Graphchain

What is graphchain?

Graphchain is like joblib.Memory for dask graphs. Dask graph computations are cached to a local or remote location of your choice, specified by a PyFilesystem FS URL.

When you change your dask graph (by changing a computation's implementation or its inputs), graphchain will take care to only recompute the minimum number of computations necessary to fetch the result. This allows you to iterate quickly over your graph without spending time on recomputing previously computed keys.

Source: xkcd.com/1205/

The main difference between graphchain and joblib.Memory is that in graphchain a computation's materialised inputs are not serialised and hashed (which can be very expensive when the inputs are large objects such as pandas DataFrames). Instead, a chain of hashes (hence the name graphchain) of the computation object and its dependencies (which are also computation objects) is used to identify the cache file.

Additionally, the result of a computation is only cached if it is estimated that loading that computation from cache will save time compared to simply computing the computation. The decision on whether to cache depends on the characteristics of the cache location, which are different when caching to the local filesystem compared to caching to S3 for example.

Usage by example

Basic usage

Install graphchain with pip to get started:

pip install graphchain

To demonstrate how graphchain can save you time, let's first create a simple dask graph that (1) creates a few pandas DataFrames, (2) runs a relatively heavy operation on these DataFrames, and (3) summarises the results.

import dask
import graphchain
import pandas as pd

def create_dataframe(num_rows, num_cols):
    print("Creating DataFrame...")
    return pd.DataFrame(data=[range(num_cols)]*num_rows)

def expensive_computation(df, num_quantiles):
    print("Running expensive computation on DataFrame...")
    return df.quantile(q=[i / num_quantiles for i in range(num_quantiles)])

def summarize_dataframes(*dfs):
    print("Summing DataFrames...")
    return sum(df.sum().sum() for df in dfs)

dsk = {
    "df_a": (create_dataframe, 10_000, 1000),
    "df_b": (create_dataframe, 10_000, 1000),
    "df_c": (expensive_computation, "df_a", 2048),
    "df_d": (expensive_computation, "df_b", 2048),
    "result": (summarize_dataframes, "df_c", "df_d")
}

Using dask.get to fetch the "result" key takes about 6 seconds:

>>> %time dask.get(dsk, "result")

Creating DataFrame...
Running expensive computation on DataFrame...
Creating DataFrame...
Running expensive computation on DataFrame...
Summing DataFrames...

CPU times: user 7.39 s, sys: 686 ms, total: 8.08 s
Wall time: 6.19 s

On the other hand, using graphchain.get for the first time to fetch 'result' takes only 4 seconds:

>>> %time graphchain.get(dsk, "result")

Creating DataFrame...
Running expensive computation on DataFrame...
Summing DataFrames...

CPU times: user 4.7 s, sys: 519 ms, total: 5.22 s
Wall time: 4.04 s

The reason graphchain.get is faster than dask.get is because it can load df_b and df_d from cache after df_a and df_c have been computed and cached. Note that graphchain will only cache the result of a computation if loading that computation from cache is estimated to be faster than simply running the computation.

Running graphchain.get a second time to fetch "result" will be almost instant since this time the result itself is also available from cache:

>>> %time graphchain.get(dsk, "result")

CPU times: user 4.79 ms, sys: 1.79 ms, total: 6.58 ms
Wall time: 5.34 ms

Now let's say we want to change how the result is summarised from a sum to an average:

def summarize_dataframes(*dfs):
    print("Averaging DataFrames...")
    return sum(df.mean().mean() for df in dfs) / len(dfs)

If we then ask graphchain to fetch "result", it will detect that only summarize_dataframes has changed and therefore only recompute this function with inputs loaded from cache:

>>> %time graphchain.get(dsk, "result")

Averaging DataFrames...

CPU times: user 123 ms, sys: 37.2 ms, total: 160 ms
Wall time: 86.6 ms

Storing the graphchain cache remotely

Graphchain's cache is by default ./__graphchain_cache__, but you can ask graphchain to use a cache at any PyFilesystem FS URL such as s3://mybucket/__graphchain_cache__:

graphchain.get(dsk, "result", location="s3://mybucket/__graphchain_cache__")

Excluding keys from being cached

In some cases you may not want a key to be cached. To avoid writing certain keys to the graphchain cache, you can use the skip_keys argument:

graphchain.get(dsk, "result", skip_keys=["result"])

Using graphchain with dask.delayed

Alternatively, you can use graphchain together with dask.delayed for easier dask graph creation:

import dask
import pandas as pd

@dask.delayed
def create_dataframe(num_rows, num_cols):
    print("Creating DataFrame...")
    return pd.DataFrame(data=[range(num_cols)]*num_rows)

@dask.delayed
def expensive_computation(df, num_quantiles):
    print("Running expensive computation on DataFrame...")
    return df.quantile(q=[i / num_quantiles for i in range(num_quantiles)])

@dask.delayed
def summarize_dataframes(*dfs):
    print("Summing DataFrames...")
    return sum(df.sum().sum() for df in dfs)

df_a = create_dataframe(num_rows=10_000, num_cols=1000)
df_b = create_dataframe(num_rows=10_000, num_cols=1000)
df_c = expensive_computation(df_a, num_quantiles=2048)
df_d = expensive_computation(df_b, num_quantiles=2048)
result = summarize_dataframes(df_c, df_d)

After which you can compute result by setting the delayed_optimize method to graphchain.optimize:

import graphchain
from functools import partial

optimize_s3 = partial(graphchain.optimize, location="s3://mybucket/__graphchain_cache__/")

with dask.config.set(scheduler="sync", delayed_optimize=optimize_s3):
    print(result.compute())

Using a custom a serializer/deserializer

By default graphchain will cache dask computations with joblib.dump and LZ4 compression. However, you may also supply a custom serialize and deserialize function that writes and reads computations to and from a PyFilesystem filesystem, respectively. For example, the following snippet shows how to serialize dask DataFrames with dask.dataframe.to_parquet, while other objects are serialized with joblib:

import dask.dataframe
import graphchain
import fs.osfs
import joblib
import os
from functools import partial
from typing import Any

def custom_serialize(obj: Any, fs: fs.osfs.OSFS, key: str) -> None:
    """Serialize dask DataFrames with to_parquet, and other objects with joblib.dump."""
    if isinstance(obj, dask.dataframe.DataFrame):
        obj.to_parquet(os.path.join(fs.root_path, "parquet", key))
    else:
        with fs.open(f"{key}.joblib", "wb") as fid:
            joblib.dump(obj, fid)

def custom_deserialize(fs: fs.osfs.OSFS, key: str) -> Any:
    """Deserialize dask DataFrames with read_parquet, and other objects with joblib.load."""
    if fs.exists(f"{key}.joblib"):
        with fs.open(f"{key}.joblib", "rb") as fid:
            return joblib.load(fid)
    else:
        return dask.dataframe.read_parquet(os.path.join(fs.root_path, "parquet", key))

optimize_parquet = partial(
    graphchain.optimize,
    location="./__graphchain_cache__/custom/",
    serialize=custom_serialize,
    deserialize=custom_deserialize
)

with dask.config.set(scheduler="sync", delayed_optimize=optimize_parquet):
    print(result.compute())

Contributing

1. Clone this repository.
2. Start a Dev Container in your preferred development environment:
   • VS Code: open the cloned repository and run ⌘ + ⇧ + P → Remote-Containers: Reopen in Container.
   • PyCharm: open the cloned repository and configure Docker Compose as a remote interpreter.
   • Terminal: open the cloned repository and run docker compose run --rm dev to start an interactive Dev Container.

Developed by Radix

Radix is a Belgium-based Machine Learning company.

Our vision is to make technology work for and with us. We believe that if technology is used in a creative way, jobs become more fulfilling, people become the best version of themselves, and companies grow.