All Versions
Latest Version
Avg Release Cycle
122 days
Latest Release

Changelog History

  • v0.1.1.1 Changes

    • ⬆️ bumped a version number (the least significant) in order to upload on hackage a new version with correct github links
  • v0.1.1.0 Changes

    February 09, 2019
    • Includes various enhancements (most notable is DML operations support) and fixes
      • Issue #1: Implemented agg function string_agg (listagg in Oracle) and the corresponding Julius clause
      • Issue #2: Implemented Julius Aggregate clauses: CountDist and CountStar
      • Issue #6 DML Enhancements
        • Implement Update Julius Clause
        • Implement Insert Operation and corresponding Julius Clause (both single RTuple INSERT and INSERT INTO SELECT)
        • Implement Merge/Upsert operation and corresponding Julius clause
        • Implement semi-join operation and corresponding Julius clause
        • Implement anti-join operation and corresponding Julius clause
        • Implement Delete operation and corresponding Julius clause
      • Issue #5 : Add support for UTCTime
      • Solve the CSV orphan instances problem by defining CSV with newtype
      • Fix problem with order by. I have noticed the following bug: Haskell >>> let t1 = RDate {rdate = "01/12/1990", dtformat = "DD/MM/YYYY"} >>> let t2 = RDate {rdate = "1/12/1991", dtformat = "DD/MM/YYYY"} >>> compare t1 t2 >>> EQ 🛠 Fix:
        • Redefine the RDataType Ord instance based on the compare function instead of the (<=) function.
        • When comparing RDate types, convert them first to RTimeStamps and then compare these ones
        • The previous point apply it also to the Eq instance for RDataType.
  • v0.1.0.0 Changes

    October 10, 2018
    • Initial Version. Includes a full-working version of
      • Julius: A type-level Embedded Domain Specific (EDSL) Language for ETL
      • all common Relational Algebra operations,
      • the ETL Mapping and other typical ETL constructs and operations
      • operations applicable to all kinds of tabular data
      • In-memory, database-less data processing.