Using persistence is a powerful technique to avoid redundant computation and disk I/O in Dask. Persistence allows you to keep intermediate results in memory, so that they can be reused in subsequent computations without having to recalculate them or read them from disk.
To persist a Dask DataFrame or Dask Bag in memory, you can use the persist() method. For example:
import dask.dataframe as dd# Load CSV data into a Dask DataFramedf = dd.read_csv('data/*.csv')# Persist the Dask DataFrame in memorydf_persisted = df.persist() |
In this example, we load a set of CSV files into a Dask DataFrame using the dd.read_csv() function. We then call the persist() method to persist the DataFrame in memory. This will cause the DataFrame to be loaded into memory and cached, so that it can be reused in subsequent computations.
Once a Dask DataFrame or Bag has been persisted, you can reuse it in subsequent computations without having to read it from disk again. For example:
# Compute the mean of a column in the persisted DataFramemean1 = df_persisted['col1'].mean().compute()# Compute the sum of another column in the same DataFramesum1 = df_persisted['col2'].sum().compute() |
In this example, we compute the mean of one column and the sum of another column in the persisted DataFrame. Because the DataFrame has already been loaded into memory and cached, these computations will be very fast and won't require any disk I/O.
One important thing to keep in mind when using persistence is that it can be memory-intensive, especially if you're persisting large datasets. You should be careful not to persist more data than you actually need, and you should monitor your memory usage to ensure that you don't run out of memory. You can use the memory_usage() method of a Dask DataFrame or Bag to see how much memory it's currently using.