Structured Streaming編程 Programming Guide
? Overview
? Quick Example
? Programming Model
o Basic Concepts
o Handling Event-time and Late Data
o Fault Tolerance Semantics
? API using Datasets and DataFrames
o Creating streaming DataFrames and streaming Datasets
? Input Sources
? Schema inference and partition of streaming DataFrames/Datasets
o Operations on streaming DataFrames/Datasets
? Basic Operations - Selection, Projection, Aggregation
? Window Operations on Event Time
? Handling Late Data and Watermarking
? Join Operations
? Stream-static Joins
? Stream-stream Joins
? Inner Joins with optional Watermarking
? Outer Joins with Watermarking
? Semi Joins with Watermarking
? Support matrix for joins in streaming queries
? Streaming Deduplication
? Policy for handling multiple watermarks
? Arbitrary Stateful Operations
? Unsupported Operations
? Limitation of global watermark
o Starting Streaming Queries
? Output Modes
? Output Sinks
? Using Foreach and ForeachBatch
? ForeachBatch
? Foreach
? Streaming Table APIs
? Triggers
o Managing Streaming Queries
o Monitoring Streaming Queries
? Reading Metrics Interactively
? Reporting Metrics programmatically using Asynchronous APIs
? Reporting Metrics using Dropwizard
o Recovering from Failures with Checkpointing
o Recovery Semantics after Changes in a Streaming Query
? Continuous Processing
? Additional Information
Overview
Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. You can express your streaming computation the same way you would express a batch computation on static data. The Spark SQL engine will take care of running it incrementally and continuously and updating the final result as streaming data continues to arrive. You can use the Dataset/DataFrame API in Scala, Java, Python or R to express streaming aggregations, event-time windows, stream-to-batch joins, etc. The computation is executed on the same optimized Spark SQL engine. Finally, the system ensures end-to-end exactly-once fault-tolerance guarantees through checkpointing and Write-Ahead Logs. In short, Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing without the user having to reason about streaming.
Internally, by default, Structured Streaming queries are processed using a micro-batch processing engine, which processes data streams as a series of small batch jobs thereby achieving end-to-end latencies as low as 100 milliseconds and exactly-once fault-tolerance guarantees. However, since Spark 2.3, we have introduced a new low-latency processing mode called Continuous Processing, which can achieve end-to-end latencies as low as 1 millisecond with at-least-once guarantees. Without changing the Dataset/DataFrame operations in your queries, you will be able to choose the mode based on your application requirements.
In this guide, we are going to walk you through the programming model and the APIs. We are going to explain the concepts mostly using the default micro-batch processing model, and then later discuss Continuous Processing model. First, let’s start with a simple example of a Structured Streaming query - a streaming word count.
結構化流是基于Spark SQL引擎構建的可伸縮且容錯的流處理引擎。可以像對靜態數據進行批處理計算一樣，來表示流計算。當流數據繼續到達時，Spark SQL引擎負責遞增地，連續地運行，并更新最終結果。可以在Scala，Java，Python或R中使用Dataset / DataFrame API來表示流聚合，事件時間窗口，流到批處理聯接等。計算是在同一優化的Spark SQL引擎上執行的。最后，系統通過檢查點和預寫日志來確保端到端的一次容錯。簡而言之，結構化流提供了快速，可擴展，容錯，端到端的一次精確流處理，而用戶無需推理流。
在內部，默認情況下，結構化流查詢是使用微批量處理引擎處理的，該引擎將數據流作為一系列小批量作業處理，從而實現了低至100毫秒的端到端延遲以及一次精確的容錯保證。但是，從Spark 2.3開始，引入了一種稱為“連續處理”的新低延遲處理模式，該模式可以實現一次最少保證的低至1毫秒的端到端延遲。在不更改查詢中的Dataset / DataFrame操作的情況下，將能夠根據應用程序需求選擇模式。
本文將逐步了解編程模型和API。主要使用默認的微批處理模型來解釋這些概念，然后再討論連續處理模型。首先，讓從結構化流查詢的簡單示例開始-流字數。
Quick Example
Let’s say you want to maintain a running word count of text data received from a data server listening on a TCP socket. Let’s see how you can express this using Structured Streaming. You can see the full code in Scala/Java/Python/R. And if you download Spark, you can directly run the example. In any case, let’s walk through the example step-by-step and understand how it works. First, we have to import the necessary classes and create a local SparkSession, the starting point of all functionalities related to Spark.
假設要維護從偵聽TCP套接字的數據服務器接收到的，文本數據的運行字數。看看如何使用結構化流來表達這一點。可以在Scala / Java / Python / R中看到完整的代碼 。如果下載了Spark，則可以直接運行該示例。無論如何，逐步介紹該示例并了解其工作原理。首先，必須導入必要的類，創建一個本地SparkSession，這是與Spark相關的所有功能的起點。
? Scala
? Java
? Python
? R
import org.apache.spark.sql.functions._
import org.apache.spark.sql.SparkSession

val spark = SparkSession
.builder
.appName(“StructuredNetworkWordCount”)
.getOrCreate()

import spark.implicits._
Next, let’s create a streaming DataFrame that represents text data received from a server listening on localhost:9999, and transform the DataFrame to calculate word counts. 接下來，創建一個流數據框架，該數據框架表示從在localhost：9999上偵聽的服務器接收的文本數據，并對數據框架進行轉換以計算字數。
? Scala
? Java
? Python
? R
// Create DataFrame representing the stream of input lines from connection to localhost:9999
val lines = spark.readStream
.format(“socket”)
.option(“host”, “localhost”)
.option(“port”, 9999)
.load()

// Split the lines into words
val words = lines.as[String].flatMap(_.split(" "))

// Generate running word count
val wordCounts = words.groupBy(“value”).count()
This lines DataFrame represents an unbounded table containing the streaming text data. This table contains one column of strings named “value”, and each line in the streaming text data becomes a row in the table. Note, that this is not currently receiving any data as we are just setting up the transformation, and have not yet started it. Next, we have converted the DataFrame to a Dataset of String using .as[String], so that we can apply the flatMap operation to split each line into multiple words. The resultant words Dataset contains all the words. Finally, we have defined the wordCounts DataFrame by grouping by the unique values in the Dataset and counting them. Note that this is a streaming DataFrame which represents the running word counts of the stream.
We have now set up the query on the streaming data. All that is left is to actually start receiving data and computing the counts. To do this, we set it up to print the complete set of counts (specified by outputMode(“complete”)) to the console every time they are updated. And then start the streaming computation using start().
linesDataFrame表示一個包含流文本數據的無邊界表。該表包含一列名為“值”的字符串，流文本數據中的每一行都成為表中的一行。由于正在設置轉換，并且尚未開始轉換，目前未接收任何數據。接下來，使用DataFrame轉換為String的Dataset .as[String]，以便可以應用該flatMap算子，將每一行拆分為多個單詞。結果words數據集包含所有單詞。最后，wordCounts通過對數據集中的唯一值進行分組，對其進行計數來定義DataFrame。這是一個流數據幀，表示流的運行字數。
現在，對流數據進行了查詢。剩下的就是實際開始接收數據并計算計數了。為此，將其設置outputMode(“complete”)為在每次更新計數時，將完整的計數集（由指定）打印到控制臺。然后使用start()開始流計算。
? Scala
? Java
? Python
? R
// Start running the query that prints the running counts to the console
val query = wordCounts.writeStream
.outputMode(“complete”)
.format(“console”)
.start()

query.awaitTermination()
After this code is executed, the streaming computation will have started in the background. The query object is a handle to that active streaming query, and we have decided to wait for the termination of the query using awaitTermination() to prevent the process from exiting while the query is active.
To actually execute this example code, you can either compile the code in your own Spark application, or simply run the example once you have downloaded Spark. We are showing the latter. You will first need to run Netcat (a small utility found in most Unix-like systems) as a data server by using
執行此代碼后，流計算將在后臺開始。該query對象是該活動流查詢的句柄，已決定等待查詢終止，awaitTermination()以防止該查詢處于活動狀態時退出該過程。
要實際執行此示例代碼，可以在Spark應用程序中編譯代碼，也可以在 下載Spark之后直接 運行示例。正在展示后者。首先需要通過使用以下命令將Netcat（在大多數類Unix系統中找到的一個小實用程序）作為數據服務器運行。
$ nc -lk 9999
Then, in a different terminal, you can start the example by using
? Scala
? Java
? Python
? R
$ ./bin/run-example org.apache.spark.examples.sql.streaming.StructuredNetworkWordCount localhost 9999
Then, any lines typed in the terminal running the netcat server will be counted and printed on screen every second. It will look something like the following.
然后，每秒鐘在運行netcat服務器的終端中鍵入的任何行，都將被計數并打印在屏幕上。類似于以下內容。

TERMINAL 1:

Running Netcat

$ nc -lk 9999
apache spark
apache hadoop

… ? Scala
? Java
? Python
? R

TERMINAL 2: RUNNING StructuredNetworkWordCount

$ ./bin/run-example org.apache.spark.examples.sql.streaming.StructuredNetworkWordCount localhost 9999

---

Batch: 0

±-----±----+
| value|count|
±-----±----+
|apache| 1|
| spark| 1|
±-----±----+

---

Batch: 1

±-----±----+
| value|count|
±-----±----+
|apache| 2|
| spark| 1|
|hadoop| 1|
±-----±----+
…
Programming Model
The key idea in Structured Streaming is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model. You will express your streaming computation as standard batch-like query as on a static table, and Spark runs it as an incremental query on the unbounded input table. Let’s understand this model in more detail.
結構化流處理中的關鍵思想，將實時數據流視為被連續追加的表。這導致了一個新的流處理模型，該模型與批處理模型非常相似。將像在靜態表上一樣，將流計算表示為類似于批處理的標準查詢，Spark在無界輸入表上，將其作為增量查詢運行。更詳細地了解此模型。
Basic Concepts
Consider the input data stream as the “Input Table”. Every data item that is arriving on the stream is like a new row being appended to the Input Table.
將輸入數據流視為“輸入表”。流上到達的每個數據項都像是將新行附加到輸入表中。

A query on the input will generate the “Result Table”. Every trigger interval (say, every 1 second), new rows get appended to the Input Table, which eventually updates the Result Table. Whenever the result table gets updated, we would want to write the changed result rows to an external sink.
查詢輸入，生成“結果表”。在每個觸發間隔（例如，每1秒鐘），新行將附加到輸入表中，并最終更新結果表。無論何時更新結果表，都希望將更改后的結果行寫入外部接收器。

The “Output” is defined as what gets written out to the external storage. The output can be defined in a different mode:
? Complete Mode - The entire updated Result Table will be written to the external storage. It is up to the storage connector to decide how to handle writing of the entire table.
? Append Mode - Only the new rows appended in the Result Table since the last trigger will be written to the external storage. This is applicable only on the queries where existing rows in the Result Table are not expected to change.
? Update Mode - Only the rows that were updated in the Result Table since the last trigger will be written to the external storage (available since Spark 2.1.1). Note that this is different from the Complete Mode in that this mode only outputs the rows that have changed since the last trigger. If the query doesn’t contain aggregations, it will be equivalent to Append mode.
Note that each mode is applicable on certain types of queries. This is discussed in detail later.
To illustrate the use of this model, let’s understand the model in context of the Quick Example above. The first lines DataFrame is the input table, and the final wordCounts DataFrame is the result table. Note that the query on streaming lines DataFrame to generate wordCounts is exactly the same as it would be a static DataFrame. However, when this query is started, Spark will continuously check for new data from the socket connection. If there is new data, Spark will run an “incremental” query that combines the previous running counts with the new data to compute updated counts, as shown below.
“輸出”定義為寫到外部存儲器的內容。可以在不同的模式下定義輸出：
? 完整模式-整個更新后的結果表，將被寫入外部存儲器。由存儲連接器決定如何處理整個表的寫入。
? 追加模式-僅將自上次觸發以來追加在結果表中的新行，寫入外部存儲器。僅適用于預期結果表中現有行不會更改的查詢。
? 更新模式-僅自上次觸發以來在結果表中已更新的行，將被寫入外部存儲（自Spark 2.1.1起可用）。與完成模式的不同之處在于，此模式僅輸出自上次觸發以來已更改的行。如果查詢不包含聚合，則等效于追加模式。
每種模式都適用于某些類型的查詢。稍后將對此進行詳細討論。
為了說明此模型的用法，在上面的“快速示例”的上下文中了解該模型。第一個linesDataFrame是輸入表，最后一個wordCountsDataFrame是結果表。在流媒體的查詢lines數據幀生成wordCounts是完全一樣的，因為是一個靜態的數據幀。但是，啟動此查詢時，Spark將不斷檢查套接字連接中是否有新數據。如果有新數據，Spark將運行一個“增量”查詢，該查詢將先前的運行計數與新數據結合起來以計算更新的計數，如下所示。

Note that Structured Streaming does not materialize the entire table. It reads the latest available data from the streaming data source, processes it incrementally to update the result, and then discards the source data. It only keeps around the minimal intermediate state data as required to update the result (e.g. intermediate counts in the earlier example).
This model is significantly different from many other stream processing engines. Many streaming systems require the user to maintain running aggregations themselves, thus having to reason about fault-tolerance, and data consistency (at-least-once, or at-most-once, or exactly-once). In this model, Spark is responsible for updating the Result Table when there is new data, thus relieving the users from reasoning about it. As an example, let’s see how this model handles event-time based processing and late arriving data.
結構化流技術不會實現整個表。從流數據源讀取最新的可用數據，對其進行增量處理以更新結果，然后丟棄該源數據。僅保留更新結果所需的最小中間狀態數據（例如，前面示例中的中間計數）。
此模型與許多其它流處理引擎明顯不同。許多流系統要求用戶自己維護運行中的聚合，因此必須考慮容錯和數據一致性（至少一次，最多一次或恰好一次）。在此模型中，Spark負責在有新數據時更新結果表，從而使用戶免于推理。作為示例，看看該模型如何處理基于事件時間的處理和延遲到達的數據。
Handling Event-time and Late Data
Event-time is the time embedded in the data itself. For many applications, you may want to operate on this event-time. For example, if you want to get the number of events generated by IoT devices every minute, then you probably want to use the time when the data was generated (that is, event-time in the data), rather than the time Spark receives them. This event-time is very naturally expressed in this model – each event from the devices is a row in the table, and event-time is a column value in the row. This allows window-based aggregations (e.g. number of events every minute) to be just a special type of grouping and aggregation on the event-time column – each time window is a group and each row can belong to multiple windows/groups. Therefore, such event-time-window-based aggregation queries can be defined consistently on both a static dataset (e.g. from collected device events logs) as well as on a data stream, making the life of the user much easier.
Furthermore, this model naturally handles data that has arrived later than expected based on its event-time. Since Spark is updating the Result Table, it has full control over updating old aggregates when there is late data, as well as cleaning up old aggregates to limit the size of intermediate state data. Since Spark 2.1, we have support for watermarking which allows the user to specify the threshold of late data, and allows the engine to accordingly clean up old state. These are explained later in more detail in the Window Operations section.
事件時間是嵌入數據本身的時間。對于許多應用程序，可能希望在此事件時間進行操作。例如，如果要獲取每分鐘由IoT設備生成的事件數，則可能要使用生成數據的時間（即數據中的事件時間），而不是Spark收到的時間。此事件時間在此模型中非常自然地表達-設備中的每個事件，都是表中的一行，而事件時間是該行中的列值。允許基于窗口的聚合（例如，每分鐘的事件數），只是事件時間列上的一種特殊類型的分組和聚合-每個時間窗口都是一個組，每行可以屬于多個窗口/組。
此外，此模型自然會根據事件時間處理比預期時間晚到達的數據。由于Spark正在更新結果表，具有完全控制權，可以在有較晚數據時更新舊聚合，并可以清除舊聚合以限制中間狀態數據的大小。從Spark 2.1開始，支持水印功能，該功能允許用戶指定最新數據的閾值，并允許引擎相應地清除舊狀態。這些將在后面的“窗口操作”部分中詳細介紹。
Fault Tolerance Semantics
Delivering end-to-end exactly-once semantics was one of key goals behind the design of Structured Streaming. To achieve that, we have designed the Structured Streaming sources, the sinks and the execution engine to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing. Every streaming source is assumed to have offsets (similar to Kafka offsets, or Kinesis sequence numbers) to track the read position in the stream. The engine uses checkpointing and write-ahead logs to record the offset range of the data being processed in each trigger. The streaming sinks are designed to be idempotent for handling reprocessing. Together, using replayable sources and idempotent sinks, Structured Streaming can ensure end-to-end exactly-once semantics under any failure.
提供端到端的一次語義是結構化流設計背后的主要目標之一。為此，設計了結構化流源，接收器和執行引擎，可靠地跟蹤處理的確切進度，可以通過重新啟動和/或重新處理來處理任何類型的故障。假定每個流源都有偏移量（類似于Kafka偏移量或Kinesis序列號），跟蹤流中的讀取位置。引擎使用檢查點和預寫日志，記錄每個觸發器中正在處理的數據的偏移范圍。流接收器被設計為是冪等的idempotent，用于處理后處理。結合使用可重播的源和冪等的idempotent接收器，結構化流可以確保端到端的一次精確語義 在任何故障下。
API using Datasets and DataFrames
Since Spark 2.0, DataFrames and Datasets can represent static, bounded data, as well as streaming, unbounded data. Similar to static Datasets/DataFrames, you can use the common entry point SparkSession (Scala/Java/Python/R docs) to create streaming DataFrames/Datasets from streaming sources, and apply the same operations on them as static DataFrames/Datasets. If you are not familiar with Datasets/DataFrames, you are strongly advised to familiarize yourself with them using the DataFrame/Dataset Programming Guide.
從Spark 2.0開始，DataFrame和Dataset可以表示靜態的有界數據，以及流式無界數據。與靜態數據集/數據框類似，可以使用公共入口點SparkSession （Scala / Java / Python / R docs），從流源創建流式數據框/數據集，應用與靜態數據框/數據集相同的操作。如果不熟悉Datasets / DataFrames，強烈建議使用DataFrame / Dataset編程指南來熟悉 。
Creating streaming DataFrames and streaming Datasets
Streaming DataFrames can be created through the DataStreamReader interface (Scala/Java/Python docs) returned by SparkSession.readStream(). In R, with the read.stream() method. Similar to the read interface for creating static DataFrame, you can specify the details of the source – data format, schema, options, etc.
可以通過由返回的DataStreamReader接口（Scala / Java / Python文檔），創建Streaming DataFrames SparkSession.readStream()。在R中，使用read.stream()方法。與用于創建靜態DataFrame的讀取接口類似，可以指定源的詳細信息-數據格式，架構，選項等。
Input Sources
There are a few built-in sources.
? File source - Reads files written in a directory as a stream of data. Files will be processed in the order of file modification time. If latestFirst is set, order will be reversed. Supported file formats are text, CSV, JSON, ORC, Parquet. See the docs of the DataStreamReader interface for a more up-to-date list, and supported options for each file format. Note that the files must be atomically placed in the given directory, which in most file systems, can be achieved by file move operations.
? Kafka source - Reads data from Kafka. It’s compatible with Kafka broker versions 0.10.0 or higher. See the Kafka Integration Guide for more details.
? Socket source (for testing) - Reads UTF8 text data from a socket connection. The listening server socket is at the driver. Note that this should be used only for testing as this does not provide end-to-end fault-tolerance guarantees.
? Rate source (for testing) - Generates data at the specified number of rows per second, each output row contains a timestamp and value. Where timestamp is a Timestamp type containing the time of message dispatch, and value is of Long type containing the message count, starting from 0 as the first row. This source is intended for testing and benchmarking.
Some sources are not fault-tolerant because they do not guarantee that data can be replayed using checkpointed offsets after a failure. See the earlier section on fault-tolerance semantics. Here are the details of all the sources in Spark.
? 文件源-讀取寫入目錄中的文件作為數據流。文件將按照文件修改時間的順序進行處理。如果設置為latestFirst，則順序將相反。支持的文件格式為文本，CSV，JSON，ORC，Parquet。有關最新列表以及每種文件格式支持的選項，請參見DataStreamReader界面的文檔。文件必須原子地放在給定目錄中，在大多數文件系統中，可以通過文件移動操作來實現。
? Kafka源-從Kafka讀取數據。與0.10.0或更高版本的Kafka代理兼容。有關更多詳細信息，參見《Kafka集成指南》。
? 套接字源（用于測試） -從套接字連接讀取UTF8文本數據。監聽服務器套接字位于驅動程序處。僅應用于測試，不能提供端到端的容錯保證。
? 速率源（用于測試） -以每秒指定的行數生成數據，每個輸出行均包含timestamp和value。wheretimestamp是Timestamp包含消息分發時間的類型，value是Long包含消息計數的類型，從0開始作為第一行。此源旨在用于測試和基準測試。
一些源不是容錯的，因為不能保證故障后可以使用檢查點偏移來重放數據。參見前面有關 容錯語義的部分。以下是Spark中所有來源的詳細信息。
Source Options Fault-tolerant Notes
File source path: path to the input directory, and common to all file formats.
maxFilesPerTrigger: maximum number of new files to be considered in every trigger (default: no max)
latestFirst: whether to process the latest new files first, useful when there is a large backlog of files (default: false)
fileNameOnly: whether to check new files based on only the filename instead of on the full path (default: false). With this set to true, the following files would be considered as the same file, because their filenames, “dataset.txt”, are the same:
“file:///dataset.txt”
“s3://a/dataset.txt”
“s3n://a/b/dataset.txt”
“s3a://a/b/c/dataset.txt”
maxFileAge: Maximum age of a file that can be found in this directory, before it is ignored. For the first batch all files will be considered valid. If latestFirst is set to true and maxFilesPerTrigger is set, then this parameter will be ignored, because old files that are valid, and should be processed, may be ignored. The max age is specified with respect to the timestamp of the latest file, and not the timestamp of the current system.(default: 1 week)
cleanSource: option to clean up completed files after processing.
Available options are “archive”, “delete”, “off”. If the option is not provided, the default value is “off”.
When “archive” is provided, additional option sourceArchiveDir must be provided as well. The value of “sourceArchiveDir” must not match with source pattern in depth (the number of directories from the root directory), where the depth is minimum of depth on both paths. This will ensure archived files are never included as new source files.
For example, suppose you provide ‘/hello?/spark/’ as source pattern, ‘/hello1/spark/archive/dir’ cannot be used as the value of “sourceArchiveDir”, as '/hello?/spark/’ and ‘/hello1/spark/archive’ will be matched. ‘/hello1/spark’ cannot be also used as the value of “sourceArchiveDir”, as ‘/hello?/spark’ and ‘/hello1/spark’ will be matched. ‘/archived/here’ would be OK as it doesn’t match.
Spark will move source files respecting their own path. For example, if the path of source file is /a/b/dataset.txt and the path of archive directory is /archived/here, file will be moved to /archived/here/a/b/dataset.txt.
NOTE: Both archiving (via moving) or deleting completed files will introduce overhead (slow down, even if it’s happening in separate thread) in each micro-batch, so you need to understand the cost for each operation in your file system before enabling this option. On the other hand, enabling this option will reduce the cost to list source files which can be an expensive operation.
Number of threads used in completed file cleaner can be configured withspark.sql.streaming.fileSource.cleaner.numThreads (default: 1).
NOTE 2: The source path should not be used from multiple sources or queries when enabling this option. Similarly, you must ensure the source path doesn’t match to any files in output directory of file stream sink.
NOTE 3: Both delete and move actions are best effort. Failing to delete or move files will not fail the streaming query. Spark may not clean up some source files in some circumstances - e.g. the application doesn’t shut down gracefully, too many files are queued to clean up.

For file-format-specific options, see the related methods in DataStreamReader (Scala/Java/Python/R). E.g. for “parquet” format options see DataStreamReader.parquet().

In addition, there are session configurations that affect certain file-formats. See the SQL Programming Guide for more details. E.g., for “parquet”, see Parquet configuration section.
Yes Supports glob paths, but does not support multiple comma-separated paths/globs.
Socket Source host: host to connect to, must be specified
port: port to connect to, must be specified No
Rate Source rowsPerSecond (e.g. 100, default: 1): How many rows should be generated per second.

rampUpTime (e.g. 5s, default: 0s): How long to ramp up before the generating speed becomes rowsPerSecond. Using finer granularities than seconds will be truncated to integer seconds.

numPartitions (e.g. 10, default: Spark’s default parallelism): The partition number for the generated rows.

The source will try its best to reach rowsPerSecond, but the query may be resource constrained, and numPartitions can be tweaked to help reach the desired speed. Yes
Kafka Source See the Kafka Integration Guide.
Yes

Here are some examples.
? Scala
? Java
? Python
? R
val spark: SparkSession = …

// Read text from socket
val socketDF = spark
.readStream
.format(“socket”)
.option(“host”, “localhost”)
.option(“port”, 9999)
.load()

socketDF.isStreaming // Returns True for DataFrames that have streaming sources

socketDF.printSchema

// Read all the csv files written atomically in a directory
val userSchema = new StructType().add(“name”, “string”).add(“age”, “integer”)
val csvDF = spark
.readStream
.option(“sep”, “;”)
.schema(userSchema) // Specify schema of the csv files
.csv("/path/to/directory") // Equivalent to format(“csv”).load("/path/to/directory")
These examples generate streaming DataFrames that are untyped, meaning that the schema of the DataFrame is not checked at compile time, only checked at runtime when the query is submitted. Some operations like map, flatMap, etc. need the type to be known at compile time. To do those, you can convert these untyped streaming DataFrames to typed streaming Datasets using the same methods as static DataFrame. See the SQL Programming Guide for more details. Additionally, more details on the supported streaming sources are discussed later in the document.
這些示例生成未類型化的流式DataFrame，意味著在編譯時不檢查DataFrame的架構，僅在提交查詢時在運行時檢查。有些操作（如map，flatMap等）需要在編譯時知道類型。可以使用與靜態DataFrame相同的方法，將這些未類型化的流式數據幀轉換為類型化的流式數據集。有關更多詳細信息，請參見《SQL編程指南》。此外，本文檔后面將討論有關受支持的流媒體源的更多詳細信息。
從Spark 3.1開始，還可以使用從表DataStreamReader.table()創建流式DataFrame 。有關更多詳細信息，請參見流表API。
Since Spark 3.1, you can also create streaming DataFrames from tables with DataStreamReader.table(). See Streaming Table APIs for more details.
Schema inference and partition of streaming DataFrames/Datasets
By default, Structured Streaming from file based sources requires you to specify the schema, rather than rely on Spark to infer it automatically. This restriction ensures a consistent schema will be used for the streaming query, even in the case of failures. For ad-hoc use cases, you can reenable schema inference by setting spark.sql.streaming.schemaInference to true.
Partition discovery does occur when subdirectories that are named /key=value/ are present and listing will automatically recurse into these directories. If these columns appear in the user-provided schema, they will be filled in by Spark based on the path of the file being read. The directories that make up the partitioning scheme must be present when the query starts and must remain static. For example, it is okay to add /data/year=2016/ when /data/year=2015/ was present, but it is invalid to change the partitioning column (i.e. by creating the directory /data/date=2016-04-17/).
默認情況下，從基于文件的源進行結構化流傳輸需要指定架構，而不是依靠Spark自動推斷。此限制確保即使在發生故障的情況下，也可以將一致的架構用于流查詢。對于臨時用例，可以通過將設置spark.sql.streaming.schemaInference，重新啟用模式推斷true。
當存在命名的子目錄時，分區發現確實會發生，/key=value/列表，將自動遞歸到這些目錄中。如果這些列出現在用戶提供的架構中，Spark將根據讀取文件的路徑來填充。啟動查詢時，必須存在組成分區方案的目錄，并且這些目錄必須保持靜態。例如，添加/data/year=2016/時/data/year=2015/，但是無效的改變分區列（即通過創建目錄/data/date=2016-04-17/）。
Operations on streaming DataFrames/Datasets
You can apply all kinds of operations on streaming DataFrames/Datasets – ranging from untyped, SQL-like operations (e.g. select, where, groupBy), to typed RDD-like operations (e.g. map, filter, flatMap). See the SQL programming guide for more details. Let’s take a look at a few example operations that you can use.
可以將各種操作上的流DataFrames /數據集-從無類型，類似于SQL的操作（例如select，where，groupBy），鍵入RDD般的操作（例如map，filter，flatMap）。有關更多詳細信息，請參見SQL編程指南。可以使用的一些示例操作。
Basic Operations - Selection, Projection, Aggregation
Most of the common operations on DataFrame/Dataset are supported for streaming. The few operations that are not supported are discussed later in this section.
流上支持DataFrame / Dataset上的大多數常見操作。稍后將討論不支持的一些操作。
? 斯卡拉

? Scala
? Java
? Python
? R
case class DeviceData(device: String, deviceType: String, signal: Double, time: DateTime)

val df: DataFrame = … // streaming DataFrame with IOT device data with schema { device: string, deviceType: string, signal: double, time: string }
val ds: Dataset[DeviceData] = df.as[DeviceData] // streaming Dataset with IOT device data

// Select the devices which have signal more than 10
df.select(“device”).where(“signal > 10”) // using untyped APIs
ds.filter(.signal > 10).map(.device) // using typed APIs

// Running count of the number of updates for each device type
df.groupBy(“deviceType”).count() // using untyped API

// Running average signal for each device type
import org.apache.spark.sql.expressions.scalalang.typed
ds.groupByKey(.deviceType).agg(typed.avg(.signal)) // using typed API
You can also register a streaming DataFrame/Dataset as a temporary view and then apply SQL commands on it.
? Scala
? Java
? Python
? R
df.createOrReplaceTempView(“updates”)
spark.sql(“select count(*) from updates”) // returns another streaming DF
Note, you can identify whether a DataFrame/Dataset has streaming data or not by using df.isStreaming.
? Scala
? Java
? Python
? R
df.isStreaming
You may want to check the query plan of the query, as Spark could inject stateful operations during interpret of SQL statement against streaming dataset. Once stateful operations are injected in the query plan, you may need to check your query with considerations in stateful operations. (e.g. output mode, watermark, state store size maintenance, etc.)
可能要檢查查詢的查詢計劃，因為Spark可能在針對流數據集解釋SQL語句期間注入狀態操作。將有狀態操作注入查詢計劃后，可能需要檢查有狀態操作中的注意事項。（例如，輸出模式，水印，狀態存儲大小維護等）。
Window Operations on Event Time
Aggregations over a sliding event-time window are straightforward with Structured Streaming and are very similar to grouped aggregations. In a grouped aggregation, aggregate values (e.g. counts) are maintained for each unique value in the user-specified grouping column. In case of window-based aggregations, aggregate values are maintained for each window the event-time of a row falls into. Let’s understand this with an illustration.
Imagine our quick example is modified and the stream now contains lines along with the time when the line was generated. Instead of running word counts, we want to count words within 10 minute windows, updating every 5 minutes. That is, word counts in words received between 10 minute windows 12:00 - 12:10, 12:05 - 12:15, 12:10 - 12:20, etc. Note that 12:00 - 12:10 means data that arrived after 12:00 but before 12:10. Now, consider a word that was received at 12:07. This word should increment the counts corresponding to two windows 12:00 - 12:10 and 12:05 - 12:15. So the counts will be indexed by both, the grouping key (i.e. the word) and the window (can be calculated from the event-time).
The result tables would look something like the following.
滑動事件時間窗口上的聚合對于結構化流而言非常簡單，并且與分組聚合非常相似。在分組聚合中，在用戶指定的分組列中為每個唯一維護聚合值（例如，計數）。在基于窗口的聚合的情況下，將為行的事件時間所屬的每個窗口維護聚合值。通過插圖來了解這一點。
想象一下快速示例已被修改，并且流現在包含行以及生成行的時間。而不是運行字數統計，希望在10分鐘的窗口內對字數進行計數，每5分鐘更新一次。也就是說，在10分鐘窗口12:00-12：10、12：05-12：15、12：10-12:20等之間接收到的單詞中的單詞計數。注意，12：00-12:10表示數據12:00之后但12:10之前到達。現在，考慮一個在12:07收到的單詞。此字應增加對應于兩個窗口12:00-12:10和12:05-12:15的計數。因此，計數將通過分組鍵（即單詞）和窗口（可以從事件時間計算）來索引。
結果表如下所示。

Since this windowing is similar to grouping, in code, you can use groupBy() and window() operations to express windowed aggregations. You can see the full code for the below examples in Scala/Java/Python.
由于此窗口化類似于分組，因此在代碼中，可以使用groupBy()和window()操作來表示窗口化聚合。可以在Scala / Java / Python中看到以下示例的完整代碼 。
? Scala
? Java
? Python
? R
import spark.implicits._

val words = … // streaming DataFrame of schema { timestamp: Timestamp, word: String }

// Group the data by window and word and compute the count of each group
val windowedCounts = words.groupBy(
window($“timestamp”, “10 minutes”, “5 minutes”),
$“word”
).count()
Handling Late Data and Watermarking
Now consider what happens if one of the events arrives late to the application. For example, say, a word generated at 12:04 (i.e. event time) could be received by the application at 12:11. The application should use the time 12:04 instead of 12:11 to update the older counts for the window 12:00 - 12:10. This occurs naturally in our window-based grouping – Structured Streaming can maintain the intermediate state for partial aggregates for a long period of time such that late data can update aggregates of old windows correctly, as illustrated below.
現在考慮如果事件之一遲到了應用程序會發生什么。例如，應用程序可以在12:11接收在12:04（即事件時間）生成的單詞。應用程序應使用12:04而不是12:11來更新窗口的舊計數12:00 - 12:10。基于窗口的分組中很自然地發生-結構化流可以長時間保持部分聚合的中間狀態，以便后期數據可以正確更新舊窗口的聚合，如下所示。

However, to run this query for days, it’s necessary for the system to bound the amount of intermediate in-memory state it accumulates. This means the system needs to know when an old aggregate can be dropped from the in-memory state because the application is not going to receive late data for that aggregate any more. To enable this, in Spark 2.1, we have introduced watermarking, which lets the engine automatically track the current event time in the data and attempt to clean up old state accordingly. You can define the watermark of a query by specifying the event time column and the threshold on how late the data is expected to be in terms of event time. For a specific window ending at time T, the engine will maintain state and allow late data to update the state until (max event time seen by the engine - late threshold > T). In other words, late data within the threshold will be aggregated, but data later than the threshold will start getting dropped (see later in the section for the exact guarantees). Let’s understand this with an example. We can easily define watermarking on the previous example using withWatermark() as shown below.
但是，要連續幾天運行此查詢，系統必須限制其累積的中間內存狀態量。系統需要知道何時可以從內存中狀態刪除舊的聚合，因為應用程序將不再接收該聚合的最新數據。為此，在Spark 2.1中，引入了 水印功能，該功能使引擎自動跟蹤數據中的當前事件時間，并嘗試相應地清除舊狀態。可以通過指定事件時間列和有關數據，在事件時間方面的預期延遲時間的閾值，定義查詢的水印。對于在時間結束的特定窗口T，引擎將維持狀態并允許以后的數據更新狀態，直到(max event time seen by the engine - late threshold > T)。換句話說，閾值內的延遲數據將被匯總，但閾值后的數據將開始被丟棄（ 有關確切保證，請參閱本節后面的內容）。通過一個例子來理解這一點。可以使用withWatermark()，以下示例在上一個示例中輕松定義水印。
? Scala
? Java
? Python
? R
import spark.implicits._

val words = … // streaming DataFrame of schema { timestamp: Timestamp, word: String }

// Group the data by window and word and compute the count of each group
val windowedCounts = words
.withWatermark(“timestamp”, “10 minutes”)
.groupBy(
window($“timestamp”, “10 minutes”, “5 minutes”),
$“word”)
.count()
In this example, we are defining the watermark of the query on the value of the column “timestamp”, and also defining “10 minutes” as the threshold of how late is the data allowed to be. If this query is run in Update output mode (discussed later in Output Modes section), the engine will keep updating counts of a window in the Result Table until the window is older than the watermark, which lags behind the current event time in column “timestamp” by 10 minutes. Here is an illustration.
在此示例中，將在“時間戳”列的值上定義查詢的水印，并且還將“ 10分鐘”定義為允許數據晚到的閾值。如果此查詢在“更新輸出”模式下運行（稍后在“輸出模式”部分中討論），則引擎將在“結果表”中保持窗口的更新計數，直到該窗口早于水印為止，該時間滯后于“列”中的當前事件時間。時間戳”的時間減少了10分鐘。這是一個例子。

As shown in the illustration, the maximum event time tracked by the engine is the blue dashed line, and the watermark set as (max event time - ‘10 mins’) at the beginning of every trigger is the red line. For example, when the engine observes the data (12:14, dog), it sets the watermark for the next trigger as 12:04. This watermark lets the engine maintain intermediate state for additional 10 minutes to allow late data to be counted. For example, the data (12:09, cat) is out of order and late, and it falls in windows 12:00 - 12:10 and 12:05 - 12:15. Since, it is still ahead of the watermark 12:04 in the trigger, the engine still maintains the intermediate counts as state and correctly updates the counts of the related windows. However, when the watermark is updated to 12:11, the intermediate state for window (12:00 - 12:10) is cleared, and all subsequent data (e.g. (12:04, donkey)) is considered “too late” and therefore ignored. Note that after every trigger, the updated counts (i.e. purple rows) are written to sink as the trigger output, as dictated by the Update mode.
Some sinks (e.g. files) may not supported fine-grained updates that Update Mode requires. To work with them, we have also support Append Mode, where only the final counts are written to sink. This is illustrated below.
Note that using withWatermark on a non-streaming Dataset is no-op. As the watermark should not affect any batch query in any way, we will ignore it directly.
如圖所示，引擎跟蹤的最大事件時間是 藍色虛線，(max event time - ‘10 mins’) 在每次觸發開始時設置的水印是紅線。例如，當引擎觀察到數據時 (12:14, dog)，將下一個觸發器的水印設置為12:04。此水印可讓引擎再保持10分鐘的中間狀態，以便對較晚的數據進行計數。例如，數據(12:09, cat)不正確且延遲，并且落在windows12:00 - 12:10和中12:05 - 12:15。仍然12:04在觸發器中的水印之前，因此引擎仍將中間計數保持為狀態，并正確更新相關窗口的計數。當水印更新為12:11，(12:00 - 12:10)，清除了窗口的中間狀態，并且所有后續數據（例如(12:04, donkey)）都被認為“為時已晚”，因此被忽略。注意，在每次觸發之后，更新的計數（即紫色行）將被寫入接收器，作為更新輸出指示的觸發輸出。
某些接收器（例如文件）可能不支持更新模式所需的細粒度更新。支持追加模式，其中僅將最終計數寫入接收器。如下所示。
withWatermark在非流數據集上使用是no-op。由于水印不應以任何方式影響任何批處理查詢，將直接忽略它。

Similar to the Update Mode earlier, the engine maintains intermediate counts for each window. However, the partial counts are not updated to the Result Table and not written to sink. The engine waits for “10 mins” for late date to be counted, then drops intermediate state of a window < watermark, and appends the final counts to the Result Table/sink. For example, the final counts of window 12:00 - 12:10 is appended to the Result Table only after the watermark is updated to 12:11.
Conditions for watermarking to clean aggregation state
It is important to note that the following conditions must be satisfied for the watermarking to clean the state in aggregation queries (as of Spark 2.1.1, subject to change in the future).
? Output mode must be Append or Update. Complete mode requires all aggregate data to be preserved, and hence cannot use watermarking to drop intermediate state. See the Output Modes section for detailed explanation of the semantics of each output mode.
? The aggregation must have either the event-time column, or a window on the event-time column.
? withWatermark must be called on the same column as the timestamp column used in the aggregate. For example, df.withWatermark(“time”, “1 min”).groupBy(“time2”).count() is invalid in Append output mode, as watermark is defined on a different column from the aggregation column.
? withWatermark must be called before the aggregation for the watermark details to be used. For example, df.groupBy(“time”).count().withWatermark(“time”, “1 min”) is invalid in Append output mode.
Semantic Guarantees of Aggregation with Watermarking
? A watermark delay (set with withWatermark) of “2 hours” guarantees that the engine will never drop any data that is less than 2 hours delayed. In other words, any data less than 2 hours behind (in terms of event-time) the latest data processed till then is guaranteed to be aggregated.
? However, the guarantee is strict only in one direction. Data delayed by more than 2 hours is not guaranteed to be dropped; it may or may not get aggregated. More delayed is the data, less likely is the engine going to process it.
Join Operations
Structured Streaming supports joining a streaming Dataset/DataFrame with a static Dataset/DataFrame as well as another streaming Dataset/DataFrame. The result of the streaming join is generated incrementally, similar to the results of streaming aggregations in the previous section. In this section we will explore what type of joins (i.e. inner, outer, semi, etc.) are supported in the above cases. Note that in all the supported join types, the result of the join with a streaming Dataset/DataFrame will be the exactly the same as if it was with a static Dataset/DataFrame containing the same data in the stream.
Stream-static Joins
Since the introduction in Spark 2.0, Structured Streaming has supported joins (inner join and some type of outer joins) between a streaming and a static DataFrame/Dataset. Here is a simple example.
? Scala
? Java
? Python
? R
val staticDf = spark.read. …
val streamingDf = spark.readStream. …

streamingDf.join(staticDf, “type”) // inner equi-join with a static DF
streamingDf.join(staticDf, “type”, “left_outer”) // left outer join with a static DF
Note that stream-static joins are not stateful, so no state management is necessary. However, a few types of stream-static outer joins are not yet supported. These are listed at the end of this Join section.
Stream-stream Joins
In Spark 2.3, we have added support for stream-stream joins, that is, you can join two streaming Datasets/DataFrames. The challenge of generating join results between two data streams is that, at any point of time, the view of the dataset is incomplete for both sides of the join making it much harder to find matches between inputs. Any row received from one input stream can match with any future, yet-to-be-received row from the other input stream. Hence, for both the input streams, we buffer past input as streaming state, so that we can match every future input with past input and accordingly generate joined results. Furthermore, similar to streaming aggregations, we automatically handle late, out-of-order data and can limit the state using watermarks. Let’s discuss the different types of supported stream-stream joins and how to use them.
Inner Joins with optional Watermarking
Inner joins on any kind of columns along with any kind of join conditions are supported. However, as the stream runs, the size of streaming state will keep growing indefinitely as all past input must be saved as any new input can match with any input from the past. To avoid unbounded state, you have to define additional join conditions such that indefinitely old inputs cannot match with future inputs and therefore can be cleared from the state. In other words, you will have to do the following additional steps in the join.

1. Define watermark delays on both inputs such that the engine knows how delayed the input can be (similar to streaming aggregations)
2. Define a constraint on event-time across the two inputs such that the engine can figure out when old rows of one input is not going to be required (i.e. will not satisfy the time constraint) for matches with the other input. This constraint can be defined in one of the two ways.
3. Time range join conditions (e.g. …JOIN ON leftTime BETWEEN rightTime AND rightTime + INTERVAL 1 HOUR),
4. Join on event-time windows (e.g. …JOIN ON leftTimeWindow = rightTimeWindow).
   Let’s understand this with an example.
   Let’s say we want to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impressions led to monetizable clicks. To allow the state cleanup in this stream-stream join, you will have to specify the watermarking delays and the time constraints as follows.
5. Watermark delays: Say, the impressions and the corresponding clicks can be late/out-of-order in event-time by at most 2 and 3 hours, respectively.
6. Event-time range condition: Say, a click can occur within a time range of 0 seconds to 1 hour after the corresponding impression.
   The code would look like this.
   ? Scala
   ? Java
   ? Python
   ? R
   import org.apache.spark.sql.functions.expr

val impressions = spark.readStream. …
val clicks = spark.readStream. …

// Apply watermarks on event-time columns
val impressionsWithWatermark = impressions.withWatermark(“impressionTime”, “2 hours”)
val clicksWithWatermark = clicks.withWatermark(“clickTime”, “3 hours”)

// Join with event-time constraints
impressionsWithWatermark.join(
clicksWithWatermark,
expr("""
clickAdId = impressionAdId AND
clickTime >= impressionTime AND
clickTime <= impressionTime + interval 1 hour
“”")
)
Semantic Guarantees of Stream-stream Inner Joins with Watermarking
This is similar to the guarantees provided by watermarking on aggregations. A watermark delay of “2 hours” guarantees that the engine will never drop any data that is less than 2 hours delayed. But data delayed by more than 2 hours may or may not get processed.
Outer Joins with Watermarking
While the watermark + event-time constraints is optional for inner joins, for outer joins they must be specified. This is because for generating the NULL results in outer join, the engine must know when an input row is not going to match with anything in future. Hence, the watermark + event-time constraints must be specified for generating correct results. Therefore, a query with outer-join will look quite like the ad-monetization example earlier, except that there will be an additional parameter specifying it to be an outer-join.
? Scala
? Java
? Python
? R
impressionsWithWatermark.join(
clicksWithWatermark,
expr("""
clickAdId = impressionAdId AND
clickTime >= impressionTime AND
clickTime <= impressionTime + interval 1 hour
“”"),
joinType = “leftOuter” // can be “inner”, “leftOuter”, “rightOuter”, “fullOuter”, “leftSemi”
)
Semantic Guarantees of Stream-stream Outer Joins with Watermarking
Outer joins have the same guarantees as inner joins regarding watermark delays and whether data will be dropped or not.
Caveats
There are a few important characteristics to note regarding how the outer results are generated.
? The outer NULL results will be generated with a delay that depends on the specified watermark delay and the time range condition. This is because the engine has to wait for that long to ensure there were no matches and there will be no more matches in future.
? In the current implementation in the micro-batch engine, watermarks are advanced at the end of a micro-batch, and the next micro-batch uses the updated watermark to clean up state and output outer results. Since we trigger a micro-batch only when there is new data to be processed, the generation of the outer result may get delayed if there no new data being received in the stream. In short, if any of the two input streams being joined does not receive data for a while, the outer (both cases, left or right) output may get delayed.
Semi Joins with Watermarking
A semi join returns values from the left side of the relation that has a match with the right. It is also referred to as a left semi join. Similar to outer joins, watermark + event-time constraints must be specified for semi join. This is to evict unmatched input rows on left side, the engine must know when an input row on left side is not going to match with anything on right side in future.
Semantic Guarantees of Stream-stream Semi Joins with Watermarking
Semi joins have the same guarantees as inner joins regarding watermark delays and whether data will be dropped or not.
Support matrix for joins in streaming queries
Left Input Right Input Join Type
Static Static All types Supported, since its not on streaming data even though it can be present in a streaming query
Stream Static Inner Supported, not stateful
Left Outer Supported, not stateful
Right Outer Not supported
Full Outer Not supported
Left Semi Supported, not stateful
Static Stream Inner Supported, not stateful
Left Outer Not supported
Right Outer Supported, not stateful
Full Outer Not supported
Left Semi Not supported
Stream Stream Inner Supported, optionally specify watermark on both sides + time constraints for state cleanup
Left Outer Conditionally supported, must specify watermark on right + time constraints for correct results, optionally specify watermark on left for all state cleanup
Right Outer Conditionally supported, must specify watermark on left + time constraints for correct results, optionally specify watermark on right for all state cleanup
Full Outer Conditionally supported, must specify watermark on one side + time constraints for correct results, optionally specify watermark on the other side for all state cleanup
Left Semi Conditionally supported, must specify watermark on right + time constraints for correct results, optionally specify watermark on left for all state cleanup

Additional details on supported joins:
? Joins can be cascaded, that is, you can do df1.join(df2, …).join(df3, …).join(df4, …).
? As of Spark 2.4, you can use joins only when the query is in Append output mode. Other output modes are not yet supported.
? As of Spark 2.4, you cannot use other non-map-like operations before joins. Here are a few examples of what cannot be used.
o Cannot use streaming aggregations before joins.
o Cannot use mapGroupsWithState and flatMapGroupsWithState in Update mode before joins.
Streaming Deduplication
You can deduplicate records in data streams using a unique identifier in the events. This is exactly same as deduplication on static using a unique identifier column. The query will store the necessary amount of data from previous records such that it can filter duplicate records. Similar to aggregations, you can use deduplication with or without watermarking.
? With watermark - If there is an upper bound on how late a duplicate record may arrive, then you can define a watermark on an event time column and deduplicate using both the guid and the event time columns. The query will use the watermark to remove old state data from past records that are not expected to get any duplicates any more. This bounds the amount of the state the query has to maintain.
? Without watermark - Since there are no bounds on when a duplicate record may arrive, the query stores the data from all the past records as state.
? Scala
? Java
? Python
? R
val streamingDf = spark.readStream. … // columns: guid, eventTime, …

// Without watermark using guid column
streamingDf.dropDuplicates(“guid”)

// With watermark using guid and eventTime columns
streamingDf
.withWatermark(“eventTime”, “10 seconds”)
.dropDuplicates(“guid”, “eventTime”)
Policy for handling multiple watermarks
A streaming query can have multiple input streams that are unioned or joined together. Each of the input streams can have a different threshold of late data that needs to be tolerated for stateful operations. You specify these thresholds using withWatermarks(“eventTime”, delay) on each of the input streams. For example, consider a query with stream-stream joins between inputStream1 and inputStream2.
? Scala
inputStream1.withWatermark(“eventTime1”, “1 hour”)
.join(
inputStream2.withWatermark(“eventTime2”, “2 hours”),
joinCondition)
While executing the query, Structured Streaming individually tracks the maximum event time seen in each input stream, calculates watermarks based on the corresponding delay, and chooses a single global watermark with them to be used for stateful operations. By default, the minimum is chosen as the global watermark because it ensures that no data is accidentally dropped as too late if one of the streams falls behind the others (for example, one of the streams stops receiving data due to upstream failures). In other words, the global watermark will safely move at the pace of the slowest stream and the query output will be delayed accordingly.
However, in some cases, you may want to get faster results even if it means dropping data from the slowest stream. Since Spark 2.4, you can set the multiple watermark policy to choose the maximum value as the global watermark by setting the SQL configuration spark.sql.streaming.multipleWatermarkPolicy to max (default is min). This lets the global watermark move at the pace of the fastest stream. However, as a side effect, data from the slower streams will be aggressively dropped. Hence, use this configuration judiciously.
Arbitrary Stateful Operations
Many usecases require more advanced stateful operations than aggregations. For example, in many usecases, you have to track sessions from data streams of events. For doing such sessionization, you will have to save arbitrary types of data as state, and perform arbitrary operations on the state using the data stream events in every trigger. Since Spark 2.2, this can be done using the operation mapGroupsWithState and the more powerful operation flatMapGroupsWithState. Both operations allow you to apply user-defined code on grouped Datasets to update user-defined state. For more concrete details, take a look at the API documentation (Scala/Java) and the examples (Scala/Java).
Though Spark cannot check and force it, the state function should be implemented with respect to the semantics of the output mode. For example, in Update mode Spark doesn’t expect that the state function will emit rows which are older than current watermark plus allowed late record delay, whereas in Append mode the state function can emit these rows.
Unsupported Operations
There are a few DataFrame/Dataset operations that are not supported with streaming DataFrames/Datasets. Some of them are as follows.
? Multiple streaming aggregations (i.e. a chain of aggregations on a streaming DF) are not yet supported on streaming Datasets.
? Limit and take the first N rows are not supported on streaming Datasets.
? Distinct operations on streaming Datasets are not supported.
? Sorting operations are supported on streaming Datasets only after an aggregation and in Complete Output Mode.
? Few types of outer joins on streaming Datasets are not supported. See the support matrix in the Join Operations section for more details.
In addition, there are some Dataset methods that will not work on streaming Datasets. They are actions that will immediately run queries and return results, which does not make sense on a streaming Dataset. Rather, those functionalities can be done by explicitly starting a streaming query (see the next section regarding that).
? count() - Cannot return a single count from a streaming Dataset. Instead, use ds.groupBy().count() which returns a streaming Dataset containing a running count.
? foreach() - Instead use ds.writeStream.foreach(…) (see next section).
? show() - Instead use the console sink (see next section).
If you try any of these operations, you will see an AnalysisException like “operation XYZ is not supported with streaming DataFrames/Datasets”. While some of them may be supported in future releases of Spark, there are others which are fundamentally hard to implement on streaming data efficiently. For example, sorting on the input stream is not supported, as it requires keeping track of all the data received in the stream. This is therefore fundamentally hard to execute efficiently.
Limitation of global watermark
In Append mode, if a stateful operation emits rows older than current watermark plus allowed late record delay, they will be “late rows” in downstream stateful operations (as Spark uses global watermark). Note that these rows may be discarded. This is a limitation of a global watermark, and it could potentially cause a correctness issue.
Spark will check the logical plan of query and log a warning when Spark detects such a pattern.
Any of the stateful operation(s) after any of below stateful operations can have this issue:
? streaming aggregation in Append mode
? stream-stream outer join
? mapGroupsWithState and flatMapGroupsWithState in Append mode (depending on the implementation of the state function)
As Spark cannot check the state function of mapGroupsWithState/flatMapGroupsWithState, Spark assumes that the state function emits late rows if the operator uses Append mode.
Spark provides two ways to check the number of late rows on stateful operators which would help you identify the issue:

1. On Spark UI: check the metrics in stateful operator nodes in query execution details page in SQL tab
2. On Streaming Query Listener: check “numRowsDroppedByWatermark” in “stateOperators” in QueryProcessEvent.
   Please note that “numRowsDroppedByWatermark” represents the number of “dropped” rows by watermark, which is not always same as the count of “late input rows” for the operator. It depends on the implementation of the operator - e.g. streaming aggregation does pre-aggregate input rows and checks the late inputs against pre-aggregated inputs, hence the number is not same as the number of original input rows. You’d like to just check the fact whether the value is zero or non-zero.
   There’s a known workaround: split your streaming query into multiple queries per stateful operator, and ensure end-to-end exactly once per query. Ensuring end-to-end exactly once for the last query is optional.
   Starting Streaming Queries
   Once you have defined the final result DataFrame/Dataset, all that is left is for you to start the streaming computation. To do that, you have to use the DataStreamWriter (Scala/Java/Python docs) returned through Dataset.writeStream(). You will have to specify one or more of the following in this interface.
   ? Details of the output sink: Data format, location, etc.
   ? Output mode: Specify what gets written to the output sink.
   ? Query name: Optionally, specify a unique name of the query for identification.
   ? Trigger interval: Optionally, specify the trigger interval. If it is not specified, the system will check for availability of new data as soon as the previous processing has been completed. If a trigger time is missed because the previous processing has not been completed, then the system will trigger processing immediately.
   ? Checkpoint location: For some output sinks where the end-to-end fault-tolerance can be guaranteed, specify the location where the system will write all the checkpoint information. This should be a directory in an HDFS-compatible fault-tolerant file system. The semantics of checkpointing is discussed in more detail in the next section.
   Output Modes
   There are a few types of output modes.
   ? Append mode (default) - This is the default mode, where only the new rows added to the Result Table since the last trigger will be outputted to the sink. This is supported for only those queries where rows added to the Result Table is never going to change. Hence, this mode guarantees that each row will be output only once (assuming fault-tolerant sink). For example, queries with only select, where, map, flatMap, filter, join, etc. will support Append mode.
   ? Complete mode - The whole Result Table will be outputted to the sink after every trigger. This is supported for aggregation queries.
   ? Update mode - (Available since Spark 2.1.1) Only the rows in the Result Table that were updated since the last trigger will be outputted to the sink. More information to be added in future releases.
   Different types of streaming queries support different output modes. Here is the compatibility matrix.
   Query Type Supported Output Modes Notes
   Queries with aggregation Aggregation on event-time with watermark Append, Update, Complete Append mode uses watermark to drop old aggregation state. But the output of a windowed aggregation is delayed the late threshold specified in withWatermark() as by the modes semantics, rows can be added to the Result Table only once after they are finalized (i.e. after watermark is crossed). See the Late Data section for more details.

Update mode uses watermark to drop old aggregation state.

Complete mode does not drop old aggregation state since by definition this mode preserves all data in the Result Table.
Other aggregations Complete, Update Since no watermark is defined (only defined in other category), old aggregation state is not dropped.

Append mode is not supported as aggregates can update thus violating the semantics of this mode.
Queries with mapGroupsWithState Update Aggregations not allowed in a query with mapGroupsWithState.
Queries with flatMapGroupsWithState Append operation mode Append Aggregations are allowed after flatMapGroupsWithState.
Update operation mode Update Aggregations not allowed in a query with flatMapGroupsWithState.
Queries with joins Append Update and Complete mode not supported yet. See the support matrix in the Join Operations section for more details on what types of joins are supported.
Other queries Append, Update Complete mode not supported as it is infeasible to keep all unaggregated data in the Result Table.

Output Sinks
There are a few types of built-in output sinks.
? File sink - Stores the output to a directory.
writeStream
.format(“parquet”) // can be “orc”, “json”, “csv”, etc.
.option(“path”, “path/to/destination/dir”)
.start()
? Kafka sink - Stores the output to one or more topics in Kafka.
writeStream
.format(“kafka”)
.option(“kafka.bootstrap.servers”, “host1:port1,host2:port2”)
.option(“topic”, “updates”)
.start()
? Foreach sink - Runs arbitrary computation on the records in the output. See later in the section for more details.
writeStream
.foreach(…)
.start()
? Console sink (for debugging) - Prints the output to the console/stdout every time there is a trigger. Both, Append and Complete output modes, are supported. This should be used for debugging purposes on low data volumes as the entire output is collected and stored in the driver’s memory after every trigger.
writeStream
.format(“console”)
.start()
? Memory sink (for debugging) - The output is stored in memory as an in-memory table. Both, Append and Complete output modes, are supported. This should be used for debugging purposes on low data volumes as the entire output is collected and stored in the driver’s memory. Hence, use it with caution.
writeStream
.format(“memory”)
.queryName(“tableName”)
.start()
Some sinks are not fault-tolerant because they do not guarantee persistence of the output and are meant for debugging purposes only. See the earlier section on fault-tolerance semantics. Here are the details of all the sinks in Spark.
Sink Supported Output Modes Options Fault-tolerant Notes
File Sink Append path: path to the output directory, must be specified.
retention: time to live (TTL) for output files. Output files which batches were committed older than TTL will be eventually excluded in metadata log. This means reader queries which read the sink’s output directory may not process them. You can provide the value as string format of the time. (like “12h”, “7d”, etc.) By default it’s disabled.

For file-format-specific options, see the related methods in DataFrameWriter (Scala/Java/Python/R). E.g. for “parquet” format options see DataFrameWriter.parquet() Yes (exactly-once) Supports writes to partitioned tables. Partitioning by time may be useful.
Kafka Sink Append, Update, Complete See the Kafka Integration Guide
Yes (at-least-once) More details in the Kafka Integration Guide

Foreach Sink Append, Update, Complete None Yes (at-least-once) More details in the next section

ForeachBatch Sink Append, Update, Complete None Depends on the implementation More details in the next section

Console Sink Append, Update, Complete numRows: Number of rows to print every trigger (default: 20)
truncate: Whether to truncate the output if too long (default: true) No
Memory Sink Append, Complete None No. But in Complete Mode, restarted query will recreate the full table. Table name is the query name.

Note that you have to call start() to actually start the execution of the query. This returns a StreamingQuery object which is a handle to the continuously running execution. You can use this object to manage the query, which we will discuss in the next subsection. For now, let’s understand all this with a few examples.
? Scala
? Java
? Python
? R
// ========== DF with no aggregations ==========
val noAggDF = deviceDataDf.select(“device”).where(“signal > 10”)

// Print new data to console
noAggDF
.writeStream
.format(“console”)
.start()

// Write new data to Parquet files
noAggDF
.writeStream
.format(“parquet”)
.option(“checkpointLocation”, “path/to/checkpoint/dir”)
.option(“path”, “path/to/destination/dir”)
.start()

// ========== DF with aggregation ==========
val aggDF = df.groupBy(“device”).count()

// Print updated aggregations to console
aggDF
.writeStream
.outputMode(“complete”)
.format(“console”)
.start()

// Have all the aggregates in an in-memory table
aggDF
.writeStream
.queryName(“aggregates”) // this query name will be the table name
.outputMode(“complete”)
.format(“memory”)
.start()

spark.sql(“select * from aggregates”).show() // interactively query in-memory table
Using Foreach and ForeachBatch
The foreach and foreachBatch operations allow you to apply arbitrary operations and writing logic on the output of a streaming query. They have slightly different use cases - while foreach allows custom write logic on every row, foreachBatch allows arbitrary operations and custom logic on the output of each micro-batch. Let’s understand their usages in more detail.
ForeachBatch
foreachBatch(…) allows you to specify a function that is executed on the output data of every micro-batch of a streaming query. Since Spark 2.4, this is supported in Scala, Java and Python. It takes two parameters: a DataFrame or Dataset that has the output data of a micro-batch and the unique ID of the micro-batch.
? Scala
? Java
? Python
? R
streamingDF.writeStream.foreachBatch { (batchDF: DataFrame, batchId: Long) =>
// Transform and write batchDF
}.start()
With foreachBatch, you can do the following.
? Reuse existing batch data sources - For many storage systems, there may not be a streaming sink available yet, but there may already exist a data writer for batch queries. Using foreachBatch, you can use the batch data writers on the output of each micro-batch.
? Write to multiple locations - If you want to write the output of a streaming query to multiple locations, then you can simply write the output DataFrame/Dataset multiple times. However, each attempt to write can cause the output data to be recomputed (including possible re-reading of the input data). To avoid recomputations, you should cache the output DataFrame/Dataset, write it to multiple locations, and then uncache it. Here is an outline.
? Scala
streamingDF.writeStream.foreachBatch { (batchDF: DataFrame, batchId: Long) =>
batchDF.persist()
batchDF.write.format(…).save(…) // location 1
batchDF.write.format(…).save(…) // location 2
batchDF.unpersist()
}
? Apply additional DataFrame operations - Many DataFrame and Dataset operations are not supported in streaming DataFrames because Spark does not support generating incremental plans in those cases. Using foreachBatch, you can apply some of these operations on each micro-batch output. However, you will have to reason about the end-to-end semantics of doing that operation yourself.
Note:
? By default, foreachBatch provides only at-least-once write guarantees. However, you can use the batchId provided to the function as way to deduplicate the output and get an exactly-once guarantee.
? foreachBatch does not work with the continuous processing mode as it fundamentally relies on the micro-batch execution of a streaming query. If you write data in the continuous mode, use foreach instead.
Foreach
If foreachBatch is not an option (for example, corresponding batch data writer does not exist, or continuous processing mode), then you can express your custom writer logic using foreach. Specifically, you can express the data writing logic by dividing it into three methods: open, process, and close. Since Spark 2.4, foreach is available in Scala, Java and Python.
? Scala
? Java
? Python
? R
In Scala, you have to extend the class ForeachWriter (docs).
streamingDatasetOfString.writeStream.foreach(
new ForeachWriter[String] {

def open(partitionId: Long, version: Long): Boolean = {// Open connection
}def process(record: String): Unit = {// Write string to connection
}def close(errorOrNull: Throwable): Unit = {// Close the connection
}

}
).start()
Execution semantics When the streaming query is started, Spark calls the function or the object’s methods in the following way:
? A single copy of this object is responsible for all the data generated by a single task in a query. In other words, one instance is responsible for processing one partition of the data generated in a distributed manner.
? This object must be serializable, because each task will get a fresh serialized-deserialized copy of the provided object. Hence, it is strongly recommended that any initialization for writing data (for example. opening a connection or starting a transaction) is done after the open() method has been called, which signifies that the task is ready to generate data.
? The lifecycle of the methods are as follows:
o For each partition with partition_id:
? For each batch/epoch of streaming data with epoch_id:
? Method open(partitionId, epochId) is called.
? If open(…) returns true, for each row in the partition and batch/epoch, method process(row) is called.
? Method close(error) is called with error (if any) seen while processing rows.
? The close() method (if it exists) is called if an open() method exists and returns successfully (irrespective of the return value), except if the JVM or Python process crashes in the middle.
? Note: Spark does not guarantee same output for (partitionId, epochId), so deduplication cannot be achieved with (partitionId, epochId). e.g. source provides different number of partitions for some reasons, Spark optimization changes number of partitions, etc. See SPARK-28650 for more details. If you need deduplication on output, try out foreachBatch instead.
Streaming Table APIs
Since Spark 3.1, you can also use DataStreamReader.table() to read tables as streaming DataFrames and use DataStreamWriter.toTable() to write streaming DataFrames as tables:
? Scala
? Java
? Python
? R
val spark: SparkSession = …

// Create a streaming DataFrame
val df = spark.readStream
.format(“rate”)
.option(“rowsPerSecond”, 10)
.load()

// Write the streaming DataFrame to a table
df.writeStream
.option(“checkpointLocation”, “path/to/checkpoint/dir”)
.toTable(“myTable”)

// Check the table result
spark.read.table(“myTable”).show()

// Transform the source dataset and write to a new table
spark.readStream
.table(“myTable”)
.select(“value”)
.writeStream
.option(“checkpointLocation”, “path/to/checkpoint/dir”)
.format(“parquet”)
.toTable(“newTable”)

// Check the new table result
spark.read.table(“newTable”).show()
For more details, please check the docs for DataStreamReader (Scala/Java/Python docs) and DataStreamWriter (Scala/Java/Python docs).
Triggers
The trigger settings of a streaming query define the timing of streaming data processing, whether the query is going to be executed as micro-batch query with a fixed batch interval or as a continuous processing query. Here are the different kinds of triggers that are supported.
Trigger Type Description
unspecified (default) If no trigger setting is explicitly specified, then by default, the query will be executed in micro-batch mode, where micro-batches will be generated as soon as the previous micro-batch has completed processing.
Fixed interval micro-batches The query will be executed with micro-batches mode, where micro-batches will be kicked off at the user-specified intervals.
? If the previous micro-batch completes within the interval, then the engine will wait until the interval is over before kicking off the next micro-batch.
? If the previous micro-batch takes longer than the interval to complete (i.e. if an interval boundary is missed), then the next micro-batch will start as soon as the previous one completes (i.e., it will not wait for the next interval boundary).
? If no new data is available, then no micro-batch will be kicked off.
One-time micro-batch The query will execute only one micro-batch to process all the available data and then stop on its own. This is useful in scenarios you want to periodically spin up a cluster, process everything that is available since the last period, and then shutdown the cluster. In some case, this may lead to significant cost savings.
Continuous with fixed checkpoint interval
(experimental) The query will be executed in the new low-latency, continuous processing mode. Read more about this in the Continuous Processing section below.

Here are a few code examples.
? Scala
? Java
? Python
? R
import org.apache.spark.sql.streaming.Trigger

// Default trigger (runs micro-batch as soon as it can)
df.writeStream
.format(“console”)
.start()

// ProcessingTime trigger with two-seconds micro-batch interval
df.writeStream
.format(“console”)
.trigger(Trigger.ProcessingTime(“2 seconds”))
.start()

// One-time trigger
df.writeStream
.format(“console”)
.trigger(Trigger.Once())
.start()

// Continuous trigger with one-second checkpointing interval
df.writeStream
.format(“console”)
.trigger(Trigger.Continuous(“1 second”))
.start()
Managing Streaming Queries
The StreamingQuery object created when a query is started can be used to monitor and manage the query.
? Scala
? Java
? Python
? R
val query = df.writeStream.format(“console”).start() // get the query object

query.id // get the unique identifier of the running query that persists across restarts from checkpoint data

query.runId // get the unique id of this run of the query, which will be generated at every start/restart

query.name // get the name of the auto-generated or user-specified name

query.explain() // print detailed explanations of the query

query.stop() // stop the query

query.awaitTermination() // block until query is terminated, with stop() or with error

query.exception // the exception if the query has been terminated with error

query.recentProgress // an array of the most recent progress updates for this query

query.lastProgress // the most recent progress update of this streaming query
You can start any number of queries in a single SparkSession. They will all be running concurrently sharing the cluster resources. You can use sparkSession.streams() to get the StreamingQueryManager (Scala/Java/Python docs) that can be used to manage the currently active queries.
? Scala
? Java
? Python
? R
val spark: SparkSession = …

spark.streams.active // get the list of currently active streaming queries

spark.streams.get(id) // get a query object by its unique id

spark.streams.awaitAnyTermination() // block until any one of them terminates
Monitoring Streaming Queries
There are multiple ways to monitor active streaming queries. You can either push metrics to external systems using Spark’s Dropwizard Metrics support, or access them programmatically.
Reading Metrics Interactively
You can directly get the current status and metrics of an active query using streamingQuery.lastProgress() and streamingQuery.status(). lastProgress() returns a StreamingQueryProgress object in Scala and Java and a dictionary with the same fields in Python. It has all the information about the progress made in the last trigger of the stream - what data was processed, what were the processing rates, latencies, etc. There is also streamingQuery.recentProgress which returns an array of last few progresses.
In addition, streamingQuery.status() returns a StreamingQueryStatus object in Scala and Java and a dictionary with the same fields in Python. It gives information about what the query is immediately doing - is a trigger active, is data being processed, etc.
Here are a few examples.
? Scala
? Java
? Python
? R
val query: StreamingQuery = …

println(query.lastProgress)

/* Will print something like the following.

{
“id” : “ce011fdc-8762-4dcb-84eb-a77333e28109”,
“runId” : “88e2ff94-ede0-45a8-b687-6316fbef529a”,
“name” : “MyQuery”,
“timestamp” : “2016-12-14T18:45:24.873Z”,
“numInputRows” : 10,
“inputRowsPerSecond” : 120.0,
“processedRowsPerSecond” : 200.0,
“durationMs” : {
“triggerExecution” : 3,
“getOffset” : 2
},
“eventTime” : {
“watermark” : “2016-12-14T18:45:24.873Z”
},
“stateOperators” : [ ],
“sources” : [ {
“description” : “KafkaSource[Subscribe[topic-0]]”,
“startOffset” : {
“topic-0” : {
“2” : 0,
“4” : 1,
“1” : 1,
“3” : 1,
“0” : 1
}
},
“endOffset” : {
“topic-0” : {
“2” : 0,
“4” : 115,
“1” : 134,
“3” : 21,
“0” : 534
}
},
“numInputRows” : 10,
“inputRowsPerSecond” : 120.0,
“processedRowsPerSecond” : 200.0
} ],
“sink” : {
“description” : “MemorySink”
}
}
*/

println(query.status)

/* Will print something like the following.
{
“message” : “Waiting for data to arrive”,
“isDataAvailable” : false,
“isTriggerActive” : false
}
*/
Reporting Metrics programmatically using Asynchronous APIs
You can also asynchronously monitor all queries associated with a SparkSession by attaching a StreamingQueryListener (Scala/Java docs). Once you attach your custom StreamingQueryListener object with sparkSession.streams.attachListener(), you will get callbacks when a query is started and stopped and when there is progress made in an active query. Here is an example,
? Scala
? Java
? Python
? R
val spark: SparkSession = …

spark.streams.addListener(new StreamingQueryListener() {
override def onQueryStarted(queryStarted: QueryStartedEvent): Unit = {
println("Query started: " + queryStarted.id)
}
override def onQueryTerminated(queryTerminated: QueryTerminatedEvent): Unit = {
println("Query terminated: " + queryTerminated.id)
}
override def onQueryProgress(queryProgress: QueryProgressEvent): Unit = {
println("Query made progress: " + queryProgress.progress)
}
})
Reporting Metrics using Dropwizard
Spark supports reporting metrics using the Dropwizard Library. To enable metrics of Structured Streaming queries to be reported as well, you have to explicitly enable the configuration spark.sql.streaming.metricsEnabled in the SparkSession.
? Scala
? Java
? Python
? R
spark.conf.set(“spark.sql.streaming.metricsEnabled”, “true”)
// or
spark.sql(“SET spark.sql.streaming.metricsEnabled=true”)
All queries started in the SparkSession after this configuration has been enabled will report metrics through Dropwizard to whatever sinks have been configured (e.g. Ganglia, Graphite, JMX, etc.).
Recovering from Failures with Checkpointing
In case of a failure or intentional shutdown, you can recover the previous progress and state of a previous query, and continue where it left off. This is done using checkpointing and write-ahead logs. You can configure a query with a checkpoint location, and the query will save all the progress information (i.e. range of offsets processed in each trigger) and the running aggregates (e.g. word counts in the quick example) to the checkpoint location. This checkpoint location has to be a path in an HDFS compatible file system, and can be set as an option in the DataStreamWriter when starting a query.
? Scala
? Java
? Python
? R
aggDF
.writeStream
.outputMode(“complete”)
.option(“checkpointLocation”, “path/to/HDFS/dir”)
.format(“memory”)
.start()
Recovery Semantics after Changes in a Streaming Query
There are limitations on what changes in a streaming query are allowed between restarts from the same checkpoint location. Here are a few kinds of changes that are either not allowed, or the effect of the change is not well-defined. For all of them:
? The term allowed means you can do the specified change but whether the semantics of its effect is well-defined depends on the query and the change.
? The term not allowed means you should not do the specified change as the restarted query is likely to fail with unpredictable errors. sdf represents a streaming DataFrame/Dataset generated with sparkSession.readStream.
Types of changes
? Changes in the number or type (i.e. different source) of input sources: This is not allowed.
? Changes in the parameters of input sources: Whether this is allowed and whether the semantics of the change are well-defined depends on the source and the query. Here are a few examples.
o Addition/deletion/modification of rate limits is allowed: spark.readStream.format(“kafka”).option(“subscribe”, “topic”) to spark.readStream.format(“kafka”).option(“subscribe”, “topic”).option(“maxOffsetsPerTrigger”, …)
o Changes to subscribed topics/files are generally not allowed as the results are unpredictable: spark.readStream.format(“kafka”).option(“subscribe”, “topic”) to spark.readStream.format(“kafka”).option(“subscribe”, “newTopic”)
? Changes in the type of output sink: Changes between a few specific combinations of sinks are allowed. This needs to be verified on a case-by-case basis. Here are a few examples.
o File sink to Kafka sink is allowed. Kafka will see only the new data.
o Kafka sink to file sink is not allowed.
o Kafka sink changed to foreach, or vice versa is allowed.
? Changes in the parameters of output sink: Whether this is allowed and whether the semantics of the change are well-defined depends on the sink and the query. Here are a few examples.
o Changes to output directory of a file sink are not allowed: sdf.writeStream.format(“parquet”).option(“path”, “/somePath”) to sdf.writeStream.format(“parquet”).option(“path”, “/anotherPath”)
o Changes to output topic are allowed: sdf.writeStream.format(“kafka”).option(“topic”, “someTopic”) to sdf.writeStream.format(“kafka”).option(“topic”, “anotherTopic”)
o Changes to the user-defined foreach sink (that is, the ForeachWriter code) are allowed, but the semantics of the change depends on the code.
? Changes in projection / filter / map-like operations: Some cases are allowed. For example:
o Addition / deletion of filters is allowed: sdf.selectExpr(“a”) to sdf.where(…).selectExpr(“a”).filter(…).
o Changes in projections with same output schema are allowed: sdf.selectExpr(“stringColumn AS json”).writeStream to sdf.selectExpr(“anotherStringColumn AS json”).writeStream
o Changes in projections with different output schema are conditionally allowed: sdf.selectExpr(“a”).writeStream to sdf.selectExpr(“b”).writeStream is allowed only if the output sink allows the schema change from “a” to “b”.
? Changes in stateful operations: Some operations in streaming queries need to maintain state data in order to continuously update the result. Structured Streaming automatically checkpoints the state data to fault-tolerant storage (for example, HDFS, AWS S3, Azure Blob storage) and restores it after restart. However, this assumes that the schema of the state data remains same across restarts. This means that any changes (that is, additions, deletions, or schema modifications) to the stateful operations of a streaming query are not allowed between restarts. Here is the list of stateful operations whose schema should not be changed between restarts in order to ensure state recovery:
o Streaming aggregation: For example, sdf.groupBy(“a”).agg(…). Any change in number or type of grouping keys or aggregates is not allowed.
o Streaming deduplication: For example, sdf.dropDuplicates(“a”). Any change in number or type of grouping keys or aggregates is not allowed.
o Stream-stream join: For example, sdf1.join(sdf2, …) (i.e. both inputs are generated with sparkSession.readStream). Changes in the schema or equi-joining columns are not allowed. Changes in join type (outer or inner) are not allowed. Other changes in the join condition are ill-defined.
o Arbitrary stateful operation: For example, sdf.groupByKey(…).mapGroupsWithState(…) or sdf.groupByKey(…).flatMapGroupsWithState(…). Any change to the schema of the user-defined state and the type of timeout is not allowed. Any change within the user-defined state-mapping function are allowed, but the semantic effect of the change depends on the user-defined logic. If you really want to support state schema changes, then you can explicitly encode/decode your complex state data structures into bytes using an encoding/decoding scheme that supports schema migration. For example, if you save your state as Avro-encoded bytes, then you are free to change the Avro-state-schema between query restarts as the binary state will always be restored successfully.
Continuous Processing
[Experimental]
Continuous processing is a new, experimental streaming execution mode introduced in Spark 2.3 that enables low (~1 ms) end-to-end latency with at-least-once fault-tolerance guarantees. Compare this with the default micro-batch processing engine which can achieve exactly-once guarantees but achieve latencies of ~100ms at best. For some types of queries (discussed below), you can choose which mode to execute them in without modifying the application logic (i.e. without changing the DataFrame/Dataset operations).
To run a supported query in continuous processing mode, all you need to do is specify a continuous trigger with the desired checkpoint interval as a parameter. For example,
? Scala
? Java
? Python
import org.apache.spark.sql.streaming.Trigger

spark
.readStream
.format(“kafka”)
.option(“kafka.bootstrap.servers”, “host1:port1,host2:port2”)
.option(“subscribe”, “topic1”)
.load()
.selectExpr(“CAST(key AS STRING)”, “CAST(value AS STRING)”)
.writeStream
.format(“kafka”)
.option(“kafka.bootstrap.servers”, “host1:port1,host2:port2”)
.option(“topic”, “topic1”)
.trigger(Trigger.Continuous(“1 second”)) // only change in query
.start()
A checkpoint interval of 1 second means that the continuous processing engine will record the progress of the query every second. The resulting checkpoints are in a format compatible with the micro-batch engine, hence any query can be restarted with any trigger. For example, a supported query started with the micro-batch mode can be restarted in continuous mode, and vice versa. Note that any time you switch to continuous mode, you will get at-least-once fault-tolerance guarantees.
Supported Queries
As of Spark 2.4, only the following type of queries are supported in the continuous processing mode.
? Operations: Only map-like Dataset/DataFrame operations are supported in continuous mode, that is, only projections (select, map, flatMap, mapPartitions, etc.) and selections (where, filter, etc.).
o All SQL functions are supported except aggregation functions (since aggregations are not yet supported), current_timestamp() and current_date() (deterministic computations using time is challenging).
? Sources:
o Kafka source: All options are supported.
o Rate source: Good for testing. Only options that are supported in the continuous mode are numPartitions and rowsPerSecond.
? Sinks:
o Kafka sink: All options are supported.
o Memory sink: Good for debugging.
o Console sink: Good for debugging. All options are supported. Note that the console will print every checkpoint interval that you have specified in the continuous trigger.
See Input Sources and Output Sinks sections for more details on them. While the console sink is good for testing, the end-to-end low-latency processing can be best observed with Kafka as the source and sink, as this allows the engine to process the data and make the results available in the output topic within milliseconds of the input data being available in the input topic.
Caveats
? Continuous processing engine launches multiple long-running tasks that continuously read data from sources, process it and continuously write to sinks. The number of tasks required by the query depends on how many partitions the query can read from the sources in parallel. Therefore, before starting a continuous processing query, you must ensure there are enough cores in the cluster to all the tasks in parallel. For example, if you are reading from a Kafka topic that has 10 partitions, then the cluster must have at least 10 cores for the query to make progress.
? Stopping a continuous processing stream may produce spurious task termination warnings. These can be safely ignored.
? There are currently no automatic retries of failed tasks. Any failure will lead to the query being stopped and it needs to be manually restarted from the checkpoint.
Additional Information
Notes
? Several configurations are not modifiable after the query has run. To change them, discard the checkpoint and start a new query. These configurations include:
o spark.sql.shuffle.partitions
? This is due to the physical partitioning of state: state is partitioned via applying hash function to key, hence the number of partitions for state should be unchanged.
? If you want to run fewer tasks for stateful operations, coalesce would help with avoiding unnecessary repartitioning.
? After coalesce, the number of (reduced) tasks will be kept unless another shuffle happens.
o spark.sql.streaming.stateStore.providerClass: To read the previous state of the query properly, the class of state store provider should be unchanged.
o spark.sql.streaming.multipleWatermarkPolicy: Modification of this would lead inconsistent watermark value when query contains multiple watermarks, hence the policy should be unchanged.
Further Reading
? See and run the Scala/Java/Python/R examples.
o Instructions on how to run Spark examples
? Read about integrating with Kafka in the Structured Streaming Kafka Integration Guide
? Read more details about using DataFrames/Datasets in the Spark SQL Programming Guide
? Third-party Blog Posts
o Real-time Streaming ETL with Structured Streaming in Apache Spark 2.1 (Databricks Blog)
o Real-Time End-to-End Integration with Apache Kafka in Apache Spark’s Structured Streaming (Databricks Blog)
o Event-time Aggregation and Watermarking in Apache Spark’s Structured Streaming (Databricks Blog)
Talks
? Spark Summit Europe 2017
o Easy, Scalable, Fault-tolerant Stream Processing with Structured Streaming in Apache Spark - Part 1 slides/video, Part 2 slides/video
o Deep Dive into Stateful Stream Processing in Structured Streaming - slides/video
? Spark Summit 2016
o A Deep Dive into Structured Streaming - slides/video

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