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https://issues.apache.org/jira/browse/ARROW-1436?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16265989#comment-16265989
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Licht Takeuchi commented on ARROW-1436:
---------------------------------------
[~wesmckinn] This seems already fixed.
* Python code:
{code:java}
import pandas as pd
import pyarrow as pa
from pyarrow import parquet as pq
import numpy as np
t = pa.timestamp('ns')
start = pd.Timestamp('2001-01-01').value
data = np.array([start, start + 1000, start + 2000], dtype='int64')
a = pa.array(data, type=t)
table = pa.Table.from_arrays([a], ['ts'])
pq.write_table(table, 'test-1.parquet', use_deprecated_int96_timestamps=True)
pq.write_table(table, 'test-2.parquet', use_deprecated_int96_timestamps=False)
{code}
* Spark code:
{code:java}
import org.apache.parquet.hadoop.ParquetFileReader
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.hadoop.conf.Configuration
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext.implicits._
val conf = sc.hadoopConfiguration
val fs = FileSystem.get(conf)
// int96 timestamp case
ParquetFileReader.readAllFootersInParallel(conf, fs.getFileStatus(new
Path("test-1.parquet")))
var df = sqlContext.read.parquet("../../../arrow/python/test-1.parquet")
df.take(3)
// int64 timestamp case
ParquetFileReader.readAllFootersInParallel(conf, fs.getFileStatus(new
Path("test-2.parquet")))
var df = sqlContext.read.parquet("../../../arrow/python/test-2.parquet")
df.take(3)
{code}
> PyArrow Timestamps written to Parquet as INT96 appear in Spark as 'bigint'
> --------------------------------------------------------------------------
>
> Key: ARROW-1436
> URL: https://issues.apache.org/jira/browse/ARROW-1436
> Project: Apache Arrow
> Issue Type: Bug
> Components: Format, Python
> Affects Versions: 0.6.0
> Reporter: Lucas Pickup
> Assignee: Licht Takeuchi
> Fix For: 0.8.0
>
>
> When using the 'use_deprecated_int96_timestamps' option to write Parquet
> files compatible with Spark <2.2.0 (which doesn't support INT64 backed
> Timestamps) Spark identifies the Timestamp columns as BigInts. Some metadata
> may be missing.
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