A Deep Dive into Massively Parallel Processing (MPP) Architecture

If you’ve ever wondered why some engines stay snappy under mixed, join-heavy workloads while others slow to a crawl, the difference often comes down to how they move and combine intermediate results. This is where MPP (massively parallel processing) earns its keep.

What MPP actually does:

1. Fragment the plan
   The optimizer breaks a query into fragments: scan, filter/project, join, aggregate, exchange. Think of each fragment as a small assembly line stage.

2. Fan out fragment instances
   Every fragment is instantiated many times and scheduled across the cluster. These instances are the smallest units of parallel work and let the engine match compute to available cores.

3. Pipeline and vectorize
   Inside each instance, operators run in a pipeline (Scan → Filter/Project → Join/Aggregate → Exchange). Vectorization keeps CPU hot by processing data in columnar batches instead of row-by-row function calls. Pipelines also provide backpressure so fast stages don’t overrun slow ones.

4. Exchange data in memory
   When two big sides must meet (distributed JOIN, global GROUP BY), rows are hashed on the key and routed directly over the network to peers. Well-behaved runs never touch disk for these exchanges; spilling is a safety valve, not the plan.

5. Distribute finalization
   Partials are merged in a tree across multiple nodes—partial → merge → final—so “the last step” isn’t a single machine. The planner/coordinator orchestrates but doesn’t become the bottleneck.

MPP vs Other Compute Architectures

1. Scatter-Gather Architecture

How it works
A coordinator breaks a query into sub-tasks and dispatches them to worker shards. Each shard scans and filters its local data and often performs partial aggregations. The coordinator merges those partials, applies any remaining GROUP BY/ORDER BY/LIMIT work, and returns the final result. Most of the heavy lifting happens close to the data; the network carries compact intermediate results instead of raw rows.

Used by
ClickHouse and similar coordinator-merge systems.

Strengths
• Simple mental model—easy to reason about where work runs and to debug slow queries.
• Very fast for single-table scans with filters, projections, and rollups that push down to shards.
• Network-efficient—shards return partial aggregates or compressed blocks, not full row sets.
• Scales reads predictably by adding shards for more parallel scan bandwidth.

Limitations
• Coordinator hot spot—final merges and aggregations centralize some work; at high concurrency this can bottleneck.
• Joins are constrained—common patterns are broadcasting a small table to shards or co-locating large tables on the same shard keys; big-table-to-big-table repartitioned joins are not the default.
• Skew sensitivity—uneven key distribution can create stragglers and long tails.
• Write-time layout matters—performance depends on good partitioning, sorting, and projections.

Best for
Operational analytics on wide, flat fact tables; single-table access with selective filters and simple to moderate aggregations; high-throughput dashboards where joins are limited or dimensions are small enough to broadcast.

2. Stage-Based Execution (MapReduce, DAG)

How it works
Queries are decomposed into multiple stages separated by shuffles. A shuffle redistributes data across executors and creates a stage boundary; upstream tasks must finish before downstream tasks begin. Intermediate data typically lands on local disk between stages. Optimizers can combine, reorder, or adapt plans (e.g., dynamic partition pruning, adaptive query execution), but the fundamental rhythm is “execute → materialize → execute.”

Used by: Apache Spark, Databricks Photon, Flink (in batch mode), and similar DAG-based systems.

Strengths
• Very flexible—handles SQL, DataFrames, ETL, ML, and batch streaming patterns.
• High fault tolerance—failed tasks are retried from materialized boundaries.
• Elastic scaling—great for cloud autoscaling and spot/ephemeral compute pools.
• Rich ecosystem—connectors, libraries, and tooling are mature.

Limitations
• Higher latency—disk I/O and serialized stage transitions add seconds to minutes.
• Fragmented pipelines—more moving parts (shuffle, spill, serialization) to tune.
• Operational complexity—resource sizing, partitioning, and shuffle tuning matter a lot for large joins/aggregations.

Best for
Large-scale ETL, periodic backfills, feature engineering, and offline transformations of structured or semi-structured data where minute-level latency is acceptable.

3. In-Memory MPP Execution

How it works
Nodes execute fragments of a physical plan concurrently and exchange intermediate results directly over the network using in-memory data streams. Operators are pipelined and vectorized for high CPU efficiency. Distributed hash joins and aggregations run across nodes; engines spill to disk only when needed. Techniques like runtime filters, dictionary encoding, and code generation keep hot paths tight.

Used by
StarRocks, Apache Impala, Greenplum, and other modern MPP databases.

Strengths
• Low-latency execution for complex, multi-table queries—JOINs, GROUP BYs, windows—at interactive speeds.
• Fully parallel joins—supports repartition (hash) joins, broadcast joins, and co-located joins.
• Real-time friendly—ingests continuously and makes fresh data queryable quickly for user-facing analytics.
• Concurrency headroom—built for many short queries from dashboards and applications.

Limitations
• Needs solid infra—sufficient memory and high-throughput networking are important.
• Requires discipline—statistics, partitioning/bucketing, and materialized views should be managed to sustain performance as data and concurrency grow.

Best for
Interactive dashboards, customer-facing analytics, anomaly/fraud detection loops, and high-cardinality aggregations with JOIN-heavy query patterns.

Summary Comparison Table

Core Features of an MPP Database

To understand why MPP engines deliver high performance and scale for interactive analytics, it helps to unpack the design patterns they use:

1. Independent node design
   Each node runs its own compute with local memory and manages its slice of the data. This minimizes contention and lets the system scale out by adding nodes, each contributing parallel scan, join, and aggregate capacity.

2. Data partitioning
   Tables are horizontally partitioned (hash, range, or buckets) so each node processes just its portion. Good partition keys align with common filters and joins to reduce cross-node traffic and hotspots.

3. Query fragmentation and scheduling
   The optimizer breaks a plan into fragments—scans, joins, aggregations, exchanges—which are scheduled across nodes. Multiple instances of the same fragment run in parallel to saturate CPU and I/O. Pipelines keep operators busy without unnecessary materialization.

4. Shuffles and join strategies
   When a true distributed join or GROUP BY is required, the engine repartitions data (hash shuffle) so matching keys meet on the same node. Engines also support broadcast joins (ship the small side to all nodes) and co-located joins (plan tables on compatible partition keys to avoid shuffles). Modern systems prefer in-memory exchanges and only spill when necessary.

5. Coordinator role
   A lightweight coordinator parses SQL, builds the physical plan, and orchestrates fragment instances. Unlike coordinator-merge systems, the “last mile” aggregation is typically distributed (often a tree/partial-final pattern), so the coordinator doesn’t become a bottleneck as concurrency grows.

A Closer Look: MPP in Practice with StarRocks

StarRocks is an MPP engine by design. Its execution core leans into MPP’s strengths while sidestepping common bottlenecks in other architectures.

 Fragmentation and Parallel Execution

When a query is submitted, StarRocks decomposes it into logical fragments. Each fragment covers one part of the plan—scan, project/filter, join, aggregate, exchange—and is instantiated into multiple fragment instances, the smallest schedulable units.

Instances run in parallel on backend (BE) nodes. Operators are pipelined (for example, Scan → Project/Filter → Join/Aggregate → Exchange) and vectorized so CPUs process columnar batches efficiently instead of row by row. The engine chooses the degree of parallelism per fragment based on available cores, table partitioning/bucketing, and runtime limits, so the cluster’s compute is saturated without introducing artificial stage barriers.

.

In-Memory Data Shuffling and Merge Operations

For distributed JOINs and GROUP BYs, StarRocks redistributes rows across nodes by hashing on the relevant key (for example, HASH(id) % N). This exchange happens in memory over the network so records with the same key meet on the same node for the next step.

The engine applies the right strategy per query:
• Hash repartition join when both sides are large.
• Broadcast join when a dimension is small enough to ship to all nodes.
• Co-located join when tables share compatible partition/bucket keys and no shuffle is needed.

Aggregation finalization is distributed in a tree—local partials → merged partials → final aggregates or top-N—so there is no single “last hop” node doing all the work. Runtime filters (such as bloom/hash filters) generated by upstream joins are pushed down to scanners to prune non-matching rows early, reducing bytes on the wire and tail latency.

Why this avoids common bottlenecks
• Versus coordinator–merge (scatter–gather): final aggregation is not centralized; merges are parallelized, so the coordinator doesn’t become a hotspot at high concurrency.
• Versus stage-based (MapReduce/DAG): exchanges do not materialize to disk between stages; the pipeline keeps flowing, which removes seconds-level penalties from repeated write/read cycles.

Scaling Characteristics

Execution runs on independent compute nodes with local CPU and memory. Nodes are stateless from a query-execution perspective, so you can scale horizontally by adding more compute. As capacity grows, the scheduler increases fragment-instance parallelism to consume additional cores. Smooth scale-out depends on even partition/bucket distribution and healthy network bandwidth.

The architecture supports high-concurrency scenarios such as:
• Real-time dashboards and product analytics
• External/customer-facing analytics applications
• Lakehouse workloads that join today’s changes with deep historical context

Operating notes that help on day one

• Align partition/bucket and sort keys with hot filters and join keys to maximize pruning, co-location, and cache locality.
• Keep frequently joined dimensions compact enough to broadcast; it removes unnecessary shuffles.
• Use incremental materialized views to maintain top-N, rollups, and expensive expressions as data lands; this preserves interactive latency under load.
• Watch for key skew; rebalance or salt hot keys so hash exchanges stay balanced and the long tail shrinks.
• Maintain table and column statistics so the optimizer can choose sensible join orders and exchange strategies as data evolves.
• Isolate workloads with separate compute warehouses and admission control (dashboards vs. ELT vs. AI agents) to protect p95 latency without overprovisioning.

Putting it together

The MPP pattern here is straightforward: break the plan into small parallel units, push work down, exchange the smallest possible intermediate data in memory, and finish in parallel. That’s why StarRocks remains responsive on join-heavy, high-cardinality queries with many concurrent readers—the exact situations where coordinator-merge or disk-staged systems tend to slow down.

Why MPP Matters

Shared-memory SMP systems—multiple CPUs against one memory and I/O fabric—are excellent for OLTP but hit walls on OLAP. Long scans, big joins, and global aggregations fight over the same sockets, caches, and buses. Vertical scale is expensive, and a single heavy query can drag down everyone else.

MPP changes the shape of the problem: push the work to where the data lives, run many small pieces in parallel, move only what’s necessary, and finish in parallel.

MPP Solves These Problems by Distributin

Workload parallelism

In an MPP engine, a query is cut into fragments (scan, project/filter, join, aggregate, exchange). Each fragment spins up many instances across the cluster. Nodes scan their partitions, compute partial results, and exchange only the keys they must share.
Result: better CPU and memory utilization, lower latency, and far higher concurrency.

Concrete pattern

A GROUP BY runs as “local partials → in-memory repartition by key → distributed merge/final.” You avoid funneling every row through a single coordinator.

Horizontal Scalability
MPP scales out, not up. As data volume or query load grows, more compute nodes can be added to the cluster. Each new node contributes additional CPU, memory, and bandwidth.

• Example: At TRM Labs, their largest customer-facing workload spans 115 TB+ and grows by 2–3% monthly. Migrating to a horizontally scalable MPP engine helped maintain sub-3s latency with high concurrency—something BigQuery struggled to deliver cost-effectively, especially as dataset size and dashboard complexity increased. 

Isolation of Resources
Each MPP node is independent—there’s no shared memory or CPU. This minimizes contention between workloads, even in multi-tenant or mixed query environments.

• Contrast: In SMP systems, a large aggregation query can monopolize memory bandwidth, blocking smaller lookups.

• Case Study Insight: At Eightfold.ai, the team found that Redshift’s leader node became a bottleneck under concurrent workloads. StarRocks’ distributed architecture—featuring separate frontends for planning and backends for execution—allowed them to avoid single-node saturation, unlocking high concurrency without degrading latency across their customer-facing analytics platform

Fault Tolerance

Because tasks are distributed, MPP systems can survive node failures without crashing the entire query. Tasks can be retried or reassigned to healthy nodes.

• Example: In Kubernetes-native deployments (e.g., at NAVER), MPP engines like StarRocks automatically reassign tasks if a pod dies—keeping the system resilient without manual intervention.

Elasticity for the Cloud

Modern MPP engines decouple compute from storage, allowing compute nodes to scale elastically based on workload. Stateless nodes can spin up or down as needed, reducing idle cost.

• Implementation Tip: MPP systems with support for object storage (like S3 or HDFS) can cache hot data on local disks (e.g., EBS) to reduce I/O latency without sacrificing durability.

• Eightfold Insight: Their team found serverless OLAP offerings lacked sufficient EBS caching to maintain high concurrency. They opted for dedicated compute nodes to ensure local data residency and predictable performance— especially critical for supporting low-latency, multi-tenant analytics experiences at scale.

In summary, MPP fundamentally changes the game for analytics systems by distributing both computation and data. This architecture supports real-time responsiveness, large-scale aggregation, and concurrency at levels that centralized systems cannot sustain.