Apache Spark¶
What are The Similarities and Differences Between Apache Spark and Apache Flink?
Apache Spark and Apache Flink are both powerful distributed data processing frameworks, but they have some key similarities and differences.
In terms of similarities, both frameworks support batch and stream processing, and they both provide high-level APIs in multiple languages, like Java, Scala, and Python. They also focus on fault tolerance, scalability, and exactly-once processing semantics.
When it comes to differences, One of the main differences is that Flink is often considered to have a more native stream processing capability, meaning it was designed from the ground up to handle real-time data streams with very low latency. Spark, on the other hand, started primarily as a batch processing framework and later introduced Structured Streaming, which provides micro-batch processing.
This means that while both can handle streaming data, Flink often achieves lower latency.
when you execution a query in spark, how does it work behind the scene?
When you run a query in Spark, it first goes through a few logical and physical planning phases. Spark builds a logical plan from your code, optimizes it into a physical plan, and then breaks it into stages and tasks. Those tasks are distributed across worker nodes by the driver.
Each stage corresponds to a set of transformations that can be pipelined together, and between stages, data is shuffled if needed. Spark uses DAG scheduling to manage dependencies, and executors on worker nodes run the tasks and return results back to the driver.
Structure Streaming¶
What is Trigger Types in Apache Spark Structure Streaming
In Apache Spark Structured Streaming, trigger types determine how often the streaming query processes data and produces results. There are a few different trigger types you can use:
- Default Trigger: This is the default mode where Spark continuously processes data in micro-batches as soon as it arrives.
- Fixed Interval Trigger: You can specify a fixed processing interval (for example, every 10 seconds). Spark will wait for that interval to pass before processing the next micro-batch.
- One-Time Trigger: This trigger processes all available data once and then stops. It's useful for scenarios where you want to run a streaming job as a batch job.
- Continuous Trigger: This is an experimental trigger where Spark processes data continuously with very low latency, though it's still in development
What is Output Mode in Apache Spark Structured Streaming?
In Apache Spark Structured Streaming, the output mode basically defines how the processed data is written to the output sink. There are three main output modes:
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Append Mode: Only the new rows that were added to the result table since the last trigger are written to the sink. This is the default mode for most sinks.
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Complete Mode: The entire updated result table is written to the sink every time there's a trigger. This mode is useful for aggregations where you want to keep updating the whole result.
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Update Mode: Only the rows that were updated since the last trigger are written to the sink. This is kind of a middle ground between Append and Complete modes.
What is Watermarking in Apache Spark Structured Streaming?
Watermarking in Apache Spark Structured Streaming is a way to handle late data in event-time based processing. Essentially, it allows you to define how late data can arrive and still be considered for processing. You set a watermark on an event-time column, and Spark will use that to manage state and clean up old data, ensuring that the system doesn't hold onto too much state for too long. It's really useful for managing streaming data efficiently.