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RAPIDS Accelerator for Apache Spark Compatibility with Apache Spark

The SQL plugin tries to produce results that are bit for bit identical with Apache Spark. There are a number of cases where there are some differences. In most cases operators that produce different results are off by default, and you can look at the configs for more information on how to enable them. In some cases we felt that enabling the incompatibility by default was worth the performance gain. All of those operators can be disabled through configs if it becomes a problem. Please also look at the current list of bugs which are typically incompatibilities that we have not yet addressed.

Ordering of Output

There are some operators where Spark does not guarantee the order of the output. These are typically things like aggregates and joins that may use a hash to distribute the work load among downstream tasks. In these cases the plugin does not guarantee that it will produce the same output order that Spark does. In cases such as an order by operation where the ordering is explicit the plugin will produce an ordering that is compatible with Spark’s guarantee. It may not be 100% identical if the ordering is ambiguous.

In versions of Spark prior to 3.1.0 -0.0 is always < 0.0 but in 3.1.0 and above this is not true for sorting. For all versions of the plugin -0.0 == 0.0 for sorting.

Spark’s sorting is typically a stable sort. Sort stability cannot be guaranteed in distributed work loads because the order in which upstream data arrives to a task is not guaranteed. Sort stability is only guaranteed in one situation which is reading and sorting data from a file using a single task/partition. The RAPIDS Accelerator does an unstable out of core sort by default. This simply means that the sort algorithm allows for spilling parts of the data if it is larger than can fit in the GPU’s memory, but it does not guarantee ordering of rows when the ordering of the keys is ambiguous. If you do rely on a stable sort in your processing you can request this by setting spark.rapids.sql.stableSort.enabled to true and RAPIDS will try to sort all the data for a given task/partition at once on the GPU. This may change in the future to allow for a spillable stable sort.

Floating Point

For most basic floating-point operations like addition, subtraction, multiplication, and division the plugin will produce a bit for bit identical result as Spark does. For other functions like sin, cos, etc. the output may be different, but within the rounding error inherent in floating-point calculations. The ordering of operations to calculate the value may differ between the underlying JVM implementation used by the CPU and the C++ standard library implementation used by the GPU.

In the case of round and bround the results can be off by more because they can enlarge the difference. This happens in cases where a binary floating-point representation cannot exactly capture a decimal value. For example 1.025 cannot exactly be represented and ends up being closer to 1.02499. The Spark implementation of round converts it first to a decimal value with complex logic to make it 1.025 and then does the rounding. This results in round(1.025, 2) under pure Spark getting a value of 1.03 but under the RAPIDS accelerator it produces 1.02. As a side note Python will produce 1.02, Java does not have the ability to do a round like this built in, but if you do the simple operation of Math.round(1.025 * 100.0)/100.0 you also get 1.02.

For aggregations the underlying implementation is doing the aggregations in parallel and due to race conditions within the computation itself the result may not be the same each time the query is run. This is inherent in how the plugin speeds up the calculations and cannot be “fixed.” If a query joins on a floating point value, which is not wise to do anyways, and the value is the result of a floating point aggregation then the join may fail to work properly with the plugin but would have worked with plain Spark. Because of this most floating point aggregations are off by default but can be enabled with the config spark.rapids.sql.variableFloatAgg.enabled.

Additionally, some aggregations on floating point columns that contain NaN can produce results different from Spark in versions prior to Spark 3.1.0. If it is known with certainty that the floating point columns do not contain NaN, set spark.rapids.sql.hasNans to false to run GPU enabled aggregations on them.

In the case of a distinct count on NaN values, prior to Spark 3.1.0, the issue only shows up if you have different NaN values. There are several different binary values that are all considered to be NaN by floating point. The plugin treats all of these as the same value, where as Spark treats them all as different values. Because this is considered to be rare we do not disable distinct count for floating point values even if spark.rapids.sql.hasNans is true.

0.0 vs -0.0

Floating point allows zero to be encoded as 0.0 and -0.0, but the IEEE standard says that they should be interpreted as the same. Most databases normalize these values to always be 0.0. Spark does this in some cases but not all as is documented here. The underlying implementation of this plugin treats them as the same for essentially all processing. This can result in some differences with Spark for operations, prior to Spark 3.1.0, like sorting, and distinct count. There are still differences with joins, and comparisons even after Spark 3.1.0.

We do not disable operations that produce different results due to -0.0 in the data because it is considered to be a rare occurrence.


Spark delegates Unicode operations to the underlying JVM. Each version of Java complies with a specific version of the Unicode standard. The SQL plugin does not use the JVM for Unicode support and is compatible with Unicode version 12.1. Because of this there may be corner cases where Spark will produce a different result compared to the plugin.

CSV Reading

Due to inconsistencies between how CSV data is parsed CSV parsing is off by default. Each data type can be enabled or disabled independently using the following configs.

If you know that your particular data type will be parsed correctly enough, you may enable each type you expect to use. Often the performance improvement is so good that it is worth checking if it is parsed correctly.

Spark is generally very strict when reading CSV and if the data does not conform with the expected format exactly it will result in a null value. The underlying parser that the RAPIDS Accelerator uses is much more lenient. If you have badly formatted CSV data you may get data back instead of nulls.

Spark allows for stripping leading and trailing white space using various options that are off by default. The plugin will strip leading and trailing space for all values except strings.

There are also discrepancies/issues with specific types that are detailed below.

CSV Boolean

Invalid values like BAD show up as true as described by this issue

This is the same for all other types, but because that is the only issue with boolean parsing we have called it out specifically here.

CSV Strings

Writing strings to a CSV file in general for Spark can be problematic unless you can ensure that your data does not have any line deliminators in it. The GPU accelerated CSV parser handles quoted line deliminators similar to multiLine mode. But there are still a number of issues surrounding it and they should be avoided.

Escaped quote characters '\"' are not supported well as described by this issue.

CSV Dates

Parsing a timestamp as a date does not work. The details are documented in this issue.

Only a limited set of formats are supported when parsing dates.

  • "yyyy-MM-dd"
  • "yyyy/MM/dd"
  • "yyyy-MM"
  • "yyyy/MM"
  • "MM-yyyy"
  • "MM/yyyy"
  • "MM-dd-yyyy"
  • "MM/dd/yyyy"

The reality is that all of these formats are supported at the same time. The plugin will only disable itself if you set a format that it does not support.

As a workaround you can parse the column as a timestamp and then cast it to a date.

Invalid dates in Spark, values that have the correct format, but the numbers produce invalid dates, can result in an exception by default, and how they are parsed can be controlled through a config. The RAPIDS Accelerator does not support any of this and will produce an incorrect date. Typically, one that overflowed.

CSV Timestamps

The CSV parser does not support time zones. It will ignore any trailing time zone information, despite the format asking for a XXX or [XXX]. As such it is off by default and you can enable it by setting spark.rapids.sql.csvTimestamps.enabled to true.

The formats supported for timestamps are limited similar to dates. The first part of the format must be a supported date format. The second part must start with a 'T' to separate the time portion followed by one of the following formats:

  • HH:mm:ss.SSSXXX
  • HH:mm:ss[.SSS][XXX]
  • HH:mm
  • HH:mm:ss
  • HH:mm[:ss]
  • HH:mm:ss.SSS
  • HH:mm:ss[.SSS]

Just like with dates all timestamp formats are actually supported at the same time. The plugin will disable itself if it sees a format it cannot support.

Invalid timestamps in Spark, ones that have the correct format, but the numbers produce invalid dates or times, can result in an exception by default and how they are parsed can be controlled through a config. The RAPIDS Accelerator does not support any of this and will produce an incorrect date. Typically, one that overflowed.

CSV Floating Point

The CSV parser is not able to parse NaN values. These are likely to be turned into null values, as described in this issue.

Some floating-point values also appear to overflow but do not for the CPU as described in this issue.

Any number that overflows will not be turned into a null value.

Also parsing of some values will not produce bit for bit identical results to what the CPU does. They are within round-off errors except when they are close enough to overflow to Inf or -Inf which then results in a number being returned when the CPU would have returned null.

CSV Integer

Any number that overflows will not be turned into a null value.


The ORC format has fairly complete support for both reads and writes. There are only a few known issues. The first is for reading timestamps and dates around the transition between Julian and Gregorian calendars as described here. A similar issue exists for writing dates as described here. Writing timestamps, however only appears to work for dates after the epoch as described here.

The plugin supports reading uncompressed, snappy and zlib ORC files and writing uncompressed and snappy ORC files. At this point, the plugin does not have the ability to fall back to the CPU when reading an unsupported compression format, and will error out in that case.


The Parquet format has more configs because there are multiple versions with some compatibility issues between them. Dates and timestamps are where the known issues exist. For reads when spark.sql.legacy.parquet.datetimeRebaseModeInWrite is set to CORRECTED timestamps before the transition between the Julian and Gregorian calendars are wrong, but dates are fine. When spark.sql.legacy.parquet.datetimeRebaseModeInWrite is set to LEGACY, the read may fail for values occurring before the transition between the Julian and Gregorian calendars, i.e.: date <= 1582-10-04.

When writing spark.sql.legacy.parquet.datetimeRebaseModeInWrite is currently ignored as described here.

When spark.sql.parquet.outputTimestampType is set to INT96, the timestamps will overflow and result in an IllegalArgumentException thrown, if any value is before September 21, 1677 12:12:43 AM or it is after April 11, 2262 11:47:17 PM. To get around this issue, turn off the ParquetWriter acceleration for timestamp columns by either setting spark.rapids.sql.format.parquet.writer.int96.enabled to false or set spark.sql.parquet.outputTimestampType to TIMESTAMP_MICROS or TIMESTAMP_MILLIS to by -pass the issue entirely.

The plugin supports reading uncompressed, snappy and gzip Parquet files and writing uncompressed and snappy Parquet files. At this point, the plugin does not have the ability to fall back to the CPU when reading an unsupported compression format, and will error out in that case.

Regular Expressions

The RAPIDS Accelerator for Apache Spark currently supports string literal matches, not wildcard matches.

If a null char ‘\0’ is in a string that is being matched by a regular expression, LIKE sees it as the end of the string. This will be fixed in a future release. The issue is here.


Spark stores timestamps internally relative to the JVM time zone. Converting an arbitrary timestamp between time zones is not currently supported on the GPU. Therefore operations involving timestamps will only be GPU-accelerated if the time zone used by the JVM is UTC.


Window Functions

Because of ordering differences between the CPU and the GPU window functions especially row based window functions like row_number, lead, and lag can produce different results if the ordering includes both -0.0 and 0.0, or if the ordering is ambiguous. Spark can produce different results from one run to another if the ordering is ambiguous on a window function too.

Range Window

When the order-by column of a range based window is numeric type like byte/short/int/long and the range boundary calculated for a value has overflow, CPU and GPU will get different results.

For example, consider the following dataset:

| id   | dollars |
|    1 |    NULL |
|    1 |      13 |
|    1 |      14 |
|    1 |      15 |
|    1 |      15 |
|    1 |      17 |
|    1 |      18 |
|    1 |      52 |
|    1 |      53 |
|    1 |      61 |
|    1 |      65 |
|    1 |      72 |
|    1 |      73 |
|    1 |      75 |
|    1 |      78 |
|    1 |      84 |
|    1 |      85 |
|    1 |      86 |
|    1 |      92 |
|    1 |      98 |

After executing the SQL statement:

 COUNT(dollars) over
    ORDER BY CAST (dollars AS Byte) ASC
FROM table

The results will differ between the CPU and GPU due to overflow handling.

CPU: WrappedArray([0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0], [0])
GPU: WrappedArray([0], [19], [19], [19], [19], [19], [19], [19], [19], [19], [19], [19], [19], [19], [19], [19], [19], [19], [19], [19])

To enable byte-range windowing on the GPU, set spark.rapids.sql.window.range.byte.enabled to true.

We also provide configurations for other integral range types:

The reason why we default the configurations to false for byte/short and to true for int/long is that we think the most real-world queries are based on int or long.

Parsing strings as dates or timestamps

When converting strings to dates or timestamps using functions like to_date and unix_timestamp, the specified format string will fall into one of three categories:

  • Supported on GPU and 100% compatible with Spark
  • Supported on GPU but may produce different results to Spark
  • Unsupported on GPU

The formats which are supported on GPU and 100% compatible with Spark are :

  • dd/MM/yyyy
  • yyyy/MM
  • yyyy/MM/dd
  • yyyy-MM
  • yyyy-MM-dd
  • yyyy-MM-dd HH:mm:ss
  • MM-dd
  • MM/dd
  • dd-MM
  • dd/MM

Examples of supported formats that may produce different results are:

  • Trailing characters (including whitespace) may return a non-null value on GPU and Spark will return null

To attempt to use other formats on the GPU, set spark.rapids.sql.incompatibleDateFormats.enabled to true.

Formats that contain any of the following characters are unsupported and will fall back to CPU:

'k', 'K','z', 'V', 'c', 'F', 'W', 'Q', 'q', 'G', 'A', 'n', 'N',
'O', 'X', 'p', '\'', '[', ']', '#', '{', '}', 'Z', 'w', 'e', 'E', 'x', 'Z', 'Y'

Formats that contain any of the following words are unsupported and will fall back to CPU:

"u", "uu", "uuu", "uuuu", "uuuuu", "uuuuuu", "uuuuuuu", "uuuuuuuu", "uuuuuuuuu", "uuuuuuuuuu",
"y", "yy", yyy", "yyyyy", "yyyyyy", "yyyyyyy", "yyyyyyyy", "yyyyyyyyy", "yyyyyyyyyy",
"D", "DD", "DDD", "s", "m", "H", "h", "M", "MMM", "MMMM", "MMMMM", "L", "LLL", "LLLL", "LLLLL",

Formatting dates and timestamps as strings

When formatting dates and timestamps as strings using functions such as from_unixtime, only a subset of valid format strings are supported on the GPU.

Formats that contain any of the following characters are unsupported and will fall back to CPU:

'k', 'K','z', 'V', 'c', 'F', 'W', 'Q', 'q', 'G', 'A', 'n', 'N',
'O', 'X', 'p', '\'', '[', ']', '#', '{', '}', 'Z', 'w', 'e', 'E', 'x', 'Z', 'Y'

Formats that contain any of the following words are unsupported and will fall back to CPU:

"u", "uu", "uuu", "uuuu", "uuuuu", "uuuuuu", "uuuuuuu", "uuuuuuuu", "uuuuuuuuu", "uuuuuuuuuu",
"y", yyy", "yyyyy", "yyyyyy", "yyyyyyy", "yyyyyyyy", "yyyyyyyyy", "yyyyyyyyyy",
"D", "DD", "DDD", "s", "m", "H", "h", "M", "MMM", "MMMM", "MMMMM", "L", "LLL", "LLLL", "LLLLL",

Note that this list differs very slightly from the list given in the previous section for parsing strings to dates because the two-digit year format "yy" is supported when formatting dates as strings but not when parsing strings to dates.

Casting between types

In general, performing cast and ansi_cast operations on the GPU is compatible with the same operations on the CPU. However, there are some exceptions. For this reason, certain casts are disabled on the GPU by default and require configuration options to be specified to enable them.

Float to Decimal

The GPU will use a different strategy from Java’s BigDecimal to handle/store decimal values, which leads to restrictions:

  • It is only available when ansiMode is on.
  • Float values cannot be larger than 1e18 or smaller than -1e18 after conversion.
  • The results produced by GPU slightly differ from the default results of Spark.

To enable this operation on the GPU, set spark.rapids.sql.castFloatToDecimal.enabled to true and set spark.sql.ansi.enabled to true.

Float to Integral Types

With both cast and ansi_cast, Spark uses the expression Math.floor(x) <= MAX && Math.ceil(x) >= MIN to determine whether a floating-point value can be converted to an integral type. Prior to Spark 3.1.0 the MIN and MAX values were floating-point values such as Int.MaxValue.toFloat but starting with 3.1.0 these are now integral types such as Int.MaxValue so this has slightly affected the valid range of values and now differs slightly from the behavior on GPU in some cases.

To enable this operation on the GPU when using Spark 3.1.0 or later, set spark.rapids.sql.castFloatToIntegralTypes.enabled to true.

This configuration setting is ignored when using Spark versions prior to 3.1.0.

Float to String

The GPU will use different precision than Java’s toString method when converting floating-point data types to strings and this can produce results that differ from the default behavior in Spark.

To enable this operation on the GPU, set spark.rapids.sql.castFloatToString.enabled to true.

String to Float

Casting from string to floating-point types on the GPU returns incorrect results when the string represents any number in the following ranges. In both cases the GPU returns Double.MaxValue. The default behavior in Apache Spark is to return +Infinity and -Infinity, respectively.

  • 1.7976931348623158E308 <= x < 1.7976931348623159E308
  • -1.7976931348623159E308 < x <= -1.7976931348623158E308

Also, the GPU does not support casting from strings containing hex values.

To enable this operation on the GPU, set spark.rapids.sql.castStringToFloat.enabled to true.

String to Date

The following formats/patterns are supported on the GPU. Timezone of UTC is assumed.

Format or Pattern Supported on GPU?
"yyyy" Yes
"yyyy-[M]M" Yes
"yyyy-[M]M " Yes
"yyyy-[M]M-[d]d" Yes
"yyyy-[M]M-[d]d " Yes
"yyyy-[M]M-[d]d *" Yes
"yyyy-[M]M-[d]d T*" Yes
"epoch" Yes
"now" Yes
"today" Yes
"tomorrow" Yes
"yesterday" Yes

String to Timestamp

To allow casts from string to timestamp on the GPU, enable the configuration property spark.rapids.sql.castStringToTimestamp.enabled.

Casting from string to timestamp currently has the following limitations.

Format or Pattern Supported on GPU?
"yyyy" Yes
"yyyy-[M]M" Yes
"yyyy-[M]M " Yes
"yyyy-[M]M-[d]d" Yes
"yyyy-[M]M-[d]d " Yes
"yyyy-[M]M-[d]dT[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us][zone_id]" Partial [1]
"yyyy-[M]M-[d]d [h]h:[m]m:[s]s.[ms][ms][ms][us][us][us][zone_id]" Partial [1]
"[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us][zone_id]" Partial [1]
"T[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us][zone_id]" Partial [1]
"epoch" Yes
"now" Yes
"today" Yes
"tomorrow" Yes
"yesterday" Yes
  • [1] The timestamp portion must be complete in terms of hours, minutes, seconds, and milliseconds, with 2 digits each for hours, minutes, and seconds, and 6 digits for milliseconds. Only timezone ‘Z’ (UTC) is supported. Casting unsupported formats will result in null values.

Constant Folding

ConstantFolding is an operator optimization rule in Catalyst that replaces expressions that can be statically evaluated with their equivalent literal values. The RAPIDS Accelerator relies on constant folding and parts of the query will not be accelerated if org.apache.spark.sql.catalyst.optimizer.ConstantFolding is excluded as a rule.

JSON string handling

The 0.5 release introduces the get_json_object operation. The JSON specification only allows double quotes around strings in JSON data, whereas Spark allows single quotes around strings in JSON data. The RAPIDS Spark get_json_object operation on the GPU will return None in PySpark or Null in Scala when trying to match a string surrounded by single quotes. This behavior will be updated in a future release to more closely match Spark.