understand what Hive is doing.
Speed Up Your Queries with Hive LLAP Engine on Hadoop or in the Cloudgluent.
Hive was the first popular SQL layer built on Hadoop and has long been known as a heavyweight SQL engine suitable mainly for long-running batch jobs. This has greatly changed since Hive was announced to the world over 8 years ago. Hortonworks and the open source community have evolved Apache Hive into a fast, dynamic SQL on Hadoop engine capable of running highly concurrent query workloads over large datasets with sub-second response time.
The latest Hortonworks and Azure HDInsight platform versions fully support Hive with LLAP execution engine for production use. In this webinar, we will go through the architecture of Hive + LLAP engine and explain how it differs from previous Hive versions. We will then dive deeper and show how features like query vectorization and LLAP columnar caching bring further automatic performance improvements.
In the end, we will show how Gluent brings these new performance benefits to traditional enterprise database platforms via transparent data virtualization, allowing even your largest databases to benefit from all this without changing any application code. Join this webinar to learn about significant improvements in modern Hive architecture and how Gluent and Hive LLAP on Hortonworks or Azure HDInsight platforms can accelerate cloud migrations and greatly improve hybrid query performance!
We will talk about two real-world challenging SQL on Hadoop use cases: #1 Highly Parallel Workload Over Massive Data, #2 Sub-second SQL for Online Reporting. The challenge is to meet very strict performance requirement over hundreds of billions of data. We will introduce how we solved these challenges using Hive on Tez, Hive LLAP and Phoenix. With real-life performance number!
This deck presents the best practices of using Apache Hive with good performance. It covers getting data into Hive, using ORC file format, getting good layout into partitions and files based on query patterns, execution using Tez and YARN queues, memory configuration, and debugging common query performance issues. It also describes Hive Bucketing and reading Hive Explain query plans.
Hadoop & cloud storage object store integration in production (final)Chris Nauroth
Today's typical Apache Hadoop deployments use HDFS for persistent, fault-tolerant storage of big data files. However, recent emerging architectural patterns increasingly rely on cloud object storage such as S3, Azure Blob Store, GCS, which are designed for cost-efficiency, scalability and geographic distribution. Hadoop supports pluggable file system implementations to enable integration with these systems for use cases such as off-site backup or even complex multi-step ETL, but applications may encounter unique challenges related to eventual consistency, performance and differences in semantics compared to HDFS. This session explores those challenges and presents recent work to address them in a comprehensive effort spanning multiple Hadoop ecosystem components, including the Object Store FileSystem connector, Hive, Tez and ORC. Our goal is to improve correctness, performance, security and operations for users that choose to integrate Hadoop with Cloud Storage. We use S3 and S3A connector as case study.
CBlocks - Posix compliant files systems for HDFSDataWorks Summit
With YARN running Docker containers, it is possible to run applications that are not HDFS aware inside these containers. It is hard to customize these applications since most of them assume a Posix file system with rewrite capabilities. In this talk, we will dive into how we created a block storage, how it is being tested internally and the storage containers which makes it all possible.
The storage container framework was developed as part of Ozone (HDFS-7240). This is talk will also explore the current state of Ozone along with CBlocks. This talk will explore architecture of storage containers, how replication is handled, scaling to millions of volumes and I/O performance optimizations.
HDFS Tiered Storage: Mounting Object Stores in HDFSDataWorks Summit
Most users know HDFS as the reliable store of record for big data analytics. HDFS is also used to store transient and operational data when working with cloud object stores, such as Azure HDInsight and Amazon EMR. In these settings- but also in more traditional, on premise deployments- applications often manage data stored in multiple storage systems or clusters, requiring a complex workflow for synchronizing data between filesystems to achieve goals for durability, performance, and coordination.
Building on existing heterogeneous storage support, we add a storage tier to HDFS to work with external stores, allowing remote namespaces to be "mounted" in HDFS. This capability not only supports transparent caching of remote data as HDFS blocks, it also supports synchronous writes to remote clusters for business continuity planning (BCP) and supports hybrid cloud architectures.
This idea was presented at last year’s Summit in San Jose. Lots of progress has been made since then and the feature is in active development at the Apache Software Foundation on branch HDFS-9806, driven by Microsoft and Western Digital. We will discuss the refined design & implementation and present how end-users and admins will be able to use this powerful functionality.
Keep your hadoop cluster at its best! v4Chris Nauroth
Hadoop has become a backbone of many enterprises. While it can do wonders for businesses, it sometimes can be overwhelming for its operators and users. Amateurs as well as seasoned operators of Hadoop are caught unaware by common pitfalls of deploying, tuning and operating a Hadoop cluster. Having spent 5+ years working with 100s of Hadoop users, running clusters with 1000s of nodes, managing 10s of petabytes of data and running 100s of 1000s of tasks per day, we have seen people's unintentional acts, suboptimal configurations and common mistakes have resulted into downtimes, SLA violations, many hours of recovery operations and in some cases even data loss! Most of these traumas could have been easily avoided by applying easy to follow best practices that would protect data and optimize performance. In this talk we present real life stories, common pitfalls and most importantly, strategies on how to correctly deploy and manage Hadoop clusters. The talk will empower users and help make their Hadoop journey more fulfilling and rewarding. We will also discuss SmartSense. SmartSense can identify latent problems in a cluster and provide recommendations so that an operator can fix them before they manifest as a service degradation or outage.
Optimizing, profiling and deploying high performance Spark ML and TensorFlow ...DataWorks Summit
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool , I’ll demonstrate how to optimize, profile, and deploy TensorFlow Models in GPU-based production environment.
This talk is contains many Spark ML and TensorFlow AI demos using PipelineIO's 100% Open Source Community Edition. All code and Docker images are available to reproduce on your own CPU or GPU-based cluster.
* Bio *
Chris Fregly is Founder and Research Engineer at PipelineIO, a Streaming Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Video Series High Performance TensorFlow in Production.
Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member of the IBM Spark Technology Center in San Francisco.
Keynote slides from Big Data Spain Nov 2016. Has some thoughts on how Hadoop ecosystem is growing and changing to support the enterprise, including Hive, Spark, NiFi, security and governance, streaming, and the cloud.
We will talk about two real-world challenging SQL on Hadoop use cases: #1 Highly Parallel Workload Over Massive Data, #2 Sub-second SQL for Online Reporting. The challenge is to meet very strict performance requirement over hundreds of billions of data. We will introduce how we solved these challenges using Hive on Tez, Hive LLAP and Phoenix. With real-life performance number!
This deck presents the best practices of using Apache Hive with good performance. It covers getting data into Hive, using ORC file format, getting good layout into partitions and files based on query patterns, execution using Tez and YARN queues, memory configuration, and debugging common query performance issues. It also describes Hive Bucketing and reading Hive Explain query plans.
Hadoop & cloud storage object store integration in production (final)Chris Nauroth
Today's typical Apache Hadoop deployments use HDFS for persistent, fault-tolerant storage of big data files. However, recent emerging architectural patterns increasingly rely on cloud object storage such as S3, Azure Blob Store, GCS, which are designed for cost-efficiency, scalability and geographic distribution. Hadoop supports pluggable file system implementations to enable integration with these systems for use cases such as off-site backup or even complex multi-step ETL, but applications may encounter unique challenges related to eventual consistency, performance and differences in semantics compared to HDFS. This session explores those challenges and presents recent work to address them in a comprehensive effort spanning multiple Hadoop ecosystem components, including the Object Store FileSystem connector, Hive, Tez and ORC. Our goal is to improve correctness, performance, security and operations for users that choose to integrate Hadoop with Cloud Storage. We use S3 and S3A connector as case study.
CBlocks - Posix compliant files systems for HDFSDataWorks Summit
With YARN running Docker containers, it is possible to run applications that are not HDFS aware inside these containers. It is hard to customize these applications since most of them assume a Posix file system with rewrite capabilities. In this talk, we will dive into how we created a block storage, how it is being tested internally and the storage containers which makes it all possible.
The storage container framework was developed as part of Ozone (HDFS-7240). This is talk will also explore the current state of Ozone along with CBlocks. This talk will explore architecture of storage containers, how replication is handled, scaling to millions of volumes and I/O performance optimizations.
HDFS Tiered Storage: Mounting Object Stores in HDFSDataWorks Summit
Most users know HDFS as the reliable store of record for big data analytics. HDFS is also used to store transient and operational data when working with cloud object stores, such as Azure HDInsight and Amazon EMR. In these settings- but also in more traditional, on premise deployments- applications often manage data stored in multiple storage systems or clusters, requiring a complex workflow for synchronizing data between filesystems to achieve goals for durability, performance, and coordination.
Building on existing heterogeneous storage support, we add a storage tier to HDFS to work with external stores, allowing remote namespaces to be "mounted" in HDFS. This capability not only supports transparent caching of remote data as HDFS blocks, it also supports synchronous writes to remote clusters for business continuity planning (BCP) and supports hybrid cloud architectures.
This idea was presented at last year’s Summit in San Jose. Lots of progress has been made since then and the feature is in active development at the Apache Software Foundation on branch HDFS-9806, driven by Microsoft and Western Digital. We will discuss the refined design & implementation and present how end-users and admins will be able to use this powerful functionality.
Keep your hadoop cluster at its best! v4Chris Nauroth
Hadoop has become a backbone of many enterprises. While it can do wonders for businesses, it sometimes can be overwhelming for its operators and users. Amateurs as well as seasoned operators of Hadoop are caught unaware by common pitfalls of deploying, tuning and operating a Hadoop cluster. Having spent 5+ years working with 100s of Hadoop users, running clusters with 1000s of nodes, managing 10s of petabytes of data and running 100s of 1000s of tasks per day, we have seen people's unintentional acts, suboptimal configurations and common mistakes have resulted into downtimes, SLA violations, many hours of recovery operations and in some cases even data loss! Most of these traumas could have been easily avoided by applying easy to follow best practices that would protect data and optimize performance. In this talk we present real life stories, common pitfalls and most importantly, strategies on how to correctly deploy and manage Hadoop clusters. The talk will empower users and help make their Hadoop journey more fulfilling and rewarding. We will also discuss SmartSense. SmartSense can identify latent problems in a cluster and provide recommendations so that an operator can fix them before they manifest as a service degradation or outage.
Optimizing, profiling and deploying high performance Spark ML and TensorFlow ...DataWorks Summit
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool , I’ll demonstrate how to optimize, profile, and deploy TensorFlow Models in GPU-based production environment.
This talk is contains many Spark ML and TensorFlow AI demos using PipelineIO's 100% Open Source Community Edition. All code and Docker images are available to reproduce on your own CPU or GPU-based cluster.
* Bio *
Chris Fregly is Founder and Research Engineer at PipelineIO, a Streaming Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Video Series High Performance TensorFlow in Production.
Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member of the IBM Spark Technology Center in San Francisco.
Keynote slides from Big Data Spain Nov 2016. Has some thoughts on how Hadoop ecosystem is growing and changing to support the enterprise, including Hive, Spark, NiFi, security and governance, streaming, and the cloud.
LLAP (Live Long and Process) is the newest query acceleration engine for Hive 2.0, which entered GA in 2017. LLAP brings into light a new set of trade-offs and optimizations that allows for efficient and secure multi-user BI systems on the cloud. In this talk, we discuss the specifics of building a modern BI engine within those boundaries, designed to be fast and cost-effective on the public cloud. The focus of the LLAP cache is to speed up common BI query patterns on the cloud, while avoiding most of the operational administration overheads of maintaining a caching layer, with an automatically coherent cache with intelligent eviction and support for custom file formats from text to ORC, and explore the possibilities of combining the cache with a transactional storage layer which supports online UPDATE and DELETES without full data reloads. LLAP by itself, as a relational data layer, extends the same caching and security advantages to any other data processing framework. We overview the structure of such a hybrid system, where both Hive and Spark use LLAP to provide SQL query acceleration on the cloud with new, improved concurrent query support and production-ready tools and UI.
Speaker
Sergey Shelukin, Member of Technical Staff, Hortonworks
Stinger.Next by Alan Gates of HortonworksData Con LA
ver the last 13 months the Apache Hive community, which included 145 developers and 44 companies working together through the Stinger initiative, delivered 390,000 lines of code and 1600 resolved JIRA tickets. This is only the beginning. The Hive community has already started the next phase of extending the Speed, Scale, and SQL compliance in Hive. As Hadoop 2.0 with YARN evolves to enable a dizzying array of powerful engines that allow us to interact with ever growing data in new ways, well known tools such as SQL need to scale with it. This session will provide a technical illustration of the challenges facing SQL on Hadoop today and what the road ahead looks like as the user community drives more innovation. Stinger.next is the next multi-phase initiative to evolve Hive as the de facto SQL engine for Hadoop designed to deliver Speed, Scale and better SQL.
Apache Hive is a rapidly evolving project, many people are loved by the big data ecosystem. Hive continues to expand support for analytics, reporting, and bilateral queries, and the community is striving to improve support along with many other aspects and use cases. In this lecture, we introduce the latest and greatest features and optimization that appeared in this project last year. This includes benchmarks covering LLAP, Apache Druid's materialized views and integration, workload management, ACID improvements, using Hive in the cloud, and performance improvements. I will also tell you a little about what you can expect in the future.
Apache Hive is a rapidly evolving project, many people are loved by the big data ecosystem. Hive continues to expand support for analytics, reporting, and bilateral queries, and the community is striving to improve support along with many other aspects and use cases. In this lecture, we introduce the latest and greatest features and optimization that appeared in this project last year. This includes benchmarks covering LLAP, Apache Druid's materialized views and integration, workload management, ACID improvements, using Hive in the cloud, and performance improvements. I will also tell you a little about what you can expect in the future.
Atlanta meetup presentation, discussion around big data processing engines (Hive, HBase, Druid, Spark). Weighs the relative strengths of each engine and which use cases each of the engines are most suited for
We discuss the current state of LLAP (Live Long and Process) – the concurrent sub-second execution of analytical queries engine for Hive 2.0. LLAP is a hybrid execution model that enables performance improvement in and across queries, such as caching of columnar data with cache coherence and intelligent eviction for disaggregated storage models (like S3, Isilon, Azure), JIT-friendly operator pipelines, asynchronous I/O, data pre-fetching and multi-threaded processing. LLAP features robust machine and service failure tolerance achieved by building on top of the time-tested fault tolerant subsystems, as well as a concurrency-directed design that achieves high utilization with low latency via resource sharing, reducing overheads for multiple queries, and enabling the system to preempt tasks of lower priority without failing any query in-flight. The talk also aims to cover the novel deployment model required for hybrid execution. The elasticity demands of the system are served by a long-lived YARN service interacting with on-demand elastic containers serving as a tightly integrated DAG-based framework for query execution. We discuss the current state of the project, performance numbers, deployment and usage strategy, as well as future work, including how LLAP fits into a unified secure DataFrame access layer.
Hive on spark is blazing fast or is it finalHortonworks
This presentation was given at the Strata + Hadoop World, 2015 in San Jose.
Apache Hive is the most popular and most widely used SQL solution for Hadoop. To keep pace with Hadoop’s increasingly vital role in the Enterprise, Hive has transformed from a batch-only, high-latency system into a modern SQL engine capable of both batch and interactive queries over large datasets. Hive’s momentum is accelerating: With Spark integration and a shift to in-memory processing on the horizon, Hive continues to expand the boundaries of Big Data.
In this talk the speakers examined Hive performance, past, present and future. In particular they looked at Hive’s origins as a petabyte scale SQL engine.
Through some numbers and graphs, they showed how Hive became 100x faster by moving beyond MapReduce, by vectorizing execution and by introducing a cost-based optimizer.
They detailed and discussed the challenges of scalable SQL on Hadoop.
The looked into Hive’s sub-second future, powered by LLAP and Hive on Spark.
And showed just how fast Hive on Spark really is.
ORC files were originally introduced in Hive, but have now migrated to an independent Apache project. This has sped up the development of ORC and simplified integrating ORC into other projects, such as Hadoop, Spark, Presto, and Nifi. There are also many new tools that are built on top of ORC, such as Hive’s ACID transactions and LLAP, which provides incredibly fast reads for your hot data. LLAP also provides strong security guarantees that allow each user to only see the rows and columns that they have permission for.
This talk will discuss the details of the ORC and Parquet formats and what the relevant tradeoffs are. In particular, it will discuss how to format your data and the options to use to maximize your read performance. In particular, we’ll discuss when and how to use ORC’s schema evolution, bloom filters, and predicate push down. It will also show you how to use the tools to translate ORC files into human-readable formats, such as JSON, and display the rich metadata from the file including the type in the file and min, max, and count for each column.
Discover HDP2.1: Apache Storm for Stream Data Processing in HadoopHortonworks
For the first time, Hortonworks Data Platform ships with Apache Storm for processing stream data in Hadoop.
In this presentation, Himanshu Bari, Hortonworks senior product manager, and Taylor Goetz, Hortonworks engineer and committer to Apache Storm, cover Storm and stream processing in HDP 2.1:
+ Key requirements of a streaming solution and common use cases
+ An overview of Apache Storm
+ Q & A
Apache Hadoop 3.0 is coming! As the next major release, it attracts everyone's attention as show case several bleeding-edge technologies and significant features across all components of Apache Hadoop, include: Erasure Coding in HDFS, Multiple Standby NameNodes, YARN Timeline Service v2, JNI-based shuffle in MapReduce, Apache Slider integration and Service Support as First Class Citizen, Hadoop library updates and client-side class path isolation, etc.
In this talk, we will update the status of Hadoop 3 especially the releasing work in community and then go deep diving on new features included in Hadoop 3.0. As a new major release, Hadoop 3 would also include some incompatible changes - we will go through most of these changes and explore its impact to existing Hadoop users and operators. In the last part of this session, we will continue to discuss ongoing efforts in Hadoop 3 age and show the big picture that how big data landscape could be largely influenced by Hadoop 3.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
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Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.