Slash Polars Memory: 5 Proven Fixes for Graphs 2025

import polars as pl

# Define the optimal types for your edge list
edge_dtypes = {
    "source_id": pl.UInt32,
    "target_id": pl.UInt32,
    "weight": pl.Float32, # Do you really need Float64 precision?
    "relationship_type": pl.Categorical # More on this soon!
}

# Use the dtypes argument when scanning the file
edges_df = pl.read_csv("large_edge_list.csv", dtypes=edge_dtypes)

print(edges_df.estimated_size("mb"))

Imagine you want to find all outgoing connections from 'high-influence' nodes. Here's the memory-hungry eager way vs. the sleek lazy way:

# Assume 'nodes' and 'edges' DataFrames are loaded

# Eager Way (Bad! Creates intermediate DataFrames)
high_influence_nodes = nodes.filter(pl.col("follower_count") > 10000) # Copy 1
result_eager = edges.join(
    high_influence_nodes, 
    left_on="source_id", 
    right_on="node_id"
) # Copy 2

# Lazy Way (Good! Builds a plan, no intermediate copies)
result_lazy = (
    edges.lazy() # Start a lazy query
    .join(
        nodes.lazy().filter(pl.col("follower_count") > 10000), 
        left_on="source_id", 
        right_on="node_id"
    )
    .collect() # Execute the optimized plan
)

The lazy version looks cleaner, but the real magic is invisible. Polars might perform the filter on `nodes` while it’s performing the join, never needing to allocate memory for the full `high_influence_nodes` DataFrame. Start all your complex queries with .lazy() and end them with .collect(). Your RAM will thank you.

The Magic of Global String Caching

If you used pl.Categorical as suggested in the first fix, you're already on the right track. But what if you have multiple DataFrames with the same string-based keys? For example, a `nodes` DataFrame with `user_name` and an `events` DataFrame also with `user_name`. By default, Polars will store the strings for 'user_name' separately for each DataFrame.

Enter the global string cache. By enabling this feature, you tell Polars to create a single, process-wide pool for all categorical strings. When you enable it, every time Polars sees a string value for a categorical column, it checks if it already exists in the global cache. If so, it just points to it. If not, it adds it.

This means a string like "SuperGraphUser123" will only ever be stored once in your entire application's memory, no matter how many DataFrames it appears in.

Enabling it is laughably simple:

import polars as pl

# Enable the magic *before* you load any data
pl.enable_string_cache(True)

# Now, load your data using the Categorical type
nodes = pl.read_csv("nodes.csv", dtypes={"user_name": pl.Categorical})
events = pl.read_csv("events.csv", dtypes={"user_name": pl.Categorical})

# Disable it when you're done if you need to free the cache memory
# pl.disable_string_cache()

For any graph analysis involving multiple datasets with shared textual identifiers, this is a non-negotiable optimization. It’s a one-line change that can save you gigabytes.

Rethinking Your Graph Joins and Aggregations

Joins are the bread and butter of graph analysis—they're how you connect nodes to edges, find paths, and aggregate neighborhood information. But a standard `inner` join can often be overkill and create unnecessarily large tables.

Polars offers a variety of join strategies, and choosing the right one is key. Let’s say you want to get your edge list, but only for edges that start from a 'verified' node. The naive approach is an inner join:

# Naive approach
verified_nodes = nodes.filter(pl.col("is_verified") == True)
edges_from_verified = edges.join(verified_nodes, left_on="source_id", right_on="node_id")

This works, but `edges_from_verified` now contains all the columns from `edges` and all the columns from `verified_nodes`. You probably didn't need that. You just wanted to use `verified_nodes` as a filter.

A much more memory-efficient approach is a semi-join:

# Efficient approach using a semi-join
verified_nodes = nodes.filter(pl.col("is_verified") == True)
edges_from_verified = edges.join(
    verified_nodes, 
    left_on="source_id", 
    right_on="node_id", 
    how="semi"
)

A semi-join returns only the rows from the left DataFrame (`edges`) that have a matching key in the right DataFrame (`verified_nodes`). The columns from the right DataFrame are never added, making the resulting table much smaller and the operation much faster.

Beyond RAM: Embracing the Streaming Revolution

What happens when your graph is so enormous that even with all these optimizations, it simply won't fit in RAM? This is the reality of 2025-scale datasets. This is where Polars' streaming API becomes your superpower.

Streaming allows Polars to process a dataset in smaller, manageable chunks without ever loading the whole thing into memory. It works hand-in-hand with the lazy API. You start by *scanning* your files instead of *reading* them. A scan immediately returns a LazyFrame; it doesn't actually read any data yet.

You then build your entire lazy query plan just as before. The final step is to call .collect(streaming=True). This tells Polars to execute your plan by streaming the data from disk, applying the operations chunk by chunk, and only materializing the final, often much smaller, result.

Let's calculate the out-degree for every node in a 100GB edge list file—a task that would be impossible to do in memory on most machines.

# This will work even if 'massive_edges.parquet' is 500GB
out_degrees = (
    pl.scan_parquet("massive_edges.parquet") # Scan, don't read!
    .group_by("source_id")
    .agg(pl.count().alias("out_degree"))
    .collect(streaming=True) # EXECUTE IN STREAMING MODE!
)

print(out_degrees.head())

Polars will stream the Parquet file, calculate the counts for each chunk, and combine the results at the end. The maximum memory used will be related to the size of a chunk and the size of the final aggregated result, not the 500GB source file. This is the ultimate escape hatch and the key to working with virtually limitless data.

Your Memory-Efficient Polars Future

OOM errors don't have to be a routine part of graph analysis. By moving beyond default settings and embracing the powerful features Polars provides, you can build robust, scalable, and memory-efficient pipelines.

Let's recap the game plan:

1. Downcast aggressively: Use the smallest possible integer and float types.
2. Think lazy: Build query plans to let the optimizer save you memory.
3. Cache strings: Use the global string cache for shared text identifiers.
4. Join smartly: Use semi-joins for filtering instead of inner joins.
5. Stream big data: Use scan_* and collect(streaming=True) for datasets that exceed your RAM.

Stop fighting your tools and start building bigger, more complex, and more insightful graphs. With Polars and these techniques, memory is no longer the final frontier—your imagination is.