[VL] Add ANSI mode support

[philo-he]

Description

In this issue, we will list and track all tasks for ANSI mode support.

There are two Spark configurations directly related to ANSI usage.

• spark.sql.ansi.enabled (default is true since Spark 4.0)
• spark.sql.storeAssignmentPolicy (default is ANSI since Spark 3.0)

Tasks

Basic

• [VL] Set ANSI mode for Velox according to Spark's configuration #10387

1. Type Casting Functions (ANSI Strict)

• cast string to boolean (@malinjawi)
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L701

• Cast decimal to string (@Mariamalmesfer))
  // In ANSI mode, Spark always use plain string representation on casting Decimal values
  // as strings. Otherwise, the casting is using BigDecimal.toString which may use scientific
  // notation if an exponent is needed.
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L678

• cast string to timestamp (@infvg )
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L733

• cast String to timestampNTZ
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L775

• cast float/double to timestamp (@infvg)
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L758
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L765

• cast string to date (@malinjawi)
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L811

• cast string to time (@malinjawi)
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L826
  The implementation for codegen, assume equivalent with the above link:
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L1493

• cast string to long/int/short/byte (@malinjawi)
  As one example, here is the related code for long type:
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L883

• cast NumericType to long/int/short/byte (@minni31)
  As one example, here is the related code for long type:
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L896

• cast timestamp to int/short/byte
  As one example, here is the related code for int type:
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L933

• cast time to short/byte (requires TimeType support)
  As one example, here is the related code for short type:
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L980

• cast several types to decimal
  ANSI controls the overflow behavior in changePrecision
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L1103

• cast string to double/float
  ANSI controls the behavior in handling incorrect number format
  As one example, here is the related code for double type:
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala#L1159

2. Arithmetic Functions (ANSI Overflow Check)

• A base type: AnsiIntervalType (@malinjawi)
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/api/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala#L168

• Unary expressions like Abs, UnaryMinus (@malinjawi)
  The ANSI config controls failOnError.
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala#L152C35-L152C46

• Binary arithmetic expressions using BinaryArithmetic as base, such as add, divide, multiply, etc. (@malinjawi)
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala#L209

3. Date/Time Functions (ANSI Validation)

• Datetime expressions: ToUnixTimestamp, UnixTimestamp, GetTimestamp, TryToTimestampExpressionBuilder, NextDay, DateAddInterval, ParseToDate, TryToDateExpressionBuilder, ParseToTimestamp, MakeDate, TryMakeTimestampLTZExpressionBuilder, MakeTimestamp

4. String to Numeric Conversion (ANSI Strict)

• String expressions: Elt
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala#L286

• Collection expression Size
  Its legacySizeOfNull is impacted by ANSI config.
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala#L118

• Collection expression ElementAt
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala#L2622

• conv
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathExpressions.scala#L451

• round functions: Round, BRound, RoundCeil, RoundFloor
  As one example, see how to round to ByteType with ANSI enabled:
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathExpressions.scala#L1579

• STORE_ASSIGNMENT_POLICY defaults to ANSI
  https://github.com/apache/spark/blob/e221b56be7b6d9e48e107fc4d1cf0c15f02700f8/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala#L4487

5. Aggregation Functions (ANSI Overflow)

SUM, AVG, VAR_POP, VAR_SAMP, STDDEV_POP, STDDEV_SAMP

• In ANSI mode: overflow checks during accumulation.

TRY_SUM (Spark 3.4+)

• Returns NULL on overflow instead of error.

6. Window Functions (ANSI Overflow)

Same overflow checks apply in window operations:

• SUM(...) OVER(...)
• AVG(...) OVER(...)

7. ANSI SQL Compliant String Functions

SUBSTRING / SUBSTR

• ANSI SQL standard argument order: SUBSTRING(str FROM start [FOR len])
• Also supports classic form: SUBSTRING(str, start, len)

TRIM

• ANSI syntax: TRIM(LEADING '0' FROM col)
  Also TRIM(BOTH ...), TRIM(TRAILING ...)

OVERLAY

• ANSI SQL string replacement:
  OVERLAY(string PLACING replacement FROM start [FOR length])

---

See Spark ANSI compliance: https://github.com/apache/spark/blob/v4.0.0/docs/sql-ref-ansi-compliance.md

Related discussion: #4740.
facebookincubator/velox#3869

[FelixYBW]

@philo-he are you going to work on this?

[philo-he]

@PHILO-HE are you going to work on this?

@FelixYBW, some developers in the community are interested in this work. I will work with them. I assume it requires a lot of small changes distributed in multiple files in Velox.

[FelixYBW]

Yes, several customer required it. I added to project track. Could you update the status weekly or bi-weekly?

We may need to enable the ANSI UT firstly and list the failure cases. Also create the sub-issue for work to do. I think some of work is already done in velox.

[nimesh1601]

@philo-he We at Uber also require this support, so feel free to include me too

[philo-he]

Yes, several customer required it. I added to project track. Could you update the status weekly or bi-weekly?

We may need to enable the ANSI UT firstly and list the failure cases. Also create the sub-issue for work to do. I think some of work is already done in velox.

@FelixYBW, I will update the status bi-weekly and will also create some subtasks. Yes, there are some relevant work already done.

[malinjawi]

Hey @philo-he @FelixYBW I will be picking up cast string to boolean. Looking forward to making my first contribution!