Experiences Migrating Hive Workload to SparkSQL with Jie Xiong and Zhan Zhang
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At Facebook, millions of Hive queries are executed on a daily basis, and the workload contributes to important analytics that drive product decisions and insights. Spark SQL in Apache Spark provides much of the same functionality as Hive query language (HQL) more efficiently, and Facebook is building a framework to migrate existing production Hive workload to Spark SQL with minimal user intervention. Before Facebook began large-scale migration to SparkSQL, they worked on identifying the gap between HQL and SparkSQL. They built an offline syntax analysis tool that parses, analyzes, optimizes and generates physical plans on daily HQL workload. In this session, they’ll share their results. After finding their syntactic analysis encouraging, they built tooling for offline semantic analysis where they run HQL queries in their Spark shadow cluster and validate the outputs. Output validation is necessary since the runtime behavior in Spark SQL may be different from HQL. They have built a migration framework that supports HQL in both Hive and Spark execution engines, can shadow and validate HQL workloads in Spark, and makes it easy for users to convert their workloads.
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Native Support of Prometheus Monitoring in Apache Spark 3.0
Native Support of Prometheus Monitoring in Apache Spark 3.0Databricks All production environment requires monitoring and alerting. Apache Spark also has a configurable metrics system in order to allow users to report Spark metrics to a variety of sinks. Prometheus is one of the popular open-source monitoring and alerting toolkits which is used with Apache Spark together.
How to understand and analyze Apache Hive query execution plan for performanc...
How to understand and analyze Apache Hive query execution plan for performanc...DataWorks Summit/Hadoop Summit The document compares the query execution plans produced by Apache Hive and PostgreSQL. It shows that Hive's old-style execution plans are overly verbose and difficult to understand, providing many low-level details across multiple stages. In contrast, PostgreSQL's plans are more concise and readable, showing the logical query plan in a top-down manner with actual table names and fewer lines of text. The document advocates for Hive to adopt a simpler execution plan format similar to PostgreSQL's.
Designing Structured Streaming Pipelines—How to Architect Things Right
Designing Structured Streaming Pipelines—How to Architect Things RightDatabricks "Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark's built-in functions make it easy for developers to express complex computations. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem needs to be solved.
What are you trying to consume? Single source? Joining multiple streaming sources? Joining streaming with static data?
What are you trying to produce? What is the final output that the business wants? What type of queries does the business want to run on the final output?
When do you want it? When does the business want to the data? What is the acceptable latency? Do you really want to millisecond-level latency?
How much are you willing to pay for it? This is the ultimate question and the answer significantly determines how feasible is it solve the above questions.
These are the questions that we ask every customer in order to help them design their pipeline. In this talk, I am going to go through the decision tree of designing the right architecture for solving your problem."
The Rise of ZStandard: Apache Spark/Parquet/ORC/Avro
The Rise of ZStandard: Apache Spark/Parquet/ORC/AvroDatabricks Zstandard is a fast compression algorithm which you can use in Apache Spark in various way. In this talk, I briefly summarized the evolution history of Apache Spark in this area and four main use cases and the benefits and the next steps:
1) ZStandard can optimize Spark local disk IO by compressing shuffle files significantly. This is very useful in K8s environments. It’s beneficial not only when you use `emptyDir` with `memory` medium, but also it maximizes OS cache benefit when you use shared SSDs or container local storage. In Spark 3.2, SPARK-34390 takes advantage of ZStandard buffer pool feature and its performance gain is impressive, too.
2) Event log compression is another area to save your storage cost on the cloud storage like S3 and to improve the usability. SPARK-34503 officially switched the default event log compression codec from LZ4 to Zstandard.
3) Zstandard data file compression can give you more benefits when you use ORC/Parquet files as your input and output. Apache ORC 1.6 supports Zstandardalready and Apache Spark enables it via SPARK-33978. The upcoming Parquet 1.12 will support Zstandard compression.
4) Last, but not least, since Apache Spark 3.0, Zstandard is used to serialize/deserialize MapStatus data instead of Gzip.
There are more community works to utilize Zstandard to improve Spark. For example, Apache Avro community also supports Zstandard and SPARK-34479 aims to support Zstandard in Spark’s avro file format in Spark 3.2.0.
How to understand and analyze Apache Hive query execution plan for performanc...
How to understand and analyze Apache Hive query execution plan for performanc...DataWorks Summit/Hadoop Summit
Similar to Experiences Migrating Hive Workload to SparkSQL with Jie Xiong and Zhan Zhang (20)
Benchmarking Performance and Scalability with Web Stress
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Radical Speed for SQL Queries on Databricks: Photon Under the Hood
Radical Speed for SQL Queries on Databricks: Photon Under the HoodDatabricks Join this session to hear from the Photon product and engineering team talk about the latest developments with the project.
As organizations embrace data-driven decision-making, it has become imperative for them to invest in a platform that can quickly ingest and analyze massive amounts and types of data. With their data lakes, organizations can store all their data assets in cheap cloud object storage. But data lakes alone lack robust data management and governance capabilities. Fortunately, Delta Lake brings ACID transactions to your data lakes – making them more reliable while retaining the open access and low storage cost you are used to.
Using Delta Lake as its foundation, the Databricks Lakehouse platform delivers a simplified and performant experience with first-class support for all your workloads, including SQL, data engineering, data science & machine learning. With a broad set of enhancements in data access and filtering, query optimization and scheduling, as well as query execution, the Lakehouse achieves state-of-the-art performance to meet the increasing demands of data applications. In this session, we will dive into Photon, a key component responsible for efficient query execution.
Photon was first introduced at Spark and AI Summit 2020 and is written from the ground up in C++ to take advantage of modern hardware. It uses the latest techniques in vectorized query processing to capitalize on data- and instruction-level parallelism in CPUs, enhancing performance on real-world data and applications — all natively on your data lake. Photon is fully compatible with the Apache Spark™ DataFrame and SQL APIs to ensure workloads run seamlessly without code changes. Come join us to learn more about how Photon can radically speed up your queries on Databricks.
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Migrating Complex Data Aggregation from Hadoop to Spark-(Ashish Singh andPune...
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Spark + AI Summit 2019: Headaches and Breakthroughs in Building Continuous Ap...
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Presented by Landon Robinson and Jack Chapa
ONE FOR ALL! Using Apache Calcite to make SQL smart
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Low latency high throughput streaming using Apache Apex and Apache Kudu
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Speaker
Ananth Gundabattula, Senior Architect, Commonwealth Bank of Australia
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Apache Spark for RDBMS Practitioners: How I Learned to Stop Worrying and Lov...
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Spark can also easily integrate a large variety of data sources, from file-based formats to relational databases and more. Notably, Spark can easily scale up data pipelines and workloads from laptops to large clusters of commodity hardware or on the cloud. The talk also addresses some key points about the adoption process and learning curve around Apache Spark and the related “Big Data” tools for a community of developers and DBAs at CERN with a background in relational database operations.
Apache Spark - A High Level overview
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Hadoop Summit SJ 2016: Next Gen Big Data Analytics with Apache Apex
Hadoop Summit SJ 2016: Next Gen Big Data Analytics with Apache ApexApache Apex This is an overview of architecture with use cases for Apache Apex, a big data analytics platform. It comes with a powerful stream processing engine, rich set of functional building blocks and an easy to use API for the developer to build real-time and batch applications. Apex runs natively on YARN and HDFS and is used in production in various industries. You will learn more about two use cases: A leading Ad Tech company serves billions of advertising impressions and collects terabytes of data from several data centers across the world every day. Apex was used to implement rapid actionable insights, for real-time reporting and allocation, utilizing Kafka and files as source, dimensional computation and low latency visualization. A customer in the IoT space uses Apex for Time Series service, including efficient storage of time series data, data indexing for quick retrieval and queries at high scale and precision. The platform leverages the high availability, horizontal scalability and operability of Apex.
Website optimization with request reduce
Website optimization with request reduceMatt Wrock This document discusses RequestReduce, a tool that automatically optimizes websites for performance by reducing the number of HTTP requests. It does this through CSS and JavaScript merging, minification, automatic sprite generation, caching headers, and synchronization across multiple servers with minimal customization needed. The document provides details on how RequestReduce works, its features, limitations, and ways to optimize and troubleshoot sites optimized with RequestReduce.
Cheetah:Data Warehouse on Top of MapReduce
Cheetah:Data Warehouse on Top of MapReduceTilani Gunawardena PhD(UNIBAS), BSc(Pera), FHEA(UK), CEng, MIESL
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Data Lakehouse Symposium | Day 1 | Part 1
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Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 1 | Part 2
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Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
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Data Lakehouse Symposium | Day 2
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Data Lakehouse Symposium | Day 4
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5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
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Democratizing Data Quality Through a Centralized Platform
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At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including:
Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal
Performing data quality validations using libraries built to work with spark
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
Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time
Learn to Use Databricks for Data Science
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As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored.
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The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
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Simplify Data Conversion from Spark to TensorFlow and PyTorch
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Scaling your Data Pipelines with Apache Spark on Kubernetes
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Sawtooth Windows for Feature Aggregations
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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
· Problems with Spark Accumulators
· Utilize Redis Hashes as distributed counters
· Precautions for retries and speculative execution
· Pipelining to improve performance
Re-imagine Data Monitoring with whylogs and Spark
Re-imagine Data Monitoring with whylogs and SparkDatabricks In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data.
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
Raven: End-to-end Optimization of ML Prediction QueriesDatabricks Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components.
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.
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache SparkDatabricks Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis.
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
Massive Data Processing in Adobe Using Delta LakeDatabricks At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences.
What are we storing?
Multi Source – Multi Channel Problem
Data Representation and Nested Schema Evolution
Performance Trade Offs with Various formats
Go over anti-patterns used
(String FTW)
Data Manipulation using UDFs
Writer Worries and How to Wipe them Away
Staging Tables FTW
Datalake Replication Lag Tracking
Performance Time!
Machine Learning CI/CD for Email Attack Detection
Machine Learning CI/CD for Email Attack DetectionDatabricks Detecting advanced email attacks at scale is a challenging ML problem, particularly due to the rarity of attacks, adversarial nature of the problem, and scale of data. In order to move quickly and adapt to the newest threat we needed to build a Continuous Integration / Continuous Delivery pipeline for the entire ML detection stack. Our goal is to enable detection engineers and data scientists to make changes to any part of the stack including joined datasets for hydration, feature extraction code, detection logic, and develop/train ML models.
In this talk, we discuss why we decided to build this pipeline, how it is used to accelerate development and ensure quality, and dive into the nitty-gritty details of building such a system on top of an Apache Spark + Databricks stack.
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Ann Naser Nabil- Data Scientist Portfolio.pdf
Ann Naser Nabil- Data Scientist Portfolio.pdfআন্ নাসের নাবিল I am a data scientist with a strong foundation in economics and a deep passion for AI-driven problem-solving. My academic journey includes a B.Sc. in Economics from Jahangirnagar University and a year of Physics study at Shahjalal University of Science and Technology, providing me with a solid interdisciplinary background and a sharp analytical mindset.
I have practical experience in developing and deploying machine learning and deep learning models across a range of real-world applications. Key projects include:
AI-Powered Disease Prediction & Drug Recommendation System – Deployed on Render, delivering real-time health insights through predictive analytics.
Mood-Based Movie Recommendation Engine – Uses genre preferences, sentiment, and user behavior to generate personalized film suggestions.
Medical Image Segmentation with GANs (Ongoing) – Developing generative adversarial models for cancer and tumor detection in radiology.
In addition, I have developed three Python packages focused on:
Data Visualization
Preprocessing Pipelines
Automated Benchmarking of Machine Learning Models
My technical toolkit includes Python, NumPy, Pandas, Scikit-learn, TensorFlow, Keras, Matplotlib, and Seaborn. I am also proficient in feature engineering, model optimization, and storytelling with data.
Beyond data science, my background as a freelance writer for Earki and Prothom Alo has refined my ability to communicate complex technical ideas to diverse audiences.
Mining a Global Trade Process with Data Science - Microsoft
Mining a Global Trade Process with Data Science - MicrosoftProcess mining Evangelist
The third speaker at Process Mining Camp 2018 was Dinesh Das from Microsoft. Dinesh Das is the Data Science manager in Microsoft’s Core Services Engineering and Operations organization.
Machine learning and cognitive solutions give opportunities to reimagine digital processes every day. This goes beyond translating the process mining insights into improvements and into controlling the processes in real-time and being able to act on this with advanced analytics on future scenarios.
Dinesh sees process mining as a silver bullet to achieve this and he shared his learnings and experiences based on the proof of concept on the global trade process. This process from order to delivery is a collaboration between Microsoft and the distribution partners in the supply chain. Data of each transaction was captured and process mining was applied to understand the process and capture the business rules (for example setting the benchmark for the service level agreement). These business rules can then be operationalized as continuous measure fulfillment and create triggers to act using machine learning and AI.
Using the process mining insight, the main variants are translated into Visio process maps for monitoring. The tracking of the performance of this process happens in real-time to see when cases become too late. The next step is to predict in what situations cases are too late and to find alternative routes.
As an example, Dinesh showed how machine learning could be used in this scenario. A TradeChatBot was developed based on machine learning to answer questions about the process. Dinesh showed a demo of the bot that was able to answer questions about the process by chat interactions. For example: “Which cases need to be handled today or require special care as they are expected to be too late?”. In addition to the insights from the monitoring business rules, the bot was also able to answer questions about the expected sequences of particular cases. In order for the bot to answer these questions, the result of the process mining analysis was used as a basis for machine learning.
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.
How to Set Up Process Mining in a Decentralized Organization?
How to Set Up Process Mining in a Decentralized Organization?Process mining Evangelist
The history of a.s.r. begins 1720 in “Stad Rotterdam”, which as the oldest insurance company on the European continent was specialized in insuring ocean-going vessels — not a surprising choice in a port city like Rotterdam. Today, a.s.r. is a major Dutch insurance group based in Utrecht.
Nelleke Smits is part of the Analytics lab in the Digital Innovation team. Because a.s.r. is a decentralized organization, she worked together with different business units for her process mining projects in the Medical Report, Complaints, and Life Product Expiration areas. During these projects, she realized that different organizational approaches are needed for different situations.
For example, in some situations, a report with recommendations can be created by the process mining analyst after an intake and a few interactions with the business unit. In other situations, interactive process mining workshops are necessary to align all the stakeholders. And there are also situations, where the process mining analysis can be carried out by analysts in the business unit themselves in a continuous manner. Nelleke shares her criteria to determine when which approach is most suitable.
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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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.
Process Mining and Official Statistics - CBS
Process Mining and Official Statistics - CBSProcess mining Evangelist Johan Lammers from Statistics Netherlands has been a business analyst and statistical researcher for almost 30 years. In their business, processes have two faces: You can produce statistics about processes and processes are needed to produce statistics. As a government-funded office, the efficiency and the effectiveness of their processes is important to spend that public money well.
Johan takes us on a journey of how official statistics are made. One way to study dynamics in statistics is to take snapshots of data over time. A special way is the panel survey, where a group of cases is followed over time. He shows how process mining could test certain hypotheses much faster compared to statistical tools like SPSS.
Controlling Financial Processes at a Municipality
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The fourth speaker at Process Mining Camp 2018 was Wim Kouwenhoven from the City of Amsterdam. Amsterdam is well-known as the capital of the Netherlands and the City of Amsterdam is the municipality defining and governing local policies. Wim is a program manager responsible for improving and controlling the financial function.
A new way of doing things requires a different approach. While introducing process mining they used a five-step approach:
Step 1: Awareness
Introducing process mining is a little bit different in every organization. You need to fit something new to the context, or even create the context. At the City of Amsterdam, the key stakeholders in the financial and process improvement department were invited to join a workshop to learn what process mining is and to discuss what it could do for Amsterdam.
Step 2: Learn
As Wim put it, at the City of Amsterdam they are very good at thinking about something and creating plans, thinking about it a bit more, and then redesigning the plan and talking about it a bit more. So, they deliberately created a very small plan to quickly start experimenting with process mining in small pilot. The scope of the initial project was to analyze the Purchase-to-Pay process for one department covering four teams. As a result, they were able show that they were able to answer five key questions and got appetite for more.
Step 3: Plan
During the learning phase they only planned for the goals and approach of the pilot, without carving the objectives for the whole organization in stone. As the appetite was growing, more stakeholders were involved to plan for a broader adoption of process mining. While there was interest in process mining in the broader organization, they decided to keep focusing on making process mining a success in their financial department.
Step 4: Act
After the planning they started to strengthen the commitment. The director for the financial department took ownership and created time and support for the employees, team leaders, managers and directors. They started to develop the process mining capability by organizing training sessions for the teams and internal audit. After the training, they applied process mining in practice by deepening their analysis of the pilot by looking at e-invoicing, deleted invoices, analyzing the process by supplier, looking at new opportunities for audit, etc. As a result, the lead time for invoices was decreased by 8 days by preventing rework and by making the approval process more efficient. Even more important, they could further strengthen the commitment by convincing the stakeholders of the value.
Step 5: Act again
After convincing the stakeholders of the value you need to consolidate the success by acting again. Therefore, a team of process mining analysts was created to be able to meet the demand and sustain the success. Furthermore, new experiments were started to see how process mining could be used in three audits in 2018.
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Experiences Migrating Hive Workload to SparkSQL with Jie Xiong and Zhan Zhang
- 1. Zhan Zhang, Jie Xiong zhanzhang@fb.com, jiexiong@fb.com Experiences Migrating Hive Workload to SparkSQL
- 2. Overview • Motivation • Syntax & Semantics Gap Analysis • Offline & Online Shadowing • Performance Optimization • Challenges and Future Work
- 3. Background • Make batch compute in Facebook more efficient. • Bridge the gap between Spark and Hive so Spark can handle production workload in Facebook. • Unified User Interface for SparkSQL and HQL.
- 4. Preparation - Syntax Analysis • Syntax Gap Analysis – Use our daily hive query log to select query candidates. – A group of Spark Drivers each runs a subset of the candidates for daily syntax analysis. • Parsing, analyzing, optimization, physical plan generation, and executed plan
- 5. • 0: Success • 1: Parser • 2. Analyzer • 3. Optimization • 4. Physical Plan • 5. Execution Plan Syntax Analysis – Error Distribution on Each Stage Parser Analyzer Optimization Physical Plan Execution Plan
- 6. • The computation weight by error category. – More than 50% without errors – A small number of syntax errors take a big percentage. Syntax Analysis – Hive CPU Usage Distribution by Error Category
- 7. Preparation - Semantic Validation • Avoid affecting production pipelines – Rewrite the parsed plan by appending the output table with suffix _spark_shadow • Avoid obsolete data – Run the same query on the same source data one day after the hive query finishes. • Verify the correctness – Hash validation: sometimes even heavier than the query itself. – Count validation: fast way to filter out obvious errors.
- 8. Migration Steps Hive Query Log Offline Spark Shadow Scheduler query stats log comparison Start online shadow Pick candidates based on metrics Migrate to Spark Performance improved Back to Hive Not Improved
- 9. Migration Metrics • Correctness • Wall time • Reserved CPU time • Stability
- 10. Offline Shadow Process • Pickup the namespace • Setup pool for offline shadowing • Select the pipelines above some compute cost threshold • Set up a Spark application to continuously run the selected pipelines in shadow mode. • Pick up the candidate for online shadowing using our metrics
- 11. Online Shadow Process • Running in parallel with production pipelines in different pools • No Change to HQL – rewrite the rule to make SparkSQL and HQL consistent • No impact on production – Whether shadow job succeeded or failed, the downstream jobs are not affected
- 12. Online Shadow Operator • Unified Interface for SparkSQL and HQL – Rewrite the query plan to bridge the gap • Support Different Running mode – Hive – Shadow (Hive &Spark) – Spark ShadowOriginal Migrated
- 13. Performance Optimization • Tradeoff between Shuffle Partitions and Disk Spill – The amount of Shuffled data varies per pipeline. – Automatic choose partition number based on input data size and hive historical data. • Avoid unnecessary stage retries – Avoid retries when OOM happens. • Avoid false task failure – Do not shut down executor when one task being killed. • ReuseExchange to avoid redundant table scan. – Enable table scan reuse in Spark-SQL • More accurate input data size estimation – A new rule to estimate input data size is added before join selection strategy. – Enable more advanced query optimization, e.g., BroadcastJoin/ShuffledHashJoin.
- 14. BroadcastJoin Driver Executor HashMap Partition 1 Partition 2 Partition 3 • Driver collects and broadcast the smaller side to all tasks. • Streaming the bigger side • Overhead – best Executor Executor HashMap HashMap
- 15. ShuffledHashJoin • Build Hash on one side. • Streaming the other side. • Overhead – shuffle ExecutorExecutor
- 16. SortMergeJoin • Shuffle Rows with same keys to same tasks • Sort both sides • Join step by step • Overhead – Shuffle/Sort/Spill Executor SORT SORT
- 17. Tradeoff • BroacastJoin – No Shuffle or Spill – OutOfMemory • ShuffledHashJoin – Shuffle without Sort – OutOfMemory • SortMergeJoin – Shuffle & Sort & Spill – Robust • Fallback mechanism – Try ShuffledHashJoin. – Fallback to SortMergeJoin on failure.
- 19. JIRA • SPARK-20215: ReuseExchange is broken in SparkSQL • SPARK-20006: Separate threshold for broadcast and shuffled hash join • SPARK-19908: Direct buffer memory OOM should not cause stage retries. • SPARK-19890: Make MetastoreRelation statistics estimation more accurate • SPARK-19839: Fix memory leak in BytesToBytesMap • SPARK-17637: Packed scheduling for Spark tasks across executors
- 20. Challenges and Future Work • Non-deterministic UDF makes validation hard • Performance degradation due to lack of HiveUDF WholeStageCodegen support • Leverage Run-time/Historical data to get more accurate stats for advanced query optimization • Maximize the utilization of HashAggregation and ShuffledHashJoin (with fallback mechanism)
- 21. Question?