I have a file that has one JSON per line. Here is a sample:
{
"product": {
"id": "abcdef",
"price": 19.99,
"specs": {
"voltage": "110v",
"color": "white"
}
},
"user": "Daniel Severo"
}
I want to create a parquet file with columns such as:
product.id, product.price, product.specs.voltage, product.specs.color, user
I know that parquet has a nested encoding using the Dremel algorithm, but I haven't been able to use it in python (not sure why).
I'm a heavy pandas and dask user, so the pipeline I'm trying to construct is json data -> dask -> parquet -> pandas
, although if anyone has a simple example of creating and reading these nested encodings in parquet using Python I think that would be good enough :D
EDIT
So, after digging in the PRs I found this: https://github.com/dask/fastparquet/pull/177
which is basically what I want to do. Although, I still can't make it work all the way through. How exactly do I tell dask/fastparquet that my product
column is nested?
Parquet stores nested data structures in a flat columnar format using a technique outlined in the Dremel paper from Google.
Apache Parquet is a popular column storage file format used by Hadoop systems, such as Pig, Spark, and Hive. The file format is language independent and has a binary representation. Parquet is used to efficiently store large data sets and has the extension . parquet .
So storing JSON values in a map column in Parquet avoids parsing JSON in every query, but it still requires reading chunks of data for all map values, not just for the selected keys.
With the CData Python Connector for Parquet, you can work with Parquet data just like you would with any database, including direct access to data in ETL packages like petl. Download a free, 30-day trial of the Parquet Python Connector to start building Python apps and scripts with connectivity to Parquet data.
I know that parquet has a nested encoding using the Dremel algorithm, but I haven't been able to use it in python (not sure why).
It is important to have this functionality because other systems that use Parquet, like Impala, Hive, Presto, Drill, and Spark, have native support for nested types in their SQL dialects, so we need to be able to read and write these structures faithfully from Python.
In this example, we extract Parquet data, sort the data by the Column1 column, and load the data into a CSV file. With the CData Python Connector for Parquet, you can work with Parquet data just like you would with any database, including direct access to data in ETL packages like petl.
Implementing the conversions on both the read and write path for arbitrary Parquet nested data is quite complicated to get right -- implementing the shredding and reassembly algorithm with associated conversions to some Python data structures. We have this on the roadmap in Arrow / parquet-cpp (see https://github.com/apache/parquet-cpp/tree/master/src/parquet/arrow), but it has not been completed yet (only support for simple structs and lists/arrays are supported now). It is important to have this functionality because other systems that use Parquet, like Impala, Hive, Presto, Drill, and Spark, have native support for nested types in their SQL dialects, so we need to be able to read and write these structures faithfully from Python.
This can be analogously implemented in fastparquet as well, but it's going to be a lot of work (and test cases to write) no matter how you slice it.
I will likely take on the work (in parquet-cpp) personally later this year if no one beats me to it, but I would love to have some help.
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