#todayilearned: Julian/Georgian calendar mismatches in 'pyspark'

Working in software engineering has taught me that time is an extremely difficult concept to parse effectively. There’s an entire section on date and time falsehoods programmers believe on GitHub. Programmers therefore try and normalize time as much as they can; for example, always specify a timezone and normalize to UTC, or record milliseconds since Unix epoch.

I would like to add in my own addendum to this pile: never assume that different programming languages obey the same paradigms about date and time.

I work on an ETL tool, and it’s nominally written in Python. Although Python is fast in terms of development time and feature turnaround, Python’s system performance is slow in comparison to other programming languages, and optimizing for system performance requires certain workarounds. My workaround was to include an acceleration layer using Apache Spark and our open-source Spark connector, interfacing with Python using pyspark and py4j.

There were hiccups along the way, but it worked and it was fast. Then, when I was executing my regression tests, I noticed something rather odd about a bit of data I was ingesting.

I had a datetime value '1483-12-13 23:07:26.066', corresponding to December 13th, 1483. When I ingested this datetime value into our database using this new acceleration layer, it recorded as timestamp value -15337453953934 (units in seconds since UNIX epoch).

Okay…so what’s the point?

When I was doing per-value comparison on that record, I read the value out of the database using Python and compared it to the value from the original source, which I re-read in Python. The value in the database corresponded to the Python datetime object datetime.datetime(1483, 12, 22, 23, 11, 24, 66000) - December 22nd, 1483.

It’s eight days off.


I asked a colleague about this issue. He found a JIRA issue created for this already, SPARK-18381. To spare you from reading (perhaps even more) JIRA tickets, the gist is that pyspark uses an idealized Georgian calendar extending all the way back through time, while Apache Spark respects the actual switch from the Julian calendar that took place around mid-October 1583.

What this basically means is that if you used Python to save datetimes before October of 1583, and you started using Spark to save more datetimes before October of 1583, you would be merging two different states together which would corrupt your data. You would need to write or incorporate code in order to reconcile these two states.

Here’s a code sample in Python:

yingw787@yingw787-desktop:~/Projects/official/bytes.yingw787.com$ python
Python 3.6.5 |Anaconda, Inc.| (default, Apr 29 2018, 16:14:56)
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from dateutil import parser
>>> parser.parse('1483-12-13 23:07:26.066')
datetime.datetime(1483, 12, 13, 23, 7, 26, 66000)

Here’s the anomaly expressed in pyspark:

yingw787@yingw787-desktop:~/Projects/official/bytes.yingw787.com$ pyspark
Python 3.6.5 |Anaconda, Inc.| (default, Apr 29 2018, 16:14:56)
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
2018-11-01 23:10:33 WARN  Utils:66 - Your hostname, yingw787-desktop resolves to a loopback address:; using instead (on interface enp3s0)
2018-11-01 23:10:33 WARN  Utils:66 - Set SPARK_LOCAL_IP if you need to bind to another address
2018-11-01 23:10:34 WARN  NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
2018-11-01 23:10:35 WARN  Utils:66 - Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
2018-11-01 23:10:35 WARN  Utils:66 - Service 'SparkUI' could not bind on port 4041. Attempting port 4042.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 2.3.2

Using Python version 3.6.5 (default, Apr 29 2018 16:14:56)
SparkSession available as 'spark'.
>>> from dateutil import parser
>>> date = parser.parse('1483-12-13 23:07:26.066').date()
>>> spark.createDataFrame([(date,)], ('date',)).show(1)
2018-11-01 23:10:59 WARN  ObjectStore:568 - Failed to get database global_temp, returning NoSuchObjectException
|      date|

I’m not sure why it’s earlier instead of later like in the prior example. It is still about 8 days different, though.

Note this doesn’t happen in vanilla Java. Vanilla Java parses datetimes fine.

yingw787@yingw787-desktop:~/Projects/official/bytes.yingw787.com$ jshell
|  Welcome to JShell -- Version 9-internal
|  For an introduction type: /help intro

-> import java.text.SimpleDateFormat;

-> SimpleDateFormat dateFormat = new SimpleDateFormat("yyyy-MM-dd hh:mm:ss.SSS");
|  Added variable dateFormat of type SimpleDateFormat with initial value java.text.SimpleDateFormat@844650a5

-> import java.util.Date;

-> Date parsedDate = dateFormat.parse("1483-12-13 23:07:26.066");
|  Added variable parsedDate of type Date with initial value Sat Dec 13 23:07:26 EST 1483

-> import java.sql.Timestamp;

-> Timestamp timestamp = new Timestamp(parsedDate.getTime());
|  Added variable timestamp of type Timestamp with initial value 1483-12-13 23:07:26.066

-> timestamp
|  Variable timestamp of type Timestamp has value 1483-12-13 23:07:26.066

I only noticed this anomaly because I randomly generated my test data, and as we don’t really have any customers referencing datetimes so far back, we decided to ignore this particular situation. I filtered my test data so that all datetimes were on or after UNIX epoch, and my comparisons happily passed.

This definitely counts as one of those ‘unknown unknowns’ that may keep people up at night. If you don’t have high enough cardinality in your dataset to notice a statistically significant drift, and you didn’t record which services that piece of data touched on its way to the database, there’s really no solution I can think of that would fix the corrupted data after it’s saved.

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