Sparkler—Crawler on Apache Spark: Spark Summit East talk by Karanjeet Singh a...Spark Summit
A web crawler is a bot program that fetches resources from the web for the sake of building applications like search engines, knowledge bases, etc. In this talk, Karanjeet Singh and Thamme Gowda will describe a new crawler called Sparkler (contraction of Spark-Crawler) that makes use of recent advancements in distributed computing and information retrieval domains by conglomerating various Apache projects like Spark, Kafka, Lucene/Solr, Tika, and Felix. Sparkler is extensible, highly scalable, and high-performance web crawler that is an evolution of Apache Nutch and runs on Apache Spark Cluster.
https://github.com/USCDataScience/sparkler
This session talks about how unit testing of Spark applications is done, as well as tells the best way to do it. This includes writing unit tests with and without Spark Testing Base package, which is a spark package containing base classes to use when writing tests with Spark.
Sparkler—Crawler on Apache Spark: Spark Summit East talk by Karanjeet Singh a...Spark Summit
A web crawler is a bot program that fetches resources from the web for the sake of building applications like search engines, knowledge bases, etc. In this talk, Karanjeet Singh and Thamme Gowda will describe a new crawler called Sparkler (contraction of Spark-Crawler) that makes use of recent advancements in distributed computing and information retrieval domains by conglomerating various Apache projects like Spark, Kafka, Lucene/Solr, Tika, and Felix. Sparkler is extensible, highly scalable, and high-performance web crawler that is an evolution of Apache Nutch and runs on Apache Spark Cluster.
https://github.com/USCDataScience/sparkler
This session talks about how unit testing of Spark applications is done, as well as tells the best way to do it. This includes writing unit tests with and without Spark Testing Base package, which is a spark package containing base classes to use when writing tests with Spark.
we will see an overview of Spark in Big Data. We will start with an introduction to Apache Spark Programming. Then we will move to know the Spark History. Moreover, we will learn why Spark is needed. Afterward, will cover all fundamental of Spark components. Furthermore, we will learn about Spark’s core abstraction and Spark RDD. For more detailed insights, we will also cover spark features, Spark limitations, and Spark Use cases.
A Deep Dive into Query Execution Engine of Spark SQLDatabricks
Spark SQL enables Spark to perform efficient and fault-tolerant relational query processing with analytics database technologies. The relational queries are compiled to the executable physical plans consisting of transformations and actions on RDDs with the generated Java code. The code is compiled to Java bytecode, executed at runtime by JVM and optimized by JIT to native machine code at runtime. This talk will take a deep dive into Spark SQL execution engine. The talk includes pipelined execution, whole-stage code generation, UDF execution, memory management, vectorized readers, lineage based RDD transformation and action.
From HelloWorld to Configurable and Reusable Apache Spark Applications in Sca...Databricks
We can think of an Apache Spark application as the unit of work in complex data workflows. Building a configurable and reusable Apache Spark application comes with its own challenges, especially for developers that are just starting in the domain. Configuration, parametrization, and reusability of the application code can be challenging. Solving these will allow the developer to focus on value-adding work instead of mundane tasks such as writing a lot of configuration code, initializing the SparkSession or even kicking-off a new project.
This presentation will describe using code samples a developer’s journey from the first steps into Apache Spark all the way to a simple open-source framework that can help kick-off an Apache Spark project very easy, with a minimal amount of code. The main ideas covered in this presentation are derived from the separation of concerns principle.
The first idea is to make it even easier to code and test new Apache Spark applications by separating the application logic from the configuration logic.
The second idea is to make it easy to configure the applications, providing SparkSessions out-of-the-box, easy to set-up data readers, data writers and application parameters through configuration alone.
The third idea is that taking a new project off the ground should be very easy and straightforward. These three ideas are a good start in building reusable and production-worthy Apache Spark applications.
The resulting framework, spark-utils, is already available and ready to use as an open-source project, but even more important are the ideas and principles behind it.
Security is one of fundamental features for enterprise adoption. Specifically, for SQL users, row/column-level access control is important. However, when a cluster is used as a data warehouse accessed by various user groups via different ways, it is difficult to guarantee data governance in a consistent way. In this talk, we focus on SQL users and talk about how to provide row/column-level access controls with common access control rules throughout the whole cluster with various SQL engines, e.g., Apache Spark 2.1, Apache Spark 1.6 and Apache Hive 2.1. If some of rules are changed, all engines are controlled consistently in near real-time. Technically, we enables Spark Thrift Server to work with an identify given by JDBC connection and take advantage of Hive LLAP daemon as a shared and secured processing engine. We demonstrate row-level filtering, column-level filtering and various column maskings in Apache Spark with Apache Ranger. We use Apache Ranger as a single point of security control center.
Large Scale Lakehouse Implementation Using Structured StreamingDatabricks
Business leads, executives, analysts, and data scientists rely on up-to-date information to make business decision, adjust to the market, meet needs of their customers or run effective supply chain operations.
Come hear how Asurion used Delta, Structured Streaming, AutoLoader and SQL Analytics to improve production data latency from day-minus-one to near real time Asurion’s technical team will share battle tested tips and tricks you only get with certain scale. Asurion data lake executes 4000+ streaming jobs and hosts over 4000 tables in production Data Lake on AWS.
Extreme Apache Spark: how in 3 months we created a pipeline that can process ...Josef A. Habdank
Presentation consists of an amazing bundle of Pro tips and tricks for building an insanely scalable Apache Spark and Spark Streaming based data pipeline.
Presentation consists of 4 parts:
* Quick intro to Spark
* N-billion rows/day system architecture
* Data Warehouse and Messaging
* How to deploy spark so it does not backfire
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the CloudNoritaka Sekiyama
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud (Hadoop / Spark Conference Japan 2019)
# English version #
http://hadoop.apache.jp/hcj2019-program/
This session, led by James Hamilton, VP and Distinguished Engineer, gives an insider view of some the innovations that help make the AWS cloud unique. He will show examples of AWS networking innovations from the interregional network backbone, through custom routers and networking protocol stack, all the way down to individual servers. He will show examples from AWS server hardware, storage, and power distribution and then, up the stack, in high scale streaming data processing. James will also dive into fundamental database work AWS is delivering to open up scaling and performance limits, reduce costs, and eliminate much of the administrative burden of managing databases. Join this session and walk away with a deeper understanding of the underlying innovations powering the cloud.
Modern applications, often called “big-data” analysis, require us to manage immense amounts of data quickly. To deal with applications such as these, a new software stack has evolved.
we will see an overview of Spark in Big Data. We will start with an introduction to Apache Spark Programming. Then we will move to know the Spark History. Moreover, we will learn why Spark is needed. Afterward, will cover all fundamental of Spark components. Furthermore, we will learn about Spark’s core abstraction and Spark RDD. For more detailed insights, we will also cover spark features, Spark limitations, and Spark Use cases.
A Deep Dive into Query Execution Engine of Spark SQLDatabricks
Spark SQL enables Spark to perform efficient and fault-tolerant relational query processing with analytics database technologies. The relational queries are compiled to the executable physical plans consisting of transformations and actions on RDDs with the generated Java code. The code is compiled to Java bytecode, executed at runtime by JVM and optimized by JIT to native machine code at runtime. This talk will take a deep dive into Spark SQL execution engine. The talk includes pipelined execution, whole-stage code generation, UDF execution, memory management, vectorized readers, lineage based RDD transformation and action.
From HelloWorld to Configurable and Reusable Apache Spark Applications in Sca...Databricks
We can think of an Apache Spark application as the unit of work in complex data workflows. Building a configurable and reusable Apache Spark application comes with its own challenges, especially for developers that are just starting in the domain. Configuration, parametrization, and reusability of the application code can be challenging. Solving these will allow the developer to focus on value-adding work instead of mundane tasks such as writing a lot of configuration code, initializing the SparkSession or even kicking-off a new project.
This presentation will describe using code samples a developer’s journey from the first steps into Apache Spark all the way to a simple open-source framework that can help kick-off an Apache Spark project very easy, with a minimal amount of code. The main ideas covered in this presentation are derived from the separation of concerns principle.
The first idea is to make it even easier to code and test new Apache Spark applications by separating the application logic from the configuration logic.
The second idea is to make it easy to configure the applications, providing SparkSessions out-of-the-box, easy to set-up data readers, data writers and application parameters through configuration alone.
The third idea is that taking a new project off the ground should be very easy and straightforward. These three ideas are a good start in building reusable and production-worthy Apache Spark applications.
The resulting framework, spark-utils, is already available and ready to use as an open-source project, but even more important are the ideas and principles behind it.
Security is one of fundamental features for enterprise adoption. Specifically, for SQL users, row/column-level access control is important. However, when a cluster is used as a data warehouse accessed by various user groups via different ways, it is difficult to guarantee data governance in a consistent way. In this talk, we focus on SQL users and talk about how to provide row/column-level access controls with common access control rules throughout the whole cluster with various SQL engines, e.g., Apache Spark 2.1, Apache Spark 1.6 and Apache Hive 2.1. If some of rules are changed, all engines are controlled consistently in near real-time. Technically, we enables Spark Thrift Server to work with an identify given by JDBC connection and take advantage of Hive LLAP daemon as a shared and secured processing engine. We demonstrate row-level filtering, column-level filtering and various column maskings in Apache Spark with Apache Ranger. We use Apache Ranger as a single point of security control center.
Large Scale Lakehouse Implementation Using Structured StreamingDatabricks
Business leads, executives, analysts, and data scientists rely on up-to-date information to make business decision, adjust to the market, meet needs of their customers or run effective supply chain operations.
Come hear how Asurion used Delta, Structured Streaming, AutoLoader and SQL Analytics to improve production data latency from day-minus-one to near real time Asurion’s technical team will share battle tested tips and tricks you only get with certain scale. Asurion data lake executes 4000+ streaming jobs and hosts over 4000 tables in production Data Lake on AWS.
Extreme Apache Spark: how in 3 months we created a pipeline that can process ...Josef A. Habdank
Presentation consists of an amazing bundle of Pro tips and tricks for building an insanely scalable Apache Spark and Spark Streaming based data pipeline.
Presentation consists of 4 parts:
* Quick intro to Spark
* N-billion rows/day system architecture
* Data Warehouse and Messaging
* How to deploy spark so it does not backfire
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the CloudNoritaka Sekiyama
Amazon S3 Best Practice and Tuning for Hadoop/Spark in the Cloud (Hadoop / Spark Conference Japan 2019)
# English version #
http://hadoop.apache.jp/hcj2019-program/
This session, led by James Hamilton, VP and Distinguished Engineer, gives an insider view of some the innovations that help make the AWS cloud unique. He will show examples of AWS networking innovations from the interregional network backbone, through custom routers and networking protocol stack, all the way down to individual servers. He will show examples from AWS server hardware, storage, and power distribution and then, up the stack, in high scale streaming data processing. James will also dive into fundamental database work AWS is delivering to open up scaling and performance limits, reduce costs, and eliminate much of the administrative burden of managing databases. Join this session and walk away with a deeper understanding of the underlying innovations powering the cloud.
Modern applications, often called “big-data” analysis, require us to manage immense amounts of data quickly. To deal with applications such as these, a new software stack has evolved.
The world has changed and having one huge server won’t do the job anymore, when you’re talking about vast amounts of data, growing all the time the ability to Scale Out would be your savior. Apache Spark is a fast and general engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
This lecture will be about the basics of Apache Spark and distributed computing and the development tools needed to have a functional environment.
Analytic Insights in Retail Using Apache Spark with Hari ShreedharanDatabricks
Streamsets Data Collector is designed to make data ingest and processing easy. SDC integrates at several levels with Apache Spark to make data analysis using Spark very easy. SDC works with Databricks Cloud to trigger jobs based on incoming data.
In this talk, you will learn how a larger retail player with thousands of outlets is utilizing StreamSets to power Spark jobs on the Databricks cloud, combining real-time foot traffic data and historic behavioral & transaction data for analytic insights that improve revenue per square foot.
Interest is growing in the Apache Spark community in using Deep Learning techniques and in the Deep Learning community in scaling algorithms with Apache Spark. A few of them to note include:
· Databrick’s efforts in scaling Deep learning with Spark
· Intel announcing the BigDL: A Deep learning library for Spark
· Yahoo’s recent efforts to opensource TensorFlowOnSpark
In this lecture we will discuss the key use cases and developments that have emerged in the last year in using Deep Learning techniques with Spark.
In Apache Cassandra Lunch #50, we will discuss how you can use Apache Spark and Apache Cassandra to perform basic Machine Learning tasks.
Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-50-machine-learning-with-spark--cassandra/
Accompanying YouTube video: https://youtu.be/myIX0kkpL9U
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
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Follow Us and Reach Us At:
Anant:
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Scalable Monitoring Using Prometheus with Apache Spark Clusters with Diane F...Databricks
As Apache Spark applications move to a containerized environment, there are many questions about how to best configure server systems in the container world. In this talk we will demonstrate a set of tools to better monitor performance and identify optimal configuration settings. We will demonstrate how Prometheus, a project that is now part of the Cloud Native Computing Foundation (CNCF: https://www.cncf.io/projects/), can be applied to monitor and archive system performance data in a containerized spark environment.
In our examples, we will gather spark metric output through Prometheus and present the data with Grafana dashboards. We will use our examples to demonstrate how performance can be enhanced through different tuned configuration settings. Our demo will show how to configure settings across the cluster as well as within each node.
Spark summit 2019 infrastructure for deep learning in apache spark 0425Wee Hyong Tok
In machine learning projects, the preparation of large datasets is a key phase which can be complex and expensive. It was traditionally done by data engineers before the handover to data scientists or ML engineers. They operated in different environments due to the differences in the tools, frameworks and runtimes required in each phase. Spark's support for different types of workloads brought data engineering closer to the downstream activities like machine learning that depended on the data. Unifying data acquisition, preprocessing, training models and batch inferencing under a single platform enabled by Spark not only provided seamless experience between different phases and helped accelerate the end-to-end ML lifecycle but also lowered the TCO in the building, managing the infrastructure to cover different phases. With that, the needs of a shared infrastructure expanded to include specialized hardware like GPUs and support deep learning workloads as well. Spark can effectively make use of such infrastructure as it integrates with popular deep learning frameworks and supports acceleration of deep learning jobs using GPUs. In this talk, we share learnings and experiences in supporting different types of workloads in shared clusters equipped for doing deep learning as well as data engineering. We will cover the following topics: * Considerations for sharing the infrastructure for big data and deep learning in Spark * Deep learning in Spark in clusters with and without GPUs * Differences between distributed data processing and distributed machine learning * Multitenancy and isolation in shared infrastructure.
https://databricks.com/sparkaisummit/north-america/sessions-single-2019?id=97
Interactive querying of streams using Apache Pulsar_Jerry pengStreamNative
As applications become more reliant on real-time data, streaming/messaging platforms have become more and more popular and crucial to any data pipeline. Currently, many streaming/messaging platforms are only used to access the most recent events from streams of data, however, there is tremendous value that can be unlocked if the full history of streams can be queried in an interactive fashion. Pulsar SQL is a query layer built on top of Apache Pulsar (a next-gen messaging platform), that enables users to dynamically query all streams, old and new, stored inside of Pulsar. Thus, users can unlock insights from querying both new and historical streams of data in a single system. Pulsar SQL leverages Presto and Apache Pulsar’s unique architecture to execute queries in a highly scalable fashion irregardless of the number of partitions of topics that make up the streams. In this talk, we will examine the use cases and advantages of being able to interactively query events within an streaming messaging platform and how Pulsar enables users to do that in the most user-friendly and efficient manner.
Machine translation (MT) is one of the earliest and most successful applications of natural language processing. Many MT services have been deployed via web and smartphone apps, enabling communication and information access across the globe by bypassing language barriers. However, MT is not yet a solved problem. MT services that cover the most languages cover only about a hundred; thousands more are currently unsupported. Even for the currently supported languages, the translation quality is far from perfect.
A key obstacle in our way to achieving usable MT models for any language is data imbalance. On the one hand, machine learning techniques perform subpar on rare categories, having only a few to no training examples. On the other hand, natural language datasets are inevitably imbalanced with a long tail of rare types. The rare types carry more information content, and hence correctly translating them is crucial. In addition to the rare word types, rare phenomena also manifest in other forms as rare languages and rare linguistic styles.
Our contributions towards advancing rare phenomena learning in MT are four-fold: (1) We show that MT models have much in common with classification models, especially regarding the data imbalance and frequency-based biases. We describe a way to reduce the imbalance severity during the model training. (2) We show that the currently used automatic evaluation metrics overlook the importance of rare words. We describe an interpretable evaluation metric that treats important words as important. (3) We propose methods to evaluate and improve translation robustness to rare linguistic styles such as partial translations and language alternations in inputs. (4) Lastly, we present a set of tools intended to advance MT research across a wider range of languages. Using these tools, we demonstrate 600 languages to English translation, thus supporting 500 more rare languages currently unsupported by others.
Macro average: rare types are important tooThamme Gowda
While traditional corpus-level evaluation metrics for machine translation (MT) correlate well with fluency, they struggle to reflect adequacy. Model-based MT metrics trained on segment-level human judgments have emerged as an attractive replacement due to strong correlation results. These models, however, require potentially expensive re-training for new domains and languages. Furthermore, their decisions are inherently non-transparent and appear to reflect unwelcome biases. We explore the simple type-based classifier metric, MacroF1, and study its applicability to MT evaluation. We find that MacroF1 is competitive on direct assessment, and outperforms others in indicating downstream cross-lingual information retrieval task performance. Further, we show that MacroF1 can be used to effectively compare supervised and unsupervised neural machine translation, and reveal significant qualitative differences in the methods’ outputs.
500 languages to English Machine Translation ModelThamme Gowda
While there are more than 7000 languages in the world, most translation research efforts have targeted a few high resource languages. Commercial translation systems support only one hundred languages or fewer, and do not make these models available for transfer to low resource languages. In this work, we present useful tools for machine translation research: MTData, NLCodec and RTG. We demonstrate their usefulness by creating a multilingual neural machine translation model capable of translating from 500 source languages to English. We make this multilingual model readily downloadable and usable as a service, or as a parent model for transfer-learning to even lower-resource languages.
Large Scale Image Forensics using Tika and Tensorflow [ICMR MFSec 2017]Thamme Gowda
This paper describes the applications of deep learning-based image
recognition in the DARPA Memex program and its repository of
1.4 million weapons-related images collected from the Deep web.
We develop a fast, efficient, and easily deployable framework for
integrating Google’s Tensorflow framework with Apache Tika for
automatically performing image forensics on the Memex data. Our
framework and its integration are evaluated qualitatively and quantitatively
and our work suggests that automated, large-scale, and
reliable image classification and forensics can be widely used and
deployed in bulk analysis for answering domain-specific questions
Data Programming: Creating Large Datasets, Quickly -- Presented at JPL MLRGThamme Gowda
Presented at Machine Learning Reading Group (MLRG) at NASA Jet Propulsion Laboratory (JPL).
Data programming is helpful for creating large datasets, our application is in the project named Mars Target Encyclopedia (MTE)
IEEE IRI 16 - Clustering Web Pages based on Structure and Style SimilarityThamme Gowda
The structural similarity of HTML pages is measured by using Tree Edit Distance measure on DOM trees. The stylistic similarity is measured by using Jaccard similarity on CSS class names. An aggregated similarity measure is computed by combining structural and stylistic measures. A clustering method is then applied to this aggregated similarity measure to group the documents.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
Top 7 Unique WhatsApp API Benefits | Saudi ArabiaYara Milbes
Discover the transformative power of the WhatsApp API in our latest SlideShare presentation, "Top 7 Unique WhatsApp API Benefits." In today's fast-paced digital era, effective communication is crucial for both personal and professional success. Whether you're a small business looking to enhance customer interactions or an individual seeking seamless communication with loved ones, the WhatsApp API offers robust capabilities that can significantly elevate your experience.
In this presentation, we delve into the top 7 distinctive benefits of the WhatsApp API, provided by the leading WhatsApp API service provider in Saudi Arabia. Learn how to streamline customer support, automate notifications, leverage rich media messaging, run scalable marketing campaigns, integrate secure payments, synchronize with CRM systems, and ensure enhanced security and privacy.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
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https://sumonreview.com/ai-fusion-buddy-review
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✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
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See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
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1. Nov 15th
2016
@ Apache Big Data EU 2016, Seville, Spain
Thamme GowdaKaranjeet Singh
SPARKLER
Information Retrieval
and Data Science
Chris Mattmann
2. ABOUT: USC INFORMATION RETRIEVAL
AND DATA SCIENCE GROUP
● Established in August 2012 at the University of Southern California (USC)
● Dr. Chris Mattmann, Director of IRDS and our Advisor
● Funding from NSF, DARPA, NASA, DHS, private industry and other agencies
- in collaboration with NASA JPL
● 3 Postdocs, and 30+ Masters and PhD students, 20+ JPLers past 7 years
● Recent topical research in the DARPA XDATA/MEMEX program
Information Retrieval
and Data Science
Email : irds-L@mymaillists.usc.edu
Website : http://irds.usc.edu/
GitHub : https://github.com/USCDataScience/
3. ABOUT: US
Karanjeet Singh
Graduate Student at the University of Southern California, USA
Research Interest: Information Retrieval & Natural Language Processing
Research Affiliate at NASA Jet Propulsion Laboratory
Committer and PMC member of Apache Nutch
Information Retrieval
and Data Science
Thamme Gowda
Graduate Student at the University of Southern California, USA
Research Intern at NASA Jet Propulsion Laboratory, Co Founder at Datoin
Research Interest: NLP, Machine Learning and Information Retrieval
Committer and PMC member of Apache Nutch, Tika, and Joshua (Incubating)
Dr. Chris Mattmann
Director & Vice Chairman, Apache Software Foundation
Research Interest: Data Science, Open Source, Information Retrieval & NLP
Committer and PMC member of Apache Nutch, Tika, (former) Lucene, OODT, Incubator
4. OVERVIEW
● About Sparkler
● Motivations for building Sparkler
● Quick intro to Apache Spark
● Sparkler technology stack, internals
● Features of Sparkler
● Comparison with Nutch
● Going forward
Information Retrieval
and Data Science
5. ABOUT: SPARKLER
● New Open Source Web Crawler
○ A bot program that can fetch resources from the web
● Name: Spark Crawler
● Inspired by Apache Nutch
● Like Nutch: Distributed crawler that can scale horizontally
● Unlike Nutch: Runs on top of Apache Spark
● Easy to deploy and easy to use
Information Retrieval
and Data Science
6. Information Retrieval
and Data Science
MOTIVATION #1
● Challenges in DARPA MEMEX
○ Intro: MEMEX System has crawlers to fetch deep and
dark web data for assisting law keeping agencies
○ Crawls are kind of blackbox, we wanted real-time
progress reports
● Dr. Chris Mattmann was considering an upgrade since 3
years
● Technology upgrade needed
7. Information Retrieval
and Data Science
https://twitter.com/cutting/status/796566255830503424
Modern Hadoop cluster has no Hadoop (Map-Reduce) left in it!
WHY A NEW CRAWLER?
8. Information Retrieval
and Data Science
MOTIVATION #2
● Challenges at DATOIN
○ Intro: Datoin is a distributed text analytics platform
○ Late 2014 - migrated the infrastructure from Hadoop
Map Reduce to Apache Spark
○ But the crawler component (powered by Apache Nutch)
was left behind
● Met Dr. Chris Mattmann at USC in Web Search Engines
class
○ Enquired about his thoughts for running Nutch on Spark
○ Agreed to work on it.
9. ● High performance & Fault tolerance
● Real time crawl analysis
● Easy to customize
Is the food ready?
How is it going?
I want less salt.
Information Retrieval
and Data Science
KEY FEATURES
10. APACHE SPARK: OVERVIEW
● Introduction
● Resilient Distributed Dataset (RDD)
● Driver, Workers & Executors
Information Retrieval
and Data Science
11. APACHE SPARK: INTRODUCTION
● Fast and general engine for large scale data processing
● Started at UC Berkeley in 2009
● The most popular distributed computing framework
● Provides high level APIs in Scala, Java, Python, R
● Integration with Hadoop and its ecosystem
● Open sourced in 2010 under Apache v2.0 license
● Mattmann helped to bring Spark to Apache under
DARPA XDATA effort
Information Retrieval
and Data Science
12. Resilient Distributed Dataset (RDD)
● A basic abstraction in Spark
● Immutable, Partitioned collection of elements operated in parallel
● Data in persistent store (HDFS, Cassandra) or in cache (memory, disk)
● Partitions are recomputed on failure or cache eviction
● Two classes of operations
○ Transformations
○ Actions
● Custom RDDs can also be
implemented - we have one!
Information Retrieval
and Data Science
14. SPARKLER: TECH STACK
● Batch crawling (similar to Apache Nutch)
● Apache Solr as crawl database
● Multi module Maven project with OSGi bundles
● Stream crawled content through Apache Kafka
● Parses everything using Apache Tika
● Crawl visualization - Banana
Information Retrieval
and Data Science
17. ● Crawldb needed indexing
○ For real time analytics
○ For instant visualizations
● This is internal data structure of sparkler
○ Exposed over REST API
○ Used by Sparkler-ui, the web application
● We chose Apache Solr
● Standalone Solr Server or Solr Cloud?
● Glued the crawldb and spark using CrawldbRDD
SPARKLER #1: Lucene/Solr powered Crawldb
Information Retrieval
and Data Science
18. SPARKLER #2: Partitioning by host
Information Retrieval
and Data Science
● Politeness
* Doesn’t hit same server too many times in distributed mode
● First version
○ Group by: Host name
○ Sort by: depth, score
● Customization is easy
○ Write your own Solr query
○ Take advantage of boosting to alter the ranking
● Partitions the dataset based on the above criteria
● Lazy evaluations and delay between the requests
■ Performs parsing instead of waiting
■ Inserts delay only when it is necessary
19. SPARKLER #3: OSGI Plugins
Information Retrieval
and Data Science
● Plugins Interfaces are inspired by Nutch
● Plugins are developed as per Open Service Gateway
Interface (OSGI)
● We chose Apache Felix implementation of OSGI
● Migrated a plugin from Nutch
○ Regex URL Filter Plugin → The most used plugin in
Nutch
● Added JavaScript plugin (described in the next slide)
● //TODO: Migrate more plugins from Nutch
○ Mavenize nutch [NUTCH-2293]
20. SPARKLER #4: JavaScript Rendering
Information Retrieval
and Data Science
● Java Script Execution* has first class support
● Distributable on Spark Cluster without pain
○ Pure JVM based JavaScript engine
● This is an implementation of FetchFunction
● FetchFunction
○ Stream<URL> → Stream<Content>
○ Note: URLS are grouped by host
○ It preserves cookies and reuses sessions for each iteration
Thanks to: Madhav Sharan
Member of USC IRDS* JBrowserDriver by MachinePublishers
21. SPARKLER #5: Output in Kafka Streams
Information Retrieval
and Data Science
● Crawler is sometimes input for the applications that does
deeper analysis
○ Can’t fit all those deeper analysis into crawler
● Integrating to such applications made easy via Queues
● We chose Apache Kafka
○ Suits our need
■ Distributable, Scalable, Fault Tolerant
● FIXME: Larger messages such as Videos
● This is optional, default output on Shared File System
(such as HDFS), compatible with Nutch
*
Thanks to: Rahul Palamuttam
MS CS @ Stanford University; Intern @ NASA JPL)
22. SPARKLER #6: Tika, the universal parser
Information Retrieval
and Data Science
● Apache Tika
○ Is a toolkit of parsers
○ Detects and extracts metadata, text, and URLS
○ Over a thousand different file types
● Main application is to discover outgoing links
● The default Implementation for our ParseFunction
23. SPARKLER #7: Visual Analytics
Information Retrieval
and Data Science
● Charts and Graphs provides nice summary of crawl job
● Real time analytics
● Example:
○ Distribution of URLS across hosts/domains
○ Temporal activities
○ Status reports
● Customizable in real time
● Using Banana Dashboard from Lucidworks
● Sparkler has a sub component named sparkler-ui
* Thanks to : Manish Dwibedy
MS CS University of Southern California
24. SPARKLER #Next: what’s coming?
Information Retrieval
and Data Science
● Interactive UI
● More plugins
● Scoring Crawled Pages
● Focussed Crawling
● Crawl Graph Analysis
● Domain Discovery (another research challenge)
● Other useful plugins from Nutch
● Detailed documentation and tutorials on wiki
25. Nutch Configuration
Version : 1.12
topN : 50,000
Fetcher Thread : 1
Hadoop Configuration
Version : 2.6.0-cdh5.8.2
Slaves : 2
Memory : 8G (Map), 16G (Reduce)
22 Mappers, 11 reducers
HOW FAST IT RUNS - Comparison with Nutch
Information Retrieval
and Data Science
Crawl Iterations : 5
Fetch Delay : 1 sec
Sparkler Configuration
Version : 0.1-SNAPSHOT
topGroups : 252
topN : 1000
Spark Configuration
Version : 1.6.1 with Scala v2.11
Slaves : 2
22 Worker Instances with 210G memory
29. ● Get involved with our journey of Incubator
● Get started: Checkout README and wiki at
https://github.com/USCDataScience/sparkler
Information Retrieval
and Data Science
Questions?
THANK YOU