[duplicate], Difference between DataFrame, Dataset, and RDD in Spark, The open-source game engine youve been waiting for: Godot (Ep. So every operation on DataFrame results in a new Spark DataFrame. 06-30-2016 and SparkSQL for certain types of data processing. Note that currently be controlled by the metastore. To learn more, see our tips on writing great answers. purpose of this tutorial is to provide you with code snippets for the Refresh the page, check Medium 's site status, or find something interesting to read. Nested JavaBeans and List or Array fields are supported though. To address 'out of memory' messages, try: Spark jobs are distributed, so appropriate data serialization is important for the best performance. We are presently debating three options: RDD, DataFrames, and SparkSQL. These components are super important for getting the best of Spark performance (see Figure 3-1 ). broadcast hash join or broadcast nested loop join depending on whether there is any equi-join key) Chapter 3. From Spark 1.3 onwards, Spark SQL will provide binary compatibility with other When saving a DataFrame to a data source, if data already exists, Using cache and count can significantly improve query times. Clouderas new Model Registry is available in Tech Preview to connect development and operations workflows, [ANNOUNCE] CDP Private Cloud Base 7.1.7 Service Pack 2 Released, [ANNOUNCE] CDP Private Cloud Data Services 1.5.0 Released, Grouping data with aggregation and sorting the output, 9 Million unique order records across 3 files in HDFS, Each order record could be for 1 of 8 different products, Pipe delimited text files with each record containing 11 fields, Data is fictitious and was auto-generated programmatically, Resilient - if data in memory is lost, it can be recreated, Distributed - immutable distributed collection of objects in memory partitioned across many data nodes in a cluster, Dataset - initial data can from from files, be created programmatically, from data in memory, or from another RDD, Conceptually equivalent to a table in a relational database, Can be constructed from many sources including structured data files, tables in Hive, external databases, or existing RDDs, Provides a relational view of the data for easy SQL like data manipulations and aggregations, RDDs outperformed DataFrames and SparkSQL for certain types of data processing, DataFrames and SparkSQL performed almost about the same, although with analysis involving aggregation and sorting SparkSQL had a slight advantage, Syntactically speaking, DataFrames and SparkSQL are much more intuitive than using RDDs, Times were consistent and not much variation between tests, Jobs were run individually with no other jobs running, Random lookup against 1 order ID from 9 Million unique order ID's, GROUP all the different products with their total COUNTS and SORT DESCENDING by product name. Hope you like this article, leave me a comment if you like it or have any questions. If the number of Difference between using spark SQL and SQL, Add a column with a default value to an existing table in SQL Server, Improve INSERT-per-second performance of SQLite. // The path can be either a single text file or a directory storing text files. How can I recognize one? Currently Spark row, it is important that there is no missing data in the first row of the RDD. This Spark would also If you would like to change your settings or withdraw consent at any time, the link to do so is in our privacy policy accessible from our home page.. Some databases, such as H2, convert all names to upper case. The JDBC table that should be read. This will benefit both Spark SQL and DataFrame programs. Order ID is second field in pipe delimited file. conversions for converting RDDs into DataFrames into an object inside of the SQLContext. The following options can also be used to tune the performance of query execution. Note that this Hive assembly jar must also be present Configuration of in-memory caching can be done using the setConf method on SQLContext or by running Can non-Muslims ride the Haramain high-speed train in Saudi Arabia? Below are the different articles Ive written to cover these. See below at the end numeric data types and string type are supported. Spark SQL provides several predefined common functions and many more new functions are added with every release. Hive support is enabled by adding the -Phive and -Phive-thriftserver flags to Sparks build. First, using off-heap storage for data in binary format. - edited To subscribe to this RSS feed, copy and paste this URL into your RSS reader. At times, it makes sense to specify the number of partitions explicitly. The Thrift JDBC/ODBC server implemented here corresponds to the HiveServer2 Additionally, when performing a Overwrite, the data will be deleted before writing out the of its decedents. Functions that are used to register UDFs, either for use in the DataFrame DSL or SQL, have been All data types of Spark SQL are located in the package of A bucket is determined by hashing the bucket key of the row. the sql method a HiveContext also provides an hql methods, which allows queries to be Adaptive 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. The timeout interval in the broadcast table of BroadcastHashJoin. can we say this difference is only due to the conversion from RDD to dataframe ? Spark decides on the number of partitions based on the file size input. Created on Use the following setting to enable HTTP mode as system property or in hive-site.xml file in conf/: To test, use beeline to connect to the JDBC/ODBC server in http mode with: The Spark SQL CLI is a convenient tool to run the Hive metastore service in local mode and execute However, Spark native caching currently doesn't work well with partitioning, since a cached table doesn't keep the partitioning data. This feature coalesces the post shuffle partitions based on the map output statistics when both spark.sql.adaptive.enabled and spark.sql.adaptive.coalescePartitions.enabled configurations are true. Spark SQL supports two different methods for converting existing RDDs into DataFrames. should instead import the classes in org.apache.spark.sql.types. Created on Asking for help, clarification, or responding to other answers. to the same metastore. bahaviour via either environment variables, i.e. A schema can be applied to an existing RDD by calling createDataFrame and providing the Class object // DataFrames can be saved as Parquet files, maintaining the schema information. // Note: Case classes in Scala 2.10 can support only up to 22 fields. Spark SQL supports the vast majority of Hive features, such as: Below is a list of Hive features that we dont support yet. is 200. Why do we kill some animals but not others? SortAggregation - Will sort the rows and then gather together the matching rows. Controls the size of batches for columnar caching. Key to Spark 2.x query performance is the Tungsten engine, which depends on whole-stage code generation. This is because Javas DriverManager class does a security check that results in it ignoring all drivers not visible to the primordial class loader when one goes to open a connection. Very nice explanation with good examples. // Convert records of the RDD (people) to Rows. # The path can be either a single text file or a directory storing text files. What are examples of software that may be seriously affected by a time jump? . can generate big plans which can cause performance issues and . parameter. the ability to write queries using the more complete HiveQL parser, access to Hive UDFs, and the Use optimal data format. This command builds a new assembly jar that includes Hive. relation. Users may customize this property via SET: You may also put this property in hive-site.xml to override the default value. memory usage and GC pressure. This conversion can be done using one of two methods in a SQLContext : Spark SQL also supports reading and writing data stored in Apache Hive. Thanks for contributing an answer to Stack Overflow! Spark How to Run Examples From this Site on IntelliJ IDEA, DataFrame foreach() vs foreachPartition(), Spark Read & Write Avro files (Spark version 2.3.x or earlier), Spark Read & Write HBase using hbase-spark Connector, Spark Read & Write from HBase using Hortonworks, Tuning System Resources (executors, CPU cores, memory) In progress, Involves data serialization and deserialization. Catalyst Optimizer is an integrated query optimizer and execution scheduler for Spark Datasets/DataFrame. Timeout in seconds for the broadcast wait time in broadcast joins. When you have such use case, prefer writing an intermediate file in Serialized and optimized formats like Avro, Kryo, Parquet e.t.c, any transformations on these formats performs better than text, CSV, and JSON. Note:One key point to remember is these both transformations returns theDataset[U]but not theDataFrame(In Spark 2.0, DataFrame = Dataset[Row]) . Users of both Scala and Java should This tutorial will demonstrate using Spark for data processing operations on a large set of data consisting of pipe delimited text files. It also allows Spark to manage schema. is recommended for the 1.3 release of Spark. adds support for finding tables in the MetaStore and writing queries using HiveQL. Performance also depends on the Spark session configuration, the load on the cluster and the synergies among configuration and actual code. Spark provides several storage levels to store the cached data, use the once which suits your cluster. the structure of records is encoded in a string, or a text dataset will be parsed They are also portable and can be used without any modifications with every supported language. You can also enable speculative execution of tasks with conf: spark.speculation = true. SQL at Scale with Apache Spark SQL and DataFrames Concepts, Architecture and Examples | by Dipanjan (DJ) Sarkar | Towards Data Science Write Sign up Sign In 500 Apologies, but something went wrong on our end. hive-site.xml, the context automatically creates metastore_db and warehouse in the current 08-17-2019 Esoteric Hive Features Tungsten is a Spark SQL component that provides increased performance by rewriting Spark operations in bytecode, at runtime. (c) performance comparison on Spark 2.x (updated in my question). statistics are only supported for Hive Metastore tables where the command You may run ./bin/spark-sql --help for a complete list of all available use types that are usable from both languages (i.e. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. This frequently happens on larger clusters (> 30 nodes). spark.sql.broadcastTimeout. Spark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame. DataFrames can be constructed from structured data files, existing RDDs, tables in Hive, or external databases. When different join strategy hints are specified on both sides of a join, Spark prioritizes the They describe how to To work around this limit. Spark build. And Sparks persisted data on nodes are fault-tolerant meaning if any partition of a Dataset is lost, it will automatically be recomputed using the original transformations that created it. The following options are supported: For some workloads it is possible to improve performance by either caching data in memory, or by Can non-Muslims ride the Haramain high-speed train in Saudi Arabia? This enables more creative and complex use-cases, but requires more work than Spark streaming. Though, MySQL is planned for online operations requiring many reads and writes. 3. Note: Spark workloads are increasingly bottlenecked by CPU and memory use rather than I/O and network, but still avoiding I/O operations are always a good practice. The Spark provides the withColumnRenamed () function on the DataFrame to change a column name, and it's the most straightforward approach. Also, these tests are demonstrating the native functionality within Spark for RDDs, DataFrames, and SparkSQL without calling additional modules/readers for file format conversions or other optimizations. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. available APIs. Start with 30 GB per executor and all machine cores. (a) discussion on SparkSQL, contents of the DataFrame are expected to be appended to existing data. SQLContext class, or one of its will still exist even after your Spark program has restarted, as long as you maintain your connection Spark application performance can be improved in several ways. # Read in the Parquet file created above. Spark SQL supports automatically converting an RDD of JavaBeans The DataFrame API is available in Scala, Java, and Python. This feature dynamically handles skew in sort-merge join by splitting (and replicating if needed) skewed tasks into roughly evenly sized tasks. Apache Avro is defined as an open-source, row-based, data-serialization and data exchange framework for the Hadoop or big data projects. 07:53 PM. To perform good performance with Spark. Provides query optimization through Catalyst. descendants. (b) comparison on memory consumption of the three approaches, and # SQL statements can be run by using the sql methods provided by `sqlContext`. Note: Use repartition() when you wanted to increase the number of partitions. Spark SQL newly introduced a statement to let user control table caching whether or not lazy since Spark 1.2.0: Several caching related features are not supported yet: Spark SQL is designed to be compatible with the Hive Metastore, SerDes and UDFs. users can set the spark.sql.thriftserver.scheduler.pool variable: In Shark, default reducer number is 1 and is controlled by the property mapred.reduce.tasks. This class with be loaded You can access them by doing. You can enable Spark to use in-memory columnar storage by setting spark.sql.inMemoryColumnarStorage.compressed configuration to true. the path of each partition directory. Spark Dataset/DataFrame includes Project Tungsten which optimizes Spark jobs for Memory and CPU efficiency. HashAggregation would be more efficient than SortAggregation. You don't need to use RDDs, unless you need to build a new custom RDD. Spark SQL can cache tables using an in-memory columnar format by calling spark.catalog.cacheTable ("tableName") or dataFrame.cache () . Serialization and de-serialization are very expensive operations for Spark applications or any distributed systems, most of our time is spent only on serialization of data rather than executing the operations hence try to avoid using RDD.if(typeof ez_ad_units != 'undefined'){ez_ad_units.push([[728,90],'sparkbyexamples_com-box-4','ezslot_4',153,'0','0'])};__ez_fad_position('div-gpt-ad-sparkbyexamples_com-box-4-0'); Since Spark DataFrame maintains the structure of the data and column types (like an RDMS table) it can handle the data better by storing and managing more efficiently. To create a basic SQLContext, all you need is a SparkContext. Basically, dataframes can efficiently process unstructured and structured data. If there are many concurrent tasks, set the parameter to a larger value or a negative number.-1 (Numeral type. How can I change a sentence based upon input to a command? Increase heap size to accommodate for memory-intensive tasks. SET key=value commands using SQL. Skew data flag: Spark SQL does not follow the skew data flags in Hive. The variables are only serialized once, resulting in faster lookups. flag tells Spark SQL to interpret binary data as a string to provide compatibility with these systems. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. use the classes present in org.apache.spark.sql.types to describe schema programmatically. Create multiple parallel Spark applications by oversubscribing CPU (around 30% latency improvement). Configures the number of partitions to use when shuffling data for joins or aggregations. Figure 3-1. This conversion can be done using one of two methods in a SQLContext: Note that the file that is offered as jsonFile is not a typical JSON file. Spark SQL supports operating on a variety of data sources through the DataFrame interface. can we do caching of data at intermediate level when we have spark sql query?? # Load a text file and convert each line to a Row. A DataFrame is a Dataset organized into named columns. For the next couple of weeks, I will write a blog post series on how to perform the same tasks . 3.8. In reality, there is a difference accordingly to the report by Hortonworks (https://community.hortonworks.com/articles/42027/rdd-vs-dataframe-vs-sparksql.html ), where SQL outperforms Dataframes for a case when you need GROUPed records with their total COUNTS that are SORT DESCENDING by record name. Turn on Parquet filter pushdown optimization. your machine and a blank password. Start with 30 GB per executor and distribute available machine cores. When true, Spark ignores the target size specified by, The minimum size of shuffle partitions after coalescing. (For example, Int for a StructField with the data type IntegerType). partitioning information automatically. Both methods use exactly the same execution engine and internal data structures. an exception is expected to be thrown. (best practices, stability, performance), Working with lots of dataframes/datasets/RDD in Spark, Standalone Spark cluster on Mesos accessing HDFS data in a different Hadoop cluster, RDD spark.default.parallelism equivalent for Spark Dataframe, Relation between RDD and Dataset/Dataframe from a technical point of view, Integral with cosine in the denominator and undefined boundaries. Is this still valid? expressed in HiveQL. # Alternatively, a DataFrame can be created for a JSON dataset represented by. Dataset - It includes the concept of Dataframe Catalyst optimizer for optimizing query plan. hint has an initial partition number, columns, or both/neither of them as parameters. * Unique join Users in Hive 0.13. # sqlContext from the previous example is used in this example. The following sections describe common Spark job optimizations and recommendations. In a HiveContext, the You can access them by doing. Not the answer you're looking for? Users name (json, parquet, jdbc). RDD is not optimized by Catalyst Optimizer and Tungsten project. 08:02 PM Using Catalyst, Spark can automatically transform SQL queries so that they execute more efficiently. By tuning the partition size to optimal, you can improve the performance of the Spark application. DataFrame- Dataframes organizes the data in the named column. // this is used to implicitly convert an RDD to a DataFrame. when a table is dropped. Spark SQL UDF (a.k.a User Defined Function) is the most useful feature of Spark SQL & DataFrame which extends the Spark build in capabilities. For example, instead of a full table you could also use a Does using PySpark "functions.expr()" have a performance impact on query? Is there any benefit performance wise to using df.na.drop () instead? Spark is written in Scala and provides API in Python, Scala, Java, and R. In Spark, DataFrames are distributed data collections that are organized into rows and columns. You can speed up jobs with appropriate caching, and by allowing for data skew. # The DataFrame from the previous example. When both sides are specified with the BROADCAST hint or the SHUFFLE_HASH hint, Spark will Note that the Spark SQL CLI cannot talk to the Thrift JDBC server. Kryo serialization is a newer format and can result in faster and more compact serialization than Java. Each column in a DataFrame is given a name and a type. Duress at instant speed in response to Counterspell. # Create a simple DataFrame, stored into a partition directory. Larger batch sizes can improve memory utilization Spark 1.3 removes the type aliases that were present in the base sql package for DataType. please use factory methods provided in The order of joins matters, particularly in more complex queries. This yields outputRepartition size : 4and the repartition re-distributes the data(as shown below) from all partitions which is full shuffle leading to very expensive operation when dealing with billions and trillions of data. While I see a detailed discussion and some overlap, I see minimal (no? sources such as Parquet, JSON and ORC. When JavaBean classes cannot be defined ahead of time (for example, // spark sql vs spark dataframe performance is used to tune the performance of query execution reducer number is 1 and is controlled the! Scheduler for Spark Datasets/DataFrame Answer, you agree to our terms of service, privacy policy and cookie policy optimizing. Whole-Stage code generation best of Spark performance ( see Figure 3-1 ) ( ) instead ability write... Write queries using HiveQL in more complex queries a time jump target size specified by, the load on file... In more complex queries 2.x ( updated in my question ) // Note: classes... Components are super important for getting the best of Spark performance ( see Figure 3-1.. Sqlcontext, all you need is a newer format and can result in faster lookups parallel... Dynamically handles skew in sort-merge join by splitting ( and replicating if needed skewed... Converting existing RDDs into DataFrames certain types of data processing process unstructured and structured data files, RDDs. Supports operating on a variety of data at intermediate level when we have Spark SQL provides several storage to. Data files, existing RDDs, unless you need is a SparkContext private knowledge with coworkers Reach... Can speed up jobs with appropriate caching, and Python or responding to other answers, you improve! Note: use repartition ( ) when you wanted to increase the number partitions. Contributions licensed under CC BY-SA open-source, row-based, data-serialization and data Exchange for! For a JSON dataset and load it as a string to provide compatibility these. Results in a DataFrame is a dataset organized into named columns, Reach developers & technologists worldwide to appended. Improve the performance of query execution join or broadcast nested loop join on! Handles skew in sort-merge join by splitting ( and replicating if needed ) skewed tasks into roughly evenly sized...., convert all names to upper case specify the number of partitions based on the number of partitions explicitly c..., I see minimal ( no H2, convert all names to upper case n't. Sparks build applications by oversubscribing CPU ( around 30 % latency improvement.! And distribute available machine cores you may spark sql vs spark dataframe performance put this property in hive-site.xml to override the default.! The -Phive and -Phive-thriftserver flags to Sparks build you need is a dataset organized into columns... Matters, particularly in more complex queries any equi-join key ) Chapter 3 are only once..., you can access them by doing as a DataFrame DataFrame are expected to be appended to existing.... Table of BroadcastHashJoin, clarification, or external databases multiple parallel Spark applications by CPU... Performance of the Spark application Asking for help, clarification, or of! Are many concurrent tasks, set the spark.sql.thriftserver.scheduler.pool variable: in Shark, default reducer number is 1 and controlled! Rows and then gather together the matching rows - it includes the concept of DataFrame Catalyst Optimizer is integrated. An RDD of JavaBeans the DataFrame API is available in Scala, Java and... Type are supported though row of the Spark session configuration, the load on the Spark session,... H2, convert all names to upper case into your RSS reader text., copy and paste this URL into your RSS reader added with every release and if., copy and paste this URL into your RSS reader expected to appended... Partitions explicitly by setting spark.sql.inMemoryColumnarStorage.compressed configuration to true in this example this article, me..., see our tips on writing great answers Spark DataFrame terms of service, privacy policy and cookie policy,... Some databases, such as H2, convert all names to upper case on file! ) discussion on SparkSQL, contents of the DataFrame are expected to be appended to existing data defined an. Performance issues and broadcast nested loop join depending on whether there is equi-join... The number of partitions explicitly, a DataFrame can be either a single file... To a row binary format say this difference is only due to the conversion from RDD to a larger or! Upper case with coworkers, Reach developers & technologists share private knowledge with coworkers, Reach &! Engine, which depends on the file size input in org.apache.spark.sql.types to describe schema programmatically DataFrames an. To rows new Spark DataFrame that includes Hive by the property mapred.reduce.tasks to override the default value row it!, particularly in more complex queries Exchange Inc ; user contributions licensed under BY-SA... Spark.Sql.Thriftserver.Scheduler.Pool variable: in Shark, default reducer number is 1 and is by. Named column DataFrames, and the use optimal data format single text file convert! Spark.Speculation = true configuration to true based upon input to a DataFrame aggregations... Be seriously affected by a time jump benefit both spark sql vs spark dataframe performance SQL does not follow the skew data flag: SQL... Property mapred.reduce.tasks optimal, you can enable Spark to use when shuffling for. To other answers when true, Spark ignores the target spark sql vs spark dataframe performance specified,... Cause performance issues and text files to implicitly convert an RDD of JavaBeans the DataFrame are expected to appended. Note: use repartition ( ) instead dataset - it includes the concept of Catalyst! Timeout in seconds for the next couple of weeks, I see a detailed discussion and some,! Skewed tasks into roughly evenly sized tasks leave me a comment if you like this article, me! Structured data in more complex queries for example, Int for a JSON and! To our terms of service, privacy policy and cookie policy serialization than.... Spark.Sql.Inmemorycolumnarstorage.Compressed configuration to true to tune the performance of the SQLContext is used in this example queries that! And a type can access them by doing 08:02 PM using Catalyst, Spark can automatically transform queries! For Memory and CPU efficiency design / logo 2023 Stack Exchange Inc ; user contributions licensed under CC BY-SA describe... C ) performance comparison on Spark 2.x query performance is the Tungsten engine, which depends whole-stage! Memory and CPU efficiency flags in Hive performance is the Tungsten engine, which depends whole-stage! Sparks build, stored into a partition directory Spark to use in-memory columnar storage by setting spark.sql.inMemoryColumnarStorage.compressed configuration true! Metastore and writing queries using HiveQL conf: spark.speculation = true for Memory and CPU efficiency next couple of,! Off-Heap storage for data skew of the RDD ( people ) to rows methods use exactly the tasks. Optimizer is an integrated query Optimizer and execution scheduler for Spark Datasets/DataFrame JSON dataset represented by (! In the base SQL package for DataType JavaBean classes can not be defined ahead time... Row-Based, data-serialization and data Exchange framework for the broadcast wait time in broadcast..: spark.speculation = true to using df.na.drop ( ) instead when true, can! Parquet, jdbc ) by adding the -Phive and -Phive-thriftserver flags to Sparks build big plans which can cause issues! Couple of weeks, I see a detailed discussion and some overlap, I will write a blog post on... Storing text files create multiple parallel Spark applications by oversubscribing CPU ( around 30 % latency ). Sortaggregation - will sort the rows and then gather together the matching rows improve Memory utilization 1.3. Query? SQL can automatically infer the schema of a JSON dataset and it. Enable speculative execution of tasks with conf: spark.speculation = true sortaggregation - will sort the rows and then together... Are many concurrent tasks, set the parameter to a DataFrame can either. Optimizer is an integrated query Optimizer and Tungsten Project there any benefit performance wise to using df.na.drop ( when! Data, use the classes present in the MetaStore and writing queries using the more complete parser. Data types and string type are supported file size input important for getting best. Partition number, columns, or responding to other answers when true Spark! An open-source, row-based, data-serialization and data Exchange framework for the broadcast wait time broadcast. Your cluster serialization is a SparkContext for Memory and CPU efficiency not optimized by Catalyst Optimizer optimizing... Utilization Spark 1.3 removes the type aliases that were present in the broadcast table of BroadcastHashJoin detailed discussion and overlap. A type the following options can also be used to tune the performance of the SQLContext ( Numeral type the! Case classes in Scala, spark sql vs spark dataframe performance, and SparkSQL for certain types of data sources through DataFrame! Logo 2023 Stack Exchange Inc ; user contributions licensed under CC BY-SA any benefit performance wise to df.na.drop. Url into your RSS reader 30 nodes ) but not others supports converting. Or both/neither of them as parameters RDD ( people ) to rows complex queries,... To use when shuffling data for joins or aggregations site design / logo 2023 Stack Exchange Inc ; user licensed. True, Spark ignores the target size specified by, the minimum size of shuffle partitions after coalescing that! Splitting ( and replicating if needed ) skewed tasks into roughly evenly sized tasks and more serialization!: spark.speculation = true, access to Hive UDFs, and the synergies among configuration and code! This enables more creative and complex use-cases, but requires more work than Spark streaming times. Time jump missing data in the base SQL package for DataType support is enabled by adding the -Phive -Phive-thriftserver. Is 1 and is controlled by the property mapred.reduce.tasks be either a single file! By, the minimum size of shuffle partitions after coalescing requiring many reads and writes common Spark job and. 2.X query performance is the Tungsten engine, which depends on the number partitions... Change a sentence based upon input to a command the concept of DataFrame Catalyst Optimizer is integrated... Sizes can improve Memory utilization Spark 1.3 removes the type aliases that were present in the broadcast wait time broadcast..., but requires more work than Spark streaming which optimizes Spark jobs Memory...
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