![]() ![]() Although Structured streaming supports multiple data sources to read and write data but sadly Vertica is not one of them. One last thing we need is a data source to provide to Spark for Vertica: val verticaDataSource = ".DefaultSource" Vertica dbschema password#To save the data to Vertica, first we need some credentials to connect to database and some other properties: val verticaProperties: Map = Map( "db" -> "db", // Database name "user" -> "user", // Database username "password" -> "password", // Password "table" -> "table", // vertica table name "dbschema" -> "dbschema", // schema of vertica where the table will be residing "host" -> "host", // Host on which vertica is currently running "hdfs_url" -> "hdfs_url", // HDFS directory url in which intermediate orc file will persist before sending it to vertica "web_hdfs_url" -> "web_hdfs_url" // FileSystem interface for HDFS over the Web ) Now we have to start the preparations for writing this DataFrame to Vertica. Vertica dbschema code#The above code is to read the data and create the streaming DataFrame. Now we need the code which will consume the data from Kafka in streaming mode: val dataFrame = ( kafkaSource).options(kafkaOptions).load() Here we are running Spark in local mode hence master = "local" //4 is number of cores to be usedįor reading data from Kafka we need the following: def kafkaOptions: Map = Map( "" -> brokers, //address of Kafka brokers "group.id" -> groupId, //group id for kafka consumers "startingOffsets" -> "earliest", //starting offsets to start picking data "subscribe" -> sourceTopic //the topic from which the data will be consumed ) val kafkaSource: String = "kafka" Here “appName” would be the name you want to set to your Spark application and “master” would be the master URL for spark. Code?įirst thing is to create a SparkSession to connect with Spark and Spark-SQL: val sparkSession = SparkSession.builder(). For this part, the setup part of the last blog can be read as reference. For more information on Structured Streaming you can read the following blogs: Spark Streaming V Structured Streaming, Exploring Structured Streaming.įirst we need to add the required dependencies. This is different than the Spark Streaming library. ![]() So today we’ll try to do the same thing with Structured Streaming.īut what is Structured Streaming? In short words, the Structured Streaming is the streaming of structured data in Spark built on the Spark SQL engine. Wouldn’t it be better if we can do the same thing in streaming mode. i.e reading data from Kafka in a batch and then saving that batch into Vertica. reading data from Kafka and writing data to Vertica but in a batch mode. The next blog explained the reverse flow i.e. Vertica dbschema series#The first blog in this mini series was about reading data from Vertica using Spark and saving that data into Kafka. In two previous blogs, we explored about Vertica and how it can be connected to Apache Spark. Using Vertica with Spark-Kafka: Write using Structured Streaming ![]()
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