Parquet without Hadoop?

问题

I want to use parquet in one of my projects as columnar storage. But i dont want to depends on hadoop/hdfs libs. Is it possible to use parquet outside of hdfs? Or What is the min dependency?

回答1:

Investigating the same question I found that apparently it's not possible for the moment. I found this git issue, which proposes decoupling parquet from the hadoop api. Apparently it has not been done yet.

In the Apache Jira I found an issue, which asks for a way to read a parquet file outside hadoop. It is unresolved by the time of writing.

EDIT:

Issues are not tracked on github anymore (first link above is dead). A newer issue I found is located on apache's Jira with the following headline:

make it easy to read and write parquet files in java without depending on hadoop

回答2:

Late to the party, but I've been working on something that should make this possible: https://github.com/jmd1011/parquet-readers.

This is still under development, but a final implementation should be out within a month or two of writing this.

Edit: Months later, and still working on this! It is under active development, just taking longer than expected.

回答3:

Since it is just a file format it is obviously possible to decouple parquet from the Hadoop ecosystem. Nowadays the simplest approach I could find was through Apache Arrow, see here for a python example.

Here a small excerpt from the official PyArrow docs:

Writing

In [2]: import numpy as np

In [3]: import pandas as pd

In [4]: import pyarrow as pa

In [5]: df = pd.DataFrame({'one': [-1, np.nan, 2.5],
   ...:                    'two': ['foo', 'bar', 'baz'],
   ...:                    'three': [True, False, True]},
   ...:                    index=list('abc'))
   ...: 

In [6]: table = pa.Table.from_pandas(df)

In [7]: import pyarrow.parquet as pq

In [8]: pq.write_table(table, 'example.parquet')

Reading

In [11]: pq.read_table('example.parquet', columns=['one', 'three'])

EDIT:

With Pandas directly

It is also possible to use pandas directly to read and write DataFrames. This makes it as simple as my_df.to_parquet("myfile.parquet") and my_df = pd.read_parquet("myfile.parquet")

回答4:

What type of data do you have in Parquet? You don't require HDFS to read Parquet files. It is definitely not a pre-requisite. We use parquet files at Incorta for our staging tables. We do not ship with a dependency on HDFS, however, you can store the files on HDFS if you want. Obviously, we at Incorta can read directly from the parquet files, but you can also use Apache Drill to connect, use file:/// as the connection and not hdfs:/// See below for an example.

To read or write Parquet data, you need to include the Parquet format in the storage plugin format definitions. The dfs plugin definition includes the Parquet format.

{
  "type" : "file",
  "enabled" : true,
  "connection" : "file:///",
  "workspaces" : {
  "json_files" : {
  "location" : "/incorta/tenants/demo//drill/json/",
  "writable" : false,
  "defaultInputFormat" : json
  } 
},

回答5:

Nowadays you dont need to rely on hadoop as heavily as before.

Please see my other post: How to view Apache Parquet file in Windows?

来源：https://stackoverflow.com/questions/29279865/parquet-without-hadoop

标签

Hadoop

HDFS

parquet