Podcast
Questions and Answers
What is the primary motivation for using stream processing in industry?
What is the primary motivation for using stream processing in industry?
- Data analysis speed (correct)
- Data storage capacity
- Reliability improvements
- Cost reduction
Micro-batch processing in Spark allows for processing latencies as low as 10 milliseconds.
Micro-batch processing in Spark allows for processing latencies as low as 10 milliseconds.
False (B)
What are the two forms of streaming available in Spark?
What are the two forms of streaming available in Spark?
Micro-batch processing and Continuous Stream Processing
In Continuous Stream Processing, the latency can go down to ____ milliseconds.
In Continuous Stream Processing, the latency can go down to ____ milliseconds.
Match the following streaming concepts with their characteristics:
Match the following streaming concepts with their characteristics:
Which of the following is an advantage of Spark Structured Streaming?
Which of the following is an advantage of Spark Structured Streaming?
Spark Structured Streaming is considered a traditional form of batch processing.
Spark Structured Streaming is considered a traditional form of batch processing.
What is the maximum latency for micro-batch processing in Spark?
What is the maximum latency for micro-batch processing in Spark?
What is one of the challenges associated with streaming in Kafka?
What is one of the challenges associated with streaming in Kafka?
Append output mode allows for updating existing records in the results table.
Append output mode allows for updating existing records in the results table.
Name one output mode used in Spark Streaming.
Name one output mode used in Spark Streaming.
In Spark Streaming, a source, sink, and _______ streaming are involved in the architecture.
In Spark Streaming, a source, sink, and _______ streaming are involved in the architecture.
Match the following output modes with their descriptions:
Match the following output modes with their descriptions:
What does 'tolerating failure' mean in the context of end-to-end guarantees?
What does 'tolerating failure' mean in the context of end-to-end guarantees?
Batch API code is completely incompatible with Streaming API code.
Batch API code is completely incompatible with Streaming API code.
What is the main purpose of the sink in Spark Streaming architecture?
What is the main purpose of the sink in Spark Streaming architecture?
The aggregation in streaming allows data to be collected and processed over ________ time durations.
The aggregation in streaming allows data to be collected and processed over ________ time durations.
In the example of tracking steps taken by users, what was the trigger time mentioned?
In the example of tracking steps taken by users, what was the trigger time mentioned?
Spark Streaming can only handle structured data.
Spark Streaming can only handle structured data.
What is an example of a source mentioned that collects data for Spark Streaming?
What is an example of a source mentioned that collects data for Spark Streaming?
In Spark Streaming, the ______ table holds the results of the processed data.
In Spark Streaming, the ______ table holds the results of the processed data.
Match each character with their corresponding steps taken:
Match each character with their corresponding steps taken:
What happens in the Update output mode?
What happens in the Update output mode?
The term 'state' in Spark Streaming refers to a snapshot of data at a point in time.
The term 'state' in Spark Streaming refers to a snapshot of data at a point in time.
Flashcards
Batch Processing
Batch Processing
Processing data on a static dataset, treated as a whole.
Streaming processing
Streaming processing
Continuous analysis of data as it arrives, reducing delay.
Spark Structured Streaming
Spark Structured Streaming
A Spark feature for processing data streams efficiently.
Micro-batch processing
Micro-batch processing
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Continuous stream processing
Continuous stream processing
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Data Frequency
Data Frequency
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Data Size
Data Size
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Streaming Application
Streaming Application
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Streaming Challenges
Streaming Challenges
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Late Events
Late Events
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End-to-End Guarantee
End-to-End Guarantee
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Code Portability
Code Portability
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Spark Streaming Architecture
Spark Streaming Architecture
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Complete Output Mode
Complete Output Mode
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Update Output Mode
Update Output Mode
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Append Output Mode
Append Output Mode
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Trigger Time
Trigger Time
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Source (Streaming)
Source (Streaming)
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Sink (Streaming)
Sink (Streaming)
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Aggregation (Streaming)
Aggregation (Streaming)
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Result Table (Streaming)
Result Table (Streaming)
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State (Streaming)
State (Streaming)
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Batch API
Batch API
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Streaming API
Streaming API
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Study Notes
Introduction to Spark Streaming
- Spark Streaming is a powerful feature in Spark, used by many companies.
- It reduces the time between data acquisition and calculation on that data.
- Data is constantly changing, and new/changed data needs quick analysis.
- This is important in applications involving continuous data streams.
Streaming Concept
- Industry shows high interest in streaming applications.
- A typical data flow pipeline involves Extract, Transform, and Load (ETL) stages.
Motivation
- Data is constantly changing and needs immediate analysis.
- Streaming applications are needed to process this data quickly.
Batch vs. Stream Processing
- Batch: Processes data in fixed-size chunks. Data frequency is infrequent. The data size is large. Data analysis is performed on the whole data set.
- Streaming: Processes data as it arrives. Data frequency is constant. The data size is small. Data analysis is done on incoming data.
Stream Processing Concept
- Streaming applications minimize the time between data acquisition and analysis.
- Data changes constantly, requiring quick analysis.
Why bother with Stream Processing?
- Convenience: Stream processing simplifies managing continuously changing data.
- Critical Applications: Essential in real-time data analysis and decision-making
Spark Structured Streaming Module
- Spark Streaming is integrated within the Spark ecosystem.
- It contains modules for DataFrames, Machine Learning, and GraphX.
- Programming languages like Scala, Python, Java, and R can be leveraged for development.
Spark Structured Streaming API
- Spark Structured Streaming API offers an efficient way to work with continuous data streams.
Batch Processing Spark
- Processing static datasets is done using DataFrames or DataSets and SQL queries.
- Data is considered static, so DataFrames are static as well.
Basic Concept of Structured Streaming
- Data streams are treated as unbounded tables.
- Newly arriving data is appended to the table.
- The data stream is perpetually growing.
Forms of Streaming
- Spark supports micro-batch and continuous stream processing.
Micro-batch Processing
- Processes data in small batches (e.g., 100ms).
- Guarantees "once and only once" data processing.
Continuous Stream Processing
- Processes data continuously in real time.
- Latency is reduced down to milliseconds.
- At least one guarantee exists but duplicates are possible.
Spark Streaming vs. Kafka
- Spark Structured Streaming and Apache Kafka are tools for handling large, continuous datasets.
Streaming Challenges
- Late Events: Data might arrive after the intended processing time causing discrepancies.
- End-to-End Guarantees: Ensuring the pipeline processes data without error and tolerates failures.
- Code Portability: The differences between Batch API and Streaming API are not major, and many Batch API codes can be translated into Streaming.
Spark Streaming Architecture and Output Modes
- Basic components include source, trigger, state, result table, and sink.
Example
- Visualization of a streaming data pipeline. Demonstrates how data is processed, updated, and sent to the Sink.
Spark Streaming Output Modes
- Complete: Sends the most recent data to the sink without duplicates.
- Update: Sends the changed data to the sink.
- Append: Receives new records and appends them to the table.
Interview Question 1
- Append mode is not suitable for aggregation.
- Only new data can be added while old data cannot be modified.
- Aggregations result in updates, and this is not permitted in append mode.
Interview Question 2
- Complete output mode in Spark supports aggregation queries, but not non-aggregate queries.
Summary
- Core concepts in Spark Streaming and output modes were reviewed.
- Queries supported and not supported in append and complete output modes were described.
Experiments with Spark Streaming Modes
- Examples of using Spark Streaming in real-world scenarios were presented.
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Description
Dive into the fundamentals of Spark Streaming, a key feature that enables real-time data processing. This quiz explores the critical differences between batch and stream processing, and highlights the importance of quick data analysis in various applications. Test your knowledge on data flow pipelines and streaming concepts.
