Migrating Apache Hive Workload to Apache Spark: Bridge the Gap with Zhan Zhang and Jane Wang
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At Spark Summit 2017, we described our framework to migrate production Hive workload to Spark with minimal user intervention. After a year of migration, Spark now powers an important part of our batch processing workload. The migration framework supports syntax compatibility analysis, offline/online shadowing, and data validation. In this session, we first introduce new features and improvements in the migration framework to support bucketed tables and increase automation. Next, we will deep dive into the top technical challenges we encountered and how we addressed them. We improved the the syntax compatibility between Hive and Spark from around 51% to 85% by identifying/developing top missing features, fixing incompatible UDFs, and implementing a UDF testing framework. In addition, we developed reliable join operators to improve Spark stability in production when leveraging optimizations such as ShuffledHashJoin. Finally, we will share an update on our overall migration effort and examples of migrations wins. For example, we were able to migrate one of the most complicated workloads in Facebook from Hive to Spark with more than 2.5X performance gain.
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"
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Dynamically generating pipelines that can be abstracted away from users
Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers
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The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity.
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Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
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Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
o Custom queries on top a table; We load the data once and query N times
· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
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· Precautions for retries and speculative execution
· Pipelining to improve performance
Re-imagine Data Monitoring with whylogs and Spark
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In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.
Raven: End-to-end Optimization of ML Prediction Queries
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We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure.
This allows us to introduce optimization rules that
(i) reduce unnecessary computations by passing information between the data processing and ML operators
(ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and
(iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator.
We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.
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Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them.
Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy.
This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.
Massive Data Processing in Adobe Using Delta Lake
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What are we storing?
Multi Source – Multi Channel Problem
Data Representation and Nested Schema Evolution
Performance Trade Offs with Various formats
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(String FTW)
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Writer Worries and How to Wipe them Away
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Feature Engineering for Electronic Health Record Systems
Feature Engineering for Electronic Health Record SystemsProcess mining Evangelist Oak Ridge National Laboratory (ORNL) is a leading science and technology laboratory under the direction of the Department of Energy.
Hilda Klasky is part of the R&D Staff of the Systems Modeling Group in the Computational Sciences & Engineering Division at ORNL. To prepare the data of the radiology process from the Veterans Affairs Corporate Data Warehouse for her process mining analysis, Hilda had to condense and pre-process the data in various ways. Step by step she shows the strategies that have worked for her to simplify the data to the level that was required to be able to analyze the process with domain experts.
hersh's midterm project.pdf music retail and distribution
hersh's midterm project.pdf music retail and distributionhershtara1 midterm project for music retail and distribution
Adopting Process Mining at the Rabobank - use case
Adopting Process Mining at the Rabobank - use caseProcess mining Evangelist Frank van Geffen is a Process Innovator at the Rabobank. He realized that it took a lot of different disciplines and skills working together to achieve what they have achieved. It's not only about knowing what process mining is and how to operate the process mining tool. Instead, a lot of emphasis needs to be placed on the management of stakeholders and on presenting insights in a meaningful way for them.
The results speak for themselves: In their IT service desk improvement project, they could already save 50,000 steps by reducing rework and preventing incidents from being raised. In another project, business expense claim turnaround time has been reduced from 11 days to 1.2 days. They could also analyze their cross-channel mortgage customer journey process.
How to regulate and control your it-outsourcing provider with process mining
How to regulate and control your it-outsourcing provider with process miningProcess mining Evangelist Oliver Wildenstein is an IT process manager at MLP. As in many other IT departments, he works together with external companies who perform supporting IT processes for his organization. With process mining he found a way to monitor these outsourcing providers.
Rather than having to believe the self-reports from the provider, process mining gives him a controlling mechanism for the outsourced process. Because such analyses are usually not foreseen in the initial outsourcing contract, companies often have to pay extra to get access to the data for their own process.
文凭证书美国SDSU文凭圣地亚哥州立大学学生证学历认证查询
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Sets theories and applications that can used to imporve knowledge
Sets theories and applications that can used to imporve knowledgesaumyasl2020 This is mathematical ppt
Improving Product Manufacturing Processes
Improving Product Manufacturing ProcessesProcess mining Evangelist Giancarlo Lepore works at Zimmer Biomet, Switzerland. Zimmer Biomet produces orthopedic products (for example, hip replacements) and one of the challenges is that each of the products has many variations that require customizations in the production process.
Giancarlo is a business analyst in Zimmer Biomet’s operational intelligence team. He has introduced process mining to analyze the material flow in their production process.
He explains why it is difficult to analyze the production process with traditional lean six sigma tools, such as spaghetti diagrams and value stream mapping. He compares process mining to these traditional process analysis methods and also shows how they were able to resolve data quality problems in their master data management in the ERP system.
AI ------------------------------ W1L2.pptx
AI ------------------------------ W1L2.pptxAyeshaJalil6 This lecture provides a foundational understanding of Artificial Intelligence (AI), exploring its history, core concepts, and real-world applications. Students will learn about intelligent agents, machine learning, neural networks, natural language processing, and robotics. The lecture also covers ethical concerns and the future impact of AI on various industries. Designed for beginners, it uses simple language, engaging examples, and interactive discussions to make AI concepts accessible and exciting.
By the end of this lecture, students will have a clear understanding of what AI is, how it works, and where it's headed.
Transforming health care with ai powered
Transforming health care with ai poweredgowthamarvj Transforming health care with ai powered dieases
Patient data
Process Mining at Dimension Data - Jan vermeulen
Process Mining at Dimension Data - Jan vermeulenProcess mining Evangelist Dimension Data has over 30,000 employees in nine operating regions spread over all continents. They provide services from infrastructure sales to IT outsourcing for multinationals. As the Global Process Owner at Dimension Data, Jan Vermeulen is responsible for the standardization of the global IT services processes.
Jan shares his journey of establishing process mining as a methodology to improve process performance and compliance, to grow their business, and to increase the value in their operations. These three pillars form the foundation of Dimension Data's business case for process mining.
Jan shows examples from each of the three pillars and shares what he learned on the way. The growth pillar is particularly new and interesting, because Dimension Data was able to compete in a RfP process for a new customer by providing a customized offer after analyzing the customer's data with process mining.
Process Mining Machine Recoveries to Reduce Downtime
Process Mining Machine Recoveries to Reduce DowntimeProcess mining Evangelist
ASML provides chip makers with everything they need to mass-produce patterns on silicon, helping to increase the value and lower the cost of a chip. The key technology is the lithography system, which brings together high-tech hardware and advanced software to control the chip manufacturing process down to the nanometer. All of the world’s top chipmakers like Samsung, Intel and TSMC use ASML’s technology, enabling the waves of innovation that help tackle the world’s toughest challenges.
The machines are developed and assembled in Veldhoven in the Netherlands and shipped to customers all over the world. Freerk Jilderda is a project manager running structural improvement projects in the Development & Engineering sector. Availability of the machines is crucial and, therefore, Freerk started a project to reduce the recovery time.
A recovery is a procedure of tests and calibrations to get the machine back up and running after repairs or maintenance. The ideal recovery is described by a procedure containing a sequence of 140 steps. After Freerk’s team identified the recoveries from the machine logging, they used process mining to compare the recoveries with the procedure to identify the key deviations. In this way they were able to find steps that are not part of the expected recovery procedure and improve the process.
What is ETL? Difference between ETL and ELT?.pdf
What is ETL? Difference between ETL and ELT?.pdfSaikatBasu37 What is ETL? What is the difference between ETL and ELT?
新西兰文凭奥克兰理工大学毕业证书AUT成绩单补办
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How to regulate and control your it-outsourcing provider with process mining
How to regulate and control your it-outsourcing provider with process miningProcess mining Evangelist
Migrating Apache Hive Workload to Apache Spark: Bridge the Gap with Zhan Zhang and Jane Wang
- 1. Zhan Zhang, Jane Wang, Facebook Migrating Apache Hive Workload to Apache Spark: Bridge the Gap
- 2. Overview • Hive to Spark Migration Effort • Narrowing Down Feature Gaps – Regex Column Specification Support. – Local Writes support. – UDFs • Performance and Reliability – Dynamic Join – Bucket Join • Advanced Optimization for Extremely Large Jobs – Secondary Partitioning – Run-time Optimization.
- 3. • Why do we migrate workload from hive to Spark – Performance – Identify and narrow down the feature gap. Hive to Spark Migration
- 4. Regex Column Specification Support • One of the most failures in our syntax analysis. • Support regex column specification. – SELECT `(a)?+.+` FROM data table – SELECT t.`(a)?+.+` FROM data table • SPARK-12139 4put your #assignedhashtag here by setting the footer in view-header/footer
- 5. Local Filesystem Writes • Support Writing data into the filesystem from queries … – INSERT OVERWRITE LOCAL? DIRECTORY path=STRING rowFormat? createFileFormat? – INSERT OVERWRITE LOCAL? DIRECTORY (path=STRING)? tableProvider (OPTIONS options=tablePropertyList)? 5
- 6. UDF Support • UDAF_JAVA_F/UDTF_JAVA_F/UDF_JAVA_F • UDF_Bind • UDF_EVAL_F • Non-deterministic Expression • … 6
- 7. Narrowing Down Feature Gaps - Syntax • Regex Column Specification • Syntax parser improvement • UDF compatibility – Enum value – User defined class type – Lambda function
- 8. 3X Workload Growth in 6 Month Reserved CPU Days CPU Days
- 9. Joins Broadcast Join ShuffleHash Join SortMerge Join
- 10. Dynamic Join Build Hash table OOM Hash Join Reconstruct Iterator Sort MergeJoin Start End No Ye s • More aggressively leverage HashJoin • Provide a reliable fallback mechanim
- 11. Dynamic Join – Physical Plan
- 12. Bucket Join Bucket 1 Bucket 2 Bucket 4 Bucket 2 Bucket 3 Bucket 4 Bucket 3 Bucket 1 Split 1 Split 2 Split 3 Split 4 Bucket 1 Bucket 2 Bucket 4 Bucket 2 Bucket 3 Bucket 1 Split 1 Split 2 • Support different number (multiplier) of buckets on left/right side.
- 13. Bucket Join Validation • To verify bucket join spark generate consistent result to hive bucket join – Read Spark/Hive Table. – Zip the corresponding splits from spark/hive generated tables. – Compare the sorted column in two splits sequentially. – Sort the bucket column in each split and compare rows in two splits sequentially. 13
- 14. Challenges in Large Jobs • A large job with 10,000 mapper * 10,000 reducer – IOPS: 100,000,000 – HDFS: 10,000 result files – Scheduling Overhead: 20,000 tasks – Manual Tuning – Data skewness 14
- 15. Advanced - Secondary Partitioning
- 16. Pros and Cons • Reduce IOPS • Number of HDFS files • Runtime Optimization • Backward Compatibility – Exactly same behavior with split number = 1 • Auto-Configuration – 503 partitions and 13 buckets to achieve good performance. BUT • Reduced Parallelism • Need to fetch all before computation.
- 18. JIRA • SPARK-12139 – REGEX Column Specification for Hive Queries • SPARK-4131 – Support "Writing data into the filesystem from queries” • SPARK-23306 – Race condition in TaskMemoryManager • SPARK-19326 – Speculated task attempts do not get launched in few scenarios • SPARK-19839 – Fix memory leak in BytesToBytesMap
- 19. ACKNOWLEDGEMENTS The presentation includes the work from the Spark team in Facebook. Thanks for their contribution, esp., Lin Wang, Tejas Patil.
- 20. Question?