Hello, Per the process described at https://arrow.apache.org/docs/format/Changing.html#discussion-and-voting-process I am starting a discussion thread for the following spec change proposal:
1. Add a new time unit: PICOSECOND, which is unsupported in the existing 64-bit timestamp-related types. 2. Add support for bitWidth=128 to the timestamp data type, which supports all units, including PICOSECOND. 3. Add support for bitWidth=128 to the duration data type, which supports all units, including PICOSECOND. This is motivated by some currently experimental changes in BigQuery to support picosecond precision timestamps (source <https://docs.cloud.google.com/bigquery/docs/reference/storage/rpc/google.cloud.bigquery.storage.v1?content_ref=read%20api%20will%20return%20full%20precision%20picosecond%20value%20the%20value%20will%20be%20encoded%20as%20a%20string%20which%20conforms%20to%20iso%208601%20format#picostimestampprecision>), but from what I can tell such timestamps already have some support in IBM Db2 (source <https://www.ibm.com/docs/en/db2-for-zos/13.0.0?topic=jdbc-dbtimestamp-class&content_ref=the+com+ibm+db2+jcc+dbtimestamp+class+can+be+used+to+create+timestamp+objects+with+a+precision+of+up+to+picoseconds+and+time+zone+information>) and Trino (source <https://trino.io/docs/current/language/types.html?content_ref=heading+calendar+date+and+time+of+day+without+a+time+zone+with+pdigits+of+precision+for+the+fraction+of+seconds+a+precision+of+up+to+12+picoseconds+is+supported>). Note that reference implementation(s) are still very much a work-in-progress (https://github.com/apache/arrow/pull/48018 for a start in C++), but I figured it would be useful to kick off the conversation before diving in too much further into implementation. Inspired by other discussions, I've created a draft of a more formal RFC document here: Arrow-RFC: timestamp128 and duration128 data types with support for picosecond units <https://docs.google.com/document/d/1-S0qvYTIEGlLnNkkgyWSHfnIvU4xpFqDQuMNTojaj9A/edit?tab=t.0#heading=h.as1aixu509k7> * • **Tim Sweña (Swast)* * • *Team Lead, BigQuery DataFrames * • *Google Cloud Platform * • *Chicago, IL, USA
