Tachyon meetup San Francisco Oct 2014
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This document outlines the agenda for a Tachyon Meetup in San Francisco. The agenda includes discussing the xPatterns architecture, BDAS++, demos of Tachyon internals and APIs, and lessons learned. BDAS++ refers to enhancements made to Tachyon to support Spark SQL and the Spark job server. Lessons learned focus on issues discovered like partial in-memory file storage bugs and best practices for Tachyon usage.
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To conclude, we will also give an overview on our course ID2223 on Large Scale Learning and Deep Learning, in which 60 students designed and ran SparkML applications on the platform.
Feeding Cassandra with Spark-Streaming and Kafka
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An introduction into Spark ML plus how to go beyond when you get stuck
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Spark Summit EU talk by Mike Percy
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Spark Summit EU talk by Jorg Schad
This document discusses containers and virtual machines. It explains that containers provide a lightweight virtualization method that isolates applications but shares the host operating system kernel. Containers use resource isolation features like cgroups and namespaces to limit CPU, memory, storage, and networking usage. In contrast, virtual machines run their own full operating system and provide stronger isolation but are more resource intensive.
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Apache Kafka sits at the center of a technology ecosystem that can be a bit overwhelming to someone just getting started. Fortunately, Apache Kafka is also at the heart of an amazing community that is able and eager to help! So, if you are new, or relatively new, to Apache Kafka, welcome! I’d like to introduce you to the Kafka ecosystem, and present you with a plan for how to learn and be productive with it. I’d also like to introduce you to one of the most helpful and welcoming software communities I’ve ever encountered.
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I am still learning myself, so I will share with you what and who have helped me in my journey, and then I’ll invite you to continue that journey with me. It’s going to be a great adventure!
Lambda Architecture with Spark, Spark Streaming, Kafka, Cassandra, Akka and S...
Regardless of the meaning we are searching for over our vast amounts of data, whether we are in science, finance, technology, energy, health care…, we all share the same problems that must be solved: How do we achieve that? What technologies best support the requirements? This talk is about how to leverage fast access to historical data with real time streaming data for predictive modeling for lambda architecture with Spark Streaming, Kafka, Cassandra, Akka and Scala. Efficient Stream Computation, Composable Data Pipelines, Data Locality, Cassandra data model and low latency, Kafka producers and HTTP endpoints as akka actors...
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An introduction into Spark ML plus how to go beyond when you get stuck
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#phillyopensource
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Speakers:
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Terraform - Taming Modern Clouds
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Using Snap Clone with Enterprise Manager 12c
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Apache Deep Learning 101 - ApacheCon Montreal 2018 v0.31
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Advanced caching techniques with ehcache, big memory, terracotta, and coldfusion
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HTTP/2 Comes to Java - What Servlet 4.0 Means to You
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Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Design and optimization of ion propulsion drone
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
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This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
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efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
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Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Rainfall intensity duration frequency curve statistical analysis and modeling...
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Advanced control scheme of doubly fed induction generator for wind turbine us...
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Tachyon meetup San Francisco Oct 2014
- 1. Tachyon in xPatterns Tachyon Meetup SF 1 Oct 2014
- 2. Agenda • xPatterns architecture • BDAS++ • Demos • Tachyon internals (apis) • Lessons learned 2
- 3. 3
- 4. BDAS++ • Tachyon patch (https://github.com/amplab/tachyon/pull/482) • Jaws, xPatterns http spark sql server http://github.com/Atigeo/http-spark-sql- 4 server Backward compatible with Shark and Spark 0.x stack • Spark Job Server multiple Spark contexts in same JVM, job submission in Java + Scala https://github.com/Atigeo/spark-job-rest • Mesos framework starvation bug submitted patch… detailed Tech Blog link soon at http://xpatterns.com • *SchedulerBackend update, 0.9.0 patches (shuffle spill, Mesos fine-grained)
- 5. Demos … 5
- 6. Lessons learned • partial in-memory file storage bug • journal file on hdfs -> backup of local master disk • hdfs api • RawTable in Shark • persist(OFF_HEAP) temporary storage • RDD.persist() OFF_HEAP > MEMORY_SER_AND_DISK • native API: getInStream(CACHE|NO_CACHE) -> local workers • do not evict blocks when streaming to Tachyon/hdfs • Tachyon > Spark JVM Cache for long running jobs • kryo/defaultCodec/sequenceFile format to minimize memory footprint • 25million emails/month 2TB, 3-45 nodes, 120-170GB of RAM for Tachyon 6
- 7. Q & A 7
- 8. © 2013 Atigeo, LLC. All rights reserved. Atigeo and the xPatterns logo are trademarks of Atigeo. The information herein is for informational purposes only and represents the current view of Atigeo as of the date of this presentation. Because Atigeo must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Atigeo, and Atigeo cannot guarantee the accuracy of any information provided after the date of this presentation. ATIGEO MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.
Editor's Notes
- The physical architecture diagram for our largest customer deployment, demonstrating the enterprise-grade attributes of the platform: scalability, high availability, performance, resilience, manageability while providing means for geo-failover (warehouse), geo-replication (real-time DB), data and system monitoring, instrumentation, backup & restore. Cassandra rings are DC-replicated across EC2 east and west coast regions, data between geo-replicas synchronized in real time through an ipsec tunnel (VPC-to-VPC). Geo-replicated apis behind an AWS Route 53 DNS service (latency based resource records sets) and ELBs ensures users requests are served from the closest geographical location. Failure to an entire region (happened to us during a big conference!) does not affect our availability and SLAs. User facing dashboards are served from Cassandra (real-time store), with data being exported from a data warehouse (Shark/Hive) build on top a Mesos-managed Spark/Hadoop cluster. Export jobs are instrumented and provide a throttling mechanism to control throughput. Export jobs run on the east-coast only, data is synchronized in real time with the west coast ring. Generated apis are automatically instrumented (Graphite) and monitored (Nagios).
- Referral Provider Network: one of the 6 applications that we built for our healthcare customer using the xPatterns APIs and tools on the new beyond Hadoop infrastructure: ELT Pipeline, Export to NoSQL API. The dashboard for the RPN application was built using D3.js and angular against the generic api published by the export tool. The application allows for building a graph of downstream and upstream referred and referring providers, grouped by specialty and with computed aggregates like patient counts, claim counts and total charged amounts. RPN is used for both fraud detection and for aiding a clinic buying decision, by following the busiest graph paths. The dataset behind the app consists of 8 billion medical records, from which we extracted 1.7 million providers (Shark warehouse) and built 53 million relationships in the graph (persisted in Cassandra)