Stream Set presentation for datapipeline.

Evolution of Stream Processing Systems
Amit Sahu
Digital Specialist Engineer

Introduction
Importance of Stream Processing
• Stream processing has emerged as a critical technology for handling and analyzing high-velocity data streams.
• Traditional batch processing systems are not suitable for real-time data analysis and decision-making.
• Stream processing enables organizations to extract valuable insights from continuous data streams and make
timely and informed decisions.

Introduction
Challenges in Stream Processing
• Stream processing systems face several challenges in managing out-of-order data, handling stateful computations,
ensuring fault tolerance, managing high data loads, and supporting dynamic reconfiguration.
• Addressing these challenges is crucial for the successful implementation and adoption of stream processing
technology.

Characteristics of Data Streams and Continuous Queries
High-Volume, Real-Time Nature
• Data streams are continuous and high in volume,
often arriving at a rapid rate.
• Continuous queries are executed on these data
streams in real-time, requiring immediate
processing.
On-the-Fly Processing with Limited Memory
• Data streams and continuous queries need to be
processed on-the-fly, without the ability to store the
entire data set.
• Limited memory resources pose a challenge for
performing computations in real-time.

Continuously Producing Updated Results
• Continuous queries require continuously updated
results as new data arrives.
• The challenge lies in efficiently updating the query
results without reprocessing the entire data
stream.
Handling High Input Rates
• Data streams can have high input rates, making it
challenging to process the incoming data in a timely
manner.
• Efficient mechanisms are required to handle the high
input rates and avoid data overload.
Characteristics of Data Streams and Continuous Queries

Requirements for Streaming Systems
Correctness with Out-of-Order and Delayed Data
• Streaming systems need to handle data that arrives out of order or with delays, ensuring that the processing results
are correct and consistent.
Progress Estimation and Result Completeness
• Streaming systems should provide mechanisms to estimate the progress of data processing and ensure that the
results are complete and accurate.
Management of Accumulated State
• Streaming systems need to efficiently manage and maintain the state of ongoing computations, such as
aggregations and windowing operations.

Requirements for Streaming Systems
Fault Tolerance and Failure Handling
• Streaming systems should be designed to handle failures gracefully and recover from them without losing data or
compromising processing results.
Adaptability to Workload Variations
• Streaming systems should be able to dynamically adapt to changes in data volume, velocity, and processing
requirements to ensure optimal performance and resource utilization.

Key Concepts and Terms in Stream Processing
Data Stream
A continuous flow of data
records that are
generated and processed
in real-time.
Continuous Query
A query that is
continuously applied to a
data stream, producing
real-time results.
Out-of-Order Data
Data records in a stream
that arrive in a different
order than they were
produced.
State
The information that a
stream processing
system maintains about
the data it has seen so
far.

Fault-Tolerance
The ability of a stream
processing system to
continue operating
correctly in the presence
of failures.
Load Management
The process of efficiently
distributing the
processing workload
across multiple nodes in
a stream processing
system.
Elasticity
The ability of a stream
processing system to
dynamically scale up or
down its resources based
on the workload.
Reconfiguration
The process of modifying
the structure or behavior
of a stream processing
system while it is
running.
Key Concepts and Terms in Stream Processing

Categorization of Streaming Systems
Streaming systems canbecategorized intothreegenerations based on their data model, query language, execution
model, and system architecture. These generations represent the evolution of stream processing systems over
time.

Stream Set presentation for datapipeline.

Out-of-order Data Management
Windowing
•Use time-based or
count-based windows to
group and process
data within a specified
time or size
limit.
•Handle out-of-order
data by adjusting window
boundaries or
using watermarking
techniques.
Ordering
•Apply timestamp based
or sequence-based
ordering to ensure data is
processed in the correct
order.
•Use buffering and
buffering techniques to
handle late-arriving
events.
Revision
•Maintain a revision
history of events to
handle late-arriving
updates or corrections.
•Apply revision logic to
update previous results
based on new
information.
Progress Tracking
•Use watermarking
techniques to track the
progress of event
processing.
•Adjust processing logic
based on the current
watermark to
handle out-of-order
events.

Causes of Disorder
Stream processing refers to the real-time processing of data streams. Disorder in stream processing can have
various causes, leading to inefficiencies and inaccuracies in data analysis and decision-making. Some common
causes of disorder in stream processing include:

Effects of Disorder
Disorderinstream processing can have significant impacts on the efficiency and accuracy of data analysis. When data
is not processed in a timely and organized manner, it can lead to various negative effects.

State Management
Statemanagement is a crucial aspect of stream processing systems as it involves handling and maintaining the state
of data streams. In this section, we will explore key aspects of state management in stream processing, including
state representation, state partitioning, state persistence, and state scalability.
Key Aspects of State Management

Fault Tolerance and High Availability
Instreamprocessing systems,faulttolerance and high availability are crucial for ensuring continuous data processing
and minimizing disruptions. This section examines the key aspects of fault tolerance and high availability in stream
processing, including processing semantics, recovery strategies, and availability metrics.

Load Management, Elasticity, and Reconfiguration
Instream processingsystems,loadmanagement,elasticity,andreconfigurationplay crucial roles in ensuring efficient
and reliable data processing. This section will discuss key concepts and techniques related to load management,
elasticity, and reconfiguration.
Load Management Techniques

Elasticity and Reconﬁguration Techniques

Conclusion
Comprehensive
Overview
The survey provides a
comprehensive overview
of the fundamental
aspects and
functionalities of stream
processing systems and
their evolution over time.
Comparison of Early and
Modern Systems
The paper compares
early and modern stream
processing systems with
regard to their data
models, query languages,
execution models, and
system architectures.
Highlighting Important
Works
The survey highlights
important but overlooked
works that have
influenced today’s
streaming systems
design.
Future Trends and Open Problems
The survey discusses future trends and open problems in stream processing, such as supporting multiple time
domains, handling cyclic queries, enabling transactional stream processing, and providing user-friendly ways
to specify availability.
Establishing Common
Nomenclature
The paper establishes a
common nomenclature
for streaming concepts,
often described by
inconsistent terms in
different systems and
communities.

Stream Set presentation for datapipeline.

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