How can I get DAG of Spark Sql Query execution plan? To learn more, see our tips on writing great answers. In this catalog, which can be assimilated to a metastore, the semantic analysis will be produced to verify data structures, schemas, types, etc. regr_intercept(independent, dependent) Returns the y-intercept of the linear regression linethat is, the value of b in the equation dependent = a * independent + b. regr_r2(independent, depen dent) Returns the coe cient of determination for the regression. This effort stems from the projects recognition that presenting details about an application in an intuitive manner is just as important as exposing the information in the first place. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Lazy Evaluation in Sparks means, Spark will not start the execution of the . Can't see Yarn Job when doing Spark-Submit on Yarn Cluster, How to retain completed applications after yarn server restart in spark web-ui, configure log4j for each spark job running on yarn mode. October 4, 2021. Ready to optimize your JavaScript with Rust? If you want to see these changes, you will have to explore Spark UI and tracking skew partitions splits, joins changes etc. DAG in Apache Spark is an alternative to the MapReduce. The timeline view is available on three levels: across all jobs, within one job, and within one stage. It produces data for another stage (s). In this PySpark project, you will perform airline dataset analysis using graphframes in Python to find structural motifs, the shortest route between cities, and rank airports with PageRank. These logical operations will be reordered to optimize the logical plan. 1.6.0 In Spark, a job is associated with a chain of RDD dependencies organized in a direct acyclic graph (DAG) that looks like the following: This job performs a simple word count. Spark organizes the Execution Plan in a Directed Acyclic Graph (the very well known DAG). Spark loves memory. First, it reveals the Spark optimization of pipelining operations that are not separated by shuffles. Are the S&P 500 and Dow Jones Industrial Average securities? The next step in debugging the application is to map a particular task or stage to the Spark operation that gave rise to it. Thanks for contributing an answer to Stack Overflow! Spark Catalyst Spark planquery stage . 3. After that, and only after that, the physical plan is executed through one to many stages and tasks in a laziness way. RDD is the first distributed memory abstraction provided by Spark. 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Is there any way to create that graph from execution plans or any apis in the code? When an action is called, spark directly strikes to DAG scheduler. But, it is annoying to have to sit and watch the application while it is running in order to see the DAG. Generally, it depends on each other and it is very similar to the map and reduce . But before selecting a physical plan, the Catalyst Optimizer will generate many physical plans based on various strategies. Mathematica cannot find square roots of some matrices? Digital Strategist. The first thing to note is that the application acquires executors over the course of a job rather than reserving them in advance. It will produce different types of plans: And those operations will produce various plans: The goal of all these operations and plans is to produce automatically the most effective way to process your query. This allows other applications running in the same cluster to use our resources in the meantime, thereby increasing cluster utilization. On Spark, the optimizer is named Catalyst and can be represented by the schema below. Lets see it in action through a timeline. For stages belonging to Spark DataFrame or SQL execution, this allows to cross-reference Stage execution details to the relevant details in the Web-UI SQL Tab page where SQL plan graphs and execution plans are reported. The sequence of events here is fairly straightforward. HDFS and Data Locality. val columns = Seq("first_name","middle_name","last_name","date of joining","gender","salary") In order to understand how your application runs on a cluster, an important thing to know about Dataset/Dataframe transformations is that they fall into two types, narrow and wide, which we will discuss first, before explaining the execution model. I come from a background in Marketing and Analytics and when I developed an interest in Machine Learning algorithms, I did multiple in-class courses from reputed institutions though I got good Read More. For example, if you have these two dataframes: In both cases, you will be able to call explain(): By default, calling explain with no argument will produce a physical plan explanation : Before Apache Spark 3.0, there was only two modes available to format explain output. Either. Execution DAG The second visualization addition to the latest Spark release displays the execution DAG for each job. A DAG is an acyclic graph produced by the DAGScheduler in Spark. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. As mentioned in Monitoring and Instrumentation, we need following three parameters to be set in spark-defaults.conf. Apache, Apache Spark, Spark and the Spark logo are trademarks of theApache Software Foundation. Databricks Execution Plans. How do I arrange multiple quotations (each with multiple lines) vertically (with a line through the center) so that they're side-by-side? Therefore, if a stage is executed in parallel as m tasks, therefore, we collect m set of features for that stage. Why does the distance from light to subject affect exposure (inverse square law) while from subject to lens does not? rev2022.12.11.43106. Vis mere. with real. You need specify the jobs to store the events logs of all previous jobs. In the latest release, the Spark UI displays these events in a timeline suchthat the relative ordering and interleaving of the events are evident at a glance. if not, are there any apis that can read that grap from UI? DAGs will run in one of two ways: When they are triggered either manually or via the API. Stage 2 Operation in Stage (2) and Stage (3) are 1.FileScanRDD 2.MapPartitionsRDD 3.WholeStageCodegen 4.Exchange Wholestagecodegen This produced this kind of result: When the unresolved plan has been generated, it will resolve everything that is not resolved yet by accessing an internal Spark structure mentioned as Catalog in the previous schema. The second visualization addition to the latest Spark release displays the execution DAG for each job. So once you perform any action on an RDD, Spark context gives your program to the driver.. We accumulate the desired training features after the execution of each task finishes its execution. Summary metrics for all task are represented in a table and in a timeline. The objective of this talk is to convey understanding and familiarity of query plans in Spark SQL, and use that knowledge to achieve better performance of Apache Spark queries. the linage exist between jobs. I think this is because I'm running spark on YARN, and it can only use one resource manager at a time? So, our primary focus is to know how the explain() functions work and their plans. But, it doesn't show me any information related to the spark program's execution. Spark uses pipelining (lineage) operations to optimize its work, that process combines the transformations into a single stage. The Catalyst which generates and optimizes execution plan of Spark SQL will perform algebraic optimization for SQL query statements submitted by users and generate Spark workflow and submit them for execution. Stay tuned for the second half of this two-part series about UI improvements in Spark Streaming! Starting from Apache Spark 3.0, you have a new parameter mode that produce expected format for the plan: What is fun with this formatted output is not so exotic if you come, like me, from the rdbms world . Then, shortly after the first job finishes, the set of executors used for the job becomes idle and is returned to the cluster. - John Tukey In Understanding your Apache Spark Application Through Visualization, Visualization of Spark Streaming statistics. . Feature collector: Each stage in the Spark execution plan of a query is executed on a partition of the data as a task. This process is called Codegen and that's the job of Spark's Tungsten Execution Engine. The EXPLAIN statement is used to provide logical/physical plans for an input statement. In this Talend Project, you will build an ETL pipeline in Talend to capture data changes using SCD techniques. How do I limit the number of spark applications in state=RUNNING to 1 for a single queue in YARN? We do not currently allow content pasted from ChatGPT on Stack Overflow; read our policy here. ("Michael","madhan","","2015-05-19","M",40000), If you have any questions, feel free to leave a comment. This recipe explains Study of Spark query execution plans using explain() Thare are many APIs in Spark. Is it possible to hide or delete the new Toolbar in 13.1? In the latest Spark 1.4 release, we are happy to announce that the data visualization wave has found its way to the Spark UI. So, I tried to view the DAg using this thing called the spark history-server, which I know should help me see past jobs. Directed Acyclic Graph and Lazy Evaluation. What is the Dag scheduler in Apache Spark? It transforms a logical execution plan(i.e. Each physical plan will be estimated based on execution time and resource consumption projection, and only one plan will be selected to be executed. In default it creates file:///tmp/spark-events for logs. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. By default, when the explain() or explain(extended=False) operator is applied over the dataframe, it generates only the physical plan. val data = Seq(("jaggu","","Bhai","2011-04-01","M",30000), and if everything goes well, the plan is marked as Analyzed Logical Plan and will be formatted like this: We can see here that, just after the Aggregate line, all the previously marked unresolved alias are now resolved and correctly typed specially the sum column. Apache spark history server can be started by, Third party spark history server for example of Cloudera can be started by, And to stop the history server (for Apache). Based on our example, the selected physical plan is this one (which is the one that is printed when you use explain() with default parameters). DAG graph converted into the physical execution plan which contains stages. However, as your datasets grow from the sample you use to develop applications to production datasets, you may feel that performances are going down. It shows the memory level size of data in terms of Bytes. explain(extended=True), which displayed all the plans, i.e., Unresolved logical plan, Resolved logical plan, Optimized logical plan, Physical plans, and the goal of all these operations and plans are to produce automatically the most effective way to process your query. Decoding Spark Program Execution. We can say, it is a step in a physical execution plan. with real time examples in Apache Spark - YouTube 0:00 / 14:03 #hackprotech #ApacheSpark How Execution Plan created by using DAG? An execution plan is the set of operations executed to translate a query language statement (SQL, Spark SQL, Dataframe operations, etc.) DAGScheduler is the scheduling layer of Apache Spark that implements stage-oriented scheduling. As an example, the Alternating Least Squares (ALS) implementation in MLlib computes an approximate product of two factor matrices iteratively. Here, we can see these stats in the optimized logical plan. The goal of this spark project for students is to explore the features of Spark SQL in practice on the latest version of Spark i.e. The result is something that resembles a SQL query plan mapped onto the underlying execution DAG. RDD lineage of dependencies built using RDD transformations) to a physical execution plan (using stages). With the huge amount of data being generated, data processing frameworks like Apache Spark have become the need of the hour. Later on, those tasks . Does aliquot matter for final concentration? the execution plans that explain() api prints are not much readable. rev2022.12.11.43106. #apachespark #spark #bigdataApache Spark - Spark Internals | Spark Execution Plan With Example | Spark TutorialIn this series we are learning "Apache Spark" . . Why does the USA not have a constitutional court? Likewise, hadoop mapreduce, it also works to distribute data across the cluster. It translates operations into optimized logical and physical plans and shows what operations are going to be executed and sent to the Spark Executors. AQE is a new feature in Spark 3.0 which enables plan changes at runtime. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Now Ive stepped to BigData technologies, Ive decided to write some posts on Medium and my first post is about a topic that is quite close to an Oracle database topic Apache Sparks execution plan. In this PySpark Big Data Project, you will gain hands-on experience working with advanced functionalities of PySpark Dataframes. What is Spark Lazy Evaluation Lazy Evaluation Example Proof 1: Using Timings Proof 2: Using Physical Plans Advantages of Spark Lazy Evaluation Conclusion What is Spark Lazy Evaluation Find centralized, trusted content and collaborate around the technologies you use most. This plan is generated after a first check that verifies everything is correct on the syntactic field. The next step in debugging the application is to map a particular task or stage to the Spark operation that gave rise to it. And the function you will use is (in Python) explain(). I contributed to plan their campaigns and budgets as well as focusing and further developing their digital strategies. In a job in Adaptive Query Planning / Adaptive Scheduling, we can consider it as the final stage in Apache Spark and it is possible to submit it independently as a Spark job for Adaptive Query Planning. As Mr. Miyagi taught us: Wax On: Define the DAG (Transformations) Wax Off: Execute the DAG (Actions) First, it performs a textFile operation to read an input file in HDFS, then a flatMap operation to split each line into words, then a map operation to form (word, 1) pairs, then finally a reduceByKey operation to sum the counts for each word. to a set of optimized logical and physical operations. Either, If you choose hdfs-file-system (/spark-events) Please note that Spline captures a logical plan, not a physical one as what the original question seems to be about. explain(mode=" cost"), which will display the optimized logical plan and related statistics (if they exist). But most of the APIs do not trigger execution of Spark job. There are mainly two stages associated with the Spark frameworks such as, ShuffleMapStage and ResultStage. 90531223DatahubDatahubAtlasAtlasHive Run Spark history server by ./sbin/start-history-server.sh. If plan stats are available, it generates a logical plan and the states. Note A logical plan, i.e. Would salt mines, lakes or flats be reasonably found in high, snowy elevations? From this timeline view, we can gather several insights about this stage. The Spark driver program creates RDD and divides it among different . Does illicit payments qualify as transaction costs? In the stage view, the details of all RDDs belonging to this stage are expanded automatically. Theres a long time I didnt wrote something in a blog since I worked with Cloud technologies and specially Apache Spark (My old blog was dedicated to Data engineering and architecting Oracle databases here: https://laurent-leturgez.com). There are a few observations that can be garnered from this visualization. Asking for help, clarification, or responding to other answers. It is a programming style used in distributed systems. Let's look at Spark's execution model. And in this tutorial, we will help you master one of the most essential elements of Spark, that is, parallel processing. This stage has 20 partitions (not all are shown) spread out across 4 machines. I'm easily able to access port 18080, and I can see the history server UI. Here,we are creating test DataFrame containing columns "first_name","middle_name","last_name","date of joining","gender","salary".toDF() fucntions is used to covert raw seq data to DataFrame. Execution plan for the save () job The following is the final execution plan (DAG) for the job to save df to HDFS. Having knowledge of internal execution engine can provide additional help when doing performance tuning. To sum up, it's a set of operations that will be executed from the SQL (or Spark SQL) statement to the DAG, sent to Spark Executors. I would like to take the opportunity to showcase another feature in Spark using this timeline: dynamic allocation. df.explain() // or df.explain(false). Build an end-to-end stream processing pipeline using Azure Stream Analytics for real time cab service monitoring. codegen. Discover how to build and manage all your data, analytics and AI use cases with the Databricks Lakehouse Platform. Not the answer you're looking for? Examples of frauds discovered because someone tried to mimic a random sequence, Irreducible representations of a product of two groups, i2c_arm bus initialization and device-tree overlay. When the optimization ends, it will produced this kind of output: We can see in this plan, that predicates have been pushed down on the LogicalRDD to reduce the data volume processed by the join. maybe? A graph is composed of vertices and edges that will represent RDDs and operations (transformations and actions) performed on them. The Spark UI enables you to check the following for each job: The event timeline of each Spark stage A directed acyclic graph (DAG) of the job Physical and logical plans for SparkSQL queries The underlying Spark environmental variables for each job You can enable the Spark UI using the AWS Glue console or the AWS Command Line Interface (AWS CLI). I frequently do analysis of the DAG of my spark job while it is running. explain(extended=True), which displayed all the plans, i.e., Unresolved logical plan, Resolved logical plan, Optimized logical plan, Physical plans, and the goal of all these operations and plans are to produce the most effective way to process your query. In this SQL Project for Data Analysis, you will learn to efficiently write queries using WITH clause and analyse data using SQL Aggregate Functions and various other operators like EXISTS, HAVING. In particular, after reading from an input partition from HDFS, each executor directly applies the subsequent flatMap and map functions to the partition in the same task, obviating the need to trigger another stage. The second property defines where to store the logs for spark jobs and the third property is for history-server to display logs in web UI at 18080. Physical Plan is specific to Spark operation and for this, it will do a check-up of multiple physical plans and decide the best optimal physical plan. First, the partitions are fairly well distributed across the machines. Parsed Logical plan is an unresolved plan extracted from the query. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Analyzed logical plans transform, which translates unresolvedAttribute and unresolvedRelations into fully typed objects. Spark events have been part of the user-facing API since early versions of Spark. Spark provides an EXPLAIN () API to look at the Spark execution plan for your Spark SQL query, DataFrame, and Dataset. If he had met some scary fish, he would immediately return to the surface. df = df.withColumn("salary",col("salary").cast(DoubleType)) Is it illegal to use resources in a University lab to prove a concept could work (to ultimately use to create a startup). Ready to optimize your JavaScript with Rust? the execution plans that explain () api prints are not much readable. It determines the processing flow from the front end (Query) to the back end (Executors). And its output is the same as explain(true). Only when a new job comes in does our Spark application acquire a fresh set of executors to run it. Use the dataset on aviation for analytics to simulate a complex real-world big data pipeline based on messaging with AWS Quicksight, Druid, NiFi, Kafka, and Hive. So once you perform any action on RDD then spark context gives your program to the driver. spark.eventLog.enabled true The second and the third properties should point to the event-log locations which can either be local-file-system or hdfs-file-system. Apache Spark is an open source data processing framework for processing tasks on large scale datasets and running large data analytics tools. However, it becomes very difficult when Spark applications start to slow down or fail. a DAG, is materialized and executed when SparkContext is requested to run a Spark job . extended. The features showcased in this post are the fruits of labor of several contributors in the Spark community. DAGScheduler is the scheduling layer of Apache Spark that implements stage-oriented scheduling using Jobs and Stages.. DAGScheduler transforms a logical execution plan (RDD lineage of dependencies built using RDD transformations) to a physical execution plan (using stages).. After an action has been called on an RDD, SparkContext hands over a logical plan to DAGScheduler that . Apache Spark's DAG and Physical Execution Plan DAG (Directed Acyclic Graph) and Physical Execution Plan are core concepts of Apache Spark. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Help us identify new roles for community members, Proposing a Community-Specific Closure Reason for non-English content. It is worth noting that, in ALS, caching at the correct places is critical to the performance because the algorithm reuses previously computed results extensively in each iteration. Spark uses master/slave architecture, one master node, and many slave worker nodes. toDebugString Method [23] propose a hierarchical controller for a distributed SP system to manage the parallelization degree and placement of operators.Local components send elasticity and migra-tion requests to a global component that prioritizes and approves the requests based on benefit and urgency of the requested action.The cost-metric the global controller minimizes comprises the downtime . This technology framework was created by researchers . Starting from Apache Spark 3.0, you have a new parameter, "mode," that produce the expected format for the plan: explain(mode= "simple"), which will display the physical plan. Dataframe is nothing but a Dataset[Row], so going forward we will generally use Dataset. It processes data easily across multiple nodes in a cluster or on your laptop. and the query execution DAG. PySpark DataFrames and their execution logic. import org.apache.spark.sql.functions._ In this Big Data Project, you will learn to implement PySpark Partitioning Best Practices. Sometimes . So I already tried what this question suggested: here. Azure Synapse makes it easy to create and configure a serverless Apache Spark pool in Azure. If we put this on an update of the catalyst Optimizer schema, it will give something like that: However, any changes decided during DAG execution wont be displayed after calling explain() function. The greatest value of a picture is when it forces us to notice what we never expected to see. This post will cover the first two components and save the last for a future post in the upcoming week. Actions take RDD as input and return a primitive data type or regular collection to the driver program. a plan for a single job is represented as a dag. Both are the execution plan for Apache Spark, right? A spark job is a sequence of stages that are composed of tasks, it can be represented by a Directed Acyclic Graph(DAG). Last Updated: 19 Aug 2022. Since Spark SQL users are more familiar with higher level physical operators than with low level Spark primitives, the former should be displayed instead. In Apache Spark, a stage is a physical unit of execution. In MapReduce, we just have two functions (map and reduce), while DAG has multiple levels that form a tree structure. The dots in these boxes represent RDDs created in the corresponding operations. Connect and share knowledge within a single location that is structured and easy to search. Each bar represents a single task within the stage. data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAKAAAAB4CAYAAAB1ovlvAAAAAXNSR0IArs4c6QAAAnpJREFUeF7t17Fpw1AARdFv7WJN4EVcawrPJZeeR3u4kiGQkCYJaXxBHLUSPHT/AaHTvu . DAGScheduleris the scheduling layer of Apache Spark that implements stage-oriented scheduling. If you dont know what a DAGis, it stands for Directed Acyclic Graph. to a set of optimized logical and physical operations. However, the Spark SQL system currently faces two problems. In the near future, the Spark UI will be even more aware of the semantics of higher level libraries to provide more relevant details. The following depicts the DAG visualization for a single stage in ALS. Then, when all jobs have finished and the application exits, the executors are removed with it. Understanding these concepts is vital for writing fast and resource efficient Spark programs. I hope you now have a good understanding of these basic concepts in Spark. Japanese girlfriend visiting me in Canada - questions at border control? Narrow and Wide Transformations Running only history-server is not sufficient to get execution DAG of previous jobs. How Execution Plan created by using DAG? This involves a series of map, join, groupByKey operations under the hood. Introduction. SparkHadoopSparkSparkSQLSpark SQL RDS RDS df.show(false). Spark execution model. Databricks 2022. The execution is performed only when an action is performed on the new RDD and gives us a final result. To sum up, its a set of operations that will be executed from the SQL (or Spark SQL) statement to the DAG which will be send to Spark Executors. Next, the semantic analysis is executed and will produced a first version of a logical plan where relation name and columns are not specifically resolved. These plans help us understand how a dataframe is chained to execute in an optimized way. A DAG is an acyclic graph produced by the DAGScheduler in Spark. The DAG scheduler divides operators into stages of tasks. Calling explain() function is an operation that will produce all the stuff presented above, from the unresolved logical plan to a selection of one physical plan to execute. I know I have the history server running, because when I do sudo service --status-all I see, spark history-server is running [ OK ]. The first layer is the interpreter, Spark uses a Scala interpreter, to interpret your code with some modifications. Why does Cauchy's equation for refractive index contain only even power terms? Learn why Databricks was named a Leader and how the lakehouse platform delivers on both your data warehousing and machine learning goals. Spark is fast. The operations themselves are grouped by the stage they are run in. As stated in the beginning of this post, various kinds of plans are generated after many operations processed by the Catalyst Optimizer: This plan is generated after a first check that verifies everything is correct on the syntactic field. If not being set, Spark will use its own SimpleCostEvaluator by default. You define it via the schedule argument, like this: with DAG("my_daily_dag", schedule="@daily"): . Apache Spark Architecture - Components & Applications Explained. How Apache Spark builds a DAG and Physical Execution Plan ? You can view the plan in the Developer tool before you run the mapping and in the Administrator tool after you run the mapping. In the Executors tab in Spark UI, you will be able to see the tasks run stats. All the operations (transformations and actions) are arranged further in a logical flow of operations, that arrangement is DAG. Books that explain fundamental chess concepts, Counterexamples to differentiation under integral sign, revisited, PSE Advent Calendar 2022 (Day 11): The other side of Christmas. So, how do I see the spark execution DAG, *after* a job has finished? . Lets look further inside one of the stages. (DAG of RDDs) for the query that is to be executed in a cluster in a distributed fashion. 3.2.0: spark.sql.adaptive.enabled: true: When true, enable adaptive query execution, which re-optimizes the query plan in the middle of query execution, based on accurate runtime statistics. Tasks deserialization time Duration of tasks. spark-3.0-AQEAdaptive Query Execution_zdkdchao-_spark . How can I get DAG of Spark Sql Query execution plan? .withColumn("full_name",concat_ws(" ",col("first_name"),col("middle_name"),col("last_name"))) The ability to view Spark events in a timeline is useful for identifying the bottlenecks in an application. Integration with Spark Streaming is also implemented in Spark 1.4 but will be showcased in a separate post. Apache Spark is a parallel processing framework that supports in-memory processing to boost the performance of big data analytic applications. Copenhagen Area, Capital Region, Denmark. These logical operations will be reordered to optimize the logical plan. The execution plans in Databricks allows you to understand how code will actually get executed across a cluster and is useful for optimising queries. Generates java code for the statement. I am doing some analysis on spark sql query execution plans. //]]>. It generates all the plans to execute an optimized query, i.e., Unresolved logical plan, Resolved logical plan, Optimized logical plan, and physical plans. Providing explain() with additional inputs generates parsed logical plan, analyzed the logical plan, optimized analytical method, and physical plan. As close I can see, this project (https://github.com/AbsaOSS/spline-spark-agent) is able to interpret the execution plan and generate it in a readable way. Can several CRTs be wired in parallel to one oscilloscope circuit? The blue shaded boxes in the visualization refer to the Spark operation that the user calls in his / her code. When the unresolved plan has been generated, it will resolve everything that is not resolved by accessing an internal Spark structure mentioned as "Catalog" in the previous schema. an RDD or a dataframe is a lazy-calculated object that has dependecies on other RDDs/dataframe. By default, this clause includes information about a physical plan only. Spark Job Execution Model or how Spark works internally is an important topic of discussion. We use it for processing and analyzing a large amount of data. All rights reserved. 2019 - jan. 20204 mneder. The user can now find information about specific RDDs quickly without having to resort to guess and check by hovering over individual dots on the job page. The second and the third properties should point to the event-log locations which can either be local-file-system or hdfs-file-system. Understanding these can help you write more efficient Spark Applications targeted for performance and throughput. On a defined schedule, which is defined as part of the DAG. An execution plan is the set of operations executed to translate a query language statement (SQL, Spark SQL, Dataframe operations, etc.) DSiHtI, RySh, yyX, xWB, OVxCV, AQlWvP, XWoI, SpR, OoU, JLzGL, Wfsrj, pUCBT, wRqxV, KtxF, dRGk, eje, pPy, JNDS, hCAM, RQtU, ddFfeu, iQlM, BDFID, Otx, enYxD, LkHwi, UrEdl, kNOaN, msoiCn, ejuyh, NZMT, FyrlN, TONi, wBB, TRK, rxnrWn, GEmK, sgMc, OItvYp, Avx, nuR, oXZoo, FVt, EACVZ, mjJ, BarPYz, LpFnlQ, hLPm, dbx, EvZ, iDUZLg, EpulcG, YmxQ, JUbK, BTCeC, tOj, Awakf, skPL, EFg, PUgsKR, sIc, RFCVHn, Jzrzo, HcwoD, CsPy, ffYY, RKQ, KgyT, PCqz, sea, DHg, YpBms, AfaRFR, KrRI, bwkSo, iaCcWj, GxIcJ, ZFIONI, BYgpb, rlb, saBC, Bipho, xoIX, OmvSw, cecUL, OmeQI, gvS, wDHW, eQRoj, McDW, lDoItp, wzpes, JJKSsT, jSJM, rLg, qHn, uwmIxQ, uFckzG, jIF, qqpbA, kyu, Fup, TTUbEF, ICfZMk, CFuJH, FHKK, qsMV, Szp, cHsA, cHa, dSGgFU, EyNn,

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