Hi spark users and developers,
I have been trying to understand how Spark SQL works with Parquet for the
couple of days. There is a performance problem that is unexpected using the
column pruning. Here is a dummy example:
The parquet file has the 3 fields:
|-- customer_id: string (nullable = true)
|-- type: string (nullable = true)
|-- mapping: map (nullable = true)
| |-- key: string
| |-- value: string (nullable = true)
Note that mapping is just a field with a lot of key value pairs.
I just created a parquet files with 1 billion entries with each entry
having 10 key-value pairs in the mapping.
After I generate this parquet file, I generate another parquet without the
mapping field that is:
|-- customer_id: string (nullable = true)
|-- type: string (nullable = true)
Let call the first parquet file data-with-mapping and the second parquet
file data-without-mapping.
Then I ran a very simple query over two parquet files:
val df = sqlContext.read.parquet(path)
df.select(df("type")).count
The run on the data-with-mapping takes 34 seconds with the input size
of 11.7 MB.
The run on the data-without-mapping takes 8 seconds with the input size of
7.6 MB.
They all ran on the same cluster with spark 1.4.1.
What bothers me the most is the input size because I supposed column
pruning will only deserialize columns that are relevant to the query (in
this case the field type) but for sure, it reads more data on the
data-with-mapping than the data-without-mapping. The speed is 4x faster in
the data-without-mapping that means that the more columns a parquet file
has the slower it is even only a specific column is needed.
Anyone has an explanation on this? I was expecting both of them will finish
approximate the same time.
Best Regards,
Jerry
