Dataset is highly type safe and use encoders. to use Codespaces. The use of artificial intelligence in business continues to evolve as massive increases in computing capacity accommodate more complex programs than ever before. Apache Spark DataFrames are an abstraction built on top of Resilient Distributed Datasets (RDDs). On the other hand, if the application uses costly aggregations and does not heavily rely on caching, increasing execution memory can help by evicting unneeded cached data to improve the computation itself. conf.set(spark.serializer, org.apache.spark.serializer.KryoSerializer), val conf = new SparkConf().setMaster().setAppName(), conf.registerKryoClasses(Array(classOf[MyClass1], classOf[MyClass2])). WebInbuild-optimization when using DataFrames; Supports ANSI SQL; Apache Spark Advantages. Data Serialization. There's a reason not everyone is a Spark pro. Every partition ~ task requires a single core for processing. Spark SQL Optimization- The Spark Catalyst Optimizer. Suppose you have a situation where one data set is very small and another data set is quite large, and you want to perform the join operation between these two. Spark comes with many file formats like CSV, JSON, XML, PARQUET, ORC, AVRO and more. 3.8. Spark queries benefit from Snowflakes automatic query pushdown optimization, which improves performance. The hard part actually comes when running them on cluster and under full load as not all jobs are created equal in terms of performance. The value of this course is in showing you different techniques with their direct and immediate effect, so you can later apply them in your own projects. git It can be installed in a stand-alone mode or a Hadoop cluster. after understanding the following summary. Consider all the popular functional programming languages supported by Apache Spark big data framework like Java, Python, R, and Scala and look at the job trends.Of all the four programming languages supported by Spark, most of the big data job openings list Scala To view this data in a tabular format, you can use the Azure Databricks display() command, as in the following example: Spark uses the term schema to refer to the names and data types of the columns in the DataFrame. More executor memory, on the other hand, becomes unwieldy from GC perspective. Due to these amazing benefits, Spark is used in banks, tech firms, financial organizations, telecommunication departments, and government agencies. Is there a higher analog of "category with all same side inverses is a groupoid"? In order to be able to enable dynamic allocation, we must also enable Sparks external shuffle service. Spark 3.3.0 is based on Scala 2.13 (and thus works with Scala 2.12 and 2.13 out-of-the-box), but it can also be made to work with Scala 3. WebNow Lets see How to Fix the Data Skew issue . WebAn itemset is an unordered collection of unique items. :) Looking forward to everyone's support. They are useful when you want to store a small data set that is being used frequently in your program. You and I have had this. On the other hand, there can be limitations in I/O throughput on a node level, depending on the operations requested, so we cannot increase this indefinitely. Python 3.6 support is deprecated as of Spark 3.2.0. Operations that imply a shuffle therefore provide a numPartitions parameter that specify the new partition count (by default the partition count stays the same as in the original RDD). But, In rule-based optimization, there are rules to determine how to execute the query. WebThe most interesting part of learning Scala for Spark is the big data job trends. We can observe a similar performance issue when making cartesian joins and later filtering on the resulting data instead of converting to a pair RDD and using an inner join: The rule of thumb here is to always work with the minimal amount of data at transformation boundaries. I have a Master's Degree in Computer Science and I wrote my Bachelor and Master theses on Quantum Computation. null keys are a common special case). As Spark can compute the actual size of each stored record, it is able to monitor the execution and storage parts and react accordingly. Let me describe it, then tell me if it sounds like you: you run a 4-line job on a gig of data, with two innocent joins, and it takes a bloody hour to run. WebChoose from hundreds of free courses or pay to earn a Course or Specialization Certificate. Serialization plays an important role in the performance for any distributed application. while waiting for the last tasks of a particular transformation to finish). WebDescription. 3 This tutorial illustrates different ways to create and submit a Spark Scala job to a Dataproc cluster, including how to: write and compile a Spark Scala "Hello World" app on a local machine from the command line using the Scala REPL (Read-Evaluate-Print-Loop or interactive interpreter) or the SBT build tool; package compiled Scala classes into a jar file For that reason Spark defines a shared space for both, giving priority to execution memory. Less than 0.3% of students refunded a course on the entire site, and every payment was returned in less than 72 hours. G1GC helps to decrease the execution time of the jobs by optimizing the pause times between the processes. But before this, you need to modify and optimize the programs logic and code. There are two ways to maintain the parallelism Repartition and Coalesce. My cheesy effort to let my friends know that Quaeris will be 'general availability' in Q1 2022! Im a software engineer and the founder of Rock the JVM. If the partitions are not uniform, we say that the partitioning is skewed. Cache and persist5. But then I looked at the stats. First of all, you don't need to store the data in the temp table to write into hive table later. Your email address will not be published. This means that it is much easier to get a very low number of partitions with wholeTextFiles if using default settings while not managing data locality explicitly on the cluster. We dive deep into Spark and understand why jobs are taking so long before we get to touch any code, or worse, waste compute money. Webpublic class SparkSession extends Object implements scala.Serializable, java.io.Closeable, Due to optimization, duplicate invocations may be eliminated or the function may even be invoked more times than it is present in the query. But, this data analysis and number crunching are not possible only through excel sheets. If you are curious to learn about spark optimization, data science, check out IIIT-B & upGrads Executive PG Programme in Data Sciencewhich is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms. It uses two premises of unified memory management. There is no difference in performance or syntax, as seen in the following example: Use filtering to select a subset of rows to return or modify in a DataFrame. Not the answer you're looking for? Our input can already be skewed when reading from the data source. In the fast-changing and hyper-competitive business world, both small and large organizations must keep a close eye on their data and analytics. It can then be used to Spark DataFrames and Spark SQL use a unified planning and optimization engine, allowing you to get nearly identical performance across all supported languages on Azure You are looking at the only course on the web on Spark optimization. During computation, if an executor is idle for more than spark.dynamicAllocation.executorIdleTimeout (60 seconds by default) it gets removed (unless it would bring the number of executors below spark.dynamicAllocation.minExecutors (0 by default). As of Spark 2.0, this is replaced by SparkSession. Apache Spark DataFrames provide a rich set of functions (select columns, filter, join, aggregate) that allow you to solve common data analysis problems efficiently. Lets look at the following example: Here we can see that a is just a variable (just as factor before) and is therefore serialized as an Int. Spark provides native bindings for programming languages, such as Python, R, Scala, and Java. GC tuning is the first step to collecting statistics by selecting verbose when submitting the spark jobs. RDD.Cache()would always store the data in memory. Web2. Master tools and techniques used by the very best. So following are the few issues which I have faced in my recent interaction with Spark SQL: Too large of a query to be stored in memory. Spark must spill data to disk if you want to occupy all the execution space. Apache Spark has plenty of use cases, but there are certain specialized needs where you need other big data engines for fulfilling the purpose. RDD.Persist() allows storage of some part of data into the memory and some part on the disk. By default, tasks are processed in a FIFO manner (on the job level), but this can be changed by using an alternative in-application scheduler to ensure fairness (by setting spark.scheduler.mode to FAIR). Track, predict, and manage COVID-19 related hospital admissions. spark.executor.memory: It is the total memory available to executors. The following example is an inner join, which is the default: You can add the rows of one DataFrame to another using the union operation, as in the following example: You can filter rows in a DataFrame using .filter() or .where(). Contribute to librity/rtjvm_spark_optimizations development by creating an account on GitHub. A tag already exists with the provided branch name. It is, in fact, literally impossible for it to do that as each transformation is defined by an opaque function and Spark has no way to see what data were working with and how. You can assign these results back to a DataFrame variable, similar to how you might use CTEs, temp views, or DataFrames in other systems. XGBoost uses num_workers to set how many parallel workers and nthreads to the number of threads per worker. Short answer: no. As you can see, designing a Spark application for performance can be quite challenging and every step of the way seems to take its toll in terms of increased complexity, reduced versatility or prolonged analysis of the specific use case. Set the JVM flag to xx:+UseCompressedOops if the memory size is less than 32 GB. Develop new tech skills and knowledge with Packt Publishings daily free learning giveaway Also Read: 6 Game Changing Features of Apache Spark. The Kryo serializer gives better performance as compared to the Java serializer. Powered by Rock the JVM! This repository contains the code we wrote during Rock the JVM's Spark Optimization with Scala course. Unless explicitly mentioned, the code in this repository is exactly what was caught on camera. As you open the project, the IDE will take care to download and apply the appropriate library dependencies. Cache and Persist methods of this will store the data set into the memory when the requirement arises. Namely GC tuning, proper hardware provisioning and tweaking Sparks numerous configuration options. in Intellectual Property & Technology Law Jindal Law School, LL.M. More info about Internet Explorer and Microsoft Edge, Scala Dataset aggregator example notebook. DataSets are highly type safe and use the encoder as part of their serialization. All data blocks of the input files are added into common pools, just as in wholeTextFiles, but the pools are then divided into partitions according to two settings: spark.sql.files.maxPartitionBytes, which specifies a maximum partition size (128MB by default), and spark.sql.files.openCostInBytes, which specifies an estimated cost of opening a new file in bytes that could have been read (4MB by default). First technique is- Salting or Key-Salting. Why choose Spark compared to a SQL-only engine? spark.mls FP-growth implementation takes the following (hyper-)parameters: minSupport: the minimum support for an itemset to be identified as frequent. Big data" analysis is a hot and highly valuable skill and this course will teach you the hottest technology in big data: Apache Spark. This will reduce one step from your code. As with the other Rock the JVM courses, Spark Optimization will take you through a battle-tested path to Spark proficiency as a data scientist and engineer. A Spark job can be optimized by choosing the parquet file with snappy compression. Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. Whenever you apply the Repartition method it gives you equal number of partitions but it will shuffle a lot so it is not advisable to go for Repartition when you want to lash all the data. WebSpark Optimization. by Raja Ramesh Chindu | Jul 29, 2020 | Big Data Technology, Blog, Data Analytics, Data Science | 0 comments. Beyond RDD, Spark also makes use of Direct Acyclic Graph (DAG) to track computations on RDDs, this approach optimizes data processing by leveraging the job flows Add a new light switch in line with another switch? Serialization of closures is therefore less efficient than serialization of the data itself, however as closures are only serialized for each executor on each transformation and not for each record, this usually does not cause performance issues. Apache Spark is a quick, universal cluster computation engine. You can use the Dataset/DataFrame API in Scala, Java, Python or R to express streaming aggregations, event-time windows, stream-to-batch joins, etc. Skew can also be introduced via shuffles, especially when joining datasets. You can find more information on how to create an Azure Databricks cluster from here. Low computing capacity The default processing on Apache Spark takes place in the cluster memory. We can reduce the amount of inter-node communication required by increasing the resources of a single executor while decreasing the overall number of executors, essentially forcing tasks to be processed by a limited number of nodes. WebThe difference between Spark and Scala is that th Apache Spark is a cluster computing framework, designed for fast Hadoop computation while the Scala is a general-purpose programming language that supports functional and object-oriented programming. Install IntelliJ IDEA with the Scala plugin. The appName parameter is a name for your application to show on the A Spark job can be optimized by choosing the parquet file with snappy WebSpark 3.3.1 ScalaDoc < Back Back Packages package root package org package scala Hypothesis Testing Programs Whenever any ByKey operation is used, the user should partition the data correctly. The bottleneck for these spark optimization computations can be CPU, memory or any resource in the cluster. When using HDFS Spark can optimize the allocation of executors in such a way as to maximize this probability. The high-level APIs use their own way of managing memory as part of Project Tungsten. The high-level APIs can automatically convert join operations into broadcast joins. But, this data analysis and number crunching are not possible only through excel sheets. Spark does not have a set type, so itemsets are represented as arrays. How can I use a VPN to access a Russian website that is banned in the EU? Logistic Regression Courses On the other hand, if the size of data increases, then it is found that the Spark DataFrame is capable enough to outperform the Pandas DataFrame. To set the Kryo serializer as part of a Spark job, we need to set a configuration property, which is org.apache.spark.serializer.KryoSerializer. 1980s short story - disease of self absorption. Linux, Mac OS), and it should run on any platform that runs a supported version of Java. The task scheduler distributes these tasks to executors. You can print the schema using the .printSchema() method, as in the following example: Azure Databricks uses Delta Lake for all tables by default. In Spark 1.6, a model import/export functionality was added to the Pipeline API. You'll understand Spark internals to explain if you're writing good code or not, You'll be able to predict in advance if a job will take a long time, You'll read query plans and DAGs while the jobs are running, to understand if you're doing anything wrong, You'll optimize DataFrame transformations way beyond the standard Spark auto-optimizer, You'll do fast custom data processing with efficient RDDs, in a way SQL is incapable of, You'll diagnose hanging jobs, stages and tasks, Plus you'll fix a few memory crashes along the way, You'll have access to the entire code I write on camera (2200+ LOC), You'll be invited to our private Slack room where I'll share latest updates, discounts, talks, conferences, and recruitment opportunities, (soon) You'll have access to the takeaway slides, (soon) You'll be able to download the videos for your offline view, Deep understanding of Spark internals so you can predict job performance, understanding join mechanics and why they are expensive, writing broadcast joins, or what to do when you join a large and a small DataFrame, write pre-join optimizations: column pruning, pre-partitioning, fixing data skews, "straggling" tasks and OOMs, writing optimizations that Spark doesn't generate for us, Optimizing key-value RDDs, as most useful transformations need them, using the different _byKey methods intelligently, reusing JVM objects for when performance is critical and even a few seconds count, using the powerful iterator-to-iterator pattern for arbitrary efficient processing, performance differences between the different Spark APIs. As with the other Rock the JVM courses, Spark Optimization will take you through a battle-tested path to Spark proficiency as a data scientist and engineer. This is also beneficial in case of losing executors (e.g. That is why it is advisable to switch to the second supported serializer, Kryo, for the majority of production uses. val peopleDF = spark.read.json(examples/src/main/resources/people.json), val parquetFileDF = spark.read.parquet(people.parquet), val usersDF = spark.read.format(avro).load(examples/src/main/resources/users.avro), usersDF.select(name, favorite_color).write.format(avro).save(namesAndFavColors.avro). Did neanderthals need vitamin C from the diet? Spark uses spark.task.cpus to set how many CPUs to allocate per task, so it should be set to the same as nthreads. Spark SQL can turn on and off AQE by spark.sql.adaptive.enabled as an umbrella configuration. Spark also allows the implementation of interactive machine learning algorithms. for serializing objects that are faster than Java serialization and is a more compact process. We know that Spark comes with 3 types of API to work upon -RDD, DataFrame and DataSet. Serialization improves any distributed applications performance. So, you can write applications in various languages. In any distributed environment parallelism plays very important role while tuning your Spark job. For best effectiveness, I recommend chunks of 1 hour of learning at a time. WebFor example, when using Scala 2.13, use Spark compiled for 2.13, and compile code/applications for Scala 2.13 as well. I think using the above technique, your write time will reduce significantly. Some of the widely used spark optimization techniques are: 1. A SQLContext can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. Broadcasting plays an important role while tuning your spark job. We can also tweak Sparks configuration relating to locality when reading data from the cluster using the spark.locality.wait setting (3 seconds by default) and its subsections (same as spark.locality.wait by default). In this Spark tutorial, we will learn about Spark SQL optimization Spark catalyst optimizer framework. in Intellectual Property & Technology Law, LL.M. So this has to be the million dollar question. jersey-server json4s-ast kryo-shaded minlog scala-xml spark-launcher; spark-network-shuffle spark-unsafe xbean-asm5-shaded; Configure Hive execution engine to use Spark: More executor memory means it can enable mapjoin optimization for more queries. Scala is one language that is used to write Spark. WebRDD-based machine learning APIs (in maintenance mode). You can select columns by passing one or more column names to .select(), as in the following example: You can combine select and filter queries to limit rows and columns returned. Master tools and techniques used by the very best. What we do in this technique is . These patterns help them in making important decisions for the enhancement of the business. Lets take a look at these two definitions of the same computation: The second definition is much faster than the first because it handles data more efficiently in the context of our use case by not collecting all the elements needlessly. Shuffles are heavy operation which consume a lot of memory. Sed based on 2 words, then replace whole line with variable. To improve the performance, the classes have to be registered using the registerKryoClasses method. Quick Start RDDs, Accumulators, Design your data structures to prefer arrays of objects, and primitive types, instead of the standard Java or Write perfomant code. Broadcasting plays an important role while tuning Spark jobs. This includes reading from a table, loading data from files, and operations that transform data. We can solve this by avoiding class fields in closures: Here we prepare the value by storing it in a local variable sum. Use Git or checkout with SVN using the web URL. If you've never done Scala or Spark, this course is not for you. This Friday, were taking a look at Microsoft and Sonys increasingly bitter feud over Call of Duty and whether U.K. regulators are leaning toward torpedoing the Activision Blizzard deal. Please refer to the latest Python Compatibility page. Serialization improves any distributed applications performance. How to Updata an ORC Hive table form Spark using Scala, How to read Hive Table with Spark-Sql efficiently, mismatch input '$' expecting StringLiteral Inpath near 'inpath' in load statement, Spark sql Optimization Techniques loading csv to orc format of hive. Are there conservative socialists in the US? Tune the partitions and tasks. @Siddharth Goel I've updated my question with sample code. Linear Algebra for Analysis. The default value for all minPartitions parameters is 2. If youd like to build Spark from source, visit Building Spark . Subscribe to receive articles on topics of your interest, straight to your inbox. When using opaque functions in transformations (e.g. Lets go through the features of Apache Spark that help in spark optimization: Spark streamlines running applications in the Hadoop cluster. WebAdaptive Query Execution (AQE) is an optimization technique in Spark SQL that makes use of the runtime statistics to choose the most efficient query execution plan, which is enabled by default since Apache Spark 3.2.0. As data columns are represented only by name for the purposes of transformation definitions and their valid usage with regards to the actual data types is only checked during run-time, this tends to result in a tedious development process where we need to keep track of all the proper types or we end up with an error during execution. Parquet uses the envelope encryption practice, where file parts are encrypted with data encryption keys (DEKs), and the DEKs are encrypted with master encryption keys (MEKs). That means that in order to serialize it, Spark needs to serialize the whole instance of SomeClass with it (so it has to extend Serializable, otherwise we would get a run-time exception). You may find Memory Management as one of the easy-to-use. The first one is repartition which forces a shuffle in order to redistribute the data among the specified number of partitions (by the aforementioned Murmur hash). Now lets go through different techniques for optimization in spark: Spark optimization techniquesare used to modify the settings and properties of Spark to ensure that the resources are utilized properly and the jobs are executed quickly. Parallelism plays a very important role while tuning spark jobs. Appealing a verdict due to the lawyers being incompetent and or failing to follow instructions? You will learn 20+ techniques and optimization strategies. See also Apache Spark Scala API reference. The same is true for d as constructor parameters are converted into fields internally. Take the following example resource distribution: In all of the instances, well be using the same amount of resources (15 cores and 15GB of memory). Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program. The idea is to modify the existing key to make an even distribution of data. Making the third option usually the fastest. cache() and persist() will store the dataset in memory. It can offer these fast speeds by decreasing the number of write/read operations to disk. The datas minimum unremovable amount is defined through spark.memory.storageFraction configuration option. There are also external fields and variables that are used in the individual transformations. All of the APIs also provide two methods to manipulate the number of partitions. Spark query tuning and performance optimization SQL database integration (Postgres, and/or MySQL) Experience working with HDFS, AWS ( S3, Redshift, EMR , IAM , Spark offers built-in APIs in Python, Java, or Scala. Spill to disk when the execution memory is full. Find centralized, trusted content and collaborate around the technologies you use most. rev2022.12.9.43105. Toggle search Toggle menu. WebFor example, when using Scala 2.13, use Spark compiled for 2.13, and compile code/applications for Scala 2.13 as well. We can, however, increase this even further by good design. The only thing that can hinder these computations is the memory, CPU, or any other resource. Previous post: Attempt 2 - Resources allocated. 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Spark supports many formats, such as CSV, JSON, XML, PARQUET, ORC, AVRO, etc. We'll write it together, either in the IDE or in the Spark Shell, and we test the effects of the code on either pre-loaded data (which I provide) or with bigger, generated data (whose generator I also provide). Additionally, there are many other techniques that may help improve performance of your Spark jobs even further. Sometimes, even though we do everything correctly, we may still get poor performance on a specific machine due to circumstances outside our control (heavy load not related to Spark, hardware failures, etc.). You may find Memory Management as one of the easy-to-use pyspark optimization techniques after understanding the following summary. This is easily achieved by starting multiple threads on the driver and issuing a set of transformations in each of them. We do not currently allow content pasted from ChatGPT on Stack Overflow; read our policy here. Architecture of Spark SQL. This is done by setting spark.serializer to org.apache.spark.serializer.KryoSerializer. spark.memory.fraction: It is the fraction of the total memory accessible for storage and execution. WebSpark 3.3.1 ScalaDoc - org.apache.spark.sql.SparkSession. 8 Ways Data Science Brings Value to the Business, The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have, Top 6 Reasons Why You Should Become a Data Scientist. Top Data Science Skills to Learn in 2022 While coding in Spark, a user should always try to avoid any shuffle operation because the shuffle operation will degrade the performance. The resulting tasks are then run concurrently and share the applications resources. Spark/Scala/PySpark developer who knows how to fully exploit the potential of our Spark cluster. to send results to a database). Provides API for Python, Java, Scala, and R Programming. Akka, Cats, Spark) to 41000+ students at various levels and I've held live trainings for some of the best companies in the industry, including Adobe and Apple. The second method provided by all APIs is coalesce which is much more performant than repartition because it does not shuffle data but only instructs Spark to read several existing partitions as one. Variables in closures are pretty simple to keep track of. Also, can you please include your spark code and properties. By default, it is 1 gigabyte. SL. The case class defines the schema of the table. ByKey operation6. Update Project Object Model (POM) file to resolve Spark module dependencies. Bucketing is an optimization technique in Apache Spark SQL. This ensures that the resources are never kept idle (e.g. WebState of art optimization and code generation through the Spark SQL Catalyst optimizer (tree transformation framework). Work fast with our official CLI. Inthis case, to avoid that error, a user should increase the level of parallelism. Here we have a second dataframe that is very small and we are keeping this data frame as a broadcast variable. 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Thanks to this, they can generate optimized serialization code tailored specifically to these types and to the way Spark will be using them in the context of the whole computation. Here, an in-memory object is converted into another format that can be stored in Let us wrap our heads around the basics of this software framework. The executor owns a certain amount of total memory that is categorized into two parts i.e. Broadcast variable will make small datasets available on nodes locally. By default, Spark uses the Java serializer over the JVM platform. A join returns the combined results of two DataFrames based on the provided matching conditions and join type. They are useful when you want to store a small data set that is being used frequently in your program. Then same thing. This is where data processing software technologies come in. Spark can run multiple computations in parallel. It is half of the total memory, by default. WebTo use MLlib in Python, you will need NumPy version 1.4 or newer.. No As Spark SQL works on schema, tables, and records, you can Similarly, when storage memory is idle, execution memory can utilize the space. technique. WebRsidence officielle des rois de France, le chteau de Versailles et ses jardins comptent parmi les plus illustres monuments du patrimoine mondial et constituent la plus complte ralisation de lart franais du XVIIe sicle. Persisting & Caching data in memory. High shuffling may give rise to an OutOfMemory Error; To avoid such an error, the user can increase the level of parallelism. Generally, in an ideal situation we should keep our garbage collection memory less than 10% of heap memory. WebFrom Assortment Optimization to Pricing Optimization. Connecting three parallel LED strips to the same power supply. Spark supports two different serializers for data serialization. Experts predict that 30% of companies will base decisions on graph technologies by 2023. Azure Databricks also uses the term schema to describe a collection of tables registered to a catalog. Each of them individually can give at least a 2x perf boost for your jobs, and I show it on camera. Many data systems are configured to read these directories of files. For Python 3.9, Arrow optimization and pandas UDFs might not work due to the supported Python versions in Apache Arrow. As closures can be quite complex, a decision was made to only support Java serialization there. Well said Ayushi Mehta. are used for tuning its performance to make the most out of it. Code is king, and we write from scratch. Or another one: you have an hour long job which was progressing smoothly, until the task 1149/1150 where it hangs, and after two more hours you decide to kill it because you don't know if it's you, a bug in Spark, or some big data god that's angry at you right when you. Spark SQL can turn on and off AQE by spark.sql.adaptive.enabled as an umbrella configuration. 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