Spark Readstream Json

Compared to run our training and tuning phase in local machines or single servers, it is quite fast that we can train our model in Azure Databricks with Spark. 0 将流式计算也统一到DataFrame里去了,提出了Structured Streaming的概念,将数据源映射为一张无线长度的表,同时将流式计算的结果映射为另外一张表,完全以结构化的方式去操作流式数据,复用了其对象的Catalyst引擎。. Spark provides two APIs for streaming data one is Spark Streaming which is a separate library provided by Spark. py", line 103, in awaitTermination. Writing a Spark Stream Word Count Application to MapR Database. StreamSQL will pass them transparently to spark when creating the streaming job. --Spark website Spark provides fast iterative/functional-like capabilities over large data sets, typically by. 8 Direct Stream approach. Spark Streaming is an extension of core Spark API, which allows processing of live data streaming. In this tutorial I'll create a Spark Streaming application that analyzes fake events streamed from another. Spark on Azure HDInsight. Also, add a Kafka producer utility method to send sample data to Kafka in Amazon MSK and verify that it is being processed by the streaming query. readStream. by Andrea Santurbano. option("maxFilesPerTrigger", 1) // Treat a sequence of files as a stream by picking one file at a time. Delta Lake overcomes many of the limitations typically associated with streaming systems and files, including:. setEventHubName ("{EVENT HUB NAME}"). Spark SQL (and Structured Streaming) deals, under the covers, with raw bytes instead of JVM objects, in order to optimize for space and efficient data access. PAM Authentication for Spark. Data; using System. from method reads octets from array and returns a buffer initialized with those read bytes. You can convert JSON String to Java object in just 2 lines by using Gson as shown below. spark_read_json: Read a JSON file into a Spark DataFrame in sparklyr: R Interface to Apache Spark rdrr. L’idée de cet article est de brancher Spark Structured Streaming à Kafka pour consommer des messages en Avro dont le schéma est géré par le Schema Registry. This Spark module allows saving DataFrame as BigQuery table. Same time, there are a number of tricky aspects that might lead to unexpected results. 0+ with python 3. Spark From Kafka Message Receiver (Scala). Jump Start on Apache® Spark™ 2. We need to provide the structure (list of fields) of the JSON data so that the Dataframe can reflect this structure:. Editor's note: Andrew recently spoke at StampedeCon on this very topic. It maps data sources into an infinite-length table, and maps the stream computing results into another table at the same time. We are sending a file path as message through azure event hub and when passing received messages to spark. I have two problems: > 1. 8 est-elle un cas de "la plus longue lettre de suicide de l'histoire"? [fermé] Scala vs. An ML model developed with Spark MLlib can be combined with a low-latency streaming pipeline created with Spark Structured Streaming. Note that version should be at least 6. option("maxFilesPerTrigger", 1) // Treat a sequence of files as a stream by picking one file at a time. Introduction In a previous article, I described how a data ingestion solution based on Kafka, Parquet, MongoDB and Spark Structured Streaming could have the following capabilities: Stream processing of data as it arrives. In this example, I will process JSON deposited in the BLOB Storage Account. It is user-friendly and easy to read and write, because it looks a lot like JSON. 0 application that reads messages from kafka using spark streaming (with spark-streaming-kafka-0-10_2. I'm new to this field, but it seems like most "Big Data" examples -- Spark's included -- begin with reading in flat lines of text from a file. 0 以上) Structured Streaming integration for Kafka 0. modules folder has subfolders for each module, module. It's a radical departure from models of other stream processing frameworks like storm, beam, flink etc. 【版权声明】博客内容由厦门大学数据库实验室拥有版权,未经允许,请勿转载! [返回Spark教程首页]Structured Streaming目前的支持的数据源有两种,一种是文件,另一种是网络套接字;Spark2. Introduction In this tutorial, we will learn to store data files using Ambari HDFS Files View. select("data. StreamSQL will pass them transparently to spark when creating the streaming job. Just like SQL. You can access DataStreamReader using SparkSession. schema(schema). json(inputPath) ) That's right, creating a streaming DataFrame is a simple as the flick of this switch. KafkaSource’s Internal Registries and Counters Name Description; currentPartitionOffsets. Latest Spark 2. which tries to read data from kafka topics and write it to HDFS Location. Part 2 covers a "gotcha" or something you might not expect when using Spark SQL JSON data source. 0 release there is an option to switch between micro-batching and experimental continuous streaming mode. format We thus have to parse this towards our original JSON. NET Class file: Below is the sample code using System; using System. 12:9092" // Setup connection to Kafka val. The most awesome part is that, a new JSON file will be created in the same partition. The Spark Streaming integration for Kafka 0. If you know the schema in advance, use the version that specifies the schema to avoid the extra scan. Spark Streaming was launched as a part of Spark 0. format("parquet") Write to Parquet. format('kafka'). Bu bölümde Apache Spark ile belirli zaman gruplarında verileri analiz ederek sonuçlar oluşturacağız. For example my csv file is :-ProductID,ProductName,price,availability,type. Let’s say, we have a requirement like: JSON data being received in Kafka, Parse nested JSON, flatten it and store in structured Parquet table and get end-to-end failure guarantees. String bootstrapServers = “localhost:9092”;. Below is the schema defined based on the format defined in CloudTrail documentation. Hot-keys on this page. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58. That's really simple. *") powerful built-in Python APIs to perform complex data. 0 application that reads messages from kafka using spark streaming (with spark-streaming-kafka-0-10_2. 0: STRUCTURED STREAMING AND DATASETS Andrew Ray StampedeCon 2016. In this article, we'll show how to create a Just-In-Time Data Warehouse by using Neo4j and the Neo4j Streams module with Apache Spark's Structured Streaming Apis and Apache Kafka. How to leverage Neo4j Streams and build a just-in-time data warehouse Photo by Vanessa Ochotorena on Unsplash. Learn how to integrate Spark Structured Streaming and. Damji Apache Spark Community Evangelist Spark Saturday Meetup Workshop. Writing new connectors for the RDD API or extending the DataFrame/DataSet API allows third parties to integrate with Spark with easy. Spark on Azure HDInsight. For JSON (one record per file), set the multiLine option to true. 3)。 操作跟DF几乎一样,自动转换为累积计算形式,也能导出Spakr SQL所用的表格。. Spark Streaming uses the power of Spark on streams of data, often data generated in real time by many producers. 0版本只支持输入源:File、kafka和socket。 1. We also recommend users to go through this link to run Spark in Eclipse. format("kafka"). 加载json文件的时候,如果schema设置的属性,如果存在非字符串类型,那么转成column就都变成了null,eg. Below is the schema defined based on the format defined in CloudTrail documentation. When there is at least one file the schema is calculated using dataFrameBuilder constructor parameter function. The class is: EventHubsForeachWriter. 0 for "Elasticsearch For Apache Hadoop" and 2. A Spark Streaming application will then parse those tweets in JSON format and perform various transformations on them including filtering, aggregations and joins. 2 on Databricks 1. If you know the schema in advance, use the version that specifies the schema to avoid the extra scan. But when using Avro we are not able to decode at the Spark end. Spark Structured Streaming目前的2. Let's move on to the next step, which may be to perform aggregation on the streaming data. Last time, we talked about Apache Kafka and Apache Storm for use in a real-time processing engine. Below is what we tried, Message in Kafka:. select(from_json("json", schema). I’ll assume you have Kafka set up already, and it’s running on localhost, as well as Spark Standalone. Apache Spark is able to parallelize all processes on the executor nodes equally. {"time":1469501107,"action":"Open"} Each line in the file contains JSON record with two fields — time and action. Below is what we tried, Message in Kafka:. Name Email Dev Id Roles Organization; Matei Zaharia: matei. selectExpr("cast (value as string) as json"). Implementation of these 3 steps leads to the successful deployment of “Machine Learning Models with Spark”. Spark Streaming example tutorial in Scala which processes data in from Slack. I'm pretty new to spark and I'm trying to receive a DStream structured as a json from a kafka topic and I want to parse the content of each json. File "/home/ubuntu/spark/python/lib/pyspark. Örnek verirsek bir port üzerinden aldıgımız kelimeleri 10’er saniyelik bölümlerde sayarak kaç adet kelime geldiğini hesaplayabiliriz. These are formats supported by spark 2. Spark Structured Streaming is a stream processing engine built on Spark SQL. StructuredNetworkWordCount maintains a running word count of text data received from a TCP socket. That might be. select("data. This is not easy to programming define the Structure type. This article describes Spark Streaming example on Consuming messages from Kafa and Producing messages to Kafka in JSON format using from_json and to_json Spark functions respectively. 0+, we prefer use Structured Streaming(DataFrame /DataSet API) in, rather than Spark Core API, but when we see the Availability log data, it is XML like format, with several hierarchy. Gson g = new Gson(); Player p = g. First the Spark App need to subscribe to the Kafka topic. Part 1 focus is the “happy path” when using JSON with Spark SQL. This example assumes that you would be using spark 2. textFileStream(inputdir) # process new files as they appear data = lines. In some case, however, a separate writer needs to be implemented for writing out results into a database, queue or some other format. Spark Structured Streaming is a stream processing engine built on Spark SQL. trigger to set the stream batch period , Trigger - How Frequently to Check Sources For New Data , Triggers in Apache Beam. In this post I'll show how to use Spark SQL to deal with JSON. These are formats supported by spark 2. Learn how to consume streaming Open Payments CSV data, transform it to JSON, store it in a document database, and explore with SQL using Apache Spark, MapR-ES MapR-DB, OJAI, and Apache Drill. Spark SQL can automatically capture the schema of a JSON dataset and load it as a DataFrame. building robust stream processing apps is hard 3 4. 9% Azure Cloud SLA. Apache Spark •The most popular and de-facto framework for big data (science) •APIs in SQL, R, Python, Scala, Java •Support for SQL, ETL, machine learning/deep learning, graph …. The settings. Each new release of Spark contains enhancements that make use of DataFrames API with JSON data more convenient. This tutorial demonstrates how to use Apache Spark Structured Streaming to read and write data with Apache Kafka on Azure HDInsight. First, we need to install the spark. First, Read files using Spark's fileStream. By the way, If you are not familiar with Spark SQL, a couple of references include a summary of Spark SQL chapter post and the. We are sending a file path as message through azure event hub and when passing received messages to spark. 100% open source Apache Spark and Hadoop bits. or you can go to maven repository for Elasticsearch For Apache Hadoop and Spark SQL and get a suitable version. Read also about Triggers in Apache Spark Structured Streaming here: [SPARK-14176][SQL]Add DataFrameWriter. The following are code examples for showing how to use pyspark. I just helped my six students to learn Apache Spark, and Structured Streaming in particular. spark_read_json: Read a JSON file into a Spark DataFrame in sparklyr: R Interface to Apache Spark rdrr. Learn the Spark streaming concepts by performing its demonstration with TCP socket. View Lab Report - Lab 6 - Spark Structured Streaming - 280818 HAHA. The Spark Streaming integration for Kafka 0. This is Recipe 11. The project was inspired by spotify/spark-bigquery, but there are several differences and enhancements: Use of the Structured Streaming API. SchemaBuilder // When reading the key and value of a Kafka topic, decode the // binary (Avro) data into structured data. The usual first. Socket Socket方式是最简单的数据输入源,如Quick example所示的程序,就是使用的这种方式。. According to Spark documentation:. We are sending a file path as message through azure event hub and when passing received messages to spark. servers", "localhost:9092"). Spark Job File Configuration. readStream. Question by soumyabrata kole Dec 10, 2016 at 07:18 AM spark-sql json. In this article, we’ll show how to create a Just-In-Time Data Warehouse by using Neo4j and the Neo4j Streams module with Apache Spark’s Structured Streaming Apis and Apache Kafka. 加载json文件的时候,如果schema设置的属性,如果存在非字符串类型,那么转成column就都变成了null,eg. An Introduction to JSON. Latest Spark 2. setStartingPosition (EventPosition. Most people will use one of the built-in API, such as Kafka for streams processing or JSON / CVS for file processing. Having Spark read a JSON file. As I normally do when teaching on-site, I offered that we. Parsing billing files took several weeks. In this article, third installment of Apache Spark series, author Srini Penchikala discusses Apache Spark Streaming framework for processing real-time streaming data using a log analytics sample. In this post we are going to build a system that ingests real time data from Twitter, packages it as JSON objects and sends it through a Kafka Producer to a Kafka Cluster. json(path) and then calling printSchema() on top of it to return the inferred schema. Initially NONE and set when KafkaSource is requested to get the maximum available offsets or generate a DataFrame with records from Kafka for a batch. This function goes through the input once to determine the input schema. 0+, we prefer use Structured Streaming(DataFrame /DataSet API) in, rather than Spark Core API, but when we see the Availability log data, it is XML like format, with several hierarchy. [Spark Engine] Databricks #opensource // eventHubs is a org. zip/pyspark/sql/streaming. json file defines the Docker build process, the module version, and your docker registry, updating the version number, pushing the updated module to an image registry, and updating the deployment manifest for an edge device triggers the Azure IoT Edge runtime to. class) You can also convert a Java object to JSON by using to Json() method as shown below. This Spark module allows saving DataFrame as BigQuery table. Below is the schema defined based on the format defined in CloudTrail documentation. The easiest is to use Spark's from_json() function from the org. DStreams is the basic abstraction in Spark Streaming. This function goes through the input once to determine the input schema. Initially NONE and set when KafkaSource is requested to get the maximum available offsets or generate a DataFrame with records from Kafka for a batch. Components of a Spark Structured Streaming application. Fully Managed Service. This article was co-authored by Elena Akhmatova. json(path) and then calling printSchema() on top of it to return the inferred schema. _ import org. Following is code:- from pyspark. load("subscribe") Project result = input device, signal Optimized Operator new files. below is my code , i m reading the data from kafka having json data , and i wanted to store the data into postgresql. schema returns exactly a wanted inferred schema, you can use this returned schema as an argument for the mandatory schema parameter of spark. I am reading data from kafka topic using spark structured streaming, I want to run sql queries on this streaming data. In this article I'm going to explain how to built a data ingestion architecture using Azure Databricks enabling us to stream data through Spark Structured Streaming, from IotHub to Comos DB. You can access DataStreamReader using SparkSession. 構造化 ストリーミング + Kafka 統合ガイド (Kafkaブローカーバージョン 0. json(s3://weblogs) can be used to read log data continuously from an AWS S3 bucket in JSON format. The usual first. JSON Libraries; JVM Languages; Object/Relational Mapping; PDF Libraries; Top Categories; Home » org. 1, in this blog wanted to show sample code for achieving stream joins. 加载json文件的时候,如果schema设置的属性,如果存在非字符串类型,那么转成column就都变成了null,eg. In a few words, Spark is a fast and powerful framework that provides an API to perform massive distributed processing over resilient sets of data. The file will be read at the beginning of the Spark job and its contents will be used to configure various variables of the Spark job. readStream method. Structured Streaming in Spark July 28th, 2016. Apache Spark consume less memory and fast. We are sending a file path as message through azure event hub and when passing received messages to spark. json dosyası bulunmaktadır. modules folder has subfolders for each module, module. sparkContext to access it Working with SparkContext and SparkSession spark. Spark Streaming was launched as a part of Spark 0. SchemaBuilder // When reading the key and value of a Kafka topic, decode the // binary (Avro) data into structured data. • PMC formed by Apache Spark committers/pmc, Apache Members. 【版权声明】博客内容由厦门大学数据库实验室拥有版权,未经允许,请勿转载! [返回Spark教程首页]Structured Streaming目前的支持的数据源有两种,一种是文件,另一种是网络套接字;Spark2. In many cases, it even automatically infers a schema. The given below idea is purely for this question. What I did was to specify a one-liner sample-json as input for inferring the schema stuff so it does not unnecessary take up memory. option("maxFilesPerTrigger", 1). Support for File Types. loads) # map DStream and return new DStream ssc. > Dear all, > > > I'm trying to parse json formatted Kafka messages and then send back to cassandra. Lets assume we are receiving huge amount of streaming events for connected cars. This function goes through the input once to determine the input schema. json file defines the Docker build process, the module version, and your docker registry, updating the version number, pushing the updated module to an image registry, and updating the deployment manifest for an edge device triggers the Azure IoT Edge runtime to. In this tutorial I'll create a Spark Streaming application that analyzes fake events streamed from another. In a few words, Spark is a fast and powerful framework that provides an API to perform massive distributed processing over resilient sets of data. DataStreamReader is the Spark developer-friendly API to create a StreamingRelation logical operator (that represents a streaming source in a logical plan). select(from_json("json", schema). According to Spark documentation:. Currently, I have implemented it as follows. tags: Spark Java. in the re. x with Databricks Jules S. Streams¶ Streams are high-level async/await-ready primitives to work with network connections. Spark on Azure HDInsight. Connecting Event Hubs and Spark. ssc = StreamingContext(sc, 2) # 2 second batches lines = ssc. option("maxFilesPerTrigger", 1) // Treat a sequence of files as a stream by picking one file at a time. Note that version should be at least 6. Theo van Kraay, Data and AI Solution Architect at Microsoft, returns with a short blog on simplified Lambda Architecture with Cosmos DB, ChangeFeed, and Spark on Databricks. All they need to do is spark. String bootstrapServers = “localhost:9092”;. json dosyası bulunmaktadır. User should pass the options of the streaming source table in its TBLPROPERTIES when creating it. Each time an executor on a Worker Node processes a micro-batch, a separate copy of this DataFrame would be sent. select(from_json("json", schema). 0 for "Elasticsearch For Apache Hadoop" and 2. option("maxFilesPerTrigger", 1) // Treat a sequence of files as a stream by picking one file at a time. That might be. as[String] import org. In Databricks, we leverage the power of Spark Streaming to perform SQL like manipulations on Streaming Data. I have a requirement to process xml files streamed into a S3 folder. This can then used be used to create the StructType. First the Spark App need to subscribe to the Kafka topic. Streams allow sending and receiving data without using callbacks or low-level protocols and transports. Spark Project SQL. Apache Spark •The most popular and de-facto framework for big data (science) •APIs in SQL, R, Python, Scala, Java •Support for SQL, ETL, machine learning/deep learning, graph …. Else, an IllegalArgumentException("No schema specified") is thrown unless it is for text provider (as providerName constructor parameter) where the default schema with a single value column of type StringType is assumed. can someone point me to a good tutorial on spark streaming to use with kafka Question by Tajinderpal Singh Jun 10, 2016 at 10:18 AM Spark spark-sql spark-streaming I am trying to fetch json format data from kafka through spark streaming and want to create a temp table in spark to query json data like normal table. Compared to run our training and tuning phase in local machines or single servers, it is quite fast that we can train our model in Azure Databricks with Spark. First, Read files using Spark's fileStream. Luckily, we find out that in the azure event hub spark library, there is class that provides all of this. I wanted to use structured streaming even when the source is not really a stream but just a folder with a bunch of files in it. Using Apache Spark for that can be much convenient. This tutorial demonstrates how to use Apache Spark Structured Streaming to read and write data with Apache Kafka on Azure HDInsight. Hi All, I am trying to read a valid Json as below through. Since Spark can use multi-line JSON file as a data source, all the polygons can be load into the DataFrame with spark. You can vote up the examples you like or vote down the exmaples you don't like. Let's try to analyze these files interactively. This method is intended for testing note:: In the case of continually arriving data, this method may block forever. In some case, however, a separate writer needs to be implemented for writing out results into a database, queue or some other format. Also, the content-length is always required in the request and signing string, even if the body is empty. You need to actually do something with the RDD for each batch. In this post I'll show how to use Spark SQL to deal with JSON. 滑动窗口功能由三个参数决定其功能:窗口时间、滑动步长和触发时间 window timecolumn:具有时间戳的列; windowDuration:为窗口的时间长度; slideDuration:为. spark-window. option("maxFilesPerTrigger", 1) // Treat a sequence of files as a stream by picking one file at a time. text("papers"). 加载json文件的时候,如果schema设置的属性,如果存在非字符串类型,那么转成column就都变成了null,eg. In this article, third installment of Apache Spark series, author Srini Penchikala discusses Apache Spark Streaming framework for processing real-time streaming data using a log analytics sample. Gson g = new Gson(); Player p = g. The usual first. readStream method. Basic Example for Spark Structured Streaming and Kafka Integration With the newest Kafka consumer API, there are notable differences in usage. Another one is Structured Streaming which is built upon the Spark-SQL library. To create a Delta Lake table, you can use existing Spark SQL code and change the format from parquet, csv, json, and so on, to delta. sparkContext. The K-means clustering algorithm will be incorporated into the data pipeline developed in the previous articles of the series. Let us add a cell to view the content of the Delta table. Last time, we talked about Apache Kafka and Apache Storm for use in a real-time processing engine. Hi guys simple question for experienced guys. We are able to decode the message in Spark, when using Json with Kafka. val ds1 = spark. json文件内容如下: 代码如下: 结果显示如下: 如果将case class CdrData的reId的Long的类型改成String,则展示正常,eg. JSON Project device, signal Filter signal > 15 Write to Parquet Spark automatically streamifies! Spark SQL converts batch-like query to a series of incremental execution plans operating on new batches of data JSON Source Optimized Operator Codegen, off-heap, etc. A Spark Streaming application will then parse those tweets in JSON format and perform various transformations on them including filtering, aggregations and joins. Spark is an open source project for large scale distributed computations. 10 to poll data from Kafka. js – Convert Array to Buffer Node. 我试图重现[Databricks] [1]中的示例并将其应用于Kafka的新连接器并激发结构化流媒体,但是我无法使用Spark中的开箱即用方法正确解析JSON 注意:该主题以JSON格式写入Kafka. option("maxFilesPerTrigger", 1) // Treat a sequence of files as a stream by picking one file at a time. 23 8:30 / apache spark / configuration. 0: STRUCTURED STREAMING AND DATASETS Andrew Ray StampedeCon 2016. *") powerful built-in Python APIs to perform complex data. That might be. pdf from IF 200 at National Institute of Technology, Bandung. start() ssc. Similar to from_json and to_json, from_avro and to_avro can also be used with any binary column, but you must specify the Avro schema manually. DataStreamWriter val writer: DataStreamWriter [ String ] = papers. This Spark SQL tutorial with JSON has two parts. As soon as the new file is detected by the Spark engine, the streaming job is initiated and we can see the JSON file almost immediately. schema returns exactly a wanted inferred schema, you can use this returned schema as an argument for the mandatory schema parameter of spark. Learn the Spark streaming concepts by performing its demonstration with TCP socket. home / 2019. 0 for "Elasticsearch For Apache Hadoop" and 2. 構造化 ストリーミング + Kafka 統合ガイド (Kafkaブローカーバージョン 0. modules folder has subfolders for each module, module. Spark Streaming is an extension of core Spark API, which allows processing of live data streaming. 100% open source Apache Spark and Hadoop bits. can someone point me to a good tutorial on spark streaming to use with kafka Question by Tajinderpal Singh Jun 10, 2016 at 10:18 AM Spark spark-sql spark-streaming I am trying to fetch json format data from kafka through spark streaming and want to create a temp table in spark to query json data like normal table. This will at best highlight all the events you want to process. Let's try to analyze these files interactively. L'objectif est de se dissocier de la déclaration manuelle du schéma de données côté consommateur. WHAT'S NEW IN SPARK 2. Let’s get started with the code. Shows how to write, configure and execute Spark Streaming code. An alternative is to represent your JSON structure into case class which actually are very easy to construct. They are extracted from open source Python projects. 0: STRUCTURED STREAMING AND DATASETS Andrew Ray StampedeCon 2016. Hi everyOne! I want to convert a DStream[String] into an RDD[String]. In some case, however, a separate writer needs to be implemented for writing out results into a database, queue or some other format. json文件内容如下: 代码如下: 结果显示如下: 如果将case class CdrData的reId的Long的类型改成String,则展示正常,eg. 8 Direct Stream approach. readStream. reading of Kafka Avro messages with Spark 2. val connectionString = ConnectionStringBuilder ("{EVENT HUB CONNECTION STRING FROM AZURE PORTAL}"). signal > 15 result. IBM Spark Technology Center Origins of the Apache Bahir Project MAY/2016: Established as a top-level Apache Project. While its entirely possible to construct your schema manually, its also worth noting that you can take a sample JSON, read it into a data frame using spark. isStreaming res: Boolean = true. To create a Delta Lake table, you can use existing Spark SQL code and change the format from parquet, csv, json, and so on, to delta. import org. DataFrame object val eventHubs = spark. 读取kafka数据 key是偏移量,value是一个byte数组 如果使用聚合,将会有window的概念,对应属性watermark 01. Spark SQL (and Structured Streaming) deals, under the covers, with raw bytes instead of JVM objects, in order to optimize for space and efficient data access. Spark Structured Streaming目前的2. 6 instead use spark.