Author: Linxiao Ma

Spark Structured Streaming Deep Dive (2) – Source

Spark Structured Streaming Deep Dive (2) – Source

As mentioned in the last blog discussing the execution flow of Spark Structured Streaming queries, the Spark Structured Streaming framework consists of three main components, Source, StreamExecution, and Sink. The source interfaces defined by the Spark Structured Streaming framework abstract the input data stream from the external streaming data sources and standarise the interaction patterns between the Spark stream querying engine and the external data sources. All the concrete Spark streaming data source classes, no matter the built-in ones or custom developed, need to implement those interfaces.

This blog post discusses the streaming source interfaces and methods defined in Spark Structured Streaming. In addition, one of the concrete streaming data sources which implements those interfaces, FileStreamSource, is discussed in details. Kafka is the primary and the most used streaming source in the industry. Spark Structured Streaming framework ships the built-in KafkaSource to support Kafka input streams. I have planned a deep dive blog series of Kafka and Kafka-Spark integration. The KafkaSource class will be discussed in details there.

The diagram below depicts the Spark Structured Streaming source class hierarchy.

SparkDataStream is the root interface representing a readable data input stream in a Spark Structured streaming query. It is the top-level abstraction of input streams and all the concrete data sources should implement this interface.

Source trait extends the SparkDataStream root interface. It is defined to abstract and represent all data sources with continually arriving data. Offset is the abstract Source trait uses to represent the monotonically increasing notion of the progress of the input stream. Source trait defines the ‘getOffset‘ method for fetching the maximum available offset for a source. Based on the last committed offset and the latest available offset, Spark can fetch the batch with all new data through the ‘getBatch‘ method defined by the Source trait. The Source trait also defines the ‘commit‘ method which is used to notify the source that the current batch has been processed successfully by Spark and the source can start to conduct the cleaning, garbage collection tasks.

SupportsAdmissionControl interface also extends the SparkDataStream and represents the streaming data sources which are able to control the rate of data ingested into the Spark. The rate of data ingestion can be controlled by the ReadLimit which can be configured by the end-user developers through options when creating the data source. It can also be controlled implicitly from the trigger modes, for example, OneTimeTrigger requires a micro-batch process all data available which fits the one-off historical data loading scenario. SupportsAdmissionControl provides the ‘latestOffset‘ method which takes ReadLimit as a parameter so that the latest offset available to read is decided by considering the given read limit. The concrete data source classes which support data ingestion rate control need to implement this method instead of the ‘getOffset‘ for Source trait.

The concrete sources need to implement the Source trait to support stream inputs. If the sources need to control the ingestion rate, they need to implement the SupportsAdmissionControl interface. Both the FileStreamSource and the KafkaSource implements the Source trait and SupportsAdmissionControl. The end-user developers can also create custom sources to support all sorts of external sources by implementing those interfaces. The development of custom sources is out of the scope of this blog post, but if you are interested, here is a nice article regarding this:

Spark Custom Stream Sources by Nicolas A Perez

In the rest of the blog post, I will look into how FileStreamSource works with details and I will cover the KafkaStreamSource in the subsequent blog posts of Spark-Kafka integration.

FileStreamSource represents a file-system data source which takes the files written in a directory as a stream of data, with the file modification time as the event time. A list file formats are supported, including csv, test, json, Parquet, ORC. The schema of the files can be defined by the end-user developer when creating the DataStreamWriter. Some file formats support schema inference, but it is not recommended.

The main responsibility of the FileStreamSource is to implement the methods defined in the Source trait and SupportsAdmissionControl interface so that a FileStreamSource instance can be consumed by Spark. The core method to implement for FileStreamSource are: latestOffset, getBatch, and commit.


This is a diagram I draw to depict how FileStreamSource gets the lastestOffset:

Here is a list of the key components involved in the process:

  • The ‘fs‘ member (used in ‘fetchAllFiles‘ method), the reference for accessing the directory (source) in a Hadoop compatible file system.
  • The ‘sourceOptions‘, the configuration settings defined by the end-user developers for controlling how the source works, such as max files per trigger (read limit).
  • The ‘metadataLog‘, records the metadata of the batch and file entries.
  • The ‘metadataLogCurrentOffset‘, the latest offset in the metadataLog.
  • The ‘seenFiles‘, the log of the files that have been placed into the current batch to process.
  • The ‘unreadFiles‘, the list of files that was read in the last batch, but was not processed (due to over the max files per trigger limit.

The FileStreamSource defines the fetchMaxOffset method for getting the latestOffset. This method first checks whether or not there is unreadFiles that was fetched, but not processed in the last batch. If there is, set the unreadFiles as newFiles, if no, call the fetchAllFiles method to fetch files from the file system and applies isNewFile method (of seenFiles) to return the unprocessed files only as newFiles.

If the newFiles set contains more files than the maxFilesPerTrigger, it will be split into two sets by the maxFilesPerTrigger value. The current batch, batchFiles, is capped by the maxFilesPerTrigger number, and the remaining files will be cached in the unselectedFiles. The unselectedFiles will be marked as unreadFiles and will be processed in the next batch. However, if the number of files in the unselectedFiles is too small (maxFilesPerTrigger * DISCARD_UNSEEN_FILES_RATIO) that the next batch would have too few files to read, the unselectedFiles will be discarded and will be reread along with other new files in the next batch.

The files to return in the current batch are logged in the seenFiles. A purge operation is then applied on the seenFiles to remove aged entries (based on the maxFileAgeMs option configured by the end-user developers). The metadataLogCurrentOffset increases by 1 to update the latest offset. All the files in the current batch are associated with the new metadataLogCurrentOffset and logged in the metadataLog. The new metadataLogCurentOffset will be returned by the fetchMaxOffset method.


The getBatch method takes the start offset and end offset as arguments and return a DataFrame instance, which represents the data rows in the files recorded in the metadataLog between the start offset and the end offset. The returned Dataframe defines the data rows to process by Saprk Structured Streaming framework in a micro-batch.

Be aware that the ‘Batch’ concept of the getBatch method is different with the ‘Batch’ concept used in the fetchMaxOffset method mentioned above. The batch returned by the getBatch method refers to the batch of rows in the files associating with one of the metadataLog offset which is between the specified start offset and end offset of the getBatch method. The batch used in the fetchMaxOffset method refers to the batch of files associating with one metadataLog offset.


In the Micro-Batch processing mode, the commit method will be called by the MicroBatchExecution to inform the source that the all data up to the given offset is processed so the source can start the post-batch processing.

FileStreamSource implements the commit method for cleaning up completed files. The end-user developers can specify the option to clean, including ‘archive’, ‘delete’ and ‘off’. Internally, FileStreamSource defines the FileStreamSourceCleaner private abstract class and two concrete classes: SourceFileArchiver for ‘archive’ option and SourceFileRemover for ‘delete’ option.

To reduce the performance impacts of the cleaning task on the micro-batch processing thread, a cleanThreadPool is maintained and all the concrete implmentations (such as SourceFileArchiver and SourceFileRemover) need to implement the cleanTask with their specific cleaning logic and submit the cleanTask to execute in a seperate thread. the number of threads used in the cleanThreadPool can be specified with the spark.sql.streaming.fileSource.cleaner.numThreads.

Spark Structured Streaming Deep Dive (1) – Execution Flow

Spark Structured Streaming Deep Dive (1) – Execution Flow

From this blog post, I am starting to write about streaming processing, focusing on Spark Structured Streaming, Kafka, Flink and Kappa architecture. This is the first blog post of the Spark Structured Streaming deep dive series. This blog post digs into the underlying, end-to-end execution flow of Spark streaming queries.

Firstly, let’s have a look at the official structured streaming query example from Spark:

A typical streaming query is defined in three steps: firstly create a data stream reader in the current Spark session that will be used to read streaming data in as a Spark DataFrame, then define the transforms on the DataFrame based on the processing requirements, and finally define the data stream writer and start running the query.

Now, let’s look into how a streaming query is executed by the Spark Structured Streaming framework under the hood.

SparkSession provides a ‘readStream‘ method which can be used by end-user developers to create and access an DataStreamReader instance that is responsible for loading an input stream from external data source. The data source format and input schema can be specified in the data stream reader. The ‘load‘ method of the data stream reader defines the Spark DataFrame which logically representing the input stream.

The transformation of the DataFrame that represents the data processing logics can be defined by the end-user developers by calling the operations available on the DataFrame. Most of the common operations available for batch processing are also supported for stream processing. Here is a list of unsupported operations. Just as how it works in the batch processing workloads, the DataFrame operations for streaming processing are also executed in the lazy mode. Under the hood, a Dataframe represents a logical plan which defines the computations required for the data processing. The computations will only be triggered when an action is invoked.

After data transformations of the DataFrame are defined, a DataStreamWriter instance needs to be created for saving the processed streaming data out into external storage. The DataStreamWriter can be created by calling the ‘writeStream‘ method of the DataFrame. The end-user developers can specify the output mode for writing the data into streaming sink, the partitions of the sink file system where the data written into, and the trigger for the micro-batch streaming jobs. Up to this point, all we have done is just to define the streaming query execution. The physical streaming query execution starts when the ‘start’ method of the DataStreamWriter is called.

The ‘start‘ method of the DataStreamWriter first creates the sink instance which supports continual writing data into external storage, and then passes the sink instance to the StreamingQueryManager of the current Spark session to create and start a StreamExecution instance. The StreamExecution instance is the core component that manages the lifecycle of a streaming query execution. There are two concrete implementations of the StreamExecution (which itself is defined as an abstract class), including the MicroBatchExecution and the ContinuousExecution. The micro-batch processing mode, i.e., processing data streams as a series of small batch jobs, implemented by MicroBatchExecution is the primary stream processing mode supported by Spark Structured Streaming engine. This mode supports exactly-once fault-tolerance guarantees and is capable to achieve end-to-end latencies at 100ms level. The ContinuousExecution supports the low-latency, continuous processing mode that achieves end-to-end latencies at 1ms level. However, this mode only supports map-like operations and can only achieve at-least-once guarantees. This blog post focuses on the micro-batch processing mode. I will dedicate a few blog posts to continuous processing mode in future.

Now, let’s look into a bit deeper on how StreamQueryManager creates and runs a streaming query (i.e. a StreamExecution instance). StreamingQueryManager is responsible to manage all the active streaming queries in a Spark session. It comes with an ‘activeQueries‘ hash map which caches the reference to the active streaming queries. The ‘start’ call from the DataStreamWriter will trigger the ‘startQuery‘ method which conducts three major activities in order, including defining streaming query (i.e. creating the StreamExecution instance), registering the created StreamExecution instance in the activeQueries hash map, and invoking the ‘start’ call of the StreamExecution instance.

To create a streaming query, logical plan of the DataFrame which is defined earlier with the required transformation operations is analysed. Please refer to my earlier blog post for more details of the Spark Catalyst Analyzer. With the analysed logical plan along with other streaming writing configurations, such as sink, output mode, checkpoint location and trigger), a WriteToStreamStatement is created which is the logical node representing a stream writing plan. After the WriteToStreamStatement is analysed, depending on the trigger type (ContinuousTrigger or not), one of the StreamExecution implementation classes, ContinuousExecution or MicroBatchExecution, is created to manage the stream query execution. The created streaming query is then registered in the activeQueries.

After that, the ‘start’ method of the created StreamExecution instance is called to start a separate thread to run the streaming querying which executes repeatedly each time when new data arrives at any source specified in the query plan. Within the thread, after the initialisation of source and metadata objects, the ‘runActivatedStream‘ method is called. The execution flow of the ‘runActivatedStream‘ method is defined in each implementation class of the StreamExecution. The rest of the blog post looks into the implementation of this method of the MicroBatchExecution. However, before digging into the method, let’s have a step back and have a look at the core components in the Sparking Structured Streaming system.

Firstly, we have external streaming sources and sinks which support continuous data read and write. Within the Spark Structured Streaming framework, there are three main components: the Source interface which abstracts the data read from the external streaming sources, the Sink interface which abstracts the data write into the external Streaming sinks, and the StreamExecution which manages the lifecycle of streaming query executions. The Source interface defines three core methods for communicating with external data sources, getOffset(), getBatch(), and commit(), while the Sink interface defines one core method for output the streaming data to sink, addBatch(). I will discuss those methods in details in the subsequent blog posts which digs into the sources and sinks implementation in the Spark Structured Streaming framework.

The execution flow of each micro-batch data processing is defined in the ‘runActivatedStream‘ method of the MicroBatchExecution class. A TriggerExecutor (ProcessingTimeExecutor by default) instance, is created to schedule and trigger micro-batch processing jobs. I draw the following diagram to depict the end-to-end execution flow of a micro-batch job.

(1) The execution of micro-batch processing starts from identifying the start offsets to read from the sources. The start offsets are determined based on the last batch execution recorded in the OffsetLog and CommitLog. The OffsetLog is a Write-Ahead Log (WAL) recording the offsets of the current batch before the batch run while the CommitLog records the batch commit when the batch run is completed successfully. If the last batch id recorded in the OffsetLog equals to the last batch id recorded in the CommitLog, it implies the last batch was successfully processed, so the start offsets of current batch == the end offsets of the last batch. If the last batch id recorded in the OffsetLog equals to the last batch id recorded in the CommitLog + 1, it implies the last batch was failed and needed to rerun, so the start offsets of current batch == the start offsets of the last batch.

(2) Check each source whether new data is available and get the next available offset.

(3) Construct next batch, record the range of offsets that the next batch will process in the WAL OffsetLog.

(4) Call the getBatch method of the sources to return the DataFrame that represents the data between the start offsets and end offsets. Note that no physical data is actually read from the source at this moment, but instead the getBatch method only returns the data plan of the batch.

(5) Replace sources in the logical plan of this stream query with the data plan of the new batch to get the newBatchesPlan. Rewire the newBatchesPlan to use the new attributes that were returned by the sources to get the newAttributePlan.

(6) Create triggerLogicalPlan from newAttributePlan. Convert the newAttributePlan to WriteToMicroBatchDataSource if required.

(7) Create the IncrementalExecution for the current batch run with the triggerLogicalPlan as input. IncrementalExecution is a variant of the QueryExecution (please refer to my earlier blog post) for planning stream queries that allow incremental execution of a given logical plan. Just like the batch query planning, the stream query planning also needs to go through the same planning stages which will be covered in details in the subsequent blog posts in this series.

(8) Based on the generated execution plan, a new Dataset that logically represents the next batch is created.

(9) Add the next batch Dataset to the sink through addBatch method provided by the Sink interface.

(10) Log the batch execution into the CommitLog to record this successful batch run.

Spark SQL Query Engine Deep Dive (20) – Adaptive Query Execution (Part 2)

Spark SQL Query Engine Deep Dive (20) – Adaptive Query Execution (Part 2)

In the previous blog post, we looked into how the Adaptive Query Execution (AQE) framework is implemented in Spark SQL. This blog post introduces the two core AQE optimizer rules, the CoalesceShufflePartitoins rule and the OptimizeSkewedJoin rule, and how are implemented under the hood.

I will not repeat what I have covered in the previous blog post, which focuses on explaining the framework of AQE. However, to understand this blog post, the knowledge of the AQE framework is a prerequisite. If you are new to AQE framework, it is recommended to have a read of the previous blog post first.

Dynamically coalescing shuffle partitions

If you have been working with Spark for some time, you might get familiar with the number, 200. There seem always 200 tasks running no matter how small is your data. As you can see from the following example, 200 tasks are there to process a very small dataset, with less than1kb of data for each task.

The reason behind the 200 tasks is that the number of shuffle partitions is fixed during a spark job execution with the default value as 200. The number of shuffle partitions determines the number of buckets into which a mapper task writes and the number of output partitions on the reducer side.

The number of shuffle partitions could affect Spark performance significantly. With too less shuffle partitions, each partition contains too much data that overstretches the tasks and counteracts the benefits from parallelism. When memory is not sufficient for processing that much data, disk spills might be triggered which causes expensive I/O operations, or OOM occurs to kill the job. On the other hand, too many shuffle partitions lead to too many small tasks that causes too much overhead on task scheduling and managing.

Therefore, choosing an appropriate shuffle partition number is important for achieving a satisfied performance. The number of shuffle partitions can be manually configured by adjusting the spark.sql.shuffle.partition property. This is actually one of the most used knobs for Spark performance tuning. However, manually setting the shuffle partition number is not always effective. A Spark job normally involves multiple stages and the size of the data can change dramatically through the different stages in the pipeline, e.g., the stages involve a filter or aggregate operation. As the shuffle partition number is predefined for all of the stages in a Spark job, a shuffle partition number which is optimal for a stage might cause poor performance of another stage.

Therefore, the shuffle partition number needs to be dynamically adjusted through the job execution pipeline, according to the data volume at the specific stage at runtime. This is exactly the use scenario where AQE can help. AQE splits a Spark Job into multiple query stages and re-optimise the query plan of downstream query stages based on the runtime statistics collected from the completed upstream query stages. The CoalesceShufflePartitoins rule is the AQE optimizer rule created for dynamically configuring the shuffle partition number. This rule coalesces contiguous shuffle partitions according to a target size of the output partitions. For the example used above, if we enable AQE and rerun the same query, we can see there is only one task created this moment. As the entire dataset used in this example is very small, only one partition is required.

To coalesce shuffle partitions, the CoalesceShufflePartitoins rule first needs to know the target size of the coalesced partition, which is determined based on the advisoryTargetSize (spark.sql.adaptive.advisoryPartitionSizeInBytes, default 64MB), the minPartitionSize (spark.sql.adaptive.coalescePartitions.minPartitionSize, default 1MB), and the minNumPartitions (spark.sql.adaptive.coalescePartitions.minPartitionNum, if it is not set, fall back to Spark default parallelism). The CoalesceShufflePartitions rule sums up the total shuffle input data size from the map output statistics and divide it by the minNumPartitions to get the maximum target size, maxTargetSize, for the coalesced partitions. If the advisoryTargetSize is larger than the maxTargetSize, the target size is set to be the maxTargetSize so that the expected parallelism can be achieved. If the target size is so small that even smaller than the minPartitionSize, that is no point to make the target size smaller than the minPartitionSize, but instead to use minPartitionSize as the target size.

With the target size ready, the CoalesceShufflePartitions rule can start to coalesce the partitions according to the target size and to define the coalesced partition specs (CoalescedPartitionSpec) which will be used to create shuffle reader later. Let’s use an example to explain this process. As the diagram shows below, we have a shuffle operation with two input partitions on the map side. When the CoalesceShufflePartitions rule is not applied, the shuffle data are read into five output partitions, even though some of them are small.

When the CoalesceShufflePartitions rule is applied, it gets the size statistics of all shuffle partitions from the MapOutputStatistics collected at the map stage. In our example, we have two shuffles, each of which has five shuffle partitions:

The CoalesceShufflePartitions rule will make a pass through all the shuffle partitions, sum up the total size of a partition from all the shuffles, pack shuffle partitions with continuous indices to a single coalesced partition until adding one more partition would be over the target size. In our example, we use the default advisory target size (64MB) as the target size. The total size of the first shuffle partition is 50MB. The attempt to coalesce the first and the second shuffle partition (20MB) ends up a coalesced partition size (70MB) to be larger than the target size. Therefore, the two partitions cannot be coalesced and the first partition will be output as a separate output partition. A ColeascedPartitionSpec object is created for the first partition with the startReducerIndex as 0 (inclusive) and the endReducerIndex as 1 (exclusive).

The CoalesceShufflePartitions rule moves on to add the second and the third partitions, resulting at 50MB which is smaller than the target size.

The fourth partition is then added as well and the total size of second, third and fourth partition (60MB) is still not over the target size.

The attempt to add the fifth partition ends up a partition size (140MB) which is over the target size. Therefore, the second, thrid and fourth partitions are coalesced into one partition (60MB). A CoalescedPartitionSpec object is created which has the startReducerIndex as 1 and the endReducerIndex as 5.

As there is no further partition to process, another CoalescedPartitionSpec is created for the last (fifth) partition. Eventually, we have three output partitions now including the one made up with the three small partitions.

The example above shows the normal scenario where small partitions are coalesced as long as their total size is not over the target size. However, there is an exception scenario where a partition is smaller than the minPartitionSize but the partitions adjacent to it on both sides will exceed the target size if adding the small partition. For this scenario, the small partition will be merged with the smaller one between the two adjacent partitions.

At this point, the CoalesceShufflePartitions rule created a sequence of CoalescedPartitionSpec objects, each of which defines the spec for one of the output partition. These partition specs will be used by the shuffle reader to output partitions accordingly. The CoalesceShufflePartitoins rule creates an AQEShuffleReadExec operator which wraps the current shuffle query stage and creates the ShuffledRowRDD with the CoalescedPartitoinSpecs defined above. For each partition of the ShuffledRowRDD, which is defined by one CoalescedPartitionSpec, a shuffle reader is created to read shuffle blocks from the range of map output partitions defined by the startReducerIndex and endReducerIndex of the CoalescedPartitionSpec.

Internally, the getMapSizesByExecutorId method of MapOutputTracker is called to get the metadata of the shuffle blocks to read, including block manager id, shuffle block id, shuffle block size, and map index. A BlockStoreShuffleReader is then created, which initialises a ShuffleBlockFetcheriterator for conducting the physical read operations to fetch blocks from other nodes’ block stores.

Dynamically optimizing skew joins

With a good understanding of the previous section that explains the dynamic shuffle partition coalescence, it would be easy to understand the dynamic skew join optimisation, which is kinda the “reversed” operation of the partition coalescence that splits a skewed partition into multiple smaller partitions.

The example below shows a job execution with skewed partition. As you can see, the skewed partition has 5.8GB data while the other partitions only have less than 20MB data. It takes 6.7 minutes to run the task of the skewed partition while less than 1 second is used to run the tasks of the other partitions. The long running time of the task with skewed partition makes the total job execution time to be 7.7 minutes.

We then enable the AQE with the dynamic skew join optimisation applied and rerun the same query again. As the skewed partition has been split into multiple small partitions, the largest partition is now 234MB and it takes 29 seconds to run the partition. Thanks to the increased parallelism, the total job execution time is reduced from 7.7 minutes to 1 minute.

The OptimizeSkewedJoin rule is the AQE optimizer responsible for dynamic skew join optimisation. At a high level, this rule splits a skewed partition and replicate its matching partition on the other side of the join so that more tasks are created to do the join in parallel to avoid straggler tasks which slows down the job completion.

Internally, the OptimizeSkewedJoin rule first checks whether or not the join to optimise is either a sort merge join (SortMergeJoinExec) or a shuffle hash join (ShuffledHashJoinExec). Only these two types of join are supported by the OptimizeSkewdJoin rule in Spark 3.2.

Next, the OptimizeSkewedJoin rule detects the skewed partitions. A partition is considered as a skewed partition if it is larger than the specified skewed partition threshold (spark.sql.adaptive.skewJoin.skewedPartitionThresholdInBytes, default 256MB) and it is larger than the median partition size multiplying the skewed partition factor (spark.sql.adaptive.skewJoin.skewedPartitionFactor, default 5).

After the skewed partitions are found, we can then calculate the target size of the split partitions which is either the average size of non-skewed partition or the advisory partition size (spark.sql.adaptive.advisoryPartitionSizeInBytes, default 64MB) depends which one is larger.

The OptimizeSkewedJoin rule defines partition splits according to the map output sizes and the target size. It first gets the sizes of all the map outputs for the shuffle of the skewed partition. It then makes a pass through all the map output sizes, one by one, and makes attempts to merge the adjacent map output sizes so that the map outputs are grouped with their summed size close to the target size. A PartialReducerPartitionSpec is then created for each group, which encapsulates the id of reducer (for the skewed partition), the start map index (the start index of the map output in the group) and the end map index (the end index of the map output in the group).

Now the OptimizeSkewedJoin rule has the list of PartialReducerPartitionSpecs ready for creating the physical AQEshuffleReadExec operator. The remaining steps for physical shuffle blocks readings are mostly same as the steps mentioned earlier for coalescing shuffle partitions. The main difference is that the shuffle reader created by the OptimizeSkewedJoin rule specifies the startMapIndex and the endMapIndex for the list of map outputs to read.

While the skewed partition is split into multiple smaller partitions, its matching partition on the other side of the join are replicated to the same number of copies matching the number of the split partitions. The join happens between one split of the skewed partition and one copy of the replicated partition.

Spark SQL Query Engine Deep Dive (19) – Adaptive Query Execution (Part 1)

Spark SQL Query Engine Deep Dive (19) – Adaptive Query Execution (Part 1)

Cost-based optimisation (CBO) is not a new thing. It has been widely used in the RDBMS world for many years. However, the use of CBO in a distributed, storage/computing separated system, such as Spark, is an “extremely complex problem” (claimed by Spark guys in Databricks). It is challenging and expensive to collect and maintain a set of accurate and up-to-date data statistics of a Spark dataset which is physically distributed in nature.

The Adaptive Query Execution framework, officially shipped in Spark 3.0, takes advantage of runtime statistics to continually optimise the query plan during the query execution process. In a normal, non-adaptive query execution process, once the physical plan is created and starts to run, the physical plan cannot be updated anymore, even though the runtime statistics show the query plan which might be generated based on dated statistics is less-efficient. Instead, an Adaptive Query Execution process is allowed to reopimises based on more accurate runtime statistics and to execute the remaining query stages based on the updated query plan.

The multi-stage job execution model of Spark makes the adaptive execution of Spark query job possible. A Spark query job is separated into multiple stages based on the shuffle (wide) dependencies required in the query plan. The physical execution of a Spark query consists of a sequence or parallel of stage runs, where a TaskSet is created from a stage and the tasks in the TaskSet are distributed to and executed in executors. The by-product of a stage execution is the most up-to-date and most accurate statistics of the data processed in the query. The Adaptive Query Execution takes advantage of the runtime statistics generated from a stage execution, optimises the query plan, and executes the remaining stages with the optimised plan.

In this blog post, I will focus on how the Adaptive Query Execution framework works under the hood, and then look into the implementation details of a few important AQE physical optimizer rules in the next blog post.

Prior to diving into the Adaptive Query Execution, let’s first have a look at how a normal, non-adaptive query execution process works. To a certain extent, the Adaptive Query Execution can be viewed as a variant of the normal non-adaptive query execution. A good understanding of the non-adaptive query execution makes it easy to understand the adaptive query execution.

Non-Adaptive Query Execution

At a high level, when an action of an RDD is triggered, a Spark job is submitted to the DAGScheduler which is initialised in the current SparkContext. The DAGScheduler computes a DAG of stages for the submitted job, where stages are created by breaking the RDD graph at the shuffle boundaries. The shuffle boundaries are determined by the shuffle dependencies of RDD operations, where shuffling operations are required. Two types of Spark stages are created for a job, the ResultStage that is the final stage in the execution pipeline for executing the action, and the ShuffleMapStage that is an intermediate stage, which writes map output file for a shuffle. A set of tasks, namely TaskSet in Spark, is created for each Spark stage. Each task in the task set computes the same logic/functions on partitions of the same RDD.

Let’s take a look at the following query, which joins a “sales” table and an “items” table, and then conducts an aggregation on the joined tables.

This is the DAG built from this query. As you can see, the query execution is separated into four stages: The first two stages read data from data files; the third one joins the two tables and conduct the partial aggregation; the last one, result stage, conduct the final aggregation and project the results.

Now let’s look into how those stages are created and executed under the hood. At the core, the DAGScheduler traverses the RDD pipeline bottom up and creates stages recursively. The diagram below shows a simplified version of the RDD pipeline of our example query. The arrows highlighted in red represents the shuffle dependencies.

The execution flow of stage creation starts from the createResultStage method of the DAGScheduler. As the name implies, the createResultStage method is responsible for creating the result stage. However, before this method could create the result stage, it has to ensure all of this result stage’s parent stages are created. Therefore, it has to traverse the upstream RDD nodes, look up the ShuffleDependency and create the parent stages first. In the same way, the parent stages have to ensure their own parent stages are created before they can be created. This process carries on until reaching to the root stages (i.e., the stages without parent). From there, the root stages are physically created and the recursive function calls start the return journey. On the way back, the stages in the corresponding function call are physically created, which enables the creation of their child stages, until the creation of the result stage.

When an ShuffleMapStage is created, it is registered in the shuffledIdToMapStage hash map, which maps from shuffle dependency ID to the created ShuffleMapStage. The shuffledIdToMapStage only caches the stages that are part of currently running jobs, which will be accessed for stage submissions later.

When the createResultStage method created and returned the final result stage, the final stage is submitted to run. Similar to creating stages, the stages are submitted recursively. Before submitting the current stage, all the parent stages need to be submitted first. The parent stages are fetched by the getMissingParentStages method, which first finds the shuffle dependency of the current stage and looks up the ShuffleMapStage created for the shuffle dependency in the shuffledIdToMapStage hash map. If the ShuffleMapStage is missing from the hash map for a shuffle dependency, a ShuffleMapStage is created for the shuffle dependency and registered in the hash map.

Adaptive Query Execution

Now let’s move on to the Adaptive Query Execution (AQE). Firstly, let’s rerun the example query we discussed above, but enable the AQE this time.

From comparing the physical plan of the same query executed with AQE off and AQE on, we can see that the join algorithm has changed from the sort-merge join to the broadcast hash join when AQE is enabled. As discussed in the previous “Join Strategies” post, broadcast hash join is the preferred strategy compared to sort-merge join, which does not require additional sort steps. To enable broadcast hash join, at least one table in the join needs to be small. From the query, we can see that the “items” table is filtered before joining with the “sales” table. The query execution statistics show that the filter reduces the data size of “items” table from 30 million rows (150MB) to 300,000 rows (5MB). Therefore, the physical plan generated with AQE on is more optimised than the one generated with AQE off.


AQE Enabled

When AQE is enabled, the EXPLAIN command prints two physical plans, the initial plan and the final plan. The initial plan is the first version of the physical plan generated through the Spark Catalyst optimizer without any adjustments yet. As you can see, the initial version selects the sort-merge join algorithm, which is same as the physical plan generated when AQE is off.

Even though the EXPLAIN command does not print out, there are intermediate plans in between the initial plan and final plan. The AQE traverses the physical plan bottom-up, create and execute query stages, and re-optimise the plan and create and execute stages for the remaining physical plan segments, until the entire physical plan is executed.

Now let’s dive into the source code of AQE to see how it works under the hood. At the query execution preparation stage where the selected physical plan is being prepared for execution, an InsertAdaptiveSparkPlan rule is applied, which wraps the query plan with an AdaptiveSparkPlanExec instance. The AdaptiveSparkPlanExec encapsulates the main execution framework of AQE which drives the execution of the query plan adaptively.

When the AdaptiveSparkPlanExec is executed, it calls the getFinalPhysicalPlan method to start the execution flow. As you might have guessed, same as the non-adaptive query execution discussed above, the AQE also makes recursive function calls to traverse the physical plan for creating stages. The AdaptiveSparkPlanExec defines a private createQueryStages method. This method is called recursively to traverse the plan bottom-up. If the current node is an Exchange node and all of its child stages have been materialised, a new QueryStage, either ShuffleQueryStageExec or BroadcastQueryStageExec (depending on the type of the Exchange node), is created and returned. A list of physical optimizer rules is applied to the new stage before its execution. Those optimizer rules encapsulate the core performance optimising features offered by Spark 3.0. I will cover those rules in the next blog post.

The new stage returned by the createQueryStages method is then materialised, which internally submits the stage to DAGScheduler to run independently and returns the map output statistics of the stage execution. The query plan is then re-optimised and re-planned based on the new statistics. The cost of the newly planed physical plan is then evaluated (by an implementation of the CostEvaluator) and compared to the cost of the old physical plan. If the new physical plan is cheaper to run than the old one, use the new physical plan for the remaining processing.

I found it is difficult to explain the recursive execution flow of AQE purely using textual descriptions. Therefore, I made the following diagrams (based on the “sales/items” example query used earlier) to walk through the evolution of an adaptive query plan.

At the start, the createQueryStages method is called on the root node. If the node has a child and the child is not an Exchange node, or if the node is an Exchange node and not all of its child nodes are materialised (allChildStagesMaterialized=fasle), an inner createQueryStages method call is made on the child node of the current node. The process is repeated until any of the “if” conditions is not met. As this is the first run, no node has been materialised yet. Therefore the createQueryStages method will be recursively called down to the bottom nodes which have no child.

As there is no child of the bottom nodes, the allChildStagesMaterialized attribute of the bottom nodes are set as true. On the return journey of the recursive createQueryStages method calls, the parents of the bottom nodes are shuffle Exchange nodes. As the bottom node is not an Exchange node and the allChildStagesMaterialized attribute of the bottom node is true, the bottom node itself can be marked as materialised, therefore the allChildStagesMaterialized attribute of its parent node, the Exchange node, is true as well. Now the condition for creating new QueryStage is met: the current node is an Exchange node and all of its child stages have been materialised. In our example query, the bottom nodes are the file scan nodes for reading the “items” table and the “sales” table. Therefore, two ShuffleQueryStageExec objects are created. After that, the return journey of the recursive createQueryStages method calls continues. However, as those two Exchange nodes are not materialised yet, no query stage will be created for all the ancestor nodes of the Exchange nodes.

When the top-level createQueryStages method call is completed, the materialize method of the two newly created ShuffleQueryStageExec is called to execute the stage and return the runtime statistics. After that, the logical node corresponding to the ShuffleQueryStageExec is replaced with LogicalQueryStage.

The logical plan is re-optimised and re-planned based on the updated statistics and a new physical plan is generated. In our example, the statistics show the size of the “items” dataset is small enough for qualifying the use of broadcast hash join. Therefore, the SortMergeJoin is replaced with the BroadcastHashJoin in the new physical plan where the sort operators are removed. At this point, the first iteration of the adaptive query execution is done.

Next, the createQueryStages method is called on the new physical plan to start a new iteration and repeat the process to execute the next stage(s).

Spark SQL Query Engine Deep Dive (18) -Partitioning & Bucketing

Spark SQL Query Engine Deep Dive (18) -Partitioning & Bucketing

I was planning to write about the Adaptive Query Execution (AQE) in this and next few blog posts, and then end my Spark SQL deep dive series there and move on to another topic, either Spark Core or Pulsar. However, I realised that I haven’t covered the mechanism of partitioning and bucketing on file systems, which could be very useful techniques for speeding up Spark queries through optimising the organisation of data file storage on the disk.

The Partitioning we are discussing here is a different concept with the partitioning concept of the inter-node distribution introduced in the previous blog post. The partitioning discussed in this blog post refers to a data file organisation approach which splits a dataset and stores the underlying data files into directories based one or more partitioning columns. The Bucketing is a similar concept with the partitioning (to a certain extent), which also groups and stores data based on the values of a column. However, the bucketing technique groups data based on the hash values of a column which allows organising data in a way that already meets the requiredChildDistribution requirements of the operations requiring shuffling, such as aggregation or join, so that the expensive shuffling steps can be skipped.

In this blog post, I will first give some examples to present how partitioning and bucketing work, and then dive into the source code and look into how partitioning and bucketing are implemented in Spark SQL.

Let’s Have Some Examples First

In this example, a dataset is partitioned by the ‘order_date’ column (even though, in a real world project, partitioning by a year-month-day directories hierarchy could provide a more flexible granularity option, we directly use the date column for the sake of simplicity to explain the partitioning concept), and saved as parquet format.

From the saved parquet file on the disk (which is actually a directory structure containing multiple physical files), we can see a list of sub-directories are created based on the partitioning column values. The path of each sub-directory contains the corresponding partitioning filter value which allows a partitioning can be directly located by the directory path.

Let’s run a query to read data from a partitioned table and a non-partitioned table. Both queries contain the same filter condition on the partitioning column.

Compared the results, we can see the query on the partitioned table only read one partition of the table while the query on the table without partitioning has times higher number, size and scan time of the files read.


Let’s have a look at an aggregation query executed on a bucketed table and the same table but not bucketed.

Compared the query plan between the query on the non-bucketed table and the query on the bucketed table, we can see that the expensive reshuffling operation is skipped for the query on the bucketed table.

Non Bucketing


Bucketing – Join

Here is the example of a join query executed on two bucketed tables and the same two tables but both are not bucketed.

Similar to the aggregation example above, the reshuffling operation is skipped for both the left-side and right side of the join when querying on the bucketed tables.

Non Bucketed


In the rest of this blog post, we will look into how Spark SQL implements those optimisations under the hood.

The Implementation of Partitioning & Bucketing in Spark SQL
Partitioning & Bucketing in the Writing Journey

The partitioning and bucketing specifications can be specified by the end-user developers when calling the dataframe writing functions. Internally, a logical command, InsertIntoHaddopFsRelationCommand, is created, which is the command specifically for writing data out to a given FileFormat. When this command is run, the FileFormatWriter is called to start the physical file writing jobs.

The bucketing specifications defined by the end-user developers are encapsulated in a BucketSpec object containing the column expressions which the buckets are created based on and the number of buckets. A HashPartitioning is initialised based on the bucketing column expressions and the number of buckets. The partitionIdExpression of the created HashPartitioning is then picked up as the bucket id expression for grouping the input data rows into buckets based on the bucket id.

The use of HashPartitioning.partitionIdExpression as the bucket id expression is the key for the bucketing idea to work. As the HashPartitioning and bucketing shares the same partition/bucket id expression, that guarantees the data distribution is same between shuffle and bucketed data source so that shuffle is not required because the data distribution of the bucketed data source already meets the requiredChildDistribution requirement of the target operations.

The bucket id expression and the partitioning column specs along with other settings for this data write are encapsulated into a WriteJobDescription object and passed to the file writing tasks running in the executors. Each task initialises a data writer for the physical file writing. To support partitioning and bucketing, a dynamic data writer, which is capable to write data to multiple directories (i.e. partitions) and/or files (i.e., buckets), is required. There are two dynamic partition data writer implementations available in Spark 3.2, the DynamicPartitionDataSingleWriter and the DynamicPartitionDataConcurrentWriter. The DynamicPartitionDataConcurrentWriter is selected when the spark.sql.maxConcurrentOutputFileWriters is set larger than 0 and the data records for writing are not sorted. Otherwise, the DynamicPartitionDataSingleWriter is selected.

The DynamicPartitionDataSingleWriter has only one file output writer open at any time for writing. For the DynamicPartitionDataSingleWriter to work, the records to be written have to be sorted on partition and/or bucket columns. The records are written to into output files one by one in sequence. For each partition key and/or bucket id, a new output writer instance is created and open which writes data into a new file while the old output writer is closed. As the records are sorted on partition and/or bucket columns, all the records with the same partition and/or bucket id are located next to each other. That ensures the current open writer needs to write all the records for the current partition and/or bucket before it can be closed and a new output writer is created to write to a new file for the next partition and/or bucket.

Unlike the DynamicPartitionDataSingleWriter, the DynamicPartitionDataConcurrentWriter allows multiple output writers open for writing at the same time. The advantage of the concurrent writer is that the records to be written do not have to be sorted by the partition key and/or bucket id. Instead, a map of output writers for each partition and/or bucket has to be maintained. The records are still written one by one, and the output writer for the current record is picked up from the writer map based on the record’s partition key and/or bucket id. The disadvantage of the DynamicPartitionDataConcurrentWriter is the resource pressure when there are too many concurrent writers. Therefore, the number of concurrent writers cannot over the limit set by spark.sql.maxConcurrentOutputFileWriters. When the number of concurrent writers exceeds limit, fall back to the single writer approach for writing the rest of the records.

Partitioning & Bucketing in the Reading Journey

The FileSourceStrategy is responsible for planning the physical reading of data files, including the files that are partitioned and/or bucketed by the user-specified columns. The FileSourceStrategy exams the logical plan of a query, it looks up the HaddopFsRelation node which contains all of the metadata required to read from a file-based datasource, including the metadata of the partitioning (partitionSchema) and the bucketing (bucketSpec).

The pushdown predicates are extracted based on the partitioning columns defined in the partitionSchema for pruning the partition directories while the bucket set is extracted from the bucketSpec for pruning the buckets. The FileSourceStrategy creates a FileSourceScanExec operator for scanning data from HaddopFsRelation with the pushdown predicates and the bucket set extracted earlier. When the FileSourceScanExec is executed, the createBucketedReadRDD method or the createReadRDD method is called, depending on whether bucketing is applied, to create the FileScanRDD which is the RDD conducting the physical reading of file partitions.

The key logic of bucket reading is encapsulated in the createBucketedReadRDD method. This method first groups the data files (from the filtered partitions) by the bucket ids.

The bucket id is extracted from the file path where the bucket id is encoded in a string starting with ‘_’ and prefix ‘0’s.

Next, the grouped files are filtered based on bucket set for bucket pruning.

Then a FilePartition, an RDD partition, is defined for each of the remaining buckets after the pruning. The list of defined FilePartitions for the buckets is then passed into the constructor of the FileScanRDD to define the partitions of the FileScanRDD so that the output partitions from the FileScanRDD are in line with the data distribution defined by the bucketing specs.


Spark SQL Query Engine Deep Dive (17) – Dynamic Partition Pruning

In this blog post, I will explain the Dynamic Partition Pruning (DPP), which is a performance optimisation feature introduced in Spark 3.0 along with the Adaptive Query Execution optimisation techniques (which I plan to cover in the next few of the blog posts).

At the core, the Dynamic Partition Pruning is a type of predicate push down optimisation method, which aims to minimise I/O costs of the data read from the data sources. The Dynamic Partition Pruning is especially effective for a common query pattern in BI solutions: a large fact table joins to a number of much smaller dimension tables and the fact table needs to be sliced and diced by some attributes of the dimension tables.

How does the Dynamic Partition Pruning work?

Before diving into the technical details of how the DPP is implemented in Spark, let’s look into the following example to understand how the DPP could improve the query performance.

Here, we have two Spark SQL tables using parquet file formats. The small dimension table, Customers, has the 100 rows with unique customer_id and a grade field with values ranging from 0 to 9.

USING parquet
    id AS customer_id,
    CAST(rand()*10 AS INT) AS grade
  FROM RANGE(100);

The other table, Orders, is a fact table with 100,000 transactional records. This table has the foreign key, customer_id, to the Customers table. In addition, this table is partitioned by the customer_id, i.e., the orders made by the same customer are stored in the same partition.

USING parquet
PARTITIONED By (customer_id)
    CAST((rand()*100) AS INT) AS customer_id,
    CAST(rand() * 100 AS INT) AS quantity
FROM RANGE(100000);

Now, we want to run the following query to find all the order records made by customers in grade “5”.

SELECT o.customer_id, o.quantity
FROM Customers AS c
  JOIN orders AS o 
    ON c.customer_id = o.customer_id 
WHERE c.grade =5

Firstly, we run the query with the DPP disabled. From the physical query plan, we can see the query is executed with the “static” optimisations as expected: the small customers table is first filtered by “grade=5” and the filtered customers dataset is broadcasted to the workers to avoid shuffling.

click to enlarge

On the large Orders table side, all the 100 partitions of the parquet data source are scanned. 800 data files and 22,236 rows are read from the data source.

Now let’s run the same query with the DPP turned on to see what happens.

set spark.sql.optimizer.dynamicPartitionPruning.enabled = true;

From the physical query plan, we can see that there is not much change on the customers join branch where the customers table is filtered and broadcasted to workers for joining the orders side. However, on the orders join branch, the results of the customers broadcast is reused as the filter criteria pushed down to the parquet data source reader, which only reads the partitions with the partition key (i.e., customer_id in this example) in the broadcast results (i.e., the list of the customers with “grade=5”).

click to enlarge

The new query execution logic can also be understood as:

SELECT o.customer_id, o.quantity
FROM Orders o
WHERE o.customer_id IN (
  SELECT customer_id
  FROM Customers
  WHERE grade=5

From the data source read statistics, we can see much less data is read from the parquet data files: Only 10 out of the 100 partitions and 80 out of the 800 files are scanned this time. Meantime, we can see one additional metric, “dynamic partition pruning time”, is included which indicates the DPP is applied for this query.

How is the Dynamic Partition Pruning Implemented in Spark SQL?

The Dynamic Partition Pruning feature is implemented in Spark SQL mainly through two rules: a logical plan Optimizer rule, PartitionPruning, and a Spark planner rule, PlanDynamicPruningFilters.


The PartitionPruning rule is added to one of the default batches in SparkOptimizer so that it will be applied at the logical plan optimisation stage. The PartitionPruning rule mainly does the following things when it is applied:

  1. Check the applicability of the DPP based on the type and selectivity of the join operation
  2. Estimate whether the partition pruning will bring benefits or not
  3. Insert a DPP predicate, if all the conditions are met

The PartitionPruning rule first checks whether the DPP is applicable or not based on the type and selectivity of the join operation. It starts from checking the applicability of the DPP on the left side of the join. Firstly, the getFilterableTableScan method is used to ensure the left-side table scan can be filtered for a given column. The table scan needs to be either a V1 partitioned scan for a given partition column or a V2 scan which supports runtime filtering on a given attribute. Then the join type is checked by the canPruneLeft method to ensure the join type supports pruning partitions on the left side (the join type needs to be either Inner, LeftSemi, or RightOuter for supporting left side partition pruning). Meantime, the hasPartitionPruningFilter method is used to check whether the right-side of the join has selective predicate which can filter the join key. When all of the above checks are passed, the PartitionPruning rule calls the insertPredicate method to insert predicate on the left side of the join. If no, the same checks are conducted on the right side to evaluate the applicability of the DPP on the right side.

Prior to inserting a DPP predicate, the insertPredicate method runs the pruningHasBenefit method to estimate the costs and benefits of the DPP optimisation and only inserts the DPP predicate when the benefits are bigger than the costs or exchange reuse is enabled for the current Spark session. The pruningHasBenefit method estimates the benefits of the DPP using the size in bytes of the partitioned plan on the pruning side times the filterRatio and estimates the costs of the DPP using the total size in bytes of the other side of the join. The filterRatio is estimated using column statistics if they are available, otherwise using the configured value of spark.sql.optimizer.dynamicPartitionPruning.fallbackFilterRatio.

When the DPP is estimated as beneficial to the query plan or exchange reuse is enabled, a DPP predicate is inserted into the pruning side of the join using the filter on the other side of the join. A custom DynamicPruning expression is created that wraps the filter in an IN expression. Here is the DPP optimised logical plan of the example used above.


During the logical plan optimisation stage, the PartitionPruning rule inserts a duplicated subquery with the filter from the other side. The PlanDynamicProuningFilters Spark planner rule is then applied at the execution plan preparation stage, which aims at removing the subquery duplicate by reusing the results of the broadcast.

The PlanDynamicProuningFilters rule first checks whether the query plan can reuse the broadcast exchange that requires the exchangeResueEnabled flag set to true and the physical join operator is BroadcastHashJoinExec. If the query plan can reuse the broadcast exchange, the duplicate subquery will be replaced with the reused results of the broadcast. Otherwise, if the estimated benefit of using the duplicate subquery still outweighs the use of original non-DPP query plan, the duplicate subquery is kept. If no, the subquery will be dropped.