Timestamps

Arrow/Pandas Timestamps

Arrow timestamps are stored as a 64-bit integer with column metadata to associate a time unit (e.g. milliseconds, microseconds, or nanoseconds), and an optional time zone. Pandas (Timestamp) uses a 64-bit integer representing nanoseconds and an optional time zone. Python/Pandas timestamp types without a associated time zone are referred to as “Time Zone Naive”. Python/Pandas timestamp types with an associated time zone are referred to as “Time Zone Aware”.

Timestamp Conversions

Pandas/Arrow ⇄ Spark

Spark stores timestamps as 64-bit integers representing microseconds since the UNIX epoch. It does not store any metadata about time zones with its timestamps.

Spark interprets timestamps with the session local time zone, (i.e. spark.sql.session.timeZone). If that time zone is undefined, Spark turns to the default system time zone. For simplicity’s sake below, the session local time zone is always defined.

This implies a few things when round-tripping timestamps:

  1. Timezone information is lost (all timestamps that result from converting from spark to arrow/pandas are “time zone naive”).

  2. Timestamps are truncated to microseconds.

  3. The session time zone might have unintuitive impacts on translation of timestamp values.

Spark to Pandas (through Apache Arrow)

The following cases assume the Spark configuration spark.sql.execution.arrow.enabled is set to "true".

>>> pdf = pd.DataFrame({'naive': [datetime(2019, 1, 1, 0)],
...                     'aware': [Timestamp(year=2019, month=1, day=1,
...                               nanosecond=500, tz=timezone(timedelta(hours=-8)))]})
>>> pdf
       naive                               aware
       0 2018-10-01 2018-10-01 00:00:00.000000500-08:00

>>> spark.conf.set("spark.sql.session.timeZone", "UTC")
>>> utc_df = sqlContext.createDataFrame(pdf)
>>> utf_df.show()
+-------------------+-------------------+
|              naive|              aware|
+-------------------+-------------------+
|2019-01-01 00:00:00|2019-01-01 08:00:00|
+-------------------+-------------------+

Note that conversion of the aware timestamp is shifted to reflect the time assuming UTC (it represents the same instant in time). For naive timestamps, Spark treats them as being in the system local time zone and converts them UTC. Recall that internally, the schema for spark dataframe’s does not store any time zone information with timestamps.

Now if the session time zone is set to US Pacific Time (PST) we don’t see any shift in the display of the aware time zone (it still represents the same instant in time):

>>> spark.conf.set("spark.sql.session.timeZone", "US/Pacific")
>>> pst_df = sqlContext.createDataFrame(pdf)
>>> pst_df.show()
+-------------------+-------------------+
|              naive|              aware|
+-------------------+-------------------+
|2019-01-01 00:00:00|2019-01-01 00:00:00|
+-------------------+-------------------+

Looking again at utc_df.show() we see one of the impacts of the session time zone. The naive timestamp was initially converted assuming UTC, the instant it reflects is actually earlier than the naive time zone from the PST converted data frame:

>>> utc_df.show()
+-------------------+-------------------+
|              naive|              aware|
+-------------------+-------------------+
|2018-12-31 16:00:00|2019-01-01 00:00:00|
+-------------------+-------------------+

Spark to Pandas

We can observe what happens when converting back to Arrow/Pandas. Assuming the session time zone is still PST:

>>> pst_df.show()
+-------------------+-------------------+
|              naive|              aware|
+-------------------+-------------------+
|2019-01-01 00:00:00|2019-01-01 00:00:00|
+-------------------+-------------------+


 >>> pst_df.toPandas()
 naive      aware
 0 2019-01-01 2019-01-01
 >>> pst_df.toPandas().info()
 <class 'pandas.core.frame.DataFrame'>
 RangeIndex: 1 entries, 0 to 0
 Data columns (total 2 columns):
 naive    1 non-null datetime64[ns]
 aware    1 non-null datetime64[ns]
 dtypes: datetime64[ns](2)
 memory usage: 96.0 bytes

Notice that, in addition to being a “time zone naive” timestamp, the ‘aware’ value will now differ when converting to an epoch offset. Spark does the conversion by first converting to the session time zone (or system local time zone if session time zones isn’t set) and then localizes to remove the time zone information. This results in the timestamp being 8 hours before the original time:

>>> pst_df.toPandas()['aware'][0]
Timestamp('2019-01-01 00:00:00')
>>> pdf['aware'][0]
Timestamp('2019-01-01 00:00:00.000000500-0800', tz='UTC-08:00')
>>> (pst_df.toPandas()['aware'][0].timestamp()-pdf['aware'][0].timestamp())/3600
-8.0

The same type of conversion happens with the data frame converted while the session time zone was UTC. In this case both naive and aware represent different instants in time (the naive instant is due to the change in session time zone between creating data frames):

>>> utc_df.show()
+-------------------+-------------------+
|              naive|              aware|
+-------------------+-------------------+
|2018-12-31 16:00:00|2019-01-01 00:00:00|
+-------------------+-------------------+

>>> utc_df.toPandas()
naive      aware
0 2018-12-31 16:00:00 2019-01-01

Note that the surprising shift for aware doesn’t happen when the session time zone is UTC (but the timestamps still become “time zone naive”):

>>> spark.conf.set("spark.sql.session.timeZone", "UTC")
>>> pst_df.show()
+-------------------+-------------------+
|              naive|              aware|
+-------------------+-------------------+
|2019-01-01 08:00:00|2019-01-01 08:00:00|
+-------------------+-------------------+

>>> pst_df.toPandas()['aware'][0]
Timestamp('2019-01-01 08:00:00')
>>> pdf['aware'][0]
Timestamp('2019-01-01 00:00:00.000000500-0800', tz='UTC-08:00')
>>> (pst_df.toPandas()['aware'][0].timestamp()-pdf['aware'][0].timestamp())/3600
0.0
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