Introduction

What do you do when your data outgrows a single-machine DuckDB?

This is a question every DuckDB power user eventually faces. Your data grows from gigabytes to terabytes, even petabytes — your laptop’s 8GB/16GB of RAM isn’t enough anymore, and DuckDB’s Spill to Disk mechanism starts to struggle.

Historically, there was only one answer: Apache Spark.

But Spark is heavy. You need YARN or Kubernetes, cluster configuration, scheduler tuning, dozens of parameters to optimize, and a complex DataFrame API. If you just need to run some SQL on a few hundred GB to a few TB of data for preprocessing, setting up a Spark cluster is like using a sledgehammer to crack a nut.

In April 2025, DeepSeek open-sourced Smallpond (⭐ 5000+), offering a third path: DuckDB + 3FS distributed file system = lightweight PB-scale data processing.

This article dives deep into Smallpond’s architecture, API, performance benchmarks, and practical deployment strategies.

1. When Does Single-Node DuckDB Hit Its Limit?

Before discussing distributed solutions, let’s be clear about where single-machine DuckDB stands.

DuckDB Single-Node Performance Boundaries

DuckDB’s Spill to Disk mechanism (SET memory_limit='4GB'; SET temp_directory='/tmp/tmp_duckdb') allows an 8GB laptop to process 100GB of data, but at a significant performance cost — disk I/O becomes the bottleneck.

When you enter the terabyte range, you need a distributed solution. But Spark’s learning curve and operational overhead deter many small and medium teams.

2. What Is Smallpond?

Smallpond is an open-source lightweight distributed data processing framework from DeepSeek with a fundamentally different philosophy:

Instead of building a new distributed compute engine (with its own MapReduce/Shuffle implementation), Smallpond lets DuckDB run on multiple nodes, each processing data shards independently, sharing data through 3FS — a high-performance distributed filesystem.

Architecture Overview

┌──────────────────────────────────────────────────┐
│                 3FS (Distributed Filesystem)       │
│        /smallpond/data/*.parquet                  │
└──────┬────────────────────┬───────────────────────┘
       │                    │
       ▼                    ▼
┌──────────────┐    ┌──────────────┐    ┌──────────────┐
│  Node 1      │    │  Node 2      │    │  Node 3      │
│ DuckDB+3FS   │    │ DuckDB+3FS   │    │ DuckDB+3FS   │
│ 10 partitions│    │ 10 partitions│    │ 10 partitions│
└──────────────┘    └──────────────┘    └──────────────┘
       │                    │                    │
       └────────────────────┼────────────────────┘
                            ▼
                  ┌──────────────────┐
                  │  Aggregated Result│
                  │  output/*.parquet │
                  └──────────────────┘

Core Components

1. DuckDB — The compute engine on each node. Smallpond doesn’t reimplement compute logic; it directly leverages DuckDB’s SQL execution engine.
2. 3FS — DeepSeek’s high-performance distributed filesystem. Provides a shared storage layer so all nodes can read/write the same data.
3. Smallpond Scheduler — Handles data partitioning, task distribution, and result aggregation. Written in Python with a minimal API.

Installation is one command:

pip install smallpond

3. API Overview with Code Examples

Smallpond’s API is remarkably simple — just a handful of core functions:

3.1 Initialize Session

import smallpond

# Default: auto-detect available nodes
sp = smallpond.init()

# Custom configuration
sp = smallpond.init(
    num_nodes=10,           # Use 10 nodes
    duckdb_memory="8GB",    # Memory limit per node
    data_dir="/smallpond/data",  # 3FS data path
)

3.2 Read Data

# Read Parquet (auto-partitioned)
df = sp.read_parquet("huge_dataset/*.parquet")

# Read CSV
df = sp.read_csv("logs/*.csv")

# Read JSON Lines
df = sp.read_json("events/*.jsonl")

Smallpond automatically splits files by size. Each partition is approximately 256MB by default, and partition count determines parallelism.

3.3 Repartition

# Hash repartition by a column (like Spark's repartition)
df = df.repartition(10, hash_by="user_id")

# Random repartition
df = df.repartition(20)

Repartitioning is critical for distributed computation. It determines how data is redistributed across nodes and directly impacts JOIN and GROUP BY efficiency.

3.4 Execute SQL

Smallpond uses partial_sql to run distributed DuckDB SQL:

# Note: {0} is a placeholder for the DataFrame
df_result = sp.partial_sql(
    "SELECT user_id, COUNT(*), AVG(amount) "
    "FROM {0} "
    "WHERE event_type = 'purchase' "
    "GROUP BY user_id",
    df
)

partial_sql executes the same SQL query on every partition independently, then automatically merges results. This means your SQL must be executable per-partition — ideal for filtering, mapping, and grouped aggregations.

3.5 Write Results

# Write back to Parquet
df.write_parquet("output/")

# Convert to Pandas DataFrame (for small result sets)
pandas_df = df.to_pandas()

# Count rows
print(f"Total rows: {df.count()}")

3.6 Complete Example: E-Commerce User Behavior Analysis

import smallpond

# 1. Initialize
sp = smallpond.init()

# 2. Read 1TB of user event data
events = sp.read_parquet("s3://data/events/*.parquet")
users = sp.read_parquet("s3://data/users/*.parquet")

# 3. Repartition by user_id for local JOINs
events = events.repartition(50, hash_by="user_id")

# 4. Distributed JOIN + aggregation
result = sp.partial_sql("""
    SELECT
        u.country,
        u.tier,
        COUNT(DISTINCT e.user_id) AS active_users,
        SUM(e.revenue) AS total_revenue,
        AVG(e.revenue) AS avg_revenue_per_user
    FROM {0} e
    JOIN users u ON e.user_id = u.user_id
    WHERE e.event_date >= '2026-01-01'
    GROUP BY u.country, u.tier
""", events)

# 5. Write results
result.write_parquet("output/daily_report/")

# 6. Preview
print(result.to_pandas().head(20))

4. Performance: 110 TiB in 30 Minutes on 50 Nodes

DeepSeek published official benchmark results from their production cluster.

Sort Benchmark

These numbers are impressive. For comparison:

• On the same cluster, Apache Spark typically takes 45-60 minutes for similar sorting tasks (including scheduling and Shuffle overhead)
• Smallpond achieves near-linear scalability

TPCH Benchmark

Smallpond outperforms Spark across all TPCH queries, especially on complex JOINs and subqueries.

Why Is Smallpond Faster?

1. Zero Shuffle overhead — Spark’s Shuffle is a notorious performance killer (serialization/deserialization/network transfer/Sort). Smallpond uses 3FS shared storage + data locality scheduling to eliminate most Shuffle operations.
2. DuckDB’s native performance — DuckDB’s single-node execution is 5-10x faster than Spark SQL (columnar storage, vectorized execution, Morsel-Driven parallelism). Smallpond directly leverages DuckDB instead of implementing its own execution engine.
3. No JVM overhead — Spark runs on the JVM; GC pauses and JIT warmup are common pain points. Smallpond’s scheduler is Python and the compute layer is C++ (DuckDB) — no JVM overhead.
4. Coarser partitioning — Spark defaults to 128MB partitions; Smallpond uses 256MB, reducing task scheduling frequency.

5. Comparison: Spark vs Dask vs Smallpond

Comprehensive Comparison

When to Choose Smallpond

Data Size Decision Guide:

< 10 GB   → Single-node DuckDB (simplest, fastest)
10-100 GB → Single-node DuckDB + Spill to Disk (no distribution needed)
100 GB-1 TB → Single-node DuckDB + Large RAM (e.g., 64GB instance)
1-100 TB  → **Smallpond** (sweet spot)
> 100 TB  → Smallpond or Spark (depends on team expertise)

Best suited for:

1. Data preprocessing pipelines — Cleaning, filtering, aggregation, feature engineering
2. Log analytics — Daily TB-scale log ETL and querying
3. Large-scale reporting — Cross-source daily/weekly report generation
4. ML feature engineering — Large-scale feature extraction and transformation

Not ideal for:

1. Real-time/streaming — Smallpond is batch-only, no streaming support
2. Iterative ML algorithms — PageRank, K-means iterations; Spark MLlib is better
3. Graph computation — GraphX or dedicated graph databases are more suitable

6. Practical Case Study: E-Commerce User Behavior Pipeline

Let’s walk through a complete example simulating a real-world workload: an e-commerce platform generating 500GB of user behavior logs daily.

6.1 Generate Sample Data

import smallpond
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

# Initialize Smallpond
sp = smallpond.init()

# Generate user data (10 million users)
num_users = 10_000_000
users_df = pd.DataFrame({
    "user_id": range(1, num_users + 1),
    "country": np.random.choice(["CN", "US", "JP", "DE", "BR"], num_users),
    "tier": np.random.choice(["bronze", "silver", "gold", "platinum"], num_users,
                              p=[0.5, 0.3, 0.15, 0.05]),
    "registration_date": (
        datetime.now() - pd.to_timedelta(np.random.randint(1, 365*3, num_users), unit="D")
    ).strftime("%Y-%m-%d"),
})
users_df.to_parquet("/tmp/sample/users.parquet")
print(f"Generated {len(users_df):,} user records")

# Generate event data (50 million events/day, 3 days = 150 million)
num_days = 3
events_per_day = 50_000_000

for day in range(num_days):
    date_str = (datetime.now() - timedelta(days=day)).strftime("%Y-%m-%d")
    n = events_per_day
    events_df = pd.DataFrame({
        "event_id": range(day * n + 1, (day + 1) * n + 1),
        "user_id": np.random.randint(1, num_users + 1, n),
        "event_type": np.random.choice(
            ["page_view", "click", "add_cart", "purchase", "favorite"],
            n, p=[0.6, 0.2, 0.1, 0.07, 0.03]
        ),
        "revenue": np.where(
            np.random.random(n) < 0.07,
            np.random.uniform(10, 500, n).round(2),
            0.0
        ),
        "event_date": date_str,
        "timestamp": [
            f"{date_str} {np.random.randint(0,24):02d}:{np.random.randint(0,60):02d}:{np.random.randint(0,60):02d}"
            for _ in range(n)
        ],
    })
    events_df.to_parquet(f"/tmp/sample/events/{date_str}.parquet")
    print(f"Generated events: {date_str} ({n:,} records)")

6.2 Distributed Analysis

import smallpond

sp = smallpond.init()

# 1. Read data
print("Reading data...")
events = sp.read_parquet("/tmp/sample/events/*.parquet")
users = sp.read_parquet("/tmp/sample/users.parquet")

# 2. Repartition by user_id for local JOIN
events = events.repartition(10, hash_by="user_id")

# 3. Execute distributed SQL analysis
print("Executing distributed query...")
result = sp.partial_sql("""
    SELECT
        u.country,
        u.tier,
        e.event_date,
        COUNT(DISTINCT e.user_id) AS active_users,
        COUNT(*) AS total_events,
        SUM(CASE WHEN e.event_type = 'purchase' THEN 1 ELSE 0 END) AS purchases,
        SUM(e.revenue) AS total_revenue,
        SUM(e.revenue) / NULLIF(COUNT(DISTINCT e.user_id), 0) AS revenue_per_user,
        SUM(CASE WHEN e.event_type = 'add_cart' THEN 1 ELSE 0 END) AS cart_adds,
        SUM(CASE WHEN e.event_type = 'purchase' THEN 1 ELSE 0 END) * 1.0
            / NULLIF(SUM(CASE WHEN e.event_type = 'add_cart' THEN 1 ELSE 0 END), 0)
            AS cart_to_purchase_rate
    FROM {0} e
    JOIN users u ON e.user_id = u.user_id
    WHERE e.event_date >= '2026-04-01'
    GROUP BY u.country, u.tier, e.event_date
""", events)

# 4. Preview results
pandas_result = result.to_pandas()
print(f"\nResult rows: {len(pandas_result):,}")
print(f"\nTop 20 preview:")
print(pandas_result.head(20))

# 5. Write results
result.write_parquet("/tmp/sample/output/daily_stats/")
print("\nResults written to /tmp/sample/output/daily_stats/")

6.3 Performance Comparison

7. Production Deployment Guide

7.1 Hardware Requirements

7.2 Deployment Steps

# 1. Install 3FS on all nodes
# Reference: https://github.com/deepseek-ai/3FS

# 2. Install Smallpond on all nodes
pip install smallpond

# 3. Configure 3FS mount point (same path on all nodes)
# /smallpond/data ← shared via 3FS

# 4. Copy data to 3FS
cp /local/data/*.parquet /smallpond/data/

# 5. Submit jobs from any node
python my_etl_script.py

7.3 Performance Tuning

1. Partition size — Default 256MB. For < 100GB data, increase to 512MB to reduce scheduling overhead. For > 10TB, decrease to 128MB for higher parallelism.
2. Repartition strategy — Choose hash_by columns that match your JOIN or GROUP BY keys to minimize cross-node data transfer.
3. Memory limits — Set SET memory_limit='NGB' on each node. Reserve ~20% of system memory for OS and 3FS.
4. Data locality — Smallpond tries to execute computation where data resides. Ensure your 3FS distribution strategy matches compute requirements.

8. Monetization Strategies

8.1 Consulting Services

Target clients: SMEs with 1-100TB data currently struggling with Spark’s complexity.

Services:

• Evaluate existing data pipelines
• Migrate to Smallpond + DuckDB architecture
• Performance tuning and operations guidance

Pricing:

8.2 Training

Target audience: Teams transitioning from Spark to lighter solutions.

• Smallpond intro (2 hours) → $500/person
• Enterprise workshop (1 day) → $3,000-5,000/day
• Spark migration (2-day hands-on) → $7,000-10,000

8.3 Managed Service

For small teams who want Smallpond without managing it themselves:

• Starter (3 nodes, ≤ 10TB) → $500/month
• Standard (10 nodes, ≤ 50TB) → $1,500/month
• Enterprise (50 nodes, ≤ 500TB) → $5,000/month

8.4 Sales Pitch

“Your Spark cluster costs $5,000/month on EMR? Smallpond runs 30% faster on the same hardware with 70% lower ops cost. And your team doesn’t need to learn Scala — SQL is all you need.”

9. Summary and Future Outlook

Smallpond represents an interesting trend in data processing: instead of rebuilding everything, replace the engine while keeping the chassis.

DeepSeek didn’t reinvent the distributed compute engine — they used the best single-machine analytics engine available (DuckDB) and solved storage distribution with 3FS. This combination outperforms Spark in most scenarios while being cheaper and easier to operate.

Decision Flowchart

Your data < 100 GB → Use single-node DuckDB
You know SQL      → Smallpond is better than Spark for you
Your boss asks why
  Spark costs $60K/year → Show them this article

Limitations

1. No streaming — Batch-only, no real-time processing
2. 3FS dependency — 3FS deployment docs are still maturing
3. Young community — Much smaller ecosystem compared to Spark
4. No ML pipeline — No Spark MLlib equivalent (yet)

But if you just need “fast SQL queries on terabytes of data without Spark’s complexity”, Smallpond is the most exciting project to emerge in 2025-2026.

Further Reading:

• Smallpond GitHub Repository
• 3FS — High-Performance Distributed Filesystem
• DuckDB Official Documentation for advanced usage