I'm unable to figure out what is wrong with my CTAS query, it breaks the data into smaller files while storing inside a partition even though I haven't mentioned any bucketing columns. Is there a way to avoid these small files and store as one single file per partition as files lesser than 128 MB would cause additional overhead?
CREATE TABLE sampledb.yellow_trip_data_parquet
WITH(
format = 'PARQUET'
parquet_compression = 'GZIP',
external_location='s3://mybucket/Athena/tables/parquet/'
partitioned_by=ARRAY['year','month']
)
AS SELECT
VendorID,
tpep_pickup_datetime,
tpep_dropoff_datetime,
passenger_count,
trip_distance,
RatecodeID,
store_and_fwd_flag,
PULocationID,
DOLocationID,
payment_type,
fare_amount,
extra,
mta_tax,
tip_amount,
tolls_amount,
improvement_surcharge,
total_amount,
date_format(date_parse(tpep_pickup_datetime,'%Y-%c-%d %k:%i:%s'),'%Y') AS year,
date_format(date_parse(tpep_pickup_datetime,'%Y-%c-%d %k:%i:%s'),'%c') AS month
FROM sampleDB.yellow_trip_data_raw;
I was able to overcome the issue by creating a bucketing column month_a
. Below is the code
CREATE TABLE sampledb.yellow_trip_data_avro
WITH (
format = 'AVRO',
external_location='s3://a4189e1npss3001/Athena/internal_tables/avro/',
partitioned_by=ARRAY['year','month'],
bucketed_by=ARRAY['month_a'],
bucket_count=12
) AS SELECT
VendorID,
tpep_pickup_datetime,
tpep_dropoff_datetime,
passenger_count,
trip_distance,
RatecodeID,
store_and_fwd_flag,
PULocationID,
DOLocationID,
payment_type,
fare_amount,
extra,
mta_tax,
tip_amount,
tolls_amount,
improvement_surcharge,
total_amount,
date_format(date_parse(tpep_pickup_datetime, '%Y-%c-%d %k:%i:%s'),'%c') AS month_a,
date_format(date_parse(tpep_pickup_datetime, '%Y-%c-%d %k:%i:%s'),'%Y') AS year,
date_format(date_parse(tpep_pickup_datetime, '%Y-%c-%d %k:%i:%s'),'%c') AS month
FROM sampleDB.yellow_trip_data_raw;
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