PyData Global 2021 - Designing Functional Data Pipelines for Reproducibility and Maintainability

Designing Functional Data Pipelines for Reproducibility and Maintainability
Event: PyData Global 2021
Date: 29 October 2021
Location: Online

When building data pipelines at scale, it is crucial to design data pipelines that are reliable, scalable and extensible according to evolving business needs. Designing data pipelines for reproducibility and maintainability is a challenge, as testing and debugging across compute units (threads/cores/computes) are often complex and time-consuming due to dependencies and shared states at runtime. In this talk, I will be sharing about common challenges in designing reproducible and maintainable data pipelines at scale, and exploring the use of functional programming in Python and Apache Spark to build scalable production-ready data pipelines that are designed for reproducibility and maintainability. Through analogies and realistic examples inspired by data pipeline designs in production environments, you will learn about:

1. What is Functional Programming, and how it differs from other programming paradigms
2. Key Principles of Functional Programming
3. How "control flow" is implemented in Functional Programming
Functional design patterns for data pipeline design in Python and Apache Spark, and how they improve reproducibility and maintainability
4. Whether it is possible to write a purely functional program

This talk assumes basic understanding of building data pipelines with functions and classes/objects. While the main target audience are data scientists/engineers and developers building data-intensive applications, anyone with hands-on experience in imperative programming (including Python) would be able to understand the key concepts and use-cases in functional programming.

Transcript

1. Designing Functional Data Pipelines for Reproducibility and Maintainability By: Chin Hwee Ong (@ongchinhwee) 29 November 2021
2. About me Ong Chin Hwee 王敬惠 • Data Engineer @ DT One • Aerospace Engineering + Computational Modelling • Speaker and (occasional) writer on data processing @ongchinhwee Slides link: bit.ly/pg2021-design-fp-data
3. Basic Design Pattern: Data Pipeline @ongchinhwee
4. Designing a Data Pipeline at Scale • Reliable ◦ Data pipeline must produce the desired output → Reproducibility • Scalable ◦ Data pipeline must run independently across multiple nodes → Parallelism • Extensible ◦ Able to extend data pipeline with changing business logic → Maintainability @ongchinhwee
5. Challenges in Designing Data Pipelines at Scale @ongchinhwee • Reproducibility during Testing
6. Challenges in Designing Data Pipelines at Scale @ongchinhwee • Reproducibility during Testing
7. Challenges in Designing Data Pipelines at Scale • Reproducibility during Testing ◦ Dependencies in data pipeline design ▪ Data source ▪ Computation logic @ongchinhwee
8. Challenges in Designing Data Pipelines at Scale • Reproducibility during Testing ◦ Challenge: Given the same data source, how do we ensure that we replicate the same result every time we re-run the same process? @ongchinhwee
9. Challenges in Designing Data Pipelines at Scale @ongchinhwee • Reproducibility in Production
10. Challenges in Designing Data Pipelines at Scale @ongchinhwee • Reproducibility in Production
11. Challenges in Designing Data Pipelines at Scale @ongchinhwee • Reproducibility in Production
12. Challenges in Designing Data Pipelines at Scale @ongchinhwee • Reproducibility in Production ◦ Debugging parallel/concurrent code at runtime due to shared states ▪ E.g. What is the current state of the data source?
13. Challenges in Designing Data Pipelines at Scale @ongchinhwee • Reproducibility in Production ◦ Challenge: How do we design data pipelines that run the same computation logic across multiple nodes and reproduce predictable results every time?
14. Challenges in Designing Data Pipelines at Scale • Maintainability during Debugging ◦ “Works in testing, breaks in production” 😔 ▪ Edge cases and inefficiencies not detected in test cases causing performance issues and/or failures in production ▪ Complexities in debugging and logging for parallelism @ongchinhwee
15. Challenges in Designing Data Pipelines at Scale • Maintainability during Debugging ◦ Challenge: How do we design data pipelines that are readable and maintainable at its core to reduce inefficiencies in production debugging at scale? @ongchinhwee
16. Challenges in Designing Data Pipelines at Scale • Maintainability when Adding New Features ◦ Adding new features to an evolving (growing) codebase ▪ Code reasoning becomes more challenging with increasing code complexity ▪ Risk of introducing unintended behaviour due to dependencies @ongchinhwee
17. Challenges in Designing Data Pipelines at Scale • Maintainability when Adding New Features ◦ Challenge: How do we design data pipelines that adapts well to changing business and technical requirements and ensures developer productivity? @ongchinhwee
18. Data Pipelines as Functions @ongchinhwee
19. What is Functional Programming? • Declarative style of programming that emphasizes writing software using only: ◦ Pure functions; and ◦ Immutable values. @ongchinhwee
20. 3 Key Principles of Functional Programming • Pure functions and avoid side effects • Immutability • Referential transparency @ongchinhwee
21. Pure Function and Avoid Side Effects @ongchinhwee
22. The concept of a “pure function” • Pure function ◦ Output depends only on its input parameters and its internal algorithm ◦ No side effects ⇒ same function f, same input parameter x → same result y regardless of number of invocations @ongchinhwee
23. Pure Function: Making Pizza 160°C, 10 mins P U T T H E M T O G ET H ER @ongchinhwee
24. “Impure” Function: Making Pizza with Side Effects 160°C, 10 mins P U T T H E M T O G ET H ER @ongchinhwee Side Effect: Radiation Heat
25. “Impure” Function: Making Pizza with Side Effects 180°C, 10 mins P U T T H E M T O G ET H ER @ongchinhwee Side Effect: Oven Overheat, Burnt Pizza! 😖
26. What is a side effect? • A function with side effects changes state outside the local function scope ◦ Examples: ▪ modifying a variable or data structure in place ▪ modifying a global state ▪ performing any I/O operation ▪ throwing an exception with an error @ongchinhwee
27. The concept of Immutability @ongchinhwee
28. The concept of Immutability • Immutability of an assigned variable ◦ Once a value is assigned to a variable, the state of the variable cannot be changed. ⇒ Disciplined state management ⇒ Prevents side effect resulting from state change → “pure function” @ongchinhwee
29. The concept of Immutability: Key Implication • Key implication: Ease of writing parallel/concurrent programs @ongchinhwee
30. @ongchinhwee
31. The concept of Referential Transparency A function is referentially transparent when an expression can be substituted by its equivalent algorithm without affecting the program logic for all programs @ongchinhwee
32. Conditions for Referential Transparency • Pure function • Deterministic ◦ Expression always returns the same output given the same input @ongchinhwee
33. @ongchinhwee
34. Conditions for Referential Transparency • Pure function • Deterministic ◦ Expression returns the same output given the same input • Idempotent ◦ Expression can be applied multiple times without changing the result beyond its initial application @ongchinhwee
35. @ongchinhwee
36. Equational Reasoning • A key consequent of referential transparency ◦ Expression can be replaced with its equivalent result @ongchinhwee
37. Functional Control Flow @ongchinhwee
38. Function Composition @ongchinhwee
39. Functions are Values • In Python, functions are first-class objects. • A function can be: ◦ assigned to a variable ◦ passed as a parameter to other functions ◦ returned as a value from other functions @ongchinhwee
40. Higher-order Functions • Key consequent of first-class functions • A higher-order function has at least one of these properties: ◦ Accepts functions as parameters ◦ Returns a function as a value @ongchinhwee
41. Anonymous Functions • Also known as “lambda expressions” in Python • Using function as input without defining named function object @ongchinhwee
42. map @ongchinhwee
43. filter @ongchinhwee
44. reduce @ongchinhwee
45. map/filter/reduce vs for-loops @ongchinhwee Managing state changes of mutable variables in a for-loop
46. Recursion as a form of “functional iteration” • Recursion is a form of self-referential function composition ◦ Takes the results of itself as inputs into another instance of itself ◦ To prevent infinite recursive loop, base case required as terminating condition @ongchinhwee
47. Recursion as a form of “functional iteration” @ongchinhwee
48. Recursion as a form of “functional iteration” • Tail-call optimization ◦ Objective: reduce stack frame consumption in call stack ◦ Tail call: does nothing other than returning the value of function call ◦ Identify tail calls and compile them to iterative loops @ongchinhwee
49. Recursion as a form of “functional iteration” @ongchinhwee
50. Functional Design Patterns for Data Pipeline Design @ongchinhwee
51. Immutable Data Structures • Once an immutable data structure is created, it cannot be changed • Benefits: ◦ Easier to reason - “what you see is what you get” ◦ Easier to test - worry about the logic, not the state ◦ Thread-safe - easier for parallelism @ongchinhwee
52. Tuple vs List @ongchinhwee
53. Tuple vs List @ongchinhwee
54. Namedtuple vs Class vs Dictionary @ongchinhwee
55. Namedtuple vs Class vs Dictionary @ongchinhwee
56. Namedtuple vs Class vs Dictionary @ongchinhwee
57. Namedtuple vs Class vs Dictionary @ongchinhwee
58. Data Transformations • map/filter in data transformations @ongchinhwee
59. Data Transformations • map/filter (and its derivatives) in data transformations ◦ Keeping data and transformation logic separate ▪ Improved code reusability with better transparency of transformation logic @ongchinhwee
60. Extending map/filter to parallel/concurrent programming @ongchinhwee
61. Data Actions / Aggregations • reduce in data actions / aggregations @ongchinhwee
62. Data Actions / Aggregations • reduce (and its derivatives) in data actions / aggregations ◦ Transformations first, actions last ▪ Transformation logic can be applied to each element / partition ▪ Actions / aggregations consolidates results from partitions @ongchinhwee
63. Functional Design Patterns in Apache Spark @ongchinhwee • Resilient Distributed Datasets (RDDs) ◦ Low-level data abstraction in Apache Spark ◦ Immutable and read-only ◦ Designed for fault-tolerant parallel operations with logical partitioning across nodes
64. Transformations vs Actions in Apache Spark @ongchinhwee
65. Transformations vs Actions in Apache Spark @ongchinhwee
66. Structural Pattern Matching (PEP 634) • Python 3.10 feature inspired by similar syntax with Scala • Especially useful for conditional matching of data structure patterns match Item: case Something: do_something() @ongchinhwee
67. Structural Pattern Matching (PEP 634) • match/case expressions vs if/elif/else @ongchinhwee
68. Structural Pattern Matching (PEP 634) • Pattern matching for maintainability of data schema @ongchinhwee Note: Example based on case classes and pattern matching syntax in Scala Dataclasses used as the Python equivalent of Scala case classes
69. Type Systems • Python has support for type hints (though not enforced in runtime) @ongchinhwee
70. Type Systems • Type checking with mypy @ongchinhwee
71. Type Systems • Type checking with mypy • Preventing bugs at runtime by ensuring type safety and consistency across the data pipeline @ongchinhwee
72. Can we write a purely functional data pipeline in Python? @ongchinhwee
73. Short Answer: Not really. Can we write a purely functional data pipeline in Python? @ongchinhwee
74. “Functional Core, Imperative Shell” • I/O operations still needed for reading and writing data outside of the application domain • Keeping core domain logic and infrastructure code separate Ref: Gary Bernhardt's PyCon 2013 talk on "Boundaries" @ongchinhwee
75. “Functional Core, Imperative Shell” @ongchinhwee
76. Key Takeaways • Adopt functional design patterns when designing data pipelines at scale (parallel and distributed workflows) ◦ Reproducible ◦ Scalable ◦ Maintainable • “Functional Core, Imperative Shell” to manage side effects separately from data pipeline logic @ongchinhwee
77. Reach out to me! : ongchinhwee : @ongchinhwee : hweecat : https://ongchinhwee.me And check out my ongoing series on Functional Programming at: https://ongchinhwee.me/tag/functional -programming @ongchinhwee