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Introduction
If you are working with real-time streaming data, Apache Spark’s Structured Streaming feature allied with SQL capabilities presents a potent tool. And, when coupled with Databricks, the efficiency can scale higher. Structured Streaming in Apache Spark allows you to express your streaming computation using SQL. In this blog post, we will focus on real-time SQL streaming analytics using Apache Spark and Databricks with hands-on examples.
Apache Spark’s Structured Streaming
Structured Streaming is a scalable and fault-tolerant stream processing engine built upon the Spark SQL engine. It allows you to express your streaming computation in the same way as you would express a batch computation on static data.
Real-Time SQL Streaming Example
Suppose we want to perform a real-time count of events from an IoT device, streaming the data and storing it in a Databricks Delta table.
Initialize Spark Streaming
First, we need to initialize a SparkSession to use Spark and set up the input stream for our IoT events data.
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val spark = SparkSession .builder .appName("IoTData") .getOrCreate() import spark.implicits._ // Input stream setup val iot_raw_data = spark.readStream .format("socket") .option("host", "{host_name}") .option("port", "{port_number}") .load() |
Write SQL Queries
Once the input stream is set up, we can now write SQL queries to transform our data.
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// Register the DataFrame as a global temporary view iot_raw_data.createOrReplaceTempView("iot_raw_data") // SQL for count of events val event_count_df = spark.sql("SELECT event, COUNT(event) as count FROM iot_raw_data GROUP BY event") |
Streaming to Databricks Delta Table
Now let’s stream the data to our Databricks Delta table utilizing the writeStream function.
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event_count_df.writeStream .format("delta") .outputMode("complete") .option("checkpointLocation", "{checkpoint_URL}") .start("{output_databricks_delta_table}") |
Conclusion
Real-time SQL Streaming Analytics is a great tool for working with streaming data, especially when paired with Apache Spark and Databricks. Through this example, we hope you have a basic understanding of how you can utilize these tools for your streaming data processing needs. Begin by initializing the Spark Stream, followed by writing the SQL queries for data transformation and, finally, stream the data to Databricks Delta table.
Note:
Please replace the placeholders “{…}” in the code snippets with actual values relevant to your setup.