Join watermark in AWW

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How to Join watermark in AWW

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hi welcome back lets talk about water marking we know that in a structured streaming application there is an unbounded input table we perform our processing on top of it using a query and we produce the result in the process spark maintains the state based on the aggregation you perform lets say if you are performing a simple aggregation of a word count your word is say apple and it has come up once orange has come up once after a while lets say after 10 minutes or 20 minutes apple comes up again and your count of apple becomes two so this count has become two because the previous estate was one and apple has come up once again so there is maintenance of the state now the point is how long would you like to maintain the state the longer you maintain the state the higher memory requirement it will have and eventually you need to design a mechanism you need to put a barrier in place that a data which is very old you are not going to process it further now how old the data can be is d

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What is watermark in data engineering?

What is a Watermark? Watermarking is a useful method which helps a Stream Processing Engine to deal with lateness. Basically, a watermark is a threshold to specify how long the system waits for late events. If an arriving event lies within the watermark, it gets used to update a query.

What is the basic source of Spark streaming?

Discretized Stream or DStream is the basic abstraction provided by Spark Streaming. It represents a continuous stream of data, either the input data stream received from source, or the processed data stream generated by transforming the input stream.

What is the difference between Kafka and Spark streaming?

Spark streaming is better at processing groups of rows(groups,by,ml,window functions, etc.) Kafka streams provide true a-record-at-a-time processing capabilities. its better for functions like row parsing, data cleansing, etc.

What is spark streaming?

Spark Streaming is an extension of the core Spark API that allows data engineers and data scientists to process real-time data from various sources including (but not limited to) Kafka, Flume, and Amazon Kinesis. This processed data can be pushed out to file systems, databases, and live dashboards.

What is the difference between Spark and Spark streaming?

Spark receives real-time data and divides it into smaller batches for the execution engine. In contrast, Structured Streaming is built on the SparkSQL API for data stream processing. In the end, all the APIs are optimized using Spark catalyst optimizer and translated into RDDs for execution under the hood.

What is watermark in streaming?

Watermarking is a feature in Spark Structured Streaming that is used to handle the data that arrives late. Spark Structured Streaming can maintain the state of the data that arrives, store it in memory, and update it accurately by aggregating it with the data that arrived late.

What is the main disadvantage of Spark streaming?

Apache Spark Streaming Cons There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better.

What is watermark in Databricks?

For inner joins, Databricks recommends setting a watermark threshold on each streaming data source. This allows state information to be discarded for old records. Without watermarks, Structured Streaming attempts to join every key from both sides of the join with each trigger.

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