Janino is used to compile a Java source code into a Java class. For example, if the third project was actually a filter, they may not want to evaluate all of the operators. Next, when were trying to collect our results from this result iterator, we started off by going to the next method on the project. Its a child operator before evaluation. And finally, I need to have the sub expression must be inputs to discipline function. Each had 120 gigabytes of memory with 28 cores, and our dataset has 50 million input rows. background In previous articles Analysis and solution of DataSourceScanExec NullPointerException caused by spark DPP , we directly skipped the step of dynamic code generation failure. So why combining all the query into a single stage could significantly improve the CPU efficiency and gain performance? There are many terms and konwledges about CPU exection, which I learn so little that may make some mistakes in this blog, so it would be very nice of you to figure out any single mistake. The reason for this is that, the memory usage does not scale linearly with the size of the method. Once thats finished, (indistinct). At the beginning of the page is the summary with the count of all stages by status (active, pending, completed, skipped, and failed), In Fair scheduling mode there is a table that displays pools properties. In this case, we create two booleans. Powered by, Project Tungsten: Bringing Apache Spark Closer to Bare Metal, Apache Spark as a Compiler: Joining a Billion Rows per Second on a Laptop, Spark 2.x - 2nd generation Tungsten Engine, Vectorization: Ranger to Stampede Transition, Memory Management and Binary Processing: leveraging application semantics to manage memory explicitly and eliminate the overhead of JVM object model and garbage collection, Cache-aware computation: algorithms and data structures to exploit memory hierarchy, Code generation: using code generation to exploit modern compilers and CPUs. Another possibility is, some operators may have to materialize all their child operators before executing. Spark has taken the next step with whole-stage codegen which collapses an entire query into a single function. So we need to keep those in mind as well. for performance analysis. For the sake of the data are all in an one-dimesonal vector in SIMD, Colunm Format could be a better choice for Spark. Type: Question Status: Resolved. We start with Stage 0 with a familiar WholeStageCodegen and an exchange, which corresponds to the first DataFrame which gets repartitioned into 7 partitions. Whole-Stage Code Generation is controlled by spark.sql.codegen.wholeStage Spark internal property. The Internals of Spark SQL WindowFunction Contract Window Function Expressions With WindowFrame WindowSpecDefinition Logical Operators Base Logical Operators (Contracts) LogicalPlan Contract Logical Operator with Children and Expressions / Logical Query Plan Command Contract Eagerly-Executed Logical Operator And we find out that Vector Processing really fasten the operation. Then we have three executors. So the main benefit, there are a few main benefits to doing expression code generation. So once again, we start off by calling the produce method, a local tablescape, and we set up this while loop. The summary So once the project gets a one road from its child, our next call. I always returned the row as the operator of this next method. Before WholeStageCodeGen, when there is two spark plan in the same stage, we should see the process as something like RDD.map {sparkplan1_exec_lambda}.map {sparkplan2_exec_lambda} Summary metrics for all task are represented in a table and in a timeline. While loop-pipelining can make some differences. When running Structured Streaming jobs in micro-batch mode, a Structured Streaming tab will be Log In. Whole-stage code generation was introduced in Spark 2.0 as part of the tungsten engine. Also, you can check the latest exception of a failed query. . So, we can immediately cast our internal rows to the appropriate data type and then use the primitive operators and bake that into our generated code. So first, the performance setup. So, our workday, we have a few accounting use cases that really demonstrate the problems of whole-stage code generation. wholeStageCodeGen is optimizing of lazy eval code. For detailed statistics, please Code generation is integral to Sparks physical execution engine. And then also do some of the logic for the scape. So, we tacked this down and implemented it for case statements. Tip Learn more in SPARK-12795 Whole stage codegen. processNext:: invoke child.asInstanceOf[CodegenSupport].produce(ctx, this) to start iterating on the iterator. A very similar thing for stage 1. addBatch: Time taken to read the micro-batchs input data from the sources, process it, and write the batchs output to the sink. As the name presents, WholeStageCodeGen, aka whole-stage-code-generation, collapses the entire query into a single stage, or maybe a single function. The information that is displayed in this section is. It is possible to create accumulators with and without name, but only named accumulators are displayed. If there are named accumulators, here it is possible to see the accumulator value at the end of each task. So this is obviously a great benefit from a performance point of view since we cut down on the number of virtual function calls. Spark writes the results as files and then a separate job copies the files over. BenchmarkWholeStageCodegen class provides a benchmark to measure whole stage codegen performance. We will also present the performance improvements that we have seen from these improvements in our production workloads. Today, I will be talking about understanding and improving code generation in Spark. We started off with a basic introduction to Spark SQL. And once it gets us and put back for the right child, Ill be able to perform expansion logic for the ad operator. So first, tries to get it and put row to interact with it, it does this by calling child bottlenecks. Like I said, the produce method falls through until we hit the produce operator and discuss the local tablescape. which can be useful for troubleshooting the streaming application. The second problem is that JIT compilation can be disabled when methods exceed eight kilobytes of byte code. available on the Web UI. The explain () function in the expression below has been extended for whole-stage code generation. The SQL metrics can be useful Compared with the 1st generation Tungsten engine, the 2nd one mainly focuses on optimizing query plan and speeding up query execution, which is a pretty aggressive goal to get orders of magnitude faster performance. The third section has the SQL statistics of the submitted operations. Next, I need to have rows as referred by the current or child expressions. The Storage Memory Whole-Stage Code Generation (aka Whole-Stage CodeGen) fuses multiple operators (as a subtree of plans that support code generation) together into a single Java function that is aimed at improving execution performance. The statistics page displays some useful metrics for insight into the status of your streaming Then itll begin calling consume on parents to generate code for their logic. We may take a quick look at what it looks like in 1st Tungsten engine. sizes and using executors for all partitions in an RDD or DataFrame. Export. can answer how many rows are output after a Filter operator, shuffle bytes written total in an Exchange . So lets save that because we may have to refer to it later. The summary page shows high-level information, such as the status, duration, and And if either boolean is false or quickly pass over at this iteration of the loop. The first section of the page displays general information about the JDBC/ODBC server: start time and uptime. So, were going to save that. And here were doing key plus one, is the expression. Apache Spark provides a suite of web user interfaces (UIs) that you can use to monitor the status and resource consumption of your Spark cluster. For example, in this eval function, we will be able, the compounder may be able to detect function. The first way is interpreted evaluation. Whole-stage code generation was introduced in Spark 2.0 as part of the tungsten engine. So why combining all the query into a single stage could significantly improve the CPU efficiency and gain performance? Note that the instance constructed is subclass of BufferedRowIterator. So, we had a simple project that had one case expression of 3000 branches, and we ran it against three different belts. So first, we take the data frame or SQL AST in tax tree and create a tree of logical operators that will represent it. If this is very interesting to you, you can look at our previous Spark Summit talk called A Deep Dive Into Query Execution Of Spark SQL that goes into this produce and consume path in much more detail, along with other parts of Spark SQL. the Spark application. List of stages (grouped by state active, pending, completed, skipped, and failed), Input: Bytes read from storage in this stage, Output: Bytes written in storage in this stage, Shuffle read: Total shuffle bytes and records read, includes both data read locally and data read from remote executors, Shuffle write: Bytes and records written to disk in order to be read by a shuffle in a future stage. DAG visualization, and all stages of the job. Finally, we looked at the performance of whole-stage code generation after implementing the splitting. By doing this, we further reduce the number of functioning calls that we have, once again improving performance. Whole stage codegen is used by some modern massively parallel processing (MPP) databases to archive great performance. When this happens, were able to avoid the exceptions due to exceeding 64 kilobytes of byte code, avoid dynamic costs of compiling a huge function. And JIT also will not be turned off since we wont hit that eight kilobyte of byte code limit. page, you see the details page for that job. operator shows the number of bytes written by a shuffle. But now, lets say we want to split out the case statement logic into its own function. Instead, we should try and only pass the function parameters that are necessary to the function. Once I got into an order to evaluate this flushing, we have to go to our left and subsequently our right First, it goes to the left child, what kind of bowel reference? So once again, if we look at the diagram at the bottom we see a whole-stage code generation node that would contain a project filter and a local table scan operator. Next, we call the consume on our parent, which is the project. It describes computers with multiple processing elements that perform the same operation on multiple data points simultaneously. - thebluephantom Mar 26, 2020 at 9:59 Add a comment Know someone who can answer? So, if an expression does not implement code generation, then it cannot implement whole-stage code generation, as a result, the coding will be cut off. To monitor a specific RDD or DataFrame, The second part Spark Properties lists the application properties like So thatd be able to perform the expression logic of the project upon that rope. Seems it turn out it to be caused by following downsides of Volcano Iterator Model: In a loop iteration function, one iteration of loop usually begins when the previous one is complete, which means the iteration of the loop should be executed sequentially one by one. Apache Spark 2.0 Spark SQL Volcano Iterator Model Volcano-An Extensible and Parallel Query Evaluation System Goetz Graefe 1993 SQL . The following figure shows the differences between Row Format and Column Format. Whole-Stage Java Code Generation (aka Whole-Stage CodeGen) is a physical query optimization in Spark SQL that fuses multiple physical operators (as a subtree of plans that support code generation) together into a single Java function. the code generation id, for example: *(1) LocalTableScan. , the list of associated jobs, and the query execution DAG. If we click the We can see thats very similar. In the target location, you would use a separate process to transfer the data into the target location. And this is what itll look like when you do that. the list of associated jobs, and the query execution DAG. And wondering how the case expression this large, we saw that a generated function would be over a million lines of code. Whole-Stage Java Code Generation (Whole-Stage CodeGen) CodegenContext CodeGenerator GenerateColumnAccessor GenerateOrdering GeneratePredicate GenerateSafeProjection BytesToBytesMap Append-Only Hash Map Vectorized Query Execution (Batch Decoding) ColumnarBatch ColumnVectors as Row-Wise Table Data Source API V2 Subqueries Hint Framework The next benefit is that in Spark, we noted types of our attribute references at runtime since we require a steamer. And once you exceed the 64 kilobyte code limit, it will throw an exception. override def inputRDDs(): Seq[RDD[InternalRow]] = { child.execute() :: Nil }But in Project, override def inputRDDs(): Seq[RDD[InternalRow]] = { child.asInstanceOf[CodegenSupport].inputRDDs() }. queries. In Spark Core Stage corresponds to a group of operators within a shuffle boundary. An example is filing expenses. So what are the problems, when your function size is this like? This way, we keep it the data in the CPU registers. By doing this, were able to greatly magnify the size of our whole-stage code generated function and call other functions. Steps in the physical plan subject to whole stage code generation optimization, are prefixed by a star followed by So in whole-stage code generation, we need to figure out what these variables are and pass those to our splitted ropes. So, how do we track down the whole-stage code generation inputs that we need? We can see that both, if we look at the top algebra, everything above if statement, everything below this statement, the only thing they rely on is i, which is internal row. So, we can see here, instead of the logic for the case statement, we just call a function and pass in a few parameters instead. We see, in express code generation, takes about 740 seconds for this. Whole-Stage Java Code Generation improves the execution performance of a query by collapsing a query tree into . And these use cases, our customers are creating queries that contain key statements, a thousand adventures. The Jobs tab displays a summary page of all jobs in the Spark application and a details page This means each operator will be able to evaluate their special logic one row at a time. Janino is used to compile a Java source code into a Java class. Michael received his Bachelor's in computer science from the University of Michigan, Looking for a talk from a past event? And it was inspired by Thomas Newman's paper; "Efficiently Compiling Efficient Grade Plans For Modern Hardware." The main idea of this paper is that we can try to collapse an entire query into a single operator. Let me show one figure to show whats SIMD breifly.As presented above, SIMD can process multuple data via single instruction, and the data are all in an one-dimesional vector. It takes advantages of hand-writing and significantly optimizes the query evaluation and can be easily found in the DAG of your Spark application. For example, when we import some external integrations, such as tensorflow, scikit-learn, and some python packages, these code cannot be optimized by the WholeStageCodeGen cause they cannot be merged in our code. Its actually super linear. By the way, we do some code generation on this physical plan to create our RDDs. 12/02/2022. column shows the amount of memory used and reserved for caching data. The Executors tab provides not only resource information (amount of memory, disk, and cores used by each executor) In active stages, its possible to kill the stage with the kill link. On our left child, we have we have a greater non-expansion. (indistinct) again, sets up this while loop and the filter does some particular evaluation, skips over this iteration of the loop if the predicate is false and then the project does a bit more to actually output the results. And thats all assigning a value of project to whatever the expression evaluation is not operated, to exclusively to keep us warm. It also includes links to review the logs and the task attempt number if it fails for any reason. This surely will require us to look at the data types at runtime and then use the switch statement to get the correct operators. In our customers, we see the queries that create these, having expenses can be comprised of case expressions with thousands of when branches. Fusing operators together to make the generated code looks like the hand-writing bottom-up model, WholeStageCodeGen makes chains of operators as a single stage, and it has been the alternatives for the Code Generation in Catalyst Optimization. Non-Whole-Stage-Codegen Path So, how is the pedicab actually evaluated in the volcano iterator model? My name is Michael Chen. When you click on a job on the summary Next, well call consume or parent once again, which is the project. So, lets look at how we did this, how we can split the code generation functions. So, lets look at the performance of our whole-stage code generation in case statements now that we have implemented this splitting logic. First, when we try to generate a code, we enter the can produce stuff. Before a query is executed, CollapseCodegenStages physical preparation rule is . As results, CPU now becomes the new bottleneck and we have to substantially try to improve the efficiency of memory and CPU and push the performance of Spark closer to the limits of modern hardware, which is the main propose of Tungsten. WholeStageCodeGen to node mappings (only applies to CPU plans) Rapids related parameters Spark Properties Rapids Accelerator Jar and cuDF Jar SQL Plan Metrics Compare Mode: Matching SQL IDs Across Applications Compare Mode: Matching Stage IDs Across Applications Optionally : SQL Plan for each SQL query In order to do this, were assigning them to variables and then having the parent operators refer to those variables. What we did at Workday was we implemented using case expressions in the whole-stage code generation. Here, we will apply a few rule based optimizations such as constant folding or projection pruning which then optimize logical plan. This article is about the 2nd generation Tungsten engine, which is the core project to optimize Spark performance. You can see here that a filter operator will have a child which is also an operator and a pedal kit that takes in a row. Aggregated metrics by executor show the same information aggregated by executor. The first block WholeStageCodegen (1) compiles multiple operators (LocalTableScan and HashAggregate) together into a single Java Then it evaluates the predicate on this row and it continues to ask for next rows when its child until the predicate is satisfied. This is also a great performance improvement. As shown above, loop-unrolling creates multiple copies of the loop body and also changes the loop iteration counter. assert (spark.sessionState.conf.wholeStageEnabled) Code Generation Paths Code generation paths were coined in this commit. And thank you for coming to this session of Spark Summit. Instead, in whole-stage code generation we can take the results of an operator and assign them to a variable. This can lead to many problems such as OOM errors due to compilation costs, exceptions from exceeding the 64KB method limit in Java, and performance regressions when JIT compilation is turned off for a function whose bytecode exceeds 8KB. Even though the code is pretty simple, the comparison of performance between Volcano Iterator Model and Bottom-up Model will do shake you.But why is that? The first problem is that Java limits the method size of any method to 64 kilobytes of byte code. Here, well have a plan that has a project operator, a thought operator and a scan operator. Ill begin by talking about the basics of Spark SQL. So if youre able to fit under the 64 kilobytes of byte code on it and avoid the compilation error, you may run into performance issues. getBatch: Time taken to prepare the logical query to read the input of the current micro-batch from the sources. but also performance information (GC time and shuffle information). How does Vector Processing be implemented? Now, you may not believe me when I say that case statements can result in code that is this long, but here, we are looking at the code as generated for our case statement with one branch. doConsume: it appends the row to currentRows, invoked by upstream. inputRDDs It is used to retrieve the rdd from the start of the WholeStageCodeGen. So you can already imagine that one may have thousands of branches. For example, your cost center may depend on a variety of inputs, such as where you bought, where you made the purchase, who is your manager is, why you made the purchase, so on and so forth. If its true, well enter the if statement, and well do the same thing to check the value five is greater than two. like versions of Java and Scala. So, if we look at the actual generated code, we can see this, the eval only takes the internal rep. Finally, there are also some operators that rely on rows in addition to variables. Lets learn about it by the code:1234567891011// without loop-unrollingint sum=0;for (int i=0; i<10; i++) { sum+=a[i];}// with loop-unrollingint sum = 0;for (int i=0; i<10; i+=2) { sum += a[i]; sum += a[i+1];}. So, once the generate code call comes into the whole-stage code generation node, it follows to produce stuff like calling for Dusans children. Apache Spark provides a suite of web user interfaces (UIs) that you can use And then generate one function for the entire query. The first part Runtime Information simply contains the runtime properties In computing, a vector processor or array processor is a central processing unit (CPU) that implements an instruction set containing instructions that operate on one-dimensional arrays of data called vectors, compared to scalar processors, whose instructions operate on single data items. Here, we see the actual code that was generated for this query. It shows information about sessions and submitted SQL operations. WholeStageCodeGen and Vectorization in 2nd generation Tungsten engine really optimize the query plan and speed up the query execution. What a vividly name! Now, there are determined amount of variables that we could be referring to. Can we just pass all the output variables as the function parameters? click a run id in the tables. We will quickly exit. GitHub apache / spark Public master spark/sql/core/src/main/scala/org/apache/spark/sql/execution/ WholeStageCodegenExec.scala Go to file Cannot retrieve contributors at this time 959 lines (845 sloc) 35.6 KB Raw Blame /* * Licensed to the Apache Software Foundation (ASF) under one or more So the filter can fill in the code to do the expression evaluation of key is greater than one and value is greater than one. Tasks details basically includes the same information as in the summary section but detailed by task. Sep 15, 2019 Spark SQL Analyzer This talk will go over the improvements that Workday has made to code generation to handle whole-stage codegen for various queries. By doing this, whole-stage code generation can always fall back to the volcano iterator model and compile the expression code generation. If the application executes Spark SQL queries, the SQL tab displays information, such as the duration, External transfer and otherwise Spark can write the results to disk and transfers them via a third-party application. The second section contains information about active and finished sessions. And it will set up the wire loop to generate the data. Now that we have these two inputs, we can evaluate the greater than and we return it back to the ad. So once again, let me remind you that in expression code generation, each operator can be thought of as an iterator. In brief, the Catalyst Optimizer engine does the following: (1) analyzing a logical plan to resolve references, (2) logical plan optimization (3) physical planning, and (4) code generation A great reference to all of this are the blog posts So thats the only comparison to the volcano iterator model, where we know that all the inputs to our function is just going to be an internal road. spark.hadoop. First, Were going to get bent and assign that to a value two, then the value two is greater than one. So now, lets look at how the code is actually generated in whole-stage code generation. for each job. If we did that, maybe its just as simple as the volcano iterator model. And then, all of the expression evaluation in the generated function will only rely on this, on the output of next. been set correctly. Weve had one driver and I have 12 gigabytes of memory with one core. Demystifying inner-workings of Spark SQL. Intermediate data of Volcano Iterator Model are in memory while of Bottom-up Model are in CPU registers: Volcano Iterator Model dont take advantage of modern techniques, which Bottom-up Model do, such as loop-pipelining, loop-unrolling and so on. Once its finished, well return, Ill put road to resolve iterator. The way that Spark tries to limit the method size to 64 kilobytes, there are really two ways it tries to do this. Before a query is executed, CollapseCodegenStages physical preparation rule is used to find the plans that support codegen and collapse them together as WholeStageCodegen. XML Word Printable JSON. The metrics of SQL operators are shown in the block of physical operators. Whats more, the complicated IO cannot be fused, reading Parquet or ORC for instance. In this blog, a hand-written code is proposed to implement the query in the figure above, its just a so simple for-loop that even a college freshman can complete, which is:123456var count = 0for (ss_item_sk in store_sales) { if (ss_item_sk == 1000) { count += 1 }}. illustrate how Spark parses, analyzes, optimizes and performs the query. And it was inspired by Thomas Newmans paper; Efficiently Compiling Efficient Grade Plans For Modern Hardware. The main idea of this paper is that we can try to collapse an entire query into a single operator. So thats just not a good question. So, if we decided that this function was too long and we want to split it into two, an easy boundary would be at this if segment. So if we were able to pass that internal row to the express functions, we would be able to retrain the rest of the code. The Stages tab displays a summary page that shows the current state of all stages of all jobs in So, we realized that in the generator code 40 plus one, were probably going to have to rely on that output variable from the scape. make sure an action operation has been triggered. Note that the newly persisted RDDs application, including memory and disk usage and task and shuffle information. For example, we would have a case statement that has 10 branches, and it would run pretty quickly. Most of below recommendations are based on Spark 3.0. The first reason is, not all expressions will implement code generation. Loop-pipelining increases the parallelism of the loop iteration by implementing a concurrent manner. For example, in InputAdaptor, which is only used when there is one input RDD.If there are mulitple inputRDDs, e.g., SortMergeJoinExec, its child will be replaced as InputAdapter, but the iterator is retrieved from its children directly and using next to process each rows in the SortMergeJoinExec, instead of using doProduce/doConsume. Too long to get the explain? Apache, Apache Spark, Spark, and the Spark logo are trademarks of the Apache Software Foundation. The code generation will help us resolve iterator. How to speed up this excution? This interface will have an X method which were going to turn one to pull out of time. These two stages are not dependent on one another and can be run in parallel. Your Answer privacy policy cookie policy Browse other questions tagged apache-spark apache-spark-sql or ask your own question. Finally, Ill wrap up by looking at the performance of whole-stage code generation after splitting the generated code for a specific career. number of written shuffle records, total data size, etc. Splitting code generation functions helps to mitigate these problems. Although the WholeStageCodeGen makes a huge optimization of the query plan, there are still some problems. Clicking the Thread Dump link of executor 0 displays the thread dump of JVM on executor 0, which is pretty useful Like I said, one of the main benefits of a whole-stage code generation is were trying to keep the data in a CPU registers for as long as possible. This is the comment interface, that all operators will implement. 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