How We Cut Memory Usage from 118 GB to 28 GB by Switching to Polars

We benchmarked Pandas vs Polars on 100M rows after a 3 AM outage. The memory difference (118 GB vs 28 GB) wasn’t even the biggest surprise we found.

At 3 AM on a Wednesday, my relationship with Pandas hit a breaking point. A PagerDuty alert yanked me out of a half-dream where I was playing tabla in odd time signatures, and the message was blunt: our nightly aggregation job had hit an out-of-memory error again. The job touched 100 million log records and had been stable for months, so I knew something was off. By 3:07 AM, I was SSH’d into the problematic box, watching memory consumption spiral like a bored goat chewing through fence posts. That sleepless morning kicked off the most intense performance comparison exercise I’ve ever run, and it eventually reshaped how my team processes large datasets.

I want to walk you through the exact benchmarks, hardware specs, and real tradeoffs that came out of this migration investigation. Nothing here is theoretical. Every number comes from reproducible code, and every decision is something our team actually acted on. And yes, there were a few surprising places where the old library beat the new one convincingly. I’ll show the code for those, too.

Benchmark Setup: Hardware Specs, Dataset Design, Methodology

Benchmarking can go wrong fast when you don’t pin the environment. I learned that the hard way years ago at Airbnb when we accidentally ran A/B test simulations on inconsistent containers.

For this study, we locked down a 64-core AMD EPYC server with 512 GB RAM, NVMe SSD storage, Python 3.11, Pandas 2.x with PyArrow-backed dtypes, and the latest Polars version from 2025.

Our dataset consisted of 100 million rows of synthetic event data that mimicked our production logging structure. Fields included user_id, event_type, timestamp, device, country, and a couple of float metrics. I generated the data using both R and Python to ensure repeatability. Yes, I know that sounds like overkill. But after a decade of doing causal inference experiments, reproducibility is religion.

Here’s what the methodology looked like: warm storage cache first, then time each operation using wall clock time. We ran each task 5 times and discarded outliers, captured peak memory via psutil, and used identical logic across libraries.

This approach was important for readers asking how to handle 100 million rows in Python without blowing up memory.

Memory Showdown: One Library Keeps Its Cool

Apache Arrow powers the columnar model under the hood of our newer contender, and it shows. I actually discovered this while digging through memory profiles at 4 AM, watching allocation patterns that looked nothing like what I was used to seeing. The Arrow extensions in Pandas 2.x are better than the old NumPy engine, but they still lag noticeably.

Peak memory for loading the 100M CSV on our specific setup told the story clearly. Pandas ate approximately 118 GB, while its competitor used roughly 28 GB. Results will vary based on data types, column count, and system configuration, but the gap was consistent across our tests.

Even I did a double-take. And I’ve spent years optimizing memory paths.

Once we shifted into filtering and group-by operations, that memory gap widened further. My old go-to library held onto intermediate objects far longer than I expected. The newer option streamed intelligently, releasing memory earlier. That alone would’ve saved our 3 AM job.

Speed Tests: Read, Filter, Groupby, Join

Here are the wall clock numbers from our specific test environment that shaped our internal debate about the best DataFrame library for big data in Python. Note that these results are specific to our hardware, dataset structure, and library versions, so your mileage will vary.

Read CSV

On our setup, Pandas clocked in at approximately 96 seconds. The alternative? About 17 seconds.

Filter event_type == “click”

Around 14 seconds for the traditional approach versus approximately 3 seconds for the challenger.

Group by Country and Device

This one hurt to watch. Roughly 41 seconds with Pandas, just 6 seconds with its competitor.

Join with 10M Row Lookup Table

Approximately 52 seconds versus 9 seconds, respectively.

This is what people mean when they search for the fastest Python library for data processing in 2025. One option is clearly a rocket.

The Plot Twist: Operations Where the Old Guard Still Wins

I promised honesty, and I mean it. There are at least three operations where optimized Pandas beats the newer library in our tests. And not by a little.

1. value_counts on Low-Cardinality Categoricals

The C-optimized code path in Pandas is insanely tight here.

# Pandas
df["device"].value_counts()

# Polars
df["device"].value_counts()

In our tests, Pandas averaged around 1.1 seconds while its competitor was closer to 2.4 seconds.

2. Sorting Pre-Sorted Integer Columns

Our log data is timestamp-ordered upstream. Pandas detects that and barely touches RAM.

In our tests, Pandas took around 0.8 seconds. Polars? Approximately 4.7 seconds.

3. Small Object Column Manipulation

When we applied Python functions to small string columns, Pandas was around twice as fast. Its competitor forced a more involved expression pipeline.

These results reminded me that asking “Should I switch?” is the wrong question entirely. What you really want to know is: When should I use which tool?

Migration Reality Check: The Cost Nobody Warned Us About

Every migration has hidden costs, and ours piled up quickly. We had to retrain data scientists who relied on quirks they’d learned over the years. Refactoring a decade of utilities took longer than anyone estimated. Our custom Cython extensions needed complete rewrites. And we kept discovering that some libraries only accept traditional DataFrames.

My personal favorite headache? Debugging a subtle timestamp overflow issue at 11 PM after a tabla practice session. Nothing like trying to trace microsecond precision errors when your fingers are still sore from drum practice.

Additionally, ecosystem gaps still exist. The newer library is catching up, but the traditional option’s reach is enormous.

Decision Framework: Choose Based on Your Actual Workload

We created this flow after months of experimentation. Consider it a practical guide, not a gospel.

Does your workload touch more than 50 million rows? Lean toward the newer option. Need fast prototyping or existing integrations that only work with the traditional library? Stay where you are. Memory constraints killing you? The modern alternative wins almost every time.

What about row-wise Python UDFs? Stick with Pandas, since it’s still easier. Streaming or lazy queries? The challenger is unmatched there. Unpredictable workload spikes? Go modern to avoid scrambling for out-of-memory solutions at 3 AM.

This mirrors what I see when people ask about large-scale data processing benchmarks.

Where We Landed

That 3 AM alert pushed us to rethink our entire pipeline. Our final architecture was a hybrid model: the newer library for ingestion and heavy aggregations, the traditional one for feature engineering and modeling steps, where its ergonomics shine.

Here’s the thing. Run your own benchmarks before you commit to anything. Every workload behaves differently. And keep your pager volume turned up, just in case.