Streaming Database Query Plans

• Streaming database query plans are methodologies for continuous query evaluation over high-volume, evolving data streams with low latency and precise state management.
• They leverage techniques such as higher-order incremental view maintenance, multiway join fusion, and shared arrangements to optimize performance and manage large state efficiently.
• Practical implementations deploy adaptive cost models, parallel operator execution, and robust fault tolerance on distributed platforms to ensure reliable and scalable real-time processing.

Streaming database query plans define the methodology and physical execution strategies for evaluating complex queries over continuously evolving, high-volume data streams with strict latency and throughput requirements. Unlike traditional batch-oriented query planning, streaming plans must simultaneously address algorithmic update efficiency, large state management, fault tolerance, operator parallelism, adaptive behavior under workload shifts, and windowed or deadline-bound semantics—all within the constraints of distributed cloud-scale platforms. This entry synthesizes recent foundational techniques, operator designs, cost models, scheduling recipes, and future directions in streaming database query plan design, drawing from major research contributions including incremental dynamic query processing (Elghandour et al., 2019), deadline-aware scheduling (Chandrasekaran et al., 2023), multiway join optimization (Hu et al., 2024), inter-query state sharing (McSherry et al., 2018), hybrid SQL+ML plan optimization (Sidiq et al., 19 Sep 2025), time-centric streaming IRs (Jayarajan et al., 2023), network-aware plan adaptation (Bhatia et al., 2021), incremental re-optimization (Liu et al., 2014), and compositional micro-batch query frameworks (Fegaras, 2015).

1. Algorithmic Foundations of Streaming Query Plans

Streaming query plans depart from static batch plans by emphasizing incremental computation and stateful operator maintenance. Upon each tuple arrival, update, or window expiry, the plan’s operators must produce new outputs by updating only affected state, avoiding recomputation over the entire input.

Legacy plans utilize first-order Incremental View Maintenance (IVM), maintaining materialized views and applying delta rules: $\Delta V \gets \Delta R \bowtie S$ for a join $R \bowtie S$. However, worst-case complexity is $O(k|S|)$ per $k$-tuple update and $O(k N^{w-1})$ for $w$-way joins, quickly becoming intractable at scale (Elghandour et al., 2019). Recent advances include:

• Higher-Order IVM / DBToaster: Materializes not only query results $V$ and first-order deltas $\Delta V$, but also intermediate higher-order deltas $\Delta^2V$ etc., allowing update costs proportional to the affected keys (Elghandour et al., 2019).
• Worst-Case-Optimal Dynamic Algorithms: Employ factorized representations and trie indexes, bounding amortized update times for conjunctive queries by $O(k N^{\rho^*-1})$, where $\rho^*$ is the fractional edge cover number (Elghandour et al., 2019).
• Dynamic Yannakakis: Extends the acyclic-join algorithm to incrementally update semi-join reductions and stateful subviews up the join tree (Elghandour et al., 2019).
• Differential Dataflow: Represents plans as dataflow graphs where each operator maintains timestamp-annotated state, interleaving incremental and iterative computations (Elghandour et al., 2019).

These principle techniques form the backbone of modern streaming query algorithms.

2. Operator State Management and Multiway Joins

Managing join state under streaming and long-window workloads is a critical bottleneck. Naïve implementations maintain in-memory buffers for all historical tuples, rapidly exhausting memory when window sizes grow or data rates surge.

UMJoin Operator and LSM-Tree Backends: The UMJoin operator leverages Log-Structured Merge Trees (LSM-Trees), such as RocksDB, to efficiently spill operator state to disk, enabling memory-efficient multi-way joins. Input streams are indexed as key-to-tuple lists in memory but periodically flushed to sorted disk tables. Lookup probes exploit in-memory caches, Bloom filters, and hierarchical index blocks for efficient access, and disk writes are mostly sequential (Hu et al., 2024).

Plan Rewriting via TSC (Tree-Structured Conversion): Streaming SQL platforms typically construct plans as trees of binary joins, materializing high-overhead intermediates. TSC identifies connected binary-join subgraphs and replaces them with a single UMJoin node, effectively fusing multi-way joins and reducing intermediate memory costs (Hu et al., 2024). Correctness is preserved by restricting rewrites to connected, purely internal join chains.

Shared Arrangements: To avoid redundant index maintenance across concurrent queries, streaming engines can employ shared arrangements—single-writer multiversioned indexed views that multiple operators read at their own logical frontier, reducing memory footprint and response latency for interactive multi-query workloads (McSherry et al., 2018).

3. Cost Models and Execution Plan Optimization

Streaming query planners utilize cost models reflecting operator-level CPU, memory, and network requirements, as well as per-tuple overheads, batch sizes, and window constraints (Sidiq et al., 19 Sep 2025, Chandrasekaran et al., 2023, Liu et al., 2014). Key modeling aspects include:

• Operator DAG Representation: Plans are represented as operator DAGs, distinguishing stateful (e.g. join, window) from stateless (e.g. filter, projection) nodes (Sidiq et al., 19 Sep 2025).
• Plan Cost Equations: Each operator $op_i$ is assigned $$cost(op_i) = \alpha_i CPU_i + \beta_i MEM_i + \gamma_i NET_i$$. The total plan cost $C(P) = \sum_i cost(op_i)$ guides join order selection and operator fusion (Sidiq et al., 19 Sep 2025).
• Deadline- and Batch-Aware Scheduling: Queries with hard deadlines decompose into intermittent batches. Formal cost models derive batch sizes $b_i$ minimizing total compute and overhead costs while meeting deadline constraints. Linear formulations and MIP solvers can select batch schedules for optimal resource trade-offs (Chandrasekaran et al., 2023).
• Incremental and Adaptive Optimization: Streaming optimizers integrate runtime statistics (e.g. histograms, selectivities, resource usage) to incrementally re-optimize physical plans. Declarative Datalog-based enumerators enable pruning and selective updates without re-running the entire optimizer (Liu et al., 2014).

Optimization techniques such as plan caching, resource-aware scheduling, and cost-guided operator mapping contribute significant performance gains (e.g., in OpenMLDB, plan optimization contributed 35%, caching 25%, parallel processing 20%, and overall speedups versus conventional database engines) (Sidiq et al., 19 Sep 2025).

4. Scheduling, Parallelization, and Plan Deployment

Efficient deployment of streaming plans on distributed engines (e.g., Spark Streaming, Flink) depends on sophisticated scheduling and parallelization strategies:

• Partitioned Operator Parallelism: Stateful operators partition data by key and distribute work across threads or nodes, supporting parallel windowed aggregates and joins (Sidiq et al., 19 Sep 2025).
• Operator Chaining and Task Slot Assignment: Native streaming engines (e.g., Flink) chain compatible operators, minimize network overhead, and assign task slots and key groups for data locality and efficient parallel execution (Elghandour et al., 2019).
• Custom Query Schedulers and Multi-Query Scheduling: Intermittent batching frameworks (e.g., deadline-aware scheduler on Spark) employ dynamic queries queues, laxity-based selection (EDF, LLF), and resource slack factors to optimize multi-query throughput and minimize deadline misses (Chandrasekaran et al., 2023).
• Time-Centric IRs and Full Operator Fusion: TiLT introduces a temporal intermediate representation allowing side-effect-free, fused operators and embarrassingly parallel partitioning of time domains; code generation targets hardware-efficient, vectorized, per-partition execution (Jayarajan et al., 2023).
• Hybrid Execution (Network Streaming Analytics): DynamiQ demonstrates adaptive partitioning of operator chains between data-plane targets and user-space stream processors, with incremental mapping and cost prediction to dynamically adjust resource allocation (Bhatia et al., 2021).

Empirically, operator fusion and aggressive parallelization yield order-of-magnitude throughput improvements versus event-centric pipeline plans, and sophisticated schedulers outperform naive round-robin or SJF regimes under deadline and rate jitter (Chandrasekaran et al., 2023, Jayarajan et al., 2023).

5. Incremental Maintenance and Compositional Frameworks

Minimally stateful, exact incremental maintenance frameworks underpin scalable streaming query execution. In MRQL-Streaming, arbitrary SQL-style queries are compiled into homomorphic operators $h$ and answer functions $a$, where $h$ satisfies $(S \uplus S') = h(S) \otimes h(S')$ and $a(h(S))$ computes the snapshot answer. Operator fusion, lineage tracking, and monoid-inference derive plans wherein only the minimal aggregation skeleton is maintained, enabling low-latency and low-memory incremental updates per micro-batch (Fegaras, 2015).

Differential Dataflow extends this logic to iterative and recursive streaming computations, merging timestamped differences through operator graphs and guaranteeing amortized, key-local update cost (Elghandour et al., 2019).

6. Distributed Systems Integration and Fault Tolerance

Streaming database query plans must be mapped onto distributed platforms with robust failure recovery and consistency guarantees:

• Stateful Operator Abstractions: Spark Structured Streaming supports both micro-batch and continuous modes, with KeyedState abstractions stored in-memory or in RocksDB, checkpointed for failure recovery (Elghandour et al., 2019).
• Consistent Snapshots: Flink employs Chandy-Lamport-inspired, distributed snapshots and incremental state restoration (Elghandour et al., 2019).
• Checkpoint and Recovery Strategies: State retention and periodic checkpointing tune the trade-offs between low-latency execution and fault tolerance (Elghandour et al., 2019).

Plan deployment must co-optimize locality, parallelism, and recovery, ensuring correctness even under large-scale node failures.

7. Comparative Analysis, Best Practices, and Future Directions

The comparative landscape among streaming query plan strategies, as synthesized from recent research (Elghandour et al., 2019, Chandrasekaran et al., 2023, Hu et al., 2024), can be summarized as follows:

Approach	Throughput	Latency	Scalability	Key Trade-offs
First-Order IVM	Moderate	High	State-bounded	Simple, high state size
HIVM / DBToaster	High	Low (per update)	Scales w/ keys	Extra memory for higher-order deltas
Dynamic Yannakakis	Mod-High	Mod	Acyclic queries	Structure limits
Worst-Case Optimal	High (dense)	Mod-Low	Depends on $\rho^*$	Index complexity
Differential DF	High	Very Low	Excellent	System complexity
UMJoin + TSC	High (large wnd)	Mod-Low	Large state w/ disk	Probe overhead when memory abundant
Shared Arrangements	High (multi-Q)	Mod-Low	Q scaling	Key schema restriction

Best practices include selective materialization of high-order delta views, factorized state to minimize join blowup, key-grouped and hypergraph-local operator placement, adaptive switching between maintenance strategies, and extension of incremental logic to ML and graph queries (Elghandour et al., 2019). Future work seeks automated plan adaptivity, multi-indexed sharing, windowed arrangement integration, and robust global state management under resource constraints.

• (Elghandour et al., 2019) Incremental Techniques for Large-Scale Dynamic Query Processing
• (Chandrasekaran et al., 2023) Scheduling of Intermittent Query Processing
• (Hu et al., 2024) Streaming SQL Multi-Way Join Method for Long State Streams
• (McSherry et al., 2018) Shared Arrangements: practical inter-query sharing for streaming dataflows
• (Sidiq et al., 19 Sep 2025) Optimization techniques for SQL+ML queries: A performance analysis of real-time feature computation in OpenMLDB
• (Jayarajan et al., 2023) TiLT: A Time-Centric Approach for Stream Query Optimization and Parallelization
• (Bhatia et al., 2021) DynamiQ: Planning for Dynamics in Network Streaming Analytics Systems
• (Liu et al., 2014) Enabling Incremental Query Re-Optimization
• (Fegaras, 2015) Incremental Query Processing on Big Data Streams