Although NVMe has been more and more popular these years, a large amount of HDD are still widely used in super-large scale big data clusters. In a EB-level data platform, IO(including decompression and decode) cost contributes a large proportion of Spark jobs’ cost. In another word, IO operation is worth optimizing.
In ByteDancen, we do a series of IO optimization to improve performance, including parallel read and asynchronized shuffle. Firstly we implement file level parallel read to improve performance when there are a lot of small files. Secondly, we design row group level parallel read to accelerate queries for big-file scenario. Thirdly, implement asynchronized spill to improve job peformance. Besides, we design parquet column family, which will split a table into a few column families and different column family will be in different Parquets files. Different column family can be read in parallel, so the read performance is much higher than the existing approach. In our practice, the end to end performance is improved by 5% to 30%
In this talk, I will illustrate how we implement these features and how they accelerate Apache Spark jobs.
Improving SparkSQL Performance by 30%: How We Optimize Parquet Pushdown and P...Databricks
Parquet is a very popular column based format. Spark can automatically filter useless data using parquet file statistical data by pushdown filters, such as min-max statistics. On the other hand, Spark user can enable Spark parquet vectorized reader to read parquet files by batch. These features improve Spark performance greatly and save both CPU and IO. Parquet is the default data format of data warehouse in Bytedance. In practice, we find that parquet pushdown filters work poorly resulting in reading too much unnecessary data for statistical data has no discrimination across parquet row groups(column data is out of order when writing to parquet files by ETL jobs).
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...Databricks
Spark SQL is a highly scalable and efficient relational processing engine with ease-to-use APIs and mid-query fault tolerance. It is a core module of Apache Spark. Spark SQL can process, integrate and analyze the data from diverse data sources (e.g., Hive, Cassandra, Kafka and Oracle) and file formats (e.g., Parquet, ORC, CSV, and JSON). This talk will dive into the technical details of SparkSQL spanning the entire lifecycle of a query execution. The audience will get a deeper understanding of Spark SQL and understand how to tune Spark SQL performance.
Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...Databricks
Apache Spark is an excellent tool to accelerate your analytics, whether you’re doing ETL, Machine Learning, or Data Warehousing. However, to really make the most of Spark it pays to understand best practices for data storage, file formats, and query optimization. This talk will cover best practices I’ve applied over years in the field helping customers write Spark applications as well as identifying what patterns make sense for your use case.
Fine Tuning and Enhancing Performance of Apache Spark JobsDatabricks
Apache Spark defaults provide decent performance for large data sets but leave room for significant performance gains if able to tune parameters based on resources and job.
Properly shaping partitions and your jobs to enable powerful optimizations, eliminate skew and maximize cluster utilization. We will explore various Spark Partition shaping methods along with several optimization strategies including join optimizations, aggregate optimizations, salting and multi-dimensional parallelism.
Improving SparkSQL Performance by 30%: How We Optimize Parquet Pushdown and P...Databricks
Parquet is a very popular column based format. Spark can automatically filter useless data using parquet file statistical data by pushdown filters, such as min-max statistics. On the other hand, Spark user can enable Spark parquet vectorized reader to read parquet files by batch. These features improve Spark performance greatly and save both CPU and IO. Parquet is the default data format of data warehouse in Bytedance. In practice, we find that parquet pushdown filters work poorly resulting in reading too much unnecessary data for statistical data has no discrimination across parquet row groups(column data is out of order when writing to parquet files by ETL jobs).
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...Databricks
Spark SQL is a highly scalable and efficient relational processing engine with ease-to-use APIs and mid-query fault tolerance. It is a core module of Apache Spark. Spark SQL can process, integrate and analyze the data from diverse data sources (e.g., Hive, Cassandra, Kafka and Oracle) and file formats (e.g., Parquet, ORC, CSV, and JSON). This talk will dive into the technical details of SparkSQL spanning the entire lifecycle of a query execution. The audience will get a deeper understanding of Spark SQL and understand how to tune Spark SQL performance.
Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...Databricks
Apache Spark is an excellent tool to accelerate your analytics, whether you’re doing ETL, Machine Learning, or Data Warehousing. However, to really make the most of Spark it pays to understand best practices for data storage, file formats, and query optimization. This talk will cover best practices I’ve applied over years in the field helping customers write Spark applications as well as identifying what patterns make sense for your use case.
Fine Tuning and Enhancing Performance of Apache Spark JobsDatabricks
Apache Spark defaults provide decent performance for large data sets but leave room for significant performance gains if able to tune parameters based on resources and job.
Properly shaping partitions and your jobs to enable powerful optimizations, eliminate skew and maximize cluster utilization. We will explore various Spark Partition shaping methods along with several optimization strategies including join optimizations, aggregate optimizations, salting and multi-dimensional parallelism.
Optimizing spark jobs through a true understanding of spark core. Learn: What is a partition? What is the difference between read/shuffle/write partitions? How to increase parallelism and decrease output files? Where does shuffle data go between stages? What is the "right" size for your spark partitions and files? Why does a job slow down with only a few tasks left and never finish? Why doesn't adding nodes decrease my compute time?
Building a SIMD Supported Vectorized Native Engine for Spark SQLDatabricks
Spark SQL works very well with structured row-based data. Vectorized reader and writer for parquet/orc can make I/O much faster. It also used WholeStageCodeGen to improve the performance by Java JIT code. However Java JIT is usually not working very well on utilizing latest SIMD instructions under complicated queries. Apache Arrow provides columnar in-memory layout and SIMD optimized kernels as well as a LLVM based SQL engine Gandiva. These native based libraries can accelerate Spark SQL by reduce the CPU usage for both I/O and execution.
Dynamic Partition Pruning in Apache SparkDatabricks
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.
Spark Shuffle Deep Dive (Explained In Depth) - How Shuffle Works in SparkBo Yang
The slides explain how shuffle works in Spark and help people understand more details about Spark internal. It shows how the major classes are implemented, including: ShuffleManager (SortShuffleManager), ShuffleWriter (SortShuffleWriter, BypassMergeSortShuffleWriter, UnsafeShuffleWriter), ShuffleReader (BlockStoreShuffleReader).
Memory management is at the heart of any data-intensive system. Spark, in particular, must arbitrate memory allocation between two main use cases: buffering intermediate data for processing (execution) and caching user data (storage). This talk will take a deep dive through the memory management designs adopted in Spark since its inception and discuss their performance and usability implications for the end user.
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."
How Adobe Does 2 Million Records Per Second Using Apache Spark!Databricks
Adobe’s Unified Profile System is the heart of its Experience Platform. It ingests TBs of data a day and is PBs large. As part of this massive growth we have faced multiple challenges in our Apache Spark deployment which is used from Ingestion to Processing.
Deep Dive: Memory Management in Apache SparkDatabricks
Memory management is at the heart of any data-intensive system. Spark, in particular, must arbitrate memory allocation between two main use cases: buffering intermediate data for processing (execution) and caching user data (storage). This talk will take a deep dive through the memory management designs adopted in Spark since its inception and discuss their performance and usability implications for the end user.
Tuning Apache Spark for Large-Scale Workloads Gaoxiang Liu and Sital KediaDatabricks
Apache Spark is a fast and flexible compute engine for a variety of diverse workloads. Optimizing performance for different applications often requires an understanding of Spark internals and can be challenging for Spark application developers. In this session, learn how Facebook tunes Spark to run large-scale workloads reliably and efficiently. The speakers will begin by explaining the various tools and techniques they use to discover performance bottlenecks in Spark jobs. Next, you’ll hear about important configuration parameters and their experiments tuning these parameters on large-scale production workload. You’ll also learn about Facebook’s new efforts towards automatically tuning several important configurations based on nature of the workload. The speakers will conclude by sharing their results with automatic tuning and future directions for the project.ing several important configurations based on nature of the workload. We will conclude by sharing our result with automatic tuning and future directions for the project.
Join is one of most important and critical SQL operation in most data warehouses. This is essential when we want to get insights from multiple input datasets. Over the last year, we’ve added a series of join optimizations internally at Facebook, and we started to contribute back to upstream open source recently.
Materialized Column: An Efficient Way to Optimize Queries on Nested ColumnsDatabricks
In data warehouse area, it is common to use one or more columns in complex type, such as map, and put many subfields into it. It may impact the query performance dramatically because: 1) It is a waste of IO. The whole column (in map), which may contain tens of subfields, need to be read. And Spark will traverse the whole map and get the value of the target key. 2) Vectorized read can not be exploit when nested type column is read. 3) Filter pushdown can not be utilized when nested columns is read. Over the last year, we have added a series of optimizations in Apache Spark to solve the above problems for Parquet.
Deep Dive into the New Features of Apache Spark 3.0Databricks
Continuing with the objectives to make Spark faster, easier, and smarter, Apache Spark 3.0 extends its scope with more than 3000 resolved JIRAs. We will talk about the exciting new developments in the Spark 3.0 as well as some other major initiatives that are coming in the future.
Hive Bucketing in Apache Spark with Tejas PatilDatabricks
Bucketing is a partitioning technique that can improve performance in certain data transformations by avoiding data shuffling and sorting. The general idea of bucketing is to partition, and optionally sort, the data based on a subset of columns while it is written out (a one-time cost), while making successive reads of the data more performant for downstream jobs if the SQL operators can make use of this property. Bucketing can enable faster joins (i.e. single stage sort merge join), the ability to short circuit in FILTER operation if the file is pre-sorted over the column in a filter predicate, and it supports quick data sampling.
In this session, you’ll learn how bucketing is implemented in both Hive and Spark. In particular, Patil will describe the changes in the Catalyst optimizer that enable these optimizations in Spark for various bucketing scenarios. Facebook’s performance tests have shown bucketing to improve Spark performance from 3-5x faster when the optimization is enabled. Many tables at Facebook are sorted and bucketed, and migrating these workloads to Spark have resulted in a 2-3x savings when compared to Hive. You’ll also hear about real-world applications of bucketing, like loading of cumulative tables with daily delta, and the characteristics that can help identify suitable candidate jobs that can benefit from bucketing.
Designing ETL Pipelines with Structured Streaming and Delta Lake—How to Archi...Databricks
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. Delta Lake, on the other hand, is the best way to store structured data because it is a open-source storage layer that brings ACID transactions to Apache Spark and big data workloads Together, these can make it very easy to build pipelines in many common scenarios. 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 that needs to be solved. Apache Spark, being a unified analytics engine doing both batch and stream processing, often provides multiples ways to solve the same problem. So understanding the requirements carefully helps you to architect your pipeline that solves your business needs in the most resource efficient manner.
In this talk, I am going examine a number common streaming design patterns in the context of the following questions.
WHAT are you trying to consume? What are you trying to produce? What is the final output that the business wants? What are your throughput and latency requirements?
WHY do you really have those requirements? Would solving the requirements of the individual pipeline actually solve your end-to-end business requirements?
HOW are going to architect the solution? And how much are you willing to pay for it?
Clarity in understanding the ‘what and why’ of any problem can automatically much clarity on the ‘how’ to architect it using Structured Streaming and, in many cases, Delta Lake.
Apache Spark presentation at HasGeek FifthElelephant
https://fifthelephant.talkfunnel.com/2015/15-processing-large-data-with-apache-spark
Covering Big Data Overview, Spark Overview, Spark Internals and its supported libraries
A Deep Dive into Query Execution Engine of Spark SQLDatabricks
Spark SQL enables Spark to perform efficient and fault-tolerant relational query processing with analytics database technologies. The relational queries are compiled to the executable physical plans consisting of transformations and actions on RDDs with the generated Java code. The code is compiled to Java bytecode, executed at runtime by JVM and optimized by JIT to native machine code at runtime. This talk will take a deep dive into Spark SQL execution engine. The talk includes pipelined execution, whole-stage code generation, UDF execution, memory management, vectorized readers, lineage based RDD transformation and action.
"The common use cases of Spark SQL include ad hoc analysis, logical warehouse, query federation, and ETL processing. Spark SQL also powers the other Spark libraries, including structured streaming for stream processing, MLlib for machine learning, and GraphFrame for graph-parallel computation. For boosting the speed of your Spark applications, you can perform the optimization efforts on the queries prior employing to the production systems. Spark query plans and Spark UIs provide you insight on the performance of your queries. This talk discloses how to read and tune the query plans for enhanced performance. It will also cover the major related features in the recent and upcoming releases of Apache Spark.
"
"Structured Streaming was a new streaming API introduced to Spark over 2 years ago in Spark 2.0, and was announced GA as of Spark 2.2. Databricks customers have processed over a hundred trillion rows in production using Structured Streaming. We received dozens of questions on how to best develop, monitor, test, deploy and upgrade these jobs. In this talk, we aim to share best practices around what has worked and what hasn't across our customer base.
We will tackle questions around how to plan ahead, what kind of code changes are safe for structured streaming jobs, how to architect streaming pipelines which can give you the most flexibility without sacrificing performance by using tools like Databricks Delta, how to best monitor your streaming jobs and alert if your streams are falling behind or are actually failing, as well as how to best test your code."
Parquet performance tuning: the missing guideRyan Blue
Ryan Blue explains how Netflix is building on Parquet to enhance its 40+ petabyte warehouse, combining Parquet’s features with Presto and Spark to boost ETL and interactive queries. Information about tuning Parquet is hard to find. Ryan shares what he’s learned, creating the missing guide you need.
Topics include:
* The tools and techniques Netflix uses to analyze Parquet tables
* How to spot common problems
* Recommendations for Parquet configuration settings to get the best performance out of your processing platform
* The impact of this work in speeding up applications like Netflix’s telemetry service and A/B testing platform
Video: https://www.youtube.com/watch?v=kkOG_aJ9KjQ
This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
Accelerating Spark SQL Workloads to 50X Performance with Apache Arrow-Based F...Databricks
In Big Data field, Spark SQL is important data processing module for Apache Spark to work with structured row-based data in a majority of operators. Field-programmable gate array(FPGA) with highly customized intellectual property(IP) can not only bring better performance but also lower power consumption to accelerate CPU-intensive segments for an application.
Optimizing spark jobs through a true understanding of spark core. Learn: What is a partition? What is the difference between read/shuffle/write partitions? How to increase parallelism and decrease output files? Where does shuffle data go between stages? What is the "right" size for your spark partitions and files? Why does a job slow down with only a few tasks left and never finish? Why doesn't adding nodes decrease my compute time?
Building a SIMD Supported Vectorized Native Engine for Spark SQLDatabricks
Spark SQL works very well with structured row-based data. Vectorized reader and writer for parquet/orc can make I/O much faster. It also used WholeStageCodeGen to improve the performance by Java JIT code. However Java JIT is usually not working very well on utilizing latest SIMD instructions under complicated queries. Apache Arrow provides columnar in-memory layout and SIMD optimized kernels as well as a LLVM based SQL engine Gandiva. These native based libraries can accelerate Spark SQL by reduce the CPU usage for both I/O and execution.
Dynamic Partition Pruning in Apache SparkDatabricks
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.
Spark Shuffle Deep Dive (Explained In Depth) - How Shuffle Works in SparkBo Yang
The slides explain how shuffle works in Spark and help people understand more details about Spark internal. It shows how the major classes are implemented, including: ShuffleManager (SortShuffleManager), ShuffleWriter (SortShuffleWriter, BypassMergeSortShuffleWriter, UnsafeShuffleWriter), ShuffleReader (BlockStoreShuffleReader).
Memory management is at the heart of any data-intensive system. Spark, in particular, must arbitrate memory allocation between two main use cases: buffering intermediate data for processing (execution) and caching user data (storage). This talk will take a deep dive through the memory management designs adopted in Spark since its inception and discuss their performance and usability implications for the end user.
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."
How Adobe Does 2 Million Records Per Second Using Apache Spark!Databricks
Adobe’s Unified Profile System is the heart of its Experience Platform. It ingests TBs of data a day and is PBs large. As part of this massive growth we have faced multiple challenges in our Apache Spark deployment which is used from Ingestion to Processing.
Deep Dive: Memory Management in Apache SparkDatabricks
Memory management is at the heart of any data-intensive system. Spark, in particular, must arbitrate memory allocation between two main use cases: buffering intermediate data for processing (execution) and caching user data (storage). This talk will take a deep dive through the memory management designs adopted in Spark since its inception and discuss their performance and usability implications for the end user.
Tuning Apache Spark for Large-Scale Workloads Gaoxiang Liu and Sital KediaDatabricks
Apache Spark is a fast and flexible compute engine for a variety of diverse workloads. Optimizing performance for different applications often requires an understanding of Spark internals and can be challenging for Spark application developers. In this session, learn how Facebook tunes Spark to run large-scale workloads reliably and efficiently. The speakers will begin by explaining the various tools and techniques they use to discover performance bottlenecks in Spark jobs. Next, you’ll hear about important configuration parameters and their experiments tuning these parameters on large-scale production workload. You’ll also learn about Facebook’s new efforts towards automatically tuning several important configurations based on nature of the workload. The speakers will conclude by sharing their results with automatic tuning and future directions for the project.ing several important configurations based on nature of the workload. We will conclude by sharing our result with automatic tuning and future directions for the project.
Join is one of most important and critical SQL operation in most data warehouses. This is essential when we want to get insights from multiple input datasets. Over the last year, we’ve added a series of join optimizations internally at Facebook, and we started to contribute back to upstream open source recently.
Materialized Column: An Efficient Way to Optimize Queries on Nested ColumnsDatabricks
In data warehouse area, it is common to use one or more columns in complex type, such as map, and put many subfields into it. It may impact the query performance dramatically because: 1) It is a waste of IO. The whole column (in map), which may contain tens of subfields, need to be read. And Spark will traverse the whole map and get the value of the target key. 2) Vectorized read can not be exploit when nested type column is read. 3) Filter pushdown can not be utilized when nested columns is read. Over the last year, we have added a series of optimizations in Apache Spark to solve the above problems for Parquet.
Deep Dive into the New Features of Apache Spark 3.0Databricks
Continuing with the objectives to make Spark faster, easier, and smarter, Apache Spark 3.0 extends its scope with more than 3000 resolved JIRAs. We will talk about the exciting new developments in the Spark 3.0 as well as some other major initiatives that are coming in the future.
Hive Bucketing in Apache Spark with Tejas PatilDatabricks
Bucketing is a partitioning technique that can improve performance in certain data transformations by avoiding data shuffling and sorting. The general idea of bucketing is to partition, and optionally sort, the data based on a subset of columns while it is written out (a one-time cost), while making successive reads of the data more performant for downstream jobs if the SQL operators can make use of this property. Bucketing can enable faster joins (i.e. single stage sort merge join), the ability to short circuit in FILTER operation if the file is pre-sorted over the column in a filter predicate, and it supports quick data sampling.
In this session, you’ll learn how bucketing is implemented in both Hive and Spark. In particular, Patil will describe the changes in the Catalyst optimizer that enable these optimizations in Spark for various bucketing scenarios. Facebook’s performance tests have shown bucketing to improve Spark performance from 3-5x faster when the optimization is enabled. Many tables at Facebook are sorted and bucketed, and migrating these workloads to Spark have resulted in a 2-3x savings when compared to Hive. You’ll also hear about real-world applications of bucketing, like loading of cumulative tables with daily delta, and the characteristics that can help identify suitable candidate jobs that can benefit from bucketing.
Designing ETL Pipelines with Structured Streaming and Delta Lake—How to Archi...Databricks
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. Delta Lake, on the other hand, is the best way to store structured data because it is a open-source storage layer that brings ACID transactions to Apache Spark and big data workloads Together, these can make it very easy to build pipelines in many common scenarios. 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 that needs to be solved. Apache Spark, being a unified analytics engine doing both batch and stream processing, often provides multiples ways to solve the same problem. So understanding the requirements carefully helps you to architect your pipeline that solves your business needs in the most resource efficient manner.
In this talk, I am going examine a number common streaming design patterns in the context of the following questions.
WHAT are you trying to consume? What are you trying to produce? What is the final output that the business wants? What are your throughput and latency requirements?
WHY do you really have those requirements? Would solving the requirements of the individual pipeline actually solve your end-to-end business requirements?
HOW are going to architect the solution? And how much are you willing to pay for it?
Clarity in understanding the ‘what and why’ of any problem can automatically much clarity on the ‘how’ to architect it using Structured Streaming and, in many cases, Delta Lake.
Apache Spark presentation at HasGeek FifthElelephant
https://fifthelephant.talkfunnel.com/2015/15-processing-large-data-with-apache-spark
Covering Big Data Overview, Spark Overview, Spark Internals and its supported libraries
A Deep Dive into Query Execution Engine of Spark SQLDatabricks
Spark SQL enables Spark to perform efficient and fault-tolerant relational query processing with analytics database technologies. The relational queries are compiled to the executable physical plans consisting of transformations and actions on RDDs with the generated Java code. The code is compiled to Java bytecode, executed at runtime by JVM and optimized by JIT to native machine code at runtime. This talk will take a deep dive into Spark SQL execution engine. The talk includes pipelined execution, whole-stage code generation, UDF execution, memory management, vectorized readers, lineage based RDD transformation and action.
"The common use cases of Spark SQL include ad hoc analysis, logical warehouse, query federation, and ETL processing. Spark SQL also powers the other Spark libraries, including structured streaming for stream processing, MLlib for machine learning, and GraphFrame for graph-parallel computation. For boosting the speed of your Spark applications, you can perform the optimization efforts on the queries prior employing to the production systems. Spark query plans and Spark UIs provide you insight on the performance of your queries. This talk discloses how to read and tune the query plans for enhanced performance. It will also cover the major related features in the recent and upcoming releases of Apache Spark.
"
"Structured Streaming was a new streaming API introduced to Spark over 2 years ago in Spark 2.0, and was announced GA as of Spark 2.2. Databricks customers have processed over a hundred trillion rows in production using Structured Streaming. We received dozens of questions on how to best develop, monitor, test, deploy and upgrade these jobs. In this talk, we aim to share best practices around what has worked and what hasn't across our customer base.
We will tackle questions around how to plan ahead, what kind of code changes are safe for structured streaming jobs, how to architect streaming pipelines which can give you the most flexibility without sacrificing performance by using tools like Databricks Delta, how to best monitor your streaming jobs and alert if your streams are falling behind or are actually failing, as well as how to best test your code."
Parquet performance tuning: the missing guideRyan Blue
Ryan Blue explains how Netflix is building on Parquet to enhance its 40+ petabyte warehouse, combining Parquet’s features with Presto and Spark to boost ETL and interactive queries. Information about tuning Parquet is hard to find. Ryan shares what he’s learned, creating the missing guide you need.
Topics include:
* The tools and techniques Netflix uses to analyze Parquet tables
* How to spot common problems
* Recommendations for Parquet configuration settings to get the best performance out of your processing platform
* The impact of this work in speeding up applications like Netflix’s telemetry service and A/B testing platform
Video: https://www.youtube.com/watch?v=kkOG_aJ9KjQ
This talk gives details about Spark internals and an explanation of the runtime behavior of a Spark application. It explains how high level user programs are compiled into physical execution plans in Spark. It then reviews common performance bottlenecks encountered by Spark users, along with tips for diagnosing performance problems in a production application.
Accelerating Spark SQL Workloads to 50X Performance with Apache Arrow-Based F...Databricks
In Big Data field, Spark SQL is important data processing module for Apache Spark to work with structured row-based data in a majority of operators. Field-programmable gate array(FPGA) with highly customized intellectual property(IP) can not only bring better performance but also lower power consumption to accelerate CPU-intensive segments for an application.
Spark SQL Tutorial | Spark SQL Using Scala | Apache Spark Tutorial For Beginn...Simplilearn
This presentation about Spark SQL will help you understand what is Spark SQL, Spark SQL features, architecture, data frame API, data source API, catalyst optimizer, running SQL queries and a demo on Spark SQL. Spark SQL is an Apache Spark's module for working with structured and semi-structured data. It is originated to overcome the limitations of Apache Hive. Now, let us get started and understand Spark SQL in detail.
Below topics are explained in this Spark SQL presentation:
1. What is Spark SQL?
2. Spark SQL features
3. Spark SQL architecture
4. Spark SQL - Dataframe API
5. Spark SQL - Data source API
6. Spark SQL - Catalyst optimizer
7. Running SQL queries
8. Spark SQL demo
This Apache Spark and Scala certification training is designed to advance your expertise working with the Big Data Hadoop Ecosystem. You will master essential skills of the Apache Spark open source framework and the Scala programming language, including Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark. This Scala Certification course will give you vital skillsets and a competitive advantage for an exciting career as a Hadoop Developer.
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Visual Studio 2010 and the .NET Framework 4 enhance support for parallel programming by providing a new runtime, new class library types, and new diagnostic tools. This presentation is all about parallel programming and its features.
Don Kelly - Effective Spark for Neophytes - Codemotion Milan 2018Codemotion
Spark is a wonderful, esoteric and sometimes obtuse programming environment, but it is very difficult for the neophyte. Understanding the fundamental concepts of the system takes time and energy. To make matters worse, non-trivial example applications of any merit are hard to find. By the end of the talk, neophyte developers will realize what Spark can do for them and give them a perspective to approach their own solutions.
Stream, stream, stream: Different streaming methods with Spark and KafkaItai Yaffe
Going into different streaming methods, we will share our experience as early-adopters of Spark Streaming and Spark Structured Streaming, and how we overcame technical barriers (and there were plenty...).
We will also present a rather unique solution of using Kafka to imitate streaming over our Data Lake, while significantly reducing our cloud services’ costs.
Topics include :
* Kafka and Spark Streaming for stateless and stateful use-cases
* Spark Structured Streaming as a possible alternative
* Combining Spark Streaming with batch ETLs
* “Streaming” over Data Lake using Kafka
A new look on Spark 2 features and Under the hood. We try to look at Apache spark latest release with an examining look, while still loving it, but also criticising it.
http://www.learntek.org/product/scala-spark-training/
Scala is a modern multi-paradigm programming language designed to express common programming patterns in a concise, elegant, and type-safe way. Scala, the word came from “Scalable Language”, is a hybrid functional programming language which smoothly integrates the features of objected oriented and functional programming languages and it is compiled to run on the Java Virtual Machine.
Spark is a fast cluster computing technology, designed for fast computation in Hadoop clusters. It is based on Hadoop MapReduce programming and it extends the MapReduce model to efficiently use it for more types of computations, like interactive queries and stream processing. Spark uses Hadoop in two different ways – one is storage and another one is processing.
http://www.learntek.org/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses. We are dedicated to designing, developing and implementing training programs for students, corporate employees and business professional.
Reduce latency and boost sql server io performanceKevin Kline
Is SQL Server slow for you? Attend this webinar and learn how you can optimize your SQL Server performance. (Download the companion T-SQL scripts from Kevin's at http://blogs.sqlsentry.com/KevinKline). Hear how the pros pinpoint performance bottlenecks and leverage the latest advancements in storage technology to decrease access latency and IO wait times. By the end of the webinar you'll have the tools and information you need to recommend the best approach for your SQL Server environment.
Healthcare Claim Reimbursement using Apache SparkDatabricks
Optum Inc helps hospitals accurately calculate the claim reimbursement, detect underpayment from the Insurance company. Optum receives millions of claims per day which needs to be evaluated in less than 8 hours and the results need to be sent back to the hospitals for revenue recovery purposes.
Data Science at Scale with Apache Spark and Zeppelin NotebookCarolyn Duby
How to get started with Data Science at scale using Apache Spark to clean, analyze, discover, and build models on large data sets. Use Zeppelin to record analysis to encourage peer review and reuse of analysis techniques.
Similar to How We Optimize Spark SQL Jobs With parallel and sync IO (20)
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 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.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
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.
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.
5 Critical Steps to Clean Your Data Swamp When Migrating Off of HadoopDatabricks
In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.
Democratizing Data Quality Through a Centralized PlatformDatabricks
Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale.
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 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 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 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 IntegrationDatabricks
In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs.
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 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 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 PipelinesDatabricks
Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark.
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 AggregationsDatabricks
In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.
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 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 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 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 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!
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
How We Optimize Spark SQL Jobs With parallel and sync IO
1. How we optimize
Spark SQL jobs with
parallel and
asynchronous I/O
Guo, Jun (jason.guo.vip@gmail.com)
Lead of Data Engine Team, ByteDance
2. Who we are
▪ Data Engine team at
ByteDance
▪ Build a platform of
one-stop experience for
OLAP , on which users can
analyze PB level data by
writing SQL without caring
about the underlying
execution engine
3. What we do
▪ Manage Spark SQL /
Presto / Hive workloads
▪ Offer Open API and
self-serve platform
▪ Optimize Spark SQL /
Presto / Hive engine
▪ Design data architecture
for most business lines in
ByteDance
4. Agenda
• Spark SQL at ByteDance
• Why does I/O matter for Spark SQL
• How we boost Spark SQL jobs by parallel and
asynchronous I/O
• Prospects
6. Spark SQL at ByteDance
2016 2017 2018 2019 2020
Small Scale Experiments
Ad-hoc workload
Few ETL pipelines in production
Full-production deployment
Main engine in DW area
2021
Totally replace Hive for ETL
8. ▪ NVMe SSD perform better than HDD
by two magnitude
▪ More and more new hardware have
been invented in past years, such as
AEP
▪ Many papers show that ‘I/O is faster
than CPU’
▪ TCO is one of the most important
factors for huge data storage
▪ Most of servers have a lot of HDD,
especially for Hadoop cluster
▪ I/O cost contribute more that 30% of
total latency of Spark ETL jobs
I/O is still the bottleneck for big data
processing
I/O performance has been improved
Why does I/O matter for Spark SQL
9. How we boost Spark SQL jobs by
parallel and asynchronous IO
11. Parallel IO
• Spark SQL will split a large
Parquet file into a group of
splits, each of which contains
one or a few row groups
• Each task will read these row
group sequentially
12. Parallel IO
• Spark SQL can combine a
group of small parquet files
into a single split
• Each task will read these files
in a single group sequentially
13. Parallel I/O
▪ I/O and computation are handled
sequentially by the same thread
▪ Tuples in a single task are computed
sequentially
▪ I/O for different files or row groups
are handled sequentially
▪ Introduce a buffer to separate I/O and
computation
▪ I/O and computation will be handled
in separated threads
▪ I/O for different files or row groups
can be done in a parallel approach
I/O and computation in separated
threads
I/O and computation in a single thread
15. Parallel I/O
• Column level parallel I/O
o Split a logical Parquet file into a
group of column family, which is a
physical Parquet file
o Each column family contains a few
columns
o Spark SQL will read different column
family in parallel