![]() The important point that is very relevant this week (and maybe why you followed up!) is that "local timestamp" is not well defined. Thanks for the input - this is a topic I've been noodling on for a while (see the link to my talk above), so here are some thoughts. not sure if that's a dependency DuckDB wants to take on. I believe Postgres leans on the ICU library for time zone handling. Helpfully, Redshift has already done that, and Snowflake works essentially the same as well (if you ignore TIMESTAMP_LTZ which offers a third behavior). I would suggest DuckDB copy Postgres here. That would be a major pain for me, making sure everything was properly converted before ingesting, but it would be vitally necessary for the sanity of my users. If I were to try to use DuckDB for serious work, I would have to defy the manual and store only localized timestamps, rather than UTC. So the common advice of "just use UTC" flat-out doesn't work in an analytical environment. Where I live, for 6-7 hours each day it is a different calendar day here than it is in UTC, so any calendar-centric computation done with UTC timestamps will be egregiously incorrect. But business users and analysts who don't themselves live in UTC don't think in UTC and asking them to do so is silly. Time is a human concept, and humans generally care about their local time.Īs software engineers, we're used to thinking like machines, and UTC is made for machines so it feels natural to us. (Converting the other way does require supplying a time zone, since that determines the offset.) Why is timestamptz necessary for analytics? OffsetDateTime can be converted to an Instant without supplying a time zone, because it is unambiguous. represents "a date-time with an offset from UTC". Converting a LocalDateTime to or from an Instant requires supplying a time zone, because of its inherent ambiguity. ![]() represents "a date-time without a time-zone". holds the number of nanoseconds since the standard epoch. Java 8's date/time classes can make for a helpful comparison, as they are modeled extremely rigorously. Systems built around plain timestamp are missing critical information. When I first started working with Postgres, I found these very confusing, and elected to use plain timestamp because I felt like timestamptz involved extra complication I didn't need. When we ask Postgres for a string rendering of a timestamptz, it is shown to us in the current session timezone. Note however that the time zone we supply is not remembered. Timestamptz fixes that by forcing us to supply enough information to produce an unambiguous instant. The problem with that though is that the time zone was supplied through a side channel (I told it to you, wrote it on a napkin, etc), and isn't present in the data. If I tell you "in America/Denver" then you have enough information, and are able to compare it to any other unambiguous timestamp. The problem with a plain timestamp like ' 18:58:37' is that you don't actually know when that is, because I didn't give you enough information. TIMESTAMPTZ: unambiguous/absolute timestamp TIMESTAMP: local timestamp in an unknown/unspecified time zone That may be the fault of the standard, I'm not sure. Postgres' timestamp types are very unfortunately named. (sorry this is long, proper timezone handling is extremely important to me) Postgres It does have at time zone, so that works as well. The important component was that the inserting application inserted what it though the appropriate time zone is (within pg's parsing expectations). Granted, I believe that PG always stores in UTC, so I do not expect to see something other than +00 from queries. (I have not tested with non-hour-aligned time zones, but allegedly it will find them by their olson names or other unambiguous zone name, so the 2-digit hour on the output is likely not a limiting factor.) I've not run into a situation where the +00 varies in format or accuracy (only when the importing process did it incorrectly). ![]() ( current_timestamp returns timestamp with time zone according to, table 9.30.) Automate ETL/ELT processes and get data flowing from hundreds of potential data sources to a single destination for analytics.And have been comfortable with that output for a while. With our Runnersolution, you can easily integrate your Redshift data (or other Amazon data) into your data pipeline. Learn how Zuarcan help you manage your Redshift data. It is constructed with this syntax: DATEDIFF ( datepart, ) The Amazon Redshift DATEDIFF function returns the difference between the date parts of two date or time expressions.
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