It is easy to get started with Dask DataFrame, but using it well does require some experience. This page contains suggestions for best practices, and includes solutions to common problems.
For data that fits into RAM, Pandas can often be faster and easier to use than Dask DataFrame. While “Big Data” tools can be exciting, they are almost always worse than normal data tools while those remain appropriate.
Reduce, and then use Pandas
Similar to above, even if you have a large dataset there may be a point in your computation where you’ve reduced things to a more manageable level. You may want to switch to Pandas at this point.
df = dd.read_parquet('my-giant-file.parquet') df = df[df.name == 'Alice'] # Select a subsection result = df.groupby('id').value.mean() # Reduce to a smaller size result = result.compute() # Convert to Pandas dataframe result... # Continue working with Pandas
Pandas Performance Tips Apply to Dask DataFrame
Usual Pandas performance tips like avoiding apply, using vectorized operations, using categoricals, etc., all apply equally to Dask DataFrame. See Modern Pandas by Tom Augspurger for a good read on this topic.
Use the Index
Dask DataFrame can be optionally sorted along a single index column. Some
operations against this column can be very fast. For example, if your dataset
is sorted by time, you can quickly select data for a particular day, perform
time series joins, etc. You can check if your data is sorted by looking at the
df.known_divisions attribute. You can set an index column using the
.set_index(column_name) method. This operation is expensive though, so use
it sparingly (see below):
df = df.set_index('timestamp') # set the index to make some operations fast df.loc['2001-01-05':'2001-01-12'] # this is very fast if you have an index df.merge(df2, left_index=True, right_index=True) # this is also very fast
For more information, see documentation on dataframe partitions.
Avoid Full-Data Shuffling
Setting an index is an important but expensive operation (see above). You should do it infrequently and you should persist afterwards (see below).
Some operations like
merge/join are harder to do in a
parallel or distributed setting than if they are in-memory on a single machine.
In particular, shuffling operations that rearrange data become much more
communication intensive. For example, if your data is arranged by customer ID
but now you want to arrange it by time, all of your partitions will have to talk
to each other to exchange shards of data. This can be an intensive process,
particularly on a cluster.
So, definitely set the index but try do so infrequently. After you set the
index, you may want to
persist your data if you are on a cluster:
df = df.set_index('column_name') # do this infrequently
set_index has a few options that can accelerate it in some
situations. For example, if you know that your dataset is sorted or you already
know the values by which it is divided, you can provide these to accelerate the
set_index operation. For more information, see the set_index docstring.
df2 = df.set_index(d.timestamp, sorted=True)
This section is only relevant to users on distributed systems.
Often DataFrame workloads look like the following:
Load data from files
Filter data to a particular subset
Shuffle data to set an intelligent index
Several complex queries on top of this indexed data
It is often ideal to load, filter, and shuffle data once and keep this result in memory. Afterwards, each of the several complex queries can be based off of this in-memory data rather than have to repeat the full load-filter-shuffle process each time. To do this, use the client.persist method:
df = dd.read_csv('s3://bucket/path/to/*.csv') df = df[df.balance < 0] df = client.persist(df) df = df.set_index('timestamp') df = client.persist(df) >>> df.customer_id.nunique().compute() 18452844 >>> df.groupby(df.city).size().compute() ...
Persist is important because Dask DataFrame is lazy by default. It is a way of telling the cluster that it should start executing the computations that you have defined so far, and that it should try to keep those results in memory. You will get back a new DataFrame that is semantically equivalent to your old DataFrame, but now points to running data. Your old DataFrame still points to lazy computations:
# Don't do this client.persist(df) # persist doesn't change the input in-place # Do this instead df = client.persist(df) # replace your old lazy DataFrame
Repartition to Reduce Overhead
Your Dask DataFrame is split up into many Pandas DataFrames. We sometimes call these “partitions”, and often the number of partitions is decided for you. For example, it might be the number of CSV files from which you are reading. However, over time, as you reduce or increase the size of your pandas DataFrames by filtering or joining, it may be wise to reconsider how many partitions you need. There is a cost to having too many or having too few.
Partitions should fit comfortably in memory (smaller than a gigabyte) but also not be too many. Every operation on every partition takes the central scheduler a few hundred microseconds to process. If you have a few thousand tasks this is barely noticeable, but it is nice to reduce the number if possible.
A common situation is that you load lots of data into reasonably sized
partitions (Dask’s defaults make decent choices), but then you filter down your
dataset to only a small fraction of the original. At this point, it is wise to
regroup your many small partitions into a few larger ones. You can do this by
df = dd.read_csv('s3://bucket/path/to/*.csv') df = df[df.name == 'Alice'] # only 1/100th of the data df = df.repartition(npartitions=df.npartitions // 100) df = df.persist() # if on a distributed system
This helps to reduce overhead and increase the effectiveness of vectorized Pandas operations. You should aim for partitions that have around 100MB of data each.
Additionally, reducing partitions is very helpful just before shuffling, which
n log(n) tasks relative to the number of partitions. DataFrames
with less than 100 partitions are much easier to shuffle than DataFrames with
tens of thousands.
Joining two DataFrames can be either very expensive or very cheap depending on the situation. It is cheap in the following cases:
Joining a Dask DataFrame with a Pandas DataFrame
Joining a Dask DataFrame with another Dask DataFrame of a single partition
Joining Dask DataFrames along their indexes
Also, it is expensive in the following case:
Joining Dask DataFrames along columns that are not their index
The expensive case requires a shuffle. This is fine, and Dask DataFrame will complete the job well, but it will be more expensive than a typical linear-time operation:
dd.merge(a, pandas_df) # fast dd.merge(a, b, left_index=True, right_index=True) # fast dd.merge(a, b, left_index=True, right_on='id') # half-fast, half-slow dd.merge(a, b, left_on='id', right_on='id') # slow
For more information see Joins.
Store Data in Apache Parquet Format
HDF5 is a popular choice for Pandas users with high performance needs. We encourage Dask DataFrame users to store and load data using Parquet instead. Apache Parquet is a columnar binary format that is easy to split into multiple files (easier for parallel loading) and is generally much simpler to deal with than HDF5 (from the library’s perspective). It is also a common format used by other big data systems like Apache Spark and Apache Impala, and so it is useful to interchange with other systems:
df.to_parquet('path/to/my-results/') df = dd.read_parquet('path/to/my-results/')
Dask supports reading parquet files with different engine implementations of the Apache Parquet format for Python:
df1 = dd.read_parquet('path/to/my-results/', engine='fastparquet') df2 = dd.read_parquet('path/to/my-results/', engine='pyarrow')
These libraries can be installed using:
conda install fastparquet pyarrow -c conda-forge