The case for Hadoop performance
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The case for Hadoop performance, short presentation on the need for BDOOP, the Big Data Operations On Performance group. Presents the case why analyzing Hadoop performance is complex, but important to devise cost effective infrastructures. Originally presented at the Barcelona BDOOP meetup group: http://www.meetup.com/BDOOP-BigData-Operations-On-Perfomance-Barcelona/
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Motorized rotating pancake coil (MRPC) probes have been developed to overcome the limitation of circumferential cracking and to provide a high resolution mapping of the tube, sensitive to all types of flaws in any orientation. However, this inspection technique remains very slow and not appropriate for inspecting the entire length of the tube. Eddy current array (ECA) tubing probes were introduced in the late 1990s with success for the inspection of Steam Generators. However, these probes are very specialized, equipment and software-specific, and cost prohibitive outside the scope of SG applications.
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• Typically, the manufacturing thickness tolerance of carbon steel tubes is approximately 10 %. But such a variation in thickness can represent a rotation of about 60° on your channel, which greatly affects sizing interpretations.
• The magnetic permeability of carbon steel varies from grade to grade and according to processing history — tubes processed at high temperatures do not have the same permeability than those processed at low temperatures. Such variations in permeability have a considerable impact on RFT sensitivity and depth sizing.
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• bobbin probes are very sensitive to tube expansion and ferromagnetic materials, out of which tubesheets are usually made. When analyzing bobbin coil data, it is very difficult to
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Originally presented at: https://spark-summit.org/eu-2017/events/the-state-of-apache-spark-in-the-cloud/
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The state of Hive and Spark in the Cloud (July 2017)
Originally presented at the BDOOP and Spark Barcelona meetup groups: http://meetu.ps/3bwCTM
Cloud providers currently offer convenient on-demand managed big data clusters (PaaS) with a pay-as-you-go model. In PaaS, analytical engines such as Spark and Hive come ready to use, with a general-purpose configuration and upgrade management. Over the last year, the Spark framework and APIs have been evolving very rapidly, with major improvements on performance and the release of v2, making it challenging to keep up-to-date production services both on-premises and in the cloud for compatibility and stability. The talk compares:
• The performance of both v1 and v2 for Spark and Hive
• PaaS cloud services: Azure HDinsight, Amazon Web Services EMR, Google Cloud Dataproc
• Out-of-the-box support for Spark and Hive versions from providers
• PaaS reliability, scalability, and price-performance of the solutions
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The state of Spark in the cloud
Originally presented at Strata EU 2017: https://conferences.oreilly.com/strata/strata-eu/public/schedule/detail/57631
Cloud providers currently offer convenient on-demand managed big data clusters (PaaS) with a pay-as-you-go model. In PaaS, analytical engines such as Spark and Hive come ready to use, with a general-purpose configuration and upgrade management. Over the last year, the Spark framework and APIs have been evolving very rapidly, with major improvements on performance and the release of v2, making it challenging to keep up-to-date production services both on-premises and in the cloud for compatibility and stability.
Nicolas Poggi evaluates the out-of-the-box support for Spark and compares the offerings, reliability, scalability, and price-performance from major PaaS providers, including Azure HDinsight, Amazon Web Services EMR, Google Dataproc, and Rackspace Cloud Big Data, with an on-premises commodity cluster as baseline. Nicolas uses BigBench, the brand new standard (TPCx-BB) for big data systems, with both Spark and Hive implementations for benchmarking the systems. BigBench combines SQL queries, MapReduce, user code (UDF), and machine learning, which makes it ideal to stress Spark libraries (SparkSQL, DataFrames, MLlib, etc.).
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Using BigBench to compare Hive and Spark (Long version)
BigBench is the brand new standard (TPCx-BB) for benchmarking and testing Big Data systems. The BigBench specification describes several application use cases combining the need for SQL queries, Map/Reduce, user code (UDF), Machine Learning, and even streaming. From the available implementation, we can test the different framework combinations such as Hadoop+Hive (with Mahout) and Spark (SparkSQL+MLlib) in their different versions and configurations, helping us to spot problems and possible optimizations of our data stacks.
This talk first introduces BigBench and how problems can it solve. Then, presents both Hive and Spark benchmark results with their respective 1 and 2 versions under distinct configurations including Tez, Mahout, MLlib. Experiments are run on Cloud and On-Prem clusters of different numbers of nodes and data scales, taking into account interactive and batch usage. Results are further classified by use cases, showing where each platform shines (or doesn't), and why, based on performance metrics and logfile analysis. The talk concludes with the main findings, the scalability, and limits of each framework.
Originally presented at: https://dataworkssummit.com/munich-2017/sessions/using-bigbench-to-compare-hive-and-spark-versions-and-features/
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State-of-the-Art RFT— Meeting the Ferromagnetic Tube Challenge
Ferromagnetic heat exchanger tubing found in petrochemical plants cannot be inspected with conventional eddy current techniques. Common inspection methods for this material include IRIS, magnetic flux leakage (MFL), and remote-field testing (RFT). In general, RFT is preferred due to its defect sizing capabilities, decreased need for tube cleanliness, and high inspection speeds. This application note shows how innovative software features allow working around traditional difficulties encountered with RFT.
RFT is a through-transmission technique where the magnetic field travels twice in the tube wall, which induces important amplitude attenuation and phase lag. This double diffusion in the tube wall makes the RFT technique very sensitive to any nominal thickness variations and permeability change. This high sensitivity brings several challenges:
• Typically, the manufacturing thickness tolerance of carbon steel tubes is approximately 10 %. But such a variation in thickness can represent a rotation of about 60° on your channel, which greatly affects sizing interpretations.
• The magnetic permeability of carbon steel varies from grade to grade and according to processing history — tubes processed at high temperatures do not have the same permeability than those processed at low temperatures. Such variations in permeability have a considerable impact on RFT sensitivity and depth sizing.
• Calibration tubes matching the properties of tubes to inspect are extremely hard to come by. Differences in thickness and permeability almost always make calibrations inadequate without the proper tools to compensate.
Assigment 6
Computer ethics rules include limiting system use to business purposes only, not interfering with other users or the system, respecting software copyrights and licenses, and restricting system administrators' access to user files and activities to maintenance purposes only. Major principles are using computer resources responsibly and ethically, respecting other users' privacy and intellectual property rights.
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Heat exchangers are essential to the operation of many systems in various industries, processing plants and electric power generation plants, for example. A unique type of heat exchanger (HX) was introduced several years ago: the Twisted Tube® HX. They offer the highest levels of efficiency, but have always been a challenge for periodic inspections. Until now.
A twisted tube is composed of two straight, circular sections near the tubesheets, separated by a length of helical, oval tube. This helix creates a small, central circular passage along the length of the tube where traditional bobbin probes can be pushed and pulled.
However, standard ECT probes deliver suboptimal performances — detecting, characterizing, and sizing flaws are problematic because of poor signal-to-noise ratios. Flaws on the wall furthest away from the tube’s center (crest) produce much smaller, distorted signals than same-size flaws in the narrow section, closest to the tube’s center (dip). This is because of the liftoff created by the tube’s geometry between its walls and the ECT probe. This amount of liftoff translates into a lower probability of detection and less accurate sizing.
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The document discusses the design of a concrete chute for delivering wet concrete from a truck to a construction site. It analyzes the forces on the hydraulic cylinder that raises and lowers the chute as it varies between 0 and 50 degrees. Calculations determine the force capacity of the cylinder should be between 472.138 lbs and 884.531 lbs to safely transport the concrete through the varying lengths of the chute segments. The design of the concrete truck, mixer, and chute are considered to properly deliver wet concrete while ensuring safety.
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Welding techniques are numerous and each of them has its
peculiarities. Laser beam welding (LBW) uses a laser to
concentrate heat, very quickly producing deep, narrow welds.
Laser welding is common in nuclear applications and highvolume
applications such as automotive component
manufacturing, as well as others where speed and excellent
accuracy are necessary.
Find out more at www.eddyfi.com
Assessing Circumferential Cracking in Non-Ferromagnetic Heat Exchanger Tubes
Heat exchangers are used to heat and cool various fluids across a wide spectrum of industries. This is especially critical to the power generation and petrochemical industries where fixed equipment reliability is paramount. Tubes in heat exchangers made of such materials as austenitic stainless steel and Inconel are plagued by circumferential cracking, which conventional, single-coil eddy current testing (ECT) cannot reliably detect.
This application note explores how an innovative probe technology is able to address this challenge.
Non-ferromagnetic heat exchanger tubing is susceptible to circumferential cracking in the vicinity of the tubesheet. These cracks are induced by the localized stress created during the tube-to-tubesheet rolling process.
Circumferential cracking is very difficult to detect and characterize with standard eddy current bobbin probes, currently the norm in the industry to inspect tubes, because:
• bobbin probes are very sensitive to tube expansion and ferromagnetic materials, out of which tubesheets are usually made. When analyzing bobbin coil data, it is very difficult to
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More from Nicolas Poggi
Benchmarking Elastic Cloud Big Data Services under SLA Constraints
The document proposes a new benchmark called Elasticity Test (ET) to evaluate elastic cloud big data systems under service level agreement (SLA) constraints. The ET generates realistic workloads based on production job arrival patterns and scales of data. It measures SLA compliance by calculating the distance between actual query completion times and specified SLAs. This provides a more meaningful metric than the current TPCx-BB metric. Experimental results on Apache Hive and Spark using the new ET and metric show significant differences from the current metric, highlighting weaknesses in elasticity and isolation. Future work includes testing database-as-a-service platforms and further study of specifying and incorporating SLAs into benchmarks.
Correctness and Performance of Apache Spark SQL
1) The document discusses ensuring correctness and performance of Apache Spark SQL through extensive testing that goes beyond unit and integration testing to include techniques like random query generation, failure testing, and micro and macro benchmarks.
2) It describes Databricks' continuous integration pipeline for Spark that classifies issues, analyzes regressions, and alerts on problems to minimize impacts.
3) As an example, it details an investigation that found a 18% regression in query performance between Spark 2.3 and 2.4 due to changes in hashing functions, and the steps taken like profiling and bisection to root cause and address the issue.
State of Spark in the cloud (Spark Summit EU 2017)
Originally presented at: https://spark-summit.org/eu-2017/events/the-state-of-apache-spark-in-the-cloud/
Cloud providers currently offer convenient on-demand managed big data clusters (PaaS) with a pay-as-you-go model. In PaaS, analytical engines such as Spark and Hive come ready to use, with a general-purpose configuration and upgrade management. Over the last year, the Spark framework and APIs have been evolving very rapidly, with major improvements on performance and the release of v2, making it challenging to keep up-to-date production services both on-premises and in the cloud for compatibility and stability. Nicolas Poggi evaluates the out-of-the-box support for Spark and compares the offerings, reliability, scalability, and price-performance from major PaaS providers, including Azure HDinsight, Amazon Web Services EMR, Google Dataproc with an on-premises commodity cluster as baseline. Nicolas uses BigBench, the brand new standard (TPCx-BB) for big data systems, with both Spark and Hive implementations for benchmarking the systems. BigBench combines SQL queries, MapReduce, user code (UDF), and machine learning, which makes it ideal to stress Spark libraries (SparkSQL, DataFrames, MLlib, etc.). The work is framed within the ALOJA research project, which features an open source benchmarking and analysis platform that has been recently extended to support SQL-on-Hadoop engines and BigBench. The ALOJA project aims to lower the total cost of ownership (TCO) of big data deployments and study their performance characteristics for optimization. Nicolas highlights how to easily repeat the benchmarks through ALOJA and benefit from BigBench to optimize your Spark cluster for advanced users. The work is a continuation of a paper to be published at the IEEE Big Data 16 conference.
The state of Hive and Spark in the Cloud (July 2017)
Originally presented at the BDOOP and Spark Barcelona meetup groups: http://meetu.ps/3bwCTM
Cloud providers currently offer convenient on-demand managed big data clusters (PaaS) with a pay-as-you-go model. In PaaS, analytical engines such as Spark and Hive come ready to use, with a general-purpose configuration and upgrade management. Over the last year, the Spark framework and APIs have been evolving very rapidly, with major improvements on performance and the release of v2, making it challenging to keep up-to-date production services both on-premises and in the cloud for compatibility and stability. The talk compares:
• The performance of both v1 and v2 for Spark and Hive
• PaaS cloud services: Azure HDinsight, Amazon Web Services EMR, Google Cloud Dataproc
• Out-of-the-box support for Spark and Hive versions from providers
• PaaS reliability, scalability, and price-performance of the solutions
Using BigBench, the new Big Data benchmark standard. BigBench combines SQL queries, MapReduce, user code (UDF), and machine learning, which makes it ideal to stress Spark libraries (SparkSQL, DataFrames, MLlib, etc.).
The state of Spark in the cloud
Originally presented at Strata EU 2017: https://conferences.oreilly.com/strata/strata-eu/public/schedule/detail/57631
Cloud providers currently offer convenient on-demand managed big data clusters (PaaS) with a pay-as-you-go model. In PaaS, analytical engines such as Spark and Hive come ready to use, with a general-purpose configuration and upgrade management. Over the last year, the Spark framework and APIs have been evolving very rapidly, with major improvements on performance and the release of v2, making it challenging to keep up-to-date production services both on-premises and in the cloud for compatibility and stability.
Nicolas Poggi evaluates the out-of-the-box support for Spark and compares the offerings, reliability, scalability, and price-performance from major PaaS providers, including Azure HDinsight, Amazon Web Services EMR, Google Dataproc, and Rackspace Cloud Big Data, with an on-premises commodity cluster as baseline. Nicolas uses BigBench, the brand new standard (TPCx-BB) for big data systems, with both Spark and Hive implementations for benchmarking the systems. BigBench combines SQL queries, MapReduce, user code (UDF), and machine learning, which makes it ideal to stress Spark libraries (SparkSQL, DataFrames, MLlib, etc.).
The work is framed within the ALOJA research project, which features an open source benchmarking and analysis platform that has been recently extended to support SQL-on-Hadoop engines and BigBench. The ALOJA project aims to lower the total cost of ownership (TCO) of big data deployments and study their performance characteristics for optimization. Nicolas highlights how to easily repeat the benchmarks through ALOJA and benefit from BigBench to optimize your Spark cluster for advanced users. The work is a continuation of a paper to be published at the IEEE Big Data 16 conference. (A preprint copy can be obtained here.)
Using BigBench to compare Hive and Spark (Long version)
BigBench is the brand new standard (TPCx-BB) for benchmarking and testing Big Data systems. The BigBench specification describes several application use cases combining the need for SQL queries, Map/Reduce, user code (UDF), Machine Learning, and even streaming. From the available implementation, we can test the different framework combinations such as Hadoop+Hive (with Mahout) and Spark (SparkSQL+MLlib) in their different versions and configurations, helping us to spot problems and possible optimizations of our data stacks.
This talk first introduces BigBench and how problems can it solve. Then, presents both Hive and Spark benchmark results with their respective 1 and 2 versions under distinct configurations including Tez, Mahout, MLlib. Experiments are run on Cloud and On-Prem clusters of different numbers of nodes and data scales, taking into account interactive and batch usage. Results are further classified by use cases, showing where each platform shines (or doesn't), and why, based on performance metrics and logfile analysis. The talk concludes with the main findings, the scalability, and limits of each framework.
Originally presented at: https://dataworkssummit.com/munich-2017/sessions/using-bigbench-to-compare-hive-and-spark-versions-and-features/
Using BigBench to compare Hive and Spark (short version)
BigBench is the brand new standard for benchmarking and testing Big Data systems. This talk first introduces BigBench and how problems can it solve. Then, presents both Hive and Spark benchmark results with with their respective 1 and 2 versions under different configurations. Results are further classified by use cases, showing where each platform shines (or doesn't), and why, based on performance metrics and log-file analysis. The talk concludes with the main findings, the scalability and limits of each framework.
Accelerating HBase with NVMe and Bucket Cache
on-Volatile-Memory express (NVMe) standard promises and order of magnitude faster storage than regular SSDs, while at the same time being more economical than regular RAM on TB/$. This talk evaluates the use cases and benefits of NVMe drives for its use in Big Data clusters with HBase and Hadoop HDFS.
First, we benchmark the different drives using system level tools (FIO) to get maximum expected values for each different device type and set expectations. Second, we explore the different options and use cases of HBase storage and benchmark the different setups. And finally, we evaluate the speedups obtained by the NVMe technology for the different Big Data use cases from the YCSB benchmark.
In summary, while the NVMe drives show up to 8x speedup in best case scenarios, testing the cost-efficiency of new device technologies is not straightforward in Big Data, where we need to overcome system level caching to measure the maximum benefits.
The state of SQL-on-Hadoop in the Cloud
With the increase of Hadoop offerings in the Cloud, users are faced with many decisions to make: which Cloud provider, VMs to choose, cluster sizing, storage type, or even if to go to fully managed Platform-as-a-Service (PaaS) Hadoop? As the answer is always "depends on your data and usage", this talk will guide participants over an overview of the different PaaS solutions for the leading Cloud providers. By highlighting the main results benchmarking their SQL-on-Hadoop (i.e., Hive) services using the ALOJA benchmarking project. To compare their current offerings in terms of readiness, architectural differences, and cost-effectiveness (performance-to-price), to entry-level Hadoop based deployments. As well as briefly presenting how to replicate results and create custom benchmarks from internal apps. So that users can make their own decisions about choosing the right provider to their particular data needs.
sudoers: Benchmarking Hadoop with ALOJA
Presentation for the sudoers Barcelona group 0ct 06 2015, on benchmarking Hadoop with ALOJA open source benchmarking platform. The presentation was mostly a live DEMO, posting some slides for the people who could not attend.
http://lanyrd.com/2015/sudoers-barcelona-october/
Benchmarking Hadoop and Big Data
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An on-going presentation for the Docker workshop on how to integrate docker into Vagrant as a provider. In order to remove the requirement of having a VM, and speedup development environments. It also features Puppet as the configuration management system.
The code can be found in: https://github.com/npoggi/vagrant-docker
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State of Spark in the cloud (Spark Summit EU 2017)
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The state of Hive and Spark in the Cloud (July 2017)
The state of Hive and Spark in the Cloud (July 2017)
Using BigBench to compare Hive and Spark (Long version)
Using BigBench to compare Hive and Spark (Long version)
Using BigBench to compare Hive and Spark (short version)
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