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I have two spark streams, in the first comes data related to products: their price to the supplier, the currency, their description, the supplier id. These data are enriched by the category, guessed by the analysis of the description and the price in dollars. Then they are saved in a parquet dataset.
The second stream contains data on the auctioning of these products, then the cost at which they were sold and the date.
Given the fact that a product can arrive in the first stream today and be sold in a year, how can I join the second stream with all the history contained in the parquet dataset of the first stream?
The result to be clear should be the average daily earnings per price range ...
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Note that, if you want to receive multiple streams of data in parallel in your streaming application, you can create multiple input DStreams (discussed further in the Performance Tuning section). This will create multiple receivers which will simultaneously receive multiple data streams.
Use readStream. format("socket") from Spark session object to read data from the socket and provide options host and port where you want to stream data from.
Users specify a streaming computation by writing a batch computation (using Spark's DataFrame/Dataset API), and the engine automatically incrementalizes this computation (runs it continuously).
Spark Streaming Versus Structured Streaming Once it receives the input data, it divides it into batches for processing by the Spark Engine. DStream in Apache Spark is continuous streams of data. Spark polls the data after a configurable batch interval and creates a new RDD for the execution.
In Spark Structured Streaming, a streaming join is a streaming query that was described ( build) using the high-level streaming operators: Dataset.join Joins of a streaming query and a batch query ( stream-static joins) are stateless and no state management is required
Spark Streaming is a scalable, high-throughput, fault-tolerant streaming processing system that supports both batch and streaming workloads. It extends the core Spark API to process real-time data from sources like Kafka, Flume. Recipe Objective: How to perform a stream-stream inner join on dataframe in Spark Structured Streaming?
Spark Streaming is an engine to process data in real-time from sources and output data to external storage systems. Spark Streaming is a scalable, high-throughput, fault-tolerant streaming processing system that supports both batch and streaming workloads. It extends the core Spark API to process real-time data from sources like Kafka, Flume.
At query planning, IncrementalExecution uses the StreamingJoinStrategy execution planning strategy for planning stream-stream joins as StreamingSymmetricHashJoinExec physical operators. Stream-stream Joins in the official documentation of Apache Spark for Structured Streaming
I found a possible solution with snappydata, using its mutable DataFrame:
https://www.snappydata.io/blog/how-mutable-dataframes-improve-join-performance-spark-sql
The reported example is very similar to the one described by claudio-dalicandro
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