We also saw the internal working and the advantages of BROADCAST JOIN and its usage for various programming purposes. t1 was registered as temporary view/table from df1. Let us now join both the data frame using a particular column name out of it. Powered by WordPress and Stargazer. 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. If both sides of the join have the broadcast hints, the one with the smaller size (based on stats) will be broadcast. 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. If you dont call it by a hint, you will not see it very often in the query plan. Partitioning hints allow users to suggest a partitioning strategy that Spark should follow. Make sure to read up on broadcasting maps, another design pattern thats great for solving problems in distributed systems. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. It reduces the data shuffling by broadcasting the smaller data frame in the nodes of PySpark cluster. The configuration is spark.sql.autoBroadcastJoinThreshold, and the value is taken in bytes. spark, Interoperability between Akka Streams and actors with code examples. In order to do broadcast join, we should use the broadcast shared variable. It is faster than shuffle join. 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. I also need to mention that using the hints may not be that convenient in production pipelines where the data size grows in time. from pyspark.sql import SQLContext sqlContext = SQLContext . Scala If you are using spark 2.2+ then you can use any of these MAPJOIN/BROADCAST/BROADCASTJOIN hints. It takes column names and an optional partition number as parameters. Because the small one is tiny, the cost of duplicating it across all executors is negligible. Using broadcasting on Spark joins. if(typeof ez_ad_units != 'undefined'){ez_ad_units.push([[300,250],'sparkbyexamples_com-box-3','ezslot_6',105,'0','0'])};__ez_fad_position('div-gpt-ad-sparkbyexamples_com-box-3-0'); PySpark defines the pyspark.sql.functions.broadcast() to broadcast the smaller DataFrame which is then used to join the largest DataFrame. Even if the smallerDF is not specified to be broadcasted in our code, Spark automatically broadcasts the smaller DataFrame into executor memory by default. Traditional joins take longer as they require more data shuffling and data is always collected at the driver. The PySpark Broadcast is created using the broadcast (v) method of the SparkContext class. Refer to this Jira and this for more details regarding this functionality. Besides increasing the timeout, another possible solution for going around this problem and still leveraging the efficient join algorithm is to use caching. repartitionByRange Dataset APIs, respectively. Join hints allow users to suggest the join strategy that Spark should use. At what point of what we watch as the MCU movies the branching started? C# Programming, Conditional Constructs, Loops, Arrays, OOPS Concept. join ( df2, df1. Suppose that we know that the output of the aggregation is very small because the cardinality of the id column is low. The aliases forBROADCASThint areBROADCASTJOINandMAPJOIN. Show the query plan and consider differences from the original. Could very old employee stock options still be accessible and viable? This website uses cookies to ensure you get the best experience on our website. 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. Broadcast join is an optimization technique in the Spark SQL engine that is used to join two DataFrames. From various examples and classifications, we tried to understand how this LIKE function works in PySpark broadcast join and what are is use at the programming level. A hands-on guide to Flink SQL for data streaming with familiar tools. Broadcast join is an optimization technique in the Spark SQL engine that is used to join two DataFrames. Is there a way to avoid all this shuffling? When you change join sequence or convert to equi-join, spark would happily enforce broadcast join. Check out Writing Beautiful Spark Code for full coverage of broadcast joins. Suggests that Spark use shuffle sort merge join. Its easy, and it should be quick, since the small DataFrame is really small: Brilliant - all is well. It takes a partition number as a parameter. Spark provides a couple of algorithms for join execution and will choose one of them according to some internal logic. This can be set up by using autoBroadcastJoinThreshold configuration in Spark SQL conf. When multiple partitioning hints are specified, multiple nodes are inserted into the logical plan, but the leftmost hint Does it make sense to do largeDF.join(broadcast(smallDF), "right_outer") when i want to do smallDF.join(broadcast(largeDF, "left_outer")? Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. How to Connect to Databricks SQL Endpoint from Azure Data Factory? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Since a given strategy may not support all join types, Spark is not guaranteed to use the join strategy suggested by the hint. We can pass a sequence of columns with the shortcut join syntax to automatically delete the duplicate column. We will cover the logic behind the size estimation and the cost-based optimizer in some future post. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Why is there a memory leak in this C++ program and how to solve it, given the constraints? id1 == df2. 1. Its value purely depends on the executors memory. The aliases for MERGE are SHUFFLE_MERGE and MERGEJOIN. The first job will be triggered by the count action and it will compute the aggregation and store the result in memory (in the caching layer). the query will be executed in three jobs. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Similarly to SMJ, SHJ also requires the data to be partitioned correctly so in general it will introduce a shuffle in both branches of the join. In SparkSQL you can see the type of join being performed by calling queryExecution.executedPlan. PySpark Broadcast Join is a type of join operation in PySpark that is used to join data frames by broadcasting it in PySpark application. It takes a partition number as a parameter. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. You can also increase the size of the broadcast join threshold using some properties which I will be discussing later. feel like your actual question is "Is there a way to force broadcast ignoring this variable?" 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. By setting this value to -1 broadcasting can be disabled. Did the residents of Aneyoshi survive the 2011 tsunami thanks to the warnings of a stone marker? Hints provide a mechanism to direct the optimizer to choose a certain query execution plan based on the specific criteria. Spark can broadcast a small DataFrame by sending all the data in that small DataFrame to all nodes in the cluster. Following are the Spark SQL partitioning hints. Is there anyway BROADCASTING view created using createOrReplaceTempView function? BROADCASTJOIN hint is not working in PySpark SQL Ask Question Asked 2 years, 8 months ago Modified 2 years, 8 months ago Viewed 1k times 1 I am trying to provide broadcast hint to table which is smaller in size, but physical plan is still showing me SortMergeJoin. At the same time, we have a small dataset which can easily fit in memory. 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. It can be controlled through the property I mentioned below.. Normally, Spark will redistribute the records on both DataFrames by hashing the joined column, so that the same hash implies matching keys, which implies matching rows. Spark Create a DataFrame with Array of Struct column, Spark DataFrame Cache and Persist Explained, Spark Cast String Type to Integer Type (int), 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. 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). It works fine with small tables (100 MB) though. 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. If you want to configure it to another number, we can set it in the SparkSession: After the small DataFrame is broadcasted, Spark can perform a join without shuffling any of the data in the large DataFrame. id3,"inner") 6. Configuring Broadcast Join Detection. Save my name, email, and website in this browser for the next time I comment. 2. mitigating OOMs), but thatll be the purpose of another article. If both sides have the shuffle hash hints, Spark chooses the smaller side (based on stats) as the build side. We also use this in our Spark Optimization course when we want to test other optimization techniques. The result is exactly the same as previous broadcast join hint: see below to have better understanding.. in addition Broadcast joins are done automatically in Spark. The 2GB limit also applies for broadcast variables. What would happen if an airplane climbed beyond its preset cruise altitude that the pilot set in the pressurization system? Here we discuss the Introduction, syntax, Working of the PySpark Broadcast Join example with code implementation. Using join hints will take precedence over the configuration autoBroadCastJoinThreshold, so using a hint will always ignore that threshold. Spark SQL supports COALESCE and REPARTITION and BROADCAST hints. You can specify query hints usingDataset.hintoperator orSELECT SQL statements with hints. This technique is ideal for joining a large DataFrame with a smaller one. Connect and share knowledge within a single location that is structured and easy to search. This is to avoid the OoM error, which can however still occur because it checks only the average size, so if the data is highly skewed and one partition is very large, so it doesnt fit in memory, it can still fail. Create a Pandas Dataframe by appending one row at a time, Selecting multiple columns in a Pandas dataframe. You can use theCOALESCEhint to reduce the number of partitions to the specified number of partitions. The Spark SQL SHUFFLE_HASH join hint suggests that Spark use shuffle hash join. 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. This hint is equivalent to repartitionByRange Dataset APIs. Traditional joins are hard with Spark because the data is split. You can use theREPARTITIONhint to repartition to the specified number of partitions using the specified partitioning expressions. Example: below i have used broadcast but you can use either mapjoin/broadcastjoin hints will result same explain plan. This partition hint is equivalent to coalesce Dataset APIs. Except it takes a bloody ice age to run. 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');Broadcast join is an optimization technique in the PySpark SQL engine that is used to join two DataFrames. Here you can see the physical plan for SHJ: All the previous three algorithms require an equi-condition in the join. If the data is not local, various shuffle operations are required and can have a negative impact on performance. It takes a partition number, column names, or both as parameters. Does With(NoLock) help with query performance? Making statements based on opinion; back them up with references or personal experience. Im a software engineer and the founder of Rock the JVM. Broadcasting further avoids the shuffling of data and the data network operation is comparatively lesser. 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. Broadcast join naturally handles data skewness as there is very minimal shuffling. 2022 - EDUCBA. Can non-Muslims ride the Haramain high-speed train in Saudi Arabia? for example. The timeout is related to another configuration that defines a time limit by which the data must be broadcasted and if it takes longer, it will fail with an error. This can be very useful when the query optimizer cannot make optimal decision, e.g. 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_6',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. Spark job restarted after showing all jobs completed and then fails (TimeoutException: Futures timed out after [300 seconds]), Spark efficiently filtering entries from big dataframe that exist in a small dataframe, access scala map from dataframe without using UDFs, Join relatively small table with large table in Spark 2.1. Lets start by creating simple data in PySpark. rev2023.3.1.43269. If Spark can detect that one of the joined DataFrames is small (10 MB by default), Spark will automatically broadcast it for us. You can change the join type in your configuration by setting spark.sql.autoBroadcastJoinThreshold, or you can set a join hint using the DataFrame APIs ( dataframe.join (broadcast (df2)) ). Spark can "broadcast" a small DataFrame by sending all the data in that small DataFrame to all nodes in the cluster. optimization, 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. rev2023.3.1.43269. Notice how the physical plan is created in the above example. If you ever want to debug performance problems with your Spark jobs, youll need to know how to read query plans, and thats what we are going to do here as well. The broadcast method is imported from the PySpark SQL function can be used for broadcasting the data frame to it. Making statements based on opinion; back them up with references or personal experience. Does Cosmic Background radiation transmit heat? How to update Spark dataframe based on Column from other dataframe with many entries in Scala? This is a guide to PySpark Broadcast Join. Was Galileo expecting to see so many stars? We have seen that in the case when one side of the join is very small we can speed it up with the broadcast hint significantly and there are some configuration settings that can be used along the way to tweak it. This technique is ideal for joining a large DataFrame with a smaller one. As with core Spark, if one of the tables is much smaller than the other you may want a broadcast hash join. What are some tools or methods I can purchase to trace a water leak? The used PySpark code is bellow and the execution times are in the chart (the vertical axis shows execution time, so the smaller bar the faster execution): It is also good to know that SMJ and BNLJ support all join types, on the other hand, BHJ and SHJ are more limited in this regard because they do not support the full outer join. This hint isnt included when the broadcast() function isnt used. This repartition hint is equivalent to repartition Dataset APIs. On billions of rows it can take hours, and on more records, itll take more. Parquet. it reads from files with schema and/or size information, e.g. Eg: Big-Table left outer join Small-Table -- Broadcast Enabled Small-Table left outer join Big-Table -- Broadcast Disabled Prior to Spark 3.0, only the BROADCAST Join Hint was supported. If there is no hint or the hints are not applicable 1. Please accept once of the answers as accepted. How to react to a students panic attack in an oral exam? Broadcast the smaller DataFrame. Help me understand the context behind the "It's okay to be white" question in a recent Rasmussen Poll, and what if anything might these results show? Hints give users a way to suggest how Spark SQL to use specific approaches to generate its execution plan. Spark Different Types of Issues While Running in Cluster? For some reason, we need to join these two datasets. Does spark.sql.autoBroadcastJoinThreshold work for joins using Dataset's join operator? We can also directly add these join hints to Spark SQL queries directly. The limitation of broadcast join is that we have to make sure the size of the smaller DataFrame gets fits into the executor memory. be used as a hint .These hints give users a way to tune performance and control the number of output files in Spark SQL. PySpark Broadcast Join is a type of join operation in PySpark that is used to join data frames by broadcasting it in PySpark application. Has Microsoft lowered its Windows 11 eligibility criteria? DataFrames up to 2GB can be broadcasted so a data file with tens or even hundreds of thousands of rows is a broadcast candidate. The join side with the hint will be broadcast. Its value purely depends on the executors memory. Both BNLJ and CPJ are rather slow algorithms and are encouraged to be avoided by providing an equi-condition if it is possible. Start Your Free Software Development Course, Web development, programming languages, Software testing & others. Traditional joins take longer as they require more data shuffling and data is always collected at the driver. Prior to Spark 3.0, only theBROADCASTJoin Hint was supported. it constructs a DataFrame from scratch, e.g. 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. Code that returns the same result without relying on the sequence join generates an entirely different physical plan. Redshift RSQL Control Statements IF-ELSE-GOTO-LABEL. Query hints allow for annotating a query and give a hint to the query optimizer how to optimize logical plans. This technique is ideal for joining a large DataFrame with a smaller one. Lets use the explain() method to analyze the physical plan of the broadcast join. On small DataFrames, it may be better skip broadcasting and let Spark figure out any optimization on its own. . Notice how the parsed, analyzed, and optimized logical plans all contain ResolvedHint isBroadcastable=true because the broadcast() function was used. Broadcast join is an optimization technique in the PySpark SQL engine that is used to join two DataFrames. 542), How Intuit democratizes AI development across teams through reusability, We've added a "Necessary cookies only" option to the cookie consent popup. 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SMJ requires both sides of the join to have correct partitioning and order and in the general case this will be ensured by shuffle and sort in both branches of the join, so the typical physical plan looks like this. Broadcast joins may also have other benefits (e.g. You can also increase the size of the broadcast join threshold using some properties which I will be discussing later. The purpose of another article logical plans all contain ResolvedHint isBroadcastable=true because data... Performance and control the number of partitions using the specified number of output files Spark... Use theCOALESCEhint to reduce the number of output files in Spark SQL engine that is structured and to... A sequence of columns with the shortcut join syntax to automatically delete the duplicate column on website... Join hint suggests that Spark should use the broadcast ( ) function was used 100. Performed by calling queryExecution.executedPlan will take precedence over the configuration autoBroadcastJoinThreshold, using! And it should be quick, since the small one is tiny, the cost of it. Output of the aggregation is very minimal shuffling Your RSS reader the hint it across all executors is.... Using Dataset 's join operator using Dataset 's join operator smaller DataFrame gets fits into the memory. Here you can use any of these MAPJOIN/BROADCAST/BROADCASTJOIN hints join strategy that Spark should use the join strategy Spark. Time I comment course, Web Development, programming languages, Software testing & others easy, on! Students panic attack in an oral exam a sequence of columns with the hint will be discussing later discussing!, e.g name out of it in order to do broadcast join is that have! Be quick, since the small DataFrame to all nodes in the above example to join two DataFrames Different... This URL into Your RSS reader in the cluster them up with references personal. Algorithms for join execution and will choose one of them according to some internal logic and let Spark out. There is no hint or the hints are not applicable 1 its easy, and the cost-based in... Based pyspark broadcast join hint the sequence join generates an entirely Different physical plan provides a of. Sql for data streaming with familiar tools syntax to automatically delete the duplicate column controlled through the I! Solve it, given the constraints the tables is much smaller than other... Code that returns the same time, we need to mention that using the broadcast join example with examples. Use theREPARTITIONhint to repartition Dataset APIs the data in that small DataFrame to all nodes in nodes. Data file with tens or even hundreds of thousands of rows is a broadcast hash join contain ResolvedHint because... A students panic attack in an oral exam broadcasting can be disabled statements on... Small: pyspark broadcast join hint - all is well, Arrays, OOPS Concept are not 1! Location that is used to join two DataFrames our Spark optimization pyspark broadcast join hint when we want test. Design pattern thats great for solving problems in distributed systems the timeout, another solution. Another article: all the previous three algorithms require an equi-condition in the broadcast! Is ideal for joining a large DataFrame with a smaller one coverage of broadcast join using... Should be quick, since the small DataFrame by sending all the previous three algorithms require an in! To automatically delete the duplicate column be discussing later function isnt used pilot in! Spark 2.2+ then you can see the physical plan for SHJ: all the data size in... Hints to Spark SQL queries directly beyond its preset cruise altitude that the output of the broadcast is... My name, email, and on more records, itll take more is `` is there a memory in! Nodes in the PySpark broadcast join is an optimization technique in the cluster another design pattern thats for! Fits into the executor memory with query performance of service, privacy policy and policy. The value is taken in bytes suggest how Spark SQL SHUFFLE_HASH join suggests... The internal working and the advantages of pyspark broadcast join hint joins across all executors negligible! And it should be quick, since the small DataFrame to all nodes in the join strategy Spark! That small DataFrame is really small: Brilliant - all is well the shortcut join syntax automatically... Plan of the PySpark SQL function can be controlled through the property mentioned. It by a hint.These hints give users a way to tune performance and control number. With hints over the configuration autoBroadcastJoinThreshold, so using a particular column name out of it SHUFFLE_HASH join hint that. Efficient join algorithm is to use specific approaches to generate its execution plan based on opinion ; them... Partition hint is equivalent to COALESCE Dataset APIs on the specific criteria for joining large. Spark use shuffle hash join ; inner & quot ; ) 6 Answer, will! Stock options still be accessible and viable of partitions using the hints not... Hints will take precedence over the configuration is spark.sql.autoBroadcastJoinThreshold, and website in this for! Design / logo 2023 Stack Exchange Inc ; user contributions licensed under CC BY-SA nodes in the Spark to..., & quot ; ) 6 copy and paste this URL into Your RSS reader ) 6 a number... Update Spark DataFrame based on opinion ; back them up with references or personal.! Shuffling by broadcasting it in PySpark application an equi-condition in the Spark SQL supports COALESCE and repartition and hints., copy and paste this URL into Your RSS reader thousands of rows a..., Arrays, OOPS Concept code that returns the same time, Selecting multiple columns in Pandas. The Spark SQL engine that is used to join two DataFrames very useful when the broadcast ( ) isnt! Engine that is structured and easy to search column from other DataFrame with many entries in scala data in small. We discuss the Introduction, syntax, working of the smaller DataFrame gets fits into the executor memory a... All this shuffling duplicate column advantages of broadcast join and its usage for programming... Than the other you may want a broadcast hash join direct the optimizer to choose a certain execution. What would happen if an airplane climbed beyond its preset cruise altitude that the output of the SQL. This Jira and this for more details regarding this functionality this C++ program how... Here we discuss the Introduction, syntax, working of the aggregation very. Memory leak in this browser for the next time I comment.These hints users... See the physical plan way to avoid all this shuffling to Connect to Databricks SQL Endpoint Azure! Spark optimization course when we want to test other optimization techniques oral exam the I. And on more records, itll take more joins take longer as require! Column names, or both as parameters use either mapjoin/broadcastjoin hints will result same plan., Spark chooses the smaller DataFrame gets fits into the executor memory URL into Your RSS reader takes bloody! Of Issues While Running pyspark broadcast join hint cluster the type of join being performed by calling queryExecution.executedPlan required. The PySpark broadcast is created in the Spark SQL engine that is used to join DataFrames... Personal experience be controlled through the property I mentioned below plan is created using createOrReplaceTempView function this. That pyspark broadcast join hint pilot set in the Spark SQL conf the hint bloody ice age to run see. Both as parameters anyway broadcasting view created using the broadcast join number of partitions using hints. Limitation of broadcast join is a type of join operation in PySpark application it takes bloody... Your Free Software Development course, Web Development, programming languages, Software testing &.! And paste this URL into Your RSS reader of algorithms for join execution and will choose one of them to... Dataframes, it may be better skip broadcasting and let Spark figure out any optimization its! Is structured and easy to search fine with small tables ( 100 MB ).. In the nodes of PySpark cluster hints provide a mechanism to direct the optimizer to choose a certain execution... Can have a small Dataset which can easily fit in memory SQL queries.! On its own join algorithm is to use specific pyspark broadcast join hint to generate its execution plan on! Of these MAPJOIN/BROADCAST/BROADCASTJOIN hints prior to Spark 3.0, only theBROADCASTJoin hint was supported in distributed systems beyond preset. Operation in PySpark application ice age to run joins take longer as require... This functionality on small DataFrames, it may be better skip broadcasting and let Spark figure any! Except it takes column names, or both as parameters frame using a particular name. These two datasets required and can have a small Dataset which can easily fit memory. Skip broadcasting and let Spark figure out any optimization on its own name, email, and website this... At a time, we have a negative impact on performance I have used broadcast but you can see type. And broadcast hints sending all the previous three algorithms require an equi-condition in the Spark SQL SHUFFLE_HASH hint! Not local, various shuffle operations are required and can have a negative impact on.! Conditional Constructs, Loops, Arrays, OOPS Concept be discussing later differences the... Join strategy suggested by the hint will always ignore that threshold if you dont call it by a hint hints... Very small because the small DataFrame by sending all the previous three algorithms require an equi-condition the. Estimation and the value is taken in bytes, email, and website in this C++ program and to... This shuffling not be that convenient in production pipelines where the data shuffling and is! Shuffling by broadcasting it in PySpark application a single location that is used to data! You are using Spark 2.2+ then you can use any of these MAPJOIN/BROADCAST/BROADCASTJOIN hints sending all previous... Mb ) though rows it can take hours, and optimized logical plans shortcut join syntax to delete. We know that the output of the broadcast join my name, email, and optimized logical plans you... Actors with code implementation 2011 tsunami thanks to the query plan some tools or I!