Dynamic Partition Pruning in Apache Spark
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In data analytics frameworks such as Spark it is important to detect and avoid scanning data that is irrelevant to the executed query, an optimization which is known as partition pruning. Dynamic partition pruning occurs when the optimizer is unable to identify at parse time the partitions it has to eliminate. In particular, we consider a star schema which consists of one or multiple fact tables referencing any number of dimension tables. In such join operations, we can prune the partitions the join reads from a fact table by identifying those partitions that result from filtering the dimension tables. In this talk we present a mechanism for performing dynamic partition pruning at runtime by reusing the dimension table broadcast results in hash joins and we show significant improvements for most TPCDS queries.
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Spark + AI Summit recap jul16 2020
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Apache Spark 3 Dynamic Partition Pruning
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Informational Referential Integrity Constraints Support in Apache Spark with ...
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ETL 2.0 Data Engineering for developers
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Deep Dive into the New Features of Apache Spark 3.1
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The following features are covered: the SQL features for ANSI SQL compliance, new streaming features, and Python usability improvements, the performance enhancements and new tuning tricks in query compiler.
From Query Plan to Query Performance: Supercharging your Apache Spark Queries...
From Query Plan to Query Performance: Supercharging your Apache Spark Queries...Databricks The SQL tab in the Spark UI provides a lot of information for analysing your spark queries, ranging from the query plan, to all associated statistics. However, many new Spark practitioners get overwhelmed by the information presented, and have trouble using it to their benefit. In this talk we want to give a gentle introduction to how to read this SQL tab. We will first go over all the common spark operations, such as scans, projects, filter, aggregations and joins; and how they relate to the Spark code written. In the second part of the talk we will show how to read the associated statistics to pinpoint performance bottlenecks.
SQL Performance Improvements At a Glance in Apache Spark 3.0
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https://databricks.com/session_na20/sql-performance-improvements-at-a-glance-in-apache-spark-3-0
SQL Performance Improvements at a Glance in Apache Spark 3.0
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The Pushdown of Everything by Stephan Kessler and Santiago Mola
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Extending Apache Spark SQL Data Source APIs with Join Push Down with Ioana De...
Extending Apache Spark SQL Data Source APIs with Join Push Down with Ioana De...Databricks This document summarizes a presentation on extending Spark SQL Data Sources APIs with join push down. The presentation discusses how join push down can significantly improve query performance by reducing data transfer and exploiting data source capabilities like indexes. It provides examples of join push down in enterprise data pipelines and SQL acceleration use cases. The presentation also outlines the challenges of network speeds and exploiting data source capabilities, and how join push down addresses these challenges. Future work discussed includes building a cost model for global optimization across data sources.
Best Practices for Building Robust Data Platform with Apache Spark and Delta
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Modernizing Your Data Warehouse using APS
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Achieving Lakehouse Models with Spark 3.0
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Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...
Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...Databricks This document discusses best practices for optimizing Apache Spark applications. It covers techniques for speeding up file loading, optimizing file storage and layout, identifying bottlenecks in queries, dealing with many partitions, using datasource tables, managing schema inference, file types and compression, partitioning and bucketing files, managing shuffle partitions with adaptive execution, optimizing unions, using the cost-based optimizer, and leveraging the data skipping index. The presentation aims to help Spark developers apply these techniques to improve performance.
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Data Lakehouse Symposium | Day 1 | Part 1
Data Lakehouse Symposium | Day 1 | Part 1Databricks The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
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.
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 2
Data Lakehouse Symposium | Day 2Databricks The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
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.
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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
Learn to Use Databricks for Data ScienceDatabricks Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
Why APM Is Not the Same As ML Monitoring
Why APM Is Not the Same As ML MonitoringDatabricks Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications.
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.
In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch FixDatabricks Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.
Stage Level Scheduling Improving Big Data and AI Integration
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There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs.
The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorchDatabricks In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks.
Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model?
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.
The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters.
In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on KubernetesDatabricks There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal.
In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
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Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations.
Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.
Sawtooth Windows for Feature Aggregations
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Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen SinkDatabricks We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark.
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!
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Dynamic Partition Pruning in Apache Spark
- 1. Dynamic Partition Pruning in Apache Spark Spark + AI Summit, Amsterdam 1 Bogdan Ghit and Juliusz Sompolski
- 2. 2 About Us BI Experience team in the Databricks Amsterdam European Development Centre ● Working on improving the experience and performance of Business Intelligence / SQL analytics workloads using Databricks ○ JDBC / ODBC connectivity to Databricks clusters ○ Integrations with BI tools such as Tableau ○ But also: core performance improvements in Apache Spark for common SQL analytics query patterns Bogdan Ghit Juliusz Sompolski
- 3. TPCDS Q98 on 10 TB How to Make a Query 100x Faster?
- 4. Static Partition Pruning SELECT * FROM Sales WHERE day_of_week = ‘Mon’ Filter Scan Basic data-flow Filter Scan Filter Push-down Filter Scan Partition files with multi-columnar data
- 5. Table Denormalization SELECT * FROM Sales JOIN Date WHERE Date.day_of_week = ‘Mon’ Static pruning not possible Scan Sales Filter day_of_week = ‘mon’ Join Simple workaround Scan Sales Join Scan Date Filter day_of_week = ‘mon’ Scan Scan Date
- 6. This Talk Dynamic pruning Scan Sales Filter day_of_week = ‘mon’ Join SELECT * FROM Sales JOIN Date WHERE Date.day_of_week = ‘Mon’ Scan Countries
- 7. Spark In a Nutshell Query Logical Plan Optimization Physical Plan Selection RDD batches Cluster slots Stats-based cost model Rule-based transformations APIs
- 8. Optimization Opportunities Data Layout Partition files with multi-columnar data Scan FACT TABLE Scan DIM TABLE Non-partitioned dataset Filter DIM Join on partition id Query Shape
- 9. A Simple Approach Partition files with multi-columnar data Scan FACT TABLE Scan DIM TABLE Non-partitioned dataset Filter DIM Join on partition id Scan DIM TABLE Filter DIM Work duplication may be expensive Heuristics based on inaccurate stats
- 10. Broadcast Hash Join FileScan FileScan with Dim Filter Non-partitioned dataset BroadcastExchange Broadcast Hash Join Execute the build side of the join Place the result in a broadcast variableBroadcast the build side results Execute the join locally without a shuffle
- 11. Reusing Broadcast Results Partition files with multi-columnar data FileScan FileScan with Dim Filter Non-partitioned dataset BroadcastExchange Broadcast Hash Join Dynamic Filter
- 12. Experimental Setup Workload Selection - TPC-DS scale factors 1-10 TB Cluster Configuration - 10 i3.xlarge machines Data-Processing Framework - Apache Spark 3.0
- 13. TPCDS 1 TB 60 / 102 queries speedup between 2 and 18
- 14. Top Queries Very good speedups for top 10% of the queries
- 15. Data Skipped Very effective in skipping data
- 16. TPCDS 10 TB Even better speedups at 10x the scale
- 17. Query 98 SELECT i_item_desc, i_category, i_class, i_current_price, sum(ss_ext_sales_price) as itemrevenue, sum(ss_ext_sales_price)*100/sum(sum(ss_ext_sales_price)) over (partition by i_class) as revenueratio FROM store_sales, item, date_dim WHERE ss_item_sk = i_item_sk and i_category in ('Sports', 'Books', 'Home') and ss_sold_date_sk = d_date_sk and cast(d_date as date) between cast('1999-02-22' as date) and (cast('1999-02-22' as date) + interval '30' day) GROUP BY i_item_id, i_item_desc, i_category, i_class, i_current_price ORDER BY i_category, i_class, i_item_id, i_item_desc, revenueratio
- 18. TPCDS 10 TB Highly selective dimension filter that retains only one month out of 5 years of data
- 19. Conclusion Apache Spark 3.0 introduces Dynamic Partition Pruning - Strawman approach at logical planning time - Optimized approach during execution time Significant speedup, exhibited in many TPC-DS queries With this optimization Spark may now work good with star-schema queries, making it unnecessary to ETL denormalized tables.
- 20. 20 Thanks! Bogdan Ghit - linkedin.com/in/bogdanghit Juliusz Sompolski - linkedin.com/in/juliuszsompolski