Can I read multiple files into a Spark Dataframe from S3, passing over nonexistent ones?

Question

I would like to read multiple parquet files into a dataframe from S3. Currently, I'm using the following method to do this:

files = ['s3a://dev/2017/01/03/data.parquet',
         's3a://dev/2017/01/02/data.parquet']
df = session.read.parquet(*files)

This works if all of the files exist on S3, but I would like to ask for a list of files to be loaded into a dataframe without breaking when some of the files in the list don't exist. In other words, I would like for sparkSql to load as many of the files as it finds into the dataframe, and return this result without complaining. Is this possible?

Answer 1

Yes, it's possible if you change method of specifying input to hadoop glob pattern, for example:

files = 's3a://dev/2017/01/{02,03}/data.parquet'
df = session.read.parquet(files)

You can read more on patterns in Hadoop javadoc.

But, in my opinion this isn't elegant way of working with data partitioned by time (by day in your case). If you are able to rename directories like this:

• s3a://dev/2017/01/03/data.parquet --> s3a://dev/day=2017-01-03/data.parquet
• s3a://dev/2017/01/02/data.parquet --> s3a://dev/day=2017-01-02/data.parquet

then you can take advantage of spark partitioning schema and read data by:

session.read.parquet('s3a://dev/') \
    .where(col('day').between('2017-01-02', '2017-01-03')

This way will omit empty/non-existing directories as well. Additionall column day will appear in your dataframe (it will be string in spark <2.1.0 and datetime in spark >= 2.1.0), so you will know in which directory each record exists.

Answer 2

Can I observe that as glob-pattern matching includes a full recursive tree-walk and pattern match of the paths, it is an absolute performance killer against object stores, especially S3. There's a special shortcut in spark to recognise when your path doesn't have any glob characters in, in which case it makes a more efficient choice.

Similarly, a very deep partitioning tree,as in that year/month/day layout, means many directories scanned, at a cost of hundreds of millis (or worse) per directory.

The layout suggested by Mariusz should be much more efficient, as it is a flatter directory tree —switching to it should have a bigger impact on performance on object stores than real filesystems.

Answer 3

A solution using union

files = ['s3a://dev/2017/01/03/data.parquet',
         's3a://dev/2017/01/02/data.parquet']

for i, file in enumerate(files):
    act_df = spark.read.parquet(file)   
    if i == 0:
        df = act_df
    else:
        df = df.union(act_df)

An advantage is that it can be done regardless any pattern.

Answer 4

import sys
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.transforms import *
from awsglue.dynamicframe import DynamicFrame
from awsglue.utils import getResolvedOptions
from awsglue.job import Job

import boto3


sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)


inputDyf = lueContext.create_dynamic_frame.from_options(connection_type="parquet", connection_options={'paths': ["s3://dev-test-laxman-new-bucket/"]})

I am able to read multiple (2) parquet file from s3://dev-test-laxman-new-bucket/ and write in csv files.

As you can see i have 2 parqet file in the my bucket :

Hope it will be helpful to others.