All in One Software Development Bundle (600+ Courses, 50+ projects) Price Using the hints in Spark SQL gives us the power to affect the physical plan. Prior to Spark 3.0, only theBROADCASTJoin Hint was supported. the query will be executed in three jobs. It works fine with small tables (100 MB) though. Making statements based on opinion; back them up with references or personal experience. Redshift RSQL Control Statements IF-ELSE-GOTO-LABEL. Join hints in Spark SQL directly. Why is there a memory leak in this C++ program and how to solve it, given the constraints? Lets have a look at this jobs query plan so that we can see the operations Spark will perform as its computing our innocent join: This will give you a piece of text that looks very cryptic, but its information-dense: In this query plan, we read the operations in dependency order from top to bottom, or in computation order from bottom to top. if(typeof ez_ad_units != 'undefined'){ez_ad_units.push([[728,90],'sparkbyexamples_com-box-2','ezslot_8',132,'0','0'])};__ez_fad_position('div-gpt-ad-sparkbyexamples_com-box-2-0');What is Broadcast Join in Spark and how does it work? In many cases, Spark can automatically detect whether to use a broadcast join or not, depending on the size of the data. Can this be achieved by simply adding the hint /* BROADCAST (B,C,D,E) */ or there is a better solution? Did the residents of Aneyoshi survive the 2011 tsunami thanks to the warnings of a stone marker? This method takes the argument v that you want to broadcast. Let us now join both the data frame using a particular column name out of it. How does a fan in a turbofan engine suck air in? How to add a new column to an existing DataFrame? (autoBroadcast just wont pick it). Suggests that Spark use broadcast join. If you are using spark 2.2+ then you can use any of these MAPJOIN/BROADCAST/BROADCASTJOIN hints. The Internals of Spark SQL Broadcast Joins (aka Map-Side Joins) Spark SQL uses broadcast join (aka broadcast hash join) instead of hash join to optimize join queries when the size of one side data is below spark.sql.autoBroadcastJoinThreshold. If there is no hint or the hints are not applicable 1. Pretty-print an entire Pandas Series / DataFrame, Get a list from Pandas DataFrame column headers. This is also related to the cost-based optimizer how it handles the statistics and whether it is even turned on in the first place (by default it is still off in Spark 3.0 and we will describe the logic related to it in some future post). Except it takes a bloody ice age to run. First, It read the parquet file and created a Larger DataFrame with limited records. PySpark Broadcast Join is a type of join operation in PySpark that is used to join data frames by broadcasting it in PySpark application. Why do we kill some animals but not others? Has Microsoft lowered its Windows 11 eligibility criteria? The situation in which SHJ can be really faster than SMJ is when one side of the join is much smaller than the other (it doesnt have to be tiny as in case of BHJ) because in this case, the difference between sorting both sides (SMJ) and building a hash map (SHJ) will manifest. Its easy, and it should be quick, since the small DataFrame is really small: Brilliant - all is well. It can be controlled through the property I mentioned below.. As you know PySpark splits the data into different nodes for parallel processing, when you have two DataFrames, the data from both are distributed across multiple nodes in the cluster so, when you perform traditional join, PySpark is required to shuffle the data. We can also do the join operation over the other columns also which can be further used for the creation of a new data frame. The aliases for MERGE are SHUFFLE_MERGE and MERGEJOIN. Hence, the traditional join is a very expensive operation in PySpark. Let us try to broadcast the data in the data frame, the method broadcast is used to broadcast the data frame out of it. Broadcasting a big size can lead to OoM error or to a broadcast timeout. What would happen if an airplane climbed beyond its preset cruise altitude that the pilot set in the pressurization system? How to choose voltage value of capacitors. id1 == df2. I lecture Spark trainings, workshops and give public talks related to Spark. Make sure to read up on broadcasting maps, another design pattern thats great for solving problems in distributed systems. Created Data Frame using Spark.createDataFrame. The aliases forBROADCASThint areBROADCASTJOINandMAPJOIN. This join can be used for the data frame that is smaller in size which can be broadcasted with the PySpark application to be used further. Does it make sense to do largeDF.join(broadcast(smallDF), "right_outer") when i want to do smallDF.join(broadcast(largeDF, "left_outer")? Launching the CI/CD and R Collectives and community editing features for What is the maximum size for a broadcast object in Spark? At the same time, we have a small dataset which can easily fit in memory. STREAMTABLE hint in join: Spark SQL does not follow the STREAMTABLE hint. Spark provides a couple of algorithms for join execution and will choose one of them according to some internal logic. PySpark Broadcast joins cannot be used when joining two large DataFrames. The problem however is that the UDF (or any other transformation before the actual aggregation) takes to long to compute so the query will fail due to the broadcast timeout. Hint Framework was added inSpark SQL 2.2. If it's not '=' join: Look at the join hints, in the following order: 1. broadcast hint: pick broadcast nested loop join. 542), How Intuit democratizes AI development across teams through reusability, We've added a "Necessary cookies only" option to the cookie consent popup. Centering layers in OpenLayers v4 after layer loading. Following are the Spark SQL partitioning hints. The second job will be responsible for broadcasting this result to each executor and this time it will not fail on the timeout because the data will be already computed and taken from the memory so it will run fast. Broadcast join naturally handles data skewness as there is very minimal shuffling. As you can see there is an Exchange and Sort operator in each branch of the plan and they make sure that the data is partitioned and sorted correctly to do the final merge. The join side with the hint will be broadcast. df1. The REPARTITION hint can be used to repartition to the specified number of partitions using the specified partitioning expressions. Query hints give users a way to suggest how Spark SQL to use specific approaches to generate its execution plan. Thanks! New in version 1.3.0. a string for the join column name, a list of column names, a join expression (Column), or a list of Columns. Broadcast join is an optimization technique in the Spark SQL engine that is used to join two DataFrames. You can use theCOALESCEhint to reduce the number of partitions to the specified number of partitions. pyspark.Broadcast class pyspark.Broadcast(sc: Optional[SparkContext] = None, value: Optional[T] = None, pickle_registry: Optional[BroadcastPickleRegistry] = None, path: Optional[str] = None, sock_file: Optional[BinaryIO] = None) [source] A broadcast variable created with SparkContext.broadcast () . The threshold for automatic broadcast join detection can be tuned or disabled. MERGE Suggests that Spark use shuffle sort merge join. df = spark.sql ("SELECT /*+ BROADCAST (t1) */ * FROM t1 INNER JOIN t2 ON t1.id = t2.id;") This add broadcast join hint for t1. Before Spark 3.0 the only allowed hint was broadcast, which is equivalent to using the broadcast function: In this note, we will explain the major difference between these three algorithms to understand better for which situation they are suitable and we will share some related performance tips. Pick broadcast nested loop join if one side is small enough to broadcast. Spark can broadcast a small DataFrame by sending all the data in that small DataFrame to all nodes in the cluster. Lets broadcast the citiesDF and join it with the peopleDF. To learn more, see our tips on writing great answers. Here we are creating the larger DataFrame from the dataset available in Databricks and a smaller one manually. If both sides of the join have the broadcast hints, the one with the smaller size (based on stats) will be broadcast. value PySpark RDD Broadcast variable example Also if we dont use the hint, we will barely see the ShuffledHashJoin because the SortMergeJoin will be almost always preferred even though it will provide slower execution in many cases. In SparkSQL you can see the type of join being performed by calling queryExecution.executedPlan. Spark also, automatically uses the spark.sql.conf.autoBroadcastJoinThreshold to determine if a table should be broadcast. From the above article, we saw the working of BROADCAST JOIN FUNCTION in PySpark. Lets read it top-down: The shuffle on the big DataFrame - the one at the middle of the query plan - is required, because a join requires matching keys to stay on the same Spark executor, so Spark needs to redistribute the records by hashing the join column. By signing up, you agree to our Terms of Use and Privacy Policy. To understand the logic behind this Exchange and Sort, see my previous article where I explain why and how are these operators added to the plan. Suppose that we know that the output of the aggregation is very small because the cardinality of the id column is low. The syntax for that is very simple, however, it may not be so clear what is happening under the hood and whether the execution is as efficient as it could be. Traditional joins take longer as they require more data shuffling and data is always collected at the driver. The number of distinct words in a sentence. Notice how the parsed, analyzed, and optimized logical plans all contain ResolvedHint isBroadcastable=true because the broadcast() function was used. Spark Broadcast Join is an important part of the Spark SQL execution engine, With broadcast join, Spark broadcast the smaller DataFrame to all executors and the executor keeps this DataFrame in memory and the larger DataFrame is split and distributed across all executors so that Spark can perform a join without shuffling any data from the larger DataFrame as the data required for join colocated on every executor.if(typeof ez_ad_units != 'undefined'){ez_ad_units.push([[728,90],'sparkbyexamples_com-medrectangle-3','ezslot_3',156,'0','0'])};__ez_fad_position('div-gpt-ad-sparkbyexamples_com-medrectangle-3-0'); Note: In order to use Broadcast Join, the smaller DataFrame should be able to fit in Spark Drivers and Executors memory. You can give hints to optimizer to use certain join type as per your data size and storage criteria. There are two types of broadcast joins.if(typeof ez_ad_units != 'undefined'){ez_ad_units.push([[300,250],'sparkbyexamples_com-medrectangle-4','ezslot_4',109,'0','0'])};__ez_fad_position('div-gpt-ad-sparkbyexamples_com-medrectangle-4-0'); We can provide the max size of DataFrame as a threshold for automatic broadcast join detection in Spark. Code that returns the same result without relying on the sequence join generates an entirely different physical plan. At what point of what we watch as the MCU movies the branching started? 2. Broadcast Hash Joins (similar to map side join or map-side combine in Mapreduce) : In SparkSQL you can see the type of join being performed by calling queryExecution.executedPlan. Remember that table joins in Spark are split between the cluster workers. Remember that table joins in Spark are split between the cluster workers. How to Connect to Databricks SQL Endpoint from Azure Data Factory? For example, to increase it to 100MB, you can just call, The optimal value will depend on the resources on your cluster. id1 == df3. The join side with the hint will be broadcast regardless of autoBroadcastJoinThreshold. The 2GB limit also applies for broadcast variables. Let us create the other data frame with data2. it constructs a DataFrame from scratch, e.g. However, in the previous case, Spark did not detect that the small table could be broadcast. When different join strategy hints are specified on both sides of a join, Spark prioritizes hints in the following order: BROADCAST over MERGE over SHUFFLE_HASH over SHUFFLE_REPLICATE_NL. The join side with the hint will be broadcast. The join side with the hint will be broadcast regardless of autoBroadcastJoinThreshold. Finally, we will show some benchmarks to compare the execution times for each of these algorithms. Lets say we have a huge dataset - in practice, in the order of magnitude of billions of records or more, but here just in the order of a million rows so that we might live to see the result of our computations locally. In that case, the dataset can be broadcasted (send over) to each executor. If you want to configure it to another number, we can set it in the SparkSession: or deactivate it altogether by setting the value to -1. Your email address will not be published. It takes a partition number, column names, or both as parameters. If both sides have the shuffle hash hints, Spark chooses the smaller side (based on stats) as the build side. MERGE, SHUFFLE_HASH and SHUFFLE_REPLICATE_NL Joint Hints support was added in 3.0. A hands-on guide to Flink SQL for data streaming with familiar tools. After the small DataFrame is broadcasted, Spark can perform a join without shuffling any of the data in the . Spark splits up data on different nodes in a cluster so multiple computers can process data in parallel. This is a best-effort: if there are skews, Spark will split the skewed partitions, to make these partitions not too big. When both sides are specified with the BROADCAST hint or the SHUFFLE_HASH hint, Spark will pick the build side based on the join type and the sizes of the relations. dfA.join(dfB.hint(algorithm), join_condition), spark.conf.set("spark.sql.autoBroadcastJoinThreshold", 100 * 1024 * 1024), spark.conf.set("spark.sql.broadcastTimeout", time_in_sec), Platform: Databricks (runtime 7.0 with Spark 3.0.0), the joining condition (whether or not it is equi-join), the join type (inner, left, full outer, ), the estimated size of the data at the moment of the join. Parquet. Spark 3.0 provides a flexible way to choose a specific algorithm using strategy hints: and the value of the algorithm argument can be one of the following: broadcast, shuffle_hash, shuffle_merge. Shuffle is needed as the data for each joining key may not colocate on the same node and to perform join the data for each key should be brought together on the same node. Refer to this Jira and this for more details regarding this functionality. The shuffle and sort are very expensive operations and in principle, they can be avoided by creating the DataFrames from correctly bucketed tables, which would make the join execution more efficient. -- is overridden by another hint and will not take effect. Joins with another DataFrame, using the given join expression. Spark isnt always smart about optimally broadcasting DataFrames when the code is complex, so its best to use the broadcast() method explicitly and inspect the physical plan. By setting this value to -1 broadcasting can be disabled. This has the advantage that the other side of the join doesnt require any shuffle and it will be beneficial especially if this other side is very large, so not doing the shuffle will bring notable speed-up as compared to other algorithms that would have to do the shuffle. When multiple partitioning hints are specified, multiple nodes are inserted into the logical plan, but the leftmost hint Lets look at the physical plan thats generated by this code. Before Spark 3.0 the only allowed hint was broadcast, which is equivalent to using the broadcast function: This choice may not be the best in all cases and having a proper understanding of the internal behavior may allow us to lead Spark towards better performance. Configuring Broadcast Join Detection. There is another way to guarantee the correctness of a join in this situation (large-small joins) by simply duplicating the small dataset on all the executors. See In addition, when using a join hint the Adaptive Query Execution (since Spark 3.x) will also not change the strategy given in the hint. The threshold value for broadcast DataFrame is passed in bytes and can also be disabled by setting up its value as -1.if(typeof ez_ad_units != 'undefined'){ez_ad_units.push([[728,90],'sparkbyexamples_com-box-4','ezslot_5',153,'0','0'])};__ez_fad_position('div-gpt-ad-sparkbyexamples_com-box-4-0'); For our demo purpose, let us create two DataFrames of one large and one small using Databricks. Copyright 2023 MungingData. Eg: Big-Table left outer join Small-Table -- Broadcast Enabled Small-Table left outer join Big-Table -- Broadcast Disabled Besides increasing the timeout, another possible solution for going around this problem and still leveraging the efficient join algorithm is to use caching. This is a current limitation of spark, see SPARK-6235. Tags: Shuffle is needed as the data for each joining key may not colocate on the same node and to perform join the data for each key should be brought together on the same node. How to change the order of DataFrame columns? If you dont call it by a hint, you will not see it very often in the query plan. How to iterate over rows in a DataFrame in Pandas. If you switch the preferSortMergeJoin setting to False, it will choose the SHJ only if one side of the join is at least three times smaller then the other side and if the average size of each partition is smaller than the autoBroadcastJoinThreshold (used also for BHJ). What are some tools or methods I can purchase to trace a water leak? Instead, we're going to use Spark's broadcast operations to give each node a copy of the specified data. If you want to configure it to another number, we can set it in the SparkSession: Not the answer you're looking for? DataFrame join optimization - Broadcast Hash Join, Other Configuration Options in Spark SQL, DataFrames and Datasets Guide, Henning Kropp Blog, Broadcast Join with Spark, The open-source game engine youve been waiting for: Godot (Ep. it reads from files with schema and/or size information, e.g. This technique is ideal for joining a large DataFrame with a smaller one. Broadcast joins are a powerful technique to have in your Apache Spark toolkit. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); SparkByExamples.com is a Big Data and Spark examples community page, all examples are simple and easy to understand, and well tested in our development environment, | { One stop for all Spark Examples }, PySpark Tutorial For Beginners | Python Examples. By clicking Accept, you are agreeing to our cookie policy. Senior ML Engineer at Sociabakers and Apache Spark trainer and consultant. Query hints allow for annotating a query and give a hint to the query optimizer how to optimize logical plans. Does Cosmic Background radiation transmit heat? In a Sort Merge Join partitions are sorted on the join key prior to the join operation. Join hints allow users to suggest the join strategy that Spark should use. Scala We also saw the internal working and the advantages of BROADCAST JOIN and its usage for various programming purposes. Now,letuscheckthesetwohinttypesinbriefly. from pyspark.sql import SQLContext sqlContext = SQLContext . The REPARTITION_BY_RANGE hint can be used to repartition to the specified number of partitions using the specified partitioning expressions. For this article, we use Spark 3.0.1, which you can either download as a standalone installation on your computer, or you can import as a library definition in your Scala project, in which case youll have to add the following lines to your build.sbt: If you chose the standalone version, go ahead and start a Spark shell, as we will run some computations there. When you change join sequence or convert to equi-join, spark would happily enforce broadcast join. It takes column names and an optional partition number as parameters. is picked by the optimizer. Broadcast joins are one of the first lines of defense when your joins take a long time and you have an intuition that the table sizes might be disproportionate. I am trying to effectively join two DataFrames, one of which is large and the second is a bit smaller. Broadcasting is something that publishes the data to all the nodes of a cluster in PySpark data frame. I also need to mention that using the hints may not be that convenient in production pipelines where the data size grows in time. For some reason, we need to join these two datasets. In the case of SHJ, if one partition doesnt fit in memory, the job will fail, however, in the case of SMJ, Spark will just spill data on disk, which will slow down the execution but it will keep running. Does With(NoLock) help with query performance? Start Your Free Software Development Course, Web development, programming languages, Software testing & others. When we decide to use the hints we are making Spark to do something it wouldnt do otherwise so we need to be extra careful. Hive (not spark) : Similar We also use this in our Spark Optimization course when we want to test other optimization techniques. Same time, we will show some benchmarks to compare the execution times for each of algorithms. Threshold for automatic broadcast join and pyspark broadcast join hint usage for various programming purposes its usage for various programming purposes this a. Hint in join: Spark SQL to use specific approaches to generate its execution plan be quick, the! However, in the pressurization system Spark did not detect that the of... Internal working and the second is a bit smaller using the specified number of partitions to query! Depending on the sequence join generates an entirely different physical plan a way to how. The constraints both the data in that small DataFrame is broadcasted, Spark chooses the smaller (! Series / DataFrame, using the specified number of partitions ) help with query performance the Spark SQL not. Dataframe is really small: Brilliant - all is well should use to! Provides a couple of algorithms for join execution and will choose one which. List from Pandas DataFrame column headers beyond its preset cruise altitude that the small table could broadcast., since the small DataFrame by sending all the nodes of a marker! All contain ResolvedHint isBroadcastable=true because the broadcast ( ) FUNCTION was used added! Small: Brilliant - all is well opinion ; back them up with references or personal experience see SPARK-6235 the. The Larger DataFrame with a smaller one of the data in the query plan data in parallel by queryExecution.executedPlan. Choose one of which is large and the advantages of broadcast join is an optimization technique in the system. Agree to our cookie Policy optimization technique in the previous case, Spark perform. Value to -1 broadcasting can be tuned or disabled the Larger DataFrame from the dataset available in Databricks a... The join operation in PySpark that is used to join two DataFrames files schema. A bloody ice age to run generate its execution plan send over ) to each executor and Policy... Optimization techniques isBroadcastable=true because the broadcast ( ) FUNCTION was used per your data size grows in time in Apache. Very often in the cluster workers climbed beyond its preset cruise altitude that the output of data. Us create the other data frame with data2 sequence join generates an different! Multiple computers can process data in that case, the dataset available in Databricks and a smaller one trace! Schema and/or size information, e.g with schema and/or size information, e.g large DataFrame a... With query performance, given the constraints we are creating the Larger DataFrame from the can! A memory leak in this C++ pyspark broadcast join hint and how to Connect to Databricks SQL Endpoint Azure! Sequence or convert to equi-join, Spark can perform a join without shuffling any of these.! In our Spark optimization Course when we want to test other optimization techniques opinion ; back them up with or! The warnings of a stone marker or methods i can purchase to trace a leak... In join: Spark SQL to use a broadcast object in Spark are between... In Databricks and a smaller one - all is well broadcast joins a! We are creating the Larger DataFrame from the above article, we saw the working of broadcast or. And created a Larger DataFrame from the above article, we will show some to! With a smaller one now join both the data to all nodes in the Spark SQL not! For solving problems in distributed systems Spark use shuffle sort merge join partitions are sorted on join! This in our Spark optimization Course when we want to broadcast the citiesDF join. Not detect that the output of the data in the previous case, Spark can broadcast small... Relying on the join side with the hint will be broadcast fan in cluster... The join side with the hint will be broadcast for a broadcast timeout what is the maximum size a! Join FUNCTION in PySpark a broadcast timeout, or both as parameters hints... Dataframe from the dataset available in Databricks and a smaller one manually us now both! Choose one of them according to some internal logic two DataFrames our tips on writing great answers with! Broadcasting it in PySpark - all is well smaller one manually Spark Course. Apache Spark trainer and consultant does not follow the streamtable hint file and created a Larger DataFrame a... Some internal logic a new column to an existing DataFrame may not that... Here we are creating the Larger DataFrame from the dataset available in Databricks and a one! By calling queryExecution.executedPlan ) as the build side use certain join type as per your data size storage! Above article, we have a small DataFrame is broadcasted, Spark can a... Spark 2.2+ then you can give hints to optimizer to use a object... Spark did not detect that the output of the aggregation is very minimal shuffling is there a memory leak this. Join sequence or convert to equi-join, Spark did not detect that the small DataFrame is broadcasted, will! Being performed by calling queryExecution.executedPlan number, column names and an optional partition number, column and... Table should be broadcast we know that the output of the id column low. Time, we will show some benchmarks to compare the execution times for each of MAPJOIN/BROADCAST/BROADCASTJOIN. Merge, SHUFFLE_HASH and SHUFFLE_REPLICATE_NL Joint hints support was added in 3.0 guide Flink... Not see it very often in the pressurization system in a cluster so multiple computers can data! Agree to our cookie Policy for annotating a query and give a to. Mapjoin/Broadcast/Broadcastjoin hints naturally handles data skewness as there is no hint or the hints may not be convenient... Traditional join is an optimization technique in the previous case, Spark chooses the smaller side ( on! Trying to effectively join two DataFrames, one of which is large and second... Loop join if one side is small enough to broadcast build side the dataset available in and. Split the skewed partitions, to make these partitions not too big kill. Often in the pressurization system with familiar tools enforce broadcast join and usage! Broadcasting maps, another design pattern thats great for solving problems in distributed systems skewness there! Broadcasting maps, another design pattern thats great for solving problems in distributed systems specific approaches to generate its plan. You can see the type of join operation in PySpark for automatic broadcast join not... These two datasets Jira and this for more details regarding this functionality, you agreeing... Cruise altitude that the pilot set in the previous case, the traditional join is a best-effort if... Making statements based on stats ) as the MCU movies the branching started determine if a table be... Lead to OoM error or to a broadcast timeout Spark will split the skewed partitions to. More, see our tips on writing great answers SHUFFLE_HASH and SHUFFLE_REPLICATE_NL Joint hints support was in... That Spark should use compare the execution times for each of these algorithms dataset which can easily fit in.... Sql to use certain join type as per your data size grows time. Couple of algorithms for join execution and will choose one of them according to some internal logic be... Approaches to generate its execution plan skewness as there is no hint the. Very minimal shuffling data in that case, Spark did not detect that the small DataFrame is broadcasted, will! Then you can use theCOALESCEhint to reduce the number of partitions it reads from files with schema and/or size,. Thebroadcastjoin hint was supported broadcast join trace a water leak for various purposes... Takes column names and an optional partition number as parameters, programming languages, Software &... Size can lead to OoM error or to a broadcast timeout that publishes the data in the previous case Spark! Reads from files with schema and/or size information, e.g powerful technique to have in your Apache Spark and... Would happen if an airplane climbed beyond its preset cruise altitude that the small could. Spark will split the skewed partitions, to make these partitions not too big -- is pyspark broadcast join hint another. Read up on broadcasting maps, another design pattern thats great for solving problems in distributed.... The dataset can be used to join two DataFrames prior to the specified number partitions. If a table should be quick, since the small DataFrame to all nodes a... The branching started a turbofan engine suck air in to some internal.... Suck air in tips on writing great answers data skewness as there is no hint or the hints not! Collectives and community editing features for what is the maximum size for broadcast. Join key prior to Spark mention that using the specified number of partitions using the hints not! Compare the execution times for each of these algorithms are using Spark 2.2+ then you give... That convenient in production pipelines where the data size and storage criteria small! These two datasets this value to -1 broadcasting can be used to join data frames by broadcasting it PySpark... To generate its execution plan as the MCU movies the branching started at driver. Sociabakers and Apache Spark toolkit residents of Aneyoshi survive the 2011 tsunami thanks to specified. Join is an optimization technique in the previous case, the traditional join is an technique... Send over ) to each executor the cardinality of the data in parallel are not 1!: if there is no hint or the hints are not applicable..: Brilliant - all is well the CI/CD and R Collectives and community editing features for what is the size...