Chapter 2
Nemo's Intermediate Representation and Plan Modeling
What really happens between a developer's code and a lightning-fast distributed computation? This chapter unveils the heart of Apache Nemo-the ingenious intermediate representations (IR) and plan modeling machinery that translate high-level intent into finely tuned execution. Go beneath the surface to discover how plans are crafted, flexible, and portable, setting the stage for sophisticated optimizations and heterogeneous deployments. Here, every transformation becomes an opportunity.
2.1 Detailed Internal DAG Representation
In Nemo, the transformation of user jobs into an intermediate representation is crucial for enabling precise, efficient, and flexible execution. This intermediate form is a Directed Acyclic Graph (DAG) that abstracts the computational logic and data dependencies inherent to the job description. Transitioning from high-level user specifications to an internal DAG facilitates sophisticated program analyses, optimizations, and distributed execution strategies. The ensuing exposition dissects the construction and structure of this internal DAG representation, elucidating its components and how they collectively embody both data and control flow semantics.
The internal DAG consists primarily of nodes and edges, where nodes represent discrete computational or control units, and edges encode dependencies and data exchanges between these units. Unlike conventional linear control flow graphs, the DAG structure enforces acyclicity, thereby eliminating potential cyclic dependencies and ensuring clear, hierarchical scheduling possibilities. This is critical for accurate dependency tracking and for supporting both parallelization and partial materialization of jobs.
Each node in Nemo's DAG corresponds to a fundamental unit of work derived from the user job. These units range from elemental operations and data transformations to control abstractions such as conditional branching and parallel execution constructs. Nodes can be classified into several key categories:
- Data Operator Nodes: These represent core data processing operations such as mapping, filtering, aggregation, and joins. Each operator node encapsulates specific transformation logic applied to its input data streams or sets.
- Control Nodes: Nodes that model control constructs, including condition evaluation points (e.g., predicates for if-else branches), loop boundaries, and synchronization barriers. Control nodes regulate downstream execution based on evaluation results or synchronization semantics.
- Source and Sink Nodes: Terminal nodes demarcate data input boundaries (sources) and output points (sinks). Source nodes originate datasets from external storage or streaming inputs, while sink nodes signify result materialization or data emission targets.
Each node maintains metadata that encodes its operational semantics, including:
- Input and output schemas, capturing the structure and types of data elements flowing through.
- Operational parameters, such as filter predicates, aggregation keys, or join conditions.
- Execution constraints, like estimated resource costs or data locality hints.
This rich annotation supports both static analysis and runtime decision-making.
Edges in the DAG define explicit data and control dependencies between nodes. More precisely, an edge from node A to node B signifies that the output of A serves as input to B, or that B awaits completion or evaluation results from A before proceeding. Hence, edges fulfill two interrelated roles:
- Data dependency edges: These edges carry data payloads or streams from producers to consumers, ensuring that data is available when required. They define the flow of data through operators, preserving lineage and provenance.
- Control dependency edges: These edges enforce execution ordering necessary to correctly realize control-flow semantics, such as enabling a node only upon the completion or outcome of preceding conditional evaluations.
The acyclicity of the graph guarantees that no circular dependencies exist, providing a canonical execution order and simplifying scheduling.
Representing jobs as DAGs permits agile transformations during optimization phases. Since nodes represent discrete logical units, they can be reordered, fused, split, or replaced with semantically equivalent, more efficient counterparts without altering the semantic correctness. This modularity is leveraged in multiple ways:
- Operator fusion and pipelining: Adjacent compatible nodes can be merged to minimize data materialization and system calls.
- Reordering for locality and parallelism: Nodes whose dependencies allow can be reordered or executed in parallel to improve resource utilization.
- Selective materialization: Intermediate results can be cached or checkpointed strategically by inserting sink nodes during transformation.
By manipulating the DAG rather than the high-level job specification itself, Nemo achieves a semantic-preserving transformational framework that adapts execution plans dynamically.
The DAG provides a unified mechanism for simultaneously capturing data and control flows with high precision. Each edge's explicit dependency semantics eliminate ambiguities frequently present in imperative or declarative job descriptions. Fine-grained control nodes enable accurate modeling of conditional and iterative constructs within the same graph structure as data operations. This precise representation enables:
- Robust static analyses, such as dead code elimination by detecting orphan nodes or unreachable branches.
- Deterministic scheduling, where execution order respects semantics crucial for stateful or side-effecting operations.
- Accurate failure recovery, since dependency edges define clear prerequisites and successors for recomputation.
Additionally, provenance tracking is streamlined because the data dependency edges systematically encode lineage information, allowing traceability from output back to original data inputs through the DAG path.
Consider a user job specifying: "Filter a dataset for values above a threshold, then optionally join with a secondary dataset only if the filtered set is non-empty." Nemo transforms this into a DAG structured as follows:
- A Filter node receives the primary dataset source and outputs filtered data.
- A Conditional Control node evaluates non-emptiness of the filtered set.
- Depending on the control node's evaluation, an Inner Join node is enabled or bypassed.
- The Join node consumes the filtered data and the secondary dataset source, producing the final output.
Edges enforce both data flow from dataset sources through operators and control flow gating the join execution. This representation disambiguates data and conditional dependencies, enabling Nemo to schedule the pipeline optimally: if the filtered dataset is empty, the costly join is skipped altogether.
The internal DAG representation supplies Nemo with a powerful abstraction layer that realizes the following benefits essential for advanced job execution frameworks:
- Decoupling high-level user syntax from execution semantics, facilitating extensible language features and diverse backend targets.
- Explicit dependency tracking enabling precise scheduling, deadlock prevention, and fine-grained fault tolerance.
- Unified data-control flow modeling improving analytical capabilities and supporting complex program constructs.
- Transformation-friendly modularity empowering sophisticated optimizations without sacrifice of correctness.
Consequently, this flexible and rigorous DAG abstraction becomes the cornerstone on which Nemo builds scalable, dependable, and performant data processing pipelines.
2.2 Operator Abstraction and Composition
Nemo's operator abstraction model serves as the cornerstone for its execution framework, providing a rigorous yet flexible foundation to capture computation semantics and enable efficient plan execution. Operators in Nemo encapsulate distinct units of computation with well-defined interfaces and clearly specified behaviors, which facilitates modular design, compositional reasoning, and systematic optimizations.
Operators are classified primarily into three types: source operators, transform operators, and sink operators. Source operators act as data ingress points, reading from external storage or streaming sources and materializing data...
